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codeparrot
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github-jupyter

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FRBs/FRB
docs/nb/Using_the_CIGALE_wrapper.ipynb
4
72255
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: UnitsWarning: 'km/s/Mpc' contains multiple slashes, which is discouraged by the FITS standard [astropy.units.format.generic]\n" ] } ], "source": [ "import numpy as np\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.table import Table\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from frb.frb import FRB\n", "from frb.surveys.survey_utils import load_survey_by_name\n", "from frb.surveys.catalog_utils import convert_mags_to_flux\n", "from frb.analysis import cigale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing flux conversion " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DES " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=2</i>\n", "<table id=\"table139671973127120\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>DES_g</th><th>DES_g_err</th><th>DES_r</th><th>DES_r_err</th><th>DES_i</th><th>DES_i_err</th><th>DES_z</th><th>DES_z_err</th><th>DES_Y</th><th>DES_Y_err</th><th>DES_ID</th><th>ra</th><th>dec</th><th>DES_tile</th><th>WISE_W1</th><th>WISE_W1_err</th><th>WISE_W2</th><th>WISE_W2_err</th><th>WISE_W3</th><th>WISE_W3_err</th><th>WISE_W4</th><th>WISE_W4_err</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>25.0468</td><td>0.305723</td><td>24.5441</td><td>0.301756</td><td>23.923</td><td>0.293647</td><td>23.463</td><td>0.271636</td><td>24.7709</td><td>2.66629</td><td>209914542</td><td>326.101627</td><td>-40.899805</td><td>DES2143-4040</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td></tr>\n", "<tr><td>21.6222</td><td>0.0269829</td><td>20.5415</td><td>0.0156388</td><td>20.1379</td><td>0.0186293</td><td>19.8547</td><td>0.0200389</td><td>19.8083</td><td>0.0564316</td><td>209914488</td><td>326.105209</td><td>-40.900226</td><td>DES2143-4040</td><td>16.846</td><td>0.102</td><td>16.062</td><td>0.185</td><td>11.691</td><td>-999.0</td><td>8.501</td><td>-999.0</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=2>\n", " DES_g DES_g_err DES_r DES_r_err ... WISE_W3 WISE_W3_err WISE_W4 WISE_W4_err\n", "float64 float64 float64 float64 ... float64 float64 float64 float64 \n", "------- --------- ------- --------- ... ------- ----------- ------- -----------\n", "25.0468 0.305723 24.5441 0.301756 ... -999.0 -999.0 -999.0 -999.0\n", "21.6222 0.0269829 20.5415 0.0156388 ... 11.691 -999.0 8.501 -999.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frb180924 = FRB.by_name(\"FRB180924\")\n", "survey = load_survey_by_name(\"DES\",frb180924.coord,15*u.arcsec)\n", "survey.get_catalog()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=2</i>\n", "<table id=\"table139633015540880\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>DES_g</th><th>DES_g_err</th><th>DES_r</th><th>DES_r_err</th><th>DES_i</th><th>DES_i_err</th><th>DES_z</th><th>DES_z_err</th><th>DES_Y</th><th>DES_Y_err</th><th>DES_ID</th><th>ra</th><th>dec</th><th>DES_tile</th><th>WISE_W1</th><th>WISE_W1_err</th><th>WISE_W2</th><th>WISE_W2_err</th><th>WISE_W3</th><th>WISE_W3_err</th><th>WISE_W4</th><th>WISE_W4_err</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>0.34776029396353764</td><td>0.11309989577324552</td><td>0.5525352556488469</td><td>0.1770270619612232</td><td>0.9790390045854888</td><td>0.3040559413046528</td><td>1.495546753095973</td><td>0.4251305057911406</td><td>0.4483735593717958</td><td>4.7774650632357805</td><td>209914542</td><td>326.101627</td><td>-40.899805</td><td>DES2143-4040</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td></tr>\n", "<tr><td>8.149294155302018</td><td>0.20506524182685903</td><td>22.049563390010288</td><td>0.3198973292351718</td><td>31.97716768233088</td><td>0.5534054072117228</td><td>41.50687090621285</td><td>0.7731851361138282</td><td>43.31915677274319</td><td>2.3110707525906546</td><td>209914488</td><td>326.105209</td><td>-40.900226</td><td>DES2143-4040</td><td>56.53491951856131</td><td>5.568680812966024</td><td>64.59371982943986</td><td>11.999494472175313</td><td>667.2720168587022</td><td>-99.0</td><td>3326.3052157253505</td><td>-99.0</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=2>\n", " DES_g DES_g_err ... WISE_W4 WISE_W4_err\n", " float64 float64 ... float64 float64 \n", "------------------- ------------------- ... ------------------ -----------\n", "0.34776029396353764 0.11309989577324552 ... -99.0 -99.0\n", " 8.149294155302018 0.20506524182685903 ... 3326.3052157253505 -99.0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert_mags_to_flux(survey.catalog,fluxunits=u.microjansky)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SDSS " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=1</i>\n", "<table id=\"table140374151903696\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>ra</th><th>dec</th><th>SDSS_ID</th><th>run</th><th>rerun</th><th>camcol</th><th>SDSS_field</th><th>type</th><th>SDSS_u</th><th>SDSS_g</th><th>SDSS_r</th><th>SDSS_i</th><th>SDSS_z</th><th>SDSS_u_err</th><th>SDSS_g_err</th><th>SDSS_r_err</th><th>SDSS_i_err</th><th>SDSS_z_err</th><th>extinction_u</th><th>extinction_g</th><th>extinction_r</th><th>extinction_i</th><th>extinction_z</th><th>photo_z</th><th>photo_zerr</th><th>z_spec</th><th>separation</th></tr></thead>\n", "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>arcmin</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>334.020419873775</td><td>-7.89888531358588</td><td>1237652600644436162</td><td>1659</td><td>301</td><td>5</td><td>192</td><td>3</td><td>19.03135</td><td>18.06515</td><td>17.63091</td><td>17.26474</td><td>17.08397</td><td>0.04294281</td><td>0.008970303</td><td>0.007262514</td><td>0.007621691</td><td>0.01935624</td><td>0.1975715</td><td>0.1539464</td><td>0.1064994</td><td>0.07914045</td><td>0.05886595</td><td>-9999.0</td><td>-9999.0</td><td>0.1177805</td><td>0.05628329857046733</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1>\n", " ra dec ... z_spec separation \n", " ... arcmin \n", " float64 float64 ... float64 float64 \n", "---------------- ----------------- ... --------- -------------------\n", "334.020419873775 -7.89888531358588 ... 0.1177805 0.05628329857046733" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frb190608 = FRB.by_name(\"FRB190608\")\n", "survey = load_survey_by_name(\"SDSS\",frb190608.coord,15*u.arcsec)\n", "survey.get_catalog()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=1</i>\n", "<table id=\"table140374152285264\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>ra</th><th>dec</th><th>SDSS_ID</th><th>run</th><th>rerun</th><th>camcol</th><th>SDSS_field</th><th>type</th><th>SDSS_u</th><th>SDSS_g</th><th>SDSS_r</th><th>SDSS_i</th><th>SDSS_z</th><th>SDSS_u_err</th><th>SDSS_g_err</th><th>SDSS_r_err</th><th>SDSS_i_err</th><th>SDSS_z_err</th><th>extinction_u</th><th>extinction_g</th><th>extinction_r</th><th>extinction_i</th><th>extinction_z</th><th>photo_z</th><th>photo_zerr</th><th>z_spec</th><th>separation</th></tr></thead>\n", "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>arcmin</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>334.020419873775</td><td>-7.89888531358588</td><td>1237652600644436162</td><td>1659</td><td>301</td><td>5</td><td>192</td><td>3</td><td>0.08860535997067599</td><td>0.21574462979928918</td><td>0.32183701703197415</td><td>0.4509246681020314</td><td>0.5326132842973963</td><td>0.0035747280091641743</td><td>0.001789855963532317</td><td>0.0021599911381572647</td><td>0.0031765539944286023</td><td>0.009580444778228149</td><td>0.1975715</td><td>0.1539464</td><td>0.1064994</td><td>0.07914045</td><td>0.05886595</td><td>-9999.0</td><td>-9999.0</td><td>0.1177805</td><td>0.05628329857046733</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1>\n", " ra dec ... z_spec separation \n", " ... arcmin \n", " float64 float64 ... float64 float64 \n", "---------------- ----------------- ... --------- -------------------\n", "334.020419873775 -7.89888531358588 ... 0.1177805 0.05628329857046733" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert_mags_to_flux(survey.catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DECaLS " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=6</i>\n", "<table id=\"table140270132132560\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>DECaL_ID</th><th>brick_primary</th><th>DECaL_brick</th><th>ra</th><th>dec</th><th>gaia_pointsource</th><th>DECaL_g</th><th>DECaL_r</th><th>DECaL_z</th><th>WISE_W1</th><th>WISE_W2</th><th>WISE_W3</th><th>WISE_W4</th><th>DECaL_g_err</th><th>DECaL_r_err</th><th>DECaL_z_err</th><th>WISE_W1_err</th><th>WISE_W2_err</th><th>WISE_W3_err</th><th>WISE_W4_err</th></tr></thead>\n", "<thead><tr><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>7696603045628958</td><td>1</td><td>330371</td><td>0.997871222025512</td><td>-0.003181800279689</td><td>0</td><td>25.825</td><td>24.9819</td><td>23.5421</td><td>21.0602</td><td>20.8248</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-1.19283</td><td>-1.54654</td></tr>\n", "<tr><td>7696603045628959</td><td>1</td><td>330371</td><td>0.998236420651554</td><td>-0.0033006206513122</td><td>0</td><td>24.2206</td><td>24.3073</td><td>23.7538</td><td>25.0001</td><td>-99.0</td><td>16.9868</td><td>14.4753</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-2.78168</td><td>-99.0</td><td>-99.0</td></tr>\n", "<tr><td>7696603045694624</td><td>1</td><td>330372</td><td>1.00202546888956</td><td>-0.0009794773389576</td><td>0</td><td>24.7342</td><td>25.1195</td><td>23.9565</td><td>-99.0</td><td>-99.0</td><td>17.6525</td><td>17.2043</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-2.6537</td><td>-1.92639</td><td>-99.0</td><td>-99.0</td></tr>\n", "<tr><td>7696603045629090</td><td>1</td><td>330371</td><td>0.996710980408447</td><td>0.0011405432351369</td><td>0</td><td>24.4385</td><td>23.8904</td><td>23.4821</td><td>-99.0</td><td>-99.0</td><td>18.2956</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-2.69137</td><td>-0.311491</td><td>-99.0</td><td>-1.8065</td></tr>\n", "<tr><td>7696603045629094</td><td>1</td><td>330371</td><td>0.999330927183485</td><td>0.001700881614525</td><td>0</td><td>22.9443</td><td>22.2224</td><td>21.2398</td><td>20.8062</td><td>21.169</td><td>17.6742</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-0.184622</td></tr>\n", "<tr><td>7696603045694684</td><td>0</td><td>330372</td><td>0.999330886306945</td><td>0.0017009239077483</td><td>0</td><td>22.9455</td><td>22.2238</td><td>21.2402</td><td>20.7923</td><td>21.1589</td><td>17.5228</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-0.324041</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=6>\n", " DECaL_ID brick_primary DECaL_brick ... WISE_W3_err WISE_W4_err\n", " int64 int64 int64 ... float64 float64 \n", "---------------- ------------- ----------- ... ----------- -----------\n", "7696603045628958 1 330371 ... -1.19283 -1.54654\n", "7696603045628959 1 330371 ... -99.0 -99.0\n", "7696603045694624 1 330372 ... -99.0 -99.0\n", "7696603045629090 1 330371 ... -99.0 -1.8065\n", "7696603045629094 1 330371 ... -99.0 -0.184622\n", "7696603045694684 0 330372 ... -99.0 -0.324041" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey = load_survey_by_name(\"DECaL\",SkyCoord(1,0,unit=\"deg\"),15*u.arcsec)\n", "survey.get_catalog()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=6</i>\n", "<table id=\"table140269932810896\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>DECaL_ID</th><th>brick_primary</th><th>DECaL_brick</th><th>ra</th><th>dec</th><th>gaia_pointsource</th><th>DECaL_g</th><th>DECaL_r</th><th>DECaL_z</th><th>WISE_W1</th><th>WISE_W2</th><th>WISE_W3</th><th>WISE_W4</th><th>DECaL_g_err</th><th>DECaL_r_err</th><th>DECaL_z_err</th><th>WISE_W1_err</th><th>WISE_W2_err</th><th>WISE_W3_err</th><th>WISE_W4_err</th><th>z</th></tr></thead>\n", "<thead><tr><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>7696603045628958</td><td>1</td><td>330371</td><td>0.997871222025512</td><td>-0.003181800279689</td><td>0</td><td>0.00016982436301503037</td><td>0.0003691815546274997</td><td>0.001390464784758612</td><td>0.001165834165004409</td><td>0.0008036561924704808</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "<tr><td>7696603045628959</td><td>1</td><td>330371</td><td>0.998236420651554</td><td>-0.0033006206513122</td><td>0</td><td>0.0007443205237248634</td><td>0.0006871950053272952</td><td>0.0011441421738770923</td><td>3.095114916252917e-05</td><td>-99.0</td><td>0.005081394490707617</td><td>0.01355945071186171</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "<tr><td>7696603045694624</td><td>1</td><td>330372</td><td>1.00202546888956</td><td>-0.0009794773389576</td><td>0</td><td>0.00046378851978996717</td><td>0.0003252370358223504</td><td>0.000949292378912325</td><td>-99.0</td><td>-99.0</td><td>0.0027523452637214325</td><td>0.0010981005189436787</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "<tr><td>7696603045629090</td><td>1</td><td>330371</td><td>0.996710980408447</td><td>0.0011405432351369</td><td>0</td><td>0.000608975743467035</td><td>0.0010088811188017934</td><td>0.001469467530477905</td><td>-99.0</td><td>-99.0</td><td>0.0015221691380578274</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "<tr><td>7696603045629094</td><td>1</td><td>330371</td><td>0.999330927183485</td><td>0.001700881614525</td><td>0</td><td>0.0024114596214222564</td><td>0.004688565572821968</td><td>0.011589908152667827</td><td>0.0014731154291372922</td><td>0.000585315864488759</td><td>0.00269788176276814</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "<tr><td>7696603045694684</td><td>0</td><td>330372</td><td>0.999330886306945</td><td>0.0017009239077483</td><td>0</td><td>0.002408795850079067</td><td>0.004682523809058658</td><td>0.011585639059153531</td><td>0.0014920960417035985</td><td>0.0005907861364100322</td><td>0.0031015794632975146</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>-99.0</td><td>0.1</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=6>\n", " DECaL_ID brick_primary DECaL_brick ... WISE_W3_err WISE_W4_err z \n", " int64 int64 int64 ... float64 float64 float64\n", "---------------- ------------- ----------- ... ----------- ----------- -------\n", "7696603045628958 1 330371 ... -99.0 -99.0 0.1\n", "7696603045628959 1 330371 ... -99.0 -99.0 0.1\n", "7696603045694624 1 330372 ... -99.0 -99.0 0.1\n", "7696603045629090 1 330371 ... -99.0 -99.0 0.1\n", "7696603045629094 1 330371 ... -99.0 -99.0 0.1\n", "7696603045694684 0 330372 ... -99.0 -99.0 0.1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert_mags_to_flux(survey.catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pan-STARRS" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=10</i>\n", "<table id=\"table139673324110544\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Pan-STARRS_ID</th><th>ra</th><th>dec</th><th>objInfoFlag</th><th>qualityFlag</th><th>Pan-STARRS_g</th><th>Pan-STARRS_r</th><th>Pan-STARRS_i</th><th>Pan-STARRS_z</th><th>Pan-STARRS_y</th><th>Pan-STARRS_g_err</th><th>Pan-STARRS_r_err</th><th>Pan-STARRS_i_err</th><th>Pan-STARRS_z_err</th><th>Pan-STARRS_y_err</th><th>separation</th></tr></thead>\n", "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>arcmin</th></tr></thead>\n", "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>194962070641515671</td><td>207.06427429</td><td>72.47072443</td><td>436527104</td><td>52</td><td>22.978200912475586</td><td>22.056699752807617</td><td>21.171199798583984</td><td>20.81879997253418</td><td>20.604700088500977</td><td>0.16809199750423431</td><td>0.1003049984574318</td><td>0.058139000087976456</td><td>0.06288400292396545</td><td>0.10481999814510345</td><td>0.06154607375773434</td></tr>\n", "<tr><td>194962070655432752</td><td>207.06557723</td><td>72.46824989</td><td>436285440</td><td>52</td><td>23.081499099731445</td><td>22.094100952148438</td><td>22.37849998474121</td><td>22.177499771118164</td><td>21.631799697875977</td><td>0.18480700254440308</td><td>0.1328050047159195</td><td>0.13767799735069275</td><td>0.23336200416088104</td><td>0.4830789864063263</td><td>0.08895380876505755</td></tr>\n", "<tr><td>194962070589123840</td><td>207.05893867</td><td>72.46923579</td><td>444915712</td><td>53</td><td>21.392000198364258</td><td>20.566099166870117</td><td>20.111099243164062</td><td>19.763399124145508</td><td>19.794300079345703</td><td>0.0412990003824234</td><td>0.027608999982476234</td><td>0.013101000338792801</td><td>0.0261049997061491</td><td>0.05897799879312515</td><td>0.11336669396811025</td></tr>\n", "<tr><td>194962070629201005</td><td>207.06294605</td><td>72.46679517</td><td>444674048</td><td>53</td><td>22.12299919128418</td><td>21.675800323486328</td><td>21.502899169921875</td><td>21.707399368286133</td><td>21.247299194335938</td><td>0.07845800369977951</td><td>0.0917619988322258</td><td>0.054104000329971313</td><td>0.15350599586963654</td><td>0.2990039885044098</td><td>0.1795038049154313</td></tr>\n", "<tr><td>194962070583121611</td><td>207.05858533</td><td>72.46731982</td><td>436527104</td><td>52</td><td>22.945899963378906</td><td>21.594600677490234</td><td>21.271499633789062</td><td>21.334400177001953</td><td>20.82659912109375</td><td>0.1651419997215271</td><td>0.0853549987077713</td><td>0.037769000977277756</td><td>0.11157699674367905</td><td>0.1906830072402954</td><td>0.18498379095408174</td></tr>\n", "<tr><td>194962070604421049</td><td>207.06062466</td><td>72.46692391</td><td>436527104</td><td>52</td><td>23.347900390625</td><td>22.50279998779297</td><td>20.951099395751953</td><td>19.76099967956543</td><td>19.138399124145508</td><td>0.2384369969367981</td><td>0.1957859992980957</td><td>0.03649500012397766</td><td>0.02630399912595749</td><td>0.04225499927997589</td><td>0.18558987558276424</td></tr>\n", "<tr><td>194962070693028149</td><td>207.06930036</td><td>72.47280774</td><td>404226048</td><td>48</td><td>24.794200897216797</td><td>24.502300262451172</td><td>22.69540023803711</td><td>21.702800750732422</td><td>22.039600372314453</td><td>0.9575219750404358</td><td>0.9948269724845886</td><td>0.16877900063991547</td><td>0.15884999930858612</td><td>0.43328800797462463</td><td>0.2007801436413031</td></tr>\n", "<tr><td>194962070530925676</td><td>207.05309167</td><td>72.47076983</td><td>436527248</td><td>52</td><td>20.09040069580078</td><td>19.989700317382812</td><td>19.926700592041016</td><td>19.86090087890625</td><td>19.970199584960938</td><td>0.01399100013077259</td><td>0.01583999954164028</td><td>0.009170999750494957</td><td>0.028929000720381737</td><td>0.06363300234079361</td><td>0.22419957775476</td></tr>\n", "<tr><td>194962070764472218</td><td>207.07633366</td><td>72.46786126</td><td>1384198144</td><td>165</td><td>25.91510009765625</td><td>23.059200286865234</td><td>22.314300537109375</td><td>27.924800872802734</td><td>21.937000274658203</td><td>2.4253599643707275</td><td>0.25383898615837097</td><td>0.08086299896240234</td><td>44.88589859008789</td><td>0.44850799441337585</td><td>0.23329485719850837</td></tr>\n", "<tr><td>194962070569838430</td><td>207.0578546</td><td>72.47301528</td><td>1343750144</td><td>160</td><td>-999.0</td><td>28.999500274658203</td><td>23.214399337768555</td><td>23.06220054626465</td><td>23.11590003967285</td><td>1.1896799802780151</td><td>59.30379867553711</td><td>0.17835399508476257</td><td>0.5811989903450012</td><td>1.0535999536514282</td><td>0.23603444468990473</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=10>\n", " Pan-STARRS_ID ra ... Pan-STARRS_y_err separation \n", " ... arcmin \n", " int64 float64 ... float64 float64 \n", "------------------ ------------ ... ------------------- -------------------\n", "194962070641515671 207.06427429 ... 0.10481999814510345 0.06154607375773434\n", "194962070655432752 207.06557723 ... 0.4830789864063263 0.08895380876505755\n", "194962070589123840 207.05893867 ... 0.05897799879312515 0.11336669396811025\n", "194962070629201005 207.06294605 ... 0.2990039885044098 0.1795038049154313\n", "194962070583121611 207.05858533 ... 0.1906830072402954 0.18498379095408174\n", "194962070604421049 207.06062466 ... 0.04225499927997589 0.18558987558276424\n", "194962070693028149 207.06930036 ... 0.43328800797462463 0.2007801436413031\n", "194962070530925676 207.05309167 ... 0.06363300234079361 0.22419957775476\n", "194962070764472218 207.07633366 ... 0.44850799441337585 0.23329485719850837\n", "194962070569838430 207.0578546 ... 1.0535999536514282 0.23603444468990473" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frb190523 = FRB.by_name(\"FRB190523\")\n", "survey = load_survey_by_name(\"Pan-STARRS\",frb190523.coord,15*u.arcsec)\n", "survey.get_catalog()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=10</i>\n", "<table id=\"table140269887884880\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>Pan-STARRS_ID</th><th>ra</th><th>dec</th><th>objInfoFlag</th><th>qualityFlag</th><th>Pan-STARRS_g</th><th>Pan-STARRS_r</th><th>Pan-STARRS_i</th><th>Pan-STARRS_z</th><th>Pan-STARRS_y</th><th>Pan-STARRS_g_err</th><th>Pan-STARRS_r_err</th><th>Pan-STARRS_i_err</th><th>Pan-STARRS_z_err</th><th>Pan-STARRS_y_err</th><th>separation</th></tr></thead>\n", "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>arcmin</th></tr></thead>\n", "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>194962070641515671</td><td>207.06427429</td><td>72.47072443</td><td>436527104</td><td>52</td><td>0.0023373278255046587</td><td>0.005461602557007481</td><td>0.012345823812802456</td><td>0.01707969084133699</td><td>0.020802712110329864</td><td>0.00039137617942680016</td><td>0.0005286080602107476</td><td>0.0006791144932347553</td><td>0.0010184351163233295</td><td>0.0021084943100777337</td><td>0.06154607375773434</td></tr>\n", "<tr><td>194962070655432752</td><td>207.06557723</td><td>72.46824989</td><td>436285440</td><td>52</td><td>0.002125202677523379</td><td>0.005276666099168051</td><td>0.004060691537375596</td><td>0.004886524559347609</td><td>0.008077558558724435</td><td>0.0003943483327991366</td><td>0.000686564656632898</td><td>0.0005489929338442965</td><td>0.001171693308173678</td><td>0.00452653765478498</td><td>0.08895380876505755</td></tr>\n", "<tr><td>194962070589123840</td><td>207.05893867</td><td>72.46923579</td><td>444915712</td><td>53</td><td>0.010073952875247701</td><td>0.021555610502656475</td><td>0.03277632799851968</td><td>0.045148190061386526</td><td>0.043881348229200604</td><td>0.00039057200999074564</td><td>0.000555162579572353</td><td>0.00039789022479336114</td><td>0.0010986798971418805</td><td>0.002449597153820731</td><td>0.11336669396811025</td></tr>\n", "<tr><td>194962070629201005</td><td>207.06294605</td><td>72.46679517</td><td>444674048</td><td>53</td><td>0.0051380735619370515</td><td>0.007756751259852961</td><td>0.009095787933875017</td><td>0.007534253820077024</td><td>0.011510132327160793</td><td>0.0003850342303793361</td><td>0.00068406923321122</td><td>0.00046474113831356575</td><td>0.0011442057649592144</td><td>0.0036492641627717056</td><td>0.1795038049154313</td></tr>\n", "<tr><td>194962070583121611</td><td>207.05858533</td><td>72.46731982</td><td>436527104</td><td>52</td><td>0.0024079086615606065</td><td>0.008359103898910219</td><td>0.011256416154581709</td><td>0.010622822468564289</td><td>0.01695744221656394</td><td>0.000395567120829929</td><td>0.000683671055472669</td><td>0.0003984620966658142</td><td>0.0011497323714599874</td><td>0.003255686833876511</td><td>0.18498379095408174</td></tr>\n", "<tr><td>194962070604421049</td><td>207.06062466</td><td>72.46692391</td><td>436527104</td><td>52</td><td>0.0016627993192890455</td><td>0.003621429201179749</td><td>0.015120294010286847</td><td>0.04524807654520275</td><td>0.08028609678668759</td><td>0.0004083653866674257</td><td>0.0007156204984565379</td><td>0.0005168788127853664</td><td>0.0011096065322449065</td><td>0.003186196315121414</td><td>0.18558987558276424</td></tr>\n", "<tr><td>194962070693028149</td><td>207.06930036</td><td>72.47280774</td><td>404226048</td><td>48</td><td>0.0004388535697857522</td><td>0.000574222081760238</td><td>0.003032772958493059</td><td>0.007566232701444514</td><td>0.0055482988471717695</td><td>0.000621201495131282</td><td>0.0008613026474280501</td><td>0.000510066714819344</td><td>0.0011920648105509587</td><td>0.0027211211233817674</td><td>0.2007801436413031</td></tr>\n", "<tr><td>194962070530925676</td><td>207.05309167</td><td>72.47076983</td><td>436527248</td><td>52</td><td>0.033407172221944476</td><td>0.0366538727118693</td><td>0.03884362542279461</td><td>0.041270491662338836</td><td>0.037318154665939435</td><td>0.00043327672652524493</td><td>0.0005386697200853883</td><td>0.0003294940755397376</td><td>0.0011144161618132158</td><td>0.0022525114671399357</td><td>0.22419957775476</td></tr>\n", "<tr><td>194962070764472218</td><td>207.07633366</td><td>72.46786126</td><td>1384198144</td><td>165</td><td>0.00015630035169338639</td><td>0.002169301315156495</td><td>0.004308040519757273</td><td>2.4551591255144958e-05</td><td>0.0060981750172174965</td><td>0.0013028630926881155</td><td>0.0005713606582244242</td><td>0.00033310279229122443</td><td>22102382028907.805</td><td>0.003119111384398808</td><td>0.23329485719850837</td></tr>\n", "<tr><td>194962070569838430</td><td>207.0578546</td><td>72.47301528</td><td>1343750144</td><td>160</td><td>-99.0</td><td>9.124306897865119e-06</td><td>0.0018803567748668791</td><td>0.002163315070658567</td><td>0.0020589223994881053</td><td>-197.14689714024465</td><td>4.805287257207698e+18</td><td>0.00033570497253016446</td><td>0.001531556708379542</td><td>0.003374581209827869</td><td>0.23603444468990473</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=10>\n", " Pan-STARRS_ID ra ... Pan-STARRS_y_err separation \n", " ... arcmin \n", " int64 float64 ... float64 float64 \n", "------------------ ------------ ... --------------------- -------------------\n", "194962070641515671 207.06427429 ... 0.0021084943100777337 0.06154607375773434\n", "194962070655432752 207.06557723 ... 0.00452653765478498 0.08895380876505755\n", "194962070589123840 207.05893867 ... 0.002449597153820731 0.11336669396811025\n", "194962070629201005 207.06294605 ... 0.0036492641627717056 0.1795038049154313\n", "194962070583121611 207.05858533 ... 0.003255686833876511 0.18498379095408174\n", "194962070604421049 207.06062466 ... 0.003186196315121414 0.18558987558276424\n", "194962070693028149 207.06930036 ... 0.0027211211233817674 0.2007801436413031\n", "194962070530925676 207.05309167 ... 0.0022525114671399357 0.22419957775476\n", "194962070764472218 207.07633366 ... 0.003119111384398808 0.23329485719850837\n", "194962070569838430 207.0578546 ... 0.003374581209827869 0.23603444468990473" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert_mags_to_flux(survey.catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WISE" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=1</i>\n", "<table id=\"table139671937665296\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>tmass_key</th><th>WISE_W1</th><th>WISE_W2</th><th>WISE_W3</th><th>WISE_W4</th><th>WISE_W1_err</th><th>WISE_W2_err</th><th>WISE_W3_err</th><th>WISE_W4_err</th><th>ph_qual</th><th>moon_lev</th></tr></thead>\n", "<thead><tr><th>str20</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>str4</th><th>int64</th></tr></thead>\n", "<tr><td>3263m409_ac51-031287</td><td>326.1052734</td><td>-40.9002904</td><td>-999</td><td>16.846</td><td>16.062</td><td>11.691</td><td>8.501</td><td>0.102</td><td>0.185</td><td>-999</td><td>-999</td><td>ABUU</td><td>22</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1>\n", " source_id ra dec ... WISE_W4_err ph_qual moon_lev\n", " str20 float64 float64 ... int64 str4 int64 \n", "-------------------- ----------- ----------- ... ----------- ------- --------\n", "3263m409_ac51-031287 326.1052734 -40.9002904 ... -999 ABUU 22" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frb180924 = FRB.by_name(\"FRB180924\")\n", "survey = load_survey_by_name(\"WISE\",frb180924.coord,15*u.arcsec)\n", "survey.get_catalog()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=1</i>\n", "<table id=\"table139671936431888\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>source_id</th><th>ra</th><th>dec</th><th>tmass_key</th><th>WISE_W1</th><th>WISE_W2</th><th>WISE_W3</th><th>WISE_W4</th><th>WISE_W1_err</th><th>WISE_W2_err</th><th>WISE_W3_err</th><th>WISE_W4_err</th><th>ph_qual</th><th>moon_lev</th><th>z</th></tr></thead>\n", "<thead><tr><th>str20</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>str4</th><th>int64</th><th>float64</th></tr></thead>\n", "<tr><td>3263m409_ac51-031287</td><td>326.1052734</td><td>-40.9002904</td><td>-999</td><td>0.05653491951856131</td><td>0.06459371982943986</td><td>0.6672720168587022</td><td>3.3263052157253505</td><td>0.005568680812966024</td><td>0.011999494472175312</td><td>-99</td><td>-99</td><td>ABUU</td><td>22</td><td>0.1</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=1>\n", " source_id ra dec ... ph_qual moon_lev z \n", " str20 float64 float64 ... str4 int64 float64\n", "-------------------- ----------- ----------- ... ------- -------- -------\n", "3263m409_ac51-031287 326.1052734 -40.9002904 ... ABUU 22 0.1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert_mags_to_flux(survey.catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing the CIGALE `run` command " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run CIGALE, you need photometries and redshifts of each galaxy in your catalog. In addition to this, it is recommended to have a column with unique object IDs (preferably containing \"ID\" in the name). " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function run in module frb.analysis.cigale:\n", "\n", "run(photometry_table, zcol, data_file='cigale_in.fits', config_file='pcigale.ini', wait_for_input=False, plot=True, outdir=None, compare_obs_model=False, **kwargs)\n", " Input parameters and then run CIGALE.\n", " \n", " Args:\n", " photometry_table (astropy Table):\n", " A table from some photometric catalog with magnitudes and\n", " error measurements. Currently supports\n", " DES, DECaLS, SDSS, Pan-STARRS and WISE\n", " zcol (str):\n", " Name of the column with redshift estimates.\n", " data_file (str, optional):\n", " Path to the photometry data file generated used as input to CIGALE\n", " config_file (str, optional):\n", " Path to the file where CIGALE's configuration generated\n", " wait_for_input (bool, optional):\n", " If true, waits for the user to finish editing the auto-generated config file\n", " before running.\n", " plot (bool, optional):\n", " Plots the best fit SED if true\n", " cores (int, optional):\n", " Number of CPU cores to be used. Defaults\n", " to all cores on the system.\n", " outdir (str, optional):\n", " Path to the many outputs of CIGALE\n", " If not supplied, the outputs will appear in a folder named out/\n", " compare_obs_model (bool, optional):\n", " If True compare the input observed fluxes with the model fluxes\n", " This writes a Table to outdir named 'photo_observed_model.dat'\n", " \n", " kwargs: These are passed into _initialise()\n", " sed_modules (list of 'str', optional):\n", " A list of SED modules to be used in the \n", " PDF analysis. If this is being input, there\n", " should be a corresponding correct dict\n", " for sed_modules_params.\n", " sed_module_params (dict, optional):\n", " A dict containing parameter values for\n", " the input SED modules. Better not use this\n", " unless you know exactly what you're doing.\n", "\n" ] } ], "source": [ "help(cigale.run)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "frb180924 = FRB.by_name(\"FRB180924\")\n", "survey = load_survey_by_name(\"DES\",frb180924.coord,15*u.arcsec)\n", "survey.get_catalog()\n", "cat = survey.catalog\n", "cat['z'] = 0.1*np.ones(len(cat)) # Add redshifts" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=2</i>\n", "<table id=\"table140569840963536\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>DES_g</th><th>DES_g_err</th><th>DES_r</th><th>DES_r_err</th><th>DES_i</th><th>DES_i_err</th><th>DES_z</th><th>DES_z_err</th><th>DES_Y</th><th>DES_Y_err</th><th>DES_ID</th><th>ra</th><th>dec</th><th>DES_tile</th><th>WISE_W1</th><th>WISE_W1_err</th><th>WISE_W2</th><th>WISE_W2_err</th><th>WISE_W3</th><th>WISE_W3_err</th><th>WISE_W4</th><th>WISE_W4_err</th><th>z</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>25.0468</td><td>0.305723</td><td>24.5441</td><td>0.301756</td><td>23.923</td><td>0.293647</td><td>23.463</td><td>0.271636</td><td>24.7709</td><td>2.66629</td><td>209914542</td><td>326.101627</td><td>-40.899805</td><td>DES2143-4040</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>-999.0</td><td>0.1</td></tr>\n", "<tr><td>21.6222</td><td>0.0269829</td><td>20.5415</td><td>0.0156388</td><td>20.1379</td><td>0.0186293</td><td>19.8547</td><td>0.0200389</td><td>19.8083</td><td>0.0564316</td><td>209914488</td><td>326.105209</td><td>-40.900226</td><td>DES2143-4040</td><td>16.846</td><td>0.102</td><td>16.062</td><td>0.185</td><td>11.691</td><td>-999.0</td><td>8.501</td><td>-999.0</td><td>0.1</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=2>\n", " DES_g DES_g_err DES_r DES_r_err ... WISE_W3_err WISE_W4 WISE_W4_err z \n", "float64 float64 float64 float64 ... float64 float64 float64 float64\n", "------- --------- ------- --------- ... ----------- ------- ----------- -------\n", "25.0468 0.305723 24.5441 0.301756 ... -999.0 -999.0 -999.0 0.1\n", "21.6222 0.0269829 20.5415 0.0156388 ... -999.0 8.501 -999.0 0.1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No radio module found. Options are: radio.\n", "Initialising the analysis module... \n", "The out/ directory was renamed to 2020-01-07_12:05:12_out/\n", "\n", "Processing block 1/1...\n", "\n", "Computing models ...\n", "16800/16800 computed in 19.0 seconds (884.5/s)\n", "\n", "Estimating the physical properties ...\n", "2/2 computed in 3.5 seconds (0.6/s)\n", "\n", "Block processed.\n", "\n", "Estimating physical properties on all blocks\n", "\n", "Computing the best fit spectra\n", "2/2 computed in 0.5 seconds (4.3/s)\n", "1/2 computed in 0.5 seconds (2.2/s)\n", "0.0% of the objects have chi^2_red~0 and 0.0% chi^2_red<0.5\n", "\n", "Saving the analysis results...\n", "Run completed!\n" ] } ], "source": [ "cigale.run(survey.catalog,\"z\",compare_obs_model=True,plot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outputs" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "209914488_best_model.fits 209914542_SFH.fits photo_observed_model.dat\r\n", "209914488_best_model.pdf observations.fits results.fits\r\n", "209914488_SFH.fits observations.txt results.txt\r\n", "209914542_best_model.fits \u001b[0m\u001b[01;32mpcigale.ini\u001b[0m*\r\n", "209914542_best_model.pdf \u001b[01;32mpcigale.ini.spec\u001b[0m*\r\n" ] } ], "source": [ "ls out/" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<i>Table length=2</i>\n", "<table id=\"table140569026039888\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>id</th><th>bayes.agn.fracAGN_dale2014</th><th>bayes.agn.fracAGN_dale2014_err</th><th>bayes.attenuation.B_B90</th><th>bayes.attenuation.B_B90_err</th><th>bayes.attenuation.E_BVs.nebular.continuum_old</th><th>bayes.attenuation.E_BVs.nebular.continuum_old_err</th><th>bayes.attenuation.E_BVs.nebular.continuum_young</th><th>bayes.attenuation.E_BVs.nebular.continuum_young_err</th><th>bayes.attenuation.E_BVs.nebular.lines_old</th><th>bayes.attenuation.E_BVs.nebular.lines_old_err</th><th>bayes.attenuation.E_BVs.nebular.lines_young</th><th>bayes.attenuation.E_BVs.nebular.lines_young_err</th><th>bayes.attenuation.E_BVs.stellar.old</th><th>bayes.attenuation.E_BVs.stellar.old_err</th><th>bayes.attenuation.E_BVs.stellar.young</th><th>bayes.attenuation.E_BVs.stellar.young_err</th><th>bayes.attenuation.FUV</th><th>bayes.attenuation.FUV_err</th><th>bayes.attenuation.V_B90</th><th>bayes.attenuation.V_B90_err</th><th>bayes.attenuation.ebvs_old_factor</th><th>bayes.attenuation.ebvs_old_factor_err</th><th>bayes.attenuation.powerlaw_slope</th><th>bayes.attenuation.powerlaw_slope_err</th><th>bayes.attenuation.uv_bump_amplitude</th><th>bayes.attenuation.uv_bump_amplitude_err</th><th>bayes.attenuation.uv_bump_wavelength</th><th>bayes.attenuation.uv_bump_wavelength_err</th><th>bayes.attenuation.uv_bump_width</th><th>bayes.attenuation.uv_bump_width_err</th><th>bayes.dust.alpha</th><th>bayes.dust.alpha_err</th><th>bayes.nebular.f_dust</th><th>bayes.nebular.f_dust_err</th><th>bayes.nebular.f_esc</th><th>bayes.nebular.f_esc_err</th><th>bayes.nebular.lines_width</th><th>bayes.nebular.lines_width_err</th><th>bayes.nebular.logU</th><th>bayes.nebular.logU_err</th><th>bayes.param.EW(500.7/1.0)</th><th>bayes.param.EW(500.7/1.0)_err</th><th>bayes.param.EW(656.3/1.0)</th><th>bayes.param.EW(656.3/1.0)_err</th><th>bayes.param.restframe_u_prime-r_prime</th><th>bayes.param.restframe_u_prime-r_prime_err</th><th>bayes.sfh.age</th><th>bayes.sfh.age_err</th><th>bayes.sfh.age_burst</th><th>bayes.sfh.age_burst_err</th><th>bayes.sfh.age_main</th><th>bayes.sfh.age_main_err</th><th>bayes.sfh.f_burst</th><th>bayes.sfh.f_burst_err</th><th>bayes.sfh.tau_burst</th><th>bayes.sfh.tau_burst_err</th><th>bayes.sfh.tau_main</th><th>bayes.sfh.tau_main_err</th><th>bayes.stellar.age_m_star</th><th>bayes.stellar.age_m_star_err</th><th>bayes.stellar.imf</th><th>bayes.stellar.imf_err</th><th>bayes.stellar.metallicity</th><th>bayes.stellar.metallicity_err</th><th>bayes.stellar.old_young_separation_age</th><th>bayes.stellar.old_young_separation_age_err</th><th>bayes.universe.age</th><th>bayes.universe.age_err</th><th>bayes.universe.luminosity_distance</th><th>bayes.universe.luminosity_distance_err</th><th>bayes.universe.redshift</th><th>bayes.universe.redshift_err</th><th>bayes.attenuation.nebular.continuum_old</th><th>bayes.attenuation.nebular.continuum_old_err</th><th>bayes.attenuation.nebular.continuum_young</th><th>bayes.attenuation.nebular.continuum_young_err</th><th>bayes.attenuation.nebular.lines_old</th><th>bayes.attenuation.nebular.lines_old_err</th><th>bayes.attenuation.nebular.lines_young</th><th>bayes.attenuation.nebular.lines_young_err</th><th>bayes.attenuation.stellar.old</th><th>bayes.attenuation.stellar.old_err</th><th>bayes.attenuation.stellar.young</th><th>bayes.attenuation.stellar.young_err</th><th>bayes.dust.luminosity</th><th>bayes.dust.luminosity_err</th><th>bayes.param.restframe_Lnu(r_prime)</th><th>bayes.param.restframe_Lnu(r_prime)_err</th><th>bayes.param.restframe_Lnu(u_prime)</th><th>bayes.param.restframe_Lnu(u_prime)_err</th><th>bayes.sfh.integrated</th><th>bayes.sfh.integrated_err</th><th>bayes.sfh.sfr</th><th>bayes.sfh.sfr_err</th><th>bayes.sfh.sfr100Myrs</th><th>bayes.sfh.sfr100Myrs_err</th><th>bayes.sfh.sfr10Myrs</th><th>bayes.sfh.sfr10Myrs_err</th><th>bayes.stellar.lum</th><th>bayes.stellar.lum_err</th><th>bayes.stellar.lum_ly</th><th>bayes.stellar.lum_ly_err</th><th>bayes.stellar.lum_ly_old</th><th>bayes.stellar.lum_ly_old_err</th><th>bayes.stellar.lum_ly_young</th><th>bayes.stellar.lum_ly_young_err</th><th>bayes.stellar.lum_old</th><th>bayes.stellar.lum_old_err</th><th>bayes.stellar.lum_young</th><th>bayes.stellar.lum_young_err</th><th>bayes.stellar.m_gas</th><th>bayes.stellar.m_gas_err</th><th>bayes.stellar.m_gas_old</th><th>bayes.stellar.m_gas_old_err</th><th>bayes.stellar.m_gas_young</th><th>bayes.stellar.m_gas_young_err</th><th>bayes.stellar.m_star</th><th>bayes.stellar.m_star_err</th><th>bayes.stellar.m_star_old</th><th>bayes.stellar.m_star_old_err</th><th>bayes.stellar.m_star_young</th><th>bayes.stellar.m_star_young_err</th><th>bayes.stellar.n_ly</th><th>bayes.stellar.n_ly_err</th><th>bayes.stellar.n_ly_old</th><th>bayes.stellar.n_ly_old_err</th><th>bayes.stellar.n_ly_young</th><th>bayes.stellar.n_ly_young_err</th><th>bayes.DES_Y</th><th>bayes.DES_Y_err</th><th>bayes.DES_g</th><th>bayes.DES_g_err</th><th>bayes.DES_i</th><th>bayes.DES_i_err</th><th>bayes.DES_r</th><th>bayes.DES_r_err</th><th>bayes.DES_z</th><th>bayes.DES_z_err</th><th>bayes.WISE1</th><th>bayes.WISE1_err</th><th>bayes.WISE2</th><th>bayes.WISE2_err</th><th>bayes.WISE3</th><th>bayes.WISE3_err</th><th>bayes.WISE4</th><th>bayes.WISE4_err</th><th>best.chi_square</th><th>best.reduced_chi_square</th><th>best.agn.fracAGN_dale2014</th><th>best.attenuation.B_B90</th><th>best.attenuation.E_BVs.nebular.continuum_old</th><th>best.attenuation.E_BVs.nebular.continuum_young</th><th>best.attenuation.E_BVs.nebular.lines_old</th><th>best.attenuation.E_BVs.nebular.lines_young</th><th>best.attenuation.E_BVs.stellar.old</th><th>best.attenuation.E_BVs.stellar.young</th><th>best.attenuation.FUV</th><th>best.attenuation.V_B90</th><th>best.attenuation.ebvs_old_factor</th><th>best.attenuation.powerlaw_slope</th><th>best.attenuation.uv_bump_amplitude</th><th>best.attenuation.uv_bump_wavelength</th><th>best.attenuation.uv_bump_width</th><th>best.dust.alpha</th><th>best.nebular.f_dust</th><th>best.nebular.f_esc</th><th>best.nebular.lines_width</th><th>best.nebular.logU</th><th>best.param.EW(500.7/1.0)</th><th>best.param.EW(656.3/1.0)</th><th>best.param.restframe_u_prime-r_prime</th><th>best.sfh.age</th><th>best.sfh.age_burst</th><th>best.sfh.age_main</th><th>best.sfh.f_burst</th><th>best.sfh.tau_burst</th><th>best.sfh.tau_main</th><th>best.stellar.age_m_star</th><th>best.stellar.imf</th><th>best.stellar.metallicity</th><th>best.stellar.old_young_separation_age</th><th>best.universe.age</th><th>best.universe.luminosity_distance</th><th>best.universe.redshift</th><th>best.attenuation.nebular.continuum_old</th><th>best.attenuation.nebular.continuum_young</th><th>best.attenuation.nebular.lines_old</th><th>best.attenuation.nebular.lines_young</th><th>best.attenuation.stellar.old</th><th>best.attenuation.stellar.young</th><th>best.dust.luminosity</th><th>best.param.restframe_Lnu(r_prime)</th><th>best.param.restframe_Lnu(u_prime)</th><th>best.sfh.integrated</th><th>best.sfh.sfr</th><th>best.sfh.sfr100Myrs</th><th>best.sfh.sfr10Myrs</th><th>best.stellar.lum</th><th>best.stellar.lum_ly</th><th>best.stellar.lum_ly_old</th><th>best.stellar.lum_ly_young</th><th>best.stellar.lum_old</th><th>best.stellar.lum_young</th><th>best.stellar.m_gas</th><th>best.stellar.m_gas_old</th><th>best.stellar.m_gas_young</th><th>best.stellar.m_star</th><th>best.stellar.m_star_old</th><th>best.stellar.m_star_young</th><th>best.stellar.n_ly</th><th>best.stellar.n_ly_old</th><th>best.stellar.n_ly_young</th><th>best.DES_g</th><th>best.DES_r</th><th>best.DES_i</th><th>best.DES_z</th><th>best.DES_Y</th><th>best.WISE1</th><th>best.WISE2</th><th>best.WISE3</th><th>best.WISE4</th></tr></thead>\n", "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th></tr></thead>\n", 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"</table>" ], "text/plain": [ "<Table length=2>\n", " id bayes.agn.fracAGN_dale2014 ... best.WISE4 \n", " ... mJy \n", " int64 float64 ... float64 \n", "--------- -------------------------- ... --------------------\n", "209914542 0.10265319651287422 ... 0.042158682692117665\n", "209914488 0.11215194827232314 ... 0.13191570009051434" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = Table.read(\"out/results.fits\")\n", "results" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
joeladams/patternlets
patternlets/java-OpenMPI/notebook/Java_openmpi_patternlets.ipynb
1
112730
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The type of computing these examples illustrate is called *message passing*. Message passing is a form of programming that is based on processes that communicate with each other to coordinate their work. Message passing can be used on a single multicore computer or with a cluster of computers.\n", "\n", "### Software Patterns\n", "\n", "Patterns in software are common implementations that have been used over and over by practitioners to accomplish tasks. As practitioners use them repeatedly, the community begins to give them names and catalog them, often turning them into reusable library functions. The examples you will see in this book are based on documented patterns that have been used to solve different problems using message passing between processes. Message passing is one form of distributed computing using processes, which can be used on clusters of computers or multicore machines.\n", "\n", "In many of these examples, the pattern's name is part of the Java code file's name. You will also see that often the MPI library functions also take on the name of the pattern, and the implementation of those functions themselves contains the pattern that practitioners found themselves using often. These pattern code examples we show you here, dubbed patternlets, are based on original work by Joel Adams:\n", "\n", "Adams, Joel C. \"Patternlets: A Teaching Tool for Introducing Students to Parallel Design Patterns.\" 2015 IEEE International Parallel and Distributed Processing Symposium Workshop. IEEE, 2015.\n", "\n", "To run these examples, first you will need to install the Java mpi.jar library by running this code (this will usually take a while to install the first time):" ] }, { "cell_type": "code", "metadata": { "id": "FVgDVtdkxrgc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "47a671a6-cff0-4b48-935a-e4b2793f5468" }, "source": [ "!wget https://wsu-courses.s3.amazonaws.com/openmpi411/mpi.jar" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "GJ4ul5SKsfmM" }, "source": [ "### New to colab and jupyter notebook?\n", "\n", "If you have not used this type of notebook before, these are split into *cells*. The cell you are reading is a text cell, and the cell just above it is also. the cell with [ ] to the left of it is a code cell, which contains Java code or code that can be run as if you are in a linux shell. The latter linux shell commands always begin with an exclamation point, !, as the cell above that contains a wget command, used to download the mpi.jar file.\n", "\n", "You should execute code cells as you follow along in this notebook. Some are designed for you to re-run after changing them. You can run a cell by hovering over the [ ] and clicking on the arrow symbol.\n", "\n", "The symbol in the upper left that looks like three .__ symbols toggles the table of contents. Revealing this enables you to navigate to different pattern examples.\n", "\n", "The triangle next to some text cells below enables collapsing of sections for faster scrolling." ] }, { "cell_type": "markdown", "metadata": { "id": "uryzb1Wy-hlp" }, "source": [ "# Program structure patterns" ] }, { "cell_type": "markdown", "metadata": { "id": "sdWhWJMLzUIm" }, "source": [ "## Single Program, Multiple Data\n", "\n", "This code forms the basis of all of the other examples that follow. It is the fundamental way we structure parallel programs today.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PgRHUlUsSN5u", "outputId": "a43e537e-25a1-4d00-ff54-67ab03777d0e" }, "source": [ "%%writefile Spmd.java\n", "/* Spmd.java\n", " * ... illustrates the single program multiple data\n", " * (SPMD) pattern using basic MPI commands\n", " * and OpenMPI's Java interface.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./Spmd\n", " *\n", " */\n", "\n", "import mpi.*;\n", "\n", "public class Spmd {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " int id = MPI.COMM_WORLD.getRank();\n", " int numProcesses = MPI.COMM_WORLD.getSize();\n", " String hostName = MPI.getProcessorName();\n", "\n", " String message = \"Greetings from process #\" + id\n", " + \" of \" + numProcesses \n", " + \" on \" + hostName + \"\\n\";\n", " System.out.print(message);\n", "\n", " MPI.Finalize();\n", " }\n", "}" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fRn94fx0WB3h" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "XLFiYTIJ6ALs" }, "source": [ "Let's examine the variables created in lines 18-29 carefully.\n", "\n", "1. *comm* The fundamental notion with this type of computing is a *process* running independently on the computer. With one single program like this, we can specify that we want to start several processes, each of which can **communicate**. The mechanism for communication is initialized when the program starts up, and the object that represents the means of using communication between processes is called MPI.COMM_WORLD.\n", "\n", "2. *id* Every process can identify itself with a number. We get that number by asking *comm* for it using Get_rank().\n", "\n", "3. *numProcesses* It is helpful to know haw many processes have started up, because this can be specified differently every time you run this type of program. Asking *comm* for it is done with Get_size().\n", "\n", "4. *myHostName* When you run this code on a cluster of computers, it is sometimes useful to know which computer is running a certain piece of code. A particular computer is often called a 'host', which is why we call this variable myHostName, and get it by asking *comm* to provide it with Get_processor_name().\n", "\n", "These four variables are often used in every MPI program. The first three are often needed for writing correct programs, and the fourth one is often used for debugging and analysis of where certain computations are running.\n", "\n", "Next we see how we can compile and use the mpirun program to execute the above Java code using 4 processes. The value after -np is the number of processes to use when running the Java file saved by executing the previous code cell." ] }, { "cell_type": "code", "metadata": { "id": "zqOAtb4G4-e2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "fb0e7030-b83d-4ce6-da04-786c4e34fc6b" }, "source": [ "!javac -cp ./mpi.jar Spmd.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar Spmd" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IR2tfQ8v8RVa" }, "source": [ "The fundamental idea of message passing programs can be illustrated like this:\n", "\n", "![picture](https://drive.google.com/uc?id=1wpQaFiaubIcQBV9Lw_jwOU0y2-K-EChW)\n", "\n", "Each process is set up within a communication network to be able to communicate with every other process via communication links. Each process is set up to have its own number, or id, which starts at 0.\n", "\n", "**Note:** Each process holds its own copies of the above 4 data variables. **So even though there is one single program, it is running multiple times in separate processes, each holding its own data values.** This is the reason for the name of the pattern this code represents: single program, multiple data. The print line at the end of main() represents the multiple different data output produced by each process.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TSEhP3vv-_yX" }, "source": [ "## Master-Worker\n", "This is also a very common pattern used in parallel and distributed programming. Here's the sample small illustrative code. Review it and answer this: What is different between this example and the previous one?\n" ] }, { "cell_type": "code", "metadata": { "id": "UlF-_TYc_uKu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a00e390d-f76d-41e1-eb7b-d9388abeb592" }, "source": [ "%%writefile MasterWorker.java\n", "/* MasterWorker.java\n", " * ... illustrates the master-worker pattern\n", " * using basic MPI commands\n", " * and OpenMPI's Java interface.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./MasterWorker\n", " *\n", " */\n", "\n", "import mpi.*;\n", "\n", "public class MasterWorker {\n", "\n", " public static final int MASTER = 0;\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " int id = MPI.COMM_WORLD.getRank();\n", " int numProcesses = MPI.COMM_WORLD.getSize();\n", " String hostName = MPI.getProcessorName();\n", " String message = \"Greetings from \";\n", "\n", " if (id == MASTER) {\n", " message += \"the master, #\" + id\n", " + \" (\" + hostName + \")\"\n", " + \" of \" + numProcesses + \"\\n\";\n", " } else {\n", " message += \"a worker, #\" + id\n", " + \" (\" + hostName + \")\"\n", " + \" of \" + numProcesses + \"\\n\";\n", " }\n", "\n", " System.out.print(message);\n", "\n", " MPI.Finalize();\n", " }\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "FQ0rjAxS_9xK" }, "source": [ "The answer to the above question illustrates what we can do with this pattern: based on the process id, we can have one process carry out something different than the others. This concept is used a lot as a means to coordinate activities, where one process, often called the master, has the responsibility of handing out work and keeping track of results. We will see this in later examples.\n", "\n", "**Note:** By convention, the master coordinating process is usually the process number 0." ] }, { "cell_type": "code", "metadata": { "id": "j8HeRMx-APS2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2078fe6f-e866-4b84-91ac-506b2a3c0af9" }, "source": [ "!javac -cp ./mpi.jar MasterWorker.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar MasterWorker" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-Eyj6sa7GoXu" }, "source": [ "### Exercises:\n", "\n", "- Rerun, using varying numbers of processes from 1 through 8 (i.e., vary the argument after -np).\n", "- Explain what stays the same and what changes as the number of processes changes." ] }, { "cell_type": "markdown", "metadata": { "id": "v1uIEnUS-JGG" }, "source": [ "# Decomposition using parallel for loop patterns\n", "\n", "The most common way to complete a repeated task in any program language is a loop. We use loops because we want to do a certain number of tasks, very often because we want to work on a set of data elements found in a Buffer, an array, or some other data structure. If the work to be done in each loop is independent of previous iterations, we can use separate processes to do parts of the loop independently. This program structure pattern is called the parallel for loop pattern, which is an implementation strategy for decomposition of the work to be done into smaller parts." ] }, { "cell_type": "markdown", "metadata": { "id": "kVWqN3EhJFbJ" }, "source": [ "## Parallel Loop Split into Equal Sized Chunks\n", "\n", "In the code below, notice the use of the variable called `REPS`. This is designed to be the total amount or work, or repetitions, that the for loop is accomplishing. This particular code is designed so that if those repetitions do not divide equally by the number of processes, then the program will stop with a warning message printed by the master process.\n", "\n", "Remember that because this is still also a SPMD program, all processes execute the code in the part of the if statement that evaluates to True. Each process has its own id, and we can determine how many processes there are, so we can choose where in the overall number of REPs of the loop each process will execute." ] }, { "cell_type": "code", "metadata": { "id": "3OpxV7pnJ_u2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1d7ab552-a45b-4690-e6ed-6220eb156b63" }, "source": [ "%%writefile ParallelLoopEqualChunks.java\n", "/* ParallelLoopEqualChunks.java\n", " * ... illustrates the parallel loop pattern in OpenMPI+Java,\n", " * in which processes perform the loop's iterations in equal-sized 'chunks'\n", " * (preferable when loop iterations access memory/cache locations) ...\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " * with error-handling logic by Libby Shoop, Macalester College, 2017\n", " *\n", " * Usage: mpirun -np 4 java ./ParallelLoopEqualChunks\n", " *\n", " * Exercise:\n", " * - Compile and run, varying N: 1, 2, 4, and 8\n", " * - Change REPS to 16, save, recompile, rerun, varying N again.\n", " * - Explain how this pattern divides the iterations of the loop\n", " * among the processes.\n", " * - What if REPS is not evenly divisible by N?\n", " * What would be a better way to handle that case?\n", " */\n", "\n", "import mpi.*;\n", "\n", "public class ParallelLoopEqualChunks {\n", "\n", " public static final int REPS = 8;\n", " public static final int MASTER = 0;\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " int id = MPI.COMM_WORLD.getRank();\n", " int numProcesses = MPI.COMM_WORLD.getSize();\n", " String message = \"\";\n", "\n", " // For this first example, ensure that the REPS can be evenly divided by the\n", " // number of processes and that the number of processes doesn't exceed REPS.\n", " // If that is not the case, have the master print an error msg and stop.\n", " if ((REPS % numProcesses) > 0 || numProcesses > REPS) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run with -np divisible by and less than or equal to \"\n", " + REPS + \"\\n\\n\");\n", " }\n", " } else {\n", " int chunkSize = REPS / numProcesses; // find chunk size\n", " int start = id * chunkSize; // find starting index\n", " int stop = start + chunkSize; // find stopping index\n", "\n", " for (int i = start; i < stop; i++) { // iterate through our range\n", " message = \"Process \" + id + \" is performing iteration \" + i + \"\\n\";\n", " System.out.print(message);\n", " }\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "BBshPRYXLLhJ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "113155eb-a2d7-4ac0-ca6f-22fe20f550c2" }, "source": [ "!javac -cp ./mpi.jar ParallelLoopEqualChunks.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar ParallelLoopEqualChunks" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "LJ_ThE3-KthZ" }, "source": [ "### Exercises\n", "\n", "- Run, using these numbers of processes, N: 1, 2, 4, and 8 (i.e., vary the argument to -np).\n", "- Change REPS to 16 in the code and rerun it. Then rerun with mpirun, varying N again.\n", "- Explain how this pattern divides the iterations of the loop among the processes.\n", "\n", "Which of the following is the correct assignment of loop iterations to processes for this code, when REPS is 8 and numProcesses is 4?\n", "\n", "\n", "![picture](https://drive.google.com/uc?id=1eUsjxYdWXWqThO_rdLO91HaLqBLAAh_S)" ] }, { "cell_type": "markdown", "metadata": { "id": "_okom_QiQeHe" }, "source": [ "## Parallel for Loop Program Structure: chunks of 1\n", "\n", "In the code below, we again use the variable called `REPS` for the total amount or work, or repetitions, that the for loop is accomplishing. This particular code is designed so that the number of repetitions should be more than or equal to the number of processes requested.\n", ".. note:: Typically in real problems, the number of repetitions is much higher than the number of processes. We keep it small here to illustrate what is happening.\n", "\n", "Like the last example all processes execute the code in the part of the if statement that evaluates to True. Note that in the for loop in this case we simply have process whose id is 0 start at iteration 0, then skip to 0 + numProcesses for its next iteration, and so on. Similarly, process 1 starts at iteration 1, skipping next to 1+ numProcesses, and continuing until REPs is reached. Each process performs similar single 'slices' or 'chunks of size 1' of the whole loop.\n" ] }, { "cell_type": "code", "metadata": { "id": "15B2xax5RXEY", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "47935fb4-317a-4940-90a9-ee0a687083a3" }, "source": [ "%%writefile ParallelLoopChunksOf1.java\n", "/* ParallelLoopChunksOf1.java\n", " * ... illustrates the parallel loop pattern in OpenMPI+Java,\n", " * in which processes perform the loop's iterations in 'chunks'\n", " * of size 1 (simple, and useful when loop iterations\n", " * do not access memory/cache locations) ...\n", " * Note this is much simpler than the 'equal chunks' loop.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./ParallelLoopChunksOf1\n", " *\n", " * Exercise:\n", " * - Compile and run, varying N: 1, 2, 3, 4, 5, 6, 7, 8\n", " * - Change REPS to 16, save, recompile, rerun, varying N again.\n", " * - Explain how this pattern divides the iterations of the loop\n", " * among the processes.\n", " */\n", "\n", "import mpi.*;\n", "\n", "public class ParallelLoopChunksOf1 {\n", "\n", " public static final int REPS = 8;\n", " public static final int MASTER = 0;\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " int id = MPI.COMM_WORLD.getRank();\n", " int numProcesses = MPI.COMM_WORLD.getSize();\n", " String message = \"\";\n", "\n", " if (numProcesses > REPS) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run with -np less than or equal to \"\n", " + REPS + \"\\n\\n\");\n", " }\n", " } else {\n", " for (int i = id; i < REPS; i += numProcesses) { \n", " message = \"Process \" + id + \" is performing iteration \" + i + \"\\n\";\n", " System.out.print(message);\n", " }\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "t0ewx6nLSLgV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2a2f9d94-849c-4de3-e8e5-c9b9f2940b5f" }, "source": [ "!javac -cp ./mpi.jar ParallelLoopChunksOf1.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar ParallelLoopChunksOf1" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Vww7AO93S8QD" }, "source": [ "### Exercises\n", "- Run, using these numbers of processes, N: 1, 2, 4, and 8\n", "- Compare source code to output.\n", "- Change REPS to 16, save, rerun, varying N again.\n", "- Explain how this pattern divides the iterations of the loop among the processes.\n", "\n", "Which of the following is the correct assignment of loop iterations to processes for this code, when REPS is 8 and numProcesses is 4?\n", "\n", "\n", "![picture](https://drive.google.com/uc?id=1eUsjxYdWXWqThO_rdLO91HaLqBLAAh_S)" ] }, { "cell_type": "markdown", "metadata": { "id": "sGj_Em7xToeB" }, "source": [ "# Point to point communication: the message passing pattern\n", "\n", "The fundamental basis of coordination between independent processes is point-to-point communication between processes through the communication links in the MPI.COMM_WORLD. The form of communication is called message passing, where one process **sends** data to another one, who in turn must **receive** it from the sender. This is illustrated as follows:\n", "\n", "![picture](https://drive.google.com/uc?id=1WJcOXq6Dn5TKF9Lng8r18_y2b23tHFe8)" ] }, { "cell_type": "markdown", "metadata": { "id": "bXSiO5dGuJda" }, "source": [ "## Message Passing Pattern: Key Problem\n", "\n", "The following code represents a common error that many programmers have inadvertently placed in their code. The concept behind this program is that we wish to use communication between pairs of processes, like this:\n", "\n", "![picture](https://drive.google.com/uc?id=1UJ2acj6XzphD2W6gnutNF2YGp6wU529Z)\n", "\n", "For message passing to work between a pair of processes, one must send and the other must receive. If we wish to **exchange** data, then each process will need to perform both a send and a receive.\n", "The idea is that process 0 will send data to process 1, who will receive it from process 0. Process 1 will also send some data to process 0, who will receive it from process 1. Similarly, processes 2 and 3 will exchange messages: process 2 will send data to process 3, who will receive it from process 2. Process 3 will also send some data to process 2, who will receive it from process 3.\n", "\n", "If we have more processes, we still want to pair up processes together to exchange messages. The mechanism for doing this is to know your process id. If your id is odd (1, 3 in the above diagram), you will send and receive from your neighbor whose id is id - 1. If your id is even (0, 2), you will send and receive from your neighbor whose id is id + 1. This should work even if we add more than 4 processes, as long as the number of processes is divisible by 2.\n", "\n", "![warning sign](https://drive.google.com/uc?id=1SEqDBTBSKwNVXzn-zueWa7fBCm5b1_MB)\n", "**Warning** There is a problem with the following code called *deadlock*. This happens when every process is waiting on an action from another process. The program cannot complete. **To stop the program, choose the small square that appears after you choose to run the mpirun cell.**\n" ] }, { "cell_type": "code", "metadata": { "id": "A_-_ypqDvAM0", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a59a1fa5-100e-4a58-91d1-a1a724e5d874" }, "source": [ "%%writefile MessagePassingDeadlock.java\n", "/* MessagePassing.java\n", " * ... illustrates how use of MPI's send and receive commands\n", " * can lead to deadlock.\n", " *\n", " * Goal: Have MPI processes pair up and exchange their id numbers.\n", " *\n", " * Note: Values are sent/received in Java using arrays or buffers.\n", " * Buffers are preferred b/c they work for all communication calls.\n", " *\n", " * Joel Adams, Calvin University, November 2019,\n", " * with error-handling from Hannah Sonsalla, Macalester College 2017.\n", " *\n", " * Usage: mpirun -np 4 java ./MessagePassing\n", " *\n", " * Exercise:\n", " * - Compile, then run using 1 process, then 2 processes.\n", " * (Use Cntl-c to terminate.)\n", " * - Use source code to trace execution.\n", " * - Why does this fail?\n", "\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class MessagePassingDeadlock {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int numProcesses = comm.getSize();\n", " int id = comm.getRank();\n", "\n", " if ( numProcesses <= 1 || (numProcesses % 2) != 0) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run this program using -np N where N is positive and even.\\n\\n\");\n", " }\n", " } else {\n", " IntBuffer sendBuf = MPI.newIntBuffer(1);\n", " sendBuf.put(id);\n", " IntBuffer receiveBuf = MPI.newIntBuffer(1);\n", "\n", " if ( odd(id) ) { // odd processes receive from their 'left' neighbor, then send\n", " comm.recv(receiveBuf, 1, MPI.INT, id-1, 0); \n", " comm.send(sendBuf, 1, MPI.INT, id-1, 0);\n", " } else { // even processes receive from their 'right' neighbor, then send\n", " comm.recv(receiveBuf, 1, MPI.INT, id+1, 0); \n", " comm.send(sendBuf, 1, MPI.INT, id+1, 0);\n", " }\n", "\n", " String message = \"Process \" + id + \" sent '\" + sendBuf.get(0)\n", " + \"' and received '\" + receiveBuf.get(0) + \"'\\n\";\n", " System.out.print(message);\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " public static boolean odd(int number) { return number % 2 != 0; }\n", "\n", " private static final int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VbbAxy7vvYwW" }, "source": [ "!javac -cp ./mpi.jar MessagePassingDeadlock.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar MessagePassingDeadlock" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0MLayXixxraM" }, "source": [ "![warning sign](https://drive.google.com/uc?id=1SEqDBTBSKwNVXzn-zueWa7fBCm5b1_MB)Remember,**To stop the program, choose the small square that appears after you choose to run the mpirun cell.**" ] }, { "cell_type": "markdown", "metadata": { "id": "49L8NojO6axL" }, "source": [ "#### What causes the deadlock?\n", "\n", "Each process, regardless of its id, will execute a receive request first. In this model, recv is a **blocking** function- it will not continue until it gets data from a send. So every process is blocked waiting to receive a message.\n", "\n", "#### Can you think of how to fix this problem?\n", "\n", "Since recv is a **blocking** function, we need to have some processes send first, while others correspondingly recv first from those who send first. This provides coordinated exchanges.\n", "\n", "Go to the next example to see the solution.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "XxKI85d8LLll" }, "source": [ "## Message Passing Patterns: avoiding deadlock\n", "\n", "Let's look at a few more correct message passing examples.\n", "\n", "### Fix the Deadlock\n", "\n", "To fix deadlock of the previous example, we coordinate the communication between pairs of processes so that there is an ordering of sends and receives between them.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s)**Important:** The new code corrects deadlock with a simple change: odd process sends first, even process receives first. *This is the proper pattern for exchanging data between pairs of processes.*" ] }, { "cell_type": "code", "metadata": { "id": "OO8N49wuL0fR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "566deccb-fad7-44db-fae9-bd92340ba25c" }, "source": [ "%%writefile MessagePassing.java\n", "/* MessagePassing.java\n", " * ... illustrates the use of MPI's send and receive commands,\n", " * using OpenMPI's Java interface.\n", " *\n", " * Goal: Have MPI processes pair up and exchange their id numbers.\n", " *\n", " * Note: Values are sent/received in Java using arrays or buffers.\n", " * Buffers are preferred b/c they work for all communication calls.\n", " *\n", " * Joel Adams, Calvin University, November 2019,\n", " * with error-handling from Hannah Sonsalla, Macalester College 2017.\n", " *\n", " * Usage: mpirun -np 4 java ./MessagePassing\n", " *\n", " * Exercise:\n", " * - Compile and run, using N = 4, 6, 8, and 10 processes.\n", " * - Use source code to trace execution.\n", " * - Explain what each process:\n", " * -- sends\n", " * -- receives\n", " * -- outputs.\n", " * - Run using N = 5 processes. What happens?\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class MessagePassing {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int numProcesses = comm.getSize();\n", " int id = comm.getRank();\n", "\n", " if ( numProcesses <= 1 || (numProcesses % 2) != 0) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run this program using -np N where N is positive and even.\\n\\n\");\n", " }\n", " } else {\n", " IntBuffer sendBuf = MPI.newIntBuffer(1);\n", " sendBuf.put(id);\n", " IntBuffer receiveBuf = MPI.newIntBuffer(1);\n", "\n", " if ( odd(id) ) { // odd processes send, then receive\n", " comm.send(sendBuf, 1, MPI.INT, id-1, 0);\n", " comm.recv(receiveBuf, 1, MPI.INT, id-1, 0); \n", " } else { // even processes receive then send\n", " comm.recv(receiveBuf, 1, MPI.INT, id+1, 0); \n", " comm.send(sendBuf, 1, MPI.INT, id+1, 0);\n", " }\n", "\n", " String message = \"Process \" + id + \" sent '\" + sendBuf.get(0)\n", " + \"' and received '\" + receiveBuf.get(0) + \"'\\n\";\n", " System.out.print(message);\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " public static boolean odd(int number) { return number % 2 != 0; }\n", "\n", " private static final int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0zl-Ms_kMMql", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "78601a4f-64b7-4142-d2ec-dc21c58b6466" }, "source": [ "!javac -cp ./mpi.jar MessagePassing.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar MessagePassing" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UKgBEDizMkRM" }, "source": [ "### Exercise\n", "\n", "- Run, using N = 4, 6, 8, and 10 processes. (Note what happens if you use an odd number instead.)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Y4ybJILhM137" }, "source": [ "## Sending data structures\n", "This next example illustrates that we can exchange different Buffers of data between processes.\n" ] }, { "cell_type": "code", "metadata": { "id": "grQWl82lNWsn", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "fc82d10f-3118-4f4d-c480-21e6e68fcab6" }, "source": [ "%%writefile MessagePassing2.java\n", "/* MessagePassing2.java\n", " * ... illustrates the use of MPI's send and receive commands\n", " * to send Strings via CharBuffers, using OpenMPI's Java interface.\n", " *\n", " * Goal: Have MPI processes pair up and exchange their host-names.\n", " *\n", " * Note: Values are sent/received in Java using arrays or buffers.\n", " * Buffers are preferred as they work for both blocking and\n", " * non-blocking communication calls.\n", " * This example uses chars but the same approach works with numbers.\n", " *\n", " * Joel Adams, Calvin University, November 2019;\n", " * error-handling adapted from Hannah Sonsalla, Macalester College 2017.\n", " *\n", " * Usage: mpirun -np 4 java ./MessagePassing2\n", " *\n", " * Exercise:\n", " * - Compile and run, using N = 1, 2, 4, and 8 processes.\n", " * - Use source code to trace execution.\n", " * - Compare to MessagePassing.java; note send-receive differences.\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.CharBuffer;\n", "\n", "public class MessagePassing2 {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int numProcesses = comm.getSize();\n", " int id = comm.getRank();\n", "\n", " if ( numProcesses <= 1 || (numProcesses % 2) != 0) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run this program using -np N where N is positive and even.\\n\\n\");\n", " }\n", " MPI.Finalize();\n", " System.exit(0);\n", " } \n", "\n", " String hostName = MPI.getProcessorName();\n", " CharBuffer sendBuf = MPI.newCharBuffer(BUFFER_SIZE);\n", " //sendBuf.put(hostName); // this builds and is supposed to work but doesn't,\n", " // (UTF-16 vs UTF-8?) so we'll do it the long way\n", " for (int i = 0; i < hostName.length(); ++i) {\n", " sendBuf.put(i, hostName.charAt(i));\n", " }\n", "\n", " CharBuffer receiveBuf = MPI.newCharBuffer(BUFFER_SIZE);\n", " Status status;\n", "\n", " if ( odd(id) ) { // odd processes send, then receive\n", " comm.send(sendBuf, hostName.length(), MPI.CHAR, id-1, 0);\n", " status = comm.recv(receiveBuf, BUFFER_SIZE, MPI.CHAR, id-1, 0); \n", " } else { // even processes receive then send\n", " status = comm.recv(receiveBuf, BUFFER_SIZE, MPI.CHAR, id+1, 0); \n", " comm.send(sendBuf, hostName.length(), MPI.CHAR, id+1, 0);\n", " }\n", "\n", " String sentString = sendBuf.toString();\n", " String receivedString = receiveBuf.toString();\n", " String message = \"Process \" + id + \" sent '\" + hostName\n", " + \"' and received '\" + receivedString + \"'\\n\";\n", " System.out.print(message);\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " private static boolean odd(int number) { return number % 2 != 0; }\n", "\n", " private static final int MASTER = 0;\n", " private static final int BUFFER_SIZE = 256;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9zYlFcz8Nnx1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "88b32775-ddad-4b75-c2c1-2d7b01ad0b09" }, "source": [ "!javac -cp ./mpi.jar MessagePassing2.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar MessagePassing2" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "lFx0VyZzN1Aw" }, "source": [ "### Exercise\n", "\n", "- Run, using N = 4, 6, 8, and 10 processes. \n", "- In the above code, locate where the Buffer of elements to be sent is being made by each process. What is different about each Buffer per process?\n" ] }, { "cell_type": "markdown", "metadata": { "id": "eJQfgyqlOTCr" }, "source": [ "## Ring of passed messages\n", "Another pattern that appears in message passing programs is to use a ring of processes, where messages get sent in this fashion:\n", "\n", "![picture of ring of message passing](https://drive.google.com/uc?id=16VMF9t8nD3JcVehFvs4dbzIiU5eDuZbG)\n", "\n", "When we have 4 processes, the idea is that process 0 will send data to process 1, who will receive it from process 0 and then send it to process 2, who will receive it from process 1 and then send it to process 3, who will receive it from process 2 and then send it back around to process 0." ] }, { "cell_type": "code", "metadata": { "id": "n4PQNTd3PAGR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ddff9853-3d7f-479f-f043-0e616885d36d" }, "source": [ "%%writefile MessagePassing3.java\n", "/* MessagePassing3.java\n", " * ... illustrates the use of MPI's send and receive commands\n", " * in combination with the master-worker pattern.\n", " *\n", " * Goal: The master process sends its id to process 1\n", " * and receives a buffer of ids from process N-1.\n", " * Every other process i receives a buffer of ids from process i-1,\n", " * appends its id to the buffer, and sends the buffer to process (i+1)%N.\n", " *\n", " * Joel Adams, Calvin University, November 2019,\n", " *\n", " * Usage: mpirun -np 4 java ./MessagePassing3\n", " *\n", " * Exercise:\n", " * - Compile and run, varying N from 1-8.\n", " * - Explain the behavior you observe.\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class MessagePassing3 {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int numProcesses = comm.getSize();\n", " int id = comm.getRank();\n", "\n", " if ( numProcesses <= 1 ) {\n", " if (id == MASTER) {\n", " System.out.print(\"\\nPlease run this program using -np N where N is at least 2.\\n\\n\");\n", " }\n", " MPI.Finalize();\n", " System.exit(0);\n", " } \n", "\n", " IntBuffer sendBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", " IntBuffer receiveBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", " Status status;\n", "\n", " if ( id == MASTER ) { // MASTER:\n", " sendBuf.put(0, id); // 1. put id in buffer\n", " comm.send(sendBuf, // 2. send: buffer,\n", " 1, // number of values,\n", " MPI.INT, // type of values,\n", " id+1, // destination id,\n", " 0); // tag.\n", " status = comm.recv(receiveBuf, // 3. recv: buffer,\n", " BUFFER_SIZE, // buffer capacity,\n", " MPI.INT, // type of values,\n", " numProcesses-1, // sender id,\n", " 0); // tag.\n", " int valuesReceived = status.getCount(MPI.INT); // 4. how many did we get?\n", " output(receiveBuf, valuesReceived); // 5. output what we got.\n", " } else { // WORKERS:\n", " status = comm.recv(receiveBuf, // 1. receive: buffer,\n", " BUFFER_SIZE, // buffer capacity,\n", " MPI.INT, // type of values,\n", " id-1, // sender id,\n", " 0); // tag.\n", " int valuesReceived = status.getCount(MPI.INT); // 2. how many did we get?\n", " output(receiveBuf, valuesReceived); // 3. output what we got.\n", " receiveBuf.put(valuesReceived, id); // 4. append id to buffer\n", " comm.send(receiveBuf, // 5. send: buffer,\n", " valuesReceived+1, // number of values,\n", " MPI.INT, // type of values,\n", " (id+1) % numProcesses, // destination id,\n", " 0); // tag.\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to print an IntBuffer with descriptive labels.\n", " * @param: buf, an IntBuffer.\n", " * @param: size, the number of ints in IntBuffer\n", " * (b/c IntBuffer has no length() method,\n", " * whose bright idea was that?).\n", " * POST: The ints in buf have been displayed on System.out,\n", " * preceded by spaces, and with a newline at the end.\n", " */\n", " private static void output(IntBuffer buf, int size) throws MPIException {\n", " System.out.printf(\"Process %d of %d received:\",\n", " MPI.COMM_WORLD.getRank(),\n", " MPI.COMM_WORLD.getSize());\n", " for (int i = 0; i < size; ++i) {\n", " System.out.print( \" \" );\n", " System.out.print( buf.get(i) );\n", " }\n", " System.out.print(\"\\n\");\n", " }\n", "\n", " private static final int MASTER = 0;\n", " private static final int BUFFER_SIZE = 256;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iGTpIE-pPRIq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7026fadc-8767-4774-d5cb-46afd668c8c8" }, "source": [ "!javac -cp ./mpi.jar MessagePassing3.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar MessagePassing3" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "o6bvSqgMPeyt" }, "source": [ "### Exercises\n", "- Run, using N = from 1 through 8 processes.\n", "- Make sure that you can trace how the code generates the output that you see.\n", "- How is the finishing of the 'ring' completed, where the last process determines that it should send back to process 0?" ] }, { "cell_type": "markdown", "metadata": { "id": "bPqOxvbK5qSx" }, "source": [ "# Collective Communication: Broadcast pattern\n", "There are many cases when a master process obtains or creates data that needs to be sent to all of the other processes. There is a special pattern for this called **broadcast**. You will see examples of the master sending different types of data to each of the other processes." ] }, { "cell_type": "markdown", "metadata": { "id": "57wzDFu2xxvW" }, "source": [ "## Broadcast from master to workers\n", "\n", "We will look at three types of data that can be created in the master and sent to the workers. Rather than use send and receive, we will use a special new function called bcast.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s) **Note:** In each code example, note how the master does one thing, and the workers do another, but **all of the processes execute the bcast function.**\n" ] }, { "cell_type": "markdown", "metadata": { "id": "n3eZ1sN1yVsK" }, "source": [ "### Broadcast an integer\n", "\n", "Find the place in this code where the data is being broadcast to all of the processes. Match the prints to the output you observe when you run it." ] }, { "cell_type": "code", "metadata": { "id": "HVx87ecmynYO", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "eb94261d-4033-4e6a-946f-7efd0080c2cc" }, "source": [ "%%writefile Broadcast.java\n", "/* Broadcast.java\n", " * ... illustrates the use of MPI's broadcast command with a scalar value.\n", " *\n", " * Note: This version uses an IntBuffer of length 1 to store the scalar.\n", " *\n", " * Goal: The master process reads an 'answer' value from a file\n", " * and broadcasts it to all the other processes.\n", " * Each process outputs its 'answer' value before and after\n", " * the broadcast.\n", " *\n", " * Joel Adams, Calvin University, November 2019,\n", " *\n", " * Usage: mpirun -np 4 java ./Broadcast\n", " *\n", " * Exercise:\n", " * - Compile, then run several times,\n", " * using 2, 4, and 8 processes\n", " * - Use source code to trace execution and output\n", " * (noting contents of file \"data.txt\");\n", " * - Explain behavior/effect of MPI_Bcast().\n", " */\n", "\n", "import java.io.File;\n", "import java.io.FileNotFoundException;\n", "import java.util.Scanner;\n", "import java.nio.IntBuffer;\n", "import mpi.*;\n", "\n", "public class Broadcast {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", "\n", " IntBuffer answerBuf = MPI.newIntBuffer(1);\n", "\n", " if ( id == MASTER ) { // MASTER: read data from file\n", " int answer = 42;\n", " answerBuf.put(answer);\n", " }\n", "\n", " String beforeMsg = \"BEFORE the broadcast, process \" + id\n", " + \"'s answer is: \" + answerBuf.get(0) + \"\\n\";\n", " System.out.print(beforeMsg); // all: output 'before' values\n", "\n", " printSeparator(\"----\", id);\n", "\n", " comm.bcast(answerBuf, 1, MPI.INT, 0); // all: participate in broadcast\n", "\n", " String afterMsg = \"AFTER the broadcast, process \" + id\n", " + \"'s answer is: \" + answerBuf.get(0) + \"\\n\";\n", " System.out.print(afterMsg); // all: output 'after' values\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to print a separator string between the 'before' and 'after' parts.\n", " * @param: separator, a String.\n", " * @param: id, the rank of this MPI process.\n", " * POST: the master has printed the separator to System.out.\n", " */\n", " public static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.println(separator); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", " private static final int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "SK268l7SzB5e", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "96df228c-f1c5-411b-dff6-d948f856b45a" }, "source": [ "!javac -cp ./mpi.jar Broadcast.java \n", "!mpirun --allow-run-as-root -np 8 java -cp ./mpi.jar Broadcast" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "tNe1kQLpyqh2" }, "source": [ "#### Exercise\n", "- Run, using N = from 1 through 8 processes." ] }, { "cell_type": "markdown", "metadata": { "id": "FcckAX8G0Pd-" }, "source": [ "### Broadcast user input\n", "\n", "The following program will take extra input that will get broadcast to all processes." ] }, { "cell_type": "code", "metadata": { "id": "uI7oPCn309N_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f41fb72b-287c-45e3-a39e-65bdea3fefce" }, "source": [ "%%writefile BroadcastUserInput.java\n", "/* Broadcast.java\n", " * ... illustrates the use of MPI's broadcast command \n", " * to broadcast a value entered from the commandline.\n", " *\n", " * Note: This version uses an IntBuffer of length 1 to store the scalar.\n", " *\n", " * Goal: The master process \"reads\" an input value from the commandline\n", " * and broadcasts it to all the other processes.\n", " * Each process outputs its value before and after\n", " * the broadcast.\n", " *\n", " * Original C version by Hannah Sonsalla, Macalester College 2017,\n", " * Java version by Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./BroadcastUserInput <integer>\n", " *\n", " * Exercise:\n", " * - Compile and run several times, varying the number of \n", " * MPI processes and input value.\n", " * - Explain behavior you observe.\n", " */\n", "\n", "import java.nio.IntBuffer;\n", "import mpi.*;\n", "\n", "public class BroadcastUserInput {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", " String hostName = MPI.getProcessorName();\n", "\n", " IntBuffer answerBuf = MPI.newIntBuffer(1); // allocate buffer\n", "\n", " if (id == MASTER) {\n", " getInput(args, answerBuf); // MASTER: fill buffer\n", " }\n", "\n", " String beforeMsg = \"BEFORE the broadcast, the answer of process \" + id\n", " + \" on host \" + hostName \n", " + \" is \" + answerBuf.get(0) + \"\\n\";\n", " System.out.print(beforeMsg); // all: output 'before' values\n", "\n", " comm.bcast(answerBuf, 1, MPI.INT, 0); // all: participate in broadcast\n", "\n", " printSeparator(\"----\", id);\n", "\n", " String afterMsg = \"AFTER the broadcast, the answer of process \" + id\n", " + \" on host \" + hostName \n", " + \" is: \" + answerBuf.get(0) + \"\\n\";\n", " System.out.print(afterMsg); // all: output 'after' values\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to hide details of having the master read an int value\n", " * from the commandline (can be adapted to read from anywhere else).\n", " *\n", " * @param: args, a String array containing the commandline arguments.\n", " * @param: buf, an IntBuffer in which to the input value is to be stored.\n", " *\n", " * PRE: args[0] contains an integer input value (as a String).\n", " * POST: buf contains the integer from args[0], or else a default value.\n", " */\n", " private static void getInput(String [] args, IntBuffer buf) {\n", " int result = 0;\n", " if (args.length >= 1) {\n", " result = Integer.parseInt( args[0] );\n", " } else {\n", " System.err.println(\"\\nUsage: mpirun -np <N> java BroadcastUserInput <integer>\\n\");\n", " }\n", " buf.put(result);\n", " } \n", "\n", " /* utility to print a separator between the 'before' and 'after' parts.\n", " * @param: separator, a String.\n", " * @param: id, the rank of this MPI process.\n", " * POST: separator has been printed to System.out.\n", " */\n", " public static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.println(separator); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", "\n", " private static final int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wUUqC5t11qJW" }, "source": [ "![warning sign](https://drive.google.com/uc?id=1SEqDBTBSKwNVXzn-zueWa7fBCm5b1_MB)\n", "**Warning** This program is unlike any of the others and takes in a second argument, as shown below. " ] }, { "cell_type": "code", "metadata": { "id": "kzeQF4rB1aa3", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d21b6018-a855-4a0d-81e8-9ff1897f3408" }, "source": [ "!javac -cp ./mpi.jar BroadcastUserInput.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar BroadcastUserInput 6222021" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ocfu_ZYw3QCz" }, "source": [ "#### Exercise\n", "- Run, using N = from 1 through 8 processes, with an integer of your choosing." ] }, { "cell_type": "markdown", "metadata": { "id": "0gcBP4q93eCj" }, "source": [ "### Broadcast a Buffer\n", "\n", "This is just one more example to show that other data structures can also be broadcast from the master to all worker processes." ] }, { "cell_type": "code", "metadata": { "id": "d8k_sECN3xCv", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ab68977a-a73e-458e-eb7b-992e3782e1a6" }, "source": [ "%%writefile BroadcastArray.java\n", "/* BroadcastArray.java\n", " * ... illustrates the use of MPI's broadcast command with multiple values.\n", " *\n", " * Note: This version uses an array to store the values.\n", " *\n", " * Goal: The master process fills an array with values\n", " * and broadcasts it to all the other processes.\n", " * Each process outputs its array before and after\n", " * the broadcast.\n", " *\n", " * Joel Adams, Calvin University, November 2019,\n", " *\n", " * Usage: mpirun -np 4 java ./Broadcast2\n", " *\n", " * Exercise:\n", " * - Compile, then run several times,\n", " * using 2, 4, and 8 processes\n", " * - Use source code to trace execution and output\n", " * - Explain behavior/effect of MPI_Bcast().\n", " */\n", "\n", "import mpi.*;\n", "\n", "public class BroadcastArray {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", "\n", " int [] array = new int[ARRAY_SIZE]; // all: allocate array \n", "\n", " if ( id == MASTER ) { // MASTER: fill its array\n", " fill(array);\n", " }\n", "\n", " print(\"BEFORE\", id, array); // all: print buffers before\n", "\n", " printSeparator(\"----\", id, comm);\n", "\n", " comm.bcast(array, array.length, MPI.INT, 0); // all: participate in broadcast\n", "\n", " print(\"AFTER\", id, array); // all: print buffers after\n", "\n", " MPI.Finalize();\n", " }\n", "\n", "\n", " /* utility to fill an array with some values.\n", " * @param: a, an int array.\n", " * POST: a has been filled with int values.\n", " */\n", " private static void fill(int [] a) {\n", " for (int i = 0; i < a.length; ++i) {\n", " a[i] = i + 11;\n", " }\n", " }\n", "\n", " /* utility to print a buffer with descriptive labels.\n", " * @param: label, a String.\n", " * @param: id, this process's MPI rank.\n", " * @param, a, an int array.\n", " * POST: label, id, and a have been displayed via System.out.\n", " */\n", " private static void print(String label, int id, int [] a) {\n", " String msg = label + \" the broadcast, process \" + id\n", " + \"'s array contains:\";\n", " for (int i = 0; i < a.length; ++i) {\n", " msg += (\" \" + a[i]);\n", " }\n", " msg += \"\\n\";\n", " System.out.print(msg); \n", " }\n", " \n", " /* utility to print a separator string between the 'before' and 'after' parts.\n", " * @param: separator, a String.\n", " * @param: id, the rank of this MPI process.\n", " * @param: comm, the Communicator for the processes involved.\n", " * POST: the master has printed the separator to System.out.\n", " */\n", " public static void printSeparator(String separator, int id, Comm comm) \n", " throws MPIException {\n", " comm.barrier();\n", " if (id == MASTER) { System.out.println(separator); }\n", " comm.barrier();\n", " }\n", "\n", " private static final int MASTER = 0;\n", " private static final int ARRAY_SIZE = 8;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "vSGPEJ244FdZ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "95b0594a-3843-4c43-93ff-b478a55f8f83" }, "source": [ "!javac -cp ./mpi.jar BroadcastArray.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar BroadcastArray" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "IGWlHIPh4t08" }, "source": [ "#### Exercise\n", "- Run, using N = from 1 through 8 processes.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ogLec1ig6HZj" }, "source": [ "# Collective Communication: reduction pattern\n", "\n", "There are often cases when every process needs to complete a partial result of an overall computation. For example if you want to process a large set of numbers by summing them together into one value (i.e. *reduce* a set of numbers into one value, its sum), you could do this faster by having each process compute a partial sum, then have all the processes communicate to add each of their partial sums together.\n", "\n", "This is so common in parallel processing that there is a special collective communication function called **reduce** that does just this." ] }, { "cell_type": "markdown", "metadata": { "id": "cwiw83d44_gR" }, "source": [ "## Collective Communication: reduce function\n", "\n", "The type of reduction of many values down to one can be done with different types of operators on the set of values computed by each process.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "PrwM90WO6jKv" }, "source": [ "### Reduce all values using sum and max\n", "In this example, every process computes the square of (id+1). Then all those values are summed together and also the maximum function is applied." ] }, { "cell_type": "code", "metadata": { "id": "0jPJPMgE5U_t", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8f7cfa88-18a7-48ae-ff61-409fea63caaa" }, "source": [ "%%writefile Reduction.java\n", "/* Reduction.java\n", " * ... illustrates how to use the reduction pattern \n", " * (which combines distributed values in O(lg(P)) time)\n", " * using OpenMPI's Java interface.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./Reduction\n", " *\n", " * Exercise:\n", " * - Compile and run, varying N = 1, 2, 3, 4, 6, 8, 10.\n", " * - Explain behavior of the reduce operation.\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class Reduction {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", "\n", " int square = (id+1) * (id+1);\n", " IntBuffer squareBuffer = MPI.newIntBuffer(BUFFER_SIZE);\n", " squareBuffer.put(square);\n", "\n", " IntBuffer sumSquaresBuffer = MPI.newIntBuffer(BUFFER_SIZE);\n", " comm.reduce(squareBuffer, sumSquaresBuffer, BUFFER_SIZE,\n", " MPI.INT, MPI.SUM, MASTER);\n", "\n", " IntBuffer maxBuffer = MPI.newIntBuffer(BUFFER_SIZE);\n", " comm.reduce(squareBuffer, maxBuffer, BUFFER_SIZE,\n", " MPI.INT, MPI.MAX, MASTER);\n", "\n", " if ( id == MASTER) {\n", " String squareMsg = \"\\nThe sum of the squares from 1 to \"\n", " + numProcesses + \" is \" \n", " + sumSquaresBuffer.get(0) + \"\\n\\n\";\n", " String maxMsg = \"The max of the squares from 1 to \"\n", " + numProcesses + \" is \" \n", " + maxBuffer.get(0) + \"\\n\\n\";\n", " System.out.print(squareMsg);\n", " System.out.print(maxMsg);\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " private static int BUFFER_SIZE = 1;\n", " private static int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "8xAazDP17GJJ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "29e2feff-a2ae-4950-e6bd-ed8d35378732" }, "source": [ "!javac -cp ./mpi.jar Reduction.java\n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar Reduction" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "YRLLMm9j7iOA" }, "source": [ "#### Exercises\n", "- Run, using N = from 1 through 8 processes.\n", "- Try replacing MPI.MAX with MPI.MIN(minimum) and/or replacing MPI.SUM with MPI.PROD (product). Then save and run the code again.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JnO-S8V37tpD" }, "source": [ "### Reduction on a Buffer of values\n", "\n", "We can try reduction with a Buffer of values; note this in the following example. Then note how you can change the semantics in the exercises.\n" ] }, { "cell_type": "code", "metadata": { "id": "gsAow5Ds8DAv", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "36562141-fb8e-4081-9456-bb3a42987fff" }, "source": [ "%%writefile Reduction2.java\n", "/* Reduction2.java\n", " * ... illustrates the reduction pattern on multiple values,\n", " * using buffers in OpenMPI's Java interface.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./Reduction2\n", " *\n", " * Exercise:\n", " * - Compile, then run with N = 1, 2, 3, 4, \n", " * comparing output to source code.\n", " * - Explain behavior of reduce() in terms of\n", " * srcBuf and destBuf.\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class Reduction2 {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", "\n", " IntBuffer srcBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", " IntBuffer destBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", "\n", " if (id == MASTER) {\n", " System.out.print(\"\\nBefore reduction: \");\n", " printBuf(id, \"destBuf\", destBuf); \n", " }\n", "\n", " for (int i = 0; i < BUFFER_SIZE; ++i) {\n", " srcBuf.put(i, id * i);\n", " }\n", "\n", " printSeparator(\"\", id);\n", " printBuf(id, \"srcBuf\", srcBuf);\n", " printSeparator(\"----\", id);\n", "\n", " comm.reduce(srcBuf, destBuf, BUFFER_SIZE,\n", " MPI.INT, MPI.SUM, MASTER);\n", "\n", " if ( id == MASTER) {\n", " System.out.print(\"After reduction: \");\n", " printBuf(id, \"destBuf\", destBuf);\n", " System.out.println();\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to display the contents of an IntBuffer.\n", " * @param: id, the int MPI rank of this process.\n", " * @param: bufName, a String that is the name of the buffer.\n", " * @param: buf, the IntBuffer.\n", " * @param: size, the size of buf.\n", " */\n", " private static void printBuf(int id, String bufName, IntBuffer buf) {\n", " String msg = \"Process \" + id + \", \" + bufName + \": [\";\n", " int size = buf.capacity();\n", " int sizeLessOne = size - 1;\n", " for (int i = 0; i < size; ++i) {\n", " msg += buf.get(i);\n", " if (i < sizeLessOne ) {\n", " msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\";\n", " System.out.print(msg);\n", " }\n", "\n", " /* utility to print a separator between before and after sections.\n", " * @param: separator, a String.\n", " * @param: id, the MPI rank of this process. \n", " * POST: separator has been printed by the master process.\n", " */\n", " private static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.println(separator); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", " private static int BUFFER_SIZE = 5;\n", " private static int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Uow-rYeS8rxc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e5d72f2e-6eef-4ffc-a31d-062f91db57bf" }, "source": [ "!javac -cp ./mpi.jar Reduction2.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar Reduction2" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "P74yRNds9SBx" }, "source": [ "#### Exercises\n", "- Run, using N = from 1 through 4 processes.\n", "- Uncomment the two lines of runnable code that are commented in the main() function. Observe the new results and explain why the MPI.SUM (using the + operator underneath) behaves the way it does on Buffers, and what the new function called sumListByElements is doing instead.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s) **Note:** There are two ways in Python that you might want to sum a set of Buffers from each process: 1) concatenating the elements together, or 2) summing the element at each location from each process and placing the sum in that location in a new Buffer. In the latter case, the new Buffer is the same length as the original Buffer on each process.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "HYFc1mVp-e2r" }, "source": [ "# Collective Communication: scatter and gather pattern\n", "\n", "There are often cases when each process can work on some portion of a larger data structure. This can be carried out by having the master process maintain the larger structure and send parts to each of the worker processes, keeping part of the structure on the master. Each process then works on their portion of the data, and then the master can get the completed portions back.\n", "\n", "This is so common in message passing parallel processing that there are two special collective communication functions called **scatter** and **gather** that handle this.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gL2CQgIo-xTd" }, "source": [ "## Collective Communication: scatter and gather Buffers\n", "\n", "When several processes need to work on portions of a data structure, such as a buffer of buffers or a 1-d or 2-d array, at various points in a program, a way to do this is to have one node, usually the master, divide the data structure and send portions to each of the other processes, often keeping one portion for itself. Each process then works on that portion of the data, and then the master can get the completed portions back. This type of coordination is so common that MPI has special patterns for it called **scatter** and **gather**.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "1g5HzFU1--zY" }, "source": [ "### Scatter Buffers\n", "The following diagrams illustrate how scattering a Buffer of Buffersworks. The master contains a Buffer of Buffers and all processes participate in the scatter:\n", "\n", "![scatter lists diagram](https://drive.google.com/uc?id=1QDRW2JeAa_TelKxZTphCPF393Bxn_BbL)\n", "\n", "After the scatter is completed, each process has one of the smaller Buffers to work on, like this:\n", "\n", "![after scatter lists diagram](https://drive.google.com/uc?id=1xA2NRtm1k4_g16tJTWBFCVArKLfIEurc)\n", "\n", "In this next code example, some small Buffers are created in a Buffer whose length is as long as the number of processes.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s) **Note:** In the code below, note how all processes must call the scatter function." ] }, { "cell_type": "code", "metadata": { "id": "FI6C7UH7AiMV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ccdcdc95-72d1-4794-83f7-c92f2917cadb" }, "source": [ "%%writefile Scatter.java\n", "/* Scatter.java\n", " * ... illustrates the basic scatter pattern \n", " * using buffers in OpenMPI's Java interface.\n", " *\n", " * Note: If the number of values being scattered is not\n", " * evenly divisible by the number of processes,\n", " * use scatterv() instead of scatter.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./Scatter\n", " *\n", " * Exercise:\n", " * - Compile, then run with N = 1, 2, 4, 8. \n", " * - Trace execution through source code. \n", " * - Explain behavior/effect of scatter. \n", " * - What if BUFFER_SIZE is not evenly divisible by N?\n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class Scatter {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", "\n", " if (numProcesses > BUFFER_SIZE) {\n", " if (id == MASTER) {\n", " System.out.println(\"\\nPlease run this program with N <= 8 processes\\n\");\n", " }\n", " MPI.Finalize();\n", " System.exit(0);\n", " }\n", " IntBuffer sendBuf = null;\n", " IntBuffer recvBuf = null;\n", "\n", " if (id == MASTER) {\n", " sendBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", " for (int i = 0; i < BUFFER_SIZE; ++i) {\n", " sendBuf.put(i, (i+1) * 11);\n", " }\n", " System.out.print(\"\\nBefore scatter: \");\n", " printBuf(id, \"sendBuf\", sendBuf);\n", " }\n", " \n", " int numSent = BUFFER_SIZE / numProcesses;\n", " \n", " comm.barrier(); // see comment on next barrier\n", "\n", " recvBuf = MPI.newIntBuffer(numSent);\n", " printBuf(id, \"recvBuf\", recvBuf);\n", "\n", " printSeparator(\"----\", id);\n", "\n", " comm.scatter(sendBuf, numSent, MPI.INT, \n", " recvBuf, numSent, MPI.INT, MASTER); \n", "\n", " if (id == MASTER) {\n", " System.out.print(\"After scatter:\\n\");\n", " }\n", " comm.barrier(); // all of these barriers are here\n", " printBuf(id, \"recvBuf\", recvBuf); // just to make the output easier\n", " comm.barrier(); // to read; no effect on correctness\n", " if (id == MASTER) {\n", " System.out.println();\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to display the contents of an IntBuffer.\n", " * @param: id, the int MPI rank of this process.\n", " * @param: bufName, a String that is the name of the buffer.\n", " * @param: buf, the IntBuffer.\n", " * @param: size, the size of buf.\n", " */\n", " private static void printBuf(int id, String bufName, IntBuffer buf) {\n", " String msg = \"Process \" + id + \", \" + bufName + \": [\";\n", " int size = buf.capacity();\n", " int sizeLessOne = size - 1;\n", " for (int i = 0; i < size; ++i) {\n", " msg += buf.get(i);\n", " if (i < sizeLessOne ) {\n", " msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\";\n", " System.out.print(msg);\n", " }\n", "\n", " /* utility to print a separator between before and after sections.\n", " * @param: separator, a String.\n", " * @param: id, the MPI rank of this process. \n", " * POST: separator has been printed by the master process.\n", " */\n", " private static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.print(separator + \"\\n\"); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", " private static int BUFFER_SIZE = 8;\n", " private static int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "qgt8fHPaA0CH", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e813b0ea-23e7-4054-bc77-625240a77893" }, "source": [ "!javac -cp ./mpi.jar Scatter.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar Scatter" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "H9EYqJldBRS-" }, "source": [ "#### Exercises\n", "- Run, using N = from 2 through 8 processes.\n", "- If you want to study the code, explain to yourself what genListofLists does in the code below.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "tIw9YI6GB5go" }, "source": [ "### Gather Lists\n", "Once several processes have their own Buffers of data, those Buffers can also be gathered back together into a Buffer of Buffers, usually in the master process. All processes participate in a gather, like this:\n", "\n", "![before gather diagram](https://drive.google.com/uc?id=1OWHNMKCEKsGpExJCO6l5czW9QFyMZiT6)\n", "\n", "The gather creates a Buffer of Buffers in the master, like this:\n", "\n", "![after gather diagram](https://drive.google.com/uc?id=1W9lky1LY0L0K6iyA00jsNV4hAnmmbvP2)\n", "\n", "In this example, each process creates some very small Buffers. Then a gather is used to create a Buffer of Buffers on the master process.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s) **Note:** In the code below, note how all processes must call the gather function.\n" ] }, { "cell_type": "code", "metadata": { "id": "GPk2IBX_C46Z", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8ab9b72a-c830-4f56-b587-1c14e3d0910d" }, "source": [ "%%writefile Gather.java\n", "/* Gather.java\n", " * ... illustrates the basic gather pattern \n", " * using buffers in OpenMPI's Java interface.\n", " *\n", " * Note: If the number of values being gathered is not\n", " * evenly divisible by the number of processes,\n", " * use gatherv() instead of gather.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Usage: mpirun -np 4 java ./Gather\n", " *\n", " * Exercise:\n", " * - Compile, then run with N = 1, 2, 3, 4, 5. \n", " * - Trace execution through source code. \n", " * - Explain behavior/effect of gather. \n", " */\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class Gather {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", "\n", " IntBuffer gatherBuf = null;\n", "\n", " if (id == MASTER) {\n", " int valuesToGather = BUFFER_SIZE * numProcesses;\n", " gatherBuf = MPI.newIntBuffer(valuesToGather);\n", " System.out.print(\"\\nBefore gather: \");\n", " printBuf(id, \"gatherBuf\", gatherBuf);\n", " }\n", " \n", " IntBuffer computeBuf = MPI.newIntBuffer(BUFFER_SIZE);\n", " for (int i = 0; i < BUFFER_SIZE; ++i) {\n", " computeBuf.put(i, id * 10 + i);\n", " } \n", " comm.barrier(); // These barriers are just here\n", " printBuf(id, \"computeBuf\", computeBuf); // to make the output easier to read;\n", " comm.barrier(); // no effect on functional correctness\n", "\n", " printSeparator(\"----\", id);\n", "\n", " comm.gather(computeBuf, BUFFER_SIZE, MPI.INT, \n", " gatherBuf, BUFFER_SIZE, MPI.INT, MASTER); \n", "\n", " if (id == MASTER) {\n", " System.out.print(\"After gather: \");\n", " printBuf(id, \"gatherBuf\", gatherBuf); \n", " System.out.println();\n", " }\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to display the contents of an IntBuffer.\n", " * @param: id, the int MPI rank of this process.\n", " * @param: bufName, a String that is the name of the buffer.\n", " * @param: buf, the IntBuffer.\n", " * @param: size, the size of buf.\n", " */\n", " private static void printBuf(int id, String bufName, IntBuffer buf) {\n", " String msg = \"Process \" + id + \", \" + bufName + \": [\";\n", " int size = buf.capacity();\n", " int sizeLessOne = size - 1;\n", " for (int i = 0; i < size; ++i) {\n", " msg += buf.get(i);\n", " if (i < sizeLessOne ) {\n", " msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\";\n", " System.out.print(msg);\n", " }\n", "\n", " /* utility to print a separator between before and after sections.\n", " * @param: separator, a String.\n", " * @param: id, the MPI rank of this process. \n", " * POST: separator has been printed by the master process.\n", " */\n", " private static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.print(separator + \"\\n\"); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", " private static int BUFFER_SIZE = 3;\n", " private static int MASTER = 0;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "UvdQljANDOtE", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "224f73ab-bba2-4b7b-d259-f049541ddef5" }, "source": [ "!javac -cp ./mpi.jar Gather.java\n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar Gather" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "mPVwi5W4DpwJ" }, "source": [ "#### Exercises\n", "- Run, using N = from 2 through 8 processes.\n", "- Try with different values of SMALL_LIST_SIZE, perhaps changing printing of result for readability\n" ] }, { "cell_type": "markdown", "metadata": { "id": "-eCGJi2hEOC1" }, "source": [ "## Collective Communication: scatter and gather arrays\n", "\n", "The OpenMPI Java bindings offers several collective communication functions that are designed to work with *Buffers* (CharBuffer, IntBuffer, etc.) from the java.nio package.\n", "\n", "These Buffers behave like 1-dimensional arrays, where each value in the array is at a particular index. The MPI scatter function can be used to send portions of a larger Buffer on the master to the workers, like this:\n", "\n", "![alt text](https://drive.google.com/uc?id=1n2YmY12tBrTxtJK6MFpBX9nWmopQGT_s)\n", "\n", "The result of doing this then looks like this, where each process has a portion of the original that they can then work on:\n", "\n", "![alt text](https://drive.google.com/uc?id=19GNbTWWJEOU16wNjwzHon4jpC5fXKj_1)\n", "\n", "The reverse of this process can be done using the Gather function.\n", "\n", "In this example, a 1-D Buffer is created by the master, then scattered to the workers. After the smaller Buffer used by each process is changed, the MPI gather function brings the changed small Buffers back to the master, where they are combined into a single larger Buffer.\n", "\n", "![Important symbol](https://drive.google.com/uc?id=1AWRLAqeaqi7SG7PHyOVywZRuMDK9Z2_s) **Note:** In the code below, note how all processes must call the scatter and gather functions." ] }, { "cell_type": "code", "metadata": { "id": "cO06o4HAFBZR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ae2bf754-8b78-4b76-a7ca-799b79ef6794" }, "source": [ "%%writefile ScatterLoopGather.java\n", "/* ScatterLoopGather.java\n", " * ... uses MPI to scatter a buffer of values into chunks, \n", " * a loop to process those chunks, and \n", " * a gather to combine the piecemeal values.\n", " *\n", " * Goal: The master process fills a buffer with values\n", " * and scatters it to all the other processes.\n", " * Each process doubles the values in its buffer-chunk.\n", " * All processes then gather the chunks back to the master.\n", " *\n", " * Joel Adams, Calvin University, November 2019.\n", " *\n", " * Note: This assumes BUFFER_SIZE is evenly divisible by N.\n", " *\n", " * Usage: mpirun -np 4 java ./BroadcastLoopGather\n", " *\n", " * Exercise:\n", " * - Compile, then run, using 1, 2, 4, and 8 processes\n", " * - Use source code to trace execution and output\n", " * - Explain behavior/effect of the scatter and gather.\n", " * - Optional: change BUFFER_SIZE to be another multiple of 8, such as 16\n", " */\n", "\n", "import java.nio.IntBuffer;\n", "import mpi.*;\n", "\n", "public class ScatterLoopGather {\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", "\n", " Comm comm = MPI.COMM_WORLD;\n", " int id = comm.getRank();\n", " int numProcesses = comm.getSize();\n", " IntBuffer scatterBuffer = null;\n", " IntBuffer chunkBuffer = null;\n", " IntBuffer gatherBuffer = null;\n", "\n", " if ( BUFFER_SIZE % numProcesses != 0 || numProcesses > BUFFER_SIZE ) {\n", " String errorMsg = \"\\nPlease run this program with -np N where N is\\n\"\n", " + \" <= \" + BUFFER_SIZE + \" and divides evenly into \"\n", " + BUFFER_SIZE + \"\\n\\n\";\n", " System.err.println(errorMsg);\n", " MPI.Finalize();\n", " System.exit(0);\n", " }\n", "\n", " if ( id == MASTER ) { \n", " scatterBuffer = MPI.newIntBuffer(BUFFER_SIZE);\n", " fill(scatterBuffer);\n", " gatherBuffer = MPI.newIntBuffer(BUFFER_SIZE);\n", " }\n", "\n", " printBuffers(\"BEFORE the scatter\", id, scatterBuffer, chunkBuffer, gatherBuffer);\n", "\n", " int chunkSize = BUFFER_SIZE / numProcesses;\n", " chunkBuffer = MPI.newIntBuffer(chunkSize);\n", "\n", " comm.scatter(scatterBuffer, chunkSize, MPI.INT, \n", " chunkBuffer, chunkSize, MPI.INT, MASTER);\n", "\n", " printSeparator(\"----\", id);\n", " printBuffers(\"AFTER the scatter\", id, scatterBuffer, chunkBuffer, gatherBuffer);\n", "\n", " doubleChunk(chunkBuffer);\n", "\n", " printSeparator(\"----\", id);\n", " printBuffers(\"AFTER the doubling\", id, scatterBuffer, chunkBuffer, gatherBuffer);\n", "\n", " comm.gather(chunkBuffer, chunkSize, MPI.INT, \n", " gatherBuffer, chunkSize, MPI.INT, MASTER);\n", "\n", " printSeparator(\"----\", id);\n", " printBuffers(\"AFTER the gather:\", id, scatterBuffer, chunkBuffer, gatherBuffer);\n", "\n", " MPI.Finalize();\n", " }\n", "\n", " /* utility to fill a Buffer with some values.\n", " * @param: buf, an IntBuffer.\n", " * POST: buf has been filled with int values.\n", " */\n", " private static void fill(IntBuffer buf) {\n", " for (int i = 0; i < buf.capacity(); ++i) {\n", " buf.put(i, i + 11);\n", " }\n", " }\n", "\n", " /* utility to print a buffer with labels.\n", " * @param: label, a String.\n", " * @param: id, an int containing the MPI rank of this process.\n", " * @param: sBuf, the IntBuffer the master fills and scatters.\n", " * @param: cBuf, the IntBuffer for storing a process's chunk.\n", " * @param: gBuf, the IntBuffer for storing the gathered results.\n", " * POST: The buffers' contents have been printed, with labels.\n", " */\n", " private static void printBuffers(String label, int id, \n", " IntBuffer sBuf, IntBuffer cBuf, IntBuffer gBuf) {\n", " String msg = label + \", process \" + id\n", " + \"'s scatterBuffer is: [\";\n", " if (sBuf != null) {\n", " for (int i = 0; i < sBuf.capacity(); ++i) {\n", " msg += sBuf.get(i);\n", " if (i < sBuf.capacity()-1) msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\\t\\t\\t\\tchunkBuffer is: [\";\n", " if (cBuf != null) {\n", " for (int i = 0; i < cBuf.capacity(); ++i) {\n", " msg += cBuf.get(i);\n", " if (i < cBuf.capacity()-1) msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\\t\\t\\t\\tgatherBuffer is: [\";\n", " if (gBuf != null) {\n", " for (int i = 0; i < gBuf.capacity(); ++i) {\n", " msg += gBuf.get(i);\n", " if (i < gBuf.capacity()-1) msg += \",\";\n", " }\n", " }\n", " msg += \"]\\n\";\n", " System.out.print(msg);\n", " }\n", "\n", " /* utility to double the values in a chunk of an array.\n", " * @param: fullBuf, an IntBuffer containing all the values.\n", " * @param: id, the MPI rank of this process.\n", " * @param: chunkBuf, an IntBuffer into which we will write our values.\n", " * PRE: chunkBuf.capacity() == BUFFER_SIZE / numProcesses.\n", " * POST: chunkBuf contains the doubled values of this process's chunk\n", " * of fullBuf.\n", " */\n", " private static void doubleChunk(IntBuffer chunkBuf) {\n", " for (int i = 0; i < chunkBuf.capacity(); ++i) {\n", " int value = chunkBuf.get(i);\n", " chunkBuf.put(i, value * 2);\n", " }\n", " }\n", "\n", " /* utility to print a separator string between the 'before' and 'after' parts.\n", " * @param: separator, a String.\n", " * @param: id, the rank of this MPI process.\n", " * POST: the master has printed the separator to System.out.\n", " */\n", " public static void printSeparator(String separator, int id) throws MPIException {\n", " MPI.COMM_WORLD.barrier();\n", " if (id == MASTER) { System.out.println(separator); }\n", " MPI.COMM_WORLD.barrier();\n", " }\n", "\n", " private static final int MASTER = 0;\n", " private static final int BUFFER_SIZE = 8;\n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "uMIeHGkDFQ1V", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "838f8bb0-6b52-4139-dfc8-a5c921f2bdce" }, "source": [ "!javac -cp ./mpi.jar ScatterLoopGather.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar ScatterLoopGather" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "qKhQXI7oFRMf" }, "source": [ "#### Exercises\n", "- Run, using N = from 2 through 8 processes.\n", "- If you want to study the numpy part of the code, look up the numpy function linspace used in genArray().\n" ] }, { "cell_type": "markdown", "metadata": { "id": "dnjHsPScGpEP" }, "source": [ "# When amount of work varies: balancing the load\n", "\n", "There are algorithms where the master is used to assign tasks to workers by sending them data and receiving results back as each worker completes a task (or after the worker completes all of its tasks). In many of these cases, the computation time needed by each worker process for each of its tasks can vary somewhat dramatically. This situation is where **dynamic load balancing** can be helpful.\n", "\n", "In this example we combine the master-worker pattern with message passing. The master has many tasks that need to be completed. The master starts by sending some data needed to complete a task to each worker process. Then the master loops and waits to hear back from each worker by receiving a message from any of them. When the master receives a message from a worker, it sends that worker more data for its next task, unless there are no more tasks to complete, in which case it sends a special message to the worker to stop running.\n", "\n", "In this simple example, each worker is sent the number of seconds it should 'sleep', which can vary from 1 to 8. This illustrates varying sizes of workloads. Because of the code's simplicity, the number of tasks each worker does doesn't vary by much. In some real examples, the time for one task my be quite different than the time for another, which could have a different outcome, in which some workers were able to complete more tasks as others were doing long ones.\n", "\n", "This approach can sometimes be an improvement on the assignment of an equal number of tasks to all processes.\n", "\n", "Note in this case how the master, whose id is 0, handles the assignment of tasks, while the workers simply do what they are sent until they are told to stop." ] }, { "cell_type": "code", "metadata": { "id": "ow-7CfeCHY3n", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c54015c3-75b3-43b9-9b15-2bac38c8102e" }, "source": [ "%%writefile DynamicLoadBalance.java\n", "/* DynamicLoadBalance.java\n", " * ... illustrates how to use the dynamic load balancing pattern \n", " * using OpenMPI's Java interface.\n", " * This code was based on the dynamicLoadBalance.py script,\n", " * that was originally written by Libby Shoop (Macalester College)\n", " * \n", " * Ruth Kurniawati, Westfield State University, June 2021.\n", " *\n", " * Usage: mpirun -np 4 java ./DynamicLoadBalance\n", " *\n", " * Exercise:\n", " * - Compile and run, varying N = 4, 8.\n", " * - Explain behavior of the dynamic load balancing of the available work\n", " */\n", "\n", "import java.util.Arrays;\n", "import java.util.Random;\n", "\n", "import mpi.*;\n", "import java.nio.IntBuffer;\n", "\n", "public class DynamicLoadBalance {\n", " public static final int MASTER = 0;\n", "\n", " // tags that can be applied to messages\n", " public static final int WORKTAG = 1;\n", " public static final int DIETAG = 2;\n", "\n", " public static void main(String [] args) throws MPIException {\n", " MPI.Init(args);\n", " \n", " int id = MPI.COMM_WORLD.getRank();\n", " int numProcesses = MPI.COMM_WORLD.getSize();\n", " //String hostName = MPI.getProcessorName();\n", " \n", " if (id == MASTER) {\n", " // create an arbitrary array of numbers for how long each\n", " // worker task will 'work', by sleeping that amount of seconds\n", " int numTasks = (numProcesses-1) * 4; // avg 4 tasks per worker process\n", " int[] workTimes = genTasks(numTasks);\n", " System.out.println(\"master created \" + workTimes.length + \" values for sleep times:\" + Arrays.toString(workTimes));\n", " handOutWork(MPI.COMM_WORLD, workTimes, numProcesses);\n", " \n", " } else {\n", " worker(MPI.COMM_WORLD);\n", " } \n", " MPI.Finalize();\n", " }\n", "\n", " private static int[] genTasks(int numTasks) {\n", " int[] tasks = new int[numTasks];\n", " Random r = new Random(1000); // use the same seed\n", " for(int i = 0; i < numTasks; i++) {\n", " tasks[i] = r.nextInt(8) + 1;\n", " }\n", " return tasks;\n", " }\n", "\n", " private static void worker(Comm comm) throws MPIException {\n", " // keep receiving messages and do work, unless tagged to 'die'\n", " IntBuffer buf = MPI.newIntBuffer(1);\n", " while(true) {\n", " Status stat = comm.recv(buf, 1, MPI.INT, 0, MPI.ANY_TAG);\n", " int waitTime = buf.get(0);\n", " System.out.println(\"worker \"+comm.getRank()+\" got \"+ waitTime);\n", " if (stat.getTag() == DIETAG) {\n", " System.out.println(\"worker \"+comm.getRank()+\" dying\");\n", " return;\n", " }\n", " // simulate work by sleeping\n", " try {\n", " Thread.sleep(1000*waitTime); // sleep for waitTime seconds\n", " } catch (InterruptedException e) {\n", " e.printStackTrace();\n", " } \n", "\n", " // indicate done with work by sending to Master\n", " //System.out.println(\"worker \"+comm.getRank()+\" completed work!\");\n", " buf.put(0, waitTime);\n", " comm.send(buf, 1, MPI.INT, 0, WORKTAG);\n", " }\n", " }\n", "\n", " private static void handOutWork(Comm comm, int[] workTimes, int numProcesses) throws MPIException {\n", " int totalWork = workTimes.length;\n", " int workCount = 0, recvCount = 0;\n", " System.out.println(\"master sending first tasks\");\n", " IntBuffer sendBuf = MPI.newIntBuffer(1);\n", " \n", " for(int id = 1; id < numProcesses; id++) {\n", " int work = workTimes[workCount++];\n", " sendBuf.put(0, work);\n", " comm.send(sendBuf, 1, MPI.INT, id, WORKTAG);\n", " System.out.println(\"master sent \"+ work +\" to \"+id);\n", " }\n", "\n", " // while there is still work,\n", " // receive result from a worker, which also\n", " // signals they would like some new work\n", " IntBuffer recvBuf = MPI.newIntBuffer(1);\n", " while (workCount < totalWork) {\n", " // System.out.println(\"Master workcount \" + workCount + \", total \"+ totalWork);\n", " // receive next finished result\n", " Status stat = comm.recv(recvBuf, 1, MPI.INT, MPI.ANY_SOURCE, WORKTAG);\n", " recvCount++;\n", " int workerId = stat.getSource();\n", " int completedWorkTime = recvBuf.get(0);\n", " System.out.println(\"master received \"+completedWorkTime+\" from \"+ workerId);\n", " // send next work\n", " int newWorkTime = workTimes[workCount++];\n", " sendBuf.put(0, newWorkTime);\n", " comm.send(sendBuf, 1, MPI.INT, workerId, WORKTAG);\n", " System.out.println(\"master sent \"+newWorkTime+\" to \"+ workerId);\n", " }\n", " // Receive results for outstanding work requests.\n", " while (recvCount < totalWork) {\n", " Status stat = comm.recv(recvBuf, 1, MPI.INT, MPI.ANY_SOURCE, WORKTAG);\n", " recvCount++;\n", " int workerId = stat.getSource();\n", " int completedWorkTime = recvBuf.get(0);\n", " System.out.println(\"end: master received \"+completedWorkTime+\" from \"+ workerId);\n", " }\n", "\n", " // Tell all workers to stop\n", " sendBuf.put(0, -1);\n", " for(int id =1; id < numProcesses; id++) {\n", " comm.send(sendBuf, 1, MPI.INT, id, DIETAG);\n", " }\n", " } \n", "}\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9t30ynerHaaN", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "43447a14-a733-47d7-e464-cc8baac75103" }, "source": [ "!javac -cp ./mpi.jar DynamicLoadBalance.java \n", "!mpirun --allow-run-as-root -np 4 java -cp ./mpi.jar DynamicLoadBalance" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "t65YHXekHbBK" }, "source": [ "## Exercises\n", "- Run, using N = 4 processes\n", "- Study the execution carefully. Note that with 4 processes, 3 are workers. The total number of tasks is 3*4, or 12. Which process does the most work? You can count by looking for the lines that end with \"... from X\", where X is a worker process id.\n", "- Try with N = 8 (7 workers)." ] } ] }
gpl-2.0
sfegan/calin
unit_tests/python/notebooks/Test air_shower - atmosphere.ipynb
2
13275
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import calin.simulation.atmosphere\n", "import scipy.integrate" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "atm = calin.simulation.atmosphere.LayeredAtmosphere(\"/Users/sfegan/gd/Code/Projects/Simulations/EGS5/Parameters/atmprof6.dat\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "H = frange(0,20,0.1)\n", "nmo = asarray(list(map(lambda h: atm.n_minus_one(h), H*1e5)))\n", "rho = asarray(list(map(lambda h: atm.rho(h), H*1e5)))\n", "t = asarray(list(map(lambda h: atm.thickness(h), H*1e5)))\n", "delay = asarray(list(map(lambda h: atm.propagation_time_correction(h), H*1e5)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1077ea710>]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gpl-2.0
gevann/mir_proj
code/project analysis.ipynb
1
3826975
null
mit
ES-DOC/esdoc-jupyterhub
notebooks/mpi-m/cmip6/models/mpi-esm-1-2-lr/seaice.ipynb
1
99815
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: MPI-M \n", "**Source ID**: MPI-ESM-1-2-LR \n", "**Topic**: Seaice \n", "**Sub-Topics**: Dynamics, Thermodynamics, Radiative Processes. \n", "**Properties**: 80 (63 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/seaice?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:17" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-lr', 'seaice')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Model](#1.-Key-Properties---&gt;-Model) \n", "[2. Key Properties --&gt; Variables](#2.-Key-Properties---&gt;-Variables) \n", "[3. Key Properties --&gt; Seawater Properties](#3.-Key-Properties---&gt;-Seawater-Properties) \n", "[4. Key Properties --&gt; Resolution](#4.-Key-Properties---&gt;-Resolution) \n", "[5. Key Properties --&gt; Tuning Applied](#5.-Key-Properties---&gt;-Tuning-Applied) \n", "[6. Key Properties --&gt; Key Parameter Values](#6.-Key-Properties---&gt;-Key-Parameter-Values) \n", "[7. Key Properties --&gt; Assumptions](#7.-Key-Properties---&gt;-Assumptions) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid --&gt; Discretisation --&gt; Horizontal](#9.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Seaice Categories](#11.-Grid---&gt;-Seaice-Categories) \n", "[12. Grid --&gt; Snow On Seaice](#12.-Grid---&gt;-Snow-On-Seaice) \n", "[13. Dynamics](#13.-Dynamics) \n", "[14. Thermodynamics --&gt; Energy](#14.-Thermodynamics---&gt;-Energy) \n", "[15. Thermodynamics --&gt; Mass](#15.-Thermodynamics---&gt;-Mass) \n", "[16. Thermodynamics --&gt; Salt](#16.-Thermodynamics---&gt;-Salt) \n", "[17. Thermodynamics --&gt; Salt --&gt; Mass Transport](#17.-Thermodynamics---&gt;-Salt---&gt;-Mass-Transport) \n", "[18. Thermodynamics --&gt; Salt --&gt; Thermodynamics](#18.-Thermodynamics---&gt;-Salt---&gt;-Thermodynamics) \n", "[19. Thermodynamics --&gt; Ice Thickness Distribution](#19.-Thermodynamics---&gt;-Ice-Thickness-Distribution) \n", "[20. Thermodynamics --&gt; Ice Floe Size Distribution](#20.-Thermodynamics---&gt;-Ice-Floe-Size-Distribution) \n", "[21. Thermodynamics --&gt; Melt Ponds](#21.-Thermodynamics---&gt;-Melt-Ponds) \n", "[22. Thermodynamics --&gt; Snow Processes](#22.-Thermodynamics---&gt;-Snow-Processes) \n", "[23. Radiative Processes](#23.-Radiative-Processes) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Model \n", "*Name of seaice model used.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of sea ice model code (e.g. CICE 4.2, LIM 2.1, etc.)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.model.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Variables \n", "*List of prognostic variable in the sea ice model.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Prognostic\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the sea ice component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sea ice temperature\" \n", "# \"Sea ice concentration\" \n", "# \"Sea ice thickness\" \n", "# \"Sea ice volume per grid cell area\" \n", "# \"Sea ice u-velocity\" \n", "# \"Sea ice v-velocity\" \n", "# \"Sea ice enthalpy\" \n", "# \"Internal ice stress\" \n", "# \"Salinity\" \n", "# \"Snow temperature\" \n", "# \"Snow depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Seawater Properties \n", "*Properties of seawater relevant to sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS-10\" \n", "# \"Constant\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Ocean Freezing Point Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant seawater freezing point, specify this value.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Resolution \n", "*Resolution of the sea ice grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid e.g. N512L180, T512L70, ORCA025 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Tuning Applied \n", "*Tuning applied to sea ice model component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. Document the relative weight given to climate performance metrics versus process oriented metrics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Target\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What was the aim of tuning, e.g. correct sea ice minima, correct seasonal cycle.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Simulations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which simulations had tuning applied, e.g. all, not historical, only pi-control? *" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Metrics Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any observed metrics used in tuning model/parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Which variables were changed during the tuning process?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Key Parameter Values \n", "*Values of key parameters*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Typical Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *What values were specificed for the following parameters if used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ice strength (P*) in units of N m{-2}\" \n", "# \"Snow conductivity (ks) in units of W m{-1} K{-1} \" \n", "# \"Minimum thickness of ice created in leads (h0) in units of m\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Additional Parameters\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *If you have any additional paramterised values that you have used (e.g. minimum open water fraction or bare ice albedo), please provide them here as a comma separated list*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Assumptions \n", "*Assumptions made in the sea ice model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General overview description of any *key* assumptions made in this model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.description') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. On Diagnostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Note any assumptions that specifically affect the CMIP6 diagnostic sea ice variables.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Missing Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List any *key* processes missing in this model configuration? Provide full details where this affects the CMIP6 diagnostic sea ice variables?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the sea ice component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Provide a general description of conservation methodology.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Properties\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in sea ice by the numerical schemes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.properties') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Mass\" \n", "# \"Salt\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *For each conserved property, specify the output variables which close the related budgets. as a comma separated list. For example: Conserved property, variable1, variable2, variable3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Was Flux Correction Used\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does conservation involved flux correction?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Corrected Conserved Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *List any variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Sea ice discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Grid on which sea ice is horizontal discretised?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ocean grid\" \n", "# \"Atmosphere Grid\" \n", "# \"Own Grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the type of sea ice grid?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Structured grid\" \n", "# \"Unstructured grid\" \n", "# \"Adaptive grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the advection scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite differences\" \n", "# \"Finite elements\" \n", "# \"Finite volumes\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Thermodynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model thermodynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Dynamics Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the time step in the sea ice model dynamic component in seconds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.6. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional horizontal discretisation details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Sea ice vertical properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Layering\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What type of sea ice vertical layers are implemented for purposes of thermodynamic calculations?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Zero-layer\" \n", "# \"Two-layers\" \n", "# \"Multi-layers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Number Of Layers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using multi-layers specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional vertical grid details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Seaice Categories \n", "*What method is used to represent sea ice categories ?*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Has Mulitple Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Set to true if the sea ice model has multiple sea ice categories.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Number Of Categories\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify how many.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Category Limits\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *If using sea ice categories specify each of the category limits.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.4. Ice Thickness Distribution Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the sea ice thickness distribution scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.5. Other\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the sea ice model does not use sea ice categories specify any additional details. For example models that paramterise the ice thickness distribution ITD (i.e there is no explicit ITD) but there is assumed distribution and fluxes are computed accordingly.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.seaice_categories.other') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Grid --&gt; Snow On Seaice \n", "*Snow on sea ice details*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Has Snow On Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is snow on ice represented in this model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Number Of Snow Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels of snow on ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Snow Fraction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how the snow fraction on sea ice is determined*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify any additional details related to snow on ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamics \n", "*Sea Ice Dynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Horizontal Transport\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of horizontal advection of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Transport In Thickness Space\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice transport in thickness space (i.e. in thickness categories)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Incremental Re-mapping\" \n", "# \"Prather\" \n", "# \"Eulerian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Ice Strength Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Which method of sea ice strength formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Hibler 1979\" \n", "# \"Rothrock 1975\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Which processes can redistribute sea ice (including thickness)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.redistribution') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rafting\" \n", "# \"Ridging\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Rheology\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Rheology, what is the ice deformation formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.dynamics.rheology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Free-drift\" \n", "# \"Mohr-Coloumb\" \n", "# \"Visco-plastic\" \n", "# \"Elastic-visco-plastic\" \n", "# \"Elastic-anisotropic-plastic\" \n", "# \"Granular\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Thermodynamics --&gt; Energy \n", "*Processes related to energy in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Enthalpy Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the energy formulation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice latent heat (Semtner 0-layer)\" \n", "# \"Pure ice latent and sensible heat\" \n", "# \"Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)\" \n", "# \"Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Thermal Conductivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What type of thermal conductivity is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pure ice\" \n", "# \"Saline ice\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of heat diffusion?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Conduction fluxes\" \n", "# \"Conduction and radiation heat fluxes\" \n", "# \"Conduction, radiation and latent heat transport\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.4. Basal Heat Flux\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method by which basal ocean heat flux is handled?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heat Reservoir\" \n", "# \"Thermal Fixed Salinity\" \n", "# \"Thermal Varying Salinity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.5. Fixed Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If you have selected {Thermal properties depend on S-T (with fixed salinity)}, supply fixed salinity value for each sea ice layer.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.6. Heat Content Of Precipitation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which the heat content of precipitation is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.7. Precipitation Effects On Salinity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If precipitation (freshwater) that falls on sea ice affects the ocean surface salinity please provide further details.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Thermodynamics --&gt; Mass \n", "*Processes related to mass in sea ice thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. New Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method by which new sea ice is formed in open water.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Ice Vertical Growth And Melt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs the vertical growth and melt of sea ice.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Ice Lateral Melting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the method of sea ice lateral melting?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Floe-size dependent (Bitz et al 2001)\" \n", "# \"Virtual thin ice melting (for single-category)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Ice Surface Sublimation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method that governs sea ice surface sublimation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Frazil Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe the method of frazil ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Thermodynamics --&gt; Salt \n", "*Processes related to salt in sea ice thermodynamics.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Has Multiple Sea Ice Salinities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the sea ice model use two different salinities: one for thermodynamic calculations; and one for the salt budget?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Sea Ice Salinity Thermal Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does sea ice salinity impact the thermal properties of sea ice?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Thermodynamics --&gt; Salt --&gt; Mass Transport \n", "*Mass transport of salt*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the mass transport of salt calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Thermodynamics --&gt; Salt --&gt; Thermodynamics \n", "*Salt thermodynamics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Salinity Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is salinity determined in the thermodynamic calculation?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Prescribed salinity profile\" \n", "# \"Prognostic salinity profile\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Constant Salinity Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If using a constant salinity value specify this value in PSU?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the salinity profile used.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Thermodynamics --&gt; Ice Thickness Distribution \n", "*Ice thickness distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice thickness distribution represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Virtual (enhancement of thermal conductivity, thin ice melting)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Thermodynamics --&gt; Ice Floe Size Distribution \n", "*Ice floe-size distribution details.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How is the sea ice floe-size represented?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Parameterised\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Additional Details\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Please provide further details on any parameterisation of floe-size.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Thermodynamics --&gt; Melt Ponds \n", "*Characteristics of melt ponds.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Are Included\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are melt ponds included in the sea ice model?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Formulation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What method of melt pond formulation is used?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flocco and Feltham (2010)\" \n", "# \"Level-ice melt ponds\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.3. Impacts\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *What do melt ponds have an impact on?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Albedo\" \n", "# \"Freshwater\" \n", "# \"Heat\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Thermodynamics --&gt; Snow Processes \n", "*Thermodynamic processes in snow on sea ice*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Has Snow Aging\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has a snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Snow Aging Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow aging scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Has Snow Ice Formation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Set to True if the sea ice model has snow ice formation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Snow Ice Formation Scheme\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe the snow ice formation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Redistribution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the impact of ridging on snow cover?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.6. Heat Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *What is the heat diffusion through snow methodology in sea ice thermodynamics?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Single-layered heat diffusion\" \n", "# \"Multi-layered heat diffusion\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiative Processes \n", "*Sea Ice Radiative Processes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Surface Albedo\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used to handle surface albedo.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Parameterized\" \n", "# \"Multi-band albedo\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Ice Radiation Transmission\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method by which solar radiation through sea ice is handled.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Delta-Eddington\" \n", "# \"Exponential attenuation\" \n", "# \"Ice radiation transmission per category\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
robertoalotufo/ia898
2S2018/Seminarios/C_OpenCV.ipynb
1
1922188
null
mit
clopeau/Julia_Introduction
1 - Prise en main-Julia.ipynb
1
24119
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prise en main\n", "\n", "Pour un apprentissage rapide je recommande cette lecture (tout est dans le titre) http://learnxinyminutes.com/docs/julia/\n", "\n", "## Fonctions scientifiques\n", "\n", "Regardons les possibilités de calcul offert par cet environnement ! \n", "\n", "On est devant une superbe calculatrice comme dans la console de Matlab/Scilab ou R. On a donc l'utilisation des opérateurs classique <code>+-*/\\^</code> ainsi que toutes les fonctions scientifiques <code>sin</code>, <code>cos</code> <code>exp</code> <code>log</code> (attention ici logarithme népérien)....\n", "\n", "voir https://docs.julialang.org/en/v1/manual/mathematical-operations/\n", "\n", "A remarquer l'utilisation de la fonction <code>log</code> comme le logarithme naturel (Népérien)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.605170185988092" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log(100)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log10(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| **Fonctions usuelles**|\n", "|-|\n", "|exp, log, log10, abs, sqrt, cbrt, sign|\n", "\n", "| **Fonctions trigonométriques usuelles**|\n", "|-|\n", "| sin, cos, tan, cot, sec, csc, sinh, cosh, tanh, coth, sech, csch, asin, acos, atan |\n", "| acot, asec, acsc, asinh, acosh, atanh, acoth, asech, acsch, sinc, cosc, atan2|\n", "\n", "\n", "Il est possible d'utiliser les notations scientifiques <code>1e2</code> pour 100, ainsi que quelques nombre prédéfinis <code>pi</code>, <code>e</code>, <code>im</code> ..." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1e-2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.283185307179586" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2*pi" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1 + 0im" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "im^2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Certaines fonctions sont possèdent un domaine de définition comme <code>sqrt</code>, <code>log</code>... sur $I\\!\\!R$ extensible aux nombres complexes :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "ename": "DomainError", "evalue": "DomainError with -1.0:\nsqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).", "output_type": "error", "traceback": [ "DomainError with -1.0:\nsqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).", "", "Stacktrace:", " [1] throw_complex_domainerror(::Symbol, ::Float64) at ./math.jl:31", " [2] sqrt at ./math.jl:492 [inlined]", " [3] sqrt(::Int64) at ./math.jl:518", " [4] top-level scope at In[6]:1" ] } ], "source": [ "sqrt(-1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0 + 1.0im" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sqrt(complex(-1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'ensemble des fonctions scientifique est étendu au calcul complexe !" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.2984575814159773 + 0.6349639147847361im" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sin(1+im)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| **Fonctions d'arrondi**|\n", "|-|\n", "|round, floor, ceil, trunc|\n", "\n", "JULIA possède une algèbre étendue avec la possibilité de divisé par 0" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Inf" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1/0" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1/Inf" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Inf" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Inf+1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Inf/Inf" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Inf-Inf" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "0*Inf" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NaN+1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<code>Inf</code> et <code>NaN</code> représentant l'infini et Not A Number. Toutes les formes indéterminées donnant <code>NaN</code> et toute combinaison avec <code>NaN</code> renvoie également <code>NaN</code>." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variables \n", "\n", "L'utilisation de variable est très intuitive sans déclaration préalable de type (entier, réel, imaginaire, fraction rationnelle...). Le nom de la variable doit commencer par une lettre entre a-z ou A-Z mais aussi par un underscore ('_') ou encore un caractère Unicode (voir dernier exemple pour comprendre)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(a)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.4142135623730951" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b=sqrt(2)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(b)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3//4" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c=9//12" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rational{Int64}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(c)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c=a+b;\n", "typeof(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit dans ce qui précède que les variables sont typées, mais un mécanisme automatique de changement de type (comme <code>Int64</code> $ \\rightarrow$ <code>Float64</code>) permet la somme d'un entier et d'un réel. On peut demander à JULIA d'identifier un entier comme un réel (Float) avec l'ajout d'un <code>.</code> exemple <code>a=1.</code>.\n", "\n", "Une particularité est de ne pas avoir la possibilité de supprimer une variable ! Il est juste possible de récupérer l'espace mémoire en faisant <code>a=[]</code>." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0-element Array{Any,1}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=[]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toujours dans la rubrique particularité on verra que les fonctions sont aussi des variables, il sera donc possible de les passer en argument, de les affecter etc..." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ρ¹=1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'utilisation de caractères spétiaux comme les lettres grecques se font par utilisation d'une syntaxe de type LaTex. Dans la Plupart des éditeurs il faut commencer par <code>\\rho</code> puis la touche TAB fait afficher le charactère " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convention et style\n", "\n", "Julia impose quelques restriction de nom de variable, de plus les conventions suivantes sont d'usage :\n", "\n", "* Les noms des variables sont en minuscule.\n", "* La séparation des mots dans une variable se fait à l'aide d'un underscore ('_') mais cette pratique n'est pas recommandé pour une question de lisibilité des noms de variable.\n", "* Les noms de Type ou de Modules commencent par une lettre majuscule, les majuscules séparant les différents mots du nom de la variable (exemple \"MonModule\")\n", "* Les noms des fonctions et macros sont en minuscule sans underscores.\n", "* Les fonctions qui modifient en sortie leurs arguments d'entrée s'écrivent avec ! en fin." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Types de variable\n", "\n", "Julia n'est pas à proprement parler un \"langage objet\" néanmoins c'est ce que l'on peut appeler un \"langage typé\". En effet ce langage possède un certain nombre de types prédéfinis et permet d'en ajouter à volonter. Les nouveaux types faisant office de structure tel un objet (C++/Java...) permettant la surcharge des opérateurs standarts *, /, + ....\n", "\n", "### Les nombres scalaires\n", "On a vu précédemment que JULIA est assez flexible sur l'utilisation et affectation des variable et est capable de rendre compatible l'addition d'entier, réel (float)...\n", "\n", "De manière naturel on trouve les types :\n", "* <code>int8</code>, <code>uint8</code>, <code>int16</code>, <code>uint16</code>,<code>int32</code>, <code>uint32</code>, <code>int64</code>, <code>uint64</code>,<code>int128</code>, <code>uint128</code>.\n", "* <code>float16</code> (simple précision i.e 8 chiffres significatifs), <code>float32</code> (double précision, 16 chiffres significatifs), <code>float64</code> (32 chiffres significatifs)\n", "* <code>complex32</code>, <code>complex64</code>, <code>complex128</code>." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=1000000000000; typeof(a)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Int128" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=10000000000000000000; typeof(a)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Int64\n", "Int32\n" ] } ], "source": [ "b=10;\n", "println(typeof(b))\n", "b=Int32(b)\n", "println(typeof(b))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "convert(Float64,b)\n", "Float64(b)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Float64\n" ] } ], "source": [ "c=1.0;\n", "println(typeof(c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il est possible de forcer le type d'une variable à l'aide des commandes <code>Int8()</code>, <code>Int16()</code>...<code>Float32()</code>..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remarque : Opétations type unaire sur une variable \n", "\n", "| opération | += | -= | *= | /= | \\= | \n", "|------|------|------|------|------|------|\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=1\n", "a+=1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a*=3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Les booléens\n", "\n", "Les variables booléennes (1bit) sont naturellement définies dans JULIA à l'aide des opérateurs de comparaison \n", "\n", "| opération |égalité|différent| supérieur | supérieur ou égal | inférieur | inférieur ou égal|\n", "|------|------||------|------|------|------|\n", "| syntaxe | a==b | a!=b| a>b | a>=b | a<b | a<=b |" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "true\n" ] }, { "data": { "text/plain": [ "Bool" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = 1>0 ;println(a) ;typeof(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec les opérateurs de conjonctions\n", "\n", "| et | ou | not |\n", "|-|-|-|\n", "| & | &#124; | !|" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "false" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!((a&true) & (a|false)) " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "true" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2>1 & 0>-1 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Les chaines de caractère\n", "\n", "JULIA possède un type **Char** (Charactere) il s'agit d'une lettre délimité par '' à ne pas confondre avec la chaine de caractère." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'a': ASCII/Unicode U+0061 (category Ll: Letter, lowercase)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z='a'" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'b': ASCII/Unicode U+0062 (category Ll: Letter, lowercase)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z=z+1 # en ajoutant 1 on passe aux charactère suivant dans la table " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La chaine de caractère est délimitée par \"\" et définit unt type **ASCIIString** ou **UTF8String**" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Une chaine de caractère \\n\"" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a=\"Une chaine de caractère \\n\" # \\n est le retour à la ligne" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "String" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La concaténation de chaîne se fait par l'utilisation de * (multiplication)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Une chaine de caractère \\npuis une autre...\"" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b= a * \"puis une autre...\" " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Une chaine de caractère \n", "puis une autre...\n" ] } ], "source": [ "println(b) # \\n renvoie à la ligne" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut extraire ou affecter une partie de cette chaîne considérée comme un tableau" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Une chaine\"" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ATTENTION " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'U': ASCII/Unicode U+0055 (category Lu: Letter, uppercase)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1] # le résultat est de type Char" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"U\"" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1:1]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Char\n", "\n", "String\n" ] } ], "source": [ "println(string(typeof(a[1]))*\"\\n\")\n", "println(string(typeof(a[1:1])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'usage de $ permet comme en php de convertir et d'inclure une variable dans une chaîne de caractère (interpolation)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"le mois de novembre est le 11 ième mois \"" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m=11;\n", "a=\"le mois de novembre est le $m ième mois \"" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"racine de 2 : 1.4142135623730951\"" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b=\"racine de 2 : $(sqrt(2))\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour encore plus de possibilités avec les chaînes de caractère : https://docs.julialang.org/en/v1/manual/strings/#man-strings-1" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.1.0", "language": "julia", "name": "julia-1.1" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.1.0" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
Upward-Spiral-Science/spect-team
Code/Assignment-10/SubjectSelectionExperiments (rCBF data).ipynb
1
66247
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Subject Selection Experiments disorder data - Srinivas (handle: thewickedaxe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial Data Cleaning" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4383, 141)\n" ] } ], "source": [ "# Standard\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "# Dimensionality reduction and Clustering\n", "from sklearn.decomposition import PCA\n", "from sklearn.cluster import KMeans\n", "from sklearn.cluster import MeanShift, estimate_bandwidth\n", "from sklearn import manifold, datasets\n", "from itertools import cycle\n", "\n", "# Plotting tools and classifiers\n", "from matplotlib.colors import ListedColormap\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn import preprocessing\n", "from sklearn.datasets import make_moons, make_circles, make_classification\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA\n", "from sklearn import cross_validation\n", "from sklearn.cross_validation import LeaveOneOut\n", "\n", "\n", "# Let's read the data in and clean it\n", "\n", "def get_NaNs(df):\n", " columns = list(df.columns.get_values()) \n", " row_metrics = df.isnull().sum(axis=1)\n", " rows_with_na = []\n", " for i, x in enumerate(row_metrics):\n", " if x > 0: rows_with_na.append(i)\n", " return rows_with_na\n", "\n", "def remove_NaNs(df):\n", " rows_with_na = get_NaNs(df)\n", " cleansed_df = df.drop(df.index[rows_with_na], inplace=False) \n", " return cleansed_df\n", "\n", "initial_data = pd.DataFrame.from_csv('Data_Adults_1_reduced_inv1.csv')\n", "cleansed_df = remove_NaNs(initial_data)\n", "\n", "# Let's also get rid of nominal data\n", "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n", "X = cleansed_df.select_dtypes(include=numerics)\n", "print X.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4383, 137)\n", "(2624, 137)\n", "(2981, 137)\n" ] } ], "source": [ "# Let's now clean columns getting rid of certain columns that might not be important to our analysis\n", "\n", "cols2drop = ['GROUP_ID', 'doa', 'Baseline_header_id', 'Concentration_header_id', 'Baseline_Reading_id',\n", " 'Concentration_Reading_id']\n", "X = X.drop(cols2drop, axis=1, inplace=False)\n", "print X.shape\n", "\n", "# For our studies children skew the data, it would be cleaner to just analyse adults\n", "X = X.loc[X['Age'] >= 18]\n", "Y = X.loc[X['race_id'] == 1]\n", "X = X.loc[X['Gender_id'] == 1]\n", "\n", "print X.shape\n", "print Y.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extracting the samples we are interested in" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1056, 137)\n", "(257, 137)\n", "(1110, 137)\n", "(323, 137)\n" ] } ], "source": [ "# Let's extract ADHd and Bipolar patients (mutually exclusive)\n", "\n", "ADHD_men = X.loc[X['ADHD'] == 1]\n", "ADHD_men = ADHD_men.loc[ADHD_men['Bipolar'] == 0]\n", "\n", "BP_men = X.loc[X['Bipolar'] == 1]\n", "BP_men = BP_men.loc[BP_men['ADHD'] == 0]\n", "\n", "ADHD_cauc = Y.loc[Y['ADHD'] == 1]\n", "ADHD_cauc = ADHD_cauc.loc[ADHD_cauc['Bipolar'] == 0]\n", "\n", "BP_cauc = Y.loc[Y['Bipolar'] == 1]\n", "BP_cauc = BP_cauc.loc[BP_cauc['ADHD'] == 0]\n", "\n", "print ADHD_men.shape\n", "print BP_men.shape\n", "\n", "print ADHD_cauc.shape\n", "print BP_cauc.shape\n", "\n", "# Keeping a backup of the data frame object because numpy arrays don't play well with certain scikit functions\n", "ADHD_men = pd.DataFrame(ADHD_men.drop(['Patient_ID', 'Gender_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))\n", "BP_men = pd.DataFrame(BP_men.drop(['Patient_ID', 'Gender_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))\n", "\n", "ADHD_cauc = pd.DataFrame(ADHD_cauc.drop(['Patient_ID', 'race_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))\n", "BP_cauc = pd.DataFrame(BP_cauc.drop(['Patient_ID', 'race_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dimensionality reduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Manifold Techniques" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### ISOMAP" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1313, 133)\n", "(1433, 133)\n" ] } ], "source": [ "combined1 = pd.concat([ADHD_men, BP_men])\n", "combined2 = pd.concat([ADHD_cauc, BP_cauc])\n", "\n", "print combined1.shape\n", "print combined2.shape\n", "\n", "combined1 = preprocessing.scale(combined1)\n", "combined2 = preprocessing.scale(combined2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "combined1 = manifold.Isomap(20, 20).fit_transform(combined1)\n", "ADHD_men_iso = combined1[:1056]\n", "BP_men_iso = combined1[1056:]\n", "\n", "combined2 = manifold.Isomap(20, 20).fit_transform(combined2)\n", "ADHD_cauc_iso = combined2[:1110]\n", "BP_cauc_iso = combined2[1110:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clustering and other grouping experiments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### K-Means clustering - iso" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1313, 20)\n", "(1433, 20)\n" ] } ], "source": [ "data1 = pd.concat([pd.DataFrame(ADHD_men_iso), pd.DataFrame(BP_men_iso)])\n", "data2 = pd.concat([pd.DataFrame(ADHD_cauc_iso), pd.DataFrame(BP_cauc_iso)])\n", "\n", "print data1.shape\n", "print data2.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated number of clusters: 2\n" ] }, { "data": { "image/png": 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pkFmyno3BU9tKJMzDYshkldsbUtqrnBAWFiIrnsxlH0v59Asa687fdqttl5ai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\nuISkEo8S/nigFx2AR9fs2hU3QSlaJ8R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"text/plain": [ "<matplotlib.figure.Figure at 0x7f442843ac50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kmeans = KMeans(n_clusters=2)\n", "kmeans.fit(data1.get_values())\n", "labels1 = kmeans.labels_\n", "centroids1 = kmeans.cluster_centers_\n", "print('Estimated number of clusters: %d' % len(centroids1))\n", "\n", "for label in [0, 1]:\n", " ds = data1.get_values()[np.where(labels1 == label)] \n", " plt.plot(ds[:,0], ds[:,1], '.') \n", " lines = plt.plot(centroids1[label,0], centroids1[label,1], 'o')\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated number of clusters: 2\n" ] }, { "data": { "image/png": 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L15xr+pd3iLKg27cnRYO2b08SBmiZr6yVZ5aWwghD51pYSDsSeAH/mZl0w/rtBDIMLIzw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\nTUd9IPZfWam+Eo12E7KmTVJj0r6IIp/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4428474510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kmeans = KMeans(n_clusters=2)\n", "kmeans.fit(data2.get_values())\n", "labels2 = kmeans.labels_\n", "centroids2 = kmeans.cluster_centers_\n", "print('Estimated number of clusters: %d' % len(centroids2))\n", "\n", "for label in [0, 1]:\n", " ds2 = data2.get_values()[np.where(labels2 == label)] \n", " plt.plot(ds2[:,0], ds2[:,1], '.') \n", " lines = plt.plot(centroids2[label,0], centroids2[label,1], 'o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As is evident from the above 2 experiments, no clear clustering is apparent.But there is some significant overlap and there 2 clear groups" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classification Experiments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's experiment with a bunch of classifiers" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ADHD_men_iso = pd.DataFrame(ADHD_men_iso)\n", "BP_men_iso = pd.DataFrame(BP_men_iso)\n", "\n", "ADHD_cauc_iso = pd.DataFrame(ADHD_cauc_iso)\n", "BP_cauc_iso = pd.DataFrame(BP_cauc_iso)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "BP_men_iso['ADHD-Bipolar'] = 0\n", "ADHD_men_iso['ADHD-Bipolar'] = 1\n", "\n", "BP_cauc_iso['ADHD-Bipolar'] = 0\n", "ADHD_cauc_iso['ADHD-Bipolar'] = 1\n", "\n", "data1 = pd.concat([ADHD_men_iso, BP_men_iso])\n", "data2 = pd.concat([ADHD_cauc_iso, BP_cauc_iso])\n", "class_labels1 = data1['ADHD-Bipolar']\n", "class_labels2 = data2['ADHD-Bipolar']\n", "data1 = data1.drop(['ADHD-Bipolar'], axis = 1, inplace = False)\n", "data2 = data2.drop(['ADHD-Bipolar'], axis = 1, inplace = False)\n", "data1 = data1.get_values()\n", "data2 = data2.get_values()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Leave one Out cross validation\n", "def leave_one_out(classifier, values, labels):\n", " leave_one_out_validator = LeaveOneOut(len(values))\n", " classifier_metrics = cross_validation.cross_val_score(classifier, values, labels, cv=leave_one_out_validator)\n", " accuracy = classifier_metrics.mean()\n", " deviation = classifier_metrics.std()\n", " return accuracy, deviation" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random Forest accuracy is 0.7883 (+/- 0.409)\n", "LDA accuracy is 0.8043 (+/- 0.397)\n", "QDA accuracy is 0.7517 (+/- 0.432)\n", "Gaussian NB accuracy is 0.7890 (+/- 0.408)\n" ] } ], "source": [ "rf = RandomForestClassifier(n_estimators = 22) \n", "qda = QDA()\n", "lda = LDA()\n", "gnb = GaussianNB()\n", "classifier_accuracy_list = []\n", "classifiers = [(rf, \"Random Forest\"), (lda, \"LDA\"), (qda, \"QDA\"), (gnb, \"Gaussian NB\")]\n", "for classifier, name in classifiers:\n", " accuracy, deviation = leave_one_out(classifier, data1, class_labels1)\n", " print '%s accuracy is %0.4f (+/- %0.3f)' % (name, accuracy, deviation)\n", " classifier_accuracy_list.append((name, accuracy))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random Forest accuracy is 0.7565 (+/- 0.429)\n", "LDA accuracy is 0.7739 (+/- 0.418)\n", "QDA accuracy is 0.7306 (+/- 0.444)\n", "Gaussian NB accuracy is 0.7558 (+/- 0.430)\n" ] } ], "source": [ "for classifier, name in classifiers:\n", " accuracy, deviation = leave_one_out(classifier, data2, class_labels2)\n", " print '%s accuracy is %0.4f (+/- %0.3f)' % (name, accuracy, deviation)\n", " classifier_accuracy_list.append((name, accuracy))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
rajul/tvb-library
tvb/simulator/doc/tutorials/Tutorial_Surface_Stimuli/Tutorial_Surface_Stimuli.ipynb
2
223984
{ "metadata": { "name": "", "signature": "sha256:21534c90fb211a3704f8e4b21cb64475c5726890d0adea814577179e2f3a3243" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Tutorial: Surface Stimuli\n", "==========================\n", "\n", "This tutorial covers the basics of defining and applying surface stimuli. \n", "\n", "NOTE: Surface simulations can also be run with stimuli defined at the region\n", "level, in that case the stimuli take the same form as those discussed in \n", "\"Tutorial: Region Stimuli\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup\n", "-----\n", "\n", "The setup and initialisation is done in the usual way, see \"Tutorial: Anatomy of a Region Simulation\" and\n", "\"Tutorial: Anatomy of a Surface Simulation\" if you haven't already." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from tvb.simulator.lab import *\n", "\n", "#Initialise a Model, Coupling, and Connectivity.\n", "oscillator = models.Generic2dOscillator(d=0.1)\n", "white_matter = connectivity.Connectivity(load_default=True)\n", "white_matter.speed = 4.0\n", "\n", "white_matter_coupling = coupling.Linear(a=-2**-9)\n", "\n", "#Initialise an Integrator\n", "heunint = integrators.HeunDeterministic(dt=2**-4)\n", "\n", "#Initialise some Monitors with period in physical time\n", "mon_tavg = monitors.TemporalAverage(period=2**-2)\n", "mon_savg = monitors.SpatialAverage(period=2**-2)\n", "mon_eeg = monitors.EEG(period=2**-2)\n", "\n", "#Bundle them\n", "what_to_watch = (mon_tavg, mon_savg, mon_eeg)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Surface with Custom LocalConnectivity\n", "----------------------------------------\n", "\n", "However, this time, instead of just using the default local connectivity we'll make a\n", "custom one that has been chosen so that the influence of our stimuli will be more apparent.\n", "\n", "In specifying the LocalConnectivity, we select from a set of equations defining functions\n", "with finite support, that is they drop to zero as we move away from the origin. For practical\n", "reasons (memory) we need to truncate the evaluation of the LocalConnectivity at some specified\n", "distance, which we refer to as the \"cutoff\" distance.\n", "\n", "NOTE: Distances are in mm, so the cutoff distance and standard deviation of the Gaussian \n", "below are 60mm and 10mm, respectively." ] }, { "cell_type": "code", "collapsed": true, "input": [ "#Initialise a surface\n", "local_coupling_strength = numpy.array([0.0121])\n", "\n", "grey_matter = surfaces.LocalConnectivity(equation = equations.Gaussian(),\n", " cutoff = 60.0)\n", "grey_matter.equation.parameters['sigma'] = 10.0\n", "grey_matter.equation.parameters['amp'] = 1.0\n", "\n", "default_cortex = surfaces.Cortex(load_default=True)\n", "default_cortex.local_connectivity = grey_matter\n", "default_cortex.coupling_strength = local_coupling_strength" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a quick idea of how the LocalConnectivity we've just specified will be represented\n", "on the surface we can use the plotting tool called plot_local_connectivity. This plots \n", "the local connectivity function with different sampling based on the distribution of \n", "edge lengths in your mesh surface. If all the lines don't, at least mostly, overlap\n", "then you've probably specified a function with structure that is too fine for the \n", "resolution of your mesh surface. Also, you want the function to have essentially \n", "dropped to zero by the cutoff distance. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot_local_connectivity(default_cortex)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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c4bLDl7xS/BF2dnKxLbQjR58Nkpu7VYs+ls4wfiecEzR1u45eOStp/uiDJK9f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\nWe3uLJC1N5lLTlY4eLAr0A+u+NCncx+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"text": [ "<matplotlib.figure.Figure at 0x624c510>" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Stimulus\n", "---------\n", "\n", "As with the region level stimuli, we use an equation to define the temporal profile,\n", "however, unlike in the region level case, we also use an equation to define the spatial\n", "profile of the stimulus. We also need to specify one or more \"focal points\" (vertices on\n", "the cortical surface), about which the spatial equation will be evaluated. Here, as with\n", "the LocalConnectivity, we must use an equation \n", "which drops toward zero with increasing distance. However, as we don't need to evaluate\n", "the equation for every single vertex on the surface, but rather just for a typically \n", "small set of focal points, we don't need to truncate the evaluation with an explicit \n", "cutoff." ] }, { "cell_type": "code", "collapsed": true, "input": [ "#Define the stimulus\n", "eqn_t = equations.Gaussian()\n", "eqn_t.parameters[\"amp\"] = 1.0\n", "eqn_t.parameters[\"midpoint\"] = 8.0\n", "\n", "eqn_x = equations.Gaussian()\n", "eqn_x.parameters[\"amp\"] = -0.0625\n", "eqn_x.parameters[\"sigma\"] = 28.0\n", "\n", "stimulus = patterns.StimuliSurface(surface = default_cortex,\n", " temporal = eqn_t, \n", " spatial = eqn_x,\n", " focal_points_surface = numpy.array([8000]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simulate\n", "---------\n", "\n", "Now we bring all the pieces together into a Simulator object and configure it. We \n", "then run the simulator for a bit, throwing away the data, in order to clear the \n", "transient dynamics caused by imperfect initial conditions.\n", "\n", "NOTE: The configure method for the Simulator must calculate the LocalConnectivity\n", "here, which involves evaluating the LocalConnectivity function as a function of \n", "distance from every single vertex on the surface (about 16000 for the demo surface\n", "we're using), so this step may take a minute or two. And then the transient clearing \n", "\"dummy\" simulation will take a few more..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Initialise Simulator -- Model, Connectivity, Integrator, Monitors, and surface.\n", "sim = simulator.Simulator(model = oscillator, \n", " connectivity = white_matter,\n", " coupling = white_matter_coupling, \n", " integrator = heunint, \n", " monitors = what_to_watch, \n", " surface = default_cortex, \n", " stimulus = stimulus)\n", "\n", "sim.configure()\n", "\n", "#Clear the initial transient, so that the effect of the stimulus is clearer.\n", "#NOTE: this is ignored, stimuli are defined relative to each simulation call.\n", "LOG.info(\"Initial integration to clear transient...\")\n", "for _, _, _ in sim(simulation_length=128):\n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the main simulation loop" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Perform the simulation\n", "tavg_data = []\n", "tavg_time = []\n", "savg_data = []\n", "savg_time = []\n", "eeg_data = []\n", "eeg_time = []\n", "for tavg, savg, eeg in sim(simulation_length=2**5):\n", " if not tavg is None:\n", " tavg_time.append(tavg[0])\n", " tavg_data.append(tavg[1])\n", " \n", " if not savg is None:\n", " savg_time.append(savg[0])\n", " savg_data.append(savg[1])\n", " \n", " if not eeg is None:\n", " eeg_time.append(eeg[0])\n", " eeg_data.append(eeg[1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plots\n", "------\n", "\n", "So, let's take a look at what we've done.\n", "\n", "First, we can take a look at the stimuli we've applied to our simulation. We'll\n", "use the same plot_pattern tool we used for region stimuli. This gives us a quick\n", "overview, however, as you'll see, the representation of the space isn't all that useful." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Plot the stimulus\n", "plot_pattern(sim.stimulus)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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GIbCiQGr4FwIVBu0Gb+e7W+ZzRN1HtHt/O3ULYNsK+HqX3LCaypwGSjMtgL9k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\nQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x6242e10>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we have Mayavi available, we can get a much better representation of the spatial\n", "profile of the stimulus by plotting it directly onto our cortical surface, we'll do\n", "this using the \"surface_pattern\" plotting tool." ] }, { "cell_type": "code", "collapsed": true, "input": [ "if IMPORTED_MAYAVI:\n", " surface_pattern(sim.surface, sim.stimulus.spatial_pattern)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image\n", "sp = Image(filename='Tutorial_Surface_Stimuli_Stimulus.png')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "sp" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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xY8cmEomqqqrOzs7/+q//+uUvf+n7/gcf0dbBWSwnPH+EQUd2h5+AzYXfa2pq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cKjh1sipZwSXdOgSd37M84QkZDQOhdJq60xCHZ/575+df3dB+VjT+AQ4T4gCsLQGK0V01qNGjU4\nny8EpdAYY4wGIPfWtFJaiWIRtdJ1tWzOBePBndmMcc58X2YyvudJp75Q/ssSIXEAdvdd9wCI9o63\nAODlRWvTaZ8LVx8xz5NRpBExDKJ+/Zr+6t7jpvNjYPqL2z/xyKNPDB7SRytttL3ppvMJCJPxlO99\n/7GgFF5y6RT3wpzIVN++jevXvTdz1jghZCUkJXt2hAAG4CXsJAtgN254f/yEEQgMAQlAKf2PP/jF\ngAE9lTZw6oJ9pE7kj9SbOQlBBASWCC0h2nTGQ2SOYcS5EEK44oJxboy1ZAqFEiBHFIji5ReXLLj2\nGk/4nvScGdzqFcvPmXyun84QMkNg3UwsAhnDkFG86S0bdwweM4whTJw5cd2rbyJwIbkl7D96bBja\nMFIqjA7s3hlFNlJGCNLaqshOvuETbz7yK0B0S+OgQgIiMoDQMGi80QqRDBIiGCThpQD5J+/96RlT\nL+04uONvPnOuE+PYv2+JMVjXfzxn6NiUnCFYAwyQ0BqaNWe2tRrBAth97TuNVmFQisLSWcOmeqyf\nUorIaMG15EpxNxCsjQmDwFrLKkyYmhokY8iY73q/AMBj6hfzfel7nrMsQMYQ3BoQulIOgAF0d9/N\nwoWrpBSeFNoYay2yWOnF80SpFN519zVlKckTgj373IvvvbvX86TmTGtTkUUhAH7ly3c1t+xIXlr5\ndpBTRDZb6OzMU9JXnzxp2rFzvJV0SgtgAOGpp1YsuGq2UsoN4/Tt26SVTh6ARAdOfhH30VIyOY1K\nx0RbW05IUd8t5SZOfN/nnBlLQkgupODCCSeBs4rUlqwCLAEKQO75KU9IKaTkcubs6StXrkHAUqlk\nk+l8awnAEgIAEnYBk1Hh1jffNsaOmDhOSj5m6oQwtGFogsgAWGdd0NS3X8fRI1IZpaw21kuRMWzm\nZ+949YGfADEAsKAAiHGOyKzRDLlBcMAESMDE3I9N2LzTeJn6Hrj3sac60ul0yvfr6gfWpjOcoeCs\njEoEFgnHThovOBqjEaxDJaVCo6IwLEVBaeXa58Og2NBQl8mkhUDOmRBs+3u7ywxpAQULMHRon1j3\nO9HPxUSJlidGTr7vCcHLRrgJkLEEktz5ZgCYtWSNNcwap3BERESXXTbV9z0pK4/kSkokPvvcywCw\nZs02AGDGcM2U1paIAVaCUFkRuBwN3c9obLDrN6xKxCGIiNa8sWrKuRcmz3Ji0Fubdw4b1nflyk0m\n1mwoGTdbCLBr98ohg2f98Ufph8TpCu4jHPfd96/f+bs7stlCfX0tEezadcD3PUuIyISQbiiTcQnI\nOOcWwVoISiGiOHjgqPRTkgtPSE9IQ8gYJ4D33323z4CBBIicc+kxIWJUMoaQMWTaxLpC2tDGV9dY\ncuxnC0DoVOPALaFRprbOt2gsWUv5zk5rmUyxqr41+f1ZBGIAhkKRqq7rOcpqDUAEtl/67caevf2U\nP37gTCFE9+jN2qOtXkOD73me72UymbTvMwYcgSM6bGII/QcNFgKNVgwYWAKwCDYMgygKyOooKDlg\nKhQLmUwqLbP5fCmT9lOpdFVVuqoqdfBAi/swhw3vhwhOrykWROGxMUFZQpcz7nsSGSbnOAFQmfoE\nsRpJyVGlb/nMnb/85T8bE02YOMKPO+heTA2wxDkiJA0dxG3vvLdjx4HyN5vypXHyvszccsul1hIw\nYm7NHjBOlGLyKXbvNjqpyCrGUBK58ddef2Xa1HnJjvHYgV7q1bv70eYOrXS3bjWAQJbc8yJCKuUd\nPLSmT+8p9933ie985+GTeeCezpU+2qGUTaU5EWVzgbWQSqcAmKvhhHCtbsmFcHMIiGAtFYuB56Us\n4bMvvvKJ6659YclS6aWmz561euVqYFgqlQiASU9Yy63HpZuWQEJjCJwxo1PRNQTW0sYVr1lLxtLg\nsePjNbJ45N5Yw9w/+ZnqTLfepULJOmM51wpCSBIlskhnD2mv6zZECvef2LbujUuuvubVF58rIQwa\nOszzvKp0mseCS8AQGhsbOGdScGMUAjJgBpGQyJowDKIw0CokMlFYCsNSqVg8+MEWz5PaE54vRo4a\ncOhQK08MuwFgxMgBEDePMFHy5kIkrtmJ3YBLlyixVQOAGI+Sdnwy1RFnMS7pWLNm6/XXX9Damk0U\nmshaYkiMO5zBTZu3+p4cPXrQtm0fAMA72z7wUx5ZciOE1hJjRBYsI9fEokShvLFhOIBIekYEQBPG\nz1y77lVXwLkXVlub+bAKzv21iOh5giEWCgEy/MIXPguAAEUAam45iAgtreu//e2vnmxU+kgtpp9G\npePDrQAFgXLHd1gKq2trMR5ZZIDMWmuVBmAEaA1YoljYgktL4AlpLTq5bsYZIMt2dko/xd0UiiVu\niXseAyCDRikTe1tS1wVDDnreXfuGtdRv1DlEhqwBazN1jR2tLdaQJTc5zGd98t6XvvO34HwLGISF\nNqt1vbfxrLPHp1KNqVRKSiGE6NatW5/5V8YLgYD7du1kiNPPP9/1mwVnnDOtIrJMch8IyYI11iJq\nox0kRWFgjCKrlQqDYnH/rre4YAgACGefPcxa6t+vB+MsChUAjD5zUPJhxiM7QrCyuZNwOZJr0DH3\nioBRsjLvVv4AENxsijvfNAD8+Mf3ZzIpglh6paGhNpcrllHJIiGD1avXO21J97jL53/82eeeAEBP\nCmQotFHKAKCTx4IYa7BHU69kdb+MgKyMNel0l3JWEl1Tcslrjl/5lCmjlYoVI6ac6/hQAUAGoNjU\n2OfAwT0M2ck/707nSh/taGzqdsEFE15+eQM6HWmkbDbX0NgAhEpbAHI+IoAEgJYAkGPsRcKElJJL\nS4BMSN+/6NLLFi9aBIwZY4Bx0IbQEDOkNUdGJrLx2AJYS+6CK9DKf42xOze/CYwDE8hEcc+u6p5D\n3L+2H9qTru/tGsPOeREJkdj8G8YUD6ZSKd/9L6XMJDkRR7xo/pVLX1iIiP+HvS+Pk6q68j/n3vuW\nqup9YetmkxvFFQAAIABJREFUUTYX1DYiAhJ3ccWAJkgWNUaNiVlmnCxjkplJJjPJ5DeZmWwTY1xi\nZmIMRh1DcAGNGy6gINKKggou7NBAb9VVb7n3nN8f972iRROzNNqQ9/2Ubdfj9Xuvqut9+5xzv+d7\nJk+bHoWhoyST6P8wjpBSAQAD9vb0GRNHYRhFQRyFTIZZ6yja9Gp7suKNPH364bZeZtuF8wV/0hEH\nVW5XW0tSKmm3S8y8rdWlTMa4QEX+jEntBiwf7XFDt9Ce7xITMAwf3vjYkuesQqO3p69iNWndJtOa\nNwLAho2rjj9+0vr1W4BZKekopaT+0Y/u+JvPz0vW+5Liev/GNyvXtlon/u53f1RTW0DE6dMP7/cx\noTR363+RDEBaU2LgYvjxJ+6Zcfz5AD5AN4AHEEWRVkpu3faiMculPHbAPrUHFisdUC9mQDD5mEMe\neujZufNmQhoXI8DOjl1CSXvj2XuMma2ZCTP2t+j+1d2LbMFDuZ7jeRd89KPWPRJsN23SmIVxFEdR\nFFnf3FgnrRJ7/M3iOIriMLL/EEdxHEWlYq825FfVGUNk2BgbLol8ax4BmWnK6ePOv2h6uP3VQiFf\nSJHP+SKlpGTcG0Lb5GO1vYIw1HFkT6CjKI4io2NgK0WiMCj3dHV1d3V2d3UWe3qKvT2lYnHj+lUA\nttwPp502uVIzwtTIw75v0vKRlEIkVh6UWnITUWW4k4WNNBLDEJvQQsWfZI9r0ievuMgYamqqrfxg\nqRSkg7gxnRmV8OWddz76k2t/8/jjzwPAxRfPBAQEVI70c27O93587e0ISXNiSiuceo/0nwQD1dV5\ne66lT74Ab7pk6h8iQfJJ4bQFkhh48jETUilTDYALILU2URQH5dCQHsAPLYEa2McAXtufgSxW2hvH\nTbn02uu+ctf/PWiMEdJBgWy4deRw6wyvDUcxSSWAAJAppkRGQICCkUFIKZXjuJ7redJxAMXcj196\nxy2/TBfJBTPEUZSESNbYm9F+YwgMUeIeSUQEBEh2Tq8wzMIYeuO5ZbUjxhExEz9y87cKjaOt4GDG\nmWNGHTRaSVS2+iWlktJJhjpif2JCQB3HtrxsYmZSjpIsBUsBII1xABwA7O7sDoJyEJT7isWgXELg\noPeNoBwk1CzkeecdT0TpHzaCNwc2AJDUa2wyKwAS4rbulySE6r83EelYk+3DB0ZkiZUsSaa2kDxs\naEPC7AhaG2CuFM4rzLh+/ZadHd0jRjQNHVq/Y0dnY8MxAHz137YB8A03/AgsXWoJKHCP+NuC09Qs\nGQZ3+x0Lzp017bHHnu/tLQHA0qUvTpt2WLpD6iOcXCQDwLe/fe0ZZ06ZNOkgrPxB28Nc+QUL7mgd\n2Wyvc4SJBvBDy5BN9D7QYcOZfN4NAqqqKvi+L4UAtMM+QDkQx4QamAkFG611zLmCwwSIQkglpGQA\nEAJQcNKKpqWTQ0Qi0iay9SNrBkDpZHAGTLzcrHskMQESIzOQjUyE88ryJQBKr3iycfSkOCj7NSPK\nPV3TTzi8pXWEYEZgJ+mOUf0WucQeVhIoECccdriOIwQAYAQwcUS+4ziSSQh0SMdkHBAyDMphEMRR\nSCZ+be2Ks885LucfmrS9ETGzQAQhKjcwEcGbw5/kTmUGSIiJGe2PG0NKsu1NZwAi1jqx1pRCGEQ0\nVvJeaeZIlsNmzz7znnt/Z+nHEGNaShdSvPLKJgAol8IxY4bVN1SfeGIys2TzlmUtI6Zay6Qrrvjs\nTTddCwDCFTff/JvLPnEB7p37VGI0uO662844c4rjOEqKpsZa+35u3LDjrLNm7fUS++mVYMKEkdYA\n3X6UKpHU7Xf8LAyjvr6AiRHRUAwDB+YDKunJWOltQMYoVyqpZIEFUufuLtfzfd/3c3lEgQxSCseR\neaF27eo1zABc6isVqh0hJTHHccxQDsJw2KjRgOLO//05EUXFXm1Mob5xDyWxnV4p7PgjOyvcaGPD\nJUtJaawEryx/HIQjnSrhVnk5GRR7onI5DkpnnHGE7/uvr1k9YtRoKdB1XDtZyHUcJaUUQilrNImF\nfN7Oj508dWr78qcAKgZsqKOYSbMjlQRjYqPj3T2dcRwZHTPTU489MHfuyUIIx1VSCpuINTfXkSEA\nRkSBgpHecnsnty0zVNiJU3vMZNVMWEoiIjJ7cjcADUzc16fr6gopJVVsj1gpmYiVAKSQa9e8UZFN\nNzXVDh1aD4gVSkoRWW9/ALjssk/ffPN1ACAU/s//3nXpxz+UHrly0QRApVLfKae+z0oZpJJ7WF5K\nAAPAb46V7Fe66jNzjCG00xOwUmxiAOjp6fM8t1wOk8/YgGZwfGCVYjJWehsk4xQFK6WYoaY6J6RU\nCnft3O16HqKUylEOSgHNzXUo1IaNHUS6a/du5XpCRUIpbYgTWyW0hvmGyBDv3rGjtmlIRZVHhgkI\nkA3ZmbfG1laSKCn5CuueeVLmhtugiSKNguIwmj6lxfd9JaUUOOHItq4d23LV1QkludYXyVFKKSks\nGaUjBhiBJ0+dKqV49umnU22UQCDSulTSYVDq2o1SgusqreOnHr0PmIUQlZFqzFxTUzCGGOz8OWbL\nTWLP+roFJ8SHAHv4CBKHk0QDabM5y8YVk2+DQgjs6Slt3LT9iElt/aRDACDOmPmR+bf9FBj6SkGp\nL4hiXeoL8gW/qiqX6JXeZpXcBia2ZURceumnfnnrTQDw0Y/MBYjeVqj9y1sXnXzy0W/SMUiZSh9M\nhYYAIGVMZDbGGFtzTKyW+3npErHvOzave98xExxnIHMuymKlAx6f+8y/X3vdVwUxESulGNA2NzQ2\nVlXMicKYBBEIiQCjRg1jxrVrX+/Ytm1oSytgYClpTfuqtmnHG2JDRMQ1DY1kzRPT1W8CJmJisDZy\nRlNSVyLmJFYCZhS54UT9gg6i6ZOHKimUFI4UtjqipHQcRynlOI5jx3MLwWQYrbJKCGAB3L+J3nEk\nAAMjsAFGBgQySdLBoOMojkO7ci4EOq5yHJXLeZ7npHOKrHqIK7pnIkaJyG8uL6Xr7wQgBNnqr30p\nQRBZYdJDDz1j99ywYcecOe8XKOJY9/QW41inFWh7b9sCk5l34ad+Nf86gTaKkS0tzckanGMX9vac\nvmWEHbcdpzFLEjF99COfADAAkT1gv4o1A/DP/2fhCe8/SiaFfOxPSaeecsKbZ+SadPFOIAqtSSkg\nQgCR0nRSF/c9J5/349gQ0aOPrLrggoFsys/qSn8VEAjEJJiI2ZqQ2e5+IiY2iFJJQoiRecf2Hc1D\nWwFw4iEHM4vXN2zxcnlGZMSauoaljzyUq6pKS0j2JkYbK9nUzBZZbPWaIKl/M0BSWmLYtmEXsCAy\nUamXtJl63EjP9xwlnGQKkBCAAqFx6DA22lKSUhKY2RgQtu0OCJHsdARHrnl+VTqtkpkJRSLVBHu3\nMQEgM5fLYfvTD1tCsYOwHUd5ntsvsoAk5oH0VRhiEo6rEPYOVxgshyGLxGY7CCNjjLVJmzBh5Msv\nb9ywYQcARGGMQhR7Szo2APDMypXHvO8IAEjHB9hQRX543pUAcMON328Z0aiUUo5USp4x8ziRaqEA\nvIq5XXoJNqhRAEikEQ2iSOvolX0YgKXApAs7iZXkW9K3vdYHrQZdDGkeuXPXJiJGZGaUIg+A9rwT\nJow0iY7fjp8rDODHlfiAupEPqBczgPjUld/+yfX/QMR2NKJ0UCAScRTFdpKt7+dAICA1Nzd2de6S\n0pHK2b6tY/RBE9/YuNn1cwDY09XJWJluZEOQRLNT0S/3i5vsP6G1GenYXrSt/EkJRnqTJoLn+yrl\nI0dJ11ESMekXQUClHMdBBDIm6SUDAQiEwIRGk4nCcp8ZNXrUlk0bbGcrAgEzCpvYIQi0tegwCqMo\n5BSJkyYgM1tVJCRlbzSczLylpG+GmMH6SabvpU1nOG2mAQaOotho0sYwc09PX3d3X+WdDyNttNGG\nksJMEiFWKEmm4YkC4DlzZkihEncBKQAksEh3tlwj+0WHaPMsZiIKUZBMZAGVnRkA5t+26Liph1lh\nrDX2llKMGNEUhXEYxUQGrREw2vgvGQBlzcgRC4lbr2Ap8647Mj1+XKjKpUVDOnbyuf2sLwcAlNWV\n/krgKGmIm4c07NrVQ8S2mCtttxWx1kaoRHFXW1sFIKKYGhobg1LPiKENDLizp8xciSsw+T7hJ2Rm\nMiatw1SqD7B+zTpAZBZuvi6tCpOQLhEhgJLCkbJQVeXYspGUSiaCRJtusDFs19UFsu0XQQBEHYWh\njoENE4USJHLiNQAMSAiIEuxSX6xNGAU6jte9+LSlpNlzTuDUdNIYYkYh0Kk0jwgMypGtiFFFksTM\nzI6jbN238rqJiEhUymdGm3IQdXf3dXcXN2zYsW3b7qFD6597bn1DQ019XXV9fU19fXVVVT79hWC/\nWMnYTNBR1dr0IUsGAel8c+YKhe25V+0o9fSFAFEogGU/AxBmYtZE+sgjxyYiLIlCimJv+ZBDRoVh\nbC9BG40gmIGYlEIpgIEQBKAEYIBSU9NoY7QxWgin35VzdXXOar4PmXhKv7l1A4NsDe6vBZd/4p9/\neuM/AUBjY01XV5/1M0HbXirQLhYxszZGghBCKKVyeUWEsTZSOY21VQzY0V3kStGCIVWwWPN7MtoQ\nCAYEgNUrVgHaQSkCgErdu5xcDRML6SUyAYRcPp9Pl9L2zHEVaUNZ0txFTIbZElMSAwEAsImjMI4C\nICOEJStb5qJE2SlAa5NY8UobuPHUaZPsLEl7PxtNJKxekVNPSLSDtoMg0rE2FV4CZoZ+SmtLakka\npbUxxoRB/NrrW3fv7mXi11/fNnRofW1tVS7n+p7r+67vuyiQ2Dr2CwBO56NURq0p5VQRx0w6kZYn\nxNSflRAADJUr15SkzxQiCCKyIk+T5J/6xRdfyeVcqwsVQtTXNe6ItwOm05MQjNaAIo60MVopVA56\nnpumiQzAQsRCVDmO6lf6JzsAJVWuV65/wJDVlf6KIKX9A4h1tVU9xTJiv6gGgIlRAiSfOAJhrZqF\nUmCIUSIANtXWGKKdPX17dZ0yJwvihrSXr3rq0WXGULJaBWxrzw4qFNJ+nJty2wQ02NgJAVKdpJAi\nKXmohJWQCYHQdvNWWAmFiOMoCspSYl+pT+uwrq7a/iMgQ7LOzlKgLYmvfWG5DSpSPSfZW1obI1gk\ncnZmZuF5bhDsEQTGUQy2/c1OQxBY8RixR7BVFTsLs7Ozd9vW3Vqb555bP2JEU52lJD95AAKzHTee\nkhEToOhXzWFHVTHHUdRlBxYwJeYwb07cGJjIhMTUj5g0oSRSQkjbI2JZSSBWYsDmpiEAkKaiCgAQ\nQRtNBEEQah07jsiB57osklTTkpPdnxFV/wqUpaRDDzmxMpKXeRXiXgqGPxNZrPRXhMsv/br9ZuF9\nP6muyhVLUbpQw8BgyBCDUgIQrFesvXuJkQmMiYl5xJiDUcgRUqKQICUKySiiOC6Xy48++NAnr/nC\nFbM/UtPUKpTHaExsqppbbayTfDVJU9yWdS+0tJxiN9nhayqZaWB78WXS20HEwqTEJMA6DQgQYFMp\nLvUV4zgMgz6oyXEq/LNz5Wz8JiSuXfmUDe1GjRpmAyUbKyX+UMQkklZbpRwlVWNjw/r1G8IgCoIo\njDRg2vlh3UuEEImRADNzFOogjO0UzG3bdrmuWrKkvbW1uba24Puu77m+53q+m0wuIeZkDUAkBAr9\nYw2U0nG4ilnHcS+iQDTIwlN+P46ogIlCIsNJGzAbVGCIQRlNhjQZs2rVS7V1VULgmNGjKj9mi2gA\nAKAQ0E6vC4LQGIPo2jqRFJWA2H4+TMpNWPGoZOIJ44+vqDSZI8QBk3dneqW/Rsw669O/XfCtGg8r\nPkEbd7gAYLQmQuWgkPa2sdplqIgkN7y6npi3bt16wpnn2EJFbBJ5y9T3zzCa60cczMkyDjEYSyMV\nVrJNuuNbAzlyetoabx8GmJT1x9zDSmmgJIiJwE5vtLKo5I85Gx3rKNBR+MZrr44a02pvEjI2rrEN\ntARJr79QSiY9XfaLJpZCOLYQ7Ni1eKmUQJw4cexTT7WHQaS1ESkrVd4rVyZBIBGFkS6Vgr6+oLe3\nlM/7Uoi6uqrGplrPd+3D9ZxkvhslL5WZcM+kuUpHCAHEiDEKUtJjiokjJswValLmqnS3JY+NG7dZ\ncSMivPbqVkA4/vgjOanW61t+cT8AnHDiUePG7hlvG4RFpd50w4dhWCoFQRA5jkrK/IbS6ZMVVqrA\nVK527Njj0quyFx/ywJWWDO+z+ZfvBTJW+mNx3ge+Zr/p7l4MALSs3Ub7EyYMu+/R3Q4IkUzpARQA\nJlHzCQRgHDZ8xJpVK3fv3n3CWeeCMWA75FE++8RSoRwmBAIU4BbyxljrWkqjIpowMky6eq0Uz9h/\nIDLGxLGSQkrlKKWkFAhsrZXIrqYhgKi02pLRcVSObfc/aynhoIOGA/Cr61+3vbZEKAXmnT6RGBuy\nVNLGAjbnkjKqrq5KFwCT1XglHWuQNOP4yffc8wgxo0ijJCmMMVrbDjWwMtE41nGso0jnct7M04/7\nwhf/e9SooUomU3nr6qqsx4Bd7HccqaRktqNoK4ESVERMiDGCBtRCIBmorq7txwtpNQoYgJcue9aO\neLNXYgOpJ594ftr0w6+77rf5nGd/5vHHn5s0aQJCqyW1++//hUiDPvsLHXPQ8FIpcBxJxJaSbIZr\n3ezSU3M/IQKkwy9Nv0XACCASA2eKlGVwf+2orT0DAO695984tSU9+6TG3yze7ufRcTz711gINARC\nCBsDoUBkrmtoeOjuBcrxlOtObDvmjXUvgZBSOUSIhERAlHZSgB0NwPXqNYTh9qYQwmZkVg0d60hq\ngUqgsSMtpZBSpmJrZAJCTgWbFIblMChFYRBHAYCZe4H17WYiWrRm3Tf+4WsA/Mtf38pCCB9POKHt\nsSWrxowZBsAIoI0p9pUNURh6YRi3jBi6x1wybddHZAAxa9ap1177q9GjhxmVaAVstGMTQMtKOtZx\nbHzPOemkY6655ifDhzUoJR3HspJTKPhCoFLKdZTrOlJKsm2/DEIwYiW4sBGHBtCIMULMENfV1vdr\nmoV+FMZ33HlvHBtrns3MrS3NFQKpUFLFrw4YABMJ0nmzLr7vvl+iVWYiAEBPT1/qG4HMnPiIamMt\ns/p9TCLbpQcAAEbrSCkXAImIOSKOhIgA3mrb9GfiXcvgHMc5//zzx44d+8ILL/SfSbnX9rfuduih\nh55zzjnt7e0PPPCA4zhz5sw5+OCDly9f/uCDDwLAKaecMmXKlBUrVvzud7+DjJX+bJx9zlf6P920\n8dcAcNf9u/xcAYUCUIiYrvsLgcyELKCxeUh3d3cUhatXPC0cBximtB20vH0TIQCiMbats1JS4aTz\nPBlFhAIFM+k40gKVEEaiFkIJ1AjAZOzQWQBgAraG38RAURQEpWJQKoblvicfeaClZYjnTxVCDKlv\nA4BLLpFr1z8VhDxp0lhE2LJhnRDi1NMmr391MySlqMCWgQLr/hZGhx4yDvsxU//34cor5/z0p3eN\nG9dqjDFGWmaKotiWpbQ2cWxOPukYAPjHf7y+obHG+hvYkXATJ460B5VCOK4SQtiFTmKDhhGNVMpR\nlVQltpTEHBPp6qqaZIIuQNqRl0QrN//8dq11HJmPf/yc3mKRyI4lhrvvXlouh/mcJ9+cozEAQCeC\n5Tg866zpAHLx/U8AACLGsfZ911IYWrGrNloaKaWUlca3CICZ7ABkQURRHCqVZ+ZSqey4hjlKo7+B\nwbsWK02bNs3zvPnz51900UUPP/xwT0/P225va2vr/7Svr++888574oknZsyY8cADD5x22mlRFC1Y\nsGDu3LnLli2rqamZPHnyokWLZs6cmbHSQKJ15NzK9z+88foLPvLh39z5WxSKUSTrbrb8C1xdW2eI\nykGgtQHBKGTbpBHPrt6GAIKRiZKVOuaW4SboAPvZTSarSDHrwxf/7q5f22zOaG1EFAGT0U6sjKNM\n2kQK6aKVjsJyqVguFRH000881NIypLGxVgohBOzqXgXArquEwEPGTl2z7rG7br/nfcdMsHf2ZZfO\n+9nN8+0rYoZyOfWkDKNV7S9OmdL2ZkpCAC4Wu1zXufLKOT/4wW2+7x599HgiNjqJAG3cNH36kStX\nro1jfcaZU55+eq0tTDmumjbt8ISSpEBEYyiOE/k12tlUiA4RMCjlIAJADGAAHEN9hnRFjpBcDSIA\n3HTTHQBAROfOmu46jjamtqYWBRSLRQCePXsGANxx+6OVX1zy47YIh2WEHAADHAzw2szTZxgK//tH\nd3i+6/tuVVXO/shxxx2qtZHKSGPSaCvitNOECQGkIY6jiH1tTBxFJQaSUjNLrQesQZfeLWXA+PHj\nly5dumnTJgCI4/j3bd/raaFQ8H0fETs7OwHgsMMOu/3223fv3g0AWuutW7dee+21p59+ekdHhz3a\nAZWODhJ8/vJPtuSrRe/yOAp1FBmtKVlRY2sAhEJ4fs7zfSkVMRPRSSe/74STjgJI9QPAsvhc95ZX\nZTqpfk/ChHDWBRcmPRBC2GY3HUVBuRSWSmE5CMvlsFw2cYRAOg7jKEAwOV8teeDexoba+oaatqMP\n6ezsEUIoJRwlq6vc+jp/45alvidee23zsytfsj1dTy5b9olLP/yJS+dhOqUWAIIg6u4pdXUWH3ts\nub2cfq8bK022QgpAfPHFN8hQFMUjRw4ZNWromDHDXnl5Y/tzr1QUTUe3jZs4caRS8txzp9kOO9u+\nVy6HUaSjKA7DKAzDMAjDMIhSxHGU2nx7AK4xpqpQna4u2Idhphtu+LU2JtZ6xvuPjFL7PG1iBGxs\nGFJVqMrlfM/zP/KRmRUJASJefvm5DMBAwLZLzhawDkaUShY+9/l5H/jA8VVVuaam2paWprFjR5TL\noTYmMXswRBQlDxMxh8ShNpHRURyHWpcNBVFUjqJSX1+f0ab/Xf0XglkM7KP/wfP5fE1NTaFQAICm\npqadO3c2NjaWSqVyuVzZZ6/tez2trq4GgFNOOcXyFACMGjVqypQp69evD8MQAEaOHDlp0qSuri77\nrxkr7St8+qrv/82lF//Nxz+qoyiOI621rRsDJFolK622vrEdWzbs2rLpsIkNwAQAqrw2Vyjk8nnP\n9wE4URqnprKIcNqs2VY5mbIV2lZ1HUdRGMRhYEtIiOR5Muer+xbcWddQU9dQe8SRE4lZSiwWS2EY\nKSUcR/pernV46/Kn2gHgiCPGYtJggitWrgBQl358XoVMbdWXmKMwvv+BJXu5lxhjjDYPP/KMTD2P\nXnpp40EHD++/z4svvPbyyxsrxDRt2uEf+tDJqWFbYksUJb6YccJNURRGURRF9pswjMrlYMeOHa++\n+jKAV8jnASqjkRJKuvnnCxhgwvjWtrZx1mUztiafOop1FOuy61YL4TuO5/neJZfMSnSkAFEUAwQA\nYfo1tNUrq8mUIjdsWCsRPf/cq729pd6eUlCO4lj3L+Qnj8RZNDI60iaO4qhY7DEmjOJSsbc3DCNt\nTKwHjJWI1cA++h/84osvvvrqq88991wb8nR2djY1NVXiGgDYa/teTxHxwgsvXLBgwe233z5p0iQA\nWLly5UknnTR9+vTFixc7juN53po1a2699dZDDjnEHjDL4PY5vnj5Jfab5zp6GfG3d95BgARgpUiG\nrMcu2KDpkAkNq1/YXihUKSWVY4dcY7/K8h4Lt5POPPupRx4SQtgpHYggEiGA0TpiQiDLVGbBr2+r\nb6itr6+tr69BRCZe+cya448/Sgo0hlxXCaEYcO4HPxiFQWIMlPAQ2nazj1/yQQC45Zd34ZsAixY/\nXBE9NjbWuK4jhKiuzst0yBIKfOihlaec8r693pNjJk+0WmdrqMRplssMO3bsBoA0CUv+h4hC2JZA\nIMPGlHuLfcVi39JlvztuyiSA/npyvvXWxfX11VKIONZHjh8HjAzQ1Dja1r+7ezYLgY4qOEoQaUSW\nDl9+2QU33XTnrPOmh0HMDJ4LUvbXHwFANUAJAAS6F849+/Y77nvmmZePPnq877tBEDlKaWU4Aptt\n2uuwNTUpiQzGcWhMVOzjKIp37eweMrTeGKMHbpruPs3grrvuOvtNTU0NAIwePfroo49evXq1lLK+\nvn7Xrl1W0tV/e/+nhUKhurq6XC63trZu27YNAB555JFhw4atW7euo6NjxowZxx577K233nr44Ye/\n9tpr9kRZrPTu4cjm6peX3nHIcHHocFnq6Q6KveXe7taWERMmjJ88uo5NDGyYTa2z0/U8x3WlFUYy\np2EK2oIqpiZCx59yqrTLcwgCWSAgshAskJl0HAc6Dpl0fUNtQ0NtfX3t8BFDEJGImeHRR59JVrLT\nUsxzq5+293U/2sEX165ML58/9tHZCSkmZiFJv66FXRtc+cxLNTV5G9Sl++ITT6zu/z6cf8GJkOSk\n/dlWAODGjTuiSL8pVgrjKErEBGEYhWEURlFPT7G3p7dcDpg5yepscx3TggWP2aKPcuTUaUc5yncc\n33Fy6cmxtmZEzm8AKElZJYQvhC/QF6Lpiis+bYytx+soirUOAULm0FAY697UgUCEYU8URR+84Kyv\nfuXLZ535gUceWRWUI5vEVTLMONZxnChF4ziO4rCrs2fduo2rV7+2ccP2JHDTpqlx1kB9tPZpBldB\nT0/PqlWr5s6dGwRBe3v7yJEjL7/8cmbea/teT4vF4rJlyy644IIxY8YsXLgQAM4880xmXr58OQCs\nWrWqt7f3sssuy+fz9l/hLfrXDO8NHl7+uCHQBgzx9bfcZZvaBIKSQgL5uVzO9z3f91z33A/NkwhS\n4JY3XheIJxxzxJ0L70G0NkoMVpHJhtkAGAR+6vHHa2rztTX5IUMbPM/xXIfZ6Di2JZizzpzuukop\nV6D//OpVq559uba2UFNbcF3lKOV6TlUhVyjkhw8bmrIX//r2e5VM5nUqKR1H+b7b1FxrPeE2bdrR\n3FzehDokAAAgAElEQVT3u9894zjSmp+4rnIdx3HV5MkTFy96GgDOv+BES6xc0cMzE3G5HG54Yzsk\nhtfJSnyFIS2LOcoFgN5iXxzH9gcnTRqLAIBARA/cvxwRhRQ5383lvMMPH1dVVWvHaudzNf0UlZVe\n/3pmDRgySQDZsXOt4yRXa+eGM3lEwoocqqvqAaDYtx1BeJ6nVH0/O93cps3tyXUiokDLbszgOKqj\no+v559aXyuGY0cMYoFDwhw1tKFTlWlsGzGLp9d5goA5lMabaf8d9WltbJ02atGjRooE9NWQZ3CDB\nycfOsN/M/vQXHceRtk0DQQKjMchkbZkE4pY3XhcinQuAEDPMOvuMe+67TyC/+PzqS664FIFWLFtq\nJ+8iQn1DbU1tvqYmD5C0CFvDIGYExvvvX3buOTMQ+PnV7QDQdvSEV9dvYmYbjblOYrYbBGXftzpD\nnvuhswBgwW8f7GcyWRGcU6GQcxzV1VUcMqQ+jZUEChQCVz//GgCcf8EJFfGgEMAkQHBPT58QqJQc\nNXqotd9OEzeoiM7tf2QgjCKtTcVqLooiROjuLvX09AGiUtLz3VzOzeW8IIxdT+fz1U7Sg9IfVva9\nC4CYJLECOwhCSqmEkoKItDbGlGwMaAwBNO7a/ZoQQgjlkEpNLC3BFVpb3ved7/y/QlVOII4aNQQA\nxo1vBYDt23evfOblceNbgeGllzeMG9fqeY7nO+XyQPIIMb/zTgON7u7uxYsX74sjZ7HS4MKHPn+N\nUtLzc0op5aigpwt05HleLpfzc7mcn8unU5WGDB1mx5YcecShTDGz/vH3fnDJFZciEgKtWLYMmNa+\n+EJtbd5zpRCQjMB2lVKCyEBqUctMs8876fnVLwOAvXU7OjrzOc/PeVWFfKGQy+d8z3dyvveWG5sX\n3/+ElKK5qc5xbWQkoyiuqsoNaR75q/kLEnmk47iuclxVW1PwfFf1m91mH1aHZZnLlo1feOE1ADz0\n0NFWJZCOVBJC4Jo1b6RaLt68eSczG0MCUTkJodhmOrt+X8gXqmvqa6rrPC/Xv/UkZROOojIgCxTE\nEkFKieVgt+sqKYXNGa2VuBWCBuXQz3lCCKWcnO8vXvzk7NmnJ223wAKHA4j//K9/nzhxVOWNyuf9\nr371hm9+8xMA8Oyzrxx55FgAGDGiMZfzrvzkfzzyyKqB+tis6y6/805/CsbV5t55p32GjJUGEWZf\n9eWc71hTFIuoVEQde57r5xLk8/lCoVCoKlSlE99GHzSGTMwmIop0FBgdGR0aHS68a0F1dQ6BEclR\nwrKS7zqup5iI2HDigEnM1I8mhFLSdZTnub7vep7rea7vObmcl46Q3fvP8rOrXrSU5DhSCHzppY2e\n56x6dl2hys/5np9zcznP/ng+79tpev1ZyfOcdIwlWJmlTX/2GqmU+EIK+4lFRLh74ZNnnT2NyCDi\nY489J6Ww1+w4UinleU51dXVNdX11dW3q2F1J3BiAoygIw5KUKCQCSETbIlNyHKW1sbWtiqFdd3df\nGER+zsvl3OrqqkI+f/c9jzlKzTpvhi1tCzzIusfdc+98ADj55KMBVD43bPH999oioE5thUePHhrH\npu2oTwzgJ+flrtIAHg0AJtTl33mnfYYsgxtE8D0HIBFnMxkmlAJRpvkLpi34In2KGEfh+pfWIJDv\ne0RRHAVxVC73Fbdt2ZLzfaMNAiGSko6V8mhjIEq8cVNfXoNoF4wwfXCsddKNZ4hsuzAwIvv+m7pA\ne3p6gzBiBjJEEonE88+/6rkOE0857tCVK19xlNKxiZW2BBTHxhqDEJFJJYS25u06ggFqaxIZQVf3\nprTFN5ENxLGu5HH2r+mc80+QolbriCg+6aRjVqx4MZnrIoQU6Pte4ogC9kx+2q9LxoTGmHK5ZExM\nhJJQyESu6ShlDMWxXea3AycpCOPNmzuUlNzVWyqF759xlFLCGJKSEJgRgCteTnjO2R/sT39nzDxr\n0eL7Kl7ihuiFF16f+6GvD+wnR9N7kMHtO2SsNFjw0S99nY2xRV7bxAlErp835aK9LWU/SrKPKAri\niBEIgcJyr1KyVOot9nT39nQVe7uJiY1GYClt/xzFsbZFICERIImSgDlxbtvjR2C7wCAMI0qcpgk4\nMfv1PQcAoigKQ7tMFqfFZl658mXPdaw03HFUFMWx50htZGyU0kpJrTWzdf62bpxkEOIYpdRayf5j\nP4gI05ZkWzNxHGULUpallJykzWpD3Y7TBABaB4n9nUAhhOd5vuf7nu84kkEDAILevn294/qu4wmh\n4jgqlUtC2BGhAg27rsMsiYV9o3RsdJwkcJs3dXR0dBWL5TFjhstk4t8eUXs/k3Lrlqn6zYkiAHHm\nGe//zYIHOR11c+Hcbwz4h+c9qSvtO2SsNFggEFkgMCcf8qSUTDKNld68lI5xFOiIpBSOEkoJqQSR\nBuvcyGR30oaJtBDSTkxLgiOWDgur4WQmgUAMQHvCJQPMttueGcAOaCFroGu9RKQQlcX73t4S7Kle\nC0BQSuVybs53Z8+esXjx01pLm5cppePYaK2FcJhTXxLiONae61Tk4xbWkN968uOeoXJoRaeOOgIA\nLDFps1PJoUoVjpsyddWqZ6xUynHtpBcFoG07246OddoQasMUMUexjuIoEhKJRHdXecjQ+orEwfbQ\nWtGl0bR9e+dLL28844xjX399m9aGpXj00VWnnTYZkv4WTK9qA+KYdGiKUxkiAFAGgNkf+Dhiy777\n8JgDipQyVho0sMokAEaExFtSSiUlGblHv41oV9+YDAuWUiiJ6YxcjLSxlMRMUggSKIQwBIk3654e\nMTj0sLFr16xnNmSIBAoCFpD29xtI5pCISgUpCKNYx2EYlfqU6zlKWuUm2JKwzTpffPF1R8l8zsvn\nvXzO93xXSjn7Ayc4jrrjjoexrkqphJ5cd08aGASR1qaqKgeAQohysD3nDwXLl3uICbq6ipUak5Si\njCtqayZXDqLNdiVHKFUtpGhrm6Z1+Mq6F5SU1TV5bSIhoFzui2OjNTNHzBEwxDpiNpJx8+bOiRNH\nMXPqrsmWH4k4js3u3T0rnnlp1qzpW7fuqsxHYOabbry7obGGASqOlymlVuxTEmJiDhEBYCPz5n1H\nTFmslGHg8fFrvgEAiMCAUgjXsY5JUkkZ61AmNV6bfhnSJiiVUEBjY52UQkqUEokMGc2JR1olqkLh\nOvX1Nb09vf1F2cYYKYX1wI3jqLcU1NVVM7MxJr2pRP8VK601AwdBKFMxJyTXA4iopFi7doNyZD7v\n53Ke6zqiUgsTgEJ89KNnOo5z889/W1Nd8Hxn4sRRQog41lZnCAB9fcFRR9pOF+wtbqmuGmGnJxTL\ngQ0aRepFhEkICd09y2trjq28geVgY84/+H1HnwjQR6TL5UBJpXWMyDbT1FrHMUVRZL0otY6NMZs2\ndUw97jBg6zXAKSslIduaNW+89NKG888/sbOzVykJ6bDhtWs2MEBHR1dLS1M/H17bPpIHCNLKOgII\n5jCKpOfZAGpf4cAqK2WsNDjw8+9849Jrvm6LSpaQXNdVUm56dd2wYUOFENYigIw2xsRxJCUymR3b\ntksJo0aPFALjWBNpMsk4gEpwpaTjOupzn/rb63/235WalNZmyNCGnR27mYUCdeopkx98aEV1dd51\nVUJJzIllD4PWmpiZWAiwRwijOAxjMpQv+I0NNS+/ssFx5BuvbzviyIPTyk6i/xYChR0hLOCKK2Yp\nWQWgfnbzbZ7ruJ7jeY7rOpZyn3xy9amnHoOIS59czbB60qSDDSZW/2nvSdIlV8nmKu9eGEZSKoAQ\ngIhiQ3EU6SFDavr6SlEU27TRLu1V0rTO3b2bNnWcedZx1o7qgQdWVMrqZ511XEdHl5Ri0qSDDjt0\ntNZaKUkmKbEff/z09ufW2/Nu3NjRf+C4Ma9IOb4SLhX7XjKalJJSOr3FsLpqH35+DrBYKVMGDBZc\n9pVv2H4wz/c8z3c913XcTa++0tzcbGslUkjf93K5nKOElILIIJISKCVKJUaMGBqFYRQF5VIxDqMo\nDuMo1HHkeaqurjqf98+dOefnt1zvONJRcvSYYUE56OnpJTI53xk1sjkIQ2PwpbWv5wt+WsCyXnXW\nHS4x57D0F4bxk088f1TbOKsGOPvsqbbX/4YbFtrBmVLJT1yatFMwIJMmNpZcpGgGkAAKQN555y2u\n57iOqsi4oVLcBpg8eWKSeVJSaxOpe0GlOU7H2nGV6zpCKCmUUgWtwzCMVz773PjxrTrWsTa2dB1G\nsUknsGzf1snMpXJovZzsoTZs3HH44WM8z/F9V0opbT+0EI4jyVh3N9N21PsA4Pvf/xkkonNAIT73\n2Xl20c0YHcfa90f29L5attOotJFKFgp+HOuOjq7DD7tkH314lm7tHtgDThteO7AH/JOQsdIgwhVf\n+QYwe57rer7ne7lcbvOr6xoaG1XKSlIK13WUEkoKZkNGS4lKCc91hEQpQEpE5DiK4iiMoyiOw0Le\nLxT8fMH3PWfTxi2OIydOHBNYlINC3i0U3O/82//809cvS1vtnOfaX3FcZekIkpkrybAoY2jNmteB\nefSYYUqJs8+eapf8HUcKlEKIm266Wyphk8+LPnYWABAZYhOGse8n1otSjEpnkID1jV20+HZMmkww\nrSEjIGze3FFdnQeAk046uqmx8YYbf9PcnNwtRCyleP/7j7R9LZaVmJGIwjBevuK5iRNHhkEca22X\n+cMw0rEh5i2bdxqi+vrqzt29dpXP0lwcG993rALT9vt0dRZRYBBEYRAfO+WQ++9frmPjek4u5xaL\n5UofzGc/8wmAolU7RHHse4ft6FheLkdBEIVhpKQslYM4No8+uuor11y/jz45T2zpGtgDHj+ibmAP\n+Cchy+AGEYRAZEAAKdBRavWKp4cMHabSuW82DGEmHWlWApiItI4JPEdJAEaQKFAwADAjgk24oGIn\njTB+wpg3Xt9k/UZ0rC0NRVGcz/s33fjbhobqq/9uno71CSceYe2Ilix5noGB4eST2wDgxhsXVi71\njde3to4cUrkzIVE0VIRVKATedtsDF154+l6UBACpM6y9LA3AK1e+4jjKdZTjKtdV+bxfV1dVV1vV\n1jZeSjFsaAMz79q964rLZ//v/97tOHa+imDmJ55Yfcop70MQiKKvFIRB7LqSAeJYa01RHKeUFIdh\npLU5/bQv/DG/iDvv/CYKIQQabXRsykF4//3L7Z+EQt6LYu26ju3fQ8Cf/vR/r7zyIoBeAGCGjp0r\n7OnCICoHYbG3bL1Nrvn7r+87VsrqShn2FQSwEMJxlOs6ruu6jpPL+XEUFerr7QgTgSxQGhOT0czG\nVl/jOJISQAlESQRp9z4RsagsDaWjKnM533a0a2OUwCiKr/vJHaNGDq2tK3zmsx9kYqUkChAIQsBp\np73PpkqQVnWsVyMAtI4cAgD/939LAOBjHzvdzvtGgCsu/8DPf363SBtVf/GLewtVOd93eQ+A6Amr\niiKiqqr82WefXzEh2QNiYnYcmct5nMy1w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"prompt_number": 18, "text": [ "<IPython.core.display.Image at 0x119c3dfd0>" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the spatially down-sampled monitors, which in this example are SpatialAverage and EEG, we can plot the time-series in the usual way. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "#Make the lists numpy.arrays for easier use.\n", "TAVG = numpy.array(tavg_data)\n", "SAVG = numpy.array(savg_data)\n", "EEG = numpy.array(eeg_data)\n", "\n", "#Plot region averaged time series\n", "figure(3)\n", "plot(savg_time, SAVG[:, 0, :, 0])\n", "title(\"Region average\")\n", "\n", "#Plot EEG time series\n", "figure(4)\n", "plot(eeg_time, EEG[:, 0, :, 0])\n", "title(\"EEG\")\n", "\n", "#Show them\n", "show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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/yjcPflNjeeHmQtLnptPxQEf+O13Nxo2wbZvl2H3d0tJg9WrYsQPz7t0kZ2aS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"text": [ "<matplotlib.figure.Figure at 0x126cb13d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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VKzm3oICbZeH/NmuTSeZt3EjOcbhjzBim7KeUs1M2r4x/hTF/GEPB7IIjun/H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\n", "text": [ "<matplotlib.figure.Figure at 0x16b1bae50>" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, we can plot the full spatial resolution time-series as a movie on the \n", "cortical surface.\n", "\n", "NOTE: it's also possible to plot the stimuli using the surface_timeseries plotting tool." ] }, { "cell_type": "code", "collapsed": true, "input": [ "if IMPORTED_MAYAVI:\n", " st = surface_timeseries(sim.surface, TAVG[:, 0, :, 0])\n", "else:\n", " print \"Sorry, you don't seem to have been able to import Mayavi.\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's All Folks, so, what now?\n", "-------------------------------\n", "\n", "..." ] } ], "metadata": {} } ] }
gpl-2.0
olgabot/prettyplotlib
ipython_notebooks/.ipynb_checkpoints/plot-checkpoint.ipynb.BASE.98994.ipynb
1
162318
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import prettyplotlib as ppl\n", "from prettyplotlib import plt\n", "from prettyplotlib import mpl\n", "from prettyplotlib import brewer2mpl\n", "\n", "# Set the random seed for consistency\n", "np.random.seed(12)\n", "\n", "fig, ax = plt.subplots(1)\n", "\n", "# Show the whole color range\n", "for i in range(8):\n", " y = np.random.normal(size=1000).cumsum()\n", " x = np.arange(1000)\n", "\n", " # For now, you need to specify both x and y :(\n", " # Still figuring out how to specify just one\n", " ppl.plot(ax, x, y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "//anaconda/lib/python2.7/site-packages/matplotlib/figure.py:1533: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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OQ1nLVZ3UejidfZXu1CtcW/PR87l0s0KSJUKfWItthlJJgkvHRLGBrq4u6uvr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\nA98iUqZB2mHV5iLtCMb0BzDuf7FOySHRezhizI2qj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"text": [ "<matplotlib.figure.Figure at 0x107927650>" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Note to self: need to do this on oolite because I have non-defaults set in my .matplotlibrc file\n", "\n", "import matplotlib.pyplot as mpl_plt\n", "# Set the random seed for consistency\n", "np.random.seed(12)\n", "\n", "#fig, ax = mpl_plt.subplots(1)\n", "\n", "# Show the whole color range\n", "for i in range(8):\n", " y = np.random.normal(size=1000).cumsum()\n", " mpl_plt.plot(y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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BxWtVfPDe2sBg9ghPtn8G29ZynP+pniPDP7noODPBVmkFYMxWXpL7pjD+dzTO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njxYux2Ox6Ph7Vr15I+9iPSQUga9eitPlxNQovQWtHKxMBeDM4qbDUrifW/gqbmZ1Ct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\nSTqYjvP+BddxW82is16zyfN9e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WvXsiPg0degGg0MiuRoeYH7ttM0qW1wsPHUwRbPLamr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\nI8L7MLvN1w7RSSqN/flFP8jWJBdCByhN0/pWSLQ7A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"text": [ "<matplotlib.figure.Figure at 0x10898a1d0>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
saketkc/gencode_regions
notebooks/GRCg6.ipynb
1
15085
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict, OrderedDict\n", "import warnings\n", "import gffutils\n", "import pybedtools\n", "import pandas as pd\n", "import copy\n", "import os\n", "import re\n", "from gffutils.pybedtools_integration import tsses\n", "from copy import deepcopy\n", "from collections import OrderedDict, Callable\n", "import errno\n", "\n", "def mkdir_p(path):\n", " try:\n", " os.makedirs(path)\n", " except OSError as exc: # Python >2.5\n", " if exc.errno == errno.EEXIST and os.path.isdir(path):\n", " pass\n", " else:\n", " raise\n", " \n", "class DefaultOrderedDict(OrderedDict):\n", " # Source: http://stackoverflow.com/a/6190500/562769\n", " def __init__(self, default_factory=None, *a, **kw):\n", " if (default_factory is not None and\n", " not isinstance(default_factory, Callable)):\n", " raise TypeError('first argument must be callable')\n", " OrderedDict.__init__(self, *a, **kw)\n", " self.default_factory = default_factory\n", "\n", " def __getitem__(self, key):\n", " try:\n", " return OrderedDict.__getitem__(self, key)\n", " except KeyError:\n", " return self.__missing__(key)\n", "\n", " def __missing__(self, key):\n", " if self.default_factory is None:\n", " raise KeyError(key)\n", " self[key] = value = self.default_factory()\n", " return value\n", "\n", " def __reduce__(self):\n", " if self.default_factory is None:\n", " args = tuple()\n", " else:\n", " args = self.default_factory,\n", " return type(self), args, None, None, self.items()\n", "\n", " def copy(self):\n", " return self.__copy__()\n", "\n", " def __copy__(self):\n", " return type(self)(self.default_factory, self)\n", "\n", " def __deepcopy__(self, memo):\n", " import copy\n", " return type(self)(self.default_factory,\n", " copy.deepcopy(self.items()))\n", "\n", " def __repr__(self):\n", " return 'OrderedDefaultDict(%s, %s)' % (self.default_factory,\n", " OrderedDict.__repr__(self))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "gtf = '/home/cmb-panasas2/skchoudh/genomes/GRCg6/annotation/Gallus_gallus.GRCg6a.96.chr.gtf'\n", "gtf_db = '/home/cmb-panasas2/skchoudh/genomes/GRCg6/annotation/Gallus_gallus.GRCg6a.96.chr.gtf.db'\n", "prefix = '/home/cmb-panasas2/skchoudh/github_projects/riboraptor/riboraptor/annotation/GRCg6/v96'\n", "mkdir_p(prefix)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def create_gene_dict(db):\n", " '''\n", " Store each feature line db.all_features() as a dict of dicts\n", " '''\n", " gene_dict = DefaultOrderedDict(lambda: DefaultOrderedDict(lambda: DefaultOrderedDict(list)))\n", " for line_no, feature in enumerate(db.all_features()):\n", " gene_ids = feature.attributes['gene_id']\n", " feature_type = feature.featuretype\n", " if feature_type == 'gene':\n", " if len(gene_ids)!=1:\n", " logging.warning('Found multiple gene_ids on line {} in gtf'.format(line_no))\n", " break\n", " else:\n", " gene_id = gene_ids[0]\n", " gene_dict[gene_id]['gene'] = feature\n", " else:\n", " transcript_ids = feature.attributes['transcript_id']\n", "\n", " for gene_id in gene_ids:\n", " for transcript_id in transcript_ids:\n", " gene_dict[gene_id][transcript_id][feature_type].append(feature)\n", " return gene_dict" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "db = gffutils.create_db(gtf, dbfn=gtf_db, merge_strategy='merge', force=True, disable_infer_transcripts=True, disable_infer_genes=True)\n", "#db = gffutils.FeatureDB(gtf_db, keep_order=True)\n", "#gene_dict = create_gene_dict(db)\n", "db = gffutils.FeatureDB(gtf_db, keep_order=True)\n", "gene_dict = create_gene_dict(db)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CDS\n", "Selenocysteine\n", "exon\n", "five_prime_utr\n", "gene\n", "start_codon\n", "stop_codon\n", "three_prime_utr\n", "transcript\n" ] } ], "source": [ "for x in db.featuretypes():\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def get_gene_list(gene_dict):\n", " return list(set(gene_dict.keys()))\n", "\n", "def get_UTR_regions(gene_dict, gene_id, transcript, cds):\n", " if len(cds)==0:\n", " return [], []\n", " utr5_regions = []\n", " utr3_regions = []\n", " utrs = gene_dict[gene_id][transcript]['UTR']\n", " first_cds = cds[0]\n", " last_cds = cds[-1]\n", " for utr in utrs:\n", " ## Push all cds at once\n", " ## Sort later to remove duplicates\n", " strand = utr.strand\n", " if strand == '+':\n", " if utr.stop < first_cds.start:\n", " utr.feature_type = 'five_prime_UTR'\n", " utr5_regions.append(utr)\n", " elif utr.start > last_cds.stop:\n", " utr.feature_type = 'three_prime_UTR'\n", " utr3_regions.append(utr)\n", " else:\n", " raise RuntimeError('Error with cds')\n", " elif strand == '-':\n", " if utr.stop < first_cds.start:\n", " utr.feature_type = 'three_prime_UTR'\n", " utr3_regions.append(utr)\n", " elif utr.start > last_cds.stop:\n", " utr.feature_type = 'five_prime_UTR'\n", " utr5_regions.append(utr) \n", " else:\n", " raise RuntimeError('Error with cds') \n", " return utr5_regions, utr3_regions\n", " \n", "def create_bed(regions, bedtype='0'):\n", " '''Create bed from list of regions\n", " bedtype: 0 or 1\n", " 0-Based or 1-based coordinate of the BED\n", " '''\n", " bedstr = ''\n", " for region in regions:\n", " assert len(region.attributes['gene_id']) == 1\n", " ## GTF start is 1-based, so shift by one while writing \n", " ## to 0-based BED format\n", " if bedtype == '0':\n", " start = region.start - 1\n", " else:\n", " start = region.start\n", " bedstr += '{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n'.format(region.chrom,\n", " start,\n", " region.stop,\n", " re.sub('\\.\\d+', '', region.attributes['gene_id'][0]),\n", " '.',\n", " region.strand)\n", " return bedstr\n", "\n", "def rename_regions(regions, gene_id):\n", " regions = list(regions)\n", " if len(regions) == 0:\n", " return []\n", " for region in regions:\n", " region.attributes['gene_id'] = gene_id\n", " return regions\n", "\n", "def merge_regions(db, regions):\n", " if len(regions) == 0:\n", " return []\n", " merged = db.merge(sorted(list(regions), key=lambda x: x.start))\n", " return merged\n", "\n", "def merge_regions_nostrand(db, regions):\n", " if len(regions) == 0:\n", " return []\n", " merged = db.merge(sorted(list(regions), key=lambda x: x.start), ignore_strand=True)\n", " return merged" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<BedTool(/home/cmb-panasas2/skchoudh/github_projects/riboraptor/riboraptor/annotation/GRCg6/v96/cds.bed.gz)>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "utr5_bed = ''\n", "utr3_bed = ''\n", "gene_bed = ''\n", "exon_bed = ''\n", "intron_bed = ''\n", "start_codon_bed = ''\n", "stop_codon_bed = ''\n", "cds_bed = ''\n", "\n", "gene_list = []\n", "\n", "for gene_id in get_gene_list(gene_dict):\n", " gene_list.append(gene_dict[gene_id]['gene'])\n", " \n", " utr5_regions, utr3_regions = [], []\n", " exon_regions, intron_regions = [], []\n", " star_codon_regions, stop_codon_regions = [], []\n", " cds_regions = []\n", " \n", " for feature in gene_dict[gene_id].keys():\n", " if feature == 'gene':\n", " continue\n", " cds = list(gene_dict[gene_id][feature]['CDS'])\n", " exons = list(gene_dict[gene_id][feature]['exon'])\n", " merged_exons = merge_regions(db, exons)\n", " introns = db.interfeatures(merged_exons)\n", " #utr5_region, utr3_region = get_UTR_regions(gene_dict, gene_id, feature, cds)\n", " utr5_region = list(gene_dict[gene_id][feature]['five_prime_utr'])\n", " utr3_region = list(gene_dict[gene_id][feature]['three_prime_utr'])\n", " utr5_regions += utr5_region\n", " utr3_regions += utr3_region\n", " exon_regions += exons\n", " intron_regions += introns\n", " cds_regions += cds\n", " \n", " merged_utr5 = merge_regions(db, utr5_regions)\n", " renamed_utr5 = rename_regions(merged_utr5, gene_id)\n", " \n", " merged_utr3 = merge_regions(db, utr3_regions)\n", " renamed_utr3 = rename_regions(merged_utr3, gene_id)\n", " \n", " merged_exons = merge_regions(db, exon_regions)\n", " renamed_exons = rename_regions(merged_exons, gene_id)\n", " \n", " merged_introns = merge_regions(db, intron_regions)\n", " renamed_introns = rename_regions(merged_introns, gene_id)\n", " \n", " merged_cds = merge_regions(db, cds_regions)\n", " renamed_cds = rename_regions(merged_cds, gene_id)\n", " \n", " utr3_bed += create_bed(renamed_utr3)\n", " utr5_bed += create_bed(renamed_utr5)\n", " exon_bed += create_bed(renamed_exons)\n", " intron_bed += create_bed(renamed_introns)\n", " cds_bed += create_bed(renamed_cds)\n", " \n", " \n", "gene_bed = create_bed(gene_list)\n", "gene_bedtool = pybedtools.BedTool(gene_bed, from_string=True)\n", "utr5_bedtool = pybedtools.BedTool(utr5_bed, from_string=True)\n", "utr3_bedtool = pybedtools.BedTool(utr3_bed, from_string=True)\n", "exon_bedtool = pybedtools.BedTool(exon_bed, from_string=True)\n", "intron_bedtool = pybedtools.BedTool(intron_bed, from_string=True)\n", "cds_bedtool = pybedtools.BedTool(cds_bed, from_string=True)\n", "\n", "utr5_cds_subtracted = utr5_bedtool.subtract(cds_bedtool)\n", "utr3_cds_subtracted = utr3_bedtool.subtract(cds_bedtool)\n", "utr5_cds_subtracted.remove_invalid().sort().saveas(os.path.join(prefix, 'utr5.bed.gz'))\n", "utr3_cds_subtracted.remove_invalid().sort().saveas(os.path.join(prefix, 'utr3.bed.gz'))\n", "gene_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'gene.bed.gz'))\n", "exon_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'exon.bed.gz'))\n", "intron_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'intron.bed.gz'))\n", "cds_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'cds.bed.gz'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<BedTool(/home/cmb-panasas2/skchoudh/github_projects/riboraptor/riboraptor/annotation/GRCg6/v96/stop_codon.bed.gz)>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for gene_id in get_gene_list(gene_dict):\n", " start_codons = []\n", " stop_codons = []\n", " for start_codon in db.children(gene_id, featuretype='start_codon'):\n", " ## 1 -based stop\n", " ## 0-based start handled while converting to bed\n", " start_codon.stop = start_codon.start\n", " start_codons.append(start_codon)\n", " for stop_codon in db.children(gene_id, featuretype='stop_codon'):\n", " stop_codon.start = stop_codon.stop\n", " stop_codon.stop = stop_codon.stop+1\n", " stop_codons.append(stop_codon)\n", " merged_start_codons = merge_regions(db, start_codons)\n", " renamed_start_codons = rename_regions(merged_start_codons, gene_id)\n", " merged_stop_codons = merge_regions(db, stop_codons)\n", " renamed_stop_codons = rename_regions(merged_stop_codons, gene_id)\n", " \n", " start_codon_bed += create_bed(renamed_start_codons) \n", " stop_codon_bed += create_bed(renamed_stop_codons)\n", "\n", " \n", "start_codon_bedtool = pybedtools.BedTool(start_codon_bed, from_string=True)\n", "stop_codon_bedtool = pybedtools.BedTool(stop_codon_bed, from_string=True)\n", "start_codon_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'start_codon.bed.gz'))\n", "stop_codon_bedtool.remove_invalid().sort().saveas(os.path.join(prefix, 'stop_codon.bed.gz'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:riboraptor]", "language": "python", "name": "conda-env-riboraptor-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
IACS-CS-207/cs207-F17
lectures/L8/L8.ipynb
1
2647
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 8: Chemical Kinetics\n", "Today's lecture will provide a rapid tour through chemical kinetics. The final result is that for a system consisting of $N$ species undergoing $M$ **irreversible**, **elementary** reactions of the form \n", "\\begin{align}\n", " \\sum_{i=1}^{N}{\\nu_{ij}^{\\prime}\\mathcal{S}_{i}} \\longrightarrow \n", " \\sum_{i=1}^{N}{\\nu_{ij}^{\\prime\\prime}\\mathcal{S}_{i}}, \\qquad j = 1, \\ldots, M\n", "\\end{align}\n", "the rate of change of specie $i$ (the reaction rate) can be written as \n", "\\begin{align}\n", " f_{i} = \\sum_{j=1}^{M}{\\nu_{ij}\\omega_{j}}, \\qquad i = 1, \\ldots, N\n", "\\end{align}\n", "where the progress rate for each reaction is given by \n", "\\begin{align}\n", " \\omega_{j} = k_{j}\\prod_{i=1}^{N}{x_{i}^{\\nu_{ij}^{\\prime}}}, \\qquad j = 1, \\ldots, M\n", "\\end{align}\n", "and $k_{j}$ is the forward reaction rate coefficient.\n", "\n", "We will discuss the meaning of these expressions and each term as well as the physical units. We will also go through a few different forms for the reaction rate coefficients\n", "\n", "Your responsibility for the final project is to write a library that will return the reaction rate to a user once the user specifies the reaction set.\n", "\n", "A few notes:\n", "\n", "| Symbol | Meaning |\n", "|:--------:|:-------:|\n", "| $\\mathcal{S}_{i}$ | Chemical symbol of specie $i$ |\n", "| $\\nu_{ij}^{\\prime}$ | Stoichiometric coefficients of reactants |\n", "| $\\nu_{ij}^{\\prime\\prime}$ | Stoichiometric coefficients of products |\n", "| $N$ | Number of species in system |\n", "| $M$ | Number of elementary reactions |\n", "| $f_{i}$ | Rate of consumption or formation of specie $i$ (reaction rate) |\n", "| $\\omega_{j}$ | Progress rate of reaction $j$ |\n", "| $x_{i}$ | Concentration of specie $i$ |\n", "| $k_{j}$ | Reaction rate coefficient for reaction $j$ |" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
biokit/biokit
notebooks/viz/anova.ipynb
1
10706
{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from biokit import viz" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from biokit.viz import anova" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "X = random.random(100)\n", "\n", "df = pd.DataFrame({'A':X, 'B': X + X/10, \n", " 'C': X + random.random(100), \n", " 'D': X + random.random(100)})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "an = anova.ANOVA(df)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>A</th>\n", " <th>B</th>\n", " <th>C</th>\n", " <th>D</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>A</th>\n", " <td>1.000000e+00</td>\n", " <td>2.276745e-01</td>\n", " <td>2.641227e-23</td>\n", " <td>4.452266e-19</td>\n", " </tr>\n", " <tr>\n", " <th>B</th>\n", " <td>2.276745e-01</td>\n", " <td>1.000000e+00</td>\n", " <td>4.970184e-19</td>\n", " <td>2.411152e-15</td>\n", " </tr>\n", " <tr>\n", " <th>C</th>\n", " <td>2.641227e-23</td>\n", " <td>4.970184e-19</td>\n", " <td>1.000000e+00</td>\n", " <td>4.233366e-01</td>\n", " </tr>\n", " <tr>\n", " <th>D</th>\n", " <td>4.452266e-19</td>\n", " <td>2.411152e-15</td>\n", " <td>4.233366e-01</td>\n", " <td>1.000000e+00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " A B C D\n", "A 1.000000e+00 2.276745e-01 2.641227e-23 4.452266e-19\n", "B 2.276745e-01 1.000000e+00 4.970184e-19 2.411152e-15\n", "C 2.641227e-23 4.970184e-19 1.000000e+00 4.233366e-01\n", "D 4.452266e-19 2.411152e-15 4.233366e-01 1.000000e+00" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "an.imshow_anova_pairs()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-2-clause
aje/POT
notebooks/plot_barycenter_1D.ipynb
1
201530
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# 1D Wasserstein barycenter demo\n", "\n", "\n", "This example illustrates the computation of regularized Wassersyein Barycenter\n", "as proposed in [3].\n", "\n", "\n", "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", "Iterative Bregman projections for regularized transportation problems\n", "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe08be78ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe07faf5ef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe07fa7b6d8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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FjY2N+MxnPoN//ud/xuWXXx6zjdtuuw0tLS1YuHAhVq5ciY0bN0bc/7WvfQ3Hjx9HRUUFbrjhhpjnP/DAA1i/fj3WrFmD1atX4/zzz4+pm0qVkpISnH/++ejs7ERRURGA8MVLS0sL6urqFJ+jdCzTZenSpaitrUV9fX2EeFxwwQVwOp3YtGmT9Ngf/ehH2L9/P8rLy3HNNdfgs5/9rHRfIBDAvffei5qaGtTX12NiYgI//elPAQCvvvoqOjs7YTQaceedd+K5556DwWBAc3MzduzYgZ/85Ceora1Fc3MzHnnkkbiCK4oifvGLX6CxsRFVVVXYtWsXfvOb3wAAPvnJT6KzsxP19fWoqakBAPzbv/0bFi9ejI0bN6KsrAyXX365WvtUADDRaaUkqOM2ZpmnnnoKv/3tb6VUoUpinnzySfzud79Tj5eKSmGRUs55divqVNLC6/Xi17/+Nf7+7/9+tnelYCCEIBgMQhRFaDQasCwLlmVzuhamoqIyO6gpvnOE1157DbW1tViwYAFuueWW2d6dWUcURXg8Hvj9fgSDQfj9fng8HrhcLhw7dgxOpxMejwehUAiEEAiCEGNCUVFRKWzUFJ/KOQOtjQiFQhBFEe+//z42bdoEnuchiqIUNe3btw8bNmyIqKdgGCZcV8Gy4DguItqiEZcadamozBhqik9lbkDO9FajQgRAEhSGYaToiOO4mPuitwOEa5qi+/QxDKMoXqpwqajMHqpAqRQsNDUnCIIUIckFQxRFnDx5EpOTk9JtBoMBgUAA4+PjKCkpQXFxsWQvpo9REhwqgoIgSPZjCsdxEX9U4VJRmRlUgVIpOKgw8TwvpefkguDxeGA2m+Hz+VBRUYGOjg6IoghCCPx+Pw4ePAifzwebzQav1wtRFKHX6yXBKikpQUlJiRRxAYgrOHLhovtCYVlWiraocKkGDRWV3KEKlErBQAgBz/MRYkCjHwCYnp6GyWRCKBRCW1sbpqenUV9fL6XrWJZFcXExtFqt1IqIbtfv98Pr9cLj8WB4eBherxeCIECn00WIFn0+JZFwUQehknBFR1yqcKmopI8qUCqzDhUmKjRyYSKEwG63w2w2g2VZtLe3S0WiPT09EduIJwAMw8BgMMBgMEgFuvQ5wWAQHo8HHo8HY2Nj8Hg84HkeWq02QrRKSkqkoli6zXjCBYRnUYVCIQCAw+EAy7KorKxUhUtFJQ1UgVKZNWjqTC5M9GRNCMHExATMZjOKi4uxbNmyiKauuYBhGOh0Ouh0OlRVVUXcR4XL6/XCarXCYrEgFApBo9FERFxUuKLXt+Si4/f7JcGlwqXkLFQSLlW8VOYzqkCpzDjUkScIAoBIYRJFEaOjo+jv70dFRQXWrl0Lg8Ew4/tYVFSEoqIiVFZWRtzO87wUcdntdgwODiIQCIBl2ZiIS6/XRwiW6ixUUUkPVaBUZoToGiYgUpgEQcDQ0BCGhoZQW1uLrq4uaXRDpq+Xj5O4RqNBeXk5ysvLI24XBEFa45qensbIyIgUORFCoNfrodFoJOFSnYUqKslRBUolrySqYQKAUCiEgYEBjI2NobGxERdeeGHEXKRUth8NTZ3N5Mma4ziUlpbGpCFFUYTZbAbP83C73RgfH4fP5wMQtsRHGzTkppBMnYXqOpfKXEEVKJW8ILeKHz16FKtWrYo44fr9flgsFthsNixatAgbN26MsH2nAo1Ook++VKAKAZZlJadgQ0ODdLsoilJ7Jo/Hg8nJSfh8vghLvFy4UrXE0yg1GAyqwqVyzqMKlEpOUaphcrlcMTVMLpcLra2tWLp0aUTUkA4Mw0S0OJLfXigCFQ9qiS8uLkZtba10O7XEU4OGw+GIsMRH13LJo810nIVutxt+vx91dXWqcKkULKpAqeSEZDVMTqcTJpMJwWAQbW1t6OzszPokGC+Vdy4IVDzklng50Zb40dFReL3elC3x8r+BsEvR6/UCUJ2FKoWLKlAqWZGsholGAD09PWhvb49xxWVDPCE6lwUqHpla4qMjLmqJV+rQQVGdhSqFgipQKhmRrIbJarXCbDZDr9dDp9Ohq6sr5/tA16CimYsClYh4lvhQKCQ5C202m2SJ5zgODMOA4zjYbDYUFxfHWOLlf8tJ1VlIRUwVLpVsUAVKJWWSWcVFUcTY2BgsFgsqKiqwevVqFBcX47333svL/iSKoFQArVYb1xI/MDAAj8eDqakpDA8PS5Z4g8EQYdCQW+IB1VmoMrOoAqWSlGRW8VzXMKUKNUlQ4Yy+XUUZjuOkuqzm5mbpdkEQ4PP5pMGP0ZZ4+TpXOpZ41VmokimqQKnEJdm4C1rDNDo6isbGRlxwwQURjVYp+axLGh8fx+joKERRlJxxXq8XdrsdVVVVEakrlbMofR4cx8FoNMJoNEbcLooifD6flC60Wq3wer0ghCjWcqVqiQcinYVAuG8h3Z5Go4lIR6qf4/xDFSiVGJKNuwgEArBYLJicnERzczO6u7sT1jCxLAtRFNOuc4qHKIoYHh6GzWaDRqPBeeedJ4mg1+vFqVOn4HK5YLPZpNRVdP+8+S5c6VwwyNs4xbPEezweOBwOeDweiKKYkiVe/jfFbrdLEZ78+0cfqzoL5xeqQKlIJLOKe71emM1mOJ1OtLS0YMmSJSnVMOVKoARBwODgIIaHh7FgwQLU1NSgtbUVOp0OwWBQ6uZgMBjQ3NyMkpIS6Xnx2hDNV+GiVvJskFvia2pqIrYdCASkYz46OgqPxwNBECIs8fSPPOqm35PofVOdhfMTVaBUQAiBx+ORfuhKNUxmsxl+vx9tbW1YuXJlWj/8bNeEeJ6XUokNDQ1SO6SjR48qbjfaPBGvDdF8Fq58toJiGAZ6vR56vT7CEk/XomjENTExAY/HE2GJd7lccLlc0Ol0SbvEy7ebqrNQXec6t1AFah4jt4ofP34cbW1tKCsrk+6nc5gAZFXDRCOodAkGg+jv78fExASamppi2iFlWwc1n4VrpnsVAuHPJZkl3ul0wul0wmq1SlFx9BpX9DFXnYVzF1Wg5iFK4y44jpMcV/IapiVLlkSIViakK1CBQABmsxl2ux2LFi1Cd3e3YjoqX4W6qQrX6OgofD7fOSlcsyFQiaCWeJ1Oh9bWVqmTBs/z0jGPtsRTNyE95gaDIWXhUp2F5waqQM0TktUwsSyL8fFxHD9+HGVlZVINUy6IV1Abjc/ng9lsxtTUVEp9+ma6k0Qi4fL5fHC73XA6nTHCxfM89Ho9KioqCka4Ck2gKISQiChZo9GgrKws5iIp2hI/NjYGv98PADG1XAaDIWVLPBDrLKT3BQIBVFRUSKKlOgvzjypQc5xUapiGh4cxNjaGqqoqnHfeedDr9Tndh2QRlMfjgclkgsfjQVtbG1asWJHSD18ufNFXzjPZSSKRPdvr9WJgYAA+nw+9vb3w+/2SuaCkpARGoxHFxcUxV//5plAFShCElPYrmSWernOla4mX/02h7lCz2YwVK1ZE3Kc6C/OLKlBzlGRW8VAohMHBQYyMjKChoQELFy6UrvBzTTyBcrlc6OvrQzAYRHt7O6qrq3NiviiUVkcsy8JoNKKsrAwcx0njNqhweTyeiIiLYZiYVGG+hKtQBYrWs2WK3BIvJ9oSb7fb4fV6IyzxcuGKtsRTd6Fc0FRnYf5RBWqOoSRM8h+8vIapqalJqmEymUx5674QLSRTU1PS67W3t8c0P01nu+diLz4qXPEiLnnaKl/CVagClQv7uxLJLPG02e7IyIhkiS8qKpIEK95YF/nf0e9DdRZmjypQc4RUapgsFou0vhNdw8RxnGSayDUsy0IQBNjtdphMJnAch46OjpgecekiF6Logs5CFqh4zKRwFapAATPbS1Fuia+urpZuj7bE2+12uN1u7Nu3T7LER483UZ2FuUcVqHOcROMugHAazWQySTVM8dZ3MrWCp7J/Pp8Pp06dQmlpKZYtWxZjMsgUeQul2VyDyjfpCBc1CiQTrkIWqEIg2hJfVFQEr9eL1tZWSbi8Xi9sNhsGBgYULfElJSXQ6XSqszALVIE6R0k07gII9zQzmUwghEg1TIm+0BzHIRAI5Gz/CCEYHx+HxWKBKIpobm5GS0tLzrYPqOM2shEur9eLQCCgClWKCIIgrUtptVpUVFSgoqIi4jFyS7zD4YixxMujrnQs8XTb0c5CatCQr3HRP3MFVaDOIZJZxQkhmJychNlsRlFRUVo1TLmKoOQjNyorK7F27VqMjY1FTHjNFYVukpgtkgkXHWw4MjKCwcFBAMkjrvmOIAhJT/yJLPHRpph0LPHyvylyg4bf78fJkyexatUq6bEPP/wwHnnkkezedAGgCtQ5ADU+OJ1OqYBRLkyiKErRSmlpKTo7O2NcTMnIdg2KNnAdGBhATU1NxMiNbFsdxYMKER2HTtcB5rtAxUMuXHa7HY2NjSgrK4uwZkeP2aDFsEajUXE+1HyB5/mM6wLj1c8ls8TL17gSWeJpFEyL7QHgb3/7W4bvtLBQBaqAkY+7EAQBhw4dQnd3d0wN0+DgIKqrq7OqYco0gopu4Ko0ciNf61tA2BE4ODgIhmHA87z0Iy0uLpZcWPmI3s515Kk9uTW7rq5Oeoz8BOp2uxXnQ8nruHIhXIV6YZGt/V2JRJZ4+XgTuSVer9fHpAt5npfSjwzDwOfzzcg8tplAFagCJJFVnJ6IBwYGpBqmeHOY0iHdCIrnefT392N0dBQLFy6UGrgqQV18uYIQgtHRUZjNZhQXF2PdunXSonEoFILZbAbP87BarbBYLAiFQkm7aM83Ull7incCTUW44qWsst2n2UIQhJyNi0kGdWcWFxfHtcR7PB4MDw/D6/UiGAxCFEX09PTg448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1tm/fLplFbr/9duzfvx88z+O2227DD37wg5wdB0AVqIIimVVcFEWcPn0aVqsVCxcuxMaNG+OKQrqkGuVEN3BNNguKbjt6gTcTRFHE6Ogo+vv7UVVVhZaWFskmTGtNtFqtNO21sbExYr/pD3t8fBwejwc8z0tRljwayNUxLWSyEYNUhIt2K09HuPIRpeSKmRSoeMSzxFutVjgcDtTU1MDhcGDPnj04duwY1q1bh8rKSqxevRr/+Z//qfh5C4KAb3/723jjjTfQ1NSEDRs24Prrr8fKlSulxzz++OOorKxEb28vnnvuOdxzzz3Yvn07nn/+eQQCARw5cgRerxcrV67EF7/4RbS2tubsPc/9X+I5QDKrOK1h8nq9KCkpweLFi3P+Q04WQfn9flgslqQNXJXINsUniiJGRkYwMDCA6upqaX1rZGQkrvMwOlWk1EWbrl1FX5EKghDRXYAK12yfoHJJPqKVbIUr2pJdSBSCQMWD9nqsrKzEt771LXR1dWH79u347W9/C5vNBpPJFPe47tu3D4sXL0Z7ezsA4Oabb8aOHTsiBGrHjh146KGHAAA33ngjvvOd70jfH3qh5/P5UFRUlHVj6WhUgZpFklnFXS4XzGYzvF4v2tra4HA40NjYmJcfcTwRoQ1cp6en0draimXLlqX9+qk2i41Gbryoq6vD+vXrI9aTsu0kwTAMdDoddDpdzNwieSplcHAwYg2AihZdAyjUq/5EzGQ6LVXhGhsbg8vlwocffphVqjAfFLJAyS3wQHhdllrgq6urI6KtaIaHh9Hc3Cz9u6mpCXv37o37GI1Gg/LycthsNtx4443YsWMHGhoa4PV68R//8R8Zd4WJhypQs4DSuAv5yULeCqitrQ1VVVVgGAZmszmno9PlREdQ8gau7e3tKTdwVSJdF5/c/BHPeEG3m486qETdBWg/N7fbjcnJScl1pWTTLnThmu1oJVq4aEPdzs7OrFKF+aCQBSoUCkWI//T0dEyNVj7Yt28fOI7DyMgIHA4HNm/ejMsvv1yKxnKBKlAzRDo1TFqtVrEVEBWRfPxQaASVbQPXRNtOhtwRWF9fn9B4Qbc7k4W68fq5iaIIv98Pt9sdc0KlLiuDwYDy8vKCqS8qREMCXYNKFnF5vV643e4ZFa5CFqjoCCodgVq4cKFUCwcAQ0NDWLhwoeJjmpqawPM8pqenUV1djW3btuHTn/40tFot6urqcNFFF+G1HJk3AAAgAElEQVSjjz5SBepcItm4C0KIVNhaWlqKlStXxhRYUvLZL4+O2j59+nRW03OVSCZQPM9HdFZPJkyUQmkWKx+eJ4cWxg4ODsLv96Ovr09xwKHRaJzx9ZdCFqh4yIWrtrY24nnyyJbWEwGQRmzQlGymwpWvJra5IBQKxQjUsmXLUnruhg0b0NPTA7PZjIULF+K5557Dtm3bIh5z/fXX4/e//z26u7vxwgsv4JOf/CQYhsGiRYuwc+dO3HrrrfB4PPjggw9w11135fS9qQKVJ5KNu6DrK4ODgynPYcq1QBFCpAauGo0GOp0OXV1dOds+JZ5AUbt8KlZ1JQq9kwQtjC0tLY0Yt5FopLx8fUupCWmuUOrOPdtk6uKTXyDEEy55ISyAiLVE+txCFaBkZBNBaTQa/OpXv8KVV14JQRDw1a9+FZ2dnXjwwQexfv16XH/99fja176GW2+9FYsXL0ZVVRWee+45AMC3v/1tfOUrX0FnZycIIfjKV76CNWvW5PS9qQKVY5KNu6DRAk1jRS/8JyJXAkXTiSaTCQaDAStWrIDRaMR7772X9baViBaoUCiEgYEBjI2NoampCRs3bswofVIoEVS6xOssIO/lFt2EVC5auSo+nisCFY90hUvewUFeCFvowqUUQaWzBnX11Vfj6quvjrjtxz/+sfT/er0ezz//fMzzjEaj4u25RBWoHJGshkleP5RpDRPHcTGD0dLdx/HxcZjNZpSWluZ0rHsi6FpRKBSCxWLBxMQEmpub07KqK3GuClQ84vVy43k+In1Fi481Gk2McKVafHwupvhyRTzhoh0cqHDJTTB+vx8mk6kghUveSQKYOZPETKAKVJZQYfJ6vTh16hTWrFkT8cX1+XywWCxwOBxp1w9Fo9FoMoqg5AWulZWVeRnrngie5+F0OrFv376sj4EcuRDN5XEb1NobbZoJhUKSMSNe8TH9E30xVIjHZ7YLdeUdHKIjrg8//BClpaUxwlUIEVf0+tj09HRO15BnE1WgMiS6hkmj0UhD5ADA7XbDZDJJM2SWL1+e9RVruik+URQxNDSEwcHBmAauMwGd3mu1WsGybM6EiTLfx21otVpUVlYmLD4eHR2VJsTKi4+paaeQnGmzLVDxEEURWq0WtbW1KUdcsylcTqdTjaDmI9QqrlTDRBfsaQ2TIAhoa2vLiU2bkqpA8TyPoaGhhA1c45GL1I/f74fZbIbD4UBraytaWlpw5MiRnP9AC90kMRukWnwcCARw6NChiOJjuWlgNoSiUAUqnsU8XsQ128IVCoXUZrHziVSs4jabDR6PB2azGe3t7Xm5gkm2BiU3HzQ2NqbtiqNmhkyvqmmefmpqCm1tbVLUKD9uuYQKEc/zGB0dhV6vh9FonNcCFY/o4uPx8XGsX78+pvjYZrNFnEzla1z5Loo91wQqHukIl8/ngyiKGQtX9Jyqufa9VwUqAfJxF9SWGy1M1HRgNBqh1+tx3nnn5W1/NBqNYu85uQGjubk5Y1dcpoXAPp8PJpMJTqdTcax8vsZtCIIAl8uFvXv3oqamRmoNFQgEpNeTn2ALKZ1VKCQrPqbCFV18nI/hhqIoFmSj3lwV6SYTLlqATC8SqHDR4200GmEwGCL2JdpiLn+tuUDhfRsKgFRqmGjz0srKSqxbtw4GgwHvvfdeXt1R0Sm+bBq4KpGukHi9XphMJrjdbrS3t2PlypWK7z3XEQ0d9TEyMgKWZbFx48aIlOv09DSGh4dRXV0tNYH1eDxSOouKFv3BF+JV+2yTyKIdPU5eqfiYDjdM57dQiLVZQP67SCQaryG3w0fPhaLmF3q+4jgOfr9/zqT3AFWgIkhmFZfXMC1YsCCmhinbFFkyqEB5vV6YzWY4nc6MG7gm2n4yPB6P1BWhvb0dnZ2dCV8/VycdeWFvU1MTzj//fJw+fRoajQaiKEY4+liWRVVVVcw6jFIvPQARV6mZnFznC/HGyScrPpYf23jFx4Vm2qDMVpujeNGt/Htss9ng9/tx4MAB/OIXv4DD4YDb7ca2bdvQ2dmJZcuWJXTsZjoL6g9/+EPESPnDhw9j//79WLduXU6PgSpQSD7ugqbQxsfHE9YwaTQaaaprPggGg7BarVIqLV7EkinJIii3242+vj74/X50dHTk1ACSiGhhoilMv9+flkkiUTqLnlydTqd0cuU4LqJNjtFoVKfzxiFe8bEgCBERgLz4OLprxlxZg8o38u8xHfW+ePFiPP3009i5cyceffRRDA0N4bXXXsPg4CDefPPNnM+C+tKXvoQvfelLAIAjR47ghhtuyLk4AfNcoJKNu5C70Zqbm7Fp06aEPyAqULkOsZ1OJ0wmE3w+H/R6PTZs2JAXYYgXQdEGsqFQCO3t7VJ39XwTT5gouSrUTRQVyM0D/f39MdN56Qm2ENdOCgGO4xIWH9NODhaLRTrONputoCYfF5pAyZEX6XIch/LycixduhT/9E//lPS52c6Cojz77LO4+eabc/iuzjIvf1XJxl243W6YzWa43e4IN1oyqEDliqmpKfT19QEA2tvbodfrceLEibyJQ3QE5XQ60dfXB0EQJGGaCaJ79MUzfeS7k0S8k6u8QHZsbAxut1uqM5JHW4XUbaDQUCo+Pn36NKqrq8GybEbFx/niXBEoIHIWVDKymQUlz0Bs374dO3bsyOZtxGXeCFSycRdAuALbZDJJkUK6KaxcCFR0A9fFixdLP+JQKJRTAYyGZVkIgoDp6Wn09fWBEIKOjo4ZK/pLVZjk+xtPiPJpt41XIEvrjNxut7SgTb93er0eGo0mp663uYYgCCgqKkJpaWnMsZVfFMQrPs7X5GNBEGY9iotHdMZmptsc7d27F8XFxVi1alVetj/nBYrWMNntdimFE20Vp4LAcVxWNUzZNHMlhMBqtcJsNkc0cM3V9lMhGAyip6cHer1ecR5VvpCP20hFmCiF1Isv0ZBDs9ksnWCjXW/R61vzWbjiufgYhkFRUZGi6UVefDw0NBTh1sxV8XGhR1CZDivMZhYU5bnnnsMXv/jFLN9FfOa0QAmCgFAoBEIIjh49iu7u7pgaJovFguLiYkVBSJdMIih5LVVZWVnCBq6Zjk5Pht1uR19fHwKBABoaGtDR0ZHz11BC7orMpKt5ok4ShQI9uRoMBmncBnDW9eZ2u+FwODA0NIRAICBFWfL1rUK9es816c5cSmXyMXW6RXdySGc+VKELlPz7MVOzoIDwBcUf//hHvPPOO7l7Q1HMaYGi0C8gPaHRGqaKigqsXbsWBoMhJ6+TjkDNdgNXGjn29fWhqKgIy5cvh91uz+sPkS6uRkdM3d3d82rcBpB45IZ8Mq/b7ZbWYKI7lxfqSTNTcuXiS2TPpp0c0ik+LnSBku/bTM2CAoDdu3ejubk5pxN0Y/Yxb1suAFiWjbiaNpvNGBkZQW1tbV4ap9KGsYkQBEEaVFhbW5vWPKhcQNsy9fX1wWAwREzwnZ6ezlsKkWVZhEIhDA8Pp53Ki0e8DubngkDFQ6PRoKKiIuIkI28A63a7IwqPM4kICpV828wTdSv3+Xxwu92KxcdutxtGoxFarbbg6uOUIqiZmAUFAJdeeik++OCDNPc4Pea0QAHhdZWBgQHJDZRuf7p0SBRByaOG+vr6tBq45gL5kEK6qCnPXQNnRSTXCIKAQCCAffv25USYknEuC5QSiRrA+v3+iIhLHhHII65CO7EqMVt1UPJiYjk0DXvixAmpjiu6+Hi21w/n8iwoYI4LFCEEBw4cQGNjI2pra9HQ0JBXa6qSQGXbwFWJdNopUfOFyWSC0WhMusaVy555giBgYGBAaknU1dWVs3RqIuaaQMVDnspSakfkdrsjujrQ4lg6biMYDBZU4XGhFerSNKxWq0V7e7t0QSkvPpavH8qPr7xrRj6JbhY7l2ZBAXNcoGifNkIInE5nXi3aQKRABYNBaRZSNg1co6FOvmQiF22+SGWtLVcuQUEQJPMDFeXDhw/P2BXmfBGoeMQrPKaDNV0uFwRBwLFjxyIKj+UR12wUHhfilF8gdg0qleLjycnJnEw+TgX5MZtLs6CAOS5QcrRabV7SV3Jot/ETJ07A4XCgpaUFixcvzulVYTKBIoRgbGwMZrMZFRUVaZkvaB1UplBhonZVebSYr47mhBBMTEzAZDKBYRjJUhwKhQp6cXs2oCPlS0pKMDY2JnXel69vyWuMZmNOVCEKVKruwkSTj+nxlRcfa7XaGOHK9sJgLs2CAuaBQNGraa1Wm9cIyuv1oq+vD1NTU2hqasrJBF0l4kU5oihibGwMFosFVVVVOP/889N2BXIcl5GIREdMSr0K41nCM4WuqXm9XkxMTKCzsxMApC7bwWAQBw4ckIwE0R3MC/FEOFNERypFRUUoKipSLDym61vUqg1A0Zgxn49nMrRaraLxJZXi40SOzejPcS5mDea8QFE0Gk1eIig62t3n86G1tRUulyui3iXXRAuU3DZfXV2dlTsx3RSfUiov3hVgLiMom82G3t5eFBcXw2AwYNWqVVKHEJ1Oh8rKSkxMTEgD+eTW4vHxccmhJT/JzqdGsKmk0uQ1RtGNdenxjHa8pdq1XCVx8XEwGJSEK3pUjPwYa7XauC3A5grzRqC0Wi08Hk/Otkf71PE8H9FAlfbOyxd0nUsURQwPD2NgYCBndvVURUQuTA0NDSkZP3IhUHa7Hb29vdDpdJIL8b333ot5XLT9XMlanKgRrFy05mK9UTZrPXIhqqurk26XFx7Lu5bTwmN5Kmu+FB5ngtyxmaj42G63w+12w+/348iRI9i7dy8YhpEyRamkCjMdtQGEx2vccccdcDqdYFkWH374YV7qOOe8QNEfYq4auTocDphMJgDhBq4z7ZhhWRajo6M4fvw46urqcmpXTxZBZSJM8v3OVKCmpqbQ29sLjUYTUbclJ/qEmyzdEW+hO97V61xKE+bDjBCv8Jiuvyg1f41urFuIFEraTKn42OVyYXBwEK2trbBYLHj77bcxPj6OjRs3AgCWL1+Op556SnH9LJtRGzzPY+vWrXj66aexdu1a2Gy2vF10zHmBomRjkpD369NqtViyZEnMiS3fCIKAoaEhjIyMoLq6Oi91VPFEhL720NBQ2sIk33a6P/bp6Wn09vaCYRgsXbp0Ro55vLQLLeSkJ9p00oSFcpKjzKRbTqvVorLMgWrjaTB1IQjchRCZmogLgcHBQWke15EjRwqq8LiQjTbUaFFcXIzrrrsOS5cuxfT0NLZv345QKASLxRL32GUzauP111/HmjVrsHbtWgCIiPRyzZwXKPpDzESgUmngGu95uZwiSwt8Gxoa0NLSAoPBkJcrlmiByoUwxdt2IlwuF3p6ekAIiejmng65PAHL04RyUk0T5sO9mA0zJlAkBA3/EjhhN4CwSHPCW+A1V4HRXR6RxhJFER9//DE6OjoUWxFFF8bqdLoZeQ+FLlDRRbr0t0IvpOORzaiN06dPg2EYXHnllbBarbj55ptTmj+VCXNeoCjppPioVdtisSRt4BrvdbIVEPnoCbkBYWBgIG/tiGiKL5fCRElFoNxuN3p7exEKhbB48eKU06c0QpGfeGciakk1TehwOCTXYSGkCTMRqLSfQwg0/PPghH1RdwjQ8C+BMGUQuQukW+m493itiOSFx8PDwxGFsfL1rVwbXc4lgUpnFlS2r7tnzx58+OGHKC4uxmWXXYauri5cdtllOX+tOS9Q8ggqmUDJHXFVVVUZNXDNVqB4nkd/fz/GxsYU2wJxHJe3ei5RFBEMBvHBBx+gvr4+p22hEtnMPR6PNEqeNqVMZ7uFRnSacHBwEBzHoaKiIuM0YS5JR2yCwgSsgR3w8ifAsaWoKvoUyrUXJX0+J+xSEKezaEPbEWQWgLAtABJ3kUhUeBw9lVcpgi0uLs74e3wuCdRMjdpoamrCJZdcIq2FXX311di/f78qUNmQqLtALhu4ZmrGCIVC6O/vx/j4eMLOE6k0pE0XecRECMlLv0KlCIrWjnm9XnR0dKQ9IBI4N0ZuAJmnCeXRQa5OlKkKlE+wYMj7XyAk/H3mRScm/H9CQBhCnf4LcbfBiEPQ8MkmrArQhv6EYNF3gTOfYbprTfEKY+UR7MjISEzhMT2mqRQe08iuEOF5PuICOh2BymbUxpVXXol///d/h9frRVFREXbt2oXvfve7OX1vlHkjUErko4FrugIVDAbR39+PiYkJLFq0CN3d3Ql/NLkcWiiKIoaGhjA4OChFTPv27ctLmxu5QPl8PphMJrhcLnR0dKCmpiZjQYl34VFoxoR4ZOomTLVINiD6YPEdg09wYaF+CWq0C1MSqJDowKj3cUmc5EyH9kLPLUJ50abYJxICDf8n0DWnRDBkAKx4ECJ3Xk778MUzulCbttvtloq8AcQ4NOWNdaPHWRQSShFUqrOgshm1UVlZie9973vYsGEDGIbB1VdfjWuuuSYv73HOC5SS/Zim0cbHx2Na8mQLx3EpCVQwGITZbIbNZktJmOTbz1aglIQp373XWJZFIBDA8ePHMT09jfb2dqxcuTLrSIcKVKFFTNmSzE0YPZ1XKU1oD41hl/0F8CScEu7xHsQK4wVYRFYnPF6EiBj1PQGeuOM+xhrYgWLNUmjZmojbWfEjsKI55fep4V9EkF01I6M2lGZEJRq1Qbub064ahVZ4nG0n82xGbWzduhVbt25Nc4/TZ84LlByGYXDq1CnYbDY0NzenLArpoNFoEgpIIBCA2WyG3W5Ha2srlixZktY+ZCNQsyFMQPg9j42Nwe12Y/ny5VixYkXOfuhUoKKPYSGdSDKFEAJHyAVrcBqLDHUwcLqU04RuYQqm0vdBNCI0Gg4cp4GG43DCvQ+ChkDP1MZ5VcAZ2ge/MBj3fgAQSRAT/j9jYfE3ZDscgoZ/Ma33yBA7WPEARHF5wY3aoNGr3+/HiRMnpMLj6ELu2WisC8z9URvAPBAohmHg9/thNpvh8XhQX1+fF2GixEvx0X1wOBxoa2vDsmXLMjqJZiJQ0cKULJWZq4hEHiVWVlaisrIS9fX1WW9XDk0dRqdhzpUUXzwIIXht8kPsnz4NACjm9Ph07QVYblwU89joNKFIBLxhewaGkA48z4PnBYRCPgiCAEKAj5g3scxzCaqsVbHTY4kfk4GXU9pHD38CfmEIeq4pvB/C+2CIM+33quH3QBSXFtyojbKyMrhcLpSVlUkGAnnjV3rRFd0/jxoz8p0aVAVqDkAIwfHjx9HY2IhQKISampq8/hCie/75fD6YzWZMT0+jra0t6yay6QiUXJgWLFiQ0hpbvBN+OtAiwYmJCbS0tGDJkiWwWq1wuVwZbzMe1CRBa2Zot+5CYNTngp7TpC2WhBC8Yt2Hg84e6Tav4Mefx97BbQuvQJMhfvQDAH2+w3DydjAMC622CJEfeThdNSaeQqOrJSalxZbvA6+zQ8NpwKTwO3EE/4YGw98BhAcn7EzrfVIYMgCWDIBlC6+bhCAIEYapeI1flQqP892BRGnc+1yaBQXMA4FiGAZdXV3hdInDMSMjN3w+H7xeL0wmE9xuN9ra2nKW1krFhJGJMFGoAGYiUHKLfPS6Wj7GbdATw8GDB1FWVga9Xo+RkRG43W54vV4cOXJEMhREL37nE14U8dLwCewcD/dlrBI1+LuFa1J+/knPQIQ4nYXgxYn38LXmq1HEKn+eAdGH4+73E2ydAcuy8OgmYWzSoL1oNYDwidjltmIw8DH4YAg+wSetC3Gc5kyaMJwqlB9Dd+gQgkUT0MMEhkyl/B6j0eEDsOzlGT8/X/A8n3SOWqL+efJGxUqFx1S8Mik8jk5tz7VZUMA8ECg5MzETiud5jI2NwWazob29HZ2dnTk9KSaKoOQNZNMVJkomQkKLikdGRuKu7eVaoGjjWL/fj1WrVqGyshKhUEh63b1796K9vT2m67a8uJP+iV5DCPA8Ttgm0VFZhdIMyg2e6z+EfbazazjDATeeHTuOf2xogJZNLPwhkcebk/vj3u8IufC27RCuqF2veP8J914ExUBK+3nM/T7qqsIpQ47jIOqPQMdw0OFsBEpEEbxA04T+M2lCIomVRsNhgryJdr0ppdeMh449Ao69JKtt5INssgmJGhXT1k7RE3nlZQW0Y3mqzLVZUMA8ESi6kJ6rhrFK0LEbLpcLer0eXV1deblaV9pmLoSJkk4KUT6gsKmpCd3d3XF/zLkSqOnpafT09IDjOKxcuRImk0mxmJplWRQXF8d03abFnXT0Rl9fX0SNzCAfxJ/7LSAMA2NREb64chXW1NbFbD8eJ53WCHGimH3TeGXkNK5vWpHw+e85jsHJJ+66f8DZg4sqO1GiibyyD4g+mH1HUtxTBpPBEUyFrKjQ1kIkIUwFd8c+imWhZWPThIIgQhB48DwPW+A1VPkIWDDgNGEzBqfRgOO4NNLpPEqKegEsT/HxM0Mq06vTJV5jXfl3k7ZYix5sSP+OPq7n+pprPOaFQFHyEUG5XC709fUhGAyio6MDOp1OanCab+TClKvO5qkICU0hDgwMxB1QqLTdbH5EbrcbPT09EEURS5YskYoz5XVQR4cncHLMhsV1YWu2ktlDqbiT1sgM2Cbxx5NH4Q0EIQgCphngP999B3/fuRrtNbVJW+kEBB7bLYfi3r97woRLF7ShTKvcncQvBLFv6mTSYyEQAR9Nn8aW6rURt5t9R8Er1C1FI/8YTL7DOF97GVyhjxPayiNhzkRQHIqKdGDFSXDaCpSypRAEATzPIxgMQuB5iISAZWKFS6n8o6ToGIBrU9yHmUEQhBkzb6RSeExr4gRBQCAQgMlkwuDgIIxGIxiGSfm8k+moDYvFghUrVkj1Vhs3bsRjjz2WmwOgwLwQKHm7I1qcly3yeVAdHR1SvUogEMhbrzwKIQSDg4M5FSZKOinEVISJkkj4QrwA65QHDdWlMT8wr9crpfKWLFkSswhMTRK7Tlvw14+PgwDYax7C8hIO56coiAzDQKfX469Dg+B0OpSeSZMQQiDwAl4ZHsTnCaRWOnRUBP1DOxK8a+2HLRj/+xUUBbw+2osbF61SvP+Qqw8hktoF1P7p0+iuXCmtRYlEQJ/3YErPBQjoYe73ncCqkosxFXonxedGEwTgxpQAlHFl0Gg0Md8JURQh8AJ4gUfI74fA8yAAOJYNC5dGEx7Ix5kB4gKYUsVXmg0KodWRUk2c3+/H8ePHUVZWhlOnTuHVV19Ff38/1q9fj2XLlmHVqlX4/ve/r/j7zGbUBgB0dHTg4MFUv2vZMS8EipKLFN/09DT6+vpACFGcB5VqoW4mUIHweDzw+/0zNnJDFEWMjo7CYrFIgqjRaOCY8qKqMrWvULyWRB8eH8Sf3z4MUQQu7erApzcuk0oD+vr64HK5sHjx4rhtkBiGwZDDiR37T4YjgzMP+XDUgS0TdixtSOx4oxyaGMeQO9IizTAMNFoNxvkQ7MZibFy6NMKxReuOvF4vRELwv/4B+CCcOelyYBlW2h/Ku1YLLqvvQGVRZHpOJCI+mjqV0r4CgE8M4IjLjK7ypQCAIX8PvEKqEdBZeBKCybcHrDiS9nMBgCV2AARu0Q2e8NAwsd8HlmXBFrHQQvZdJYAgChD4cJowGAiAEILx8T/CTzZFdMuYzUnHhSBQSlBre01NDb7xjW/g+uuvx3e+8x289NJL6OnpwfHjx+PudzajNmaaeSFQ2YzcoExNTUnTchONgMhlKyIKbWLb39+Puro6GI1GdHR0pJ16GOybQGlFMSqq448Mke8/IUQSpurqamzYsAFFRUWYnvZhx4sfwmy2Yssly7D54qXguMT7oiR8tmkP/vedY6A3v/1xH0BEtFWxsNvt6OjoSNptgmEYvH4itnMBIcCzHx7F/ddugSbJcSKE4M3+xN0PXjH14cKGhXEdW4fsIwieHgbLE4RCIfh8PhBRPGPVJuBYDjzHgWg0+GByAFc1Rrak6fUOY5pPT2COuEySQJl9R1N/IgHkytnn3YMlGQ1DFQHiOLNJApfgQqUmRZszg7NpwjN7w3EcllQ5YQ8ulNoRyaNWubllJuqMgMIVqHg1UFqtFitXrowQm2iyGbUBAGazGeeddx7Kysrw8MMPY/Pmzbl8axHMC4GiZCJQDocDfX194R9PCoMKc7n29P9+9yZ6D5tQtqgYF/2fCySBcDgcaefGhy2T+MP/fRMCL2D9lmW48qYNio9jWRaCIGBsbAwmkwlVVVXo6uqKcAe98OePMDAQ/rK+9fZJ8LyIyy+L/4Og25ULFCEEz795GMFQWAxFIsLn9eKvOz/GXV+4GN3d3Skdy2GnF6fH7bGRJANMewM4NDiGrpbGhNvom3Kg3zmd8DF2vw8nbJNYWaMcke22WqDRcNBoOMh9VKJI4PV6pXUuXhDwsnM/mmxBlJaWSlHCR1Onk77XaEb8k7CHXNCxBBPBxJ0f5EReBwuYDA2jpciAojQveBjiAnD2YswpOlGJzOpwwprJgMMAykoZlJVFfmbyqHVoaEjqTUiNMKn2JkyXfLdgypREs6DySUNDAwYGBlBdXY2PP/4YN9xwA44dO5a3YaLzSqDSSfHZ7Xb09fVBq9Vi2bJlMY6bfCKKIg68ewivb9uJIq0OxUPFuPqmSinVQaOcVNN77mkf/vibt8CfEYMP3z6FVetbsbAt8mRL6zZGRkZQU1OD888/P8Yh198/KYkTZe8+E7o3dqCkJL7FNVqg+sccMI/YQUi4F5o/EIDBYEB5SSVOj3qwYklqJ5m9Q1aQOI1JCQje6RnA+YsaEp603hqwpPRa7w4PKgrUuN+NHtek4nNYlpFMAXo9XdsCsKACBlIUThkPW3AwdBJgAI1Uc6QBp+HAJjnZHndZUFWU2PUXy9k1KJ44QYgIG8+jIc1UGnMmeqJ4RA8EIoBjMog4CDkT0xGw4jGI3MaIu5P1JlTqo5erNGEhts3KZhZUNqM2aAYBALq6utDR0YHTp09j/XrlsodsmRcCRbLjlYQAACAASURBVL9gydJvdLR7X18fdDpdyhN0420r3S82TeVZzBa8/9QBVJRXSNX8bzy9C1964HMpvY9ojuwzweOOrI15868HcOtdn5JccJOTk1IKs6mpScpPR/POntgC0mCQx553e3DlFcqL/0Csi++jE4Pw+bzw+fzQG/SorKiUjteHxwdx2YYlMBoS13S4fAH0O1zQKAg1c+Z0N2ifhsU2hbYa5St7byiEY5PWhK9DOWq1wuH3ozJKtPfbh1N6vrRvDHDIM4mtbedhwYIFsE2dQMVkOUSRSC64QMAP3num5ojlwGk4SbxYlpME5qjLjEWG1PZfCV4Mi4wtlK5ABQFECiMBgUt0oYJLv1iURlAAwAlHYgRKCXmdkbyUQN6bUD4narbShPkgmzZH2YzasFqtqKqqAsdxMJlM6OnpiXuuyAXzQqAo8QSDnqBNJhMMBgM6OzuzapeTbrsg+RpTbW0tFpQ2wmvfG9FqpveABaZD/Whf25K2QB37yBJz20DvBE4fHkRNUwl6e3tRXFyMNWvWwG63x932+Pg0enrHFe/78CMzNnUvRmmp8mIGPSaiKKJ/YBA7PzgKVqNFZWUFGCYyhRLiRbxz0IyruhPXxBwcHA2f2JQCKObs7e+cHogrUIcmxiGkuPgrguCDkSFc1b5Yuo0Qgv329A0GBx0juGnRaug4DY65LADC0RbLaqDVnv1ZEhL+fvA8L1mLBVEAAwYaDQcnNw2WuFCh08Ycx3iQM2tQBEEIJNzZwCkICIpiymm+cPQUe9xcQmYCdTaCAljxFEACAJNZ0WmyESZut1tqR0QIgcFgiBCumeo4kg3ZzILKZtTG7t278eCDD0Kr1YJlWTz22GNpDRhNl3khUImEyWq1wmQywWg0pjXaPRE0lZhMoKKFia4x7dy2R/Hxu1/4AO1rW9JKVVpHpzE25Ii5PRQK4i9P78RVt56HVatWSYI8NTUVd53u2PH4J+JQSMDH+/tx6Zb482gCgQA++OADTHpZlJSWhV1ucfj45BCuvHAZWDb+iWJ//ygAnE3xRT2U3n5sZAL+EA+9NvbrfmBiLO72lTgwPhYhUCM+J8b96fcYDIoCTjitaC4pwWjAFvdxDANwHAuOi4xuxDMW+GneiglfAGzAL7W+0ZyJtrgz7YliDgzC0T0vRrYmsvM86lOKoggYorxm5xbdEImY8LNV3iKkCArgwYonIXJrEzwjfTJJEwaDQTgcjrS7OuSbbGZBAZmP2vjc5z6Hz33ucxnscWbMC4GSwzAMBEGQIqaysjKsXbs2ab+tdKACEq/tCLVt9/f3o6amRhImiulQv+LzBk4MwT3lSSuCOvZRpDuNdmNmWQYsU4K2RYsjosVE244XPVGOHx+OESh6EUA7NmzcuBHPvHYw6QnM5QnAMmpH+8JqxfsdHh9MVnv8qa6ykzIvijgxasV5ixoiHuMJBXHKFl8clBj1uGH1elF75kImk+iJcnRqDC6SWYqJZRgwGg14MQg3o0F5eTEAcibaEiAIPALeIEQx/FnKWxPR9DMfJTJTvID6FPSJIR6EU3yxiBDhET0o5dJbs41OibPi8ZwLlBKJ0oROpxNTU1MFmSacD53MgXkmUISEc/x79+5FRUUF1q1bl1NhosSLcKKFSWm0vM/tx0if8lU9IcDJvb2oXGxMWaBO7B8AAPB8WJgAJqL/3IkDA7jwk2fb78QrqHW5/RgZSdwMdHzCCavVhdra8MnJZrOht7cXJSUlWLduHQ4cOACW08A0bE9p34/2jcUVqIMDo9L/E0IgknAaTMNpzgYMsgzUkaGJGIE6NDEBMYXJr9Ecto7jspa2cHrPkY1AjcPLZt7ZJEh8ECHCLQABQYSOY8GyHIqKOACy79WZ7z0v8AgGQwgGgwDrB6vzgGFYMAzAMCymhLOdHxLBIPH3wC260xYoeYoPAFjxRPgLP0upNtqz0WAwYOnSpdLthZImVAVqjjEyMgKLxQJRFNHZ2ZnXtvTRUYhcmKqrqxWFiWI5MoBESyIn3j+Nzcs2pCRQbqcP4yP2M8JEzgxXi0xTnNjfHyFQ8SKo3t6JpK8HAMeOD2Pd2nr09PSgqKgoIn0IAOYRO3ghtZ58R/pGcd1m5TqoE6NnjQHBQBBerxcaLjwskoCAiOErcm2RFhpOgxOjVgR5AUWas1e7h62JI8J4HJ4IC9So3wVbIF0H3VmmQj70uZ0o12V2Be4Tzr62LSSgMV4t2plWQ5xGA50uvNZFODcIGx4FIooEIhEg8ATDU1OoOFNorNFowHGaM2lW+hkIQJKZT27RnbZJKDLFBzDECYYMgTDNcZ+Tb5RqoOKlCWnz10RuwlymCVWBmmOEQiF0dXWht7c373UNNIJKR5gofXHSexTLsUFc6F0H1pj4Pbjdbrz1+l643W6UlJTE/WEMmScxbfegvCosIvEiqGTpPQDgBR5vvX0QZaVLsXz5ckVrfs9g6o4zpycAy6gDbY2Ri7AhXkDfhB1+nx8+rw9arRaVlZURLkGXywWWY8OOOH8ATpeA/931LtY018NoNEJXbMDpNNN7FPP0FJyBAI5NZSZwFI/gA+/nMxIoQgC/eFag7EEejfpUT34EIlxgEO7dJp2DOSBUpEWxNvz9DQVD8Al+iKIAhgmvbem0Hmi5sEkjZmnrDEESRJAEoUvH5BAVQQHhNJ/AFpZAKcEwjNSBPFU3obz5ayZpQiWBmmuzoIB5IlAMw6C1tVXqaJ7vkRscx8FqtaK3tzdlYaIMnUqcMhJFgv4jw1h8QYvi/R6PB319ffD7/SCBopSuqk4c6MfGM4W2ShGUKBL09cWPoOgPUSQijCVGNDUtjls31jOoXC8UjyO9oxECRQjBvpM9sE7aUFRUBEOxASzDRjSNBcKfuVarjfgR+wxlqK2thdvtxr7eXkw67CAk/J41Gu5MQ1NN+AImwcU/AXDEOoGj7iwFivfBI/JoL0/frRY4k96jOEICBELApRC1iPCBQACD2IscBy+gTaeDTqeBvOKYkPDaFktGIQqidKwZJvwf+hmEbwRcogs6NvX3FR1BAQAnHIeguTLlbeSabLtIKLkJaassKlzy4YbyouNkacLoQv25OAsKmCcCBZztep3PmVC0NdDAwABKSkrSEiYAEHgBkymsz5j296P1/MjCOq/Xi76+Pni9Xska+tSe11N63ZMHBiSBUoqgJidd8Ptjj5koilJnZXmUduLkKOrqYivLfUEeo5PpOd5O9E/genSCECKtaR2e9KC8ohwsy8Lv8ysW6jIKCtMz4UBF91pUVlbiI58H5RUVZ9Znzsw8CvHw+/wQRREMy5ypO9KcqUHiIk6gh6zjsAix7shU4UURPjEIiICfF6HXpBfV+4XI1KIIYDokoKoo+U+aMPE/g4Aowi8SGLjI48cwLIq0PBgxAODMSfvMYSdEhEhEEJFItzl4B4wao9TBPFm6jxAS85kxZGBWm8fmo82RvFVWKmlCuhYmFy76O5Mf07k4CwqYRwJFycdMqOiedR0dHVIonw62YQeEFNZnRk6PI+APu6h8Pp80h6qjowM1NTVhpyIvYHQgNTPCsGUSfm8Q+uIixQhqaDjyRCyKIrxeL0KhkGKVPu3RF82o3ZfS/sixT3thHhzF5NgQdDod1qxZg91vfwyW9YcfwABSICFrFiuvg6J4gkEMT7nQVFmGE7YzkRzDhO3YGi4iYojowO0LghfOuuE0nAZ7/QMw1rJJexDGwyOcPRaOAI8GTerflej0nrSdlASKQGQ8SODex5TAw8Ap7E/0xFwaMDEsIk7jJJzmE4mIQIAHf8ZIELbAy8ZusFxkpBqzTwSseBoi15XkPeWHmezDl2qakM6I8vl86O3txcjICDQaTVrLFpmO2qAMDAxg5cqVeOihh/CP//iPWb/3RMwbgZI3jKVjl7MlWphozzqr1ZrRa4wPpLY+Iwoihk+PguUYTE9Po729Paap6tiQQ2ptlPx9AAN9E1i6ukkxgho6U0dFr/KCwSCKi4vjdtkYGLQjGORRFHWyHJ/yI3z1ndriOS+EB7i9+/Fx/J/LLkBpaSlcvgCGpyIX6ZPVQck5PWZDiaEIo57EjVmVO3DTTg8CJn1O+B0EOi481C/ixMtxSc1nHsEv/f9UQEBDGnXhQeKPSO9RHCl83gJxI6zo8XdwmhfQEKNPJPWR7mc8FYJWOOvmI4AoCuClThkBCKIYbhKr0YCIIkKhEDQcF1Ggzoon5oVAxUMpTSgIAj766CNUVVVh7969ePHFF9Hf34+uri4sXrwYq1evxve//33FQZ7ZjtoAgO9973u46qqr8vvGzzBvBIqi1WrhdCZ2ISVDLkxKzVQzjdIm+pMLlCiEe9f1HDBh7UWrsGLFCsX0ybA5vfY3llNjWLq6STGCGhwMj5QInOmXl2wxVhBEDAzYsHjxgojbrc4AiKY4qTwJoiClDo0lJYC+QlrT6pmINDYopfIS3X563AZDWYZOKuqG4zQIBAhKNTpUVBjO1h7xPII+n3T86NqWIER2FREJgVc423pqKiCk5XpTip4AwCOISbtB8CmIzLQgxNjNGXgRr/bp/7P3rjGSnfd55+89l7p0Vd/mQg45HIqXGVISSYkSNRK1iRebtRHb8FpZOwbixHFsGLaBBQw4BhZWEHgDJUEQBfAXB04+Jd7Y3oUsbbKwI2CjFROvbVmmREm2LGkoitNzn+6ZnulrXc85720/vOecOnXtqq4eklL7kYYz6K46tzr1Pud/e55x6JuHEuD5PiXf74u4s9EPmSSuG1OrPNryfR/P/ws6wd9iYWHYRfZB451AUKNgjMm7CX/qp36KH//xH+djH/sYX/ziF10K/BvfGJu9mcdqQwjB7//+7/Pkk0/OpbQzC44dQc2T4juImObdx72b47vKbJpWS5KESqVCe7PLmTNnxm9rfcqn3RTX33SzV8UISmvNlSvX+M6bN6lUKjN1CV25er+PoGKp2G1LlpfGz7YYa+i02wOpQ8GV29sYY/E8wdX7A3WfEam8ST+/trVL+cR8t31XS7S1tCLFQ7hrVip5UOoRn7XkunpaJ0gpiaMIz/eQnkFbnTugKmNpScNi6eDF0Nrh+lMRu1LzcHncQm7Qpkl/LnQY2lpa2rBUaMmfOnoqoGUOtg8RQhAEAcLzqNXTRc+6e0ErhdIt7q5/hd2GI7rBtu0H6RWltX5bvajGYZySue/7PPvssxMVJeax2qhUKvyrf/WveOWVV/j1X//1Iz6r0Tg2BDWPJ9S0xJThsJ5Qm9eHO+UcMXVJkl70Yq3l/o0dZKIIx9Qc7h0wVDv0+vU92s2I2mIFay03b97k1q1bGFtjZXVlbEQyDtcGIrhbm3tgRy+N1ho63S5xHBdSh71XdSLJ+v19zj28wrX7/XU1gZioZj4IZQxfX9+EOcZRWspFEp1YoY3FH1HQEYLcfiN7+qyUyxhruBvtYJVTfci64e7stQgWy7mS+TiJJ2ljNOPvrd1E83B59Mlp2xx7rQaxp1WBoDSMkTaahNjGJDahJGZc5AV4wsMrlQiBZ59W6OBi3pTTarXY2dnh5s2bJElCGIZDDsdHEfm8UyOot2sG6hOf+AS/8iu/cmgB7cPg2BBUhlkIylrL3bt3uXbt2lTElOEwEVTUjtjf7j1xWuNSeX1ptZRkBa7j7/Z3NnjyhcdHHvf9O7M/8V7/zh1WHinRbreJ45gPf/jDfOnL12YmJ4A7d/dpt+PcguPW5p47fGtz7rE4HbSoG+XnOG5fa7e3OLVaY313oANtTKQ0bjuJ1tzf63L69OFTFC3l0nPWQjuSLC1MuQAL8PCIGdZp7KQzSXGcoLVz6B2lYj4uvZdhV45PFyrjSGYaitpXOm8aEbYBI2pe06Ct25RmaAAZBc98G83fxPM8FhcXh0YYBtUd2u021tr8YSf7Uy6XZxoefqcSlJTy0F5Q81htfPnLX+Y//sf/yK/+6q+yt7eH53lUKhV+6Zd+6WhObASODUFlN+Y05HFYYspwGIK6f8ul94rElKfVRnyprLVc/9bNkQS1t91CJrNEcJY4Tvj/PvclfuAnPsDCwgIXLlwA4M4hiC7DtWv3ef75xwDn/4QQqUqGM+/rdLuUy+WRiuaDuHxrizOPLE8dAeB2M4S2lHTmGDPQ1tJRvfe3IzU9QQGxlSg7/Nm0lSUsl/o8o0apmLf9Xaxn8vSgcDpF+XYSa+kay4I/eM9olC2m3CYv1E2tUdYSCDHk+zQLWqZ1sInhAR+pZ66D7YAYLeQ8St3BpN+jVqvF/v4+6+vrxHFMEAR9Q7L1en0sCb1TCUprfWgvqHmsNr7whS/kr/nEJz5BvV5/oOQEx4igMgz6EhVRJKbV1dWZiWmafYzD1voO3U6HKIomEhOQ//z6t26P/PX9qdN72dBghyAIMNEK7373u/mzP/uz/BV3Nw/fUHLt+hbPP/+YSxne3UUASRITRRFhyQ0RT6t6ffPuLmubw0O+Y1N8Yy5dWyYksUobF2YvundU0re3djTbg0hbRSN/biw0E81K2X0lR6mYS5PQTXaw1kslilyK0Kavz3T1duKEhYUyxYugbIPpYqceGkpzMtRAZ6b3FdE27QMbQCwHNYhYPPMdjP+BqfebyQzVajUefrhXC5VS0mq1aLfb3Llzh1arhTGGarXaF21VKpV3LEGNiqDeCquNtwPHhqAmfkEGiGmUk+yDgjGGW7du8doXv4q1lpWVlb4220lYv3wHGUvCgZrDNPUnKR0xeZ7H0tISvu/T2O3S2OstRkmi2Nk5vNbcrVuuXrTT6LDbaJNIiQWWl5fxvNm++FIZvnF9hHLDRMWH4QW5nbhjiCJFrTZ76qkl+zvZokSjjcGf8jMrtpcPYj/uEdQoRKYDWeTU9xuLta6WZy3c70TUkyitg7muQ+PvjhTRnYR9rTgVHD6CBtBourbLwpjoxx3PsMzRIFy7+fQENQ6ZLFax4Wec5UYUOQuT5eXlnLiKxPB2YTCCmrUGdVirjSKyLr8Hjbf/ar9NyCKczc1Nrl69+rYQ0/r6Ojdv3uTMmTOcrJ3ibm26wdoMWhtuv3lnKM13f2N8QVspSbvVRgjBYr2OP/CFW7/qmhustdy715g5Eixi816Dzc0t/vgr3yTqdimFIdWFhZnJKTueK+tbLJ4YoT5v3R+t0ideMboGlWiNtK6W0u3IwxGU6ncmtkA70iwtHExQ2hoiM75Vez+enJYdX38SaQTlrmtXwPKyq7FprZAqQpomWJsLEWf6esITY+t1e0pBaT6CAhdFLXjjCWqUzNEgPPPGA1M3H2e58Rd/8Rc8/PDDxHHM5uZmbhlTqVT6UoTVavUtbYGXUg6ZFc7iBfXdhGNJUJ7n5UaBD5KYRqU2MgHZ69ev89BDD/HhD3+YMAz5o/tfHrOViTtg/fLdYYIaUTfSyg29Wiy1+rCqeYbbV++zdM51Ic6T3ssm4L/0pW8SLK6wtNSh1WrCIQkvUopWFA8RlMD5e+3u7eKJ/iFjz/fwhJe2MQvahdpTtzt7HUoaTWyGSaQTSZYWDm4LnBQ9gUvxjdPTU1Yh7XRzSNK6mah64BMEIdbbx5jeV10pmUt/GWXS+5S+upYQHl2TkBhNac61t23anOb0+BdMEUE5dfN1rHhsvoOZAcYYVldX+9J81rr6aVHdodPp9CmXZ38/qBb1eSOo7yYcG4LKvpCbm5t5m+qDjJiyRolMN2tQdWLQpHDn7mxPqhnx3X6zX1xWK812gVhyIVdjJqqaZ7h9bYvn33USYwybm7O3Fg/q89XqD3O9mXXeiUO4Lzl0E0nUkX2kr6Si2WpijHGp0Wxht9DpOgFOKSXdbhdjDdtSorVGeIJuV2K0xRtqJhiPrL18EO14ujpUZ0z9KYOhvw5VxKTZp1HYk5p62iY+aEwIAuF5eH20YF09y1q0MWA1nojZSXweCuO0vtUTg50FHdNBW40vRkfO00RQkKmbv7UENRgZCSGoVqtUq1VOnTqV/7woSbS9vc2NGzdIkoRyuTzUAj9vtDVPDeq7DceGoKy1fPWrX6Ver3Py5EmeeOKJB5rOC4Igf9LJ0ojjOgKjdkSnOZs0Uka462/e6Vu097bbaG0wRtNud1BKpUOvIdOsLHdv7fC8PTVzBNUng1RboFxy53jz1ja3VTc9Zg4dQXUSiVYGGWv8ULho0FgWqgvESYzv+73oSYDv+XkbbIbN7W086yIGpQ337+9QqfRUzINMpmjMDFJbjiaobqzHzkP10K8eMQ7j6lAHtZcPYk9qHquCsTHGTiZGhyxyItU4NwhradgyDwmnq2d1QS0+TQ3mKcIDaoEd0xlrYjitioavL6GDvznFuRwdpm1Ln6RcnrXAb287RRZgqAW+VCpNva/j4gUFx4ighBC89NJLeJ7Ht7/97SMXjB2E7/tsbm6ysbHB8vLyxGht9xCRCilBtfY77N9vsPKQm4PY3Nih1WohZcLCQo3Fxf6h14OgtWHvfgetNffuHUxQWYF5nAzS9RvbRKsuxdZrM58dnUQClt3tBqUFkStNaKWJkzELf2FfUhukNQjPxQ2eD0FYZWm56kRhlSKKI7Ryc0TDFhxibARlcUO7i9Xx0WlsFXqKWaJRdShtFYk9mNz6tqOcXJEa2SI+RUqNBATsmxAhPDyPXMQcSx5t9VtvCPrb33vbm+iyO0WKD95+dfNZUVQuP3my5wydiS23Wi12d3e5detW38BxMVU4qovwuHhBwTEiKHBRjTHmgXpCWWvZ2tpie3sbrfVUtvI7h5g1EvT8j26/eYfa6gLXr1/ntT/7ZjrrUWPmXEyK7bttdnfbIy02iuh2u3S73Yn6fK1OjC5bKvVSejSzM5TUmm6cuLmgJOThswUDwwlSR8UGj7YaPpduRyJOLhCEAUFY+CpYpweoVM+CI9KSSMcuYkjTXcUn3k40maA6ZjqCGVWH6s6Y3gNQFtpa4x9gzz4aFnAPcIn16FqPBVEg16xeVby/0vk25847HG3tm31Oc8otrAORwrQpPtdu/m2M/+FDnNM7B57n5ZFTEVm01W63WV9fz1PzxWirVqsNEdT3qhcUHDOCyvAgPKGstezs7LC2tpZ3A505c+ZAcoLDRVAiXYCtMXz9i99gX2zz+OOPc3r1EW5XDj+3ArB9pzkxeorjmE6nkw7Zrk5MTUSxxLQdQSFmq0FZLFE3YqfZQgj3uamkfwvTqly0k+HoJ4rU6PSScBGw7/csOJKoQ9CVA3WaXuSw34o4UQ/GyhR1TcwIf8AhGKCVaJYLab5Z03sZtpM2p0uzS26Bi1Yz7OuQBe8AghXuP3lNbyDaklbSiJp42l2ETPk98AOMnV6lwtevvyUENU/36mExzcDxxsYGnU6HP//zP+fy5cvcueNS/IOdfeNwWKuN1157jV/8xV8E3LX5xCc+wY/92I8d7QUYgWNJUEftCZURU7lc5vnnn6dWq3HlypWp97E7Y4MEuEUxiiKklNy7Uefv/q9/G9/3+aN7b8y8rUFsbTTZHEFQmRNoGIb9TQkT0E0UtmVYfjglk6m++DYnwVKpRFCp4Em3iMlEo6QmCAudVeMMCws/7oyIoIy1xLGmUjn4a9BScd8MUpFrrDFE0hBFMUan3ke+MzkMgoDESKTVeNMwFC7NlxGUtnrm9F6GXRlxemwj2fjPTtB/rfZUyCPh4Y6hL9oqw0qwkiuYa6VIZIJMEkz6s5y4RvlFkbWbKxAPduka1SDxdmDUwPFrr73G+973PoQQ3Lp1i+3tbX7oh36IdrvNU089xb/7d/+uj+QyzGO18fzzz/PVr36VIAi4c+cO73//+/nRH/3RBz4XdqwIqigYG0XTFI4nY29vj8uXLxOGIe9973v7QvZZSHDv/gwRVNrmGscxYejUGDpbkXMz9WFnirrRQYg6kls3esrqUkra7Ta+76dDttN/ceNEYlWajpyCnxLpSDBTUfY8n/vb/XWUqCOpL6cENS7FV4AyhniMeG/UlQcSlB2QNxpENljthRUWF4M0RZiqcStFM+mg0b3ZI5E2F4wh+P2CTFVXH6wIPhqGfakxlonmhMNQDOru7evwSEaQWqbFCU7kCuZBEFAGkiBAaU2lXEFphVaKTpK4jktII60s4jJ45grGf7BzP+9UFYks4g/DkA9+8IN84AMf4LOf/Sxf/OIXMcZw/fr1sbp881htLCz05tiiKJpJ03AeHCuCyjBvim9/f5+1tTWEELz73e8eEq8EZqpz7d+fglRsf1RRqVQJAmfuprXh7rV7nH78NM39+c0YhRCsX91Gl0Pa7XSod3Fx5i+s0galLWiLljo1vx3NJkop2u0WQgiWFhfx/ezWtHST/uvoCMqlM6aROmpP+By6XcnK6uQ0bFvJscddRCdW1CpBmiJ0MkWlcon7poFv/VQCy6QRRH9zgZdq6nnCo5H0/Ji6U1hWjIK1CmMFbR2wGAw8KE04FcFwpKQRtIzPon+YdGEPbdPGWDMkb5Utup7vUfJLMMIvSimV+0Vtrf9ndro/2NcFV61Wj3TRfKcSlNa67wExiqK8K9jzvJx8RmEeq41Tp07x5S9/mZ/7uZ/jxo0b/O7v/u5boqpxrAiqGEEdJsXXbDa5fPky1lrOnz8/UUE4CIKpXHWttezfb056Qa6XF4YBy8sreL5HN7XRznD7O3fwK7PrBo6CMZaNG/epnV2iXju8vEtUIJa4LfEXBAy49WbmhEYbavUa4cAAcaxcC3ffdjsFwpkQQWXENUkctjuuDlVAe0z33tDrIsXpgVtCW0tkZX6sQnjDKUJrscam7e8Ki+XuboN6WRB73Vxjb/qmF4tJ03QNNYKgxmI4esqwp8O5Ccpg6JgOdb+/OcBZsIw+t2K0lWFpucVJc55WOne0ublJt9vF9/28A25xcZFarXboe/edSlDjvKDeCnzkIx/h0qVLfPvb3+ZnfuZn+OEf/uEHrrxzrAgqw6xdfK1Wi7W1NaSUnD9/fqqWzmlTgOP7+QAAIABJREFUfK3dNkqN/uLLtObjUmtLeIUvTDYHlWFj7S6Lj5wctZmpkc1OdbsRKrKsLM/XGRQlvfOP2wm1hUrOJc6csIOUSZ854SA6yfDnFHcV1th8XmlsDSrFpAhKKYNShjAcvxg11XT1l048THYdHXGQQaAQAjHQXCD9EO03sQaMUblLyXAr9yilew1p40FDhZxlunS2mOCYu6tKnCvNnxZvmuYQQR1weYYg2Gehcp/qwrs4fbqnUKFStZRWq8Xdu3dptVporUcKwR4UbX03EdS0HXzzWG0U8Z73vId6vc63vvUtPvShD81xNgfjWBHUrKaF7XabtbU14jjOlX2nxbQEtb81nN5Tac1HCOd/M6iXB+5cirI+G1fucurZc0OvmwbW9tx6FxZqJIlBddt9JHAYRHE/QdUfcmaI7Y7zm1qoVqnXV5m0Og2m99zxWuJIUVkIx3fxpZGVtpZYT/4coq4aS1DKGKID3p9BG0ssNZWCiWRLHS7l2pSGajVO1RfSY7M9tYd+JXORpwmFENhCk0NTBVPWoRRMMEJsGh9lBYGYr7utZVpDJG6xU6vaZ/D1N1Heu/p+lqWkihHFoBDsnTt3iKII3/f7SGvQduOdTFBFNZhZIqh5rDauXbvGuXPnCIKAGzdu8MYbb/DEE08c5amNxLEiqAwHOd52Oh2uXLlCp9PJiWnW/Pa0rrp7haaGjJgQwn1hJqQnBiOo7Tt7bN6aTWw2H7KNIqoLVVZXnMXH3l4HayxJJ6FcP3zasC+C6iTEcUKcONfc1ZXJ7ekZRhEUuDRfZSE8MMXXSRXUJ+4jkiwujT7PccO549COegRlrD1Qf28c9mLJqpH9AdJYJXOXIjTWYI3BiiR9jWvrb0iflQPazUfVngZfsa8DTgbzjWckNiGxCWVRuN5TDuoW4Zm/BPsjB3ZujBOCVUrlCg9F241s5khrnT8MvFUNAdNAqX6zy1m8oOax2vjTP/1TPvnJTxKGIZ7n8W//7b/tk3p6UDiWBDXuhut2u1y5coVWq8XTTz/NqVOnDn1zTh1B3W/0hFytdXnzA/TyYJigANbfvDPdwVlLN4rodrtUKhVWBmaZEukWs7gZHZqgtLHINHVpjEFL5QhvocxCdYL1QgHGGiI5+hpGnQTo346UEk94Tq4oXfLGyRMV0e2O/5xacrb26k6sOJkOT3V0PJvBYgGJVXSVx0J40IxQSlrpmmVshLUeGWtbC/vSpyac/UY2i2atST9zgZt7OngWaU+FcxMUuDRf2evdV9MP6vYg7H2EvY0Vh8saBEHAyspK3+KeyXVlda1Op8Pu7m5uclgcln27oqtREdRbYbXx0z/90/z0T//0IY54PhwrghpHNlEUcfXqVfb393nqqad47rnn5n5qmoagut0ur3/9DZrNphNynUH9eBRB3bm2yeIjk55qip2A451sZWKwWOJWBByuABsnEmsNSmnXFhuECDvb7dZN1NjlvdgoYa1ld3fXLRrWpWcsFizsa+lSlQPSO0UkscIYOzxgaw8TQfUEbdv68B2Vxio6yTQE1fcubNaQkcVQAlqmQhi687DWoIzOU4RY8P0I0tRdHp+NuFa7R9Ru3jRNTtG7T621Uw9cF+Hrv0B5hyOoURBC5DNHSilOnjzJ2bNnc5PDVquVKzyMspSfRU/vsBiMoL6XdfjgmBFUEdmg6/Xr19nZ2eGpp57iPe95z5HdYJNSfHEcc+XKFfb390EKd4PN+gQ5oGtnlKGx3R5DUCM6AcfMMrmpdA0W4ubhhjO11uzsNXKx3OyayrYkmMGDaVx6D0BJQ9xNiJKus0VYWe1bXKWUtDtd4qSXqsl+6Xn9TQYWNw+1MHBsXS1RM6gcAEhtkcoQBj6tA9TLx0FbR8xt6XOK6btNe+TUj5YK8vqR6yLUhTb+CGF7WVKTXacCEWXpwhifyHpUxWzXZBAd00FaSSjCfF+HUeXyzF+A/dH5GXMEMkk0GG1yeJCeXjHaOsqBX6VU30zS97IXFBwzgsoWyiRJiOOYr371qzz11FM8++yzR/7kM2p7SZJw7do1tre3c0L8+qe/c6gvmCuG9xgqSRRRY1gSJxuyLTrnToKUOt+q7CQYbfCmtEYvWm1Y4Q95TsUdSdUenL7MMKqDz8GilWZ3u8HJh5dp6ZabMTIFIrIQWzNkjthrMrCu2w0Awd5emyCw+AUFg+aM6b0M7VgRComZIm02Cto6UupIf4aIxWLGEBS4ZonVcPD32qlGiF7klO/L9rojbfZvC1txwNlSNFIQdhY0dZMTwYl0V4er8wi7i7DXseLJwx3EBCilJrZQH6Sn12q1uHXrlqspQ5/B4TxeUX8VQX0Pw1rL2toam5ublEol3v/+91Or1R74fpVSXL9+nc3NTd71rndx4cKFdGjT9jVJzAIh+mUZZKKQUYJKJEEpzFtugZmsqpOk/4k9bsVUlycPshatNrKW8Z317aHXqVhj9LSLth1JUFq79JTv+wRemSAIsFiajSZ+4BP4LrUaJ/GAYE+/4rZDTzBOKovSmjhOFQyEYNdEGMyQMOxB6EQKPzgcuVlrcmIzlinrUGBtwqQJ3D0ZDhCURUxqPxf96T7h3sKeLXOWqJcizH6fDhlP6xnVNE1OcCI7+MPyHL7+Cso7eoI6rNTROD29TqdDs9nMvaKklJRKpZm9ouatQX234VgRlBAunfbUU0/x+uuvP3DLDWst165dy6ezP/rRj/bdgN1WRBIfrujs2swLBJW2dHf2WlDxpzYoHEQcq77GuLgZjSUoS9YF2G+1YazNGy36jhlIOhqmcAZIlEYV2uiNMWnrr5efU9SVYGFlZQWtNVEU0ew2c0JpxDEam6s0DNc5erklmViq+XyMQBpFst+BAWFYCu3c4+aQWpHCrx4uvacGUnrT1aEM5gC33Ybqvw9c196MEZ6Apgkwnt9rN8/+slkXYS9fmM9rjfCMapt2bmJ4mCaJDL7+c1TwP4M4WvfaQdfaeTAq2hrnFZV1HS4uLubvKX6Hj5MXFBwzggI4ffo01tpDq0lMA2MMt2/fzuXyP/rRj45MrTW2JihIHATR31+dxBKtFLub2zzy7Lmxg68HIY+g0gU5bo5eaKO0C7BcLrOyutK3+MfjmhsEyGhK99k0erLGoFJ5l0GyjVN1caVVrt93YvUEwhMoYzBb9/FSNW0XuWVRVM8uI1NoMMYJx5bLAWBoyiRdXL1h1YfCHFK6wb45pE4iWTCW2R/ALcb2X59p6lAHkRNApH0i7VHxDcJTMENtq/8IBXs65FSQ7jMLmITAxy++8EDPqH21z4nwxKGbJNIzwzN/ifEvHvL9o6GUeqBiseO8orTWebR1//59rl27hlIqd+Ztt9skSZJLO30ve0HBVAYA31vIUhAPwhPKWsv6+jqvvvoqcRyzsrLCuXPnxtZ9ptLgO2ifxtButWk22wjPw9dQKpU5bHEgTlTfW+NWf6oqSRJ2d3fRWrOyssLCwsLQ4hIl4xc/2ZluYWx2I5SSGGMIU6fbQRhj2d7ao9vt5k+c2WBxW0pHGp5rOw/DgDAMCYIw7dazGKNRSqKURGtFo9FNG1uEU4/I0qiFPwLwhHDCpWFIEIaurV0IZxCoFYmWRB2dpyOnbTTPmiOK6EgfM2kDVo9tjhjEvgoBiSfmu+931BRRuQDhOX09P/Cd51YQ4Kf1TGMNW9F99vb2UErlppda6wPFfwfh6y8f/KIZkaWR32r4vs/i4iKPPvoozzzzDB/84Ae5ePEizz77LMvLyyiluHXrFr/xG7/Byy+/zPr6Op/61Kf4whe+4JquJuBzn/sczz77LOfPn+eTn/zk0O/jOObv/J2/w/nz5/nIRz7C9evXAXjllVd46aWXeOGFF3jppZf4wz/8wwdx6iNx7Agqw1F6QllruXv3Lq+++iqtVouLFy9y4cKFA6O0vXkIKn2K393bc4uACPA8j6hxeC8ol3boP14dK7R0Yp17e3vEcczy8jK1Wm182/7YtKVARXqi147WmkZjn2Y3wvcDN6w8uB9rc6VwYf2RzR+tEf5P4DaVkVYQpKQVhnieTxxrojhir7HPbreNUmooUioewxBp+T6+H2A9sDrIRwGy9KSUEq00Ro++BsoO3yvGQleO+5pa9JQyRuDmoSbWnabErgonk+Y4pHNYnu+uvwo1taUavucRhAFGazrtNnv7e+zv79NutYiiCCXlxHvGM2sIc+/Q5zMK7yQlCSEElUqFU6dOEYYhL7zwAv/wH/5DXnnllTyy+sxnPsPHPvYxrl69OnIbmdXGf/kv/4XXX3+dT33qU7z++ut9rylabfzKr/wKH//4xwE4deoUn/3sZ/nmN7/Jb//2b7+l81DHLsVXjKDi+JAeNyky99wrV66wtLQ0ZOt+0CzU/mFSfKndRiZEe2J1FaOdOjaAiiUqTgjKs+fks/ZyQc9Y0FrLzt1tSktl6ot1Av/gW2ZSBGW0QcWacMDiwmnztZFSUl2oYUU0IgZ055k93YaeN2RgmGEcQY2CwD3tK2Wp1+o0VEzQjnspKmux2chAIZ2X/dsdmiMrZRVYkAnU0hRR1qbt+VmnocmVCtwmBVYYLAYGajUA7cSnVhquF1krc829g2FoqAOisSmh8GjogJWpRWhHw2JpmAYBPuVSqe8BwOYPIZoojtHtNhYXYWQ+W34QOIkkAb7+E5T3E3OeWQ/vJIIah3q9jhCCn//5nz+wiWceq40PfOAD+Wuee+65PNrNVNQfJI4dQWUIw5BW67BeOz2Twkqlwvve976+2YQMBxLUvdl8oIp2GysrK+ztOaPDZNCOotGhPt6pbiziuHisFqVS8VPJ1Hpf1lriMeoP+X7aSU5QmdxSFEcsLCxQq9dpRfFQlieLQgZrUX3K5tn2lUaa2ZW3M+HYhoxyYdZ80fTTYn6RtIquup6ra0mrQYDWrq4lhE2tM0hniwTC84fqWrFJ8n9nvRvZktNOMmWIYgSnMVMZGVpcM4RFW0FTlVn054+itlRpboIC2Nf7aTdf/wIrhMhTqDmsU79XSpFIie5GqX2HwPP/kB31ErX66am64Q7CdwNBZZimw3Req40M/+k//Sc++MEPviXkBMeQoOa13Njf3+fy5cv4vj9kUjiIo4qgMlXz3MQv/eJkKSQZ9+/DEdTsnT1xIrHY/Ole+B6B56EmSAENIkoUE7Ixbj+dhPrJKlFKuJVyhdXUoddaS7uQIsybJIQgHJHuU9IMOezOKk9URLub0Biz8Lt+ijSCKvzcZsdpdZ+Ab9xRBCWL53tuHqswY1R8t8kbFgS5sIftvbAjBVGsCLxek4EVB6lUWHrk1MOurBwJQe3oEtZ25p6R7douCcl0JVNBmkb1+xZIk5pDhvI1btx4Pu+Gq9Vqfd1ws3blvZM0+GC49b3oBfVW4NKlS3z84x/n85///Fu2z2NHUBlmbZJoNpusra1hjOGZZ55haWlpqn1MJKgDalB9quYj6iyZmkQygqBmhrW0W12kVPi+hxAefvpliFvx1KKZ4+tPPXSaEd6uya3jvfQ8TFrP6SQyN6oD+tQoRu6z6LALNGdI7w1irxVhF2bLg7kUoYcxKp9vs9ailEe5arEWZOrIO2hOaAUoU7hmBWLq7UCQEFIJTBq9RVhrCq8ThYjLFv4MY1dWOHcEa5q0Hg0TsOzPH0W1vBYP8dDBLxwDz/PwSiXOlF/nxJkfBxGitfMZazabbG5ucuXKlT7rjYy4yuXyO46IxmEeL6h5rTZu377Nj/3Yj/E7v/M7PP3000dwNtPh2BLUtE0SnU6HtbU1oijiwoULM7V0TiIomSha+6OJZFrx2ExNQibDBDW9CrMlimK6nQ5JognDAIHIa1oARmpUrAgrB3dvdSfUn9zC7QjvkfopgtDPiQncAqu0ptnpYq1TdZiKFAsOu9baif5PB6HZjhALYmb1HWMtypiUONI5NeXj+xlR+KlIa88yQ2mFFhIjioOqmYBrARZacchSqYMlAqELZDQbYhPQNQH1Oc0HwaX5joKg2mK00+6sEHYfX7+KDv57fN81zxQfJIvWG41Gg/X1deI4JgiCvkjrrRjePwzmmYGax2pjb2+PH/mRH+GTn/wkf+2v/bUjPaeDcOwIalpPqCiKuHLlCs1mk/Pnz3Py5MmZn7QmueqOmoEy6VOf1noq8dhMTWIwglKJRMWSsDL5/UmaOgzDgKWlZe5v3R+7KMeteCqCGhlBWafS4EgTfD9AxRov8Ppe0+l22e90Xbv8DDWEYh2qdUDH1yRYC7HUlLUPvhi5/I+6PhaIdZIrhGf3iVakIrS99xbrWsY3KGMRtjfTlkkKQX82s5X4KJuk7SuD12Zy1DSIPVWhHrTHn9CU2JIlnix1pvCamgyDoWEarPjzD5z6+r+i/Y+CGL5Xx1lvSClpNpt98kTtdpvXX3997MDs24F5Iqh5rDZ+8zd/k7W1Nf7ZP/tnufL55z//+b5r+KBw7AgqQ5aKGUSSJFy9epXd3V2eeuop3vve9x46BRAEwVjB2GJ6zxpDu9NBJpJabcEN2U6xz0xNYjCCAhdFjSMopSStVr8+XxRNToslzQhOja+3Qc+wrwdbkCZybfBSuoU8aseUa6kiRGb9Ua3ilyp4yWwpyiTqqZE35ujMlKk2n0ksfnVUD+EwBRjj5p20MK5RYnCbMZRHCHFYQJr0WEX+n94/C6k+iyXRhlhaygGFdF62t/6h7eGj7f/dnqxyttzKd1HY/ZDiwyQoPHb10Vhw7Kgdlr3ludNtwjbw9RfQwf849XvCMOyTJzLG8LWvfY1z587RarX6BmYrlUpOWIuLi1O58x4VBglqFi8oOLzVxq/92q/xa7/2a4c44vlx7Ahq3M0kpeT69evcu3ePJ5988kgEZCel+Hbv7WONyVs2FxYWqNdqM0m+CCHSWZ1hoo0abRYf6r95XV6+hTV2qGgcD0RhbsnrTfgPDuyOQlToJjRao43G93zCMCNKi+85lfe9+w1MKDHWOr+dxUWCIGCztTfl2fdgrROirdRK8xFU2vlnE2AEqRQ/mayrUHge1hdgh5NuApCJoFzt/3wsIG3CxBHevKHC5E3/HVmmEnbTVCH0GiCKNajim0dJO0FTlUkIKHturCAXHba4Y5qBtO7J8pEQVNd26dgONTF/ei1Qn0f7F0EsHur9mczR4uIii4uLPPLII4BLEUZRlMsTbW5u0u128+HaB+0XddxkjuAYEtQglFLcvHmTO3fu8Pjjjw/p5c2DcQRljOHK61fY3dujWqm4utYhFc0H03sZio0SxqQzRkpRH5M6HCSood+3ogPrWt1Ypgu3KrSD9z/de76PsAIda/wgYKFUQqdELZVyKT5S/bai+OgBiLoSUxboGe0xMmhr0WlEbeLxxGHTdCW4z9cKiLUcSQUWSxK7GS8hUnt2BMrqXLF8+F2k7DMcr7VkiRNEBfLotQX2SMv2PVgMRlvuNT7bySqPVLogDI7oNEIYBGZ60sJ5REkrCA9rBV9425baolY6ivpPRKD+Myr8qUO9e1yLuRCCarVKtVrl9OnT+c8zd95msznSLyojr8MqmBf381cEdUxgjEFKyZe+9CUee+wxXn755SN/6vF9v4+grLVsbGxw/fp19u41XGv1HGQohBhqMc8QNTqYdOFPUpv1+mKdcY/C8ajmhkKngNUW2Uko1Ua3gEkp2d1rptJEYYFweyuQtaC1xFoIAp+Fco2gVOi+i2KCVF/PGCdFNKjh5nlerp/Xd75tSVQ95CIJJIW5KSudhbooFFcsqRFiOiScPcRk80uDyEjBGrDaw0u/adomadfebHUjgI4M0QaG3U9EH2kVY7lsrMqm0VYWDW0nJR6pxLh6lgXC3pEIR1hgEELnpOW2mpJ4wdbkbhxwthT3mhwOmXhomRaRiah4420upoWvv4L2P4z1Lsz83llnoEa58xb9onZ2dnIF80z5oahgPu1DmFKqj+S+172g4JgS1O3bt7lx4wZCCF588cWJs0zzIIugrLXcu3ePK1eucPLkSS5evMgbn70xFzkBrv14DEHJKGZ78z71lUVWVw6K0CxxNJCmGfH6qBkNEVTWzmutxeARBH2DPPk/tdYYm9WiXKda3E4ISr1cWitOAEdE/euDTYUaDEbrXtdfTlqCqCOJpxSiHTp7C6pIULgoyq+65Joxbr+e7+OHYb7+amumithkAl4A2kqkVem1LV7frDHiANKy0JYllsrTtNGnFClsLsaai17gTAzbsabs9xo73B8PZ0PiU4ij+iMtDEKY/Lg31QKPBjGa4QeKWX2j7ql7PF56fLoXH4BQ/p8kpV8FMTxEPwlHMaQ7ScE8a8i4f/8+nU4nf20xTThq/6PMCv8qgvoehLWWD3/4w0NaVEeNIAiIoojXXnuNWq3WJ4U0lw5fCoEYapAoKi6URUi1evCXU0o9VMfKEnPFdSVuxnDG/dtaS7vt9OpqtRrGCsxuzODi6qSJ0lpUEPZtMW4n1FYHCWrMmQoQwgev4OJUUMtOpCJuA6FXcMz1JvNyisToIUqwMZhyr84UFIiJ9CwjM13tJYnBq8ZoO661W8BQVDg63deMpyUoW3AI6SfEbFf7tsajQZReR5PqDqZirWLAeTgnraAYE4PQRBgaCFaDDpA43hqh+j6WtAqn3TRNOqbDgjcbqYyCsHuE8jPI8GdmSqE/KBWJooJ5UZ0h825rtVrcuXOHVquFMabPUn5xcREp5V+l+L7XIYTg8ccfzy03jlrRPEOj0eDNN98kjmM++MEP9s1WyETR2ht2v50VQmQ+UGKk4kLU6LB05sSB24nGRR7W9n2x41aExdLtFBo76nUslt1GlyI59UkT9aX8eojbvWufaE2iZpvNyRc6z0Mqha8FourlKUJrFRnNCm/Ax6lwiokZPn8Vafy6GDsknORpuvFwdSFDHBsCo2csM2bFnv43daSHc8HVLm03IoKzKakNEtMgtpIyj5Qzd9zJzsPuVhAMR1suRbihFlgKHwWrQUQIESOI8IiAOCdaY1PfKF2kOScbJdIU4abc5InSE0fSHeeZr+Prx2fq6nurZY4yhZhiy3hmAtpqtdjf32d9fZ39/X2azSZRFHHp0iW2t7enntn63Oc+xy//8i+jtebnf/7n+Uf/6B/1/T6OY/7BP/gHfO1rX+PkyZN8+tOf5oknnmB7e5uf+Imf4Ctf+Qo/+7M/y2/+5m8e6bkfhGNHUEAuqfMgPKHa7TaXL19GKcWFCxe4dOnS0E20tzmDBt8k2Ezg1RHJ4GI6raJEPKVpYtSM2N3aoVKr5h5Q2dNxN5aAwFqDUs6RNgjCiYtM0knyWk/rgDb3ybAuRRd7eIt+mvYr/DZNDzp1ikyBwS2yaqCXLtPCExb8VIi0z7rdgrI6b0kfjnl6tvO9RgOBlh7BCMHXWaGtR0dWqJfTbQkLuMjHWO1IWYCYwqigq33a2qcejDCX7CPynvNwrz5o8rk2ITy2jaQdBtSCEKhjqfWuq7WOtIgRIiUtP4L8M9GptqET2m3QYFNushqsOFsT3z9UE1GGQP1nrDiB8V+c6vXvBB2+TKqpVqvx8MMPA/CNb3yDJ554glu3brGxscGbb77J3//7f59qtcoLL7zAP/7H/zgXgy0iUzJ/5ZVXeOyxx7h48SIf+9jH+oRii0rmv/d7v8fHP/5xPv3pT1OpVPjn//yf861vfYtvfetbb9n5ZziWBJXhKD2hioO9Fy5c6DMhG8RR+EBprWnst7DGuPTTqJpRoz2VokQ0WH+CvpXXFKKzSlBlobrgnnoLc2SdboJSEotrgBBTqAJYC0lXUq6VaEbztIenJBPbkeebLaJAXttyChaGROnCyFFGXO5NJoGg2u/Eq9AkVvWR0DRtDir2j4SgABpJQD1P8wmwHloZhAgJ/Er62TlVC5s2O1hG+yzdT8rUg2nnzkbVB3vkf73d4Vx6SEEQEPgBfuAsSIRYwNqFvu5ARIwxHZRpUQ0tEAGOpHbZpWZqiK7s65rMtnmQ/NUgQvm7SHyM/8KBr30nENQoZDWo5557jn/6T/8pf/qnf8qf/MmfYK3l0qVLfTbzRcyjZF6r1fjrf/2vs7a29sDPbxSOJUFNqyYxDaSUXL16le3tbZ5++umRg72Di+buLCrmA7DG0G63kVIh8HMV7VEw2pC0I8r10Zbt6RZHtphn0ZGUEtK0oRCCuNVvAW+tpdFs0Y3jfBh3FkSthKAa0B5bfzoYMpNlMhaUhfDghUsIR2xAXmwTwsv6sF07eUdjAxcNCs/DYEisShsO+jv8RkZOBejYw9bnCgRyNCM/3VYq7GusM03su/Y+QvgICuoHIq0xpYRlMWwlZR6vdvDnOK6s1tcUUK7VqHgeWju/rjiOUaoN1o0YhGGQWmYEKO3TaftUKg+jRSm9kAohIiwRu0GVs9WH8MVOHmkppUhSkWFr7bD9xtj7TxPK/x3J38P4H5p4Pkdp936UGCTOrCtQCMGHPjT+nI5KyfztwDvvU3gLMY8nlFKKGzducPfuXd71rnfxzDPPjCQK3/eHbvjdw6T40px0sfZz/87BQ61RozORoKQ0fbp7blcWbVwqLAhCJ+aa/i9pxJBqTHa7XWcoh1+YeZoNcSvBLvkzNFv3Y7CTzsZ2lMrNEJQxxNoRc7GdPGMQAQgJfuCDtSRWIQdqVfnnLcRY0spmiqz2sNpDBPNHUcoKGhEsBArf8/DDab/GXh5J9o7U0NI1TpcTjI0wtouZ0qF3ENbCrTjhmYUqQeDci4u/1Fo7x+EkoRm3wFqCMECnIx+BHyC8EEuIpc6egYD/gSXvRYRdJ/DuEpTWKZkNPO6CNWPsNzyCwHfpwSDAz5XkDaH8P9BmHRX8CIjR101r/ZaqhE+L4oPuYeW8vttwrAnqMBGUMYbbt29z69Ytzp49e+Bgb9ZqXiSonTu70+/Q9gwKKwNDvUms8if+cejut1l+dHy6sVh/yqw2jLFpQ4F7Mi5uH6pEAAAgAElEQVRGBHErIoqiXOp/dXWVja0Ghx1+iVox3c7hH9+TQXKNDbbee8ocGqBNW967Jh2unRDSWA1aGqSvsNj8c7bZhrJ/F2zdB0nL/T8lPVmhXEo75tIUnMFMmSTsnYC1sB8FLK8O9lkeBh4bkcejleWcqC0qJStHWNpGWDvs0TUK96XkEV1icTBFJgR+4COlRErJ4mKdUlhK08e9aCtrrMlSeffN/02ldo5q+CzKXEBam3bAKzw28fwN/GCDCuv4dgNsjLEGpRRaKTpxjDaulT7wM9J6hUCvoUt/F+s9OnQO79QU3yhMk+acV8n87cSxJKjDeEJZa7lz5w7Xrl3j4Ycf5iMf+chUaYBRahJTEVQ6M9Fut3ODwsG5KRmrAxeNqDG5W7DbdQSltUYbg+97hKHvZn+MRRQGVq2xRO2IuBOxvJouaNZ5KPUKHLMtmFoZOq0Ywtlnwqy1ffNL4CIo0idN10XXI1htnC6gAuwBc2HZ+3RkYKBRqkhs2b8nkZYjK5CRoFoDT/j4uKf67B3GOkddM460bO+4hBC0VQlt1Iih3dnR1oYdqTlZCtJzCvBFHV8U5wMNxkZo2+2RF9HIJ/mr3Yj31foHUJ3+Yyu1WFnNf+f5PiXfp1Qa9ndSWtHptnh9/9cRe3+bxerZfFaoVqshxOMYew6Vp2ot2C0CsYEf3KFk16mwgbANd6+kpBVFEVp9E2sv0ZQfIhF/g1r9TC4I+04kqMEywSxeUPMomb/dOJYElWGaJonM1n1tbY2VlRUuXrw4k2TJIEEZY9i9OznFN86gsAijjevgOwBxsztkdNaDpdXqkkiJ73m51YbFplbaBm0MVpu8TuN5HiQ9ko8ShTbZk3xxyHTK6XhjsBGIQxBUPEqIV/fqUNm8jzU2n2cSno82LiLC9ghiLNHHAmoHxw7TkFaSuIFoP+i1amfeUL5I54wKw7QGjU3TWCZ16s0eFoyFRhywWj2aLtTb3SQnqNHw8MQCXt/Qq8HYxEVZRDlxtbRmU0rOlEpYa2i3O2itWKwv4k/xUJf5O4X0vmfBqS+wan+ObksMzQotLS1Rq9WcvmR4BmMfRpn3u8/eWjzRxLMb+OU7hKV1SnYdjy2wlrp+HaXeYKf1Xt64+SzduIaUMleaWVxcfEd4Rg2S5lulZA7wxBNP0Gg0SJKE3//93+fzn/98X4PFg8SxJKhpmyR2d3e5fPky1WqVF198kWp1UrPBaAwSVGOriRoz76NTTS8hBIuLk7/MQ8oPY2CtJW52qS73hwFSSlrNFt0oyRsgsjoTkJOR1RoQeIHvKMhaWttNbKpG04o1RutUO2/U3M24xd29Tirtmrdm1PU01o61dbeRq0O5p2add5VJa4iyulOefusdS4+0CoSVCMcGh4hUhkgLgdEh5bLrILS5h1Sq9OANzBml3Xm+F1IK3AUvpgYbEZyoHhxFT4NdqWlIzVI4S+Tg4YkKnqgUFhKLtQmbSnFCnaXduEp9pURYl0z70DIKyu6z6/0Wjz3yizz2mFscMzmhZrPJ7u4uN2/eJEkSKpVKHmktLi7ihytYVpD2PSS5RFOEsHcIvA2C0gZnKus8eur/wYgnuHzjBOHCQzSbTTY2NojjmDAM+9QejsJWfqbzn1OH77BK5gDXr1+f7WCPEMeSoDKME3NtNpu8+eabeJ53oK37rPvYGdHYUPSBck+BB1f54+70tbNov50TVGaGCBCWKgR+3E9M+TGZPOXne8WajgAJK6srrh14Y9s97euiWZ9HJkE0nrTcPI22FmI989KVNTiMgo0Mquran30/QHiCWKs+vb1RGKwZuSO1eNLHVjIx2fnoQEaChZp1M1bFY8ZFei5iMthU2SMjrSzF4+OR+cJrXWJRnKIcaqRJkDZ2f0wyW10rxdVOzPuXqnNGCwJjfFqtLm/4Xf7WU/8b5VIFZRpE+jaxvk2kbxHrdRKzPdOWldnnZus3OFP9eyyVPtAnJzSoON5sNmk2m9y9e5dut0sYhn2kVaksIMR5lHlqoK51D2u/yJnVrxOGiwhvCeM9TixP00odere3t3Nb+aLSwziJoqPAPF5Q3804lgSVfQEHv4iZe24cx1y4cOFIZEQGCWq7UH+y6RNgMqMPFEAyZQQFrg6VKZor7aSJwjBke7s1TEypAoSzyRh9eyTtBKOMU/KWZuBL2Rvm1CNIK1N0AEGSpeiUxSqDGKXjN4K6lDG9usMArLXYrkac8AiCEG0MsZK5UvmsEAi8RBAuuHM09NQQDMYNmM5ABkoKtLZDs0QCwBNY7c4hCFzbuLVOZUEXJIPyBhZPsNGWPHuiSskrk4Wh1oKyCdImSNMjrb6B4xHYk7qvFjUrMvUDKROnMBLEvN59lQ+U/gaBt0Tdey/1sJca0rZLrNdzwor0bRJ9d+L1NDZho/MfaKlv8nDlb+N7/ZmBouJ40VAv08BrNpvcuHGDdrs9pIFXKpVYu9Yhit7Do8F7McIDYxFmh0BcZ2WxzOpyGeE9AtTRxs+tNwbTjkUynFfFHOb3gvpuxbEkqEHEccyVK1doNBqHds8dh8E61/bGLlibt2hXq1VWV2fzgQIONBgsorm1T2Vvj4VajXqpnjcPdAtRmEnrNE4qKTwgG2OJmxHxyOGZcWKvTiHcaNcWbq2bX7Kkpx5pqHv5NgrvLNSKXGqvq4bJ2WYnlRaeTGKIQ31oYirCdC122UUwHlndqBcB2ZS0tDUpgU3uzEu6UB0IynX+YOBsSrIrIIQA3y9oOdB3LTcaHVZtRCV0LdXZn9BLazh+Pb8+Tqy2n7Q0/VHl1XbMauinDxHTImvo6VCtVqjVVsg+wzfbf04tWOKZ2ktD7/JFlYXgPAvB+fxnxkpivZETVqxvE5t1zIA9SSP5Gm15iZOVH2Sl9H14B8wWlEolTp482deZprXO7d+vXr3K3t4epVKJ5eVl7t27l9e1/OAhrD2NMcbp5RrAdrFWU6+FLNZXeeTMSTwvxFhBt9vtSztm80r9EdxsRofH0WoDjjlBSSmJooivfe1rPPXUU7znPe858mJoJhgLbmG5/p0b7O7uDrWMzwRjSaZQ7jZao7XB15rFhTpBOUxr9q4bLIqc9YVOIzw3nT/dIUSNiGZ52hx8WlPxe6WcWCn3dJq2TZu2xJZ7Eju5UGlfrcgSKd07B3oLdo70d7pj0UtH81laAzYBMaZpSiDwhehL21lcnclY40ZiC6QVR4JKzckEFf2lwiA88Po7kQtBb7LWpxuUObHgo5QmSWI6nTYmHWINs1mgICDwQgJCqr6LOqwFg+qlB01CZGJudyWPL0z31G+MzuumK8vLIxX6v77/R4SizJMLzx+4PU+EVIN3UQ3elf/MWkNi7hVShO5vbbvc6/4BO9F/Y6X8fayUPkrgTZ/28n2fUqnE1tYWlUqF7/u+7yMIgqnqWmGYXUObCy1r7fQZyyWfyqkTPHT6FJ7nZvziOM49ozKjwyAI+iK4Wq02tq71VwR1zHDt2jU2NjYIgoCXXnrpgQ3mZRHUvXv3WFtbY2t9Z2TL+CxIYtW/KGctdilyoVYhCEvOI7yz32axnH15Bd1uTJLIfBq/b1h1CnT3u3SWDpe6sNYis/Re2oItEhDpFzBTKB+01YiNRhu3sDsHW+uk6EQvNZiRlhdbZq9sjYfpWrzy9NsTOC0/X3i5lkNOWsZSNgJFTGwUfjBrxNKP9XbC2cU6lUoAuPs4m/fKhlhVp+vsTjy/L9LyvICKH1Ch150XG3hh8SU80WFXbrIr79FSuwMxoU3T0wm1Wj0d1B4NC7y29/8Smy7vrl+c+fyE8Cj7Zyj7Z4APpednUXa3QFo3udH6EmX/ERbDD1APX8AX45uarLW5UekzzzzTJxM0a10rI5gsKspU3N1wsov2A99ndWWFE6urjoSEcI1KKWndunUrrw0P1rVGzVIeBy8oOMYEVS6Xefnll/nGN76BHtWufETodrvcuXMHKSXPP/cC/zV6dW4fqKjbS+8JyPkpm/UQoqdo7mCJ9lssPrSCtZZut83WVgvhefgzElOG9l4Hu1jUQ5t+O4nOoqACtAVpEaVUusnz8rSWsYauVE4xwvYiJpG2aGft5MX2Bl8LSiLEeDZ3y3VRzOGgI4NvxVwRtgDXvm8M8Z7l3EMnKVfKJEYRm4TYSPe3ljPVtbSxrLcS3rXUe8gSwmkiBoFPkbSMcUOsKpsHMtopL4RO5y4InBTRqzs3+JlzP0iQNshIk7Cn7rMrN1nfu8bNnSt4C376FD/dNfnLxp+wr7Z4aen7Cbz56jJCCEJxgtA7wWL4vvznyjSJ9Tr7yZcQBATeIoFYoeI/hkiVIxqNBm+88QYnTpzg4sWLBzY2zFPXcvNaws0VOtXivvVmaXGR5aWlfJQk81fLLOWvXLni5KxSXTwhBEmS/FUN6nsZQgjOnj2bFqNHd/LNi1arxeXLl/NZihdeeIGNK3cPEn6YCt1OQZ4pfWLLbuJ+Ec3eTFJ3r00Udel2I6qVChDgeYfXIUwShRdrqIT0zz/19lxssM6QCbSORKSh1E/eyhgilc4tpbNW/Z2Bqe9R3/hVGpVFlrDuF9Xo0lpRWi9K/57mI7EarAQxx7pqrEWnDxCxDQhLJSfA64dU/N5RWuvsPDLCioxMSWt8k8N6K+HReolwwgOHK2d5+H6Jcrl3IjobjFWKdifGaMO+2OO39xN+6OTFfM5o0ZzkzpVtTtjzfPTZHyEsB+zLLXblPfbUPfe3vD/Gzt7heud1tpM7vLT8AzxcPhpjwiICb5HAezc13p3/zNiY2NzFaMHNG+s0Gh3e/Z7nWFo8PWFLB2NSXavZbE6e1woCip5ZOm36yYioXq9z5swZN+phLW+88QalUonr16/zT/7JP+H27du8+uqr/PEf/zEvvvgi3//9399nQ1/EYa02AP7lv/yX/Pt//+/xfZ9//a//NT/4gz841zWbFceSoIA8FD9qT6goilhbW6PdbnPhwgUWFha4dOkSAPdubh3NPtpZBOWK5cpolybqi8x6FGGMobm9zwmpWFlZxhiI4sML1mrt8u6iIxGV0Rp8IiOOfBTW/bc7ylo+PzENS2E+H5SY1Iah0AAx7M4qilzVG7y1oNsKXbZ9BoZeOhwbFgabTCHCyqKtkYKvHYNXmr2N2AJaKyfqGgQunWdht5VwennY3lwIKPshZT+ENPVmLUiriHVKWCl5mbRLUhvLjUbM+ZXZ7dJ9z8Mvlfq6zYyx3FDb/HlrjSd2TrC7u4uUktXVVR566KF8NuhE6QwnSmd677OGptphV95jV26yJ++xq+4hTS/qb6pd/mj7/+KxygWeX/zvWA4frCCpJ8q0diusra1x9uxZnj3/GKDRtouzJcn0CUUeZR0Wvu8PeTtNO69VKpX6SAvIoy1rLSdPnuTJJ5/klVde4Wd/9mf55V/+ZdrtNl//+tdZX18fSVDzWG28/vrr/N7v/R6XLl1iY2ODH/iBH+DNN998a72y3rI9vUNxVJ5QUkquXbvG1tYWTz/9NM899xxCiDydArB5/f7c+9FKkyQqb4BAuPx2L22YEZNw5KVV2lXn4xsnFtppd+c6BplGQKYrJ8yv9izGSY8oURnhFEmroOPQscRx7MpL6QuyLj5E//bG7LIXtQlApsoXgiEDw1GkxUjS6kVbxW6+aWAZtIv3+rh1qxFxcqk8Vf1JCCiJgJIX5DPNrp1cE6XpwUY3QdYDwmD++9nzBKVSyFflFZrdBh85+17OnTuXL7S3b9+m1WoBUKvVWFpayusxy+EplsNTPMF70+O0tPQee/J+j7TkJrejy9yOLnO28jRPL7yfM+UnjrxJKY5j3nzzTYwxvPjii7mjNQT4A8ufIwc3mO4wehxlVhxmXisjrnK5zO3bt2m325TLZbTWJEnCt7/9bZ544gkef/xxfviHf3jsvuex2viDP/gDfvInf5JyucyTTz7J+fPnee211/joRz861/WYBceeoOb1hDLGcPPmTdbX13n88cd5+eWX+yKZTM0cjiCCspbGXguZSDzfIyyFaOUaJoQTaQMGUn4F8urutagu12i1D++9ZC2oVKDVduWQRtg4SK2JU2LL59Dy34p0cYBAgQqFiwwyYjpsyc4CscVb8AcMDNOnVGuxepTrbpG0IDPsMxZWqSAqgq6WdLUa0gLMkKfzRtjFZ1DKsNuKObk4e9QD7uMOhU/oVVnENQRU9DL/y4WL3I/3uBPvcDfa5k68Q1PN5uBsjKXdbmOM5s264MnViKfCkJWVlb7ahzEmT2ndvXuXy5cv96W0ssV2sbTKYrDKueozQLpAm3behHGl8w2+3foyJ0pneKxygZPhI0zjKTYO1lo2Nja4efMm58+fH5v+KqJnbd+/nVH3+LT3/aR9HVTXunLlCru7u7nrwr/5N/+Gs2fP8lu/9Vu88MILY/2fipjHamN9fZ2XX365773r6+uHPufD4NgSVFHu6DCWG0Xx2DNnzvDyyy+PDH2LN/E8EVSmz9dpJm6ANhPb9Dy0NmitCqku15nnBT5FGujut9HazEVQiSw8nRsLkYLq5BkUqTWRnPxUL9Jmh1IMfkkgvAA8r5d2M7MNxGawXQML/Z9L3soO+Xrk6tdmAmm5v2Xb8NhqL32jrHFkpSSRVnRUQiQTLPTSeROwtR9xon50Wm/r3X3+5O5NPnbuvVyoP5b/vK0iNuMd7sQ73Im2uRvvsCebQ++3FqLYqefXFhYoleoIAf9t62vciXf4nx5+mZLX+7w9z2NpaYmlpaVcIdtaR27NZpOtrS2uXbuGlJJqtdpHWpVyjUcrT/No5el8e7Hpsivvca17ibJXJRRlqn6dur8y9TVqt9u88cYb1Ot1Ll68OJe306R99jXrHBFKpRKrq6vs7e0hpeTixYssLCywtrbGpz71KT7zmc8QhiGXL1/mF37hF/gX/+JfjHTR/V7BsSWoDEEQ5O2d0+Cw4rGtvTbtxuyptUF9vub2TkpOmRSOR+AJNwCbC6J66eCtI60sKujuNmk2uyMVqKeBMRap+gv1piPxxxCUtZZY6V5L+QGw1kBHEa4uFDoQC7NFg7WiKUjLRqOfgAfhGgK9kaRVtIrf31csLnpUqqX/v703D4+qsPe4P2e2TCaTDQiEJBAIWQg7SRBcqliv2tqW9rpi9WKrtmoXUNsqXlu1rVpRX62V1qVatbZqfWvfarmorVq1bhDABVmyEALZ98y+neX9Y3IOZ7KQyR7gfJ4nDySZZM5MZs7v/LbvNzr1ZjaTbEnAabERCAQIKQoJySlIZoGgJPYErsiAMkvhiEyXN8yU5NFbc/hnUyV5yVNYlHakN5RksZNnySIv6Yi9REAK0RLq0gJWnaeFw64mLBYLaalpmHoNXOz1HKQh2MZXpq+K+T290UsA6Uta6gJrd3c3dXV1hEIhEhIStKCVkpKC3W4nMyE35vdF5DBeqVtTgRcEExbBirlXv0iWZQ4ePEhHRwdFRUVjKgUUd5l3iJmWy+Vi//79zJgxg7KyMkwmE5999hkbNmzg3HPP5ZlnniEhIQFRFKmsrBw0MxyJ1UY8PzvWCEM8WY2GLuWkQN0R6erqoqmpKS51XpfLRWVlJQkJCeTn5+NwOAb9GYAPPviAzKRsnv3FX+M+PlmS8fm8SJLUs2diQRJlavY30XsAQhIlTGahx5it15tBLWUpUckcy+xMJMGM6hJr6jN0oP/Z2D94MBTpUS4/guCwYsmJHXeVe5ZPw5IcVzBUesZv1VKkkJOokz0a/GcHC1qmqRZMjtFp7CoKpKbaSEmJ9i4lKSpGK0syVpsVh8OBxdz3uk9SZAKSSFCK9GRcIuEeA0SL2URhTgrmURQfTTRbub74NLIdg5+kRVHUlFTyCvPx2yJa0GoOdtIW7u7znBYmzeL0qUvJtA9eZhoIRYn2HD0eD263G4/Ho/Vh9JmWOl6tR1YkZGR6rCLp6uqmsrKSmZkzmT179rgKuR6NeAOUJEnU1NTgcrkoLi4mKSmJUCjEvffey9tvv80jjzzCsmXLhnz/oihSWFjIm2++SXZ2NitWrOC5555j4cKF2m1++9vfsnv3bh599FFeeOEF/va3v/Hiiy+yZ88evvnNb7J9+3YaGxs566yzqKqqGq0hibii9gmfQcUzxefz+aiqqkKSJIqKikhJSRnSfQiCQH1lY1y3jerzBQiHo8656gKxoij4PEH1N6LIMqIUtSO3WC0Dvwl0vRRZMCF6Q1jTU3qClowoRU/oR9S0dUFLF7siEann5A9q5FIA/GGkcARFXVDs+YjrsaqBqWfvScMvQUp8JxhBELD0GnDoHbQIAPFdS8Rxf+D1Rpg+PRkFBZ83mn07EhORZBm/z9+TyQpRySGLBYvFitlsxmmx4bToxrt71NUDUoQk0UFqipXWYF99xOEQkCI8vP8Dri8+jczE/qXiFUWhpaWFgwcPMnv2bM0VeiowK/FIXyQii7SFu2kOdvb0tTqp9jdQ6atjriOLsrRC8pOyeyxD4kcQBOx2O3a7PSYTUPswbrebtrY2/H4/ZrNZC1jquLbFFH3vVlVVEgwGWbpkqeY4MBblt+EQz/13dXVRUVFBVlYWpaWlCILAzp07ueGGGzj//PN59913j7oIfTRGYrWxcOFCLr74YhYsWIDFYuG3v/3tuPtknbAZlOr3EgwG2bNnD6WlfbXC9Bp9BQUFw3aYLC8vZ/+rtdR8enjgGymxzrm932gALfVduLv9SKIEREeWh/IGDIVEcNix50zv+00FLWgpcmzQUhQIho9SppuZjJwYte7WxsKPhjrJJwyw+JpoxjRjeIMD/SEIkF0whTDRhd+AGCEoigPadQyGAqSkWEi0M6CKgurqqn5I6sWETsVBn2mZTQI3rzqVaQ4H9X4Xdb5u6vwuDvu6aQ54hh20Uqx2vluwkjnO9Jiv+3w+KioqsNvt5OfnD1nQVFIk2sMumoKdNIc6cUd8pFmdzHXMZI4jE6tpdK99RVHUhgfcbjc+n09z583IyCAnJydqrTHACVQfsEY64DBaiKKoraQsWLCAxMREAoEAd999N9u3b+exxx4bN9+lCSCuP8AJH6AkSaK8vDxmWkUURQ4ePEhbWxt5eXnMmDFjRC/oXbt28dr/8x8i/ennKbHOuQ7HESdS/d9GVhSq9zQgiVHjsqGWMBRZwR8II5hNOApnx/d4FBAliWBY1O00qaO3aP83pSdiznBqPyT3lNqi/8YGrai6+SCKDAIIsxxDll86GtNmOkmdGptGibJMQBQJitGgFYgMHrRUGSmbzcK8/GlRZYg4URQZUZSIiJHYoGW2YLFayE5O5X9POx17r4AXlkQaAm7qfGrg6qbR7xlUnVzFIpj55txlrJiao/VpOjs7KSwsHFU1AlmR6Qi76Qi7sZrMJJhsJJisTLElDzm7OhqBQID9+/djtVqZOXMmgUAAt9utLcWqe0Xqx3Czj7Gko6ODqqoqZs2aRVZWFoIg8MEHH3DTTTdx+eWXs379+hENdxwDGAHqaKgBCqI9olNOOQVZlqmrq6Ouro7Zs2eTk5MzKrXs/7zxPv/8zX/6+DyJPVpcZrM5Riiy998kEAjgcfnobg30664bD+GwSKQnC3LMy8ZkH/yKWRRlQhGxn4xInyUpYDVjzk3T9or6G8kVxR7nXZMQLQPKR88JhIwEhKTRe4MmJFrIzksfNDBLclQtPdAraEXHxqUeNYaoqO706U7S0oZuYqlHfW6iHxHmJybxXxkztFKWWs7q/TqMyBKNfreWZdX5umkMuKNyUAOQb0tlsd9CQU7uqL22B0NRFDxiIPq8CWbMPRcnVmFo2T+gvT/708/T30adIFT7WqIYtZjRlwhHwwJjOERLklWEQiGKi4ux2+34fD5+/vOfs2fPHh577DEKCwsn5NjGGaMHdTT0bw51ZLympoYZM2awatWqUb166azrjgk6kiTh83pRFCWmLNE7MIVCYfwBP/aEBATZEn9w6r0IqxBjDy/6g9iOEqBkWSEckbR9p77ol2aFqMV6REaxmHpkbhRtlFudhDObzVitJvSyD30yLX3Q8oowigEqFBAJBUTsjqNfTZtNAk6bDWfPCUyRFTw+L75wGCHRQbhHFzAiS3R0+ElOTsBsHv6JXhAErFZrz1V+Ig2AJ2MqBWlTNI03r9erLXuqQcvpdJLrTCdXV7oTZZkmNdPyd3PY102D301IjODzetkleKhNSeUsYSrTZBHHCPXw4n18KdbYzDUq9CpxZG25R5rqKJl1vPp5JpNJC0RZWVna/akLxp2dnRw6dEhTctBPEI61tXtbWxvV1dXMmTOHzMzohOU777zDLbfcwne+8x0eeuihce/xTHZO2AxK6SmtdXR0sGvXLrKzs5k3b96YqJo/ecefqNlxGKvV2lM7F0lyJmHryaj0fwNBEAhHIvh9PiwWS8+koMDB/U2arP9QCQTCSNIRwTpTsoOErOmo7rGKovSoHhwJFEPFnOHElK5mE9GxbFmSdFOFSkyGpX7EEhu0rLlJhGQ57qGLwUhKSSBzdvyjx2pP0JHoIMEe+7pQM615WVPIyHBy2O2i3e8fleMEuGLxUk7KOjLSK0lSTFagqjjos4Lejq6yLHPwUC37Gg9jy5xGt1mm3t9NvT8qc3XStNmcmpFLjiN1UvRk+jsXiaJITU0NHo+H+fPnj8jduvd9qUoO6nMaDAax2WwxE4T6kvtwCYfDVFRUoCgK8+fPx2az4Xa7+elPf8rhw4d5/PHHNe27EwijxHc0ZFnmww8/xGKx4PV6OeWUU8ak5KEoCndd/iBdLd1IkkRiogO7/chknooqi6TuZCUlJWlZnLvLT0tDV99fHgeRiEQ41Kv3ZTGRVDAbuUfdWuopuQ03AMKRcXO1z6LKK+mFa7W9Ilmd9IsNWtHpwSOv2ymzUknOcBAWJQIRkWA4WnILRsRhB63ZhVOxDqKnJ0ZEvD4vVkt0bPxovTCLycSPzj2FzFtTGqYAACAASURBVNRk/JEIh90u6twuDrvd1LldtA0zaJkEgYuKF3D6rNwBb6M33FN7MIAWqDo6OsjMzGTu3Lkxr21ZUWgNerXSYFiWmJrgoCBlGrlJ6SOy/hhN2traqKquYlbOLLKzs2MCxVgF1N5j736/H4vFEpNpORyOuM4ViqLQ2tpKTU0N8+bNY/r06SiKwhtvvMHPfvYz1q9fz5VXXjlpRuLHGSNADUZ3dzeJiYns2LGDxYsXj3r2pCgKn+/cy+9/9CcsViupuvF0/fOuliBEUcSRdCSzUqmvaSPgj99BV0USZYIDWMMn5mVhTox9vIpCT6CSkeSenaJ4Xx8CkJsW1Qa0mOOUqVF04pjqfR0JWnanjaz5GX1+l4KiBa1AOEJwCEErdWoi02YOMHYtK3h9PY32JCdmS3zlluy0FK4/exWWfsoz/kiEOrdbF7iGFrRW587hG4VFWE3xHUsgEGDfvn0Eg0GSk5MJBALa4IBaHuxv2k1RFFqDPtpDPhLNVuxmCwlmC2k2e4wR43gQCoWoqKgAoKioqM/7cqDX5FgFrUgkEhO0VFsN/SBG7+w1FAqxf/9+zGYzRUVFWK1Wurq6uOWWW+jq6uKRRx4hJyfnKPd63GMEqMEIh8MoisKnn37KvHnzRq18ANEpncrKSg7vaOKTV/dq+mSxgSl6QgmFgtrOU+83WcAfpr5m6BJJoihFx8oH+IvZpqdhy0jv/5s61KCllv76C1pqYDFnJmNOTSTO195A9xgTtNLnJmGymjCbzbq9IstRglaEQFgkEBEJ9RO0BJPA7IIpWKxm/Q8TCAYIBaO7Z7aEofdmzpw/lzXL5g9+Q44ErTqPqydwuWk9iprJTKeT/1m0hNzUgSfuFEWhvr6e+vp67WpdRdXLU0+wHo8nZtpNDVq9+67RAYcQZsGkuQabBAFLP4Mwo4GiKDQ0NFBXVxe3fp7+Z/WMdclS7FF46V1yTUpKQlEUXC4XBQUFzJgxA0VR+L//+z9++ctfctNNN3HZZZeNetZ05ZVXsmXLFqZPn87nn3/e5/uKorBhwwa2bt2Kw+Hg6aefpqSkZFSPYYgYAWow1AC1Z88esrOzR2Xk1uPxaJL0hYWFPP/Lv1Ozu1ZTI7ZYrVjMlqg1dyCAPSFB23lS0f9JGmvbh5Q9RXtrImLk6CPIpkQbjrzhyZYoPaVBUYqW8xR13Nxpw5w1uvIyKdOTmJKTgtij/CFGRERJ1IRwLT2LsP1lbQpRqSW1NBgtD0ZwptmZnh3NZiORCD5vdMQ/0ZE4ohPbZauWUDZneM+pPxKh3hPNtAYKWqUzZ/KVeQXMSIq9kHK5XFRUVJCenk5eXl5cjXZ12k0ftKKqJUkxQav3iLa6BC309C+1MYcRBgSv18v+/ftJTk5m3rx5oz5i3bucPhb4fD727t2rCcG+/vrrmjSRyWTitttu46yzziI9ffALw6Hy7rvv4nQ6WbduXb8BauvWrTz88MNs3bqVbdu2sWHDhj6iseOMMcU3GKPpCRUMBqmqqiIQCFBYWEhqairuTg/1FY2ahH5EFAn4/UQiEW16S+096Y0G1feP1x3A7wtpqg36ZUNda0fLNkRJjmrGxXEZIQfCyKLUIyg7dBRZxmISSEhMIOrmC7KokJxkJ9yzO9VbFmk4eDv8pM1M7tG9s6jmsCgomlxVKBTC54ugQE/QskYDl9WC3RL9UC891EzrjLmzqG1upEUKY0tLRRmFk9YL2z7HmWBj/syhG+E5rFYKp0ylcMqRZfCAGKHe7eaw+0jguvP9/7BgWgan5cwiPzWN2poa/H4/CxYsGFIFQF+iUlF9i9xuN62trZqbq16ZPCUlpd+gNdyym14/b/78+UNWaYmX3ruFo7m4q2avDQ0N2vi7ajKYmJjIFVdcQUZGBh988AGbN2/mmWeeITd34N7icDj99NOpra0d8Psvv/wy69atQxAEVq1aRXd3N01NTZpW4mTlhA5QKiPxhFKnjNrb28nPz2fatKj5miRJ7PuwMubFHw6FEASB9PR0TCZTjPV29P6FqE231YpJMNHe5EI16FMNKhRFQZaihn6yrCBL8uDKDQMgef2Y0vrvx/SLgmbjYTabY4YHonsuAg5JYUZmGihR36hgWCQYipbdgmFxyIMYsqTg7QyQkpEU83UBYeCgFREJhYL4fGJs0LJG7cylcIhPdlXx/Yu+wLRp0xBlmWaXl/pOF4c7XTR0uWns9iDK8S3CqkiKzB/e+5jLVi1h6azMwX9gEBItVgqmTKWgV9Cqc7n57HAtW8vLycuaSWFONhb7yJU39L5F+hHt/pTJ1aClBq7+9ooGq86oEj+ZmZmaMOpYc+QicHSyKL/fz759+zTldLPZTHNzMzfeeCNJSUm89dZb2jlh3bp1o3Kfw6E/242GhgYjQE1m1BfpcDyhZFmmvr6euro6Zs2axapVqxAEQTuBA+z8525kRcHv9WrlE/3V55H9lyiKohARRcRIhMb6TgK+sE6NPCruKggCZouAWa89p+sRyVL0//FkUaLHjzXOACVLUVtqs9mM+ShTbd42D8mZKSCA1WrGajWTnKRFEMKiRDAU6Qlc0SGHwQKsp9VL8rTBx31jghb2nrtUNDtzv68nezUJtElW3v+sltWlVpxOJznpKeSkp7BqXvRNLMoyzd0eDne6qO9yU9fpotnlHTRoRSSJZ97/hPOWFHBWcd6ol5OkYAhPbS2LkpL4+jnnYrVaCYoiTV4PJkEgwWLBZjKTaLWQaBm5gsJAyuRqptXR0RETtPQLxgMtw+qXVZcuXdqnxD3eDOdvpCgKhw8fprm5maKiItLS0pBlmT//+c/85je/4c4772TNmjWTYnz/WOaEDlAq6n5SPKijowcOHCAjI4OVK1diNps1CRyIvuAPfFrLoYqotbPD4cBms8Vh+SBgtVjobvMhhhWsNit6NXJZ7Al+OgsNwRTVy7P0mvJSp/HULEuSlT5BS/IFBi1vRG07JMwmU9SHahCCriBiSMSS0M9tBbBZzdisZrRCjgLhiBSdxgtFR8mPSCtFiYQk/N1BktKHfiJTlz9DoTAms4kpyVMQTAKSKPLe7nqmJpmwEr046T3pljMllZwpOu8nSaKx20t9l4u6Tjd1XS6au7191BsUFP7vs0r2NbVz6UmLmJYcm/0NB1V+q7u7u49gsd1iYW5abF8jLEn4IxFMQnS4AQHMqhDwCBEEgaSkJJKSkvrYabjdbrq6urRl2MTExJieVnd3NwcPHmTu3LmahNhk0caLF6/Xy759+0hPT2fFihWYTCYaGhrYsGEDmZmZvPvuu2PSZxoJk8E6Yzic0AFKn0HFU+Lr6uqisrKSpKQkSkpKSEhIiA4LqK62goAkybS0NPPKH7ZiMplIS4vfaE2MSLQ0dOH36gwFdWrkR3yK1Ek3GVmM7hcJApoauSBELc1N/QUtSY6dyvMFsDj7Sn0rPYFJDZrxD+YpeFvdpM2K04ZBAJvNjM1mJtWpPr6oMaKaYQVDIq4mD45U+5D0+RQl2lOJRCIkOZ1YdRmFpef/22u8XPvfq0iwmrWpLL2duT4jcDqdzJ6ayuypR4JWRJJo6vZS1+mirstFfaebJpcHWVGoaetk06vvcfK8WZy1II/UxKGX4RRFoa2tjQMHDjBr1izy8/Pjej3ZzGZsvYYlVMUO1RxSZTSCgyAIOBwOHA6HppKgLsO63W7a29vZs2cPiqKQkpKCz+ejra1NU3CIh4kOZLIsc+jQIdra2rR+mSzLPPXUUzz++ONs2rSJc889d1IG2zVr1rB582bWrl3Ltm3bSE1NnfTlPTjBA5TKYEMSPp+PyspKZFlm4cKFJCUlaYEJjji0tre3c+DAAUS3gqc+gM1qQ4xIKApYrOY+BnAQfdOFAhE8rgCuTl9ce0dHVBhMsUFLjno+SUo0W4qWBY+4wfYJWoqCw6SQMj2VUChCIBghFIog9WRqUbX0IT2VAHia3KRmpw9b7FUQIMFmIcFmIVUt1Smwcn4uU2Ym09DqoqHNRXOnd4CelkIwFCLgD5CYaCcpKYmBImxbl48/bt3JVWtOIjU1NcbkTr8IW1dXpxlHqplWamoqSUlJ/Qathi4PDV1uDne6ONDWyUdb6lmSM4NV82YxL2NwTUCI9jcqKiqw2WyUlpaOWD+u9yI0HLnY6S39NVpBKyEhgUAggMvlYunSpaSlpcUoONTX18cYF6qZlt1un1Qneo/Hw759+5g2bZrWL6utreWHP/whhYWFvPfeezEDJ+PNpZdeyttvv017ezs5OTn8/Oc/185p1157Leeddx5bt27VfOyeeuqpCTvWoXBCj5mrU2DqiWD58uUx3w+Hw1RXV2t2G1OmTEGW5Zg3tSAIuN1uqqqqSEhIIC8vj+d/8XeaDrRov0eRFUKhCGJYIhKRiIREwqEIkhgd0x6JgsPRUE0KVZ8mFLSApQUti5l5py9BMAnRnaxgELMlAVkWCIUiBIMRwmFpyHYPGUUzcGaM7hs2yZHAD773Rez2aPYTESWaOjw0tLqob4sGrcbWbjxeLxazhaQkR5wLwzAvZyqXnbucxISj9230kkOqeoM6EXdUcdeeoFXX6cITDJHmsDPV6WDutHRslt6ZrkxtbS1tbW0UFhaOe7lotAKUqp83derUPmoWve8vFAppI+9ut5tgMEhCQkLM8zpY0BoLDyhZlqmpqaGrq4vi4mKcTieSJPHEE0/wzDPP8OCDD7J69epJFUyPEYw9qMFQFc3D4TCffvopK1asAKInodraWpqbm5k7d66WCqsDEOoJPhgMUl1dTSgUoqCggJSUFMpf/YTX//DvuO47FIgQDIS1fyNH81waJdSgpfa1UGBq0UwS0pOw2RL6dS9VZCU62BCMlttCwQjhyNFLognJdrKWjv6m/JLFOfz31/suGGoXEx4fKVNn0uUXaWhzUd/qor3bF9ek4/T0JNadV8bU1KG5G/b2KlKDlnpiHUgeJySKtLp9mE0mbBYzVrMJv9vNoYM1ZGZmTipn2N4cLYipDr0j1c/rHbQCgQA2my0maCUmjmx37Wh0d3ezf/9+Zs6MuvQKgkBVVRXr16+npKSEO++8syc7NxgGRoAaDDVAKYrCRx99xKpVq2hoaODQoUNkZWWRm5uLIAg9wwbRRri6t1RbW0tnZyd5eXlMmzYt+uLdWcP/e98/kAdUAT86kijHBKxQIIwoDu93xYMiK4iSSOIUJ1lL5iKKEpKkjrvrjPUsZnq/nmRJjg419JQFg8EIETE2wM5cko09ZfQntC48v4yFC6Jj0LIs09DQQH19fUzjXU8wHKGx3aOVButbXXS4+pcbslnNfPnkIlYujNMzawDUoKVmWj6fT3OF1Wda6n0Eg0EqKyuJSBLz8gtwJCZG+4o9Qw7HyhW6qtg9a1Zf/bzRIBwOa8+pqpWnt4hXLwZGcr+SJFFdXY3X66W4uBiHw4Eoivz2t7/lr3/9K7/5zW849dRTR/FRnZAYAWowVEVzQLNVTk9P1zbZewcmVYqlvr5eMxpTr3Aryg/w/z24FXGQzGKoiBGJYCBMMBAh5A8TDIRHXBJUFEXLBi1mCyaziXmnL8bcM6WnKNHR7EiPR5EkRlXJrT3Lr6qFeW8kSe7JsiKEghFMiTamFE0f9ZNUot3GVd8+DZMpQmVlpVZCGopVgT8YobE9Gqwa2tw0tLro8gS072dlpHDOykIKZ00btePXa7q53W7NyhyiAWru3Ln9ntRVuabeRzGZgtZg+nljid4iXn1eVYFXNXD1Vxnoj87OTiorK8nOziYnJwdBENi7dy8bNmzgC1/4AnfccQf2Udg5MzAC1KAoikJ7ezuVlZV0d3dzyimnkJiYqA1AmExHNMfa2tqoqakhIyOD3NxcTYqlo7GTd178iL3vV4zbMUfCEqFA+EjgCkTiE3XtmeKSJRmzJdaVN6Mghym5/VjBaz8afU4ikSMW5iaTScuyrJb+/arOu3wVCamJNDZ209jUTVOTi0Bw6MK3emRJRhAinPNfc1i2bGGPJcnI8QXCNLQdybIa2twkJlhZuXAWS/Jn4ojD5HEoqCUkp9OJw+HA6/VqJ1d9ptVfRjAe0j3xMBL9vLFkoIsB/ci7vlcoiqKmBFNcXExiYiKRSIQHH3yQrVu38rvf/Y6ysrIJflTHFUaAGgxRFNmxYwd5eXns2bOHk046KeaNHwlGaG1uY//nFcghheSEVMwmM2JYpKvFRVNNCy21QxdyHW0URSEcEqNlQX+0PBgORmL+WLIU3dMymU09Bnuxrw+r3cbcUxcO6WSnyLKWZYkREUmWMfXsS6lyQ9Oz0rhq43maqZ+iKHR1+WlsigasxsZo0BqspxX9YVVcN4QjycGsnGlc/s1VOJ1jd0Xr8YdoaHXR1BENVs7EBLIyUpiSMvygGA6HtUXV+fPn9wmwkUhEO7GqJ1e1jDXU3stYjmaPtX7eaKMGLTVwqarkVqsVj8dDdnY22dnZ2O12Pv30UzZs2MCXv/xlbr311jFz4H3ttdfYsGEDkiRx9dVXs3HjxpjvHz58mCuuuILu7qhdzz333MN55503JscyzhgBKh6CwSAAO3fuxGKxkJaWRmpqKiaTiZqaGiRJoqCgIDq9I0q0Hm6nsbqZxuoWGqubaavvmJTPiiwrhIIR/N6oXbwYkVEk+owZ68laPJfkGSObGJNlKSbTkmWZsi/mcdKZ87WTa28tN1lW6Ojw0tDYTVNP4GpudiNKR3pa4VAYv99Pgiqu2/MwUpMTWbv2JDJnjK5I7UAoioLbF8IfCmOzWLBaTFjMJhITrIMGAn22kZeXx/Tp8Zc/++u9qOZ6R5tyG4tMS5IkDh48SGdn55jq5401kUiEffv2EQqFmDJlCm1tbVx33XXIsozb7ebaa6/lG9/4BgsXLhyTACVJEoWFhfzrX/8iJyeHFStW8Pzzz7NgwQLtNt/97ndZvnw51113HXv37uW88847qubeMYQhFjsYzc3NbNmyhZKSEhYuXEgwGKS+vl4LTHa7nSlTotbb6iLizLwZzMybQek50d8RCoRprmml8cCRoOVqc0/sA6OnJCeFMNsUcuZOx2KxIEkyoWCEoD/cUyKMIOqs4LsOt444QJlM5p7F2yM9iAMfd7JgeZhwuF17bh0OB6mpqVq5JSMj+rFsaVRqSJJk2to81Bxs4dNPq+jqNmGzpfYJsC5PgD889R6rz5jPypPmjsh+PR4EQSDVaSdVl7UpikI4Imkivuq/Zl0J1ePxsH//flJTU1mxYsWQsw2bzca0adM0XTeIDVpNTU3alNtgQUt/3OpjijfTUns0M2fOHDf9vLFAVYPRXyh0dXXhdDr5+te/zurVq/n000956KGHOPXUU/nOd74z6sewfft28vPzycvLA2Dt2rW8/PLLMQFKXWOBqGq9qpF4onBCZ1AtLS08+eST7Ny5k4qKCiRJwu/3c+2113LRRReRkZGhlQNcLpd21aqeWNUTQG98Lj+N1c00VDfT1BO0At7guDymqE5agHA4FJfEkihKR6YG/RFySgvAOnINt95My0zh2z/5ElabRRMgVZ9X1Z+od9+ltraWrq4uCgoKSE9PJxKRaGlxa6XBxqZu2tu92o5W5vRUVp9RRGFh30m+8UbdPZNEiZqa6Mh1b4misUAdzVY/1H0ifdDqz3dssEwrEokOpITDYebPnz/h+nnDJRwOs3//fgRBoKioCJvNRiAQ4K677mLHjh08+uijMQFiLPnrX//Ka6+9xhNPPAHAs88+y7Zt29i8ebN2m6amJs455xy6urrw+Xy88cYblJaWjsvxjTFGiS9e3nnnHTZs2MB5553HsmXL+OSTTygvL6e5uZm8vDxKS0spLS3V5I08Hg8ulwu32x3th+iUnQeyI+huddFY1RO0DrTQdKB1VCf+osuOYfwBv+YxNZyT9Kz5WXz9+q/QXNdJ0+FOGg910Hy4k9AAzrxDoXBxNv995Wn9OtWqpnoul4uWlhZcLhc2m42pU6dqFwT9LcCGQiJNza6eXlY0aFmtZkqW57J4UTaJiWPTOxgMRVFoaWnh4MGD5ObmHlVWZqyDqSo3pF+CtdvtfZZg+0NRFJqbm6mtrR1wjP9YQP841GEORVH48MMPuemmm7j88stZv379uPbR4glQDzzwAIqi8KMf/YgPP/yQq666is8///yYzVx1GAEqXmpra3E4HDEupBA9aVZVVbF9+3a2b9/Ozp07CQQCLFiwgNLSUsrKyli0aBGyLGsBy+12I0lSjBxOcnJynxeUJMm013X0ZFnNNFS30FbXjjKMEfJIJKLt2fR3Eh8qF9z4FYpPLtQ+VxSFzlY3jYc6aTrcQdOhDlrqu4a1o7WwLJc1607p9ySnmj06HA7mzZuH2Wzuo9qgTmKpQau/CTe/P0xzs4vmFjcJCRaSk+1kTHOSnj4+S5U+n4/9+/fjcDjIz8/vc8Gip7/331gHAL1yg/oRCoWw2+0xF1qyLLNv3z7sdjsFBQUDPo6J1sgbjGAwyL59+0hISNAeh9fr5ec//zn79u3jscceo6CgYNyP68MPP+SOO+7g9ddfB+BXv/oVALfccot2m4ULF/Laa69pVhl5eXl89NFHfc5VxyBGgBoLwuEwn332Gdu2bWP79u3s3r0bq9XK8uXLKSkpoaysjHnz5hEMBrWgpfaw9CfW/vYyIqEIzQfbtPJgY3Uz3S2uAY9FkiR8Pj+KIpOUlDRqV3+pGSlc++srsNoG/n2SKNHW7KLpUAdNPYGrrckV145W/sIs1qw7Bbsjmt1EIhEOHDiA1+ulsLDwqGUw/fiwWna1Wq19yq69n1uvN0ggEMFqNWOxmDGbBez2wQcbhoJ+eKCoqChG12+4jNc4uV7Y1eVy0dbWRjAYJCUlhalTp2qv3aH4Pk100NLvLRYUFDB16lQUReGdd97hlltu4ZprruHaa6+dsGxEFEUKCwt58803yc7OZsWKFTz33HMsXLhQu82Xv/xlLrnkEr71rW+xb98+zjrrLBoaGib8uR0FjAA1HiiKgsfjYefOnWzbto3y8nKqqqqYNm2aVhosKytj+vTpMSdWn88Xc2JNTU3ttzfgdwdiBjAaq5vxufxD6jMNh5O/XsZZl39hSD8TCYu01HfRdLiDxp7A1dnm6fe2UzKS+erlq8AWoq6ujjlz5pCZmTmsx6EOC6gXBPq+i/r89l4cVRSFSJ/BBqFfQd94UBXH9Queo4mapfR+v472/aj28VOnTmXOnDkxgxhut1uz0NBnWgMFrd4utmNxvAMRCATYt2+flsVaLBbcbjc//elPqaur47HHHmPOnDnjcixHY+vWrVx//fVIksSVV17Jrbfeym233UZZWRlr1qxh7969fOc739GEiu+9917OOeeciT7s0cAIUBOFWu/evn27FrRUXT81YJWUlGC322P6WcFgUHvzqyfW3oaGTU1N7P1kP+aQlYhbormmlaYDrURCI+8R9eaSjV+noDRvRL8j6A9Hy4KH1fJgJ+5uP2Ikgtfno7h0Fmu+eTpTp4/emHjvEpbL5dJOrPpMq79eYX8c7aQaCASoqKjAbDZTWFg4rgoKo1la0+vnFRcXD6gxp/d9Uj96O+z299yqPwtjnwnW1dXR2NhIUVER6enpKIrCv/71L2677TY2bNjAt7/97eOhh3OsYwSoyYQsy1RXV2ulQX0/Sy0NLlq0CEALWC6XS3PitdlsdHZ2kpaWxrx582KuWmVZpr2+k8YDLTRWRbOs1sPtw9YEVLEnJXD1pstIG8Udo1AoxGef7KG5rgu7KYXOFi8t9V3kFs5g6cnzyC0Ymyb8QCfWpKSkmF5h7zLpQO8PRVE4dOgQLS0tFBYWMmVKnP5XkxBVP2/27NlkZWUN+fnXO+yqVQL1udUPYhytFzca+Hw+9u3bR2pqKnl5eZjNZjo7O7nllltwuVw88sgjx4RJ3wmCEaAmO/p+Vnl5Obt378ZiscT0s0wmE//4xz84+eSTSUpK0haL1Te96knUp58VFmmpbdPKgo3VLXQ2dQ35GFMzUvifOy4ibfrIxqNlWdYssufNm6cJ7ELPlGOHl6bDnfjcQZJTE3GmJjIjJ/2ofbCRoh93Vz9kWY4ZcHE6nX00/jo6OqisrGTGjBnMmTMHk8k06QcF+iMUCrF//35MJtOoZ3/6oKV+iKKoXRCo+2/xDJAM9ryqr62Wlhbmz59PamoqiqKwZcsW7rzzTjZu3Mill15qZE2TCyNAHWvo+1n/+c9/eOGFF2hra2PZsmUsXbqU0tJSVqxYwfTp07WRbFWyRRXHVEtY/Q0KBLxBmmqOZFkN1c34uvtX9daTMi2Zb/70fKZlDy9L6OjooKqqiunTp5ObmxuXqKssy7g6fJhMAharGbPFjNkan+38SFDH3fWqDRB11nU4HHR2diIIQp9doN4n0/EoZw0XRVGor6/XhgemTZs2buU3/QWBx+PRKgR6NfKhDPuoRoJ6z6m2tjZ+8pOfoCgKmzdvZsaMGWP2mAyGjRGgjlUkSeKMM87gkksu4ZprrqGjo0MbdS8vL6epqSmmn7V8+XISExNjhjDUXRd90OrdzFYUBU+HV9vNaqhqpulAM+F+dp4sNgtfuvqLLF29IO6TWCAQoLKyEkEQKCwsHLEKtCRF1eVVZ1j9cMNYovZnmpubSUpKQhTFGOHR1NTUAQVd9YMNkyFYeb1erQymjvKrTEQWKMtyn0xLluU+mVbvoCXLMgcPHqSjo4Pi4mKSk5NRFIWXXnqJ++67j9tuu40LL7xwUjznBv1iBKhjGVEUB7yS7K+f5ff7++xnATGDAqIoahJDas+ldzajKArtDZ00Vbdoo+4ttW1aP2vu4tl88fLTmJk38FWpavjY3t6uORGPFWM9IaZOtU2ZMiXG0kMUxZiTqjqVOVgW2/uYx/LY9YxEP2+sJwd7I8tyn0xLX3o1mUzU19fHmDo2Nzdz44034nQ6+fWvfx0jCTXaZB9KQgAAGEtJREFUDCbwCvDiiy9yxx13IAgCS5cu5bnnnhuz4zlGMQLUiUQ4HGb37t0x+1kWi4Vly5Zp/ayCgoKYXRePx4OiKDGZQH+LvmJEpPVQe4x8U9r0FErPXUresjkxSuWtra3U1NRo49YTXfcfbvYSiUSorq7G7/czf/78uJxTe49kBwKBGJkhdZVgtI4xXvT6ebNmzRrR36S/YDUemZcq4FpTU4PH48Fms7Fr1y7eeecd0tPT+fDDD/nVr3415llTPAKvVVVVXHzxxbz11lukp6fT2tp6PCzWjjZGgDqR6b2ftWPHDs3cT93PUvtZPp9P62epag36TKA/2aSgL0TzwRa6W92kTE0Gi0Kbu5XkVCf5+fljZk8wXOI9iaqj/IcOHRrRbpaKekGgV2yId49Iz3D3w6qqqsZcP288FnW7urqoqKggKyuLWbNmIQgCBw4c4Oabb0YURbKysti3bx+yLPPoo4+OmV5dPOoPN910E4WFhVx99dVjcgzHCYaa+YmMIAikpKRw5plncuaZZwJH9OHU/aynnnqKxsbGPvtZDodD62c1NzdrmYB+qdielMCcRbOJRCLU1NTgdrvJnTWblJRUBEVAkuQxVxYfCvFYYagSRU6nk7KyslEZi7bb7djtdu0KWj/u3tnZSW1tbcy4u/oxUHk3nsCl150bqq3HcBjoGHqrpQ9026MhiiLV1dX4fD6WLl2qGYo+9dRTPP7449x3332cc8452u8NhUIjfDRHp6GhQZMdAsjJyWHbtm0xt6msrATg1FNPRZIk7rjjDr70pS+N6XEdr5xQASqe2vHxjCAIZGZmsmbNGtasWQPE9rP++c9/cvfdd8f0s0pLS1m+fDkQ7Wd1d3dz+PBhwuEwJpOJYDBIVlYWy5Yt6/eELolSz8kq+vkRw8TJgyiK1NTU4HK5YqSWxqJ0pdq2OBwOMjMztftRey6tra1UV1fH9FzUQQGz2RxzPPogoOL3+6moqMBut49akB0OvZ83NUgNJVCp05+zZs2iqKgIQRCora3lhz/8IUVFRbz//vskJyfH/Mx4LkoPhOrO+/bbb1NfX8/pp5/O7t27SUtLm+hDO+Y4YQKUJEl8//vfj6kdr1mzZtyk9Scr6g5MYWEh//M//wPE9rOefvppPvvss5j9LIvFwt/+9jduu+02srKy8Pl8fPzxxyiKgtPp1DItp9PZR7lcEiXCwXDMOLY1DrO/sUDfM5s1axYFBQUTchyCIOB0OnE6nZrfj37cvbGxMWbcXQ1aTqdT6yfJssyhQ4dobm7WFBTUx6jex0QT7zFEIhHNcXjZsmXY7XYkSeL3v/89zz77LA888ACrV6+ekMeUnZ1NXV2d9nl9fX2f5d+cnBxWrlyJ1Wpl7ty5FBYWUlVVxYoVK8b7cI95TpgeVDy1Y4P+UftZb775JnfffTfNzc2aNba+n5WZmamdVF0uV0w/Sy0N9tfPioRF1BRLzVqsCWN75e/3+9m/f7+mcD3Untl4T7ZB9CKrt7q7yWQiISEBt9tNRkYGBQUF/U5m9j7WybpYrKpa6Pt/lZWVbNiwgZKSEu666y4cDseEHV88Aq+vvfYazz//PM888wzt7e0sX76cTz75hKlTp07YcU9CjB6Unnhqxwb9o/azXn75ZTZu3Mj5558PENPPevrpp2lqamLOnDkx/aykpCRNb7C1tVWzbT+akCvQR1tQMAlYRmFJVz8CX1RUNOyyy0B9l7E86ZvNZtLS0rRjFkWRyspK3G43M2bMIBgMsn37dm3cXf2IxxtsojOtcDhMRUUFiqJQWlqKzWZDFEU2b97MSy+9xG9+8xtOPfXUCTk2PRaLhc2bN3PuuedqAq8LFy6MEXg999xz+ec//8mCBQswm83cd999RnAaJidMBhWPOZjByJBlmQMHDmij7jt27MDn88XsZy1ZsgSI3c8Kh8MxFvD9DQkoioIYFo9YvitKtJ/Vj/nhQLS3t1NdXT0q49YTjWpZ3p9+3mDj7gMZFPa3UzbWQVdfZp03b542TLJ3717Wr1/PGWecwe233z7iJW+DSYeRQemJp3ZsMDJMJhMFBQUUFBRw+eWXA7H9rGeeeYbPPvsMs9nM8uXLWb58OWVlZSxdulRTH9cPCej7LcnJyX3KfpIkR8uDEC0RClFZpD4j8cEgFRUVCIKg9TQmkpEsF6uPxWQyaZlGb2w2G9OmTYtZVtWPu9fX18c97t77eIdzzAOhagFaLBZtoCMcDvPggw/y6quv8rvf/Y6ysrJRuS+DY5MTJoOKp3ZsMPYoioLX643xz6qsrGTKlCl9+ln6/SyPx4PJZIrpZ/UnLySJkmaaKMsy9XV1tHW0aYZ1xyqqfl5DQwP5+fkjVkpQDQr1TtDxjLuPhgqGftdM1QIE+PTTT9mwYQPnnXce//u//zvpdukMRhVjUbc3/ZmDGUw8+v0sVW+wsbGR3NzcmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMsrOyMZmOlAIFgVHpZ40XR9PPG00GU3fXj7sP9PN6+gtcgUCA/fv3azbyFouFUCjEpk2bePfdd3n00Ue1MrDBcY0RoCYbdXV1rFu3jpaWFgRB4Lvf/S4bNmygs7OTSy65hNraWubMmcOLL76ojQmfqPTuZ+3cuROfz0dxcXFMP0sQhD5uupIkYTKZmDt3LhkZGf0aE4oRSd/OmpT7WXr9vOLiYpxO54SIufbOZGHgcfeB0GeAev+s8vJybrzxRi666CJ+9KMfTdjelsG4YwSoyUZTUxNNTU2UlJTg8XgoLS3l73//O08//TRTpkxh48aN3HPPPXR1dbFp06aJPtxJRyQSidEb/OyzzzCZTCxfvpxly5ZRU1NDe3s7t9xyi2bx7Xa7kSQpxuMpOTm5zwlVURQkUYr5mtnSt581XqhLqnppn8nCQOPu+tKg3qPM7/ezb98+nM6oDJbZbMbv93P33Xezc+dOHn30UYqLiyf4URmMM0aAmux8/etf5wc/+AE/+MEPePvtt5k5cyZNTU2sXr2aioqKiT68SY/az3r++ef51a9+xdSpU5EkidTUVEpLSykpKdH6WYFAICYLEASB5ORkrTTYn+mjJMkoclTFXf2eyWwa02ARDoeprKwkEomMWD9vPHed+lN3t1gsCIJAMBhk7ty5zJw5E0EQ+OCDD7jppptYt24d69evH7OSpUq8CjIvvfQSF154IeXl5cZwxthjBKjJTG1tLaeffjqff/45s2fPpru7G4ieVNLT07XPDY5OIBDgsssu4xe/+AWLFi3SxpbV/azy8nIaGhr67GclJyfH9LNUuwy93mBCQkK/Qxi9Gcqo+0DoBwdGWz9PH6jGK2h5vV727NlDYmIiSUlJlJeXc9ddd2Gz2QgEAtx8882sWbNmzCdp41Efh6jx4Ve+8hXC4TCbN28elQDlcrloaWmhsLBwxL/rOMQYM5+seL1eLrjgAn7961/38eUZDwO+44nExET+9re/aZ8LgsCMGTP42te+xte+9jUgtp/1xhtvcM899+D1emP6WcuWLcNsNmtZVmNjI8FgUBvFVgPXQHqDegSTMKQdK1XVIjExcUz08/Svp/GwxTh06BBtbW0UFxeTkpKCoijU1dXhdDq59NJLKS4uZufOnVx99dWsW7eOSy+9dMyOZ/v27eTn55OXlwfA2rVrefnll/sEqJ/97GfcfPPN3HfffaNyvzt27OC5555j/vz5FBYWTlrljsmOEaDGmUgkwgUXXMBll12mKTLMmDGDpqYmrcRneMeMLv3tZ+n7WX/84x9j+lmqf9bixYuJRCK4XC46OjqoqanRLMrVgJWcnNwng+rTzxIETKa+Fx7qyby1tXVEqhaTBdV+PSMjg7KyMkwmEy6Xi5/97GfU19fzyiuvkJubC0TL2+NBPAoyu3btoq6ujq985SsjDlDt7e184xvfYNq0aezcuZO1a9cCk0ML8VjECFDjiKIoXHXVVRQXF3PjjTdqX1+zZg3PPPMMGzdu5Jlnnhm3N++JjNVqpaSkhJKSEq677rqY/azt27ezadMmKioqSE9P77Ofpdpl9BZxVZ2Kk5KS+g9aktrPgu7ubqqqqsjIyGDFihXHtKqFLMvU1NTQ1dXFggULcDqdKIrC66+/zu23387111/Pt771rUn5GGVZ5sYbb+Tpp58eld934MABzj77bG6//Xa+973vceDAAe1+JuPjn+wYPahx5L333uMLX/gCixcv1l6sd999NytXruTiiy/m8OHD5Obm8uKLL46pTbpBfAzWz1IHMVJSUvB6vVp5UB0Q6M/+Xe9vVFRU1Ef49Fg7iXV3d1NRUaHZrwuCQGdnJxs3bsTtdvPII49MqGLLYCLRLpeLefPm4XQ6AWhubmbKlCm88sorw+pD3X333ezevZvnn3+eQCBASUkJL730klZSNEp9GsaQhEH8SJJEWVkZ2dnZbNmyhYMHD7J27Vo6OjooLS3l2WefNTb7OZIt6PUGe/ezlixZgtlsjtEbDAaDCIJAKBQiMzOTOXPmDGj/3lsKaTKe0CRJorq6WnvsDocDRVHYsmULd955Jxs3buTSSy+d8IA7VAWZ1atXc//99w97SOLjjz/m8ccfZ/369RQXF7NkyRLsdjunnXYad91115i5Gh+DGEMSBvHz0EMPUVxcjNvtBuDmm2/mhhtuYO3atVx77bU8+eSTXHfddRN8lBOPyWQiPz+f/Px8LrvsMqBvP2v37t2a7l9paSnZ2dk8+uij3HjjjeTn5+P3+9m9e7cmLaQXye1tSghHgtZ4Djscjc7OTiorK8nOzqawsBBBEGhra+PHP/4xgiDwxhtvMGPGjAk7Pj3xqI+PJg6Hg8TERP7zn/8AcPbZZ1NSUsIXv/hFIzgNAyODMqC+vp4rrriCW2+9lQceeIB//OMfZGRk0NzcjMVi6VMmMTg6aj9r+/btPPzww7z33nsUFRVhsVi00mBZWRlZWVlaP8vlcuHxeFAURVNpUPtZ/S0V92Y8ApbqFBsIBCguLtbs11966SXuv/9+br/9di644IJJmfGNJ3/5y194/fXX+cc//sH999/PFVdcARjlvV4YGZRBfFx//fXce++9WsO/o6ODtLQ0TSg0JyeHhoaGiTzEYwp1CXjXrl0UFxfz3HPPkZiYSGtrK+Xl5Wzbto1nn32W+vp6cnNzKSsri+lnqdJChw4dijF9VDOtgfydxtJEsb29naqqKnJzc5k/fz6CINDc3MwNN9xASkoK//73v0csYHu8cMkll/C1r32Ne++9V3tOjOA0PIwAdYKzZcsWpk+fTmlpKW+//fZEH85xhVryUpkxYwZf/epX+epXvwrE9rPefPNNNm3ahNfrZf78+VqWtXTpUsxms7ZU3NzcrPk76ZeKbTZbv6VBleGeHCORCBUVFUiSRElJCQkJCciyzJ///Gc2b97MXXfdxVe/+lXj5NsLh8OBw+FAkqR+y7YG8WEEqBOc999/n1deeYWtW7dqnkEbNmygu7sbURSxWCyGd9YwGeykNFA/6/PPP2fbtm38+c9/5ic/+Qkmk0nrZ5WVlbFo0SJNWqi7u5vDhw8TDoc1qwxVb7C3VQYcuZKPx0FXNUXUK1vU19ezfv16cnJyePfdd4/53a2xZqxlnI53jB6Ugcbbb7/N/fffz5YtW7jooou44IILtCGJJUuW8L3vfW+iD/GEo/d+Vnl5ORUVFaSlpWkBS+1n9fZ3Uk0f1UwrHtVxiOoB7t+/H0EQKCoqwmazIcsyTz/9NL///e+59957Oeecc4yswGAkGGPmBkNDH6BqampYu3YtnZ2dLF++nD/96U/9jkUbjD+KotDW1hazn6XvZ5WUlFBaWkpqaiper1cbwtD3s9QPvemjoig0NzdTW1tLfn4+GRkZABw8eJAf/vCHFBcXc88995CcnDyRD9/g+MAIUAbHNt3d3Vx99dV8/vnnCILAH/7wB4qKigzvrH7Q97PKy8vZsWMHHo+nTz/LYrHg8Xi0TMvv95OQkIDD4aC7u5ukpCTmz5+P1WpFkiQef/xx/vSnP/Hggw9yxhlnjHnWNJjy+AMPPMATTzyBxWIhIyODP/zhD5p8ksExhRGgDI5trrjiCr7whS9w9dVXEw6HNQ8hwzsrPvT9rPLycj755BNMJhNLly7VglZBQQFPPvkk+fn5ZGZmEg6Huf3224lEInR0dLBo0SIefvjhcdlrikd5/N///jcrV67E4XDwyCOP8Pbbb/OXv/xlzI/NYNQxApTBsYvL5dJMCPVX7UVFRYZ31jDR97PKy8v597//zUcffURBQQErV65k5cqVLF26lJdffpmtW7dy1lln4XK52LlzJ1arlbfeemtMM6jBZIl68/HHH/ODH/yA999/f8yOyWDMMPagDI5dDh48SEZGBt/+9rf59NNPKS0t5aGHHqKlpYWZM2cCkJmZSUtLywQf6bGDup+1evVqRFHkhRde4OWXX6aoqEjrZ23atIni4mLefPNN7Ha79rOSJI15eS8e5XE9Tz75JF/+8pfH9JgMJhYjQBlMSkRRZNeuXTz88MOsXLmSDRs2cM8998TcZrLq1B0LnHTSSbz33nua/I66n/XLX/6y39tPtnHpP/3pT+zYsYN33nlnog/FYAw5tqSTDU4YcnJyyMnJYeXKlQBceOGF7Nq1S/POAgzvrBGgKlJMJrKzs6mrq9M+H2j/7o033uCuu+7ilVdeMSZLj3OMAGUwKcnMzGTWrFlaf+nNN99kwYIFmncWYHhnHWesWLGCqqoqDh48SDgc5oUXXugj5vrxxx9zzTXX8MorrxgXJycAxpCEwaTlk08+0Sb48vLyeOqpp5Bl2fDOOo7ZunUr119/vaY8fuutt8Yoj//Xf/0Xu3fv1vqQs2fP5pVXXpngozYYBsYUn8HYUF5ezlVXXcX27duRJImTTjqJv/zlLyxatGiiD21cePDBB3niiScQBIHFixfz1FNP0dTUZPhnGRjEjxGgDMaOn/70pwSDQQKBADk5OQOOAh9vNDQ0cNppp7F3714SExO5+OKLOe+889i6dSvnn3++Jg21dOlSwz/LwGBg4gpQRg/KYFjcdttt/Otf/2LHjh3cdNNNE30444ooigQCAURRxO/3M3PmTN566y0uvPBCILpg/Pe//32Cj9LA4NjHCFAGw6KjowOv14vH4yEYDE704Ywb2dnZ/PjHP2b27NnMnDmT1NRUSktLDf8sA4MxwAhQBsPimmuu4Ze//CWXXXYZN99880QfzrjR1dXFyy+/zMGDB2lsbMTn8/Haa69N9GEZGByXGIu6BkPmj3/8I1arlW9+85tIksQpp5zCW2+9xRe/+MWJPrQx54033mDu3Lma0vf555/P+++/b/hnGRiMAUYGZTBk1q1bx0svvQREFQa2bdt2QgQniI41f/TRR/j9fhRF0fazzjzzTP76178CJ85+1muvvUZRURH5+fl9VD4AQqEQl1xyCfn5+axcuZLa2trxP0iDYxojQBkYDIGVK1dy4YUXUlJSwuLFi5Flme9+97ts2rSJBx54gPz8fDo6Orjqqqsm+lDHFEmS+P73v8+rr77K3r17ef7559m7d2/MbZ588knS09Oprq7mhhtuOKFKwQajgzFmbmBgMGTiUR4/99xzueOOOzj55JMRRZHMzEza2toM/UQDMMbMDQxODK688kqmT58esyjd2dnJ2WefTUFBAWeffTZdXV1A1HJj/fr15Ofns2TJEnbt2jWs++xPebz35KL+NhaLhdTUVDo6OoZ1fwYnJkaAMjA4xvnWt77VZ5Lwnnvu4ayzzqKqqoqzzjpL6xG9+uqrVFVVUVVVxeOPP24sExtMaoZa4jMwMJiECIIwB9iiKMqins8rgNWKojQJgjATeFtRlCJBEB7r+f/zvW83xPs7GbhDUZRzez6/BUBRlF/pbvN6z20+FATBAjQDGYpx0jGIEyODMjA4PpmhCzrNgOrZng3U6W5X3/O1oVIOFAiCMFcQBBuwFuit2voKcEXP/y8E3jKCk8FQMPagDAyOcxRFUQRBGNXAoCiKKAjCD4DXATPwB0VR9giC8Atgh6IorwBPAs8KglANdBINYgYGcWMEKAOD45MWQRBm6kp8rT1fbwBm6W6X0/O1IaMoylZga6+v3ab7fxC4aDi/28AAjBKfgcHxir68dgXwsu7r64QoqwDXUPtPBgbjhTEkYWBwjCMIwvPAamAa0ALcDvwdeBGYDRwCLlYUpVOILiFtBr4E+IFvK4qyYyKO28BgMIwAZWBgYGAwKTFKfAYGBgYGkxIjQBkYGBgYTEqMAGVgYGBgMCkxApSBgYGBwaTk/wd1sUcynvMIGwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe07fa1b0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Author: Remi Flamary <[email protected]>\n", "#\n", "# License: MIT License\n", "\n", "import numpy as np\n", "import matplotlib.pylab as pl\n", "import ot\n", "# necessary for 3d plot even if not used\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", "from matplotlib.collections import PolyCollection\n", "\n", "#\n", "# Generate data\n", "# -------------\n", "\n", "#%% parameters\n", "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", "x = np.arange(n, dtype=np.float64)\n", "\n", "# Gaussian distributions\n", "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", "\n", "# creating matrix A containing all distributions\n", "A = np.vstack((a1, a2)).T\n", "n_distributions = A.shape[1]\n", "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", "M /= M.max()\n", "\n", "#\n", "# Plot data\n", "# ---------\n", "\n", "#%% plot the distributions\n", "\n", "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", "pl.tight_layout()\n", "\n", "#\n", "# Barycenter computation\n", "# ----------------------\n", "\n", "#%% barycenter computation\n", "\n", "alpha = 0.2 # 0<=alpha<=1\n", "weights = np.array([1 - alpha, alpha])\n", "\n", "# l2bary\n", "bary_l2 = A.dot(weights)\n", "\n", "# wasserstein\n", "reg = 1e-3\n", "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", "\n", "pl.figure(2)\n", "pl.clf()\n", "pl.subplot(2, 1, 1)\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", "\n", "pl.subplot(2, 1, 2)\n", "pl.plot(x, bary_l2, 'r', label='l2')\n", "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", "pl.tight_layout()\n", "\n", "#\n", "# Barycentric interpolation\n", "# -------------------------\n", "\n", "#%% barycenter interpolation\n", "\n", "n_alpha = 11\n", "alpha_list = np.linspace(0, 1, n_alpha)\n", "\n", "\n", "B_l2 = np.zeros((n, n_alpha))\n", "\n", "B_wass = np.copy(B_l2)\n", "\n", "for i in range(0, n_alpha):\n", " alpha = alpha_list[i]\n", " weights = np.array([1 - alpha, alpha])\n", " B_l2[:, i] = A.dot(weights)\n", " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n", "\n", "#%% plot interpolation\n", "\n", "pl.figure(3)\n", "\n", "cmap = pl.cm.get_cmap('viridis')\n", "verts = []\n", "zs = alpha_list\n", "for i, z in enumerate(zs):\n", " ys = B_l2[:, i]\n", " verts.append(list(zip(x, ys)))\n", "\n", "ax = pl.gcf().gca(projection='3d')\n", "\n", "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", "poly.set_alpha(0.7)\n", "ax.add_collection3d(poly, zs=zs, zdir='y')\n", "ax.set_xlabel('x')\n", "ax.set_xlim3d(0, n)\n", "ax.set_ylabel('$\\\\alpha$')\n", "ax.set_ylim3d(0, 1)\n", "ax.set_zlabel('')\n", "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", "pl.title('Barycenter interpolation with l2')\n", "pl.tight_layout()\n", "\n", "pl.figure(4)\n", "cmap = pl.cm.get_cmap('viridis')\n", "verts = []\n", "zs = alpha_list\n", "for i, z in enumerate(zs):\n", " ys = B_wass[:, i]\n", " verts.append(list(zip(x, ys)))\n", "\n", "ax = pl.gcf().gca(projection='3d')\n", "\n", "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", "poly.set_alpha(0.7)\n", "ax.add_collection3d(poly, zs=zs, zdir='y')\n", "ax.set_xlabel('x')\n", "ax.set_xlim3d(0, n)\n", "ax.set_ylabel('$\\\\alpha$')\n", "ax.set_ylim3d(0, 1)\n", "ax.set_zlabel('')\n", "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", "pl.title('Barycenter interpolation with Wasserstein')\n", "pl.tight_layout()\n", "\n", "pl.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
swara-salih/Portfolio
Group Project--Chicago WNV Kaggle Competition/NileVirus_Prediction-kh (1).ipynb
1
2948954
null
mit
cosmoscalibur/herramientas_computacionales
Presentaciones/Notas/09_Extraccion_web.ipynb
1
60122
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extracción de datos web (_Web scrapping_) \n", "\n", "Ante la generación masiva a traves de la red es importante tener herramientas que permitan la extracción de datos a partir de fuentes cuya ubicación es esta. De esto se trata el _web scrapping_. \n", "\n", "Se pueden tener elementos poco especificos mediante las mismas alternativas del procesamiento de texto para algunos casos, sin embargo esto no siempre será efectivo ni eficiente. Por ejemplo, podemos usar `wget` para descargar una página y hacer la búsqueda de elementos `html` en ella por medio de expresiones regulares, pero la descarga de la página implica que el contenido debio ser estatico. Igualmente, las expresiones regulares no son la mejor herramienta siempre, y es más eficiente usar elementos especialmente diseñados para recorrer la estructura html sin depender de la generación de expresiones de coincidencia sino obedeciendo exclusivamente a los patrones que ya sabemos que existirán por defecto. \n", "\n", "## Herramientas (en python)\n", "\n", "Para esta labor contamos con algunas herramientas como lo son: \n", "\n", "+ `urllib`: Modulo incluido en python para la recuperación de contenido de una url. \n", "+ `webbrowser`: Modulo incluido en python para la apertura de url's en una instancia del navegador predefinido. \n", "+ `html`: Modulo incluido en python para el analisis sintactico html. \n", "+ Request: Reemplazo externo para `urllib` con mayores caracteristicas. \n", "+ Beautiful Soup: Reemplazo externo para `html` con mayores caracteristicas. \n", "+ Selenium: Reemplazo externo para `webbrowser` con mayores caracteristicas. \n", "+ Wget: _Port_ de `wget` para python. \n", "\n", "## Instalar requisitos\n", "\n", "Primero que todo, partimos que ya tenemos instalado al menos un navegador (firefox por defecto en la mayor parte de las distribuciones linux). Se puede trabajar con otros navegadores, y es de especial interes PhantomJS, una opción de navegador que no genera interface gráfica, ideal para pruebas o automatización (en caso de ser molesto que el navegador se vea abrir y cerrar, etc...). \n", "\n", " pip install selenium beautifulsoup4 Requests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aplicando" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import webbrowser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Al usar la función `open` de `webbrowser` se abrirá el navegador o una pestaña nueva si el navegador ya estaba abierto. Ya que no indicamos el navegador, esto se realiza con el navegador configurado por defecto en nuestro sistema. Se puede usar para abrir pestañas y ventanas nuevas, cerrarlo, y tambien usar un navegador especifico." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "webbrowser.open('http://github.com/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sin embargo, la labor de extracción web depende de obtener el código fuente o elementos disponibles en las páginas, lo cual es imposible con solo abrir el navegador. Para este fin es posible usar `urllib` o como lo haremos en esta sesión, con `request`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Project Gutenberg's Latin for Beginners, by Benjamin Leonard D'Ooge\r\n", "\r\n", "This eBook is for the use of anyone anywhere at no cost and with\r\n", "almost no restrictions whatsoever. You may copy it, give it away or\r\n", "re-use it under the terms of the Project G\n" ] } ], "source": [ "import requests\n", "res = requests.get('http://www.gutenberg.org/files/18251/18251-0.txt')\n", "res.status_code == requests.codes.ok # Validar código 200 (ok)\n", "type(res)\n", "len(res.text)\n", "print(res.text[:250])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ante un fallo en el proceso de obtención del código con la función `get`, es posible generar una notificación del motivo de fallo." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "ename": "HTTPError", "evalue": "404 Client Error: Not Found for url: https://github.com/yomeinventoesto", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-577eab1b3e67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://github.com/yomeinventoesto'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://github.com/yomeinventoesto" ] } ], "source": [ "res = requests.get('http://github.com/yomeinventoesto')\n", "res.raise_for_status()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cuando obtenemos un elemento de una dirección, este se encuentra como binario y no como texto plano. Esto nos facilita algunas cosas. Nos permite descargar contenido que no se solo texto plano (archivos de texto o código fuente) sino tambien directamente archivos binarios como imagenes, ejecutables, videos, archivos de word y otros. Es importante aclarar, que si vamos a almacenar el archivo de texto plano, debemos hacerlo con creación de archivos binarios para no perder la codificación original que tenga el archivo." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res = requests.get('http://www.programmableweb.com/sites/default/files/github-jupyter.jpg')\n", "archivo_imagen = open('github-jupyter.jpg', 'wb')\n", "for bloques in res.iter_content(100000):\n", " archivo_imagen.write(bloques)\n", "archivo_imagen.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![github jupyter](github-jupyter.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el bloque anterior, el método `iter_content` genera bloques del archivo con el tamaño indicado en su argumento. Esto conviene para la escritura de archivos de gran tamaño." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import bs4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usarmos ahora `bs4` (forma de importar Beautiful Soup), lo cual nos permitirá la búsqueda de texto y estructuras html especificas. Este es más conveniente que usar expresiones regulares directamente en el código fuente. \n", "\n", "Al crear el objeto, debemos indicar el texto sobre el cual actuará (puede ser obtenido directamente de un archivo abierto tambien) y el tipo de analizador sintactico, en este caso `lxml`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bs4.BeautifulSoup" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = requests.get('https://github.com/cosmoscalibur/herramientas_computacionales')\n", "gh = bs4.BeautifulSoup(res.text, \"lxml\")\n", "type(gh)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora, buscaremos todas las estructuras `td` que tengan el atributo `class` con valor `content`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "bs4.element.ResultSet" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tabla_archivos = gh.find_all('td', {'class':'content'})\n", "type(tabla_archivos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El resultado es una lista con todos los resultados obtenidos. Tambien es posible una búsqueda uno a uno, usando `find` en lugar de `find_all`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(tabla_archivos)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[<td class=\"content\" colspan=\"3\">Failed to load latest commit information.</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/tree/master/Evaluaci%C3%B3n\" id=\"f65268c13fd62c5d4e9269ebcd9e11ca-8ed6e5c0b159f72f1829ff55eb7c4856e58e43b9\" title=\"Evaluación\">Evaluación</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/tree/master/Proyecto\" id=\"c126d9cdd553787e63d4a48608f608cc-5099d554c9d22731a50e7784cee7e458bcc9b81f\" title=\"Proyecto\">Proyecto</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/.gitignore\" id=\"a084b794bc0759e7a6b77810e01874f2-8cec0970c2bcc6a64ca702e6ace3072bec1db708\" title=\".gitignore\">.gitignore</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/Git.ipynb\" id=\"c24916e53ed7ca5bfbd5552ec60b9864-c89b09c88c856d8d41558fa24d8da697173a834c\" title=\"Git.ipynb\">Git.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/Jupyter%20Notebook%20Basico.ipynb\" id=\"56994abf3c173eef1f731f415a113e73-649ceea4101089ff2d9e22c518e09e713604115a\" title=\"Jupyter Notebook Basico.ipynb\">Jupyter Notebook Basico.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/Jupyter%20Notebook%20Intermedio.ipynb\" id=\"ffc05e347c4818563b4f8fe19ecf90f5-639517140f687747088d911f602f1ed76aea5d13\" title=\"Jupyter Notebook Intermedio.ipynb\">Jupyter Notebook Intermedio.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/LICENSE\" id=\"9879d6db96fd29134fc802214163b95a-cd3541098e80cceefd8bca945781da52d50826ae\" itemprop=\"license\" title=\"LICENSE\">LICENSE</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/LaTeX_basico.pdf\" id=\"445e300dd1ef8a87aca1c1f559f6d865-04e35031b5713ccaa5eca1aec68cee2fe0709006\" title=\"LaTeX_basico.pdf\">LaTeX_basico.pdf</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/LaTeX_basico.tex\" id=\"d183b1b27b2723139e34a2c2c2563d3b-16930e488482ae4f67e88156c0e24c35b4d1a3c6\" title=\"LaTeX_basico.tex\">LaTeX_basico.tex</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/Linux%20Bash.ipynb\" id=\"ae97da1784350f80f600c7a109ee6021-f5c0a06f7de9516a809c6978b59dd966d71015ed\" title=\"Linux Bash.ipynb\">Linux Bash.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/Linux%20Basico.ipynb\" id=\"67a20284f502e3f94cd48308516bece6-84b42c08ef7ecfa057f547a1b8aed4e7274b7665\" title=\"Linux Basico.ipynb\">Linux Basico.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/README.md\" id=\"04c6e90faac2675aa89e2176d2eec7d8-a7ef4975f70435aebf7a78f843df649b2006ac49\" title=\"README.md\">README.md</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/R_basico.ipynb\" id=\"a374c2b16cceed1093078668f0f2a077-b76411f9d8e075cb40f4428a2d83d945b7ed2972\" title=\"R_basico.ipynb\">R_basico.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/algebra_computacional.ipynb\" id=\"4484cecc709691bd6f7aa604fa5e619d-6990c171d5cfa11a4d226871f107a7ed02072ac3\" title=\"algebra_computacional.ipynb\">algebra_computacional.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/datos_abiertos.md\" id=\"11f66d58f88e6022a18fe1cbf9b3bbf4-24664abb3ee537b8311fdeea217ab11a53c85fc5\" title=\"datos_abiertos.md\">datos_abiertos.md</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/historial_talleres.md\" id=\"3919bdb26f7850379608f0e7af744435-35692b7cc202043e2c8196abad636ff1865d32b3\" title=\"historial_talleres.md\">historial_talleres.md</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/hoja_de_ruta.md\" id=\"200c2939ccd869061076b9c85f6ab4e5-b5f00e541809bea4070d4220a4e9e7a1cfbb6e18\" title=\"hoja_de_ruta.md\">hoja_de_ruta.md</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/jupyter.png\" id=\"965598871a36b41718db396637a2afe8-480932523cd389416c1b7dfffb8b0580616e9051\" title=\"jupyter.png\">jupyter.png</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/presentacion_herramientas.md\" id=\"aa6132db9bd8b7f9301a40f9347bacc3-817b6de1714a3015409eddbd7b4a25d0931600c4\" title=\"presentacion_herramientas.md\">presentacion_herramientas.md</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/procesamiento_de_texto.ipynb\" id=\"fa6d566a808c31e0fc9045b530040e88-a14c94d5ce44f69024491a037df19e7768f61d4b\" title=\"procesamiento_de_texto.ipynb\">procesamiento_de_texto.ipynb</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/r_datos.txt\" id=\"70ff05431638f94f914ae68cc1eecc6a-9eb903b0113089f99b4a8349f95731e42a9a9dd4\" title=\"r_datos.txt\">r_datos.txt</a></span>\n", "</td>, <td class=\"content\">\n", "<span class=\"css-truncate css-truncate-target\"><a class=\"js-navigation-open\" href=\"/cosmoscalibur/herramientas_computacionales/blob/master/web_scrapping.ipynb\" id=\"634ac137a7e35d0e03e15bc603462ab7-983a57f7dab52e377388e0e5e2a7df7750a7dde4\" title=\"web_scrapping.ipynb\">web_scrapping.ipynb</a></span>\n", "</td>]\n" ] } ], "source": [ "print(tabla_archivos)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En el filtrado anterior, ahora buscaremos todas las etiquetas `a` las cuales asociamos con la presencia del atributo `href`. De esta forma localizaremos la lista de archivos. Para obtener el texto al interior de una etiqueta, usamos la propiedad `string` y el valor de un atributo con el método `get`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Se encontro el archivo 'b'Evaluaci\\xc3\\xb3n'' con enlace '/cosmoscalibur/herramientas_computacionales/tree/master/Evaluaci%C3%B3n'.\n", "Se encontro el archivo 'b'Proyecto'' con enlace '/cosmoscalibur/herramientas_computacionales/tree/master/Proyecto'.\n", "Se encontro el archivo 'b'.gitignore'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/.gitignore'.\n", "Se encontro el archivo 'b'Git.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/Git.ipynb'.\n", "Se encontro el archivo 'b'Jupyter Notebook Basico.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/Jupyter%20Notebook%20Basico.ipynb'.\n", "Se encontro el archivo 'b'Jupyter Notebook Intermedio.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/Jupyter%20Notebook%20Intermedio.ipynb'.\n", "Se encontro el archivo 'b'LICENSE'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/LICENSE'.\n", "Se encontro el archivo 'b'LaTeX_basico.pdf'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/LaTeX_basico.pdf'.\n", "Se encontro el archivo 'b'LaTeX_basico.tex'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/LaTeX_basico.tex'.\n", "Se encontro el archivo 'b'Linux Bash.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/Linux%20Bash.ipynb'.\n", "Se encontro el archivo 'b'Linux Basico.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/Linux%20Basico.ipynb'.\n", "Se encontro el archivo 'b'README.md'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/README.md'.\n", "Se encontro el archivo 'b'R_basico.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/R_basico.ipynb'.\n", "Se encontro el archivo 'b'algebra_computacional.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/algebra_computacional.ipynb'.\n", "Se encontro el archivo 'b'datos_abiertos.md'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/datos_abiertos.md'.\n", "Se encontro el archivo 'b'historial_talleres.md'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/historial_talleres.md'.\n", "Se encontro el archivo 'b'hoja_de_ruta.md'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/hoja_de_ruta.md'.\n", "Se encontro el archivo 'b'jupyter.png'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/jupyter.png'.\n", "Se encontro el archivo 'b'presentacion_herramientas.md'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/presentacion_herramientas.md'.\n", "Se encontro el archivo 'b'procesamiento_de_texto.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/procesamiento_de_texto.ipynb'.\n", "Se encontro el archivo 'b'r_datos.txt'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/r_datos.txt'.\n", "Se encontro el archivo 'b'web_scrapping.ipynb'' con enlace '/cosmoscalibur/herramientas_computacionales/blob/master/web_scrapping.ipynb'.\n" ] } ], "source": [ "for content in tabla_archivos:\n", " lineas_a = content('a')\n", " if lineas_a:\n", " texto = \"Se encontro el archivo '{}'\".format(lineas_a[0].string.encode(\"utf-8\"))\n", " texto += \" con enlace '{}'.\".format(lineas_a[0].get(\"href\"))\n", " print(texto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nos vimos en la necesidad de usar `encode(\"utf-8\")` ya que la codificación de la página es utf-8 y no ascii (el usado por defecto en python). Podemos consultar los atributos de una etiqueta o si posee un atributo especifico, y no solo obtener el valor, de la siguiente forma." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lineas_a[0].has_attr(\"href\") # Existencia de un atributo" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'class': ['js-navigation-open'],\n", " 'href': '/cosmoscalibur/herramientas_computacionales/blob/master/web_scrapping.ipynb',\n", " 'id': '634ac137a7e35d0e03e15bc603462ab7-983a57f7dab52e377388e0e5e2a7df7750a7dde4',\n", " 'title': 'web_scrapping.ipynb'}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lineas_a[0].attrs # Atributos existentes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from selenium import webdriver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Invocar la instancia del controlador del navegador depende del navegador de interes. Hay que tener encuenta que no todos los navegadores son soportados. Podemos encontrar soporte para Chrome, Firefox, Opera, IE y PhantomJS. Este último permite realizar la labor sin la generación de una ventana para el navegador (en caso de ser necesario, incluso se puede generar capturas de pantalla para su validación con ayuda del controlador). \n", "Acorde a cada navegador, se puede tener requerimientos especificos. En el caso de firefox, se presenta la necesidad de indicar el directorio del perfil de usuario, en el caso de chrome se requiere indicar la ruta del controlador (se [descarga](https://sites.google.com/a/chromium.org/chromedriver/) ya que no viene incluido como si sucede en firefox o phantomjs). \n", "Podría ser posible (no he verificado) usar otros navegadores si usan el mismo motor de navegación realizando la indicación explicita de la ruta del ejecutable. Por ejemplo, se podría controlar vivaldi realizando el cambio de ruta de chrome (usan el mismo motor de navegación)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "browser = webdriver.Chrome(\"/home/cosmoscalibur/Downloads/chromedriver\")\n", "browser.get('http://github.com')\n", "username = browser.find_element_by_id(\"user[login]\")\n", "username.send_keys(\"[email protected]\")\n", "dar_click = browser.find_element_by_link_text(\"privacy policy\")\n", "dar_click.click()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resulta bastante útil el uso de `selenium` no tanto en los casos que requieran de interacción sino en los casos donde los contenidos (incluye elementos de interacción) son de generación dinámica o tras la interacción el nuevo enlace o contenido tiene retrasos apreciables, lo cual evitaría que `Request` obtenga el código adecuado. Podemos extraer el código fuente de la página en la cual se encuentra el foco del navegador de la siguiente forma." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<!DOCTYPE html><html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"en\" class=\" is-copy-enabled is-u2f-enabled\"><head prefix=\"og: http://ogp.me/ns# fb: http://ogp.me/ns/fb# object: http://ogp.me/ns/object# article: http://ogp.me/ns/article# profile: http://ogp.me/ns/profile#\">\n", " <meta charset=\"utf-8\" />\n", " <meta content=\"origin-when-cross-origin\" name=\"referrer\" />\n", "\n", " <link crossorigin=\"anonymous\" href=\"https://assets-cdn.github.com/assets/frameworks-cb37473586c0bff2206ff7c864d9afda5e2063afb40364d87d64eefa2536d1c0.css\" integrity=\"sha256-yzdHNYbAv/Igb/fIZNmv2l4gY6+0A2TYfWTu+iU20cA=\" media=\"all\" rel=\"stylesheet\" />\n", " <link crossorigin=\"anonymous\" href=\"https://assets-cdn.github.com/assets/github-1d1dd698ec7e26200c41f689c19707b13992a803afc430c1abe93cc850c026a7.css\" integrity=\"sha256-HR3WmOx+JiAMQfaJwZcHsTmSqAOvxDDBq+k8yFDAJqc=\" media=\"all\" rel=\"stylesheet\" />\n", " \n", " \n", " <link crossorigin=\"anonymous\" href=\"https://assets-cdn.github.com/assets/site-b637b3b72afffd79585a758c94c7bd5bc8d451dd7ff634ca3a1b23221da39613.css\" integrity=\"sha256-tjeztyr//XlYWnWMlMe9W8jUUd1/9jTKOhsjIh2jlhM=\" media=\"all\" rel=\"stylesheet\" />\n", " \n", "\n", " <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\" />\n", " <meta http-equiv=\"Content-Language\" content=\"en\" />\n", " <meta name=\"viewport\" content=\"width=device-width\" />\n", " \n", " <title>How people build software · GitHub</title>\n", " <link rel=\"search\" type=\"application/opensearchdescription+xml\" href=\"/opensearch.xml\" title=\"GitHub\" />\n", " <link rel=\"fluid-icon\" href=\"https://github.com/fluidicon.png\" title=\"GitHub\" />\n", " <link rel=\"apple-touch-icon\" href=\"/apple-touch-icon.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"57x57\" href=\"/apple-touch-icon-57x57.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"60x60\" href=\"/apple-touch-icon-60x60.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"72x72\" href=\"/apple-touch-icon-72x72.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"76x76\" href=\"/apple-touch-icon-76x76.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"114x114\" href=\"/apple-touch-icon-114x114.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"120x120\" href=\"/apple-touch-icon-120x120.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"144x144\" href=\"/apple-touch-icon-144x144.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"152x152\" href=\"/apple-touch-icon-152x152.png\" />\n", " <link rel=\"apple-touch-icon\" sizes=\"180x180\" href=\"/apple-touch-icon-180x180.png\" />\n", " <meta property=\"fb:app_id\" content=\"1401488693436528\" />\n", "\n", " <meta property=\"og:url\" content=\"https://github.com\" />\n", " <meta property=\"og:site_name\" content=\"GitHub\" />\n", " <meta property=\"og:title\" content=\"Build software better, together\" />\n", " <meta property=\"og:description\" content=\"GitHub is where people build software. 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"print(codigo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si se desea hacer pasar algún navegador especifico o una solicitud por `request` o `urllib` como un navegador dado (por ejemplo, evitar bloqueos de contenido por navegador o por politicas contra la extracción web), es necesario realizar la modificación del `user-agent`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bibliografía\n", "\n", "+ [How to fetch Internet Resources Using The urllib Package](https://docs.python.org/3/howto/urllib2.html). \n", "+ [Structured Markup Processing Tools](Structured Markup Processing Tools). \n", "+ [Internet Protocols and Support](https://docs.python.org/3/library/internet.html). \n", "+ Automate the boring stuff. [Chapter 11 – Web Scraping](https://automatetheboringstuff.com/chapter11/). \n", "+ [First web scraper](https://first-web-scraper.readthedocs.io/en/latest/#act-3-web-scraping). \n", "+ [HOW TO DOWNLOAD DYNAMICALLY LOADED CONTENT USING PYTHON](https://dvenkatsagar.github.io/tutorials/python/2015/10/26/ddlv/). " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
awagner-mainz/notebooks
gallery/DHD2019_Azpilcueta.ipynb
1
203332
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multimodale Versuche der Alignierung historischer Texte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_Andreas Wagner und Manuela Bragagnolo, Max-Planck-Institut für europäische Rechtsgeschichte, Frankfurt/M._\n", "\n", "&lt;<[email protected]>&gt; &lt;<[email protected]>&gt;" ] }, { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Multimodale-Versuche-der-Alignierung-historischer-Texte\" data-toc-modified-id=\"Multimodale-Versuche-der-Alignierung-historischer-Texte-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Multimodale Versuche der Alignierung historischer Texte</a></div><div class=\"lev2 toc-item\"><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Introduction</a></div><div class=\"lev1 toc-item\"><a href=\"#Preparations\" data-toc-modified-id=\"Preparations-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Preparations</a></div><div class=\"lev1 toc-item\"><a href=\"#TF/IDF-\" data-toc-modified-id=\"TF/IDF--3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>TF/IDF </a></div><div class=\"lev1 toc-item\"><a href=\"#Translations?\" data-toc-modified-id=\"Translations?-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Translations?</a></div><div class=\"lev2 toc-item\"><a href=\"#New-Approach:-Use-Aligner-from-Machine-Translation-Studies-\" data-toc-modified-id=\"New-Approach:-Use-Aligner-from-Machine-Translation-Studies--41\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>New Approach: Use Aligner from Machine Translation Studies </a></div><div class=\"lev1 toc-item\"><a href=\"#Similarity-\" data-toc-modified-id=\"Similarity--5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Similarity </a></div><div class=\"lev1 toc-item\"><a href=\"#Word-Clouds-\" data-toc-modified-id=\"Word-Clouds--6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Word Clouds </a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This file is the continuation of preceding work. Previously, I have worked my way through a couple of text-analysing approaches - such as tf/idf frequencies, n-grams and the like - in the context of a project concerned with Juan de Solórzano Pereira's *Politica Indiana*. This can be seen [here](TextProcessing_Solorzano.ipynb).\n", "\n", "In the former context, I got somewhat stuck when I was trying to automatically align corresponding passages of two editions of the same work ... where the one edition would be a **translation** of the other and thus we would have two different languages. In vector terminology, two languages means two almost orthogonal vectors and it makes little sense to search for similarities there.\n", "\n", "The present file takes this up, tries to refine an approach taken there and to find alternative ways of analysing a text across several languages. This time, the work concerned is Martín de Azpilcueta's *Manual de confesores*, a work of the 16th century that has seen very many editions and translations, quite a few of them even by the work's original author and it is the subject of the research project [\"Martín de Azpilcueta’s Manual for Confessors and the Phenomenon of Epitomisation\"](http://www.rg.mpg.de/research/martin-de-azpilcuetas-manual-for-confessors) by Manuela Bragagnolo. \n", "\n", "(There are a few DH-ey things about the project that are not directly of concern here, like a synoptic display of several editions or the presentation of the divergence of many actual translations of a given term. Such aspects are being treated with other software, like [HyperMachiavel](http://hyperprince.ens-lyon.fr/hypermachiavel) or [Lera](http://lera.uzi.uni-halle.de/).)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the previous case, the programming language used in the following examples is \"python\" and the tool used to get prose discussion and code samples together is called [\"jupyter\"](http://jupyter.org/). (A common way of installing both the language and the jupyter software, especially in windows, is by installing a python \"distribution\" like [Anaconda](https://www.anaconda.com/what-is-anaconda/).) In jupyter, you have a \"notebook\" that you can populate with text (if you want to use it, jupyter understands [markdown](http://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) code formatting) or code, and a program that pipes a nice rendering of the notebook to a web browser as you are reading right now. In many places in such a notebook, the output that the code samples produce is printed right below the code itself. Sometimes this can be quite a lot of output and depending on your viewing environment you might have to scroll quite some way to get to the continuation of the discussion.\n", "\n", "You can save your notebook online (the current one is [here at github](https://github.com/awagner-mainz/notebooks/blob/master/gallery/TextProcessing_Azpilcueta.ipynb)) and there is an online service, nbviewer, able to render any notebook that it can access online. So chances are you are reading this present notebook at the web address [https://nbviewer.jupyter.org/github/awagner-mainz/notebooks/blob/master/gallery/TextProcessing_Azpilcueta.ipynb](https://nbviewer.jupyter.org/github/awagner-mainz/notebooks/blob/master/gallery/TextProcessing_Azpilcueta.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A final word about the elements of this notebook:\n", "\n", "<div class=\"alert alertbox alert-success\">At some points I am mentioning things I consider to be important decisions or take-away messages for scholarly readers. E.g. whether or not to insert certain artefacts into the very transcription of your text, what the methodological ramifications of a certain approach or parameter are, what the implications of an example solution are, or what a possible interpretation of a certain result might be. I am highlighting these things in a block like this one here or at least in <font color=\"green\">**green bold font**</font>.</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alertbox alert-danger\">**NOTE:** As I am continually improving the notebook on the side of the source text, wordlists and other parameters, it is sometimes hard to keep the prose description in sync. So while the actual descriptions still apply, the numbers that are mentioned in the prose (as where we have e.g. a \"table with 20 rows and 1.672 columns\") might no longer reflect the latest state of the sources, auxiliary files and parameters and you should take these with a grain of salt. Best double check them by reading the actual code ;-)\n", "\n", "I apologize for the inconsistency.</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparations" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-02-24T10:19:35.089085Z", "start_time": "2019-02-24T10:19:35.076888Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'1_0': ' ... ', '1_9': 'aun que el amor de Dios ha de ser grandissimo ..., como despues de. S. Tho. b , poco ha lo tratamos 1. Sec. quaestio 109. ar. 3. c . Anadimos, (virtual) in addit. ca. Quoniam. de consec. disti. 1. nu. 10.', '1_10': 'porque aquella basta, ... d , que pone exemplo ..., que Gabriel sigue in 4. dis. 14. q. 1. art. 3. e . in 4. dis. 14. q. 1. col. 12. & 13. & in. 3. di. 27. q. 1. co. 15. Y aun, aquel doctissimo, ... f , y con razon, ..., el martyrio atribuya esto In Codice de poeni. q. 2. g , porque mas haze para esto el amor, ... que lo que se padece Lib. 2. c. 16. de natu. & gra. h . Y puede ser que mas ame, ..., como lo prueua bien Medina Arg. c. 13. 1. ad Corinth. i . Por lo qual largamente paresce quan lexos esta esto dela opinion de Luthero in predi. q. 2. . De lo dicho se collige la razon, ..., segun Syluestro k . Diximos verb. Contritio. q. 1.', '1_11': '(auer pecado,) porque el arrepentimiento ...'}\n" ] } ], "source": [ "from typing import Dict\n", "import lxml\n", "from lxml import etree\n", "\n", "document=etree.fromstring(\"\"\"\n", "<TEI xmlns=\"http://www.tei-c.org/ns/1.0\">\n", "<text>\n", " <body>\n", " <div n=\"1\">\n", " <p>\n", " ... <milestone unit=\"number\" n=\"9\"/>aun que el amor de Dios ha de ser\n", " grandissimo ..., como despues de. S. Tho.\n", " <ref target=\"#nm-0406\">b</ref><note xml:id=\"nm-0406\"><p>1. Sec. quaestio\n", " 109. ar. 3.</p></note>, poco ha lo tratamos\n", " <ref target=\"#nm-0407\">c</ref><note xml:id=\"nm-0407\"><p>in addit. ca.\n", " Quoniam. de consec. disti. 1. nu. 10.</p></note>. Anadimos, (virtual)\n", " <milestone unit=\"number\" n=\"10\"/>porque aquella basta, ...\n", " <ref target=\"#nm-0408\">d</ref><note xml:id=\"nm-0408\"><p>in 4. dis. 14.\n", " q. 1. art. 3.</p></note>, que pone exemplo ..., que Gabriel sigue\n", " <ref target=\"#nm-0409\">e</ref><note xml:id=\"nm-0409\"><p>in 4. dis. 14.\n", " q. 1. col. 12. &amp; 13. &amp; in. 3. di. 27. q. 1. co. 15.</p></note>.\n", " <milestone unit=\"other\" rendition=\"#asterisk\"/> Y aun, aquel doctissimo,\n", " ... <ref target=\"#nm-040a\">f</ref><note xml:id=\"nm-040a\"><p>In Codice de\n", " poeni. q. 2.</p></note>, y con razon, ..., el martyrio atribuya esto\n", " <ref target=\"#nm-040b\">g</ref><note xml:id=\"nm-040b\"><p>Lib. 2. c. 16.\n", " de natu. &amp; gra.</p></note>, porque mas haze para esto el amor, ...\n", " que lo que se padece <ref target=\"#nm-040c\">h</ref><note xml:id=\"nm-040c\">\n", " <p>Arg. c. 13. 1. ad Corinth.</p></note>. Y puede ser que mas ame, ...,\n", " como lo prueua bien Medina\n", " <ref target=\"#nm-040d\">i</ref><note xml:id=\"nm-040d\"><p>in predi.\n", " q. 2.</p></note>. Por lo qual largamente paresce quan lexos esta esto\n", " dela opinion de Luthero<milestone unit=\"other\" rendition=\"#asterisk\"/>.\n", " De lo dicho se collige la razon, ..., segun Syluestro\n", " <ref target=\"#nm-040e\">k</ref><note xml:id=\"nm-040e\"><p>verb. Contritio.\n", " q. 1.</p></note>. Diximos <milestone unit=\"number\" n=\"11\"/> (auer\n", " pecado,) porque el arrepentimiento ...\n", " </p>\n", " </div>\n", " </body>\n", "</text>\n", "</TEI>\"\"\")\n", "\n", "def segment(chapter: lxml.etree._Element) -> Dict[str, str]:\n", " segments = {} # this will be returned\n", " t = [] # this is a buffer\n", " chap_label = str(chapter.get(\"n\"))\n", " sect_label = \"0\"\n", " for element in chapter.iter():\n", " if element.get(\"unit\")==\"number\":\n", " # milestone: fill and close the previous segment:\n", " label = chap_label + \"_\" + sect_label\n", " segments[label] = \" \".join(t)\n", " # reset buffer\n", " t = []\n", " # if there is text after the milestone,\n", " # add it as first content to the buffer\n", " if element.tail:\n", " t.append(\" \".join(str.replace(element.tail, \"\\n\", \" \").strip().split()))\n", " # prepare for next labelmaking\n", " sect_label = str(element.get(\"n\"))\n", " else:\n", " if element.text:\n", " t.append(\" \".join(str.replace(element.text, \"\\n\", \" \").strip().split()))\n", " if element.tail:\n", " t.append(\" \".join(str.replace(element.tail, \"\\n\", \" \").strip().split()))\n", " # all elements are processed,\n", " # add text remainder/current text buffer content\n", " label = chap_label + \"_\" + sect_label\n", " segments[label] = \" \".join(t)\n", " return segments\n", "\n", "nsmap = {\"tei\": \"http://www.tei-c.org/ns/1.0\"}\n", "xp_divs = etree.XPath(\"(//tei:body/tei:div)\", namespaces = nsmap)\n", "\n", "segmented = {}\n", "divs = xp_divs(document)\n", "segments = (segment(div) for div in divs)\n", "for d in segments:\n", " print(d)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "ExecuteTime": { "end_time": "2019-02-26T22:10:09.213202Z", "start_time": "2019-02-26T22:10:09.201031Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...+ms_9+aa ab ac ad ae afag ah ai ajak +ms_10+ba bb bc bd be bfbg bh bi bj bk bl bm bn bobp bq br bsbt bu bvbw bx. by +ms_11+ca cb ...\n" ] } ], "source": [ "document=etree.fromstring(\"\"\"\n", "<TEI xmlns=\"http://www.tei-c.org/ns/1.0\">\n", "<text><body>\n", " <div n=\"1\">\n", " <p>... <milestone unit=\"number\" n=\"9\"/>aa ab ac<ref target=\"#nm-0406\">ad</ref><note xml:id=\"nm-0406\"><p>ae af</p></note> ag\n", " <ref target=\"#nm-0407\">ah</ref><note xml:id=\"nm-0407\"><p>ai aj</p></note> ak\n", " <milestone unit=\"number\" n=\"10\"/>ba bb bc<ref target=\"#nm-0408\">bd</ref><note xml:id=\"nm-0408\"><p>be bf</p></note> bg\n", " <ref target=\"#nm-0409\">bh</ref><note xml:id=\"nm-0409\"><p>bi bj</p></note><milestone unit=\"other\" rendition=\"#asterisk\"/> bk bl<ref target=\"#nm-040a\">bm</ref><note xml:id=\"nm-040a\"><p>bn bo</p></note> bp\n", " <ref target=\"#nm-040b\">bq</ref><note xml:id=\"nm-040b\"><p>br bs</p></note> bt\n", " <ref target=\"#nm-040c\">bu</ref><note xml:id=\"nm-040c\"><p>bv</p></note> bw<milestone unit=\"other\" rendition=\"#asterisk\"/>bx. by <milestone unit=\"number\" n=\"11\"/>ca cb ...</p>\n", " </div>\n", "</body></text>\n", "</TEI>\"\"\")\n", "\n", "import lxml\n", "from lxml import etree\n", "\n", "def flatten(element: lxml.etree._Element):\n", " t = \"\"\n", " if element.text:\n", " t += \" \".join(str.replace(element.text, \"\\n\", \" \").strip().split())\n", " if element.get(\"unit\")==\"number\":\n", " t += t + \"+ms_\" + str(element.get(\"n\")) + \"+\"\n", " if element.tail:\n", " t += \" \".join(str.replace(element.tail, \"\\n\", \" \").strip().split())\n", " if element.getchildren():\n", " t += \" \".join((flatten(child)) for child in element.getchildren())\n", " if element.tail and not(element.get(\"unit\")==\"number\"):\n", " t += \" \".join(str.replace(element.tail, \"\\n\", \" \").strip().split())\n", " # all elements are processed, add text remainder/current text buffer content\n", " return t\n", "\n", "nsmap = {\"tei\": \"http://www.tei-c.org/ns/1.0\"}\n", "xp_divs = etree.XPath(\"(//tei:body/tei:div)\", namespaces = nsmap)\n", "divs = xp_divs(document)\n", "\n", "segments = \"\".join(flatten(div) for div in divs)\n", "print(segments)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike in the previous case, where we had word files that we could export as plaintext, in this case Manuela has prepared a sample chapter with four editions transcribed *in parallel* in an office spreadsheet. So we first of all make sure that we have good **UTF-8** comma-separated-value files, e.g. by uploading a **csv** export of our office program of choice to [a CSV Linting service](https://csvlint.io/). (As a side remark, in my case, exporting with LibreOffice provided me with options to select UTF-8 encoding and choose the field delimiter and resulted in a valid csv file. MS Excel did neither of those.) Below, we expect the file at the following position:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:44:59.257315Z", "start_time": "2018-09-04T12:44:59.254296Z" }, "collapsed": true }, "outputs": [], "source": [ "sourcePath = 'DHd2019/cap6_align_-_2018-01.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can go ahead and open the file in python's csv reader:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:44:59.353554Z", "start_time": "2018-09-04T12:44:59.306432Z" }, "collapsed": true }, "outputs": [], "source": [ "import csv\n", "\n", "sourceFile = open(sourcePath, newline='', encoding='utf-8')\n", "sourceTable = csv.reader(sourceFile)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-02-05T15:33:14.158534Z", "start_time": "2018-02-05T15:33:14.154549Z" } }, "source": [ "And next, we read each line into new elements of four respective lists (since we're dealing with one sample chapter, we try to handle it all in memory first and see if we run into problems):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*(Note here and in the following that in most cases, when the program is counting, it does so beginning with zero. Which means that if we end up with 20 segments, they are going to be called segment 0, segment 1, ..., segment 19. There is not going to be a segment bearing the number twenty, although we do have twenty segments. The first one has the number zero and the twentieth one has the number nineteen. Even for more experienced coders, this sometimes leads to mistakes, called \"off-by-one errors\".)*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:44:59.474870Z", "start_time": "2018-09-04T12:44:59.434766Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41 rows read.\n", "\n", "1556 SPA\n", "¶ Capitulo. 6. De las circunstancias del pecado.\n", "Sumario.\n", "1 Circunstancia que es? nu. I. y que ay siete especies della. nu. 2. Y que se ha de confessar de necessidad, la que muda la especie. nu. 3. Pero no la de aver pecado en confinança de se confessar. n. 4. /Circunstancia de homicidio, y de fornicacion en lugar sagrado se ha de confessar, y la vedada por otra ley diversa &c. nu. 5/ Circunstancia de mentira iocosa, y la que alivia el pecado quando se ha de confessar. nu. 6. 7. & 8. Y quando la del dia de fiesta, de ayuno, o de oracion, o del lugar sagrado. nu. 9 & 10. Y la de la proprioa persona, y de la religion. nu. 11. Y ha de pecar contra consciencia. nume. 12/ [p. 32, corretto 31; 24 pdf] Circunstancia como no es el numero de los pecados nu. 14. Pecaodo multipliarse tantas vezes, quantas se itera, como se ha de entender, y si crece el numero de los pecados por se interpolar la voluntad. nu. 16. Y por mudar el proposito, para no acabar el pecado con otras muchas consideraciones quotidianas. num. 17 & 18. /Confessar puede el penitente mil pecados en una sola palabra n. 18. /Circunstancia del pecado quando se ha de confessar de necessidad. num 19. Y la oluidada en la confession como se confessara sin tornar a confessar el pecado. nu 20\n", "2 [1] Para fundamento desto dezimos, quanto a lo primero, que la circunstancia del pecado, segun la mente del derechoa (ca. Consideret. de poeni. d. 5 l. Aut facta ff. de poen. c. Sicut dignum de homic) y de sus interpretes (b : I. Sec. qo. 7 & in 4 dist 16 ubi etiam omnes alii & Anton. 3. part. tit. 17 ca. 17§ 4 & Gerson. 2. part. fol 170 quos retulimus in princ. d. c. Consideret & Pau. & aliorum in d. c. Sicut & alibi) es un accidente de aquello, che es pecado. Diximos (accidente) porque ninguna circunstancia de la obra, es la substancia della. Diximos de aquello (que es peccado) y no del peccado, porque muchas vezes la obra en si no es pecado, y se haze tal por la circunstancia, y como entonces ella es lo, en que consiste el peccado, no es tan accidente de pecado, quanto de aquello che es pecado, segun lo declaramos en otra parte (c : In d. c. Consideret. nu. 3), siguiendo a Alexandro Halense (d : In 4 par q. 77 ar. 2 col2).\n", "3 [2] ¶ Lo II † que la circunstancia se parte en siete species, que se contienen en un versillo (e : f. Quis, quid, ubi, quibus auxiliis, cur, quomodo, quando), referido por S. Thomas (f : In dict. q. 7. articu. 3). Quien, que, donde, con que, porque, como, y quando. Al qual versillo tenemos por mejor que al de Paludano (g : In 4. dist. 16. quaest. 3. articu. I. ), como lo diximos alli (h : f. in princ. dict. capitu. Consideret. numero. 4). Porque enel se añade, quoties, quantas vezes que denota numero, el qual no es circunstanci, sino multiplicacion del pecado, como alli (i : In dict. cap. Consideret. numero quarto) lo diximos.\n", "4 [3] ¶ Lo III. †que destas circunstancias, todas, y solas aquellas se han de confessar de necessidad, que hazen, que las obras cuyas son, sean pecados mortales, o las que son mortales de una especie, lo sean de otra, o lo que es mortal por un respecto, lo sea tambien por otro, hora muden las obras de una especie en outra, hora no, segun la commun opinion, que copiosamente alibik (K : In d. c. Consideret à numero 5) tratamos. Y solas, y todas aquellas circunstancias son desta qualidad, segun S. Thomas (l : In dict. dist. 16 quaestione sexta articu. 2. quaestione 3), que allende la [p. 32, 25 pdf] malicia de la misma obra, repugnan especialmente ala razon, y segun Scoto, las que se vedan por diversos, y especiales mandamientos. Diximos (especiales) porque no basta, que sean tales, que el uno dellos se incluya enel otro, quales son la ley, que veda todo mal, y la que veda el homicidio, como los provamos alibi (a : in d. princi. nume. 74). Lo qual todo por los siguientes corolarios se desmenuzara. * Antes de los quales avisamos, que despues que esto se imprimio, declaro por herege el concilio Tridentino (b : Sessio quarta sub Iul. 3. c. 5. & cano 7), al que dixere, que no somos obligados a confessar la circunstancia, que muda la especie de pecado. Lo qual se ha de entender, de la circunstancia, que muda la especie de pecado venial en mortal, o la del mortal en otro mortal, y no de la que muda en otro venial, que no es necessario (c : glo. c. c omnis de poeni. & re. recepta ibi, & in 4 dist. 17) confessarlo. Y aunque el concilio no expressa, sino de la que muda la especie del pecado, per tambien (y por mas fuerte razon) se ha de entender de la que haze a la obre, que de suyo es buena, o no mala, mortal. Y aun, de la que haze que una obra, quae por un respecto es mortal, lo sea tambien por otro tal, aunque la especie della (quanto a su ser) no se mudasse, como se ha dicho en esta quarta ilacion. Ca la razon que a ello movio al convilio, es que el confessor es juez y no podria bien sencenciar el caso del penitente, sin se le manifestar la circunstancia, que muda la especie del pecado, la qual razon milita en las tres dichas circunstancias (d : Quare idem iuris debet esse de omnibus. l. illud. ff. ad leg Aquil. ). *\n", "5 ¶ El primero de los quales sea, que no se han de confessar las circunstancias, de aver se cometido el pecado lunes, o martes, enel prado, o en la viña, con la mano derecha, o con la yzquierda. Porque po restas, no se haze alguna de las tres cosas suso dichas, pues no se haze mortal, lo que sin ellas no fuera tal, ni de otra especie mortal, ni por otro respecto mortal.\n", "¶ 6 [4] El segundo † que pecar con confiança, de que despues se confessara, y alcançara pardon perdon, no se ha de confessar necessariamente, perque no es circunstancia, que tanto agrava, antes aliuvia, como lo apunto un Cardenal (e : Caietan. 2. Sec. q. 21. ar. 2 quicquid dicat Bonaventura in apologia)\n", "7 ¶ El tercero, que al que hurto alguna cosa sagrada, o de lugar sagrado, no basta dezir que la hurto, porque le es necessario confessar, que la hurto de lugar sagrado, o era cosa sagrada. Ca esta circunstancia haze, que lo que era pecado mortal de una especie, o por nu respecto, lo sea de otra, o por otro respecto, por ser especialmente vedada por otra ley diversa, de la que veda el hurto conviene saber, que ninguna cosa sagrada, ni de lugar sagrado se hurte (f : c. Quisquis n. 7 quaest 4).\n", "8[5] Lo mesmo † es del homicidio, y de la fornication hecha en lugar sagrado [p. 33, 25 pdf]. Porque po resta circunstancia se hazen de otra especie, o por otro respecto mortales, por ser vedados por ley, especial humana (a : Cap. Proposuisti, de consec. eccl. cap. Ecclesiis de consec. distin. I).\n", "9 ¶ El IIII que quien se echo con muger casada, religiosa, o parienta, no satisfaze confessando, que uvo parte con muger, porque ha de declarar, que la ovo con casada, religiosa, o parienta. Ca enel primero caso es adulterio corporal, enel segundo, sacrilegio, o adulterio espiritual, enel tercero, incesto, y por conseguiente lo mortal de una especie, lo haze de otra. U si uno propuso de hurtar para tener parte con una, que es religiosa, y con otra casada, ha de confessar, hurto, sacrilegio, y adulterio : Porque puesto que estas tres cosas sean un acto interior de la voluntad, empero por tres respectos diversos es pecado mortal, pues por tres repugna a la razon, y por tres leyes diversas especiales esta vedado.\n", "10 ¶ El. V. que toda circunstancia de fin vedada por otra ley especial diversa dela que veda el acto principal, se deve confessar, como la circunstancia del que hurta para fornicar, matar, o herir a otro\n", "11 ¶ El. VI † que quien miente para dar plazer, sin daño de nadie (que es mentira iocosa, y pecado venial) con tal intencion, que no la dexaria de dezir puesto que supiera, que era mortal, es obligado a confessar a quella circunstancia, porque con ella es mortal, y sin ella no.\n", "12 ¶ El VII [6] que nadie es obligado a confessar las circunstancias que alivian el pecado, y assi el que peco con una muger, porque ella lo provoco, no es obligado a confessar, que lo provoco, pues diminuye el pecado (b : Pet. c. Significavit-de poe. & remis. quod i hoc dixit fing. Feli in c. Dilecti. de except. col. antepe. & per . l. Si adulterium cum incestu § imperatores . ff. ad. l. Iuliam de adulte. quem in hoc aiebat. fin. Iason. i. l. Ut vim. ff. de iusti & iur. co. 2 aptissimus tum ad propositum tex. in. d. c. Consideret, verb. tentatione). Antes (segun la Commun, que nos seguimos alibi (c : f. in princ. d. c. Consideret. numero. 5. ) la deve callar, por no se dever escusar el penitente enla confession. Agora empero mejor nos parece lo contrario, porque no ay ley ni razon, que efficazemente prueve aquello (d : Ergo nec asserendum. c. 2. de trans. praela . .c. Legat. 24. q. 2), y porque, aunque no la calle, bastantemente se acusa, echando se la culpa que tiene, fin quitar, ni poner mas. Y aun es obligado a confessar las, según. S. Bona. (e: in. 4. d. 16. ) y la Comun, que ali (f: in d. princ. ) seguimos, quando tanto alivia, que de mortal lo haze que no sea pecado, o no mas de venial, como la circunstancia de la grave enfermedad alivia al comer de la carne en quaresma. Y quando se las pregunta el confessor, o temiesse, que por se las callar tomaría occasion de algún mal (g: Arc. c. Nihil de praescript. ).\n", "13 ¶ El VIII † [7] que aun que es loable cosa confessar las circunstancias, que agravan el pecado, haziendolo de pequeño grande, o de grande mayor, pero la opinion mas commun, y provable es, que no es necessario, quando aquel argumento no es cause, que lo venial se haga mortal, o de otra especie, o por otro respecto, como copiosamente provamos alibi (h : f. in. d. prin. nu. 12. part. 4 pag. 36 * & satis declaratur ex nunc pro conci. Trident. sess. 4 sub Iuli. 3. cap. 5 & can. 7 à contrario sensu), apartandonos de Marsilio (i : in. li. 4. q. 12. art. I. corol. 4) (en quanto limitava esta commun, que no oviesse lugar en la circunstancia, que muy clara [p. 34, 26 pdf] y norablemente augmenta el pecado) por las muchas, y solidas consideraciones, que alli escrivimos. Parecenos empero, que se davan limitar enla que augmenta el pecado, y haze que por ello sea reservado almenos por constitucion synodal, que a las vezes reserva algunos hurtos, o daños de ciertta quantidad para cima, o añade, que la absolucion, o restitucion se haga en cierta manera.\n", "14 Y † enla que [8] haze, que tenga annexa descomunion, o que la descomunion annexa sea papal, como la descomunion dela herida ligera del clerigo, es obispal (a : ca. Pervenit de setentia exco. ubi text. singu. ), y de la grande, papal (b : c. Si quis suadente. 17 q. 4).\n", "15 Y en la que pregunta el confessor, y no se puede callar, sin peligro de algun inconveniente espiritual, como lo dixo bien Silves (c : f. verb. Contessio. I. § 9) aunque Ioan de Friburgo (el autor dela Summa confessorum) no lo dize donde el lo allegad (f. li. 3. ti. 33 q. 11), niaun donde desto tracta (e : f. lib. 3 ti 34 quaest 81).\n", "16 ¶ El IX † que la circunstancia del dia dela fiesta, no se ha de confessar [9] necessariamente : Porque no haze mal, lo que sin ella no lo fuera tal ni de otra especie, ni por otro respecto, sino fuere obra servil, vedada en fiestas, qual no es pecado, Segun S. Tho. (f : in-3 sent. d. 37 art. 5. q. 2). Esta ilagion alibi (g : f. in. d. c. Consideret. a. n. 17 usque ad 40) por otros fundamentos confirmamos, respondiendo a otros tantos contrarios, seguiendo en ello al cardenal Caieta (h : 2. Sec. q. 7. & . 2. Sec. q. 122 art. 4. i. Quodl 10 & in summa verb dies festos) en muchas partes, y al resoluto Sylvestro en otras (i : f. verb. Cincunstantia. q. 3. & ver. Dies dominica. q. si. & . in aurea rosa. casu. 63 ubi testatur doctissimos quosque ordinis dominicani convenisse & hanc opinione suscepisse), y a Iacobo Almayno (k : in 4 dist 17 col. 24). Aunque la Comun opinion en dos casos se puede guardar como alli lo diximos (l: ubi supra n. 40), conviene a saber quando el pecado se haze a fin de hazer obras manual verdada en aquel dia, o quando se peca mortalmente, con intencion, y proposito de quebrantar la fiesta.\n", "17 ¶ El X que la circunstancia del dia de ayuno, o de oracion, no se ha de confessar necessariamente, fino quando se peca con proposito delo quebrantar, por ello por que no haze alguna delas dichas tres cosas, segun lo provamos alibi (m : in d. c. Consideret nu. 32 ver. Ad primum)\n", "18 ¶ El XI que la circunstancia del lugar sagrado, puesto que accidentalmente agrave todo pecado, per no se ha de confessar necessariamente, sino quando la obra del pecado es directamente contraria a su sanctidad, o immunidad, qual es el derramiento de humana sangre, o simiente, o faca forçosa de los, que a ella se acogen. Porque en estos pecado de suyo mortal por un respecto, se haze mortal por otro (n : arg. c. Ecclesiis de concec. d. I cap. proposuit de consecra. ecclesi. c. i eo tit lib. 6), o lo que no era peccado, o no mas de venial de suyo, por ella se haze mortal, como la copula entre marido y muger sin causa justa en el avida, que en los otros lugares no lo feria.\n", "19 ¶ El xii† que deste precedente se sigue, que los que en la yglesia co [10] meten pecado de sobervia, perjjuro, o gula & c. no han de confessar de necessidad la circunstancia del lugar sagrado, ni los que estando en sagrado dessan matar, herir o fornicar, con tanto, que no lo sesseen cometer, ni poner por obra enel Ca. si esto dessean, aun [p. 35, 26 pdf] que estuviiessen fuera de sagrado, serian obligados confessar la circunstancia del sacrilegio, que enello cometen como lo diximos alibia.\n", "20 ¶ El XIII †[11] que aunque la circunstancia de la propria persona, alguna vezes acrescienta el pecado (caeteris paribus) assi como el que tiene dignidad mas peca, que aquello que no la tiene (b : cap. Homo 40. d. ), y el perlado mas que le subdito (c : Praecipue. II. quaest. 3) y el sabio mas que el ignorante (d : sicut dignum de homic), y mas el que ama la ignorancia (e : c. pen. 37. d) para pecar mas libremente, que pecaria sabiendo, y mas el bueno, que el malo, y el mejor que el menos bueno. Y aunque por est sea provechoso confessa resta circunstancia, per no es necesasrio comunmente : Porque comunmente ni haze de venial mortal, ni de mortal de una especie, mortal de outra, ni de mortal por un respecto, mortal por otro. Mas quando esto se hiziesse, lo qual feria, quando se pecasse contra voto, o estado votado, como peca el religioso en fornicar entonces avia se de confessar porque haze una de las dichas tres cosas, lo que no haze empero quando el religioso blasphemasse, o hizesse otro pecado, que no fuesse contra sus votos, o regla professada, como despues de Caietano (f : I. Sec. q. 17. art. I) lo diximos alibi (g : f. in d. princ. num. 50). Porque la circunstancia de religion, comunmente no haze mortal, lo que de suyo no es tal, ni de otra especie, lo quesuyo es de otra.\n", "21 ¶ El XIIII † [12] que la confession dela circunstancia de precar contra la consciencia, entonces soamente es necessaria, quando la obra que hizo, por ninguna ley era pecado, sino por ser hecha contra su consciencia erronea. Porque entonces solamente haze una delas dichas tres cosas, y no otras vezes, como nuevemente lo declaramos alli (h : f. ubi supra num 58 & 65).\n", "22 ¶ El XV† [13] que el numero delos pecados no es circunstancia, mas addicion de pecado a pecado, porque la frequentacion es circunstancia, que constituye nuevo pecado. Acerca delo qual alibi (i : f. in c. Consideret. nu. 41) diximos primeramente, que no basta dezir, peque muchas vezes en este pecado, porque esta diction (muchas vezes) tanto se verifica en diez, y aun en dos, como en ciento (k : glo. I. c. Monasteria. de vit. & honesta. cler. Gregorios I pri. Clemen. Saepe de verb. sign), aunque el Arcidiano (l. c. Imitare 6 q. I & in princi. d. c. Consideret), tuuo quasi, a quien no quiso reprovar Ange. (m : verb. Confessio. I. § 23) empero reprovou lo, y con razon un Cardenal (n : Alexandrinus in di. c. Imitare).\n", "23 Lo II† [14] que alli diximos es, que el pecador deve exprimir el numero cierto, si lo sabe, dizendo, esto hize tantas vezes, y si no sabe el numero cierto, ha de echar cuenta, quantas vezes el dia, o semana, o al mes (poco mas, o menos) peco, y dezir el numero cierto mas verisimil. Ca pecaria mortalmente, el que por verguença, o hypocrisia callasse algo del numero delas vezes, que se acuerda, y aun si por su lata culpa dexa de acordarse, por no aver pensado enello nada, podiendo lo hazer, y aun la confession no le valdria nada.\n", "24 Lo III† [15] que alli dizimos es, que bastaria sin algun numero declarar bastantemente su estado, como si la muger publica, que por diez [p. 36, 27 pdf] años ha estado aparejada para fornicar, assi con clerigos, religiosos, y virgines, como con legos, sueltos, y casados, y despues de convertida se confessasse, y dixesse, que tanto tiempo estuvo dela dicha manera, aparejada para tan torpe cosa, como despues de un Cardenal (a : Caiet. in. q. 3 de confessio) concluimos alibi (b : in d. c. Consideret, ibi quantum perseveraverit & defleat, quod perseveranter pecavit) ponderando ay el testo para esto singular, y añadiendo, que aquien dexo de rezar un año, bastava dezir, Dexe de razar un año.\n", "25 Lo IIII. [16] † que se augmenta el numero de los pecados, todas las vezes que el pecado, o la voluntad de pecar enterrompida se itera, segun Ioan And (c : in regul. Delictum col. penu. de reg. iuris lib. 6 i mercurial). Lo qual llanamente procede en los pecados interiores, que dentro del alma se consuman, qual es el odio, qual la heregia. No empero en os que se consuman de fuera por obra exterior : Ca ellos no se dizen iterarse, hasta que no seacabe la obra exterior, o no se interrompa, como acontesce, quando alguno va a matar otro, y caminnado todo el dia, orapiensa enello, ora en al. Ca este no peca en ello mas de un pecado aunque muy mas grave, segun un Cardenal (d : Caiet. in libello 17 respondo 15 resp e in d. princi. nu. 48), que alli (e : ind. princi. nu. 48) seguimos, y ponderamos un testo para ello muy apto.\n", "26 De lo qual inferimos, que no se itera, ni multiplica el pecado, aun que durante la exteriod muchas vezes la voluntad interior se interrompa, y renueve, ni aun por el contrario, si durando la mesma voluntad, la obra exterior se multiplique antes, que el delicto se acabe, como mas largamente lo provamos alli (g : in d. princi. nu. 48), Donde inferimos tambien, ser solo un pecado, todos los actos interiores y exteriores que solamente son camino para un solo pecado, aun que sean enterrompidos, quales son los passos, y el andar, aparejar de cavallo, lança y otras armas, con los desseos enterrompidos, por diversas vezes, hablando, comiendo, y dormiendo, y otras tantas renovados del qual va a matar a otro, de aquí a diez leguas. Ca aun que considerandos en si, son muchas, y diversas cosas, pero considerandos como amino, y partes del pecado, que contos ellos se ha de acabar, no hazen mas de uno. Como también las piedras, colunas, vigas y otros materiales de una casas, muchas cosas son, cada una dellas por si, pero todas ellas consideradas como partes de la casa, no hazen mas de una (h: Eum qui . ff. de usucapio).\n", "27 Desto † [17] inferemos la razón porque, quien ha tenido parte con una, no es obligado a confessar las platicas, besos, y otros actos, preámbulos y immediatos della: y el que la ha tenido dos vezes (aunque immediatas) es obligado a confessar, que la ovo dos vezes: ca la razón es, que la una de las dos copulas no es camino, ni preámbulo, que se ordena a la otra, y las platicas, besos y abraço, si, ala que preceden. Todo lo que es muy quotidiano. No diximos empero ociosamente, que son camino, porque si ovo interrompimiento por [p. 37, 27 pdf] proponer de no acabar el pecado, o por arrepentirse, o por otro respecto, y después otra vez lo quisiesse acabar, dos pecados distinctos serian. Aun que el dicho Cardenal no aviso esto, que es muy quotidiano. Tampoco se dixo sin causa (que solamente son camino para un solo pecado) porque si ellos de suyo son pecados, o se ordenan para ortos pecados, tantos serán ellos, quandos de suyo se son, o quantos los fines malos para que se ordenan: como quien va a matar a un hombre, y de camino hurta, roba, perjura, reniega, o ordena su comer, y beber, su andar, y hablar, no solamente para acabar el homicidio concebido, pero aun para adulterar, infamar, y hazer sacrilegios, y aun añadimos, que como este pecado por mucho tiempo continuado es muy mayor, que si fuera momentáneo, assi quien lo cometiere, y quisiere seguir nostro consejo, se dolerá, mas del, y confessara el tiempo, que poco mas, o menos en ello se ocupo.\n", "28 [18] Desto inferimos† la respuesta de la question que el muy reverendo señor, y padre fray Antonio de Zurara, a quien yo mucho devo y qioero por sus muy grandes virtudes, saber, prudencia, y otros muchos respectos, nos pregunto, del que mucho tiempo anda tras una mujer, con illigitos amores, si alcançar effecto, quantos pecados peca? Ca dezimos, que peca (almenos) tantos quantas vezes interrompe, y renueva aquella mala voluntad, que concibe sin meter, o querer meter por entonces obra exterior alguna para ello, y tantas vezes quantas interrompe aquella mala voluntad, y mala obra exterior, que para ello por entonces pone. De manera que si anduvo un dia, o una noche o parte dellos dándole músicas, o esperando oportunidad de hablarle, o servirle para este, mal din mientras que esta voluntad y obra exterior no se interrompieron, no abra mas de un pecado, aunque tanto mas grave quanto mas diuturno. Pero si acabada aquella obra exterior, que por entonces quiso hazer, entiende eon otros negocios que no son camino, o preámbulos para ello, y rotna otra vez ala mesma mala voluntad sola, o a la de hazer otra obra exterior semejante, o desemejante dela otra para alcançar su mal fin, hara otro pecado y tiendra tantos que confessar quantos interrompimientos, y renovaciones tales hizo, allende las malas voluntades absolutas, que tuvo sin meter obra exterior, y confessando el numero verisímil dellos, satisfará al piadosissimo señor, cuya misericordia y paciencia es milagrosa en sufruir nos estas muy atrevidas, y desuergonçadas continuaciones de sus ofensas gravissimas podiendo las con un solo ceño assi asperrimantamente casticar, como los castagara si nos castigaremos a nos mesmos antes*.\n", "29 ¶ El XVI que en una palabra puede el penitente confessar mil pecados mortales como diziendo mil vezes blaspheme, mil vezes perjure, mil vezes fornique, mil vezes perpuse de matar, mil vezes hize contra mi voto, o juramento, diez vezes aconseje que alguno perseverasse en pecado mortal, talc osa hize tantas vezes a fin de fornicar & c. Porque a esta confession no le galta nada, por dezir los todos, con tan pocas palabras pues son tan claras como lo provamos alibi (a : In princ. di Consideret. numero 110) despues de un cardenal (b : Caiet. tom. 2. de contri. q. 2).\n", "30 ¶ El XVII † [19] que la circunstancia del escandalo, en dos casos se ha de confessar necessariamente, segun todos, como alibi (c. In c. I § Animadvertere n. 5 de poen. d. 5) lo diximos. Porque enellos haze alguna de las tres cosas suso dichas. El primero quando el escandalo es formal, esto es quando alguna cosa se dixo, o hizo, con animo de provocar a otro a pecado mortal, y no solamente ha de confessar lo que dizo, o hizo con la dicha intencion, mas tambien ha de dezir el genero de pecado, al qual entendia provocar (d : per dicta supra in c. praecedenti). El segundo quando con obra buena, o indifferente de su casta, y mala en la especie o muestra, da occasion de pecar mortalmente. En otro tercero, son diversos los doctores, conviene saber, quando uno peca mortalmente en presencia de otros, sin intencion de los atraher apecar mortalmente. Ca Adriano (e : in 4. de consacr. confess. q. 4. co. 4), Mayor (f : In. 4. d. 38. quaest. 3), & Sylvestro (g : verb. Scandalum quaest. 3) sienten que si : Pero S. Thoh (2. Sec. q. 43. artic. 3) siente que no, en quanto dize, que puesto que mar gravemente peca el que peca en publico, que el que en secreto : pero esto, no lo passa en pecado de escandalo especial. Lo mesmo tiene Caiet (i. In summa verb. Scandaum). A nosotros parece nos lo que alli (k : f. § animadvertere. nume. 9) nos parecio, es a saber que la opinion de los primeros, proceda, quando el tal pecado se comete port al persona, o en presencia de tales, que probable, y verisimilmente tomaran nueva occasion de pecar : y la de S. Tho quando no se haze port al persona ni delante tales : como alli lo extendimos mas.\n", "31 ¶ El XVIII [20] es de notar que el que confessando se, olvido la circunstancia necessaria, no es obligado a confessar otra vez el pecado ya confessado, mas basta que confiesse la circunstancia sola. Exemplo, juro uno de no poner manos violentas en clerigo, de no hurtar, no fornicar, no blasphemar, y despues hirio, hurto, fornico, y blasfpemo, y confesso que avia hecho tales cosas, mas oluidose, que avia jurado de no hazerlas : no es necesasrio a este conessar los pecados otra vez, para confessar la circunstancia del juramento, mas basta que diga, que dos, tres, quatro, o tantas vezes quebranto juramentos licitos, y sanctos : o diga que hizo unas obras en si, o por circunstancias malas, contra lo que avia jurado como alibi (l : In d. c. Consideret. nume. 104) lo provamos mas largamente\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "import re\n", "\n", "# Initialize a list of lists, or two-dimensional list ...\n", "Editions = [[]]\n", "\n", "# ...with four sub-lists 0 to 3\n", "for i in range(3):\n", " a = []\n", " Editions.append(a)\n", "\n", "# Now populate it from our sourceTable\n", "sourceFile.seek(0) # in repeated runs, restart from the beginning of the file\n", "for row in sourceTable:\n", " for i, field in enumerate(row): # We normalize quite a bit here already:\n", " p = field.replace('¶', ' ¶ ') # spaces around ¶ \n", " p = re.sub(\"&([^c])\",\" & \\\\1\", p) # always spaces around &, except for &c\n", " p = re.sub(\"([,.:?/])(\\S)\",\"\\\\1 \\\\2\", p) # always a space after ',.:?/'\n", " p = re.sub(\"([0-9])([a-zA-Z])\", \"\\\\1 \\\\2\", p) # always a space between numbers and word characters\n", " p = re.sub(\"([a-z]) ?\\\\(\\\\1\\\\b\", \" (\\\\1\", p) # if a letter is repeated on its own in a bracketed\n", " # expression it's a note and we eliminate the character\n", " # from the preceding word\n", " p = \" \".join(p.split()) # always only one space\n", " Editions[i].append(p)\n", "\n", "print(str(len(Editions[0])) + \" rows read.\\n\")\n", "\n", "# As an example, see the first seven sections of the third edition (1556 SPA):\n", "for field in range(len(Editions[2])):\n", " print(Editions[2][field])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually, let's define two more list variables to hold information about the different editions - language and year of print:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:44:59.548092Z", "start_time": "2018-09-04T12:44:59.496928Z" }, "collapsed": true }, "outputs": [], "source": [ "numOfEds = 4\n", "language = [\"PT\", \"PT\", \"ES\", \"LA\"] # I am using language codes that later on can be used in babelnet\n", "year = [1549, 1552, 1556, 1573]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TF/IDF <a name=\"tfidf\"></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous (i.e. Solórzano) analyses, things like tokenization, lemmatization and stop-word lists filtering are explained step by step. Here, we rely on what we have found there and feed it all into functions that are ready-made and available in suitable libraries..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we build our lemmatization resource and \"function\":" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:03.381090Z", "start_time": "2018-09-04T12:44:59.682440Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "878594 PT wordforms known to the system.\n", "878594 PT wordforms known to the system.\n", "613097 ES wordforms known to the system.\n", "1854 LA wordforms known to the system.\n" ] } ], "source": [ "lemma = [{} for i in range(numOfEds)]\n", "# lemma = {} # we build a so-called dictionary for the lookups\n", "\n", "for i in range(numOfEds):\n", " \n", " wordfile_path = 'Azpilcueta/wordforms-' + language[i].lower() + '.txt'\n", "\n", " # open the wordfile (defined above) for reading\n", " wordfile = open(wordfile_path, encoding='utf-8')\n", "\n", " tempdict = []\n", " for line in wordfile.readlines():\n", " tempdict.append(tuple(line.split('>'))) # we split each line by \">\" and append\n", " # a tuple to a temporary list.\n", "\n", " lemma[i] = {k.strip(): v.strip() for k, v in tempdict} # for every tuple in the temp. list,\n", " # we strip whitespace and make a key-value\n", " # pair, appending it to our \"lemma\"\n", " # dictionary\n", " wordfile.close\n", "\n", " print(str(len(lemma[i])) + ' ' + language[i] + ' wordforms known to the system.')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, a quick test: Let's see with which \"lemma\"/basic word the particular wordform \"diremos\" is associated, or, in other words, what *value* our lemma variable returns when we query for the *key* \"diremos\":" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:03.392118Z", "start_time": "2018-09-04T12:45:03.382093Z" } }, "outputs": [ { "data": { "text/plain": [ "'dizer'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lemma[language.index(\"PT\")]['diremos']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we are going to need the stopwords lists:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:03.628776Z", "start_time": "2018-09-04T12:45:03.393120Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "690 PT stopwords known to the system, e.g.: ['ii', 'iii', 'iv', 'v', 'vi', 'vii', 'viii', 'ix', 'x', 'xii', 'xiii', 'xiv', 'xv', 'acerca', 'ad', 'adeus', 'agora', 'ainda', 'alem']\n", "\n", "690 PT stopwords known to the system, e.g.: ['ii', 'iii', 'iv', 'v', 'vi', 'vii', 'viii', 'ix', 'x', 'xii', 'xiii', 'xiv', 'xv', 'acerca', 'ad', 'adeus', 'agora', 'ainda', 'alem']\n", "\n", "756 ES stopwords known to the system, e.g.: ['cierta', 'ciertas', 'cierto', 'ciertos', 'cinco', 'claro', 'comentó', 'como', 'cómo', 'con', 'conmigo', 'conocer', 'conseguimos', 'conseguir', 'considera', 'consideró', 'consigo', 'consigue', 'consiguen']\n", "\n", "396 LA stopwords known to the system, e.g.: ['ac', 'ad', 'adhic', 'adhuc', 'ae', 'ait', 'ali', 'alii', 'aliis', 'alio', 'aliqua', 'aliqui', 'aliquid', 'aliquis', 'aliquo', 'am', 'an', 'ante', 'apud']\n", "\n" ] } ], "source": [ "stopwords = []\n", "\n", "for i in range(numOfEds):\n", " \n", " stopwords_path = 'DHd2019/stopwords-' + language[i].lower() + '.txt'\n", " stopwords.append(open(stopwords_path, encoding='utf-8').read().splitlines())\n", "\n", " print(str(len(stopwords[i])) + ' ' + language[i]\n", " + ' stopwords known to the system, e.g.: ' + str(stopwords[i][100:119]) + '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(In contrast to simpler numbers that have been filtered out by the stopwords filter, I have left numbers representing years like \"1610\" in place.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, later on when we try sentence segmentation, we are going to need the list of abbreviations - words where a subsequent period not necessarily means a new sentence:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:03.717344Z", "start_time": "2018-09-04T12:45:03.630782Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "229 abbreviations known to the system, e.g.: ['in', 'ind', 'ing', 'Io', 'iul', 'Iuli', 'iur', 'iust', 'IV', 'iv', 'IX', 'ix', 'J', 'K', 'l', 'L', 'li', 'lib', 'M']\n" ] } ], "source": [ "abbreviations = [] # As of now, this is one for all languages :-(\n", "\n", "abbrs_path = 'DHd2019/abbreviations.txt'\n", "abbreviations = open(abbrs_path, encoding='utf-8').read().splitlines()\n", "\n", "print(str(len(abbreviations)) + ' abbreviations known to the system, e.g.: ' + str(abbreviations[100:119]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we should find some very characteristic words for each segment for each edition. (Let's say we are looking for the \"Top 20\".) We should build a vocabulary for each edition individually and only afterwards work towards a common vocabulary of several \"Top n\" sets." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:25.521218Z", "start_time": "2018-09-04T12:45:03.718346Z" }, "collapsed": true }, "outputs": [], "source": [ "import re\n", "import pandas as pd\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "numTopTerms = 20\n", "\n", "# So first we build a tokenising and lemmatising function (per language) to work as\n", "# an input filter to the CountVectorizer function\n", "def ourLaLemmatiser(str_input):\n", " wordforms = re.split('\\W+', str_input)\n", " return [lemma[language.index(\"LA\")][wordform].lower().strip() if wordform in lemma[language.index(\"LA\")] else wordform.lower().strip() for wordform in wordforms ]\n", "def ourEsLemmatiser(str_input):\n", " wordforms = re.split('\\W+', str_input)\n", " return [lemma[language.index(\"ES\")][wordform].lower().strip() if wordform in lemma[language.index(\"ES\")] else wordform.lower().strip() for wordform in wordforms ]\n", "def ourPtLemmatiser(str_input):\n", " wordforms = re.split('\\W+', str_input)\n", " return [lemma[language.index(\"PT\")][wordform].lower().strip() if wordform in lemma[language.index(\"PT\")] else wordform.lower().strip() for wordform in wordforms ]\n", "\n", "def ourLemmatiser(lang):\n", " if (lang == \"LA\"):\n", " return ourLaLemmatiser\n", " if (lang == \"ES\"):\n", " return ourEsLemmatiser\n", " if (lang == \"PT\"):\n", " return ourPtLemmatiser\n", "\n", "def ourStopwords(lang):\n", " if (lang == \"LA\"):\n", " return stopwords[language.index(\"LA\")]\n", " if (lang == \"ES\"):\n", " return stopwords[language.index(\"ES\")]\n", " if (lang == \"PT\"):\n", " return stopwords[language.index(\"PT\")]\n", "\n", "topTerms = []\n", "for i in range(numOfEds):\n", "\n", " topTermsEd = []\n", " # Initialize the library's function, specifying our\n", " # tokenizing function from above and our stopwords list.\n", " tfidf_vectorizer = TfidfVectorizer(stop_words=ourStopwords(language[i]), use_idf=True, tokenizer=ourLemmatiser(language[i]), norm='l2')\n", "\n", " # Finally, we feed our corpus to the function to build a new \"tfidf_matrix\" object\n", " tfidf_matrix = tfidf_vectorizer.fit_transform(Editions[i])\n", "\n", " # convert your matrix to an array to loop over it\n", " mx_array = tfidf_matrix.toarray()\n", "\n", " # get your feature names\n", " fn = tfidf_vectorizer.get_feature_names()\n", "\n", " # now loop through all segments and get the respective top n words.\n", " pos = 0\n", " for j in mx_array:\n", " # We have empty segments, i.e. none of the words in our vocabulary has any tf/idf score > 0\n", " if (j.max() == 0):\n", " topTermsEd.append([(\"\", 0)])\n", " # otherwise append (present) lemmatised words until numTopTerms or the number of words (-stopwords) is reached\n", " else:\n", " topTermsEd.append(\n", " [(fn[x], j[x]) for x in ((j*-1).argsort()) if j[x] > 0] \\\n", " [:min(numTopTerms, len(\n", " [word for word in re.split('\\W+', Editions[i][pos]) if ourLemmatiser(language[i])(word) not in stopwords]\n", " ))])\n", " pos += 1\n", " topTerms.append(topTermsEd)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-30T12:30:15.482766Z", "start_time": "2017-08-30T14:30:15.474338+02:00" } }, "source": [ "# Translations?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maybe there is an approach to inter-lingual comparison after all. After a first unsuccessful try with [conceptnet.io](http://conceptnet.io), I next want to try [Babelnet](http://babelnet.org) in order to lookup synonyms, related terms and translations. I still have to study the [API](http://babelnet.org/guide)...\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, let's take this single segment 19:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:25.526157Z", "start_time": "2018-09-04T12:45:25.523180Z" }, "collapsed": true }, "outputs": [], "source": [ "segment_no = 18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then first let's see how this segment compares in the different editions:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:25.621560Z", "start_time": "2018-09-04T12:45:25.528164Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Comparing words from segments 18 ...\n", " \n", "Here is the segment in the four editions:\n", " \n", "Ed. 0:\n", "------\n", "6. ¶ A circunstancia do dia da festa (segundo algũs) [8] he necessaria na confissam : como o que em tal dia fornicou : porque fex obra servuil f. pecado mortal que he obra do diabo. Eam quebrou dous mandamentos . f. ho sexto & ho terceyro. Outros tem que nam he necessaria porque segundo S. Thomas 3 sententiarum : ho precepto de sanctificar ho sabado, entendido literalmente nam defende obra servil spiritual f. peccado Silvest. confessio. Ho doutor Navarro, de poenitentia d. 5. c. Consideret : tem que entonces a circunstancia do dia de festa se ha de confessar de necessidade : quando ho peccado fosse feyto a fim de fazer obra manual defesa em aquelle dia : ou quando fiz esse peccado mortal com [p. 27, 73 pdf] intençam & proposito de quebrantar a festa. Esta [9] opiniam parece razoavel & assaz secura.\n", " \n", "Ed. 1:\n", "------\n", "9. ¶ A VIII que a circunstancia do dia [9] da festa não se ha de confessar necessariamente, porque não faz mortal, o que sem ella não fora tal ne de outra specie, ne por outro respeyto : se não for obra servil defendida em festas : o qual não he ho peccado, segundo S. Tho. (m : in 3. d. 37 ar 5 q. z). Esta illaçã por muytos fundamentos confirmamos em outra parten (f. in d. c. consideret a. 17 usque ad 40) respondendo aos contraytos, seguindo nisso ao Cardenal Caietano (o : i. sede q. 7 z Se q iz art. 4 & in quod ii 10 & in suma verb dies festos) em muytas partes, & ao resoluto Sylvestre em outras (p : f. verb circunstantia & verb Dies dnica q. 6 & in Rosa aurea cas 63 ubi testat doctissimos quos que ordinis dominicani convenisse & haec opinione suscepisse) & a Jacopo Almayno (q : in 4 d 17 col z4) : posto que a openião commũ em dous casos se pode guardar, como ally dissemos (r : ubi supra a. 40) f. quando ho peccado se faz a fim, de fazer obra manual vedada naquelle dia, ou com tençao de quebrãtar a festa.\n", " \n", "Ed. 2:\n", "------\n", "16 ¶ El IX † que la circunstancia del dia dela fiesta, no se ha de confessar [9] necessariamente : Porque no haze mal, lo que sin ella no lo fuera tal ni de otra especie, ni por otro respecto, sino fuere obra servil, vedada en fiestas, qual no es pecado, Segun S. Tho. (f : in-3 sent. d. 37 art. 5. q. 2). Esta ilagion alibi (g : f. in. d. c. Consideret. a. n. 17 usque ad 40) por otros fundamentos confirmamos, respondiendo a otros tantos contrarios, seguiendo en ello al cardenal Caieta (h : 2. Sec. q. 7. & . 2. Sec. q. 122 art. 4. i. Quodl 10 & in summa verb dies festos) en muchas partes, y al resoluto Sylvestro en otras (i : f. verb. Cincunstantia. q. 3. & ver. Dies dominica. q. si. & . in aurea rosa. casu. 63 ubi testatur doctissimos quosque ordinis dominicani convenisse & hanc opinione suscepisse), y a Iacobo Almayno (k : in 4 dist 17 col. 24). Aunque la Comun opinion en dos casos se puede guardar como alli lo diximos (l: ubi supra n. 40), conviene a saber quando el pecado se haze a fin de hazer obras manual verdada en aquel dia, o quando se peca mortalmente, con intencion, y proposito de quebrantar la fiesta.\n", " \n", "Ed. 3:\n", "------\n", "16[9] Nono afferimus, quod circunstantia diei festi non est necessario confitenda, quoniam non facit esse lethale quod sine illa non est tale, neque alterius speciei talis, neque alio respectu nisi esset opus servile in festis prohibitum, quale non est peccatum iuxta S. Tho, in 3. sent dist. 37 ar. 5. q. 2. Hanc assertionem confirmavimus in dicto c. Consideret a nu. 17 usque ad 40 multis fundamentis respondendo totidem aliis oppositis, sequuti in eo Cardinalem Caietanum 1 sec. q. 7 & 2. sec. q. 122 ar. 4 & in quolib. 10 & in Summa verb Dies festos resolutumque Sylvestrum verb Circunstantia q. 3 & verb. Dies dominica, q. fin. & in Aurea rosa casu 63 ubi testatur doctissimos quosque ordinis Dominicani convenisse & hanc opinionem suscepisse & Iacobum Almaynu in 4 dist. 17 col. 24. Quamvis communis opinio posset in duobus casibus observari, sicuti diximus ibidem n. 40 nempe, quando eo sine peccatum committitur, ut in eo fiat aliquod opus manuale prohibitum in illo die, & quando intentione & proposito violandi festum, lethale crimen admittirur.\n", " \n", " \n", " \n", "Most significant words in the segment:\n", " \n", "Ed. 0:\n", "------\n", "[('festa', 0.34863692292301829), ('obrar', 0.33955916715483048), ('necessaria', 0.21405051755373969), ('pecado', 0.20815569459179345), ('circunstancia', 0.18815368130568774), ('mortal', 0.18815368130568774), ('spiritual', 0.12916072576503207), ('assaz', 0.12916072576503207), ('servuil', 0.12916072576503207), ('servil', 0.12916072576503207), ('sententiarum', 0.12916072576503207), ('secura', 0.12916072576503207), ('feyto', 0.12916072576503207), ('sanctificar', 0.12916072576503207), ('sabado', 0.12916072576503207), ('parecer', 0.12916072576503207), ('entendido', 0.12916072576503207), ('razoavel', 0.12916072576503207), ('eam', 0.12916072576503207), ('quebrar', 0.12916072576503207)]\n", " \n", "Ed. 1:\n", "------\n", "[('festa', 0.31994878266859988), ('cuestión', 0.28646118020658023), ('verbo', 0.23304120092157468), ('dies', 0.21329918844573326), ('ne', 0.17674413568897199), ('distinción', 0.15065523209893053), ('z', 0.14723123391972537), ('artículo', 0.14723123391972537), ('ubi', 0.13397746553979423), ('obrar', 0.11880574785762184), ('aurea', 0.10664959422286663), ('quos', 0.10664959422286663), ('convenisse', 0.10664959422286663), ('doctissimos', 0.10664959422286663), ('dominicani', 0.10664959422286663), ('usque', 0.10664959422286663), ('muytos', 0.10664959422286663), ('parten', 0.10664959422286663), ('resoluto', 0.10664959422286663), ('illaçã', 0.10664959422286663)]\n", " \n", "Ed. 2:\n", "------\n", "[('fiesta', 0.29452405651852892), ('cuestión', 0.26507029360770062), ('verbo', 0.22594871397313046), ('40', 0.19634937101235261), ('f', 0.18936767661285653), ('uve', 0.15894984674151075), ('sección', 0.15894984674151075), ('distinción', 0.15413557318341206), ('artículo', 0.13707252749825752), ('a el', 0.13138768411571633), ('obra', 0.12155032247066888), ('convenisse', 0.10911334512780292), ('dominicani', 0.10911334512780292), ('guardar', 0.10911334512780292), ('verdada', 0.10911334512780292), ('suscepisse', 0.10911334512780292), ('iacobo', 0.10911334512780292), ('doctissimos', 0.10911334512780292), ('hanc', 0.10911334512780292), ('testatur', 0.10911334512780292)]\n", " \n", "Ed. 3:\n", "------\n", "[('quaestio', 0.26318404821900721), ('dies', 0.24338573344629424), ('prohibitum', 0.20978391894792311), ('verbum', 0.17527166109296996), ('lethale', 0.17383131040523994), ('casus', 0.17383131040523994), ('opinio', 0.15280038260466319), ('section', 0.1448047949374944), ('opus', 0.13176945480408642), ('articulus', 0.12630454708817965), ('sine', 0.12630454708817965), ('quando', 0.11684777406197998), ('distinctio', 0.11684777406197998), ('facio', 0.11269606686848271), ('afferimus', 0.10489195947396156), ('usque', 0.10489195947396156), ('nono', 0.10489195947396156), ('quolibet', 0.10489195947396156), ('aureus', 0.10489195947396156), ('servile', 0.10489195947396156)]\n", " \n" ] } ], "source": [ "print(\"Comparing words from segments \" + str(segment_no) + \" ...\")\n", "print(\" \")\n", "print(\"Here is the segment in the four editions:\")\n", "print(\" \")\n", "for i in range(numOfEds):\n", " print(\"Ed. \" + str(i) + \":\")\n", " print(\"------\")\n", " print(Editions[i][segment_no])\n", " print(\" \")\n", "\n", "print(\" \")\n", "print(\" \")\n", "\n", "# Build List of most significant words for a segment\n", "\n", "print(\"Most significant words in the segment:\")\n", "print(\" \")\n", "for i in range(numOfEds):\n", " print(\"Ed. \" + str(i) + \":\")\n", " print(\"------\")\n", " print(topTerms[i][segment_no])\n", " print(\" \")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we look up the \"concepts\" associated to those words in babelnet. Then we look up the concepts associated with the words of the present segment from another edition/language, and see if the concepts are the same." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But we have to decide on some particular editions to get things started. Let's take the Spanish and Latin ones:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:25.678804Z", "start_time": "2018-09-04T12:45:25.622535Z" }, "collapsed": true }, "outputs": [], "source": [ "startEd = 1\n", "secondEd = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we can continue..." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:35.649041Z", "start_time": "2018-09-04T12:45:25.679806Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "For each of the 'PT' words, here are possible synsets:\n", " \n", "festa: bn:00008828n, bn:00001736n, bn:00036825n, bn:15095656n, bn:00033859n, bn:00040340n, bn:00060836n, bn:10812634n, bn:00016986n, bn:00016987n, bn:04048895n, bn:00060835n, bn:00034150n, bn:10733905n, bn:00008436n, bn:00034151n, bn:02506874n, bn:00017345n, bn:00008433n, bn:00071089n, bn:06971214n, bn:18397962n, bn:10858695n\n", " \n", "verbo: bn:00079779n, bn:00079778n, bn:00060722n, bn:00081546n\n", " \n", "ne: bn:00006824n, bn:03518732n, bn:00035065n, bn:03295403n\n", " \n", "z: bn:01487626n, bn:00032569n, bn:03226685n, bn:04052201n, bn:02173555n, bn:04959525n, bn:14056020n, bn:13940586n, bn:00682740n, bn:01436748n\n", " \n", "artículo: bn:00006137n\n", " \n", "ubi: bn:03316041n\n", " \n", "obrar: bn:00084350v\n", " \n", "aurea: bn:03183579n, bn:00034247n, bn:04307446n, bn:04508374n, bn:00844080n, bn:02427608n, bn:15419989n\n", " \n", "resoluto: bn:00101015a\n", " \n", " \n", " \n", " \n", " \n", "For each of the 'ES' words, here are possible synsets:\n", " \n", "fiesta: bn:16356131n, bn:00036825n, bn:02951623n, bn:17131948n, bn:01840936n, bn:00033860n, bn:03465309n, bn:17761686n, bn:02179813n, bn:00060835n, bn:00034150n, bn:17983073n, bn:17170376n, bn:00346838n, bn:00034100n, bn:03778418n, bn:00044416n, bn:00033859n, bn:00060836n, bn:00016986n, bn:14801008n, bn:00011467n, bn:00008436n, bn:00034151n, bn:00008434n, bn:17973136n, bn:00015479n\n", " \n", "cuestión: bn:00030951n, bn:03093952n, bn:00047208n, bn:00043267n, bn:00047690n, bn:00016404n, bn:00001734n, bn:00028426n, bn:00053868n\n", " \n", "verbo: bn:00079779n, bn:00079778n, bn:00903497n, bn:03340431n\n", " \n", "40: bn:00096004a, bn:00000113n, bn:02663913n, bn:02874439n\n", " \n", "f: bn:00006921n, bn:00032580n, bn:00438340n, bn:00726588n, bn:00025971n, bn:01757412n, bn:00032575n, bn:00032574n, bn:00061560n\n", " \n", "uve: bn:08330895n, bn:17767217n, bn:00079432n\n", " \n", "sección: bn:16489807n, bn:00070196n, bn:00023976n, bn:00070194n, bn:00070192n, bn:00075239n, bn:00027912n, bn:00027914n, bn:02309211n, bn:00070191n, bn:00074938n, bn:00062773n, bn:00774652n, bn:00072554n, bn:16919748n, bn:00074957n, bn:00070193n, bn:00074884n, bn:00005513n, bn:00071435n, bn:00073698n, bn:03039137n, bn:00070190n, bn:00002861n, bn:00181691n\n", " \n", "distinción: bn:00063703n, bn:00027767n, bn:03143940n, bn:00027765n, bn:00027766n, bn:00027534n, bn:00027644n, bn:00027769n, bn:00027036n, bn:03801415n, bn:00024357n, bn:00022249n, bn:00014485n, bn:00000704n\n", " \n", "artículo: bn:00021045n, bn:04102092n, bn:00254490n, bn:00060466n, bn:00057551n, bn:00058291n, bn:00006124n, bn:00062785n, bn:00000739n, bn:00006121n, bn:00054416n, bn:00006123n, bn:00006122n, bn:00019864n\n", " \n", "obra: bn:00062299n, bn:16901954n, bn:00042804n, bn:00025808n, bn:00021408n, bn:15338434n, bn:00062928n, bn:03118618n, bn:00062309n, bn:00013733n, bn:00011767n, bn:01110328n, bn:00062931n, bn:00028604n\n", " \n", "guardar: bn:00094404v, bn:00090043v, bn:00090040v, bn:00088697v, bn:00089368v, bn:00909251n, bn:00092208v, bn:00093281v, bn:00085714v, bn:00089102v, bn:00089103v, bn:00091983v, bn:00089407v, bn:00090229v, bn:00090039v, bn:00090044v, bn:00090045v\n", " \n", "hanc: bn:00780509n\n", " \n" ] } ], "source": [ "import urllib\n", "import json\n", "from collections import defaultdict\n", "\n", "babelAPIKey = '18546fd3-8999-43db-ac31-dc113506f825'\n", "babelGetSynsetIdsURL = \"https://babelnet.io/v5/getSynsetIds?\" + \\\n", " \"targetLang=LA&targetLang=ES&targetLang=PT\" + \\\n", " \"&searchLang=\" + language[startEd] + \\\n", " \"&key=\" + babelAPIKey + \\\n", " \"&lemma=\"\n", "\n", "# Build lists of possible concepts\n", "top_possible_conceptIDs = defaultdict(list)\n", "for (word, val) in topTerms[startEd][segment_no]:\n", " concepts_uri = babelGetSynsetIdsURL + urllib.parse.quote(word)\n", " response = urllib.request.urlopen(concepts_uri)\n", " conceptIDs = json.loads(response.read().decode(response.info().get_param('charset') or 'utf-8'))\n", " for rel in conceptIDs:\n", " top_possible_conceptIDs[word].append(rel.get(\"id\"))\n", "\n", "print(\" \")\n", "print(\"For each of the '\" + language[startEd] + \"' words, here are possible synsets:\")\n", "print(\" \")\n", "\n", "for word in top_possible_conceptIDs:\n", " print(word + \":\" + \" \" + ', '.join(c for c in top_possible_conceptIDs[word]))\n", " print(\" \")\n", "\n", "print(\" \")\n", "print(\" \")\n", "print(\" \")\n", "\n", "babelGetSynsetIdsURL2 = \"https://babelnet.io/v5/getSynsetIds?\" + \\\n", " \"targetLang=LA&targetLang=ES&targetLang=PT\" + \\\n", " \"&searchLang=\" + language[secondEd] + \\\n", " \"&key=\" + babelAPIKey + \\\n", " \"&lemma=\"\n", "\n", "# Build list of 10 most significant words in the second language\n", "top_possible_conceptIDs_2 = defaultdict(list)\n", "for (word, val) in topTerms[secondEd][segment_no]:\n", " concepts_uri = babelGetSynsetIdsURL2 + urllib.parse.quote(word)\n", " response = urllib.request.urlopen(concepts_uri)\n", " conceptIDs = json.loads(response.read().decode(response.info().get_param('charset') or 'utf-8'))\n", " for rel in conceptIDs:\n", " top_possible_conceptIDs_2[word].append(rel.get(\"id\"))\n", "\n", "print(\" \")\n", "print(\"For each of the '\" + language[secondEd] + \"' words, here are possible synsets:\")\n", "print(\" \")\n", "for word in top_possible_conceptIDs_2:\n", " print(word + \":\" + \" \" + ', '.join(c for c in top_possible_conceptIDs_2[word]))\n", " print(\" \")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:38.284604Z", "start_time": "2018-09-04T12:45:35.652049Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overlaps: {'bn:00036825n', 'bn:00060836n', 'bn:00034151n', 'bn:00016986n', 'bn:00034150n', 'bn:00079779n', 'bn:00079778n', 'bn:00008436n', 'bn:00033859n', 'bn:00060835n'}\n", "bn:00036825n: solemnidad (es)\n", "bn:00060836n: festa (pt)\n", "bn:00034151n: festival (es)\n", "bn:00016986n: celebración (es)\n", "bn:00034150n: festival (es)\n", "bn:00079779n: verbo (es)\n", "bn:00079778n: verbo (pt)\n", "bn:00008436n: banquete (es)\n", "bn:00033859n: fiesta (es)\n", "bn:00060835n: fiesta (es)\n" ] } ], "source": [ "# calculate number of overlapping terms\n", "values_a = set([item for sublist in top_possible_conceptIDs.values() for item in sublist])\n", "values_b = set([item for sublist in top_possible_conceptIDs_2.values() for item in sublist])\n", "overlaps = values_a & values_b\n", "print(\"Overlaps: \" + str(overlaps))\n", "\n", "babelGetSynsetInfoURL = \"https://babelnet.io/v5/getSynset?key=\" + babelAPIKey + \\\n", " \"&targetLang=LA&targetLang=ES&targetLang=PT\" + \\\n", " \"&id=\"\n", "\n", "for c in overlaps:\n", " info_uri = babelGetSynsetInfoURL + c\n", " response = urllib.request.urlopen(info_uri)\n", " words = json.loads(response.read().decode(response.info().get_param('charset') or 'utf-8'))\n", " \n", " senses = words['senses']\n", " for result in senses[:1]:\n", " lemma = result['properties'].get('fullLemma')\n", " resultlang = result['properties'].get('language')\n", " print(c + \": \" + lemma + \" (\" + resultlang.lower() + \")\")\n", "\n", "# what's left: do a nifty ranking" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actually I think this is somewhat promising - an overlap of four independent, highly meaning-bearing words, or of forty-something related concepts. At first glance, they should be capable of distinguishing this section from all the other ones. However, getting this result was made possible by quite a bit of manual tuning the stopwords and lemmatization dictionaries before, so this work is important and cannot be eliminated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Approach: Use Aligner from Machine Translation Studies <a name=\"newApproach\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In contrast to what I thought previously, there is a couple of tools for automatically aligning parallel texts after all. After some investigation of the [literature](https://www.zotero.org/groups/2198990/hyperazpilcueta/items/collectionKey/KQ84ZD4G), the most promising candidate seems to be [*HunAlign*](https://github.com/danielvarga/hunalign). However, as this is a commandline tool written in C++ (there is [*LF Aligner*](https://sourceforge.net/projects/aligner/), a GUI, available), it is not possible to run it from within this notebook.\n", "\n", "First results were problematic, due to the different literary conventions that our editions follow: Punctuation was used inconsistently (but sentence length is one of the most relevant factors for aligning), as were abbreviations and notes.\n", "\n", "My current idea is to use this notebook to preprocess the texts and to feed a cleaned up version of them to hunalign..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Coming back to this after a first couple of rounds with *Hunalign*, I have the feeling that the fact that literary conventions are so divergent probably means that Aligning via sentence lengths is a bad idea in our from the outset. Probably better to approach this with GMA or similar methods. Anyway, here are the first attempts with *Hunalign*:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:50.410521Z", "start_time": "2018-09-04T12:45:38.287583Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sentence-split of ed. 0:\n", "------\n", "1549 por\n", "<p>\n", "¶\n", "[1]Capitulo sexto das circunstancias.\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "1. Preguntelhe tambẽ as circunstancias necessarias quando ho penitente as nam sabe dizer.\n", "As quaes segundo sam Boãventura & Ricardo 4 dist 17 sam em tres maneyras :\n", "hunas que mudan em otra specie.\n", "Estas de necessidade se ham de confessar :\n", "assi como em ho furto, a circumstancia do lugar sacrago, ou da cousa sagrada :\n", "mudaa ē sacrilegio.\n", "Polo qual o que furtou algũa cousa de lugar sagrado .\n", "f. da igreja :\n", "nam nastaria dizer que fes hum furto :\n", "mas he necessario dizer que ho furtou da igreja :\n", "ou que a cousa era sagrada.\n", "posto que a uvesse tomado de lugar nam sagrado.\n", "Ho mesmo he do homicidio quando ho ouuesse cometido na igreja.\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "2. [2] Ou se cometeo peccado de fornicaçã com molher casada :\n", "nam satisfas dizendo que cometeo peccado de luxuria :\n", "mas he necessario declarar, que era com casada, religiosa ou parenta.\n", "Porque primeyro he adulterio :\n", "o segundo sacrilegio :\n", "ho terceyro incesto.\n", "E assi de todos os outros :\n", "como sam strupo, rapto &c. Onde se hum propos furtar :\n", "porque tenha parte cõ huna que he religiosa & como outra que he casada :\n", "de necessidade ha de confessa restas tres couss .\n", "f. ho furto & ho sacrilegio & adulterio.\n", "Porque posto que estas tres cousas sejam hũ acto soo interior da võtade :\n", "empero sam tres tres passamentos distintos, contra tres prohibiçoes .\n", "f. de furto :\n", "sacrilegio & adulterio :\n", "pelo qual convem em a confissao declarar cada hun per si Adr.\n", "4 de sacramento confessionis\n", "<p>\n", "[p. 24, 70 pdf] ¶\n", "Mudasse tambem a specie, quando ho proposito do peccar, se funda em causa final por outro particular precepto defesa, do que he a obra que faz :\n", "assi como o que furta porque fornique, porque o proposito de furtar, depende o da vontade de luxuriar que he ho fim porque foy incitado a furtar :\n", "o qual fim hep or outro special mandamento defeso do que he ho furtar.\n", "Polo qua lesta circunstantia, se ha de necessidade de confessar.\n", "Adriano .\n", "4. de sacrament. conf.\n", "<p>\n", "¶\n", "Outras circumstantias sam que agravam mortalmente na mesma specie, quaes sam aquellas, que de nam mortal fasem mortal & estas de necessidade se ham de confessar.\n", "Exemplo.\n", "Dizer mentira iocosa he peccado venial, ma se tanto ihe apras [4], & a ella se affeiçoa, que a nam deyxaria de dizer, posto que soubesse que era contra precepto de deos y da igreja, seria mortal.\n", "Item comer mais do necessario ao corpo, peccado he venial :\n", "mas se o faz porque seja incitado a luxuria, he mortal, ou dizer alcua palavra ociosa, porque provoque ao tal vicio.\n", "Mas quando estas circunstantias nam agravan mortalmente, nam sam de necessidade que se confessem, mas de perfeytam Alexan.\n", "lombardo.\n", "<p>\n", "3. Outras circunstancias sam que aliuiam & estas nam se devem confessar Comos e alcun confessasse, que peccou com hua molher, porque ella ho provocou.\n", "Ea causa he porque nam deve ho penitente em a confissam excusarse mas accusarse E entonces feria necessario confessalas segundo S. Boaventura :\n", "quando tanto diminuissem, que pasassem [p. 25, 71 pdf] em genero de venial.\n", "Como se alcum disesse que [5] comera carne na quaersma & calase a infirmidade, ou uvvesse furtado pera socorer ao pbre en estrema necessidade ou porque satisfaça aas preguntas do confessor, ou se temesse que por as calar tomaria o confessor ocasião de alcum mal.\n", "As taes circunstantias & outras semelhantes hã se de declarar ao confessor assi porque nam se escandalize da obra que ho penitente julgou ser boa :\n", "como tambem porque conheça o vulto de sua ouelha & lhe imponha penitencia segundo requere a culpa.\n", "Ma rtor.\n", "4. d. 17 q. 4.\n", "<p>\n", "4. ¶\n", "Outras circunstancias sam que agravam na mesma specie :\n", "quaes sam aquelas que fazem differir entre muyto & poco.\n", "Estas segundo S. Boaventura [6] & S. Thomas 4 dist XVII posto que seja louvavel ho confessalas, a opinam mays comuun & probavel, he que nam he de necessidade, como nam induça nova specie de peccado :\n", "como o que furta muyto ou poco, com tanto que seja notavel :\n", "porque assi o hun como ho outro procedẽ de huna mesma causa .\n", "f. de contratar a couda alhea contra vontade de seu dono :\n", "& por soo esta causa he peccado mortal :\n", "como bem ho declara & prova o doutor Navarro de peniten.\n", "d. 5. c. Consideret.\n", "<p>\n", "5. ¶\n", "Segundo Pedro de palude, posto que esta opiniam he mais comũa :\n", "mais seguro he confessalas quando notavelmente agravam :\n", "como o furtar cen cruzados, muyto he mais grave que furtar hum.\n", "E se ao sacerdote nam Ihe consta da quantidade nam sabe o que deve fazer, nem ho modo que ha [p. 26, 72 pdf] [7] de ter em impoer a restituyçã :\n", "ou se he caso do bis porque muytas vezes ordenam acerca destas cousas :\n", "ou se ho penitente pos mãos yrosas em clerigo :\n", "nam ihe labera ho sacerdote dar remedio :\n", "porque se a injuria foy leve, he caso do bispo :\n", "se foy enorme he do papa.\n", "<p>\n", "Polo qual Silvestr.\n", "confessio t. § 9 cree ser mortal em duos casos .\n", "f. quando ho sacerdote pregunta ao penitente.\n", "& sem causa nega.\n", "E quando sabe a quantidade de seu peccado & acinte ho casa :\n", "ou preguntado nam quer falar :\n", "porque sepone a perigo de nã receber conveniente mezinha pera sua suade :\n", "oua poer obstaculo porque nã receba a graça pola absoluiçã.\n", "E na maneyra sobredita se deve limitar o que diz Antisiodoro & Maior 4 d. 17. q. 4. f. que as circumstancias que agravã na mesma specie se ham de confessar .\n", "f. em caso :\n", "como se pode collegir da suma das cõnfissões li. 3 tit 3 q. 3.\n", "<p>\n", "6. ¶\n", "A circunstancia do dia da festa (segundo algũs) [8] he necessaria na confissam :\n", "como o que em tal dia fornicou :\n", "porque fex obra servuil f. pecado mortal que he obra do diabo.\n", "Eam quebrou dous mandamentos .\n", "f. ho sexto & ho terceyro.\n", "Outros tem que nam he necessaria porque segundo S. Thomas 3 sententiarum :\n", "ho precepto de sanctificar ho sabado, entendido literalmente nam defende obra servil spiritual f. peccado Silvest.\n", "confessio.\n", "Ho doutor Navarro, de poenitentia d. 5. c. Consideret :\n", "tem que entonces a circunstancia do dia de festa se ha de confessar de necessidade :\n", "quando ho peccado fosse feyto a fim de fazer obra manual defesa em aquelle dia :\n", "ou quando fiz esse peccado mortal com [p. 27, 73 pdf] intençam & proposito de quebrantar a festa.\n", "Esta [9] opiniam parece razoavel & assaz secura.\n", "<p>\n", "¶\n", "A circunstancia do dia deputado a jejuum, ou oraçam :\n", "nam he de necessidade confessata :\n", "salvo quando fizesse peccado com proposito de ho quebrantar como acima do dia da festa.\n", "Segundo Navarro, ubi supra.\n", "<p>\n", "7. ¶\n", "A circunstancia do lugar sacrado :\n", "posto que accidentalmente agrave mais todo peccado :\n", "nam he sempre necessario confessala :\n", "mas soomente quando a obra do peccado he dereytamente contra tra aa sactidade do lugar sagrado ou a sua immunidade :\n", "como he derramamento de sangue, ou semente humana :\n", "ou quando forzosamente se tiram os que a ella se acolhem :\n", "porque [10] se contraria a sua immunidade :\n", "& casos semelhantes.\n", "Caietano 2. secunde q. 17 art. 1 e Navarro de penitentia dist. 1 c. Consideret.\n", "De maneyra que aquelles que na igreja cometem peccado de soberba/ perjuro/ ou gula &c. nam he necessario que confessem a circunstancia do lugar sagrado :\n", "nem quando estam dentro da igreja :\n", "& defesam matar, ou ferir ou ter ajuntamento com algua.\n", "Com tanto que estas cousas as nam deseje come ter em lugar sagrado :\n", "Mas se desejasse estas cousas fora de lugar sagrado :\n", "& de as poer por obra em lugar sagrado :\n", "seria necessario confessar a circunstancia do lugar.\n", "Navarro.\n", "ubi supra.\n", "<p>\n", "<p>\n", "8. ¶\n", "A circunstancia da propria pessoa :\n", "algũas vezes acrecenta ho peccado ceterios paribus assi como o que he contituydo em dignidade :\n", "mays pecca que aquelle que ho nam he dist. 40 cap. Homo.\n", "[p. 28, 74 pdf] Eho prelado mais que ho subdito .\n", "c. Precipue.\n", "ii. q. [11] 3 mays o sabedor que ho ignorante, & mais o que ama a ignorancia porque peque mais livremente que o que pecca sabendoo, & mays ho que he bõ, que o que he menos boõ, ou mao :\n", "mas esta circunstandia, posto que o manifestala ao confessor, seja pueitoso, nã he de necessidade :\n", "porque nam muda de hũa specie, em outra, salvo se ho peccado fosse feito contra seu voto ou stado videlicet si religiosus fornicetur :\n", "porque entonces passa em sacrilegio, mas ho contrayro he se blasphema porque entonces soo de peccado de blasfemia seria convexido como qualquer outro secular.\n", "Caieta.\n", "2. q. 7. art. i & Navarro, ubi supra.\n", "<p>\n", "9. ¶\n", "A circunstantia do peccar contra sua consciencia entonces fomente he necessaria, quando a obra que fez por nenhũa ley era peccado senam porque foy feyta contra sua conscientia erronea.\n", "Navarro ubi supra.\n", "<p>\n", "10. ¶\n", "O numero dos peccados nã he circunstantia [12] mas acrecentamento de peccado a peccado & por tanto he necessario que se declare na confissam ho numero delles :\n", "porque a frequentaçam he circunstantia, que constitue novo peccado.\n", "Demaneyra que nao pasta dizer pequey muytas veçes neste peccado porque esta diçam muytas veçes :\n", "tanto se verifica de dez como de cento\n", "<p>\n", "11. & por isso o peccador deve exprimir o numero certo se ho sabe.\n", "Assi como isto fix tantas vezes.\n", "Rosella.\n", "Confessio 2§3.\n", "Onde peccaria mortalmente, o que por vergonha ou hipocresia calasse algũa cousa do numero das vezes que tem na memoria.\n", "E se por lata culpa, alguũ [p. 29, 75 pdf] [13] se ham lembra do numero dos peccados assi como que nenhũa cousa cuidou, pore ser que a confissam ihe nam aproveite, & peque mortalmente de novo :\n", "faziendo irreverentia ao sacramento que anulla.\n", "Silvest.\n", "confesio i§2.\n", "Mas se ham sabe ho conto determinadamente, nem ho tem na memoria ha de lançar conta, quantas vezes ao dia podia peccar ou na somana :\n", "& assi o declarar como o tem na memoria.\n", "<p>\n", "Mas quando alcun fosse tam rudo, & simprez que nam podesse em algũa maneira das sobreditas, specificar as vezes que pecou :\n", "podesse ter por descarrego dos confessores a opinam do Archidiacono .\n", "f. que basta que diga que por tanto tempo perseverou em tal peccado :\n", "assi como ha huũ anno que estaa amancebado.\n", "Angelo, ou se he molher publica, & converdita a penitencia se confessa, basta que diga [14], que tantos annos perseverou nsquelle estado, nam negando seu ajuntamento a solteiros & casados & sacerdotes.\n", "Caietano de confessio q. 3 inparius opusculis.\n", "E quando nem assi pode declarar o numero, basta que diga, que muytas vezes peccou.\n", "Segundo Archidiacono .\n", "6. q. 1. imitare :\n", "& alegao Angelo.\n", "interroga.\n", "23. mas isto nam he seguro.\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "12. ¶\n", "A circunstancia do escandalo em tres casos he necessaria confessarse.\n", "O primeiro quando o scandalo he formal, como quando alguna cousa disse, ou fez, com proposito de provocar a outro a peccado mortal.\n", "Enam somente ha de confessar o que disse, ou fez com a dita entençam mas tambem ha de dizer ho genero do peccado, ao qual o entndia provocar.\n", "O segundo quando com sua obra que de seu genero [p. 30, 76 pdf] he boa ou indifferente :\n", "mas tem specie de mal he a outro ocasiam de cayda.\n", "Nestes sous casos to [15] dos concordan Terceyro quando alcun pecca mortalmente em presença de outros :\n", "posto que nam con intençam de os atraer a peccar mortalmente/ segundo Adriano de sacramento confess Maior 4. dist 33. q. 3. Silvest.\n", "scandalum q. 3. Mas S, Thom.\n", "secunda decundae q. 43 ar. 3 diz que posto que mais gravemente pecca o que pecca em publico que em secreto :\n", "com todo nam passa em peccado de escandalo special :\n", "ho mesmo tem Caietano in summa scandalum.\n", "Estas suas opiniones concordando ho doutor Navarro de penit. d. 5 Consideret.\n", "Diz que quantas vezes ho tal peccado se comete por tal pessoa:\n", "& em presenza de taes, que prebavel & verusimilmente tomaran nova ocasiam de cayda :\n", "tantas vezes de necessidade se ha de confessar.\n", "Mas quando ho [16] peccado em presenza de taes he cometido , que nã he verisimil :\n", "que polo tal peccado tomaran nova occasiam de cayda :\n", "entonces esta circunstantia nam he de necessidade confessarse.\n", "Assi como quando algun comete peccado de fornicaçam, em presença dos que estavan aparelhandos pera fornicar, ou fere em presença de algũs que sam de mãsa condiçam :\n", "dos quaes se cree que da tal percussam nã feram movidos a ferir:\n", "& casos semelhantes.\n", "De maneyra natureza occasiã a outro de cayda:\n", "mas também que a qualidade o a quelles em cuja presença se faz segundo iuyzo de varã prudente seja occasiã de cayda:\n", "posto que nam cayam.\n", "[p. 31, 77 pdf].\n", "<p>\n", "13. ¶\n", "Se o que se confessou, foy esquecido de confessar a corcunstancia necessária:\n", "nã he obrigado a confessar [17] outra vez ho pecado:\n", "mas basta que confesse somente a circunstância.\n", "Assi como se avia jurado de nã poer mãos yrosas en cledigo, nã furtar:\n", "nã fornicar, nã blasfemar rc ferio/ furtou/ fornicou, & blasfemou despoys disso:\n", "& confessou que avia feito taes cousas mas es que ceo lhe que avia jurado de nam fazer tal despois lembralhe do juramento:\n", "nam he necessário confessar os pecados outra vez, os quês ja confessou, por que confesse a circunstância do juramento, mas basta que diga [18] que duas vezes ou três ou quatro três passou ho iuramento lícito & sancto:\n", "ou diga que fez hũa obra em si maa, contra o que avia iurado ou fez algũa obra bõa:\n", "mas por rezam da circunstância maa, assi como se fez esmola por vaãgloria nam he necessário na confissam dizer que fez esmola por aquelle fim:\n", "mas basta que diga que fez hũa obra de género de bem, por vaãgloria constituindo a hi seu fim.\n", "Navarro c. Consideret in glossa verbo Oportet de penit. d. 5\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "Sentence-split of ed. 1:\n", "------\n", "1552 por\n", "<p>\n", "¶\n", "Capitolo VI. Das circunstancias.\n", "<p>\n", "<p>\n", "<p>\n", "1. [1] Pera fundamento disto diremos :\n", "lho primeiro, que a circumstancia do peccado, segundo a mente de S. Tho P & outros, he hum accidente daquilo, que he peccado.\n", "Dissemos (he accidente) porque nenhũa circũstãcia da obra, he a substancia della.\n", "Dissemos (da quilo que he peccado) & nã do peccado :\n", "porque muytas vezes a obra em si não he peccado, & pola circũ se faz peccado :\n", "& como então ella he aquilo, em que consiste ho peccado, não he tãto accidente do peccado, quanto da quilo que he peccado :\n", "segundo que ho declaramos em outra parte (q. in d. c. Consideret n. 3), seguindo a Alex.\n", "de Ales (r in. 4 pt. q. 77 ar. z. co. la.\n", "z).\n", "<p>\n", "2. ¶\n", "[2] Ho II. Que a circunstancia se parte em sete species, que se conte nem aquelle verso :\n", "Quis, quid, ubi, quibus auxiliis, cur, quomodo, quando :\n", "Referido por S. Tho.\n", "s (in d. q. 7. ar 3) .\n", "Quem, Que, Onde, Com que ajudas, Porque, Em que maneira, Quando.\n", "O qual verso temos por melhor, que ho de Paudano (t. in. 4. d. 16. q. 3. art. 1. u), como ho dissemos em outra parte (u in princi. d. e. Consideret n. 4) :\n", "porque nelle se acrecenta Quotiens, quantas vezes :\n", "que denota ho numero :\n", "o qual não he circunstancia, se não multiplicação de peccado, como aly ho dissemos (x. in. d. n. 4).\n", "[p. 33, 41 pdf]\n", "<p>\n", "3. ¶\n", "[3] Ho III que destas circũstancias :\n", "todas, & soos aquellas, se hão de confessar de necessidade, que fazen que as obras cujas são, sejão peccados mortae :\n", "ou as que sam mortaes de hũa especie, que ho sejão da outra :\n", "ou o que he mortal mor hum respeito, ho seja tãben por outro :\n", "ora muden as obras de hũa especie em outra, ora não, segundo a comũ opiniam, que copiosamente em outra parte (x. f. in d. Consideret a n. 5) tractamos :\n", "& soos e todas aquellas circunstancias são desta qualidade (segũdo S. Tho (y. in d. d. 10 q. 6 ar. z. q. 3)) que alen da malicia da mesma obra, repugnã especialmente aa rezão segundo Scoto :\n", "as quaes se defendem por diversos, & especiaes mandamentos.\n", "Dissemos (especiaes) porque não abasta, que sejã taes :\n", "que hũ delle; se incluya no outro :\n", "quaes são, a ley que defende todo mal, & a que defende homicidio, como ho provamos en outra parte (z :\n", "in d. c. consideret n. 74).\n", "O qual todo, polas seguintes illaçones se decrara.\n", "<p>\n", "¶\n", "A primeyra he, que não se hão de confessar as circunstancias, de quãdo ho peccado se fex .\n", "f. se aa segunda feira, se aa terça :\n", "se no campo, se na vinha, se co a mão direyta, se co a esquerda :\n", "porque per estas não se faz algũa das tres cousas acima ditas :\n", "Pois não se faz mortal, o que sem elas ho não fora, nem de outra especie mortal, nem por outro respeito.\n", "<p>\n", "4. ¶\n", "[4] A II que o que furtou algũa cousa sagrada, ou de lugar sagrado :\n", "não cumpre dizendo, que furtou :\n", "Porque ha de confessar, que furtou em lugar sagrado, ou cousa sagrada de lugar não sagrado :\n", "porque esta circunstancia faz, que o que era peccado mortal de hũa especie, ou por hũ respeyto, ho sela dovira, ou por outro :\n", "por ser especialmente defendido por outra ley diversa, da que defende ho furto .\n", "f. que nenhũa cousa sagrada, nem de lugar [p. 34, 42 pdf] sagrado se furte.\n", "<p>\n", "<p>\n", "5. Do mesmo he do homicidio, & fornicaçao cometida em lugar sagrado :\n", "porque po resta circũstãcia se fazen mortaes outra specie :\n", "ou por outro respeito, por serem specialmente defendidos por ley humana (a :\n", "e proposuisti d. confe. cecie c. Eccesiis de consecr. d. 1)\n", "<p>\n", ".\n", "¶\n", "[5] A III que quem conhece carnalmente molher casada, ou parenta, ou religiosa :\n", "não satisfaz confessando somente, que tuve parte con molher :\n", "porque ha de decrarar, que era con casada, religiosa, ou parenta :\n", "porque non primeiro caso, he adulterio corporal, no ii sacrilegio, ou adulterio spiritual, no iii incesto :\n", "& por conseguinte ao mortal de hũa specie ou por hũ respecto, ho faz mortal de outra specie, ou por outro respecto.\n", "E se propos de furtar, para ter parte com hũa que he religiosa, & con outra que he casada :\n", "ha de confessar furto & adulterio, & sacrilegio :\n", "porque ainda que estas tres cousas sejão hũ auto soo interior da vontade :\n", "san poren peccado mortal, por tres respectos diversos :\n", "pois por tres repugna aa rezão, & por tres leys diversas speciaes estaa veadado.\n", "<p>\n", "¶\n", "A IIII que toda circunstancia de fim defendido por outra ley special diversa, da que veda o auto principal, se deve confessar contro a circunstancia do que furta para fornicar, matar, ou ferir a outro.\n", "<p>\n", "¶\n", "[6 ] A V. que mente pera dar prazer, sem dãno doutro :\n", "que he mintira jocosa, & peccado venial :\n", "com tal tençao, que nao a deixaria de dizer, posto que soubesse que era mortal, he obrigado a confessar aquella circunstancia :\n", "porque con ella he mortal & sem ella não.\n", "<p>\n", "6. A VI. que nenhũ he obrigado a confessar as circunstancias, que alivião o peccado :\n", "& assi o que peccou com hũa molher, porque ella ho provocou :\n", "nam he obrigado a confessar, que ella ho provocou, pois diminue ho peccado (b :\n", "c. significavit de penit & re quo in hoc dixit sing.\n", "Felinus in ea.\n", "Dilecti de except. col. ante penui.\n", "& l. si adulterium.\n", "c3.\n", "incestu § imperatores ff. ad Il iul. de adult. quam in hoc aiebat ing. Ia fon. in l. vt vim.\n", "ff. de iust & iur col. z. aptissimus tamen ad perpesitum tex in d. c. Consideret verb. tentatione) :\n", "antes a deve calar (segundo a Comũ, que nos em outra parte seguimos : )\n", "c?\n", "(c. .\n", "f. in princ. d. c. Consideret n. 5) [p. 35, 43 pdf] porque não deve ho peccador escusas na confissam mas accusarse Posto que agora, milhor nos parece ho contrario :\n", "porque dado caso, que a não case, sufficientemente se accusa lançando a si mesmo a culpa que ten, sem tirar nem poer mais.\n", "Deve porem confessalas, segundo S. Bonaventura (d. in 4. d. 16. c), & a Comũ, que ally seguimos, quando tanto aliutão, que de mortal, ho faz que não seja peccado, ou soomente venial como a circunstancia da grave e fermida de aliuia o comer carne na coresma & quando ho confessor lho pregunta :\n", "& quando temesse, que polas callar, tomaria ho confessor occasion de algũ mal (e :\n", "Arg. c. Nialn de praescriptio).\n", "<p>\n", "7. ¶\n", "[7] A VII que posto que cousa de louuar, confessar as circumstancia , que agravão ho peccado, fazendoo de pequeno grande ou de grande mayor, poren a openião mais commũa, & provavel he, que não he necessario, quando aquelle acreentamento não he causa, que ho venial se faça mortal, ou de outra specie, ou por outro respecto :\n", "como copiosamente allegamos, & provamos em outra parte (f :\n", "in d. c. Consideret), apartandonos de todo em todo do parecer de Marsilio (g. in lib. 4. q. 12. ar. 1 col4) que limitava, que esta Commũ não ovvesse lugar quando a circunstancia muy crara, & notavelmente acrecenta ho peccado, polas muytas & efficaces consideraçones, que alli (f :\n", "in d. prin. pag 36 naz. )\n", "escrevemos.\n", "<p>\n", "8. † [8] Parece nos poren, que a Comũ nã ha lugar, quãdo ao menos por constituçones synodaes, aquelle argumento do peccado ha causa, que seja reservado :\n", "ou que a restituoção se faça doutra maneira :\n", "ou que tenha annexa excomunhão :\n", "ou que a excomunhã ãnexa seja Papal :\n", "como a excomunhão da ferida pequena do clerico he do bispo (g :\n", "c. Pervenit defent.\n", "exco.\n", "ubi rex tus singularis) :\n", "& se he grãde, he do Papa (h :\n", "c. Si qs suas dente 17 q. 4)\n", "<p>\n", "ou quando preguntado polo confessor se cala sen outra algũa causa, ponendose (ao menos) en perigo dalgũ inconveniente [p. 36, 44 pdf] como ho disse bem Sylvestre (i :\n", "verb. Confessio i. q. 9 k) :\n", "ainda que Joam de Friburgo, autor da Summa confessorum, não ho diz onde elle ho allega (k f. lib. 3. t. 33. q. 11) :\n", "nem ainda onde disto tracta (l :\n", "f. lib. 3. tit. 34. q. 81).\n", "<p>\n", "9. ¶\n", "A VIII que a circunstancia do dia [9] da festa não se ha de confessar necessariamente, porque não faz mortal, o que sem ella não fora tal ne de outra specie, ne por outro respeyto :\n", "se não for obra servil defendida em festas :\n", "o qual não he ho peccado, segundo S. Tho.\n", "(m :\n", "in 3. d. 37 ar 5 q. z).\n", "Esta illaçã por muytos fundamentos confirmamos em outra parten (f. in d. c. consideret a.\n", "17 usque ad 40) respondendo aos contraytos, seguindo nisso ao Cardenal Caietano (o :\n", "i. sede q. 7 z Se q iz art. 4 & in quod ii 10 & in suma verb dies festos) em muytas partes, & ao resoluto Sylvestre em outras (p :\n", "f. verb circunstantia & verb Dies dnica q. 6 & in Rosa aurea cas 63 ubi testat doctissimos quos que ordinis dominicani convenisse & haec opinione suscepisse) & a Jacopo Almayno (q :\n", "in 4 d 17 col z4) :\n", "posto que a openião commũ em dous casos se pode guardar, como ally dissemos (r :\n", "ubi supra a.\n", "40) f. quando ho peccado se faz a fim, de fazer obra manual vedada naquelle dia, ou com tençao de quebrãtar a festa.\n", "<p>\n", "¶\n", "A IX que a circunstancia do dia de jejuũ, ou de oração, não se ha de confessar necessariamente, se não quando se pecca com proposito de ho quebrãtar :\n", "porque nã faz algũa das ditas tres cousas, segũdo em outra parte ho provamos (s :\n", "f. in d. c. Consideret n. 32 vers. sic. Ad primum)\n", "<p>\n", "10. ¶\n", "A X que a circunstãcia do lugar sagra [10] do, posto que accidentalmente agrave todo peccado :\n", "porem não se ha de confessar necessariamente, salvo quando a obra do peccado he dereytamente contra sua sanctidade, ou sua immunidade :\n", "modo he ho derramamendo de sangue, ou semente humana :\n", "ou quãdo forçosamente se tirão delle os que ally se acolhem :\n", "Porque nestes, ho peccado de si mortal, por hũ respeyto :\n", "se faz mortal por outros (s :\n", "Arg. c. Ecclesiis de consecr. d. 1. c. Proposuisti de conseer eccle.\n", "c. eod. tit. lib. 6):\n", "ou que não era peccado, po nã mais de venial de si po resta circunstancia se faz mortal :\n", "como a copula ãtre ho marido & molher sem causa justa [p. 37, 45 pdf] avida nelle, seria mortal, que nos outros lugares o não feria.\n", "<p>\n", "11. ¶\n", "A XI [11] que deste precedente se seque que aquelles que na igreja cometem peccado de soberba, perjuro, ou gula &c. não hão de confessar de necessidade a circunstancia do lugar sagrado :\n", "nem os que estãdo nelle, desejã matar, ou ferir, ou fornicar :\n", "com tãto, que o não desejem cmeter, nem ppoer por obra nelle :\n", "porque se isto desejassem (ainda que este evessem fora de sagrado) ferião obrigados a confessar a circumstancia do sacrilegio, que nisso cometem, como aly (t :\n", "ubi supra n. zi. )\n", "ho dissemos.\n", "<p>\n", "12. ¶\n", "A XII que posto que a circunstancia da propria [12] pessoa, algũas vezes acrecenta ho peccado, sendo as outras cousas yguaes :\n", "assi como o que ten dignidade, mais pecca, que aquelle que a não temu (u :\n", "c. Ho.\n", "40. d) :\n", "& ho prelado mais que o subdito (x. c. Preciput ii. q. 3) :\n", "& ho sabedor mais que ho ignorante (y :\n", "Sicud d. isgnu, de homicid) & mas o que ama a ignorantia (y :\n", "c. penult.\n", "37 d. ), porque peque mais livremente, que o que pecca sabendo :\n", "& mais ho boo, que ho mao :\n", "& mais ho melhor, que ho menos boo :\n", "porem ainda que seja provetioso confessa resta circunstancia, não he poren necessario comũmente, nem faz de venial mortal :\n", "ne de mortal de hũa specie, mortal de outra :\n", "ne de mortal por hũ respeito, mortal por outro.\n", "Mas sy, quãdo isto se fizesse :\n", "como seria, se peccasse contra seu voto, ou estado votado .\n", "f. se ho religioso fornicasse :\n", "necessario seria confessar a circũstancia de pessoa, porque entã passaria em sacrilegio ou adulterio spiritual, & faz huã das ditas tres cousas :\n", "e que nã se faz, quãdo ho religioso blasfema, ou faz outro peccado, que uã seja contra seus votos, ou regra prometida; como despois de Caietano (z :\n", "1. Sec. q. 7. ar. 1) ho dissemos em outra parte (a :\n", "ubi supra n. 50).\n", "Porque a circunstancia de religião comũmente não [p. 38, 46 pdf] faz mortal o que de seu não he mortal, nem de outra specie, o que de si he doutra.\n", "<p>\n", "13. ¶\n", "A XIII Que a circunstãcia do peccar contra sua [13] consciencia :\n", "entam somente he necessaria, quando a obra que fez, por nenhũa ley era peccado, se não porque foy feyta contra sua consciencia erronea :\n", "Porque entam somente faz hũa das ditas tres cousas, & não outras vezes; como novamente ho decraramos em outra parte (b :\n", "f. ubi supra n. 52 ed seq. )\n", "<p>\n", "14.\n", "¶\n", "A XIIII que ho numero dos peccados, não he corcunstancia, mas addição ou acrecentamento de peccado a peccado.\n", "Porque a frequencia constuve novo peccado acerca do qual dissemos em outra partec (ubi supra, n. 41), que nã abasta dizer :\n", "Pequey muytas vezes neste peccado :\n", "Pois esta dição muytas vezes tãto se verifica em dez, & ainda em eous, como en cento (d :\n", "in c. Monasteria do vita & hon & Geor.\n", "in prunei Clement.\n", "saepe de verbo signifi) :\n", "porque ho Arcediago (e :\n", "in c. imitare 6 q. 1 & in princip. d. c. Consideret) teve que si ao qual nã quis reprovar o Ango (f :\n", "verb Confessio 1. § z3 )poren reprovouho ho Cardenal Alexandrino (g :\n", "in eod. e. imitare).\n", "<p>\n", "15.\n", "† Dissemos [14] tamben que ho peccador deve decrarar ho numero certo se ho sabe dizendo :\n", "Isto fiz tantas vezes.\n", "E se não sabe o conto certo, ha de lançar contra, quantas vezes no dia, ou na somana, ou no mes, pouco mais, ou menos peccou :\n", "& dizer o conto certo, que lhe parece mais verissimel :\n", "porque peccaria mortalmente, o que por vergnoha ou hypocresia calasse algũa cousa do numero das vezes, que Ihe lembra :\n", "& ainda nenhũa causa valeria a confissão, se por sua lata culpa se não lembra, por não aver cuydado nenhũa cousa de seus peccados podendo ho fazer.\n", "<p>\n", "16.\n", "† Dissemos [15] tamben, que abastaria sem algũ numero decrarar sufficientenente seu estado :\n", "como se he molher pubrica, que por espaço de dex annos perseverou naquelle peccado de fornicação :\n", "tam aparelhada [p. 39, 47 pdf] pera clerigos, religioso, & casados, como pera solteiros :\n", "& despois de convertida se confessasse & disesse, que tanto tempo perseverou no proposito sobredito, como despois de Caietano (h :\n", "q. 3 de cons. tell per quo pondero esse text. singularem, i d. c. Consideret ibi quandum perseveraverit & deffleat quod perseveranter peccavit) nos ho concluymos em outra parte (h :\n", "in d. c. consideret), ponderando a hiho texto pera isto singular.\n", "Acrecentamos, que aquelle que deixou por espaço de hũ anno de rezar, compriria com dizer :\n", "Deixey de rezar hum anno.\n", "<p>\n", "17.\n", "Dissemos que se acrecenta ho numero dos peccados, todalas vezes que ho peccado, ou a vontade de peccar interpolada se itera, segundo Joam Andr (i :\n", "in regu.\n", "Delictum.\n", "col. pen.\n", "de regul. iur. ii. 6 in mercuriali) :\n", "o qual nam ha lugar, quando a mesma obra exterior não se interrompe :\n", "como acontece quando algum vay a matar a outro, & caminhando todo ho dia :\n", "ora cuydando nisso, ora naquillo :\n", "porque este não pecca naquillo mais de soo hum peccado, segundo Caietano (k :\n", "in lib. 17 respond.\n", "respons. 15), a quem aly seguimos (l :\n", "in d. c. Consideret pa.\n", "53) & ponderamos hũ texto (m :\n", "c. Cum pro causa de senten.\n", "exco. )\n", "pera isso muy conveniente :\n", "do qual inferimos, que aquella conclusam f. multiplicarse ho peccado tantas vezes quantas se itera :\n", "se ha de entender, quando ho peccado despois de hũa vez acabado, se itera :\n", "como quando a fornicaçao por obra comprida, se comete outra vez.\n", "<p>\n", "18.\n", "[16] † Quando porem se itera antes que per obra se acabe, nam se multiplica :\n", "posto que (ourãdo a obra exterior) muytas vezes a voluntade interior se renove :\n", "nem ainda polo contrairo, se rendo a mesma vontade, a obra exterior se multiplique antes que ho delicto se acabe, como mais compridamente ho provamos aly (n :\n", "ubi supra).\n", "Donde tambem inferimos, ser soo hum peccado todolos autos exteriores, & interiores que somente sam caminho pera hum soo peccado, posto que sejam interpolados :\n", "quaes sam os passos [p. 40, 48 pdf] ho aparelhar do cavalo, a lança & outras armas con os defejtos interpolados, & renovandos diversas vezes, falando comendo, & dormindo, do que vay matar a outro daqui a dez legoas.\n", "Porque posto que considerados, como caminho, & partes do peccado, que con todos elles se ha de acabar :\n", "não fazen mais de hũ :\n", "como tãben as pedras, colũnas, & traves, & barrotes de hũa casa, que sam muytas cousas cada hũa dellas por si :\n", "porem todas ellas consideradas, como partes da casa não fazem de hũa (e :\n", "Eum qui f. de viue).\n", "<p>\n", "19.\n", "† Nã dissemos [17] porem ociosamente no corollario (quando sã caminho para outra cousa) porque se ouue intervalo, por mudança de proposito, pera não acabar ho peccado :\n", "ou por se arrepender delle, ou por outro respeyto :\n", "& despois outra vez ho quisesse acabar, sertão dous peccados distinctos.\n", "Disto inferimos a rezam, porque quem peccou hũa vez como hũa, não he obrigado confessar as palavras, betjios, abraços, & outros autos, ou preambulos immediatos da obra da quella vez primeira.\n", "E se duas vezes teve parte com ella (ainda que fossem immediatas) obrigato feria a vonfessalas, porque a hũa das duas copulas, nã he caminho, ne preambulo, que se ordena pera a outra.\n", "Mas as falas, betjos, & abraços, que a ella precederão si o que todo he muy quotidiano.\n", "<p>\n", "<p>\n", "20.\n", "¶\n", "Ho XV [18] que em hũa soo palavra pode o penitente confessar mil, cento, trinta, & dez peccados mortaes, dizendo :\n", "mil vezes perjurey, cem vezes forniquey, trinta propus de matar, dez fiz contra meu voto &c. Porque a esta confissam não lhe falta nada polos dizer todos com tã paucas palavras, pois sam tam craras :\n", "como em outra parte (o :\n", "in d. c. Consideret n. 110) provamos [p. 41, 49 pdf] despois de Caietano (p :\n", "Tomo z, d. contritione q. z).\n", "<p>\n", "21.\n", "¶\n", "[19] Ho XVI que a circunstancia do escãdalo, em duo casos se ha de confessar necessariamente, segundo todos, como em outra parte ho dissemos (q :\n", "in c. Consideret § Animadvertere n. 5) porque nelles faz algũa das tres cousas sobreditas.\n", "Lho primeyro he, quando ho escandalo he formal .\n", "f. quando algũa cousa se disse, ou fez, com proposito de provocar a outro a peccado mortal :\n", "& não somente ha de confessar o que fez ou disse com a dita tençao :\n", "mas tambem ha de dizer ho genero do peccado, ao qual o entendia provocar (q :\n", "Per dicta supra in c. Praecedenti).\n", "Lho II quando com sua obra em si boa, ou indifferente, mas maa ao que de fora parece, daa occasião a outro de cayda mortal.\n", "Em o terceyro sam diversos os doctores .\n", "f. quando hũ pecca mortalmente en presença de outros, sem tenção de os trazer a peccar mortalmente, porque Adriano (q :\n", "de sacramento confess. q. 4. colu. 4) Mator (r :\n", "in 4. d. 38 q. 3) & Silvestre (s :\n", "verb. Scandalum.\n", "q. 2. §3)senten que si poren S. Thomast sente que não em quãto diz que posto que mais gravemente pecca, o que pecca em publico, que o que em secreto :\n", "Porem isto não passa em peccado de escandalo special :\n", "ho mesmo tem, Caietanou.\n", "(u:\n", "in Summa verb. Scandalum).\n", "Mas a nos parecenos o que hahi nos pareceo (x :\n", "in §animadvertere n. 9) .\n", "f. que a opentão dos primeyros proceda, quando ho tal peccado se comete port al pessoa, & em presença de taes, que provavel & verisimilmente tomrão nova occasião de peccar & a de sancto Tho.\n", "quãdo não se faz port al pessoa, nem em presença de taes, como ally mais nos extendemos.\n", "<p>\n", "22.\n", "¶\n", "Ho XVII [20] he de notar que aquelle que confessandose, lhe esqueceo a circunstancia necessaria :\n", "não he obrigado a confessar outra vez ho peccado ja confessado, porque confesse a circunstancia :\n", "mas abasta que confesse somente a circunstancia.\n", "Exemplo [p. 42, 50 pdf] jourou hũ de não poer mãos violentas em clerigo, ou não furtar :\n", "& despois ferio ou furtou, & confessa, que auta feyto taes cousas, mas esque ceolhe que avia jurado de as não fazer :\n", "não he necessario a este confessar, os peccados ja confessados outre avez, porque confesse a circunstancia do juramento mas abasta que diga que duas ou tres ou quatro out tantas vezes quebrantou os juramentos licitos & sanctos ou diga que fez hũas obras em si, ou por circumstancia maa, contra o que auta jurando, como em outra parte (y :\n", "in c. Consideret d. penit. d. 5. n. 104 ca. seq. )\n", "ho provamos mais largamente\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "Sentence-split of ed. 2:\n", "------\n", "1556 SPA\n", "<p>\n", "¶\n", "Capitulo.\n", "6. De las circunstancias del pecado.\n", "<p>\n", "Sumario.\n", "<p>\n", "1 Circunstancia que es?\n", "nu. I. y que ay siete especies della.\n", "nu. 2. Y que se ha de confessar de necessidad, la que muda la especie.\n", "nu. 3. Pero no la de aver pecado en confinança de se confessar.\n", "n. 4. /Circunstancia de homicidio, y de fornicacion en lugar sagrado se ha de confessar, y la vedada por otra ley diversa &c. nu. 5/ Circunstancia de mentira iocosa, y la que alivia el pecado quando se ha de confessar.\n", "nu. 6. 7. & 8. Y quando la del dia de fiesta, de ayuno, o de oracion, o del lugar sagrado.\n", "nu. 9 & 10. Y la de la proprioa persona, y de la religion.\n", "nu. 11. Y ha de pecar contra consciencia.\n", "nume. 12/ [p. 32, corretto 31; 24 pdf] Circunstancia como no es el numero de los pecados nu. 14.\n", "Pecaodo multipliarse tantas vezes, quantas se itera, como se ha de entender, y si crece el numero de los pecados por se interpolar la voluntad.\n", "nu. 16.\n", "Y por mudar el proposito, para no acabar el pecado con otras muchas consideraciones quotidianas.\n", "num. 17 & 18.\n", "/Confessar puede el penitente mil pecados en una sola palabra n. 18.\n", "/Circunstancia del pecado quando se ha de confessar de necessidad.\n", "num 19.\n", "Y la oluidada en la confession como se confessara sin tornar a confessar el pecado.\n", "nu 20\n", "<p>\n", "2 [1] Para fundamento desto dezimos, quanto a lo primero, que la circunstancia del pecado, segun la mente del derechoa (ca. Consideret.\n", "de poeni. d. 5 l. Aut facta ff. de poen. c. Sicut dignum de homic) y de sus interpretes (b :\n", "I. Sec. qo.\n", "7 & in 4 dist 16 ubi etiam omnes alii & Anton.\n", "3. part. tit. 17 ca. 17§ 4 & Gerson.\n", "2. part. fol 170 quos retulimus in princ. d. c. Consideret & Pau.\n", "& aliorum in d. c. Sicut & alibi) es un accidente de aquello, che es pecado.\n", "Diximos (accidente) porque ninguna circunstancia de la obra, es la substancia della.\n", "Diximos de aquello (que es peccado) y no del peccado, porque muchas vezes la obra en si no es pecado, y se haze tal por la circunstancia, y como entonces ella es lo, en que consiste el peccado, no es tan accidente de pecado, quanto de aquello che es pecado, segun lo declaramos en otra parte (c :\n", "In d. c. Consideret.\n", "nu. 3), siguiendo a Alexandro Halense (d :\n", "In 4 par q. 77 ar. 2 col2).\n", "<p>\n", "3 [2] ¶\n", "Lo II † que la circunstancia se parte en siete species, que se contienen en un versillo (e :\n", "f. Quis, quid, ubi, quibus auxiliis, cur, quomodo, quando), referido por S. Thomas (f :\n", "In dict. q. 7. articu. 3).\n", "Quien, que, donde, con que, porque, como, y quando.\n", "Al qual versillo tenemos por mejor que al de Paludano (g :\n", "In 4. dist. 16. quaest. 3. articu. I. ), como lo diximos alli (h :\n", "f. in princ. dict. capitu.\n", "Consideret.\n", "numero. 4).\n", "Porque enel se añade, quoties, quantas vezes que denota numero, el qual no es circunstanci, sino multiplicacion del pecado, como alli (i :\n", "In dict. cap. Consideret.\n", "numero quarto) lo diximos.\n", "<p>\n", "4 [3] ¶\n", "Lo III. †que destas circunstancias, todas, y solas aquellas se han de confessar de necessidad, que hazen, que las obras cuyas son, sean pecados mortales, o las que son mortales de una especie, lo sean de otra, o lo que es mortal por un respecto, lo sea tambien por otro, hora muden las obras de una especie en outra, hora no, segun la commun opinion, que copiosamente alibik (K :\n", "In d. c. Consideret à numero 5) tratamos.\n", "Y solas, y todas aquellas circunstancias son desta qualidad, segun S. Thomas (l :\n", "In dict. dist. 16 quaestione sexta articu. 2. quaestione 3), que allende la [p. 32, 25 pdf] malicia de la misma obra, repugnan especialmente ala razon, y segun Scoto, las que se vedan por diversos, y especiales mandamientos.\n", "Diximos (especiales) porque no basta, que sean tales, que el uno dellos se incluya enel otro, quales son la ley, que veda todo mal, y la que veda el homicidio, como los provamos alibi (a :\n", "in d. princi. nume. 74).\n", "Lo qual todo por los siguientes corolarios se desmenuzara.\n", "* Antes de los quales avisamos, que despues que esto se imprimio, declaro por herege el concilio Tridentino (b :\n", "Sessio quarta sub Iul. 3. c. 5. & cano 7), al que dixere, que no somos obligados a confessar la circunstancia, que muda la especie de pecado.\n", "Lo qual se ha de entender, de la circunstancia, que muda la especie de pecado venial en mortal, o la del mortal en otro mortal, y no de la que muda en otro venial, que no es necessario (c :\n", "glo. c. c omnis de poeni. & re.\n", "recepta ibi, & in 4 dist. 17) confessarlo.\n", "Y aunque el concilio no expressa, sino de la que muda la especie del pecado, per tambien (y por mas fuerte razon) se ha de entender de la que haze a la obre, que de suyo es buena, o no mala, mortal.\n", "Y aun, de la que haze que una obra, quae por un respecto es mortal, lo sea tambien por otro tal, aunque la especie della (quanto a su ser) no se mudasse, como se ha dicho en esta quarta ilacion.\n", "Ca la razon que a ello movio al convilio, es que el confessor es juez y no podria bien sencenciar el caso del penitente, sin se le manifestar la circunstancia, que muda la especie del pecado, la qual razon milita en las tres dichas circunstancias (d :\n", "Quare idem iuris debet esse de omnibus.\n", "l. illud.\n", "ff. ad leg Aquil.\n", ").\n", "*\n", "<p>\n", "5 ¶\n", "El primero de los quales sea, que no se han de confessar las circunstancias, de aver se cometido el pecado lunes, o martes, enel prado, o en la viña, con la mano derecha, o con la yzquierda.\n", "Porque po restas, no se haze alguna de las tres cosas suso dichas, pues no se haze mortal, lo que sin ellas no fuera tal, ni de otra especie mortal, ni por otro respecto mortal.\n", "<p>\n", "¶\n", "6 [4] El segundo † que pecar con confiança, de que despues se confessara, y alcançara pardon perdon, no se ha de confessar necessariamente, perque no es circunstancia, que tanto agrava, antes aliuvia, como lo apunto un Cardenal (e :\n", "Caietan.\n", "2. Sec. q. 21. ar. 2 quicquid dicat Bonaventura in apologia)\n", "<p>\n", "7 ¶\n", "El tercero, que al que hurto alguna cosa sagrada, o de lugar sagrado, no basta dezir que la hurto, porque le es necessario confessar, que la hurto de lugar sagrado, o era cosa sagrada.\n", "Ca esta circunstancia haze, que lo que era pecado mortal de una especie, o por nu respecto, lo sea de otra, o por otro respecto, por ser especialmente vedada por otra ley diversa, de la que veda el hurto conviene saber, que ninguna cosa sagrada, ni de lugar sagrado se hurte (f :\n", "c. Quisquis n. 7 quaest 4).\n", "<p>\n", "8[5] Lo mesmo † es del homicidio, y de la fornication hecha en lugar sagrado [p. 33, 25 pdf].\n", "Porque po resta circunstancia se hazen de otra especie, o por otro respecto mortales, por ser vedados por ley, especial humana (a :\n", "Cap. Proposuisti, de consec. eccl. cap. Ecclesiis de consec. distin. I).\n", "<p>\n", "9 ¶\n", "El IIII que quien se echo con muger casada, religiosa, o parienta, no satisfaze confessando, que uvo parte con muger, porque ha de declarar, que la ovo con casada, religiosa, o parienta.\n", "Ca enel primero caso es adulterio corporal, enel segundo, sacrilegio, o adulterio espiritual, enel tercero, incesto, y por conseguiente lo mortal de una especie, lo haze de otra.\n", "U si uno propuso de hurtar para tener parte con una, que es religiosa, y con otra casada, ha de confessar, hurto, sacrilegio, y adulterio :\n", "Porque puesto que estas tres cosas sean un acto interior de la voluntad, empero por tres respectos diversos es pecado mortal, pues por tres repugna a la razon, y por tres leyes diversas especiales esta vedado.\n", "<p>\n", "10 ¶\n", "El.\n", "V. que toda circunstancia de fin vedada por otra ley especial diversa dela que veda el acto principal, se deve confessar, como la circunstancia del que hurta para fornicar, matar, o herir a otro\n", "<p>\n", "11 ¶\n", "El.\n", "VI † que quien miente para dar plazer, sin daño de nadie (que es mentira iocosa, y pecado venial) con tal intencion, que no la dexaria de dezir puesto que supiera, que era mortal, es obligado a confessar a quella circunstancia, porque con ella es mortal, y sin ella no.\n", "<p>\n", "12 ¶\n", "El VII [6] que nadie es obligado a confessar las circunstancias que alivian el pecado, y assi el que peco con una muger, porque ella lo provoco, no es obligado a confessar, que lo provoco, pues diminuye el pecado (b :\n", "Pet.\n", "c. Significavit-de poe. & remis.\n", "quod i hoc dixit fing.\n", "Feli in c. Dilecti.\n", "de except. col. antepe.\n", "& per .\n", "l. Si adulterium cum incestu § imperatores .\n", "ff. ad.\n", "l. Iuliam de adulte.\n", "quem in hoc aiebat.\n", "fin. Iason.\n", "i. l. Ut vim.\n", "ff. de iusti & iur. co. 2 aptissimus tum ad propositum tex.\n", "in. d. c. Consideret, verb. tentatione).\n", "Antes (segun la Commun, que nos seguimos alibi (c :\n", "f. in princ. d. c. Consideret.\n", "numero. 5. ) la deve callar, por no se dever escusar el penitente enla confession.\n", "Agora empero mejor nos parece lo contrario, porque no ay ley ni razon, que efficazemente prueve aquello (d :\n", "Ergo nec asserendum.\n", "c. 2. de trans.\n", "praela .\n", ".c. Legat.\n", "24. q. 2), y porque, aunque no la calle, bastantemente se acusa, echando se la culpa que tiene, fin quitar, ni poner mas.\n", "Y aun es obligado a confessar las, según.\n", "S. Bona.\n", "(e:\n", "in. 4. d. 16. )\n", "y la Comun, que ali (f:\n", "in d. princ. ) seguimos, quando tanto alivia, que de mortal lo haze que no sea pecado, o no mas de venial, como la circunstancia de la grave enfermedad alivia al comer de la carne en quaresma.\n", "Y quando se las pregunta el confessor, o temiesse, que por se las callar tomaría occasion de algún mal (g:\n", "Arc.\n", "c. Nihil de praescript.\n", ").\n", "<p>\n", "13 ¶\n", "El VIII † [7] que aun que es loable cosa confessar las circunstancias, que agravan el pecado, haziendolo de pequeño grande, o de grande mayor, pero la opinion mas commun, y provable es, que no es necessario, quando aquel argumento no es cause, que lo venial se haga mortal, o de otra especie, o por otro respecto, como copiosamente provamos alibi (h :\n", "f. in. d. prin. nu. 12. part. 4 pag. 36 * & satis declaratur ex nunc pro conci. Trident.\n", "sess. 4 sub Iuli.\n", "3. cap. 5 & can. 7 à contrario sensu), apartandonos de Marsilio (i :\n", "in. li. 4. q. 12. art. I. corol. 4) (en quanto limitava esta commun, que no oviesse lugar en la circunstancia, que muy clara [p. 34, 26 pdf] y norablemente augmenta el pecado) por las muchas, y solidas consideraciones, que alli escrivimos.\n", "Parecenos empero, que se davan limitar enla que augmenta el pecado, y haze que por ello sea reservado almenos por constitucion synodal, que a las vezes reserva algunos hurtos, o daños de ciertta quantidad para cima, o añade, que la absolucion, o restitucion se haga en cierta manera.\n", "<p>\n", "14 Y † enla que [8] haze, que tenga annexa descomunion, o que la descomunion annexa sea papal, como la descomunion dela herida ligera del clerigo, es obispal (a :\n", "ca. Pervenit de setentia exco.\n", "ubi text. singu. ), y de la grande, papal (b :\n", "c. Si quis suadente.\n", "17 q. 4).\n", "<p>\n", "15 Y en la que pregunta el confessor, y no se puede callar, sin peligro de algun inconveniente espiritual, como lo dixo bien Silves (c :\n", "f. verb. Contessio.\n", "I. § 9) aunque Ioan de Friburgo (el autor dela Summa confessorum) no lo dize donde el lo allegad (f. li. 3. ti.\n", "33 q. 11), niaun donde desto tracta (e :\n", "f. lib. 3 ti 34 quaest 81).\n", "<p>\n", "16 ¶\n", "El IX † que la circunstancia del dia dela fiesta, no se ha de confessar [9] necessariamente :\n", "Porque no haze mal, lo que sin ella no lo fuera tal ni de otra especie, ni por otro respecto, sino fuere obra servil, vedada en fiestas, qual no es pecado, Segun S. Tho.\n", "(f :\n", "in-3 sent. d. 37 art. 5. q. 2).\n", "Esta ilagion alibi (g :\n", "f. in. d. c. Consideret.\n", "a. n. 17 usque ad 40) por otros fundamentos confirmamos, respondiendo a otros tantos contrarios, seguiendo en ello al cardenal Caieta (h :\n", "2. Sec. q. 7. & .\n", "2. Sec. q. 122 art. 4. i. Quodl 10 & in summa verb dies festos) en muchas partes, y al resoluto Sylvestro en otras (i :\n", "f. verb. Cincunstantia.\n", "q. 3. & ver. Dies dominica.\n", "q. si.\n", "& .\n", "in aurea rosa.\n", "casu.\n", "63 ubi testatur doctissimos quosque ordinis dominicani convenisse & hanc opinione suscepisse), y a Iacobo Almayno (k :\n", "in 4 dist 17 col. 24).\n", "Aunque la Comun opinion en dos casos se puede guardar como alli lo diximos (l:\n", "ubi supra n. 40), conviene a saber quando el pecado se haze a fin de hazer obras manual verdada en aquel dia, o quando se peca mortalmente, con intencion, y proposito de quebrantar la fiesta.\n", "<p>\n", "17 ¶\n", "El X que la circunstancia del dia de ayuno, o de oracion, no se ha de confessar necessariamente, fino quando se peca con proposito delo quebrantar, por ello por que no haze alguna delas dichas tres cosas, segun lo provamos alibi (m :\n", "in d. c. Consideret nu. 32 ver. Ad primum)\n", "<p>\n", "18 ¶\n", "El XI que la circunstancia del lugar sagrado, puesto que accidentalmente agrave todo pecado, per no se ha de confessar necessariamente, sino quando la obra del pecado es directamente contraria a su sanctidad, o immunidad, qual es el derramiento de humana sangre, o simiente, o faca forçosa de los, que a ella se acogen.\n", "Porque en estos pecado de suyo mortal por un respecto, se haze mortal por otro (n :\n", "arg. c. Ecclesiis de concec. d. I cap. proposuit de consecra. ecclesi. c. i eo tit lib. 6), o lo que no era peccado, o no mas de venial de suyo, por ella se haze mortal, como la copula entre marido y muger sin causa justa en el avida, que en los otros lugares no lo feria.\n", "<p>\n", "19 ¶\n", "El xii† que deste precedente se sigue, que los que en la yglesia co [10] meten pecado de sobervia, perjjuro, o gula & c. no han de confessar de necessidad la circunstancia del lugar sagrado, ni los que estando en sagrado dessan matar, herir o fornicar, con tanto, que no lo sesseen cometer, ni poner por obra enel Ca. si esto dessean, aun [p. 35, 26 pdf] que estuviiessen fuera de sagrado, serian obligados confessar la circunstancia del sacrilegio, que enello cometen como lo diximos alibia.\n", "<p>\n", "20 ¶\n", "El XIII †[11] que aunque la circunstancia de la propria persona, alguna vezes acrescienta el pecado (caeteris paribus) assi como el que tiene dignidad mas peca, que aquello que no la tiene (b :\n", "cap. Homo 40. d. ), y el perlado mas que le subdito (c :\n", "Praecipue.\n", "II. quaest. 3) y el sabio mas que el ignorante (d :\n", "sicut dignum de homic), y mas el que ama la ignorancia (e :\n", "c. pen.\n", "37. d) para pecar mas libremente, que pecaria sabiendo, y mas el bueno, que el malo, y el mejor que el menos bueno.\n", "Y aunque por est sea provechoso confessa resta circunstancia, per no es necesasrio comunmente :\n", "Porque comunmente ni haze de venial mortal, ni de mortal de una especie, mortal de outra, ni de mortal por un respecto, mortal por otro.\n", "Mas quando esto se hiziesse, lo qual feria, quando se pecasse contra voto, o estado votado, como peca el religioso en fornicar entonces avia se de confessar porque haze una de las dichas tres cosas, lo que no haze empero quando el religioso blasphemasse, o hizesse otro pecado, que no fuesse contra sus votos, o regla professada, como despues de Caietano (f :\n", "I. Sec. q. 17. art. I) lo diximos alibi (g :\n", "f. in d. princ. num. 50).\n", "Porque la circunstancia de religion, comunmente no haze mortal, lo que de suyo no es tal, ni de otra especie, lo quesuyo es de otra.\n", "<p>\n", "21 ¶\n", "El XIIII † [12] que la confession dela circunstancia de precar contra la consciencia, entonces soamente es necessaria, quando la obra que hizo, por ninguna ley era pecado, sino por ser hecha contra su consciencia erronea.\n", "Porque entonces solamente haze una delas dichas tres cosas, y no otras vezes, como nuevemente lo declaramos alli (h :\n", "f. ubi supra num 58 & 65).\n", "<p>\n", "22 ¶\n", "El XV† [13] que el numero delos pecados no es circunstancia, mas addicion de pecado a pecado, porque la frequentacion es circunstancia, que constituye nuevo pecado.\n", "Acerca delo qual alibi (i :\n", "f. in c. Consideret.\n", "nu. 41) diximos primeramente, que no basta dezir, peque muchas vezes en este pecado, porque esta diction (muchas vezes) tanto se verifica en diez, y aun en dos, como en ciento (k :\n", "glo. I. c. Monasteria.\n", "de vit. & honesta.\n", "cler. Gregorios I pri. Clemen.\n", "Saepe de verb. sign), aunque el Arcidiano (l. c. Imitare 6 q. I & in princi. d. c. Consideret), tuuo quasi, a quien no quiso reprovar Ange.\n", "(m :\n", "verb. Confessio.\n", "I. § 23) empero reprovou lo, y con razon un Cardenal (n :\n", "Alexandrinus in di.\n", "c. Imitare).\n", "<p>\n", "23 Lo II† [14] que alli diximos es, que el pecador deve exprimir el numero cierto, si lo sabe, dizendo, esto hize tantas vezes, y si no sabe el numero cierto, ha de echar cuenta, quantas vezes el dia, o semana, o al mes (poco mas, o menos) peco, y dezir el numero cierto mas verisimil.\n", "Ca pecaria mortalmente, el que por verguença, o hypocrisia callasse algo del numero delas vezes, que se acuerda, y aun si por su lata culpa dexa de acordarse, por no aver pensado enello nada, podiendo lo hazer, y aun la confession no le valdria nada.\n", "<p>\n", "24 Lo III† [15] que alli dizimos es, que bastaria sin algun numero declarar bastantemente su estado, como si la muger publica, que por diez [p. 36, 27 pdf] años ha estado aparejada para fornicar, assi con clerigos, religiosos, y virgines, como con legos, sueltos, y casados, y despues de convertida se confessasse, y dixesse, que tanto tiempo estuvo dela dicha manera, aparejada para tan torpe cosa, como despues de un Cardenal (a :\n", "Caiet.\n", "in. q. 3 de confessio) concluimos alibi (b :\n", "in d. c. Consideret, ibi quantum perseveraverit & defleat, quod perseveranter pecavit) ponderando ay el testo para esto singular, y añadiendo, que aquien dexo de rezar un año, bastava dezir, Dexe de razar un año.\n", "<p>\n", "25 Lo IIII.\n", "[16] † que se augmenta el numero de los pecados, todas las vezes que el pecado, o la voluntad de pecar enterrompida se itera, segun Ioan And (c :\n", "in regul. Delictum col. penu. de reg. iuris lib. 6 i mercurial).\n", "Lo qual llanamente procede en los pecados interiores, que dentro del alma se consuman, qual es el odio, qual la heregia.\n", "No empero en os que se consuman de fuera por obra exterior :\n", "Ca ellos no se dizen iterarse, hasta que no seacabe la obra exterior, o no se interrompa, como acontesce, quando alguno va a matar otro, y caminnado todo el dia, orapiensa enello, ora en al.\n", "Ca este no peca en ello mas de un pecado aunque muy mas grave, segun un Cardenal (d :\n", "Caiet.\n", "in libello 17 respondo 15 resp e in d. princi. nu. 48), que alli (e :\n", "ind. princi. nu. 48) seguimos, y ponderamos un testo para ello muy apto.\n", "<p>\n", "26 De lo qual inferimos, que no se itera, ni multiplica el pecado, aun que durante la exteriod muchas vezes la voluntad interior se interrompa, y renueve, ni aun por el contrario, si durando la mesma voluntad, la obra exterior se multiplique antes, que el delicto se acabe, como mas largamente lo provamos alli (g :\n", "in d. princi. nu. 48), Donde inferimos tambien, ser solo un pecado, todos los actos interiores y exteriores que solamente son camino para un solo pecado, aun que sean enterrompidos, quales son los passos, y el andar, aparejar de cavallo, lança y otras armas, con los desseos enterrompidos, por diversas vezes, hablando, comiendo, y dormiendo, y otras tantas renovados del qual va a matar a otro, de aquí a diez leguas.\n", "Ca aun que considerandos en si, son muchas, y diversas cosas, pero considerandos como amino, y partes del pecado, que contos ellos se ha de acabar, no hazen mas de uno.\n", "Como también las piedras, colunas, vigas y otros materiales de una casas, muchas cosas son, cada una dellas por si, pero todas ellas consideradas como partes de la casa, no hazen mas de una (h:\n", "Eum qui .\n", "ff. de usucapio).\n", "<p>\n", "27 Desto † [17] inferemos la razón porque, quien ha tenido parte con una, no es obligado a confessar las platicas, besos, y otros actos, preámbulos y immediatos della:\n", "y el que la ha tenido dos vezes (aunque immediatas) es obligado a confessar, que la ovo dos vezes:\n", "ca la razón es, que la una de las dos copulas no es camino, ni preámbulo, que se ordena a la otra, y las platicas, besos y abraço, si, ala que preceden.\n", "Todo lo que es muy quotidiano.\n", "No diximos empero ociosamente, que son camino, porque si ovo interrompimiento por [p. 37, 27 pdf] proponer de no acabar el pecado, o por arrepentirse, o por otro respecto, y después otra vez lo quisiesse acabar, dos pecados distinctos serian.\n", "Aun que el dicho Cardenal no aviso esto, que es muy quotidiano.\n", "Tampoco se dixo sin causa (que solamente son camino para un solo pecado) porque si ellos de suyo son pecados, o se ordenan para ortos pecados, tantos serán ellos, quandos de suyo se son, o quantos los fines malos para que se ordenan:\n", "como quien va a matar a un hombre, y de camino hurta, roba, perjura, reniega, o ordena su comer, y beber, su andar, y hablar, no solamente para acabar el homicidio concebido, pero aun para adulterar, infamar, y hazer sacrilegios, y aun añadimos, que como este pecado por mucho tiempo continuado es muy mayor, que si fuera momentáneo, assi quien lo cometiere, y quisiere seguir nostro consejo, se dolerá, mas del, y confessara el tiempo, que poco mas, o menos en ello se ocupo.\n", "<p>\n", "28 [18] Desto inferimos† la respuesta de la question que el muy reverendo señor, y padre fray Antonio de Zurara, a quien yo mucho devo y qioero por sus muy grandes virtudes, saber, prudencia, y otros muchos respectos, nos pregunto, del que mucho tiempo anda tras una mujer, con illigitos amores, si alcançar effecto, quantos pecados peca?\n", "Ca dezimos, que peca (almenos) tantos quantas vezes interrompe, y renueva aquella mala voluntad, que concibe sin meter, o querer meter por entonces obra exterior alguna para ello, y tantas vezes quantas interrompe aquella mala voluntad, y mala obra exterior, que para ello por entonces pone.\n", "De manera que si anduvo un dia, o una noche o parte dellos dándole músicas, o esperando oportunidad de hablarle, o servirle para este, mal din mientras que esta voluntad y obra exterior no se interrompieron, no abra mas de un pecado, aunque tanto mas grave quanto mas diuturno.\n", "Pero si acabada aquella obra exterior, que por entonces quiso hazer, entiende eon otros negocios que no son camino, o preámbulos para ello, y rotna otra vez ala mesma mala voluntad sola, o a la de hazer otra obra exterior semejante, o desemejante dela otra para alcançar su mal fin, hara otro pecado y tiendra tantos que confessar quantos interrompimientos, y renovaciones tales hizo, allende las malas voluntades absolutas, que tuvo sin meter obra exterior, y confessando el numero verisímil dellos, satisfará al piadosissimo señor, cuya misericordia y paciencia es milagrosa en sufruir nos estas muy atrevidas, y desuergonçadas continuaciones de sus ofensas gravissimas podiendo las con un solo ceño assi asperrimantamente casticar, como los castagara si nos castigaremos a nos mesmos antes*.\n", "<p>\n", "29 ¶\n", "El XVI que en una palabra puede el penitente confessar mil pecados mortales como diziendo mil vezes blaspheme, mil vezes perjure, mil vezes fornique, mil vezes perpuse de matar, mil vezes hize contra mi voto, o juramento, diez vezes aconseje que alguno perseverasse en pecado mortal, talc osa hize tantas vezes a fin de fornicar & c. Porque a esta confession no le galta nada, por dezir los todos, con tan pocas palabras pues son tan claras como lo provamos alibi (a :\n", "In princ. di Consideret.\n", "numero 110) despues de un cardenal (b :\n", "Caiet.\n", "tom. 2. de contri. q. 2).\n", "<p>\n", "30 ¶\n", "El XVII † [19] que la circunstancia del escandalo, en dos casos se ha de confessar necessariamente, segun todos, como alibi (c. In c. I § Animadvertere n. 5 de poen. d. 5) lo diximos.\n", "Porque enellos haze alguna de las tres cosas suso dichas.\n", "El primero quando el escandalo es formal, esto es quando alguna cosa se dixo, o hizo, con animo de provocar a otro a pecado mortal, y no solamente ha de confessar lo que dizo, o hizo con la dicha intencion, mas tambien ha de dezir el genero de pecado, al qual entendia provocar (d :\n", "per dicta supra in c. praecedenti).\n", "El segundo quando con obra buena, o indifferente de su casta, y mala en la especie o muestra, da occasion de pecar mortalmente.\n", "En otro tercero, son diversos los doctores, conviene saber, quando uno peca mortalmente en presencia de otros, sin intencion de los atraher apecar mortalmente.\n", "Ca Adriano (e :\n", "in 4. de consacr. confess. q. 4. co. 4), Mayor (f :\n", "In. 4. d. 38. quaest. 3), & Sylvestro (g :\n", "verb. Scandalum quaest. 3) sienten que si :\n", "Pero S. Thoh (2. Sec. q. 43. artic. 3) siente que no, en quanto dize, que puesto que mar gravemente peca el que peca en publico, que el que en secreto :\n", "pero esto, no lo passa en pecado de escandalo especial.\n", "Lo mesmo tiene Caiet (i. In summa verb. Scandaum).\n", "A nosotros parece nos lo que alli (k :\n", "f. § animadvertere.\n", "nume. 9) nos parecio, es a saber que la opinion de los primeros, proceda, quando el tal pecado se comete port al persona, o en presencia de tales, que probable, y verisimilmente tomaran nueva occasion de pecar :\n", "y la de S. Tho quando no se haze port al persona ni delante tales :\n", "como alli lo extendimos mas.\n", "<p>\n", "31 ¶\n", "El XVIII [20] es de notar que el que confessando se, olvido la circunstancia necessaria, no es obligado a confessar otra vez el pecado ya confessado, mas basta que confiesse la circunstancia sola.\n", "Exemplo, juro uno de no poner manos violentas en clerigo, de no hurtar, no fornicar, no blasphemar, y despues hirio, hurto, fornico, y blasfpemo, y confesso que avia hecho tales cosas, mas oluidose, que avia jurado de no hazerlas :\n", "no es necesasrio a este conessar los pecados otra vez, para confessar la circunstancia del juramento, mas basta que diga, que dos, tres, quatro, o tantas vezes quebranto juramentos licitos, y sanctos :\n", "o diga que hizo unas obras en si, o por circunstancias malas, contra lo que avia jurado como alibi (l :\n", "In d. c. Consideret.\n", "nume. 104) lo provamos mas largamente\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "<p>\n", "Sentence-split of ed. 3:\n", "------\n", "1573 LAT\n", "<p>\n", "[p. 65 v, 143 pdf] Caput VI de peccati circunstantiis\n", "<p>\n", "SUMMARIUM\n", "<p>\n", "1 Circunstantia quid nu. 1 & septem eius species, nu. Circunstantia mutans speciem necessario confitenda, nu. 3 & 19\\Circunstantia homicidii & fornicationis ratione loci sacri, confitenda est, & illa etiam quae prohibita est propter diversam legem, & c. nu. 5\\Circunstantia mendacii iocosi, & illa, quae extenuat peccatum, quando confitenda, nu. 6. 7 & 8\\Cicrunstantia diei festi, ieiunii, orationis aut loci sacri, regulariter non necessario cnfitenda.\n", "nu. 9 & 10\\Circunstantia personae & religionis quando confitenda.\n", "nu. 11\\Circunstantia conscientiae contrariae quando confitenda.\n", "nu. 12\\Circunstantia numeri non est proprie circunstantia nu. 13, 14 & 15\\PEccati numerus quando dicatur augeri per iterationem, vel continuationem, vel propositi mutationem, nu. 16.\n", "17 & 18\\Poenitens potest vel unico solo verbo mille peccata confiteri.\n", "nu. 19\\Circunstantiam per oblivionem in una confesione omissam potest quis in alia sine confessione nova peccati confiteri.\n", "nu. 20\n", "<p>\n", "2 [1] Pro fundamento materia praesentis, in primis asserimus circunstantiam peccati iuxta mente iuris c. Consideret, de Poenit, dist. 5. l. Aut facta ff. de Pen.\n", "c. sicut dignum, de homicid. Panor.\n", "& aliorum interpretum, ibidem Tho.\n", "I. sec. q. 7. & in 4 dist. 16 & omnium aliorum ibidem, & Anton.\n", "3. par. tit. 17 c. 17 § 4. Gerson.\n", "2. par. fol.\n", "170. quos retulimus in principio dicti c. Consideret, & alibi, esse quoddam [p. 66 r, 144 pdf] accidens rei, quae peccatum est, Diximus, Accidens, quoniam nulla operis circunstantia est substantia eiusdem.\n", "Diximus etiam (rei quae peccatum est) non autem diximus peccati, quoniam saepe sit, quod opus secundum se non est peccatum, sit tamen tale, ob adiunctam circunstantiam.\n", "Cum enim tunc ipsamet circunstantia sit illud, in quo peccatum consistit, non est accidens peccati, sed illius rei, quae peccatum est, sicut nos, in d. c. Consderet, nu. 3 elucidavimus :\n", "sequentes Alex.\n", "Halensem, in 4. par. q. 77. ar. 2. col. 2.\n", "<p>\n", "3[2] Secundo, Quod circunstantia in septem species dividitue, quae continentur in illo versiculo.\n", "Quis, quid, ubi, quibus auxiliis, cur, quomodo, quando, relato à S. Tho.\n", "in d. q. 7 ar. 3. qui magis nobis placet, quam illud Palud.\n", "in 4. dist. 16. q. 3. ar. 1 uti nos diximus in princip. dicti c. Consideret, nu. 4. Quoniam in illo Palud.\n", "ponitur verbum, Quoties, quod numerum denotat, loco, Quibus auxiliis, & numeros non est circunstantia, sed potius peccati multiplicatio, ut ibidem tradidimus.\n", "<p>\n", "4[3] Tertio, Quod harum circunstantiarum, omnes illae & solae in confessione necessario exponi debent, quae efficiunt opera quorum sunt circunstantiae peccata mortalia, vel quod illa, quae sunt mortalia unius speciei, fiant etiam, ut sint alterius, vel quod est mortale, ob unam causam sit etiam propter aliam, sive mutent opera ipsa, ab una in alteram speciem, sive secus :\n", "iuxta communem opinionem, quam copiose ibidem nu. 5 pertractavimus.\n", "Et solae illae & omnes sunt huius generis, quae secundum Tho.\n", "in dicta dist. 16 q. 6 ar. 2 q. 3. ultra malitiam eiusdem operis habent specialem repugnantiam cum ratione.\n", "Secundum Scotum vero illae sunt in confessione dicendae, quae diversis specialibusque praeceptis prohibentur.\n", "Diximos, (specialibus) Quoniam non sufficit esse talia, quod unum in altero includatur, qualia sunt lex prohibens omne malum, & prohibens homicidium, quod etiam in dicto principio nu. 74. probavimus, & corollariis sequentibus speciatim explicabitur.\n", "Ante quorum explicationem, admonemus, quod postquam haec fuerunt hispane excusa, Concilium Tridentinum, sess. 4, cap. 5. can. 7 diffinuit illum esse hereticum qui dixerit, nos non teneri ad confitendam circunstantiam quae mutat speciem peccati.\n", "Quod quidem est intelligendum, de circunstantia, quae speciem peccati venialis mutat in mortale, vel speciem uniusmortalis in aliud mortale :\n", "non autem de illa, quae mutat speciem unius venialis in aliud veniale, quod non est necessario confitendum, glo. c. Omnis, de Poenit. & remiss. recepta ibi, & in 4 dist. 17 & quamvis concilium non loquatur expresse, nisi de illa circnustantia quae [66 v, 145 pdf] mutat speciem peccati, intelligendum tamen est etiam de illa quae opus alioquin ex se bonum, vel saltem non malum, facit mortiferum :\n", "imo etiam de illa, quae facit ut opus, quod ob unum respectum est mortiferum, sit tale ob alterum, quamvis species operis, quantum ad suam essentiam minime mutetur.\n", "Quoniam circunstantia, quae facit de bono malum, vel de veniali mortale, vel de mortali, uno respectu, ut sit tale, altero dici potest mutare speciem per praedicta in praelud. 9. nu. 6 uti in quarta illatione dicetur.\n", "Ratio enim, quae ad hoc diffiniendum concilium movit, est, Quoniam confessarius est iudex, qui in causa poenitentis non posset aequam ferre sententiam absque manifestatione circunstantiae, quae mutat speciem peccati, quae quidem ratio militat etiam in tribus praedictis circunstantiis.\n", "Quare idem iuris debet esse de ominibus l. Illud, ff. ad l. Aquil.\n", "<p>\n", "5 Si ergo primum corollarium circunstantias illas, scilicet esse commissum peccatum in secunda vel tertia feria, aut in prato, vel vinea, manu dextra, vel sinistra, non esse confitendas, quoniam per hos nullum praedictorum trium sit, siquidem non sit mortale, quod sine illis non esset tale, neque etiam sit lethale alterius speciei, neque alio respectu.\n", "<p>\n", "6 [4] Secundo infertur, quod peccans ob fiduciam, quod postea per confessionem veniam obtinebit, non tenetur de necessitate id confiteri, quia non est circunstantia adeo peccatum aggravans, imo potius minuit, sicuti Caiet.\n", "2. sec. q. 21 ar. 2. quicquid Bonavent.\n", "in Apolog.\n", "insinuavit.\n", "<p>\n", "7 Tertio, quod is qui furatus est aliquid sacrum, vel ex loco sacro non satisfacit dicendo se id furtum fecisse, sed oportet exprimere, esse rem sacram, vel de loco sacro surreptam.\n", "Nam haec circunstantia efficit, ut quod est mortale unius speciei, aut uno respectu, fiat etiam alterius, vel atero respectu, eo quod specialiter prohibetur lege particualari alia distincta ab illa, quae futurum prohibet, quae quidem praecipit, ne quid sacrum, aut de loco sacro furripiatur.\n", "c. Quisquis 17. q. 4.\n", "<p>\n", "8[5] Idipsum est dicendum de homicidio & fornicatione in loco sacro perpetratis.\n", "Nam per hanc circunstantiam fiunt mortalia alterius speciei, vel alio respectu, eo quod lege humana speciali prohibentur c. Proposuisti, de consec. eccles. vel altar.\n", "& c. Ecclesiis de consec. dist. 1.\n", "<p>\n", "9 Quarto, infertur eum qui cum coniugata, vel religiosa, vel cognata [p. 67 r, 146 pdf] rem habiot non satisfacere confitendo se cum foemina contubuisse :\n", "quoniam debet exprimere, quod erat coniugata, religiosa, vel consanguinea.\n", "Nam in primo casu, est corporale adulterium, in secundo vero sacrilegium vel spirituale adulteruim, in tertio autem incaestus; & per consequens quod erat mortale unius generis, efficitur alterius.\n", "Et si quis deliberavit furari, ut possit rem habere cum una religiosa, & altera coniugata, tenebitur confiteri, furtum, sacrilegium, & adulterium.\n", "Nam & si tria haec sunt unus interiur actus voluntatis, ille tame ex triplici diversaque causa est crimen lethale.\n", "Si quidem trifariam rationi repugnat, & tribus etiam distinctis, & specialibus legibus prohibetur.\n", "Ex quibus facile colligas responsum ad quaestionem nuper nobis propositam, scilicet, An qui coniugatus rem habuit cum aliena coniugata, teneatur declarare non solum se esse coniugatum, sed etiam illam cum quarem habuit?\n", "resolvenda est enim affirmative; quia est circunstantia, quae per se sola mutat speciem peccati, & ita est confitenda per praedicta.\n", "<p>\n", "10 Quinto, quod quaecunque circunstantia finis prohibita per aliam legem specialem & diversam ab illa, qua principalis actus prohibetur, debet in confessione etiam explicari circunstantia furtatis ad fornicandum, occidendum ver percutiendum alterum.\n", "<p>\n", "11 Sexto, qui mentitur ut aliis praebeat voluptatem sine alterius detrimento, quod est mendacium iocosum & veniale tantum, hac in entione, ut etiam si intelligeret esse mortale, nihilominus mentiretur tenetur illam circunstantiam confiteri, quam per illam efficitur mortale, sine qua non esset tale.\n", "<p>\n", "12 Septimo quod tenetur illas circunstantias confiteri, quae peccatum extenuant.\n", "Unde qui peccavit, cum muliere quadam ab eadem provocatus, non tenetur confiteri se esse rprovocatum ab illa.\n", "Si quidem id peccatum extenuat, per c. Significavit, de Poenit. & remiss. quod in hoc dixit singulare Fely in c. Dilecti, de except. col. antepen. & per l. Si adulterium cum incestu.\n", "§ Imperatores ff. ad l. Iuliam de adult. quem ad hoc aiebat singularem lafo.\n", "in l. Ut vim ff. de iust. & iur. col. 2. aptissimus tamen ad hoc propositum textus in dicto c. Consideret, verb. Tentatione; Imo opinio communis quam sequebamur in princip. dicti. c. Consideret, nu. 5. videlicet, quod Poenitens talem illam circunstantiam subticere debet, quia in confessione se ipsum excusare non debet :\n", "nunc desplicet.\n", "Tum quia nulla lege aut ratione fulcitur, qua id possit efficaciter comprobari.\n", "Tum quia [67 v, 147 pdf] ut licet illam exprimat, se ipsum sufficienter accusat, imputando sibi culpam quam habet, nihil illi detrahens, neque adijciens.\n", "Tum quia secundum Bonaventuram, in 4. dist. 16 & communem, quam ubi subra sequuti fuimus, tenetur manifestare illas circunstantias extenuantes, quae ita extenuant, ut efficiant non esse peccatum, vel ad summum veniale tantum, quod alias fuerat mortale; qualis est circunstantia gravis morbi, quae leviorem facit esum carnium in quadragesima.\n", "Item tenetur tales circunstantias exponere, quando eas confessarius poenitentem interrogat, vel quando ex earum occultatione occasionem alicuius mali idem confessarius apprehenderet.\n", "arg. c. Nihil, de Praescript.\n", "<p>\n", "13[7] Octavo, quod quamvis sit laudabile confiteri circunstantias, quae ita peccatum adaugent, ut ex parvo magnum, vel ex magno maius efficiant, opinio tamen communior & probabilior habet, hoc non esse necessarium, quando augmentum illud non est in causa, ut veniale mortale fiat, vel alterius speciei mortalis, vel alio respectu; quod nos ubi supra copiose confirmavimus, & satis declaratum est nunc, per Concil.\n", "Trid.\n", "sess. 4. & .\n", "5 & can. 7 a contrario sensu, licet Marsilius in lib. 4 q. 12 ar. 1 corol. 4. communem istam sententiam restringat, ne site i locus in circunstantia, quae clare & manifeste peccatum adauget, a quo ibi discessimus ob multas variasque considerationes.\n", "Et quamvis post haec Dominicus Scotus asserverit idem quod Marsilius contra communem non tamen debemus ab ea discedere; Tum quia confessarii tam magnam inveniunt difficultatem in perquirendis omnibus circunstantiis mutantibus speciem etiam sine perquisitione non mutantium sed notabiliter aggravantium, ut putent quasi impossibile perquiere has, & illas :\n", "& Deus non obligat ad impossibile, neque ad nimis difficile, iuxta mentem Tho.\n", "in 4. dist. 15.\n", "Tum quia multi poenitentes remanent cum magnis scrupulis, an confessi fuerint bene aut male, videntes pauca peccata esse unius speciei, quae non sint maiora, vel minora notabiliter, quam alia eiusdem, ratione personae, loci, morae, modi, praeludiorum, aut consequentium, & ita fiunt importunissimi confessariis, redeuntes ad se reconciliandum, & dicentes confessi fuimus, peieravimus, percusissimus, fornicati fuimus, sed non diximus tempus, quod in eo consumpsimus, neque quod paulum ante, aut post diximus, aut fecimus hoc, vel illud, quando augebat peccatum & vix est ulla via alia, sedandi et tranquillandi eos, que docendo illas circunstantias non esse necessario confitendas, quia non mutat speciem saltem in genere [c. 68 r, 148 pdf] in genere moris; Item quia peccator non tenetur ut asserit communis, & idem Sotus in 4. dist. 18 q. 2. ar. 4 confiteri mala verba, signa & genstus, quae antecedunt homicidium, aut percussionem, sufficit enim dicere, interfeci, percussi :\n", "neque oscula, & alia multa turpia, quae precedunt fornicationem :\n", "sufficit enim dicere fornicatus sum :\n", "neque cogitationes, & voluntates apparatuum, ad furandum, vel committendum alia delicta, imo satisfacit ultimum actum confitendo, ut dixit Scotus post alios in dicto art. 2. dicendo :\n", "percussi, interfeci, decies, fornicatus, furatus fui toties, &c. Tum quia S. Concilium Trident.\n", "in sess. 14 cap. 5 & can. 7 cui interfuerunt viri doctissimi, & sanctissimi, qui de his opinionibus in scholis disseruerunt, declaravit circunstantias, quae mutant speciel esse confitendas :\n", "significans a contrario sensu, alias non esse confitendas.\n", "arg. l. i. ff. eius cui, & c. Cum apostolica de his, quae sunt a prael.\n", "Tum denique quod interest plurimum ad salutem animarum, ut amenus & non horreamus confessionem; in quod iuvabit allevatio eius, ab omnibus illis oneribus, & difficultatibus a quibus potest levari, iuxta eius institutionem divinam & declarationem Sanctae matris Ecclesiae :\n", "iugum enim Domini suave est, & onus leve.\n", "Math.\n", "11. Addo praedictis, quod quamvis haec nostra, quae communis est opinio, videatur facere pro alia conclusione praedicti Soti in dicta dist. 18. q. 2. ar. 4. de qua nuper interrogati fuimus, non tamen videtur nobis vera; illa enim conclusio habet, non esse necesse confiteri virginitas circunstantiam, idest, quod is qui confitetur est virgo, si voluntas fornicandi non pervenit ad actum, quamvis secus sit quando pervenit; Non inquam videtur vera, Tum quia peccata voluntatis, oris, & operis sunt eiusdem specieo & non differunt, nisi quod alia sunt magin perfecta quam alia, ut dicemus in cap. 16 nu 9 post.\n", "S. Tho.\n", "I. sec. q. 52. ar. 7 & consequenter stuprum mentale, quod est voluntas habendi copula cum virgine, aut cum sit virgo, erit eiusdem speciei, cuius stuprum reale :\n", "quod est ipsa copula :\n", "& si quis eam habuit, debet confiteri illam circunstantiam, ut ipsemet ait.\n", "Etiam debet confiteri qui eam voluit habere, licet non habuerit :\n", "Cum alia species luxuriae sit velle fornicari cum vergine, vel cum sit virgo, & alia velle fornicari cum corrupta, vel cum sit corruptus, vel corrupta, Tum quia Concilium Trid.\n", "declaravit in sess. 14 cap. 5 can. 7 quod circunstantia, quae mutat speciem, confitenda est necessario, qualis est haec de qua loquimus, ut praedictum est; Tum quia non facit pro eo praedicta opinio communis, quia loquitur de circunstantia non mutante speciem peccati [p. 68 v, 149 pdf], conclusio vero eius, de circunstantia illam mutante.\n", "Videtur tamen nobis haec opinio communis restringenda.\n", "Prmo ne procedant in circunstantia, quae sic auget peccatum, ut ratione ipsius fiat reservatum saltem ex vi constitutionum synodalium, quae solent aliquando furta aliqua, & damna certae cuiusdam quantitatis Episcopo reservare vel addere, quod absolutio sive restitutio fiat certo quodam modo.\n", "<p>\n", "14[8] Secundo posset etiam restringi ne procedat in ea circunstantia, quae habet annexam excommunicationem, vel efficit, ut ea sit Papalis quemadmodum levis percussio clerici est episcopalis c. Pervenit, de sent. excom. ubi text. singularis :\n", "gravis vero, est Papalis c. Si quis suadente 17. q. 4.\n", "<p>\n", "15 Tertio, limitatur in ea circunstantia, quam confessarius interrogat, & non potest fieri absque periculo alicuus spiritualis incommodi, ut recte asservit Sylvest.\n", "in Summa.\n", "verb. Confessio.\n", "1. § 9. .\n", "Quamvis Iohannes de Fributgo, qui fuit author Sumae confessorum id non dicit in loco citato ab eo lib. 3. tit. 33. q. 11 neque etiam ubi de haec materia tractat, lib. 3. tit. 34. q. 81.\n", "<p>\n", "16[9] Nono afferimus, quod circunstantia diei festi non est necessario confitenda, quoniam non facit esse lethale quod sine illa non est tale, neque alterius speciei talis, neque alio respectu nisi esset opus servile in festis prohibitum, quale non est peccatum iuxta S. Tho, in 3. sent dist. 37 ar. 5. q. 2. Hanc assertionem confirmavimus in dicto c. Consideret a nu. 17 usque ad 40 multis fundamentis respondendo totidem aliis oppositis, sequuti in eo Cardinalem Caietanum 1 sec. q. 7 & 2. sec. q. 122 ar. 4 & in quolib. 10 & in Summa verb Dies festos resolutumque Sylvestrum verb Circunstantia q. 3 & verb. Dies dominica, q. fin. & in Aurea rosa casu 63 ubi testatur doctissimos quosque ordinis Dominicani convenisse & hanc opinionem suscepisse & Iacobum Almaynu in 4 dist. 17 col. 24.\n", "Quamvis communis opinio posset in duobus casibus observari, sicuti diximus ibidem n. 40 nempe, quando eo sine peccatum committitur, ut in eo fiat aliquod opus manuale prohibitum in illo die, & quando intentione & proposito violandi festum, lethale crimen admittirur.\n", "<p>\n", "17 Decimo.\n", "Quod circunstantia diei ieiunio vel orationi consecrati, licet videatur aliquantulum augere peccatum, non est de necessitate confitenda, nisi peccatum perpetretur cum proposito violandi per illud huiusmodi diem; Quoniam non efficit mortale, quod alias non [p. 69 r, 150 pdf] esset tale, nec mutat in speciem mortalis, nec facit ut novo respectu sit tale quorum aliquod requiritur ad hoc un circunstantiae fonfessio sit necessaria, ut supra dictum est nu. 3. & probabimus in princip. dicti. c. Consideret, nu. 32 verb. Ad primum.\n", "<p>\n", "18.\n", "Undecimo, quod quamvis circunstantia loci sacri omne peccatum gravius reddat secundum accidens, tamen non est necessario confitenda, nisi quando ipsum opus peccati sanctitati vel immunitati eiusdem loci directe contrariaretur :\n", "qualis est humanis sanguinis, vel feminis effusio, vel violenta abstractio, eorum qui ad loca sacra confugiunt.\n", "Quoniam in his, quod era mortale propter unam causa sit etiam mortale propter alteram.\n", "ar. c. Ecclesiis, de consec. dist. 1. c. Proposuisti, de consec. eccles. c. 1. eod. tit. lib. 6. vel quod peccatum non erat, aut solum veniale ex genere, ratione talis circunstantiae sit mortale, ut est actus coniugalis inter virum & uxorem ibidem sine causa iusta exercitus, quiquidem in alijs locis peccatum non esset.\n", "<p>\n", "19[10] Duodecimo, quod qui in ecclesia committunt peccatum aliquod superbiae, periurij, aut gulae & c. non tenentur necessario confiteri circunstantiam loci sacri :\n", "neque qui in eodem loco existens desiderant occidere, ferire, vel fornicari, extra illum, dum modo non percupiant id committere in eodem.\n", "Nam qui tale desiderium conciperent, etiam si extra locum sanctum existerent, circunstantiam sacrilegij quod in eo admittunt, confiteri tenerentur, ut in principio dicti cap. Consideret num. 22 diximus.\n", "<p>\n", "20 [11] Tertiodecimo, quod quamvis circunstantia propriae personae aliquando peccatum augeat; Quia caeteris paribus plus peccat dignitate aliqua praeditus quam privatus c. Homo 40 dist. & praelatus, plusque subditur cap. Praecipue 11. q. 3. & sapiens plus quam ignorans, cap. Sicut dignum; de homic.\n", "& multo plus ille, qui affctat ignorantiam, ut liberius peccet quam peccare sciens, cap. penul. 37 dist. & bonus, plusquam malus, & melior, quam minus bonus, & licet propterea utile sit, huiusmodi circunstantiam confiteri, non tamen communiter necessarium est :\n", "quoniam illa ut plurmum neque; ex veniali facit mortale, neque ex mortali unius speciei, mortale alterius, neque ex mortali uno respectu, mortale alio, propter alium finem.\n", "At vero, quando hoc fieret puta cum agitur contra votum, vel contra statum voto firmatum, ut cum religiosus fornicatur, circunstantia personae confitenda est, quoniam tunc aliquod ex illis tribus praedictis efficit.\n", "Quod quidem non ita sit, quando religiosus blasphaemat, vel quodius [p. 69 v] aliud crimen, quod non est contra sua vota vel regulam, quam est professus admittit, ut post Caietanum I. sec. q, 17 ar. 1 diximus in dicto principio c. Consideret, nu. 50.\n", "Circunstantia enim religionis communiter non facit esse mortale, quod suapte natura, non erat tale, neque etiam facit esse alterius speciei, quod ex se erat alterius, Et ita patet responsio ad id quod nuper quaesitum fuit, ad religiosus fornicans teneatur confiteri se esse religiosum, nam per praedicta est palam, eum ad id teneri.\n", "<p>\n", "21 [12] Decimoquarto, quod tunc demum & non alias est necessarium confiteri circumstantia quod peccaverit contra conscientiam, quando opus effectum ob nullam aliam lege, sed solum propter conscientiam erroneam tantum sit peccatum, tunc enim solum modo, unum ex tribus illis praedictis & non alias efficere manifestum est, sicut nos ubi supra nu. 58 & 65 nove declaravimus.\n", "<p>\n", "22 [13] Decimoquinto, quod numerus peccatorum non est circunstantia, sed additio peccati ad peccatum :\n", "Quoniam frequentatio non est circunstantia novuum peccatum constituens.\n", "Circa quod quatuor diximus in dicto c. Consideret, nu. 41.\n", "Primum, non esse satis dicere, peccavi saepe in hoc genere peccati; quoniam verbum illud, saepe, ita verificatur in numero denario, imo & in binario, sicut in centenario, iuxta glo. I. c. Monasteria, de vita, & honest. cleric. late Georg.\n", "in principio Clement.\n", "Saepe de verb. signif. quamvis Archidiacon.\n", "in c. Imitare 6. q. I. & in princ. d. c. Consideret, parte affirmativa defentat, quem licet Angelus verb. Conessio I. § 23. noluierit reprobare, merito tamen, eum reprobavit Cardinalis Alexandrinus in dicto c. Imitare.\n", "<p>\n", "23 [14] Secundum, Peccatorem teneri ad dicendum centum numerum peccatorum si eum noverit exprimere, dicendo hoc peccatum commisi toties.\n", "Quod si certum numerum minime novit, debet secundum rationem ponere, quoties in die, vel hebdomada, aut mense, plus minusve illus peccatum admiserit, & exponere numerum verisimiliorem.\n", "Peccaret enim mortaliter, qui prae pudore vel hypocrisi aliquid ex numero civis meminit subticeret.\n", "Imo etiam si per culpam suam latam fiat quo minus recordetur, eo quod ea de re nihil antea secum praecogitarat, cum tamen id facere potuisset, & proinde confessio nihil valet.\n", "<p>\n", "24 [15] Tertium, satis esse absque; aliquo numero, suum statum sufficienter manifestare, veluti si qua publica meretrix per decennium parata & exposita ad fornicandum, tam cum clericis, religiosis, & virginitate [p. 70] praeditis, quam laicis & coniugatis aut solutis & post conversionem confiteretur, exprimeretque, se toto illo temporis spatio in statu fuisse hoc, in quam, satis esset ut post Cardinalem Caiet.\n", "in q. 3. de confess. conclusimus in dicto c. Consideret, ponderantes illa verba, quantum perseveraverit & defleat quod perseveret peccavit.\n", "Per quae praedictam sententiam singulariter probavimus ut ibi diximus & addidimus, eum qui annum integrum omisit dicere horas canonicas, satisfacere dicendo, anno integro omisi divinum officium.\n", "<p>\n", "25[16] Quartum, quod toties numerus peccatorum augetur, quoties peccatum ipsum vel voluntas peccandi, quae fuerat intercisa renovatur, secundum Io.\n", "Andre.\n", "in reg. Delictum, col. penul de reg. iur. lib. 6. in Mercurial.\n", "Quod plane verum est in peccatis internis, quae solo animo consummantur, ut odium, haeresis, non autem idem est in illis, quae opere exterior consummatur.\n", "Nam illa voluntas non dicitur iterari, donec opere exteriori perficiatur vel interrumpatur, ut evenire solet, quando quis vadit in longinquum ad homicidium perpetrandum, & in ipso itinere, quod uno die conficit, nunc de illo, nunc de aliis mediatur, hic enim non peccat in hoc amplius quam unum peccatum licet multo gravius iuxta Caiet.\n", "in libello 17 respons. 15 responsi. quem in dicto principio .\n", "n. 48 sequimur, ponderants textum ad hoc singularem in c. Cum pro causa, de sent. excom.\n", "<p>\n", "26 Ex quo primo inferimus peccatorum non iterari, aut multiplicari etiamsi durante opere exteriori multoties voluntas interior interrumpatur & renovetur, neque etiam e contrario, si manente eadem voluntate opus multiplecetur exterius anteaquam delictum finiatur, uti copiosius in dicto principio, nu. 48. comprobavimus.\n", "Secundo quod omnes actus interiores, & exteriores qui tantum modo sunt veluti quaedam via ad unisum peccatum perpetrandum, unicum tantum efficiunt peccatum, etiamsi ili actus fuerint interrupti, quales sunt passus itineris, praeparatio equi, lanceae, aliorumque armorum una cum desideriis, variis vicibus interruptis, verbo, esu, & somno renovatis, eius qui vadit ad occidendum alium reiginta vel quadraginta milliaribus distantem.\n", "Nam licet illi actus in genere entis considerati, singulatim sint multae & variae res, tame considerati, tanquam via & partes ipsius peccati quibus totum peccatum est, consummandum, non amplius quam unum peccatum efficiunt, sicut lapides, columnae, ligna, reliquaqua materia, unius domus, multa sunt secundum se sumpta :\n", "caeterum omnia simul considerata tanquam partes unius domus non amplius quam univam domum conficiunt, l. Eum qui, ff. de usu cap. & l. Qui universa ff. de acquir. possess.\n", "<p>\n", "27[p. 70 v][17] Tertio, quid qui rem habuit cum aliqua, non tenetur confiteri colloquia, oscula & alios huiusmodi actus antecedens & immediatos ad coitum.\n", "At vero qui bis coit, etiam si unus actus sit immediatus post alterum, tenetur dicere se bis coivisse.\n", "Ratio diversitatis est, quia unus coitus istorum non est via neque praeambulum ordinatum ad alterum, colloquia vero, oscula, & amplexus sunt via ad copulam, quam praecedunt :\n", "quae omnia sunt quotidiana.\n", "Porro, non diximus (Qui sunt via, ) quoniam si acciderit interruptio, eo quod proposuit non consummare peccatum, vel quia mutavit sententiam poenitendo, vel propter alium finem, & postea velit illum consummare, tunc duo essent peccata distincta :\n", "licet praedictus Cardinalis circa hoc nihil admoneat, quod tament est quotidianum.\n", "Neque adiecimus sine causa (Qui tantummodo sunt via ad unicum peccatum perpetrandum) quoniam si actus illi sunt peccata secundum se, vel ordinantur ad alia peccata, tot peccata committuntur, quot sunt illi ex natura sua, vel quot sunt fines depravat ad quos diriguntur; Nam qui pergit ad homicirium perpetrandum, & in via furatur, rapit, peierat, blasphaemat vel suum esum, potum, iter & colloquia, non solum dirigit in homicidium conceptum, verum etiam in adulterium, infamiam, & sacrilegia perpetranda, tot peccata committit, quot sunt ibi actus in se mali, & quot fines mali in quos diriguntur.\n", "Quin eiam ibidem addidimus, quod sicut huiusmodi peccatum longo tempore continuatum, est gravius quam si momentaneum fuisset, sic etiam qui upsum commiserit meo consilio plus de illo dolebit, & spacium temporis, quod in eo insumptis in confessione explicabit.\n", "<p>\n", "28[18] Quarto, inferimus solutionem illius quaestionis quam nos interrogavit admodum R. Pater Antonius de Zurara Provinciae pietatis religiosissimae, quem ego propter maximas virtutes, scientiam, & prudentiam, & quia in hoc Manuali Hispano excudendo strenue me iuuit, plurimum quum viveret merito dilexi, quemque eo magis mortuum dilogo, qui vitam sanctam sine sanctissimo claudens, insignius seraphicae regulae observantiae exemplum, etiam eo nomine reliquit, quod nullatenus se habitu ordinis sui ante illam exui permisit, interrogavit inquam, Quot peccata mommitteret ille, qui longo tempore illicitis amoribus irretitus, mulierem persequitur, sed non potitur ea?\n", "decimus enim ut minimum toties illum peccare, quoties interrumpit malam illam voluntatem peccandi conceptam cessando ab impendenda opera exteriore pro illo tempore, & iterum postea illam continuat.\n", "[p. 71] Ita quod si diem integrum vel noctem, aut partem eorumque insumpsit musicis cantilenis, quo mulierem alliceret, vel opportunitati captandae, qua ipsam alloqueretur, vel aliis officiis, quibus in illum finem pessimum eam flecteret, quandiu talis voluntas & opera exerior non interrumpitur, unum tantum peccatum erit, licet tanto gravius, quanto diuturnius.\n", "At vero si opera illa exteriore quam tunc adhibet finita, vacat aliis negotiis, quae non sunt via, neque praeludia ad praedictum opus, & iterum redit ad eandem illam malam voluntatem solam, vel ad aliam faciendi aliud opus exterius simile vel dissimile praecedentibus, ad pravum illum finem consequendum, peccatum aliud committet, & tot suminde peccata tenebitur confiteri, quot interruptiones, aut renotationes huiusmodi intervenerint, praeter multas alias voluntates seu volitiones absolutas quas habuit absque operis consummatione, & confitendo numerum verisimilem eorum, satisfaciet pientissimo Domino, cuius misericordia, & patientia mirabilis est in ferendo hasce nostras audacissime impudentissimeque continuitas oggensas, cum posset eas vel solo nutu gravissime punire, ut re vera puniet, nisi ipsi prius nos punierimus.\n", "<p>\n", "29 Decimosexto, quod unico verbo pot poenitens mille peccata mortalia confiteri veluti dicendo, millies blasphemavi, millies peieravi, millies fornicatus sum, millies occidere statui, millies feci contra meum votum vel iuramentum, decies consului alicui, ut in peccato mortali perseveraret, illud aut illud toties feci ad finem fornicandi & c. quoniam huiusmodi confessioni nihil deest, ad ipsius integritatem, etiam si tot peccata paucis sed claris verbis expresserit, ut nos in principio dicti c. Consideret, nu. 110 post Caiet.\n", "Tom. .\n", "2 de contrituione q. 2 comprobavimus.\n", "<p>\n", "30 [19] Decimoseptimo, quod circunstantia scandali in duobus casibus est necessario confitenda, secundum omnes, uti nos, in c. 1. § Animadvertere, n. 5. de Poenit. dist. 5. diximus.\n", "Quoniam in his unum ex tribus illis supradictis efficit.\n", "Prior casus est, quando scandalum est formale, hoc est, quando aliquid dictum aut factum fuit, animo provodandi alium ad peccatum mortale, & non solum tenetur confiteri id quod praedicto animo dixit aut fecit, sed etiam tenetur explicare illam peccati speciem, ad quam provocare intendebat, per dicta supra in cap. precedenti.\n", "Posterior casus est, cum per opus suo genere bonumque, aut indifferens, secundum speciem tamen & apparentiam malum, proebetur occasio peccandi mortifere.\n", "Super alio vero tertio casu diversa est authorum sententia videlicet, Cum quis mortifere coram aliis peccat, absque proposito provodandi eos ad mortifere peddandum, nam Adrian [p. 71 v] in 4. de sacram confess. q. 4. col. 4. Io.\n", "Maior in 4. dist. 38 q. 3. & Sylvest.\n", "in summa verb scandalum, q. 3. sentiunt partem affirmativam; Beatus autem Tho.\n", "2. sec. q. 43. ar. 3. negativam, quatenus ait, Quod licet gravius peccet, qui peccat publice, quam qui peccat occulte, non tamen ob hoc dicitur communiter peccatum scandali speciale.\n", "Quod ipsum affirmat Caiet.\n", "in summa verb. scandalum.\n", "Nobis idem nunc videtur, quod ubi supra § Animadvertere, visum fuit, nempe, quod priori opinioni sit locus, quando eiusmodi peccatum, per eiusmodi personam, vel in conspectu eiusmodi hominum admittitur, quod verimiliter id videntes, peccati occasionem accipient.\n", "Opinio vero posteriori Thomae sit locus, quando non sit per talem personam, nec coram talibus; ut ibidem latius extendimus.\n", "<p>\n", "31[20] Deceimoctavo, notandum est, quod qui obliviscitur confiteri circunstantiam necessario confitendam, non obligatur ad iterum confitendum peccatum, quod iam est confessus, sed satis ei, ut solam circunstantiam confiteatur, verbi gratia, Iuravit quis se non iniecturum manus violentas in clericum, non furaturum, non blasphematurum, deinde percussit clericum, furatus est, blasphemavit, post quod confessus est se illa fecisse, oblitus dicere, quod antea iuraverat, se illa non commissurum, non tenetur ad iterum confitendum illa, sed satis est, confiteri, quod semel, bis, ter, vel toties, licita iuramenta fuit transgressus, vel confiteri se fecisse aliqua opera in se mala, quae se non facturum antea iuraverat, quod in dicto c. Consideret, nu. 104 latius tradidimus.\n", "<p>\n", "Secundum dubium, P. Vinc.\n", "est circa praedicta nu. 7 quod si ut nos teneamus, & ipse approbat, non est necessum confiteri, illas circunstantias quae notabiliter aggravant non mutando speciem sequeretur quod sufficeret fornicanti corde, & opere confiteri peccasse corde, non exprimendo quod opere compleverit.\n", "Et quod qui ter solamente peccavit, & bis mente, & opere satisfaceret confitendo se quinquies decrevisse fornicari, & quod etiam sequeretur posse confitentem respondere confessario interroganti an opere, peccaverit, quod non, quia potest negare illa quae non tenetur necessario confiteri, qualia sunt venialia & etiam mortalia iam legitime confessa.\n", "ad quae respondetur negando eiusmodi consequentias, quia conclusio nostra quam ipse quoque probat loquitur de circunstantia peccati, & non de peccato, neque de ipsius perfectione ad quae fiunt consequentiae, nam peccatum operis non est circunstantia peccati pentis, sed [p. 72 r] peccatum & perfectio peccati cordis, & a diversis non fit illatio.\n", "l. Papinianus exuli, ff. de minor. & c. Ad audientiam de Decim & peccata operis oportet confiteri, quia tenemur confiteri omnia peccata c. Omnis, de poenit. & remiss. ut supra in praeludio 7 nu. 23 post Thom.\n", "ibi citatum declaravimus, addentes rationem, quare sufficiat confiteri peccatum operis sine confessione peccati cordis, & non contra peccatum cordis sine peccato operis.\n", "<p>\n", "32\n", "<p>\n", "Contra praedicta errat & peccat.\n", "<p>\n", "I. Qui credit nullam circunstantiam peccati esse necessario confitentum.\n", "nu. 3\n", "<p>\n", "II. Qui credit numerum peccatorum non augeri per eiusdem peccati iterationem.\n", "nu. 16\n", "<p>\n", "III. Qui verum, vel verisimilem numerum iterationum pecati non confitetur.\n", "nu. 16.\n", "& sequenti\n", "<p>\n", "Quando vero ij censendi haeretici, consule Praeludium I. nu. 10\n", "<p>\n" ] } ], "source": [ "from nltk import sent_tokenize\n", "\n", "## First, train the sentence tokenizer:\n", "from pprint import pprint\n", "from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktLanguageVars, PunktTrainer\n", " \n", "class BulletPointLangVars(PunktLanguageVars):\n", " sent_end_chars = ('.', '?', ':', '!', '¶')\n", "\n", "trainer = PunktTrainer()\n", "trainer.INCLUDE_ALL_COLLOCS = True\n", "tokenizer = PunktSentenceTokenizer(trainer.get_params(), lang_vars = BulletPointLangVars())\n", "for tok in abbreviations : tokenizer._params.abbrev_types.add(tok)\n", "\n", "## Now we sentence-segmentize all our editions, printing results and saving them to files:\n", "\n", "# folder for the several segment files:\n", "outputBase = 'Azpilcueta/sentences'\n", "dest = None\n", "\n", "# Then, sentence-tokenize our segments:\n", "for i in range(numOfEds):\n", " dest = open(outputBase + '_' + str(year[i]) + '.txt',\n", " encoding='utf-8',\n", " mode='w')\n", " print(\"Sentence-split of ed. \" + str(i) + \":\")\n", " print(\"------\")\n", " for s in range(0, len(Editions[i])):\n", " for a in tokenizer.tokenize(Editions[i][s]):\n", " dest.write(a.strip() + '\\n')\n", " print(a)\n", " dest.write('<p>\\n')\n", " print('<p>')\n", " dest.close()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "... lemmatize/stopwordize it---" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:46:34.337476Z", "start_time": "2018-09-04T12:46:34.304391Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cleaned/lemmatized ed. 0 [PT]:\n", "------\n", "1549\n", "<p>\n" ] }, { "ename": "TypeError", "evalue": "string indices must be integers", "output_type": "error", "traceback": [ 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\u001b[0mwordform\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mwordform\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mwordforms\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mourLemmatiser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlang\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: string indices must be integers" ] } ], "source": [ "# folder for the several segment files:\n", "outputBase = 'Azpilcueta/sentences-lemmatized'\n", "dest = None\n", "\n", "# Then, sentence-tokenize our segments:\n", "for i in range(numOfEds):\n", " dest = open(outputBase + '_' + str(year[i]) + '.txt',\n", " encoding='utf-8',\n", " mode='w')\n", " stp = set(stopwords[i])\n", " print(\"Cleaned/lemmatized ed. \" + str(i) + \" [\" + language[i] + \"]:\")\n", " print(\"------\")\n", " for s in range(len(Editions[i])):\n", " for a in tokenizer.tokenize(Editions[i][s]):\n", " dest.write(\" \".join([x for x in ourLemmatiser(language[i])(a) if x not in stp]) + '\\n')\n", " print(\" \".join([x for x in ourLemmatiser(language[i])(a) if x not in stp]))\n", " dest.write('<p>\\n')\n", " print('<p>')\n", " dest.close()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these preparations made, *Hunaligning* 1552 and 1556 reports \"Quality 0.63417\" for unlemmatized and \"Quality 0.51392\" for lemmatized versions of the texts for its findings which still contain many errors. Removing \":\" from the sentence end marks gives \"Quality 0.517048/0.388377\", but from a first impression with fewer errors. Results can be output in different formats, xls files are [here](Azpilcueta/align_2018.07.05_16.10.43/sentences_1552-sentences_1556.xls) and [here](Azpilcueta/align_2018.07.05_15.45.13/sentences-lemmatized_1552-sentences-lemmatized_1556.xls)." ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-07-11T07:42:37.598721Z", "start_time": "2017-07-11T09:42:37.587926+02:00" } }, "source": [ "# Similarity <a name=\"DocumentSimilarity\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems we could now create another matrix replacing lemmata with concepts and retaining the tf/idf values (so as to keep a weight coefficient to the concepts). Then we should be able to calculate similarity measures across the same concepts...\n", "\n", "The approach to choose would probably be the \"cosine similarity\" of concept vector spaces. Again, there is a library ready for us to use (but you can find some documentation [here](http://blog.christianperone.com/2013/09/machine-learning-cosine-similarity-for-vector-space-models-part-iii/), [here](http://scikit-learn.org/stable/modules/metrics.html#cosine-similarity) and [here](https://en.wikipedia.org/wiki/Cosine_similarity).)\n", "\n", "**However, this is where I have to take a break now. I will return to here soon...**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:51.511891Z", "start_time": "2018-09-04T12:45:01.008Z" }, "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "similarities = pd.DataFrame(cosine_similarity(tfidf_matrix))\n", "similarities[round(similarities, 0) == 1] = 0 # Suppress a document's similarity to itself\n", "print(\"Pairwise similarities:\")\n", "print(similarities)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:51.512867Z", "start_time": "2018-09-04T12:45:01.011Z" }, "collapsed": true }, "outputs": [], "source": [ "print(\"The two most similar segments in the corpus are\")\n", "print(\"segments\", \\\n", " similarities[similarities == similarities.values.max()].idxmax(axis=0).idxmax(axis=1), \\\n", " \"and\", \\\n", " similarities[similarities == similarities.values.max()].idxmax(axis=0)[ similarities[similarities == similarities.values.max()].idxmax(axis=0).idxmax(axis=1) ].astype(int), \\\n", " \".\")\n", "print(\"They have a similarity score of\")\n", "print(similarities.values.max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alertbox alert-success\">Of course, in every set of documents, we will always find two that are similar in the sense of them being more similar to each other than to the other ones. Whether or not this actually *means* anything in terms of content is still up to scholarly interpretation. But at least it means that a scholar can look at the two documents and when she determines that they are not so similar after all, then perhaps there is something interesting to say about similar vocabulary used for different puproses. Or the other way round: When the scholar knows that two passages are similar, but they have a low \"similarity score\", shouldn't that say something about the texts's rhetorics?</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Word Clouds <a name=\"WordClouds\"/>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a library that takes word frequencies like above, calculates corresponding relative sizes of words and creates nice wordcloud images for our sections (again, taking the fourth segment as an example) like this:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:51.513869Z", "start_time": "2018-09-04T12:45:01.274Z" } }, "outputs": [], "source": [ "from wordcloud import WordCloud\n", "import matplotlib.pyplot as plt\n", "\n", "# We make tuples of (lemma, tf/idf score) for one of our segments\n", "# But we have to convert our tf/idf weights to pseudo-frequencies (i.e. integer numbers)\n", "frq = [ int(round(x * 100000, 0)) for x in Editions[1][3]]\n", "freq = dict(zip(fn, frq))\n", "\n", "wc = WordCloud(background_color=None, mode=\"RGBA\", max_font_size=40, relative_scaling=1).fit_words(freq)\n", "\n", "# Now show/plot the wordcloud\n", "plt.figure()\n", "plt.imshow(wc, interpolation=\"bilinear\")\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to have a nicer overview over the many segments than is possible in this notebook, let's create a new html file listing some of the characteristics that we have found so far..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2018-09-04T12:45:51.513869Z", "start_time": "2018-09-04T12:45:01.340Z" }, "collapsed": true }, "outputs": [], "source": [ "outputDir = \"Azpilcueta\"\n", "htmlfile = open(outputDir + '/Overview.html', encoding='utf-8', mode='w')\n", "\n", "# Write the html header and the opening of a layout table\n", "htmlfile.write(\"\"\"<!DOCTYPE html>\n", "<html>\n", " <head>\n", " <title>Section Characteristics</title>\n", " <meta charset=\"utf-8\"/>\n", " </head>\n", " <body>\n", " <table>\n", "\"\"\")\n", "\n", "a = [[]]\n", "a.clear()\n", "dicts = []\n", "w = []\n", "\n", "# For each segment, create a wordcloud and write it along with label and\n", "# other information into a new row of the html table\n", "for i in range(len(mx_array)):\n", " # this is like above in the single-segment example...\n", " a.append([ int(round(x * 100000, 0)) for x in mx_array[i]])\n", " dicts.append(dict(zip(fn, a[i])))\n", " w.append(WordCloud(background_color=None, mode=\"RGBA\", \\\n", " max_font_size=40, min_font_size=10, \\\n", " max_words=60, relative_scaling=0.8).fit_words(dicts[i]))\n", " # We write the wordcloud image to a file\n", " w[i].to_file(outputDir + '/wc_' + str(i) + '.png')\n", " # Finally we write the column row\n", " htmlfile.write(\"\"\"\n", " <tr>\n", " <td>\n", " <head>Section {a}: <b>{b}</b></head><br/>\n", " <img src=\"./wc_{a}.png\"/><br/>\n", " <small><i>length: {c} words</i></small>\n", " </td>\n", " </tr>\n", " <tr><td>&nbsp;</td></tr>\n", "\"\"\".format(a = str(i), b = label[i], c = len(tokenised[i])))\n", "\n", "# And then we write the end of the html file.\n", "htmlfile.write(\"\"\"\n", " </table>\n", " </body>\n", "</html>\n", "\"\"\")\n", "htmlfile.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This should have created a nice html file which we can open [here](./Solorzano/Overview.html)." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "245px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "skip_h1_title": false, "threshold": "2", "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
natronics/rust-fc
analysis/results.ipynb
1
10605
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing `rust-fc` To Simulation Output\n", "\n", "The simulator proccessing code adds realisic noise to the IMU input before sending it to `rust-fc`.\n", "\n", "We'll compare the clean \"ideal\" simulator numbers to what was actually received by `rust-fc`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import psas_packet\n", "from psas_packet.io import BinFile\n", "import csv\n", "import matplotlib.pyplot as plt\n", "from matplotlib import gridspec\n", "%matplotlib inline\n", "\n", "FPS2M = 0.3048\n", "LBF2N = 4.44822\n", "LBS2KG = 0.453592\n", "\n", "# Extend PSAS Packet to include our state message\n", "psas_packet.messages.MESSAGES[\"STAT\"] = psas_packet.messages.Message({\n", " 'name': \"State Vector\",\n", " 'fourcc': b'STAT',\n", " 'size': \"Fixed\",\n", " 'endianness': '!',\n", " 'members': [\n", " {'key': \"time\", 'stype': \"Q\"},\n", " {'key': \"accel\", 'stype': \"d\"},\n", " {'key': \"vel\", 'stype': \"d\"},\n", " {'key': \"alt\", 'stype': \"d\"},\n", " {'key': \"roll_rate\", 'stype': \"d\"},\n", " {'key': \"roll_angle\", 'stype': \"d\"},\n", " ]\n", "})\n", "\n", "\n", "# Read data from rust-fc\n", "logfile = BinFile('../logfile-000')\n", "max_acc = 0\n", "rust_time = []\n", "rust_accel_x = []\n", "rust_accel_y = []\n", "rust_accel_z = []\n", "rust_state_time = []\n", "rust_vel = []\n", "rust_alt = []\n", "for fourcc, data in logfile.read():\n", " if fourcc == 'ADIS':\n", " if data['Acc_X'] > max_acc:\n", " max_acc = data['Acc_X']\n", " rust_t = data['timestamp']/1.0e9\n", " rust_time.append(data['timestamp']/1.0e9)\n", " rust_accel_x.append(data['Acc_X'])\n", " rust_accel_y.append(data['Acc_Y'])\n", " rust_accel_z.append(data['Acc_Z'])\n", " if fourcc == 'STAT':\n", " rust_state_time.append(data['timestamp']/1.0e9)\n", " rust_vel.append(data['vel'])\n", " rust_alt.append(data['alt'])\n", "\n", "# Read data from JSBSim\n", "max_accel = 0\n", "sim_time = []\n", "measured_accel_x = []\n", "sim_vel_up = []\n", "sim_alt = []\n", "with open('../simulation/data.csv') as datafile:\n", " reader = csv.reader(datafile, delimiter=',')\n", " for row in reader:\n", " # ignore first line\n", " if row[0][0] == 'T':\n", " continue\n", " sim_time.append(float(row[0]))\n", " force_x = float(row[18]) * LBF2N\n", " weight = float(row[6]) * LBS2KG\n", " measured_accel_x.append(force_x/weight)\n", " if (force_x/weight) > max_accel:\n", " max_accel = force_x/weight\n", " sim_t = sim_time[-1]\n", " sim_vel_up.append(-float(row[10]) * FPS2M)\n", " sim_alt.append(float(row[2]))\n", "\n", "# line up time\n", "sim_offset = rust_t - sim_t\n", "sim_time = [t + sim_offset for t in sim_time]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Message Receive Time\n", "\n", "In JSBSim the IMU messages are requested to be sent at the real IMU rate of 819.2 Hz:\n", "\n", " <output name=\"localhost\" type=\"SOCKET\" protocol=\"UDP\" port=\"5123\" rate=\"819.2\">\n", "\n", "But there they are then processed in python for noise and binary packing. Then it's sent as UDP packets which may get lost. Let's see how they appear in the flight comptuer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the time difference between each ADIS message\n", "diff = [(rust_time[i+1] - t)*1000 for i, t in enumerate(rust_time[:-1])]\n", "\n", "fig, ax1 = plt.subplots(figsize=(18,7))\n", "plt.title(r\"rust-fc ADIS Message Interval\")\n", "plt.ylabel(r\"Time Since Last Sample [ms]\")\n", "plt.xlabel(r\"Sample Number [#]\")\n", "\n", "plt.plot(range(len(diff)), diff, 'r.', alpha=1.0, ms=0.3, label=\"rust-fc Sample Interval\")\n", "plt.plot((0, len(diff)), (1.2207, 1.2207), 'k-', lw=0.6, alpha=0.7, label=\"Expected Sample Interval\")\n", "\n", "ax1.set_yscale(\"log\", nonposy='clip')\n", "plt.ylim([0.1,100])\n", "#plt.xlim()\n", "ax1.legend(loc=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax1 = plt.subplots(figsize=(18,7))\n", "plt.title(r\"rust-fc ADIS Message Interval\")\n", "plt.ylabel(r\"Number of Samples [#]\")\n", "plt.xlabel(r\"Time Since Last Sample [ms]\")\n", "\n", "n, bins, patches = plt.hist(diff, 1000, histtype='step', normed=1, alpha=0.8, linewidth=1, fill=True)\n", "plt.plot((1.2207, 1.2207), (0, 1000), 'k-', lw=0.6, alpha=0.7, label=\"Expected Sample Interval\")\n", "\n", "plt.ylim([0, 35])\n", "#plt.xlim()\n", "ax1.legend(loc=1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IMU Noisy Acceleration\n", "\n", "Here we see the noise put into the IMU data and the true acceleration." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax1 = plt.subplots(figsize=(18,7))\n", "plt.title(r\"rust-fc Recorded IMU Acceleration\")\n", "plt.ylabel(r\"Acceleration [m/s${}^2$]\")\n", "plt.xlabel(r\"Run Time [s]\")\n", "\n", "plt.plot(rust_time, rust_accel_x, alpha=0.8, lw=0.5, label=\"rust-fc IMU 'Up'\")\n", "plt.plot(rust_time, rust_accel_y, alpha=0.8, lw=0.5, label=\"rust-fc IMU 'Y'\")\n", "plt.plot(rust_time, rust_accel_z, alpha=0.6, lw=0.5, label=\"rust-fc IMU 'Z'\")\n", "\n", "plt.plot(sim_time, measured_accel_x, 'k-', lw=1.3, alpha=0.6, label=\"JSBSim True Acceleration\")\n", "\n", "#plt.ylim()\n", "#plt.xlim()\n", "ax1.legend(loc=1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## State Tracking\n", "\n", "The flight comptuer only knows the Inertial state (acceleration). It keeps track of velocity and altitude by integrating this signal. Here we compare `rust-fc` internal state to the exact numbers from the simulator." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Computer difference from FC State and simulation \"real\" numbers\n", "\n", "sim_idx = 0\n", "vel = 0\n", "alt = 0\n", "i_count = 0\n", "sim_matched_vel = []\n", "vel_diff = []\n", "alt_diff = []\n", "for i, t in enumerate(rust_state_time):\n", " vel += rust_vel[i]\n", " alt += rust_alt[i]\n", " i_count += 1\n", " if sim_time[sim_idx] < t:\n", " sim_matched_vel.append(vel/float(i_count))\n", " vel_diff.append(sim_vel_up[sim_idx] - (vel/float(i_count)))\n", " alt_diff.append(sim_alt[sim_idx] - (alt/float(i_count)))\n", " vel = 0\n", " alt = 0\n", " i_count = 0\n", " sim_idx += 1\n", " if sim_idx > len(sim_time)-1:\n", " break\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(18,9))\n", "plt.subplots_adjust(hspace=0.001) # no space between vertical charts\n", "gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) # stretch main chart to be most of the width\n", "\n", "ax1 = plt.subplot(gs[0])\n", "plt.title(r\"rust-fc State Tracking: Velocity And Velocity Integration Error\")\n", "plt.ylabel(r\"Velocity [m/s]\")\n", "\n", "plt.plot(rust_state_time, rust_vel, alpha=0.8, lw=1.5, label=\"rust-fc State Vector Velocity\")\n", "plt.plot(sim_time, sim_vel_up, 'k-', lw=1.3, alpha=0.6, label=\"JSBSim True Velocity\")\n", "\n", "plt.ylim([-60,400])\n", "ticklabels = ax1.get_xticklabels()\n", "plt.setp(ticklabels, visible=False)\n", "\n", "ax2 = plt.subplot(gs[1])\n", "plt.xlabel(r\"Run Time [s]\")\n", "plt.ylabel(r\"Integration Drift Error [m/s]\")\n", "\n", "plt.plot(sim_time, vel_diff)\n", "\n", "ax1.legend(loc=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(18,9))\n", "plt.subplots_adjust(hspace=0.001) # no space between vertical charts\n", "gs = gridspec.GridSpec(2, 1, height_ratios=[2, 1]) # stretch main chart to be most of the width\n", "\n", "ax1 = plt.subplot(gs[0])\n", "plt.title(r\"rust-fc State Tracking: Altitude And ALtitude Integration Error\")\n", "plt.ylabel(r\"Altitude MSL [m]\")\n", "\n", "plt.plot(rust_state_time, rust_alt, alpha=0.8, lw=1.5, label=\"rust-fc State Vector Altitude\")\n", "plt.plot(sim_time, sim_alt, 'k-', lw=1.3, alpha=0.6, label=\"JSBSim True Velocity\")\n", "\n", "plt.ylim([1390, 7500])\n", "ticklabels = ax1.get_xticklabels()\n", "plt.setp(ticklabels, visible=False)\n", "\n", "ax2 = plt.subplot(gs[1])\n", "plt.xlabel(r\"Run Time [s]\")\n", "plt.ylabel(r\"Integration Drift Error [m]\")\n", "\n", "plt.plot(sim_time, alt_diff)\n", "\n", "#plt.xlim()\n", "ax1.legend(loc=1)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1+" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
DillonNovak/Programming-for-Chemical-Engineering-Applications
SRK EOS.ipynb
1
37162
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import math as mm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import odeint\n", "import scipy.optimize as op\n", "from ipywidgets import interact" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# truncates a number to n decimal points\n", "\n", "def trunk(x,n):\n", " return (int(x*(10**n))/(10**n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Soave Redlich-Kwong equation of state\n", "\n", "P = (RT)/(V-b) - a/(V(V+b))\n", "\n", " a = 0.42748*((R*Tc)^2/Pc)*(1+m*(1-sqrt(Tr))**2\n", " b = 0.08664*((R*Tc)/Pc)\n", " \n", " Tr = T/Tc\n", "\n", " m = 0.480 + 1.574*w - 0.176*w**2 \n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "def PVchart(T = 288):\n", " # declare variables\n", " P = 3.310656891 # MPa\n", " R = 8.314472 # gas constant (cm^3*MPa/mol*K)\n", " #T = 278 # temperature (K)\n", "\n", " # Ethane's critical parameters\n", " Tc = 305.4 # critical temp (K)\n", " Pc = 4.88 # critical pressure (MPa)\n", " w = 0.099 # acentric factor\n", "\n", " Tr = T/Tc # reduced temperature\n", "\n", " # Define functions\n", " m = 0.480 + 1.574*w - 0.176*(w**2)\n", " a = 0.42748*(((R*Tc)**2)/Pc)*(1+m*(1-mm.sqrt(Tr)))**2 \n", " b = 0.08664*((R*Tc)/Pc)\n", "\n", " # function to find roots\n", " def srk(Vm):\n", " return ((R*T)/(Vm-b) - a/(Vm*(Vm+b)) - P)\n", "\n", " # function to find P and plot\n", " def srkP(Vm):\n", " return (R*T)/(Vm-b) - a/(Vm*(Vm+b))\n", "\n", " i = 2\n", " lastVal = srk(i)\n", " cont = True\n", " roots = []\n", " \n", " while(cont):\n", " currentVal = srk(i) \n", " if (lastVal/currentVal < 0):\n", " roots.append(op.newton(srk,i))\n", " elif (len(roots) >= 4) or (i > 1e6):\n", " cont = False \n", " lastVal = currentVal\n", " i = i + 1\n", " \n", " if len(roots)>3:\n", " roots = roots[1:4]\n", "\n", " print(roots)\n", " print(op.fsolve(srk,[1,150]))\n", " # simulation\n", " V = np.linspace(1,2200,2200)\n", " P = []\n", " P = (srkP(V))\n", "\n", " #visualization\n", " plt.loglog(V,P)\n", "\n", " plt.xlabel('Volume [$cm^3/mol$]')\n", " plt.ylabel('Pressure [MPa]')\n", " plt.title('PV Diagram for Ethane')\n", " plt.grid()\n", " plt.plot([roots[0],roots[2]],[srkP(roots[0]),srkP(roots[2])],'o-')\n", " plt.text(200,7.5,[ \"vapor vol: \", trunk(roots[2],4) ])\n", " plt.text(200,5.5,[ \"liquid vol: \", trunk(roots[0],4) ])\n", " plt.axis([50,1000,-200,10])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[87.25638369878233, 276.3795371971188, 337.052119083776]\n", "[ 8.72347478e+01 -3.21140869e+08]\n" ] }, { "data": { "image/png": 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qsMz7779PtWrVcq4nJibSpk0bl8dis9lITU0t9Pbdu3djs9kKjdMVPHGOQBIe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\nGVdHTCSWzKwJ0A2YW97mQYp+HeiRtt81wC3AIDNbGV6edlm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"text/plain": [ "<matplotlib.figure.Figure at 0x218584a53c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(PVchart, T = (279,293,1));" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": { "d17a22f5ad554b1f971857ca17c28781": { "views": [ { "cell_index": 4 } ] } }, "version": "1.2.0" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
EconForge/dolo
examples/notebooks/consumption_savings.ipynb
1
120884
{ "cells": [ { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [], "source": [ "from dolo import *" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "model = yaml_import(\"../models/consumption_savings.yaml\")" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Solving WITH complementarities.\n", "------------------------------------------------\n", "| N | Error | Gain | Time | nit |\n", "------------------------------------------------\n", "| 1 | 8.000e-01 | nan | 0.024 | 5 |\n", "| 2 | 8.027e-02 | 0.100 | 0.031 | 6 |\n", "| 3 | 4.881e-02 | 0.608 | 0.019 | 4 |\n", "| 4 | 3.369e-02 | 0.690 | 0.023 | 5 |\n", "| 5 | 2.470e-02 | 0.733 | 0.019 | 4 |\n", "| 6 | 1.938e-02 | 0.785 | 0.023 | 5 |\n", "| 7 | 1.541e-02 | 0.795 | 0.023 | 5 |\n", "| 8 | 1.223e-02 | 0.794 | 0.020 | 4 |\n", "| 9 | 9.744e-03 | 0.797 | 0.021 | 4 |\n", "| 10 | 7.958e-03 | 0.817 | 0.020 | 4 |\n", "| 11 | 6.347e-03 | 0.798 | 0.020 | 4 |\n", "| 12 | 5.251e-03 | 0.827 | 0.019 | 4 |\n", "| 13 | 5.288e-03 | 1.007 | 0.023 | 5 |\n", "| 14 | 5.270e-03 | 0.997 | 0.023 | 5 |\n", "| 15 | 5.295e-03 | 1.005 | 0.023 | 5 |\n", "| 16 | 5.280e-03 | 0.997 | 0.024 | 5 |\n", "| 17 | 5.154e-03 | 0.976 | 0.024 | 5 |\n", "| 18 | 5.190e-03 | 1.007 | 0.024 | 5 |\n", "| 19 | 5.256e-03 | 1.013 | 0.016 | 3 |\n", "| 20 | 5.356e-03 | 1.019 | 0.016 | 3 |\n", "| 21 | 5.478e-03 | 1.023 | 0.016 | 3 |\n", "| 22 | 5.613e-03 | 1.025 | 0.016 | 3 |\n", "| 23 | 5.752e-03 | 1.025 | 0.016 | 3 |\n", "| 24 | 5.891e-03 | 1.024 | 0.016 | 3 |\n", "| 25 | 6.022e-03 | 1.022 | 0.019 | 4 |\n", "| 26 | 6.143e-03 | 1.020 | 0.019 | 4 |\n", "| 27 | 6.251e-03 | 1.018 | 0.019 | 4 |\n", "| 28 | 6.343e-03 | 1.015 | 0.019 | 4 |\n", "| 29 | 6.420e-03 | 1.012 | 0.019 | 4 |\n", "| 30 | 6.479e-03 | 1.009 | 0.020 | 4 |\n", "| 31 | 6.520e-03 | 1.006 | 0.019 | 4 |\n", "| 32 | 6.542e-03 | 1.003 | 0.019 | 4 |\n", "| 33 | 6.545e-03 | 1.001 | 0.020 | 4 |\n", "| 34 | 6.530e-03 | 0.998 | 0.019 | 4 |\n", "| 35 | 6.495e-03 | 0.995 | 0.020 | 4 |\n", "| 36 | 6.442e-03 | 0.992 | 0.019 | 4 |\n", "| 37 | 6.371e-03 | 0.989 | 0.019 | 4 |\n", "| 38 | 6.283e-03 | 0.986 | 0.024 | 5 |\n", "| 39 | 6.180e-03 | 0.984 | 0.025 | 5 |\n", "| 40 | 6.062e-03 | 0.981 | 0.023 | 5 |\n", "| 41 | 5.930e-03 | 0.978 | 0.024 | 5 |\n", "| 42 | 5.786e-03 | 0.976 | 0.023 | 5 |\n", "| 43 | 5.631e-03 | 0.973 | 0.023 | 5 |\n", "| 44 | 5.465e-03 | 0.971 | 0.016 | 3 |\n", "| 45 | 5.289e-03 | 0.968 | 0.016 | 3 |\n", "| 46 | 5.105e-03 | 0.965 | 0.015 | 3 |\n", "| 47 | 4.915e-03 | 0.963 | 0.015 | 3 |\n", "| 48 | 4.719e-03 | 0.960 | 0.015 | 3 |\n", "| 49 | 4.519e-03 | 0.958 | 0.015 | 3 |\n", "| 50 | 4.317e-03 | 0.955 | 0.015 | 3 |\n", "| 51 | 4.114e-03 | 0.953 | 0.015 | 3 |\n", "| 52 | 3.911e-03 | 0.951 | 0.016 | 3 |\n", "| 53 | 3.710e-03 | 0.949 | 0.016 | 3 |\n", "| 54 | 3.512e-03 | 0.947 | 0.015 | 3 |\n", "| 55 | 3.318e-03 | 0.945 | 0.015 | 3 |\n", "| 56 | 3.128e-03 | 0.943 | 0.015 | 3 |\n", "| 57 | 2.944e-03 | 0.941 | 0.015 | 3 |\n", "| 58 | 2.765e-03 | 0.939 | 0.015 | 3 |\n", "| 59 | 2.593e-03 | 0.938 | 0.015 | 3 |\n", "| 60 | 2.429e-03 | 0.936 | 0.015 | 3 |\n", "| 61 | 2.271e-03 | 0.935 | 0.014 | 3 |\n", "| 62 | 2.121e-03 | 0.934 | 0.015 | 3 |\n", "| 63 | 1.978e-03 | 0.933 | 0.015 | 3 |\n", "| 64 | 1.843e-03 | 0.932 | 0.015 | 3 |\n", "| 65 | 1.715e-03 | 0.931 | 0.015 | 3 |\n", "| 66 | 1.595e-03 | 0.930 | 0.016 | 3 |\n", "| 67 | 1.481e-03 | 0.929 | 0.016 | 3 |\n", "| 68 | 1.375e-03 | 0.928 | 0.015 | 3 |\n", "| 69 | 1.275e-03 | 0.928 | 0.015 | 3 |\n", "| 70 | 1.182e-03 | 0.927 | 0.015 | 3 |\n", "| 71 | 1.095e-03 | 0.926 | 0.014 | 3 |\n", "| 72 | 1.014e-03 | 0.926 | 0.010 | 2 |\n", "| 73 | 9.379e-04 | 0.925 | 0.010 | 2 |\n", "| 74 | 8.674e-04 | 0.925 | 0.011 | 2 |\n", "| 75 | 8.019e-04 | 0.924 | 0.011 | 2 |\n", "| 76 | 7.410e-04 | 0.924 | 0.011 | 2 |\n", "| 77 | 6.845e-04 | 0.924 | 0.011 | 2 |\n", "| 78 | 6.321e-04 | 0.923 | 0.011 | 2 |\n", "| 79 | 5.835e-04 | 0.923 | 0.011 | 2 |\n", "| 80 | 5.386e-04 | 0.923 | 0.011 | 2 |\n", "| 81 | 4.969e-04 | 0.923 | 0.011 | 2 |\n", "| 82 | 4.584e-04 | 0.922 | 0.011 | 2 |\n", "| 83 | 4.228e-04 | 0.922 | 0.011 | 2 |\n", "| 84 | 3.899e-04 | 0.922 | 0.011 | 2 |\n", "| 85 | 3.595e-04 | 0.922 | 0.011 | 2 |\n", "| 86 | 3.314e-04 | 0.922 | 0.011 | 2 |\n", "| 87 | 3.054e-04 | 0.922 | 0.011 | 2 |\n", "| 88 | 2.815e-04 | 0.922 | 0.010 | 2 |\n", "| 89 | 2.594e-04 | 0.921 | 0.011 | 2 |\n", "| 90 | 2.390e-04 | 0.921 | 0.011 | 2 |\n", "| 91 | 2.202e-04 | 0.921 | 0.011 | 2 |\n", "| 92 | 2.029e-04 | 0.921 | 0.011 | 2 |\n", "| 93 | 1.869e-04 | 0.921 | 0.011 | 2 |\n", "| 94 | 1.721e-04 | 0.921 | 0.011 | 2 |\n", "| 95 | 1.585e-04 | 0.921 | 0.011 | 2 |\n", "| 96 | 1.460e-04 | 0.921 | 0.011 | 2 |\n", "| 97 | 1.345e-04 | 0.921 | 0.011 | 2 |\n", "| 98 | 1.238e-04 | 0.921 | 0.011 | 2 |\n", "| 99 | 1.140e-04 | 0.921 | 0.011 | 2 |\n", "| 100 | 1.050e-04 | 0.921 | 0.011 | 2 |\n", "| 101 | 9.666e-05 | 0.921 | 0.011 | 2 |\n", "| 102 | 8.900e-05 | 0.921 | 0.012 | 2 |\n", "| 103 | 8.194e-05 | 0.921 | 0.011 | 2 |\n", "| 104 | 7.544e-05 | 0.921 | 0.011 | 2 |\n", "| 105 | 6.945e-05 | 0.921 | 0.011 | 2 |\n", "| 106 | 6.394e-05 | 0.921 | 0.012 | 2 |\n", "| 107 | 5.886e-05 | 0.921 | 0.011 | 2 |\n", "| 108 | 5.419e-05 | 0.921 | 0.011 | 2 |\n", "| 109 | 4.988e-05 | 0.921 | 0.012 | 2 |\n", "| 110 | 4.592e-05 | 0.921 | 0.011 | 2 |\n", "| 111 | 4.227e-05 | 0.921 | 0.011 | 2 |\n", "| 112 | 3.891e-05 | 0.920 | 0.011 | 2 |\n", "| 113 | 3.581e-05 | 0.920 | 0.010 | 2 |\n", "| 114 | 3.296e-05 | 0.920 | 0.010 | 2 |\n", "| 115 | 3.034e-05 | 0.920 | 0.010 | 2 |\n", "| 116 | 2.793e-05 | 0.920 | 0.011 | 2 |\n", "| 117 | 2.571e-05 | 0.920 | 0.011 | 2 |\n", "| 118 | 2.366e-05 | 0.920 | 0.011 | 2 |\n", "| 119 | 2.178e-05 | 0.920 | 0.011 | 2 |\n", "| 120 | 2.004e-05 | 0.920 | 0.012 | 2 |\n", "| 121 | 1.845e-05 | 0.920 | 0.008 | 1 |\n", "| 122 | 1.698e-05 | 0.920 | 0.007 | 1 |\n", "| 123 | 1.563e-05 | 0.920 | 0.007 | 1 |\n", "| 124 | 1.438e-05 | 0.920 | 0.007 | 1 |\n", "| 125 | 1.324e-05 | 0.920 | 0.006 | 1 |\n", "| 126 | 1.218e-05 | 0.920 | 0.007 | 1 |\n", "| 127 | 1.121e-05 | 0.920 | 0.007 | 1 |\n", "| 128 | 1.032e-05 | 0.920 | 0.007 | 1 |\n", "| 129 | 9.499e-06 | 0.920 | 0.006 | 1 |\n", "| 130 | 8.742e-06 | 0.920 | 0.006 | 1 |\n", "| 131 | 8.046e-06 | 0.920 | 0.006 | 1 |\n", "| 132 | 7.405e-06 | 0.920 | 0.006 | 1 |\n", "| 133 | 6.815e-06 | 0.920 | 0.007 | 1 |\n", "| 134 | 6.272e-06 | 0.920 | 0.007 | 1 |\n", "| 135 | 5.772e-06 | 0.920 | 0.007 | 1 |\n", "| 136 | 5.313e-06 | 0.920 | 0.006 | 1 |\n", "| 137 | 4.889e-06 | 0.920 | 0.006 | 1 |\n", "| 138 | 4.500e-06 | 0.920 | 0.006 | 1 |\n", "| 139 | 4.141e-06 | 0.920 | 0.007 | 1 |\n", "| 140 | 3.811e-06 | 0.920 | 0.006 | 1 |\n", "| 141 | 3.508e-06 | 0.920 | 0.006 | 1 |\n", "| 142 | 3.228e-06 | 0.920 | 0.006 | 1 |\n", "| 143 | 2.971e-06 | 0.920 | 0.007 | 1 |\n", "| 144 | 2.734e-06 | 0.920 | 0.006 | 1 |\n", "| 145 | 2.516e-06 | 0.920 | 0.007 | 1 |\n", "| 146 | 2.316e-06 | 0.920 | 0.006 | 1 |\n", "| 147 | 2.131e-06 | 0.920 | 0.006 | 1 |\n", "| 148 | 1.961e-06 | 0.920 | 0.006 | 1 |\n", "| 149 | 1.805e-06 | 0.920 | 0.006 | 1 |\n", "| 150 | 1.661e-06 | 0.920 | 0.007 | 1 |\n", "| 151 | 1.529e-06 | 0.920 | 0.007 | 1 |\n", "| 152 | 1.407e-06 | 0.920 | 0.007 | 1 |\n", "| 153 | 1.295e-06 | 0.920 | 0.014 | 1 |\n", "| 154 | 1.192e-06 | 0.920 | 0.014 | 1 |\n", "| 155 | 1.097e-06 | 0.920 | 0.007 | 1 |\n", "| 156 | 1.009e-06 | 0.920 | 0.007 | 1 |\n", "| 157 | 9.288e-07 | 0.920 | 0.007 | 1 |\n", "------------------------------------------------\n", "Elapsed: 2.1186208724975586 seconds.\n", "------------------------------------------------\n" ] } ], "source": [ "dr = time_iteration(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can also try the faster version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stochastic Simulations" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Shocks are discretized as a markov chain by default:\n", "dp = model.exogenous.discretize()\n", "sim_shock = dp.simulate(10, 100, i0=1)\n", "for i in range(10):\n", " plt.plot(sim_shock[:,i,0], color='red', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [], "source": [ "sim = simulate(model, dr, i0=1, N=100)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(121)\n", "for i in range(10):\n", " plt.plot(sim.sel(N=i,V='c'), color='red', alpha=0.5)\n", "plt.ylabel(\"$c_t$\")\n", "plt.xlabel(\"$t$\")\n", "plt.subplot(122)\n", "for i in range(10):\n", " plt.plot(sim.sel(N=i,V='w'), color='red', alpha=0.5)\n", "plt.xlabel(\"$t$\")\n", "plt.ylabel(\"$w_t$\")\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ergodic distribution" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "sim_long = simulate(model, dr, i0=1, N=1000, T=200)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[33mFutureWarning\u001b[0m:/opt/pablo/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713\n", " Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n" ] }, { "data": { "text/plain": [ "Text(0.5, 0, '$w$')" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn\n", "seaborn.distplot(sim_long.sel(T=199, V='w'))\n", "plt.xlabel(\"$w$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Decision Rule" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [], "source": [ "tab = tabulate(model, dr,'w')" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stable_wealth = model.eval_formula('1/r+(1-1/r)*w(0)', tab)\n", "plt.plot(tab['w'], tab['w'],color='black', linestyle='--')\n", "plt.plot(tab['w'], stable_wealth,color='black', linestyle='--')\n", "plt.plot(tab['w'], tab['c'])\n", "plt.xlabel(\"w_t\")\n", "plt.ylabel(\"c_t\")\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
bosscha/alma-calibrator
notebooks/sample/J0501_B6.ipynb
1
85943
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/scratch/home/rwibowo/anaconda3/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_toolkits.axes_grid module was deprecated in version 2.1. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist provies the same functionality instead.\n", " warnings.warn(message, mplDeprecation, stacklevel=1)\n" ] } ], "source": [ "import numpy as np\n", "import sewpy\n", "import aplpy\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "import matplotlib.pyplot as plt\n", "import aplpy\n", "from astropy.io import fits\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def runsextractor(image_file, detect_thresh=3.5, analysis_thresh=3.0):\n", " params = ['NUMBER', 'FLUX_ISO', 'FLUXERR_ISO', 'FLUX_AUTO', 'FLUXERR_AUTO', 'FLUX_BEST', 'FLUXERR_BEST', 'BACKGROUND', \n", " 'THRESHOLD', 'FLUX_MAX', 'XMAX_IMAGE', 'YMAX_IMAGE', 'XPEAK_IMAGE', 'YPEAK_IMAGE', 'ALPHAPEAK_J2000', \n", " 'DELTAPEAK_J2000', 'X_IMAGE', 'Y_IMAGE', 'ALPHA_SKY', 'DELTA_SKY', 'ALPHA_J2000', 'DELTA_J2000']\n", "\n", " config = {\"DETECT_THRESH\":detect_thresh, \"ANALYSIS_THRESH\":analysis_thresh}\n", "\n", " sew = sewpy.SEW(params=params, config=config)\n", "\n", " out = sew(image_file)\n", " data = out[\"table\"]\n", " \n", " ra, dec, flux, label = data['ALPHA_J2000'], data['DELTA_J2000'], data['FLUX_MAX'], data['NUMBER'].astype('int')\n", " \n", " return ra, dec, flux, label" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "imgfile = \"./images/concat2.ms.cont.image.fits\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Ouch, SExtractor complains :\n", "b''\n" ] } ], "source": [ "ra, dec, flux, label = runsextractor(imgfile)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(<Column name='ALPHA_J2000' dtype='float64' unit='deg' description='Right ascension of barycenter (J2000)' length=3>\n", " 75.3035913\n", " 75.3036114\n", " 75.3030971,\n", " <Column name='DELTA_J2000' dtype='float64' unit='deg' description='Declination of barycenter (J2000)' length=3>\n", " -1.9877503\n", " -1.987177\n", " -1.9874529,\n", " <Column name='FLUX_MAX' dtype='float64' unit='ct' description='Peak flux above background' length=3>\n", " 0.0006485541\n", " 0.0008014459\n", " 0.0008301067,\n", " <Column name='NUMBER' dtype='int64' description='Running object number ' length=3>\n", " 1\n", " 2\n", " 3)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ra, dec, flux, label" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#rms = 1.92070720004e-05\n", "#rms = 1.92046941265e-05\n", "rms = 5.4e-05\n", "#c = SkyCoord('11h39m10.702595s -13d50m43.63856s', unit=(u.hourangle, u.deg), frame='icrs')\n", "c = SkyCoord('05h01m12.8s -01d59m14s', unit=(u.hourangle, u.deg), frame='icrs')\n", "\n", "center_x, center_y = [c.ra.value, c.dec.value]\n", "\n", "PB = 23./3600.0 \n", "\n", "\n", "\n", "multp = np.array([5, 7, 10])\n", "lvl = rms*multp" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "hdu_list = fits.open(imgfile)\n", "image_data = hdu_list[0].data\n", "vmax = image_data.max()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Setting slices=[0, 0] [aplpy.core]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: PC01_01 = 1.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC02_01 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC03_01 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC04_01 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC01_02 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC02_02 = 1.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC03_02 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC04_02 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC01_03 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC02_03 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC03_03 = 1.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC04_03 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC01_04 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC02_04 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC03_04 = 0.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n", "WARNING: FITSFixedWarning: PC04_04 = 1.000000000000E+00 \n", "indices in parameterized keywords must not have leading zeroes. [astropy.wcs.wcs]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x648 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = aplpy.FITSFigure(imgfile)#, figsize=(10, 10), dpi=300)\n", "\n", "#image.show_colorscale()#(vmin=0, vmax=0.002)\n", "image.show_grayscale(vmin=0, vmax=vmax, invert=True)\n", "#image.show_colorbar()\n", "image.tick_labels.set_font(size='small')\n", "#image.show_contour()#colors='white', levels=lvl, alpha=0.75)\n", "image.hide_xaxis_label()\n", "image.hide_yaxis_label()\n", "image.hide_tick_labels()\n", "\n", "# BEAM\n", "image.add_beam()\n", "image.beam.set_color('black')\n", "\n", "# SCALE\n", "image.add_scalebar(5 * u.arcsecond)\n", "image.scalebar.set_label('5\"')\n", "image.scalebar.set_color(\"black\")\n", "image.scalebar.set_font_size(18)\n", "\n", "# CENTER source (calibrator)\n", "image.show_markers(center_x, center_y, marker='X', edgecolor=\"red\", facecolor=\"none\", s=100)\n", "\n", "# PB circle\n", "image.show_circles(center_x, center_y, PB/2.0, edgecolor='red', facecolor=\"none\")\n", "\n", "\n", "# DETECTED SOURCE\n", "image.show_markers(ra, dec, edgecolor='black', facecolor='none', marker='s', s=400)\n", "\n", "# for i, lbl in enumerate(label):\n", "# image.add_label(ra[i], dec[i]-0.0004, lbl, color='black', size=18)\n", " \n", "\n", "\n", "image.set_title(\"J0501-0159 in B6\", size=18)\n", "#image.add_label(ra[i], dec[i]-0.0004, \"J1139-1350 in B3\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# image.savefig(\"J0501_B6.png\", dpi=300, transparent=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#hdu_list = fits.open(imgfile)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'numpy.ndarray'>\n", "(1, 1, 200, 200)\n" ] } ], "source": [ "# image_data = hdu_list[0].data\n", "# print(type(image_data))\n", "# print(image_data.shape)\n", "# img = image_data[0][0]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.28495097160344" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# hdu_list[0].header['BMAJ']*3600" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7050560712814801" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# hdu_list[0].header['BMIN']*3600" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "87.55899047852" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# hdu_list[0].header['BPA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
gaufung/Data_Analytics_Learning_Note
DesignPattern/BridgePattern.ipynb
1
5403
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "桥梁模式(Bridge Pattern) \n", "# 1 代码 \n", "在一个画图程序中，常会见到这样的情况：有一些预设的图形，如矩形、圆形等，还有一个对象-画笔，调节画笔的类型（如画笔还是画刷，还是毛笔效果等）并设定参数（如颜色、线宽等），选定图形，就可以在画布上画出想要的图形了。要实现以上需求，先从最抽象的元素开始设计，即形状和画笔（暂时忽略画布，同时忽略画笔参数，只考虑画笔类型）。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Shape(object):\n", " name=\"\"\n", " param=\"\"\n", " def __init__(self,*param):\n", " pass\n", " def getName(self):\n", " return self.name\n", " def getParam(self):\n", " return self.name,self.param\n", "\n", "class Pen(object):\n", " shape=\"\"\n", " type=\"\"\n", " def __init__(self,shape):\n", " self.shape=shape\n", " def draw(self):\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "形状对象和画笔对象是最为抽象的形式。接下来，构造多个形状，如矩形和圆形：" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Rectangle(Shape):\n", " def __init__(self,long,width):\n", " self.name=\"Rectangle\"\n", " self.param=\"Long:%s Width:%s\"%(long,width)\n", " print (\"Create a rectangle:%s\"%self.param)\n", "class Circle(Shape):\n", " def __init__(self,radius):\n", " self.name=\"Circle\"\n", " self.param=\"Radius:%s\"%radius\n", " print (\"Create a circle:%s\"%self.param)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "紧接着是构造多种画笔，如普通画笔和画刷：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NormalPen(Pen):\n", " def __init__(self,shape):\n", " Pen.__init__(self,shape)\n", " self.type=\"Normal Line\"\n", " def draw(self):\n", " print (\"DRAWING %s:%s----PARAMS:%s\"%(self.type,self.shape.getName(),self.shape.getParam()))\n", "class BrushPen(Pen):\n", " def __init__(self,shape):\n", " Pen.__init__(self,shape)\n", " self.type=\"Brush Line\"\n", " def draw(self):\n", " print (\"DRAWING %s:%s----PARAMS:%s\" % (self.type,self.shape.getName(), self.shape.getParam()))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Create a rectangle:Long:20cm Width:10cm\n", "Create a circle:Radius:15cm\n", "DRAWING Normal Line:Rectangle----PARAMS:('Rectangle', 'Long:20cm Width:10cm')\n", "DRAWING Brush Line:Circle----PARAMS:('Circle', 'Radius:15cm')\n" ] } ], "source": [ "normal_pen = NormalPen(Rectangle('20cm','10cm'))\n", "brush_pen = BrushPen(Circle('15cm'))\n", "normal_pen.draw()\n", "brush_pen.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2 Discriptions \n", "桥梁模式又叫桥接模式，定义如下：将抽象与实现解耦（注意此处的抽象和实现，并非抽象类和实现类的那种关系，而是一种角色的关系，这里需要好好区分一下），可以使其独立变化。在形如上例中，Pen只负责画，但没有形状，它终究是不知道要画什么的，所以我们把它叫做抽象化角色；而Shape是具体的形状，我们把它叫做实现化角色。抽象化角色和实现化角色是解耦的，这也就意味着，所谓的桥，就是抽象化角色的抽象类和实现化角色的抽象类之间的引用关系。\n", "\n", "# 3 Advantages \n", "+ 抽象角色与实现角色相分离，二者可以独立设计，不受约束；\n", "+ 扩展性强：抽象角色和实现角色可以非常灵活地扩展。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 Usages\n", "+ 不适用继承或者原继承关系中抽象类可能频繁变动的情况，可以将原类进行拆分，拆成实现化角色和抽象化角色。\n", "+ 重用性比较大的场景。比如开关控制逻辑的程序，开关就是抽象化角色，开关的形式有很多种，\n", "\n", "# 5 Disadvantages \n", "+ 增加对系统理解的难度" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
llulai/deep-learning
transfer-learning/Transfer_Learning.ipynb
1
1342995
null
mit
joelowj/Udacity-Projects
Udacity-Deep-Learning-Foundation-Nanodegree/Project-4/dlnd_language_translation.ipynb
1
150646
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Language Translation\n", "In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n", "## Get the Data\n", "Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import problem_unittests as tests\n", "\n", "source_path = 'data/small_vocab_en'\n", "target_path = 'data/small_vocab_fr'\n", "source_text = helper.load_data(source_path)\n", "target_text = helper.load_data(target_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the Data\n", "Play around with view_sentence_range to view different parts of the data." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset Stats\n", "Roughly the number of unique words: 227\n", "Number of sentences: 137861\n", "Average number of words in a sentence: 13.225277634719028\n", "\n", "English sentences 0 to 10:\n", "new jersey is sometimes quiet during autumn , and it is snowy in april .\n", "the united states is usually chilly during july , and it is usually freezing in november .\n", "california is usually quiet during march , and it is usually hot in june .\n", "the united states is sometimes mild during june , and it is cold in september .\n", "your least liked fruit is the grape , but my least liked is the apple .\n", "his favorite fruit is the orange , but my favorite is the grape .\n", "paris is relaxing during december , but it is usually chilly in july .\n", "new jersey is busy during spring , and it is never hot in march .\n", "our least liked fruit is the lemon , but my least liked is the grape .\n", "the united states is sometimes busy during january , and it is sometimes warm in november .\n", "\n", "French sentences 0 to 10:\n", "new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n", "les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n", "california est généralement calme en mars , et il est généralement chaud en juin .\n", "les états-unis est parfois légère en juin , et il fait froid en septembre .\n", "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n", "son fruit préféré est l'orange , mais mon préféré est le raisin .\n", "paris est relaxant en décembre , mais il est généralement froid en juillet .\n", "new jersey est occupé au printemps , et il est jamais chaude en mars .\n", "notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n", "les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n" ] } ], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n", "\n", "sentences = source_text.split('\\n')\n", "word_counts = [len(sentence.split()) for sentence in sentences]\n", "print('Number of sentences: {}'.format(len(sentences)))\n", "print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n", "\n", "print()\n", "print('English sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n", "print()\n", "print('French sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement Preprocessing Function\n", "### Text to Word Ids\n", "As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of each sentence from `target_text`. This will help the neural network predict when the sentence should end.\n", "\n", "You can get the `<EOS>` word id by doing:\n", "```python\n", "target_vocab_to_int['<EOS>']\n", "```\n", "You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n", " \"\"\"\n", " Convert source and target text to proper word ids\n", " :param source_text: String that contains all the source text.\n", " :param target_text: String that contains all the target text.\n", " :param source_vocab_to_int: Dictionary to go from the source words to an id\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: A tuple of lists (source_id_text, target_id_text)\n", " \"\"\"\n", " \n", " source_id_text = [[source_vocab_to_int.get(letter, source_vocab_to_int['<UNK>'])\n", " for letter in line.split(' ')] for line in source_text.split('\\n')]\n", " target_id_text = [[target_vocab_to_int.get(letter, target_vocab_to_int['<UNK>'])\n", " for letter in line.split(' ')] + [target_vocab_to_int['<EOS>']]\n", " for line in target_text.split('\\n')]\n", " return source_id_text, target_id_text\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_text_to_ids(text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "helper.preprocess_and_save_data(source_path, target_path, text_to_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "import helper\n", "\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the Version of TensorFlow and Access to GPU\n", "This will check to make sure you have the correct version of TensorFlow and access to a GPU" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TensorFlow Version: 1.0.0\n", "Default GPU Device: /gpu:0\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) in [LooseVersion('1.0.0'), LooseVersion('1.0.1')], 'This project requires TensorFlow version 1.0 You are using {}'.format(tf.__version__)\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n", "- `model_inputs`\n", "- `process_decoding_input`\n", "- `encoding_layer`\n", "- `decoding_layer_train`\n", "- `decoding_layer_infer`\n", "- `decoding_layer`\n", "- `seq2seq_model`\n", "\n", "### Input\n", "Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "\n", "- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n", "- Targets placeholder with rank 2.\n", "- Learning rate placeholder with rank 0.\n", "- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n", "\n", "Return the placeholders in the following the tuple (Input, Targets, Learing Rate, Keep Probability)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def model_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate, keep probability)\n", " \"\"\"\n", " \n", " inputs = tf.placeholder(tf.int32, [None, None], name = 'input')\n", " targets = tf.placeholder(tf.int32, [None, None], name = 'targets')\n", " learning_rate = tf.placeholder(tf.float32, shape = None, name = 'learning_rate')\n", " keep_prob = tf.placeholder(tf.float32, shape = None, name = 'keep_prob')\n", " return inputs, targets, learning_rate, keep_prob\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_model_inputs(model_inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process Decoding Input\n", "Implement `process_decoding_input` using TensorFlow to remove the last word id from each batch in `target_data` and concat the GO ID to the beginning of each batch." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def process_decoding_input(target_data, target_vocab_to_int, batch_size):\n", " \"\"\"\n", " Preprocess target data for dencoding\n", " :param target_data: Target Placehoder\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param batch_size: Batch Size\n", " :return: Preprocessed target data\n", " \"\"\"\n", " \n", " GO_ID = target_vocab_to_int['<GO>']\n", " target_data = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n", " concat_data = tf.fill([batch_size, 1], GO_ID)\n", " target_data = tf.concat([concat_data, target_data], 1)\n", " return target_data\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_process_decoding_input(process_decoding_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encoding\n", "Implement `encoding_layer()` to create a Encoder RNN layer using [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob):\n", " \"\"\"\n", " Create encoding layer\n", " :param rnn_inputs: Inputs for the RNN\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param keep_prob: Dropout keep probability\n", " :return: RNN state\n", " \"\"\"\n", " \n", " encoding_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size)]\n", " * num_layers)\n", " encoding_cell = tf.contrib.rnn.DropoutWrapper(encoding_cell, keep_prob)\n", " _, rnn_state = tf.nn.dynamic_rnn(encoding_cell, rnn_inputs, dtype = tf.float32) \n", " return rnn_state\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_encoding_layer(encoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Training\n", "Create training logits using [`tf.contrib.seq2seq.simple_decoder_fn_train()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_train) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). Apply the `output_fn` to the [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder) outputs." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope,\n", " output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for training\n", " :param encoder_state: Encoder State\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embed_input: Decoder embedded input\n", " :param sequence_length: Sequence Length\n", " :param decoding_scope: TenorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Train Logits\n", " \"\"\"\n", " \n", " dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, keep_prob)\n", " train_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_train(encoder_state)\n", " train_pred, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(dec_cell,\n", " train_decoder_fn,\n", " dec_embed_input,\n", " sequence_length,\n", " scope = decoding_scope)\n", " train_logits = output_fn(train_pred)\n", " return train_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_train(decoding_layer_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decoding - Inference\n", "Create inference logits using [`tf.contrib.seq2seq.simple_decoder_fn_inference()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/simple_decoder_fn_inference) and [`tf.contrib.seq2seq.dynamic_rnn_decoder()`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/seq2seq/dynamic_rnn_decoder). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id,\n", " maximum_length, vocab_size, decoding_scope, output_fn, keep_prob):\n", " \"\"\"\n", " Create a decoding layer for inference\n", " :param encoder_state: Encoder state\n", " :param dec_cell: Decoder RNN Cell\n", " :param dec_embeddings: Decoder embeddings\n", " :param start_of_sequence_id: GO ID\n", " :param end_of_sequence_id: EOS Id\n", " :param maximum_length: The maximum allowed time steps to decode\n", " :param vocab_size: Size of vocabulary\n", " :param decoding_scope: TensorFlow Variable Scope for decoding\n", " :param output_fn: Function to apply the output layer\n", " :param keep_prob: Dropout keep probability\n", " :return: Inference Logits\n", " \"\"\"\n", " \n", " dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, keep_prob)\n", " infer_decoder_fn = tf.contrib.seq2seq.simple_decoder_fn_inference(\n", " output_fn, encoder_state, dec_embeddings,\n", " start_of_sequence_id, end_of_sequence_id,\n", " maximum_length - 1, vocab_size)\n", " inference_logits, _, _ = tf.contrib.seq2seq.dynamic_rnn_decoder(\n", " dec_cell, infer_decoder_fn, scope = decoding_scope)\n", " return inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer_infer(decoding_layer_infer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Decoding Layer\n", "Implement `decoding_layer()` to create a Decoder RNN layer.\n", "\n", "- Create RNN cell for decoding using `rnn_size` and `num_layers`.\n", "- Create the output fuction using [`lambda`](https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions) to transform it's input, logits, to class logits.\n", "- Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, sequence_length, decoding_scope, output_fn, keep_prob)` function to get the training logits.\n", "- Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, vocab_size, decoding_scope, output_fn, keep_prob)` function to get the inference logits.\n", "\n", "Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size,\n", " num_layers, target_vocab_to_int, keep_prob):\n", " \"\"\"\n", " Create decoding layer\n", " :param dec_embed_input: Decoder embedded input\n", " :param dec_embeddings: Decoder embeddings\n", " :param encoder_state: The encoded state\n", " :param vocab_size: Size of vocabulary\n", " :param sequence_length: Sequence Length\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :param keep_prob: Dropout keep probability\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " \n", " dec_cell = tf.contrib.rnn.MultiRNNCell([tf.contrib.rnn.BasicLSTMCell(rnn_size)] * num_layers)\n", " dec_cell = tf.contrib.rnn.DropoutWrapper(dec_cell, keep_prob)\n", " \n", " with tf.variable_scope(\"decoding\") as decoding_scope:\n", " output_fn = lambda x: tf.contrib.layers.fully_connected(x, vocab_size,\n", " None, scope = decoding_scope)\n", " train_logits = decoding_layer_train(encoder_state, dec_cell,\n", " dec_embed_input, sequence_length,\n", " decoding_scope, output_fn, keep_prob)\n", " decoding_scope.reuse_variables()\n", " inference_logits = decoding_layer_infer(encoder_state, dec_cell,\n", " dec_embeddings, target_vocab_to_int['<GO>'],\n", " target_vocab_to_int['<EOS>'], sequence_length - 1,\n", " vocab_size, decoding_scope, output_fn, keep_prob)\n", " return train_logits, inference_logits\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_decoding_layer(decoding_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "\n", "- Apply embedding to the input data for the encoder.\n", "- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob)`.\n", "- Process target data using your `process_decoding_input(target_data, target_vocab_to_int, batch_size)` function.\n", "- Apply embedding to the target data for the decoder.\n", "- Decode the encoded input using your `decoding_layer(dec_embed_input, dec_embeddings, encoder_state, vocab_size, sequence_length, rnn_size, num_layers, target_vocab_to_int, keep_prob)`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def seq2seq_model(input_data, target_data, keep_prob, batch_size, sequence_length, source_vocab_size, target_vocab_size,\n", " enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int):\n", " \"\"\"\n", " Build the Sequence-to-Sequence part of the neural network\n", " :param input_data: Input placeholder\n", " :param target_data: Target placeholder\n", " :param keep_prob: Dropout keep probability placeholder\n", " :param batch_size: Batch Size\n", " :param sequence_length: Sequence Length\n", " :param source_vocab_size: Source vocabulary size\n", " :param target_vocab_size: Target vocabulary size\n", " :param enc_embedding_size: Decoder embedding size\n", " :param dec_embedding_size: Encoder embedding size\n", " :param rnn_size: RNN Size\n", " :param num_layers: Number of layers\n", " :param target_vocab_to_int: Dictionary to go from the target words to an id\n", " :return: Tuple of (Training Logits, Inference Logits)\n", " \"\"\"\n", " \n", " enc_embed_input = tf.contrib.layers.embed_sequence(input_data,\n", " source_vocab_size,\n", " enc_embedding_size)\n", " encoder_state = encoding_layer(enc_embed_input, rnn_size,\n", " num_layers, keep_prob)\n", " dec_input = process_decoding_input(target_data, target_vocab_to_int, batch_size)\n", " dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, dec_embedding_size]))\n", " dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n", " return decoding_layer(dec_embed_input, dec_embeddings, encoder_state,\n", " target_vocab_size, sequence_length, rnn_size,\n", " num_layers, target_vocab_to_int, keep_prob)\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_seq2seq_model(seq2seq_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `num_layers` to the number of layers.\n", "- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n", "- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `keep_probability` to the Dropout keep probability" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of Epochs\n", "epochs = 4\n", "# Batch Size\n", "batch_size = 512\n", "# RNN Size\n", "rnn_size = 100\n", "# Number of Layers\n", "num_layers = 2\n", "# Embedding Size\n", "encoding_embedding_size = 50\n", "decoding_embedding_size = 50\n", "# Learning Rate\n", "learning_rate = 0.01\n", "# Dropout Keep Probability\n", "keep_probability = 0.9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "save_path = 'checkpoints/dev'\n", "(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n", "max_source_sentence_length = max([len(sentence) for sentence in source_int_text])\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " input_data, targets, lr, keep_prob = model_inputs()\n", " sequence_length = tf.placeholder_with_default(max_source_sentence_length, None, name='sequence_length')\n", " input_shape = tf.shape(input_data)\n", " \n", " train_logits, inference_logits = seq2seq_model(\n", " tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\n", " encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\n", "\n", " tf.identity(inference_logits, 'logits')\n", " with tf.name_scope(\"optimization\"):\n", " # Loss function\n", " cost = tf.contrib.seq2seq.sequence_loss(\n", " train_logits,\n", " targets,\n", " tf.ones([input_shape[0], sequence_length]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 Batch 0/269 - Train Accuracy: 0.242, Validation Accuracy: 0.310, Loss: 5.867\n", "Epoch 0 Batch 1/269 - Train Accuracy: 0.233, Validation Accuracy: 0.310, Loss: 5.225\n", "Epoch 0 Batch 2/269 - Train Accuracy: 0.266, Validation Accuracy: 0.310, Loss: 4.425\n", "Epoch 0 Batch 3/269 - Train Accuracy: 0.278, Validation Accuracy: 0.341, Loss: 3.930\n", "Epoch 0 Batch 4/269 - Train Accuracy: 0.270, Validation Accuracy: 0.343, Loss: 3.715\n", "Epoch 0 Batch 5/269 - Train Accuracy: 0.279, Validation Accuracy: 0.352, Loss: 3.631\n", "Epoch 0 Batch 6/269 - Train Accuracy: 0.313, Validation Accuracy: 0.342, Loss: 3.469\n", "Epoch 0 Batch 7/269 - Train Accuracy: 0.309, Validation Accuracy: 0.341, Loss: 3.345\n", "Epoch 0 Batch 8/269 - Train Accuracy: 0.282, Validation Accuracy: 0.347, Loss: 3.411\n", "Epoch 0 Batch 9/269 - Train Accuracy: 0.306, Validation Accuracy: 0.347, Loss: 3.260\n", "Epoch 0 Batch 10/269 - Train Accuracy: 0.276, Validation Accuracy: 0.347, Loss: 3.302\n", "Epoch 0 Batch 11/269 - Train Accuracy: 0.313, Validation Accuracy: 0.348, Loss: 3.182\n", "Epoch 0 Batch 12/269 - Train Accuracy: 0.289, Validation Accuracy: 0.349, Loss: 3.259\n", "Epoch 0 Batch 13/269 - Train Accuracy: 0.352, Validation Accuracy: 0.349, Loss: 2.946\n", "Epoch 0 Batch 14/269 - Train Accuracy: 0.312, Validation Accuracy: 0.349, Loss: 3.082\n", "Epoch 0 Batch 15/269 - Train Accuracy: 0.334, Validation Accuracy: 0.374, Loss: 3.059\n", "Epoch 0 Batch 16/269 - Train Accuracy: 0.336, Validation Accuracy: 0.366, Loss: 3.014\n", "Epoch 0 Batch 17/269 - Train Accuracy: 0.338, Validation Accuracy: 0.374, Loss: 2.976\n", "Epoch 0 Batch 18/269 - Train Accuracy: 0.313, Validation Accuracy: 0.381, Loss: 3.047\n", "Epoch 0 Batch 19/269 - Train Accuracy: 0.373, Validation Accuracy: 0.373, Loss: 2.920\n", "Epoch 0 Batch 20/269 - Train Accuracy: 0.312, Validation Accuracy: 0.375, Loss: 3.016\n", "Epoch 0 Batch 21/269 - Train Accuracy: 0.334, Validation Accuracy: 0.394, Loss: 3.047\n", "Epoch 0 Batch 22/269 - Train Accuracy: 0.361, Validation Accuracy: 0.387, Loss: 2.881\n", "Epoch 0 Batch 23/269 - Train Accuracy: 0.379, Validation Accuracy: 0.397, Loss: 2.836\n", "Epoch 0 Batch 24/269 - Train Accuracy: 0.343, Validation Accuracy: 0.405, Loss: 2.931\n", "Epoch 0 Batch 25/269 - Train Accuracy: 0.343, Validation Accuracy: 0.401, Loss: 2.907\n", "Epoch 0 Batch 26/269 - Train Accuracy: 0.396, Validation Accuracy: 0.397, Loss: 2.602\n", "Epoch 0 Batch 27/269 - Train Accuracy: 0.382, Validation Accuracy: 0.412, Loss: 2.709\n", "Epoch 0 Batch 28/269 - Train Accuracy: 0.354, Validation Accuracy: 0.427, Loss: 2.849\n", "Epoch 0 Batch 29/269 - Train Accuracy: 0.381, Validation Accuracy: 0.439, Loss: 2.793\n", "Epoch 0 Batch 30/269 - Train Accuracy: 0.406, Validation Accuracy: 0.438, Loss: 2.654\n", "Epoch 0 Batch 31/269 - Train Accuracy: 0.416, Validation Accuracy: 0.436, Loss: 2.606\n", "Epoch 0 Batch 32/269 - Train Accuracy: 0.406, Validation Accuracy: 0.439, Loss: 2.614\n", "Epoch 0 Batch 33/269 - Train Accuracy: 0.418, Validation Accuracy: 0.444, Loss: 2.513\n", "Epoch 0 Batch 34/269 - Train Accuracy: 0.421, Validation Accuracy: 0.447, Loss: 2.521\n", "Epoch 0 Batch 35/269 - Train Accuracy: 0.432, Validation Accuracy: 0.456, Loss: 2.492\n", "Epoch 0 Batch 36/269 - Train Accuracy: 0.435, Validation Accuracy: 0.464, Loss: 2.483\n", "Epoch 0 Batch 37/269 - Train Accuracy: 0.435, Validation Accuracy: 0.459, Loss: 2.460\n", "Epoch 0 Batch 38/269 - Train Accuracy: 0.428, Validation Accuracy: 0.462, Loss: 2.437\n", "Epoch 0 Batch 39/269 - Train Accuracy: 0.435, Validation Accuracy: 0.466, Loss: 2.399\n", "Epoch 0 Batch 40/269 - Train Accuracy: 0.422, Validation Accuracy: 0.476, Loss: 2.487\n", "Epoch 0 Batch 41/269 - Train Accuracy: 0.442, Validation Accuracy: 0.471, Loss: 2.365\n", "Epoch 0 Batch 42/269 - Train Accuracy: 0.462, Validation Accuracy: 0.469, Loss: 2.255\n", "Epoch 0 Batch 43/269 - Train Accuracy: 0.426, Validation Accuracy: 0.476, Loss: 2.389\n", "Epoch 0 Batch 44/269 - Train Accuracy: 0.457, Validation Accuracy: 0.472, Loss: 2.270\n", "Epoch 0 Batch 45/269 - Train Accuracy: 0.420, Validation Accuracy: 0.470, Loss: 2.371\n", "Epoch 0 Batch 46/269 - Train Accuracy: 0.405, Validation Accuracy: 0.469, Loss: 2.390\n", "Epoch 0 Batch 47/269 - Train Accuracy: 0.476, Validation Accuracy: 0.472, Loss: 2.107\n", "Epoch 0 Batch 48/269 - Train Accuracy: 0.449, Validation Accuracy: 0.471, Loss: 2.164\n", "Epoch 0 Batch 49/269 - Train Accuracy: 0.419, Validation Accuracy: 0.473, Loss: 2.268\n", "Epoch 0 Batch 50/269 - Train Accuracy: 0.431, Validation Accuracy: 0.477, Loss: 2.247\n", "Epoch 0 Batch 51/269 - Train Accuracy: 0.462, Validation Accuracy: 0.491, Loss: 2.183\n", "Epoch 0 Batch 52/269 - Train Accuracy: 0.456, Validation Accuracy: 0.478, Loss: 2.097\n", "Epoch 0 Batch 53/269 - Train Accuracy: 0.440, Validation Accuracy: 0.487, Loss: 2.183\n", "Epoch 0 Batch 54/269 - Train Accuracy: 0.444, Validation Accuracy: 0.485, Loss: 2.162\n", "Epoch 0 Batch 55/269 - Train Accuracy: 0.449, Validation Accuracy: 0.474, Loss: 2.043\n", "Epoch 0 Batch 56/269 - Train Accuracy: 0.454, Validation Accuracy: 0.474, Loss: 2.020\n", "Epoch 0 Batch 57/269 - Train Accuracy: 0.450, Validation Accuracy: 0.468, Loss: 2.008\n", "Epoch 0 Batch 58/269 - Train Accuracy: 0.460, Validation Accuracy: 0.478, Loss: 1.993\n", "Epoch 0 Batch 59/269 - Train Accuracy: 0.450, Validation Accuracy: 0.473, Loss: 1.941\n", "Epoch 0 Batch 60/269 - Train Accuracy: 0.450, Validation Accuracy: 0.461, Loss: 1.907\n", "Epoch 0 Batch 61/269 - Train Accuracy: 0.491, Validation Accuracy: 0.484, Loss: 1.845\n", "Epoch 0 Batch 62/269 - Train Accuracy: 0.457, Validation Accuracy: 0.451, Loss: 1.875\n", "Epoch 0 Batch 63/269 - Train Accuracy: 0.444, Validation Accuracy: 0.468, Loss: 1.902\n", "Epoch 0 Batch 64/269 - Train Accuracy: 0.453, Validation Accuracy: 0.477, Loss: 1.887\n", "Epoch 0 Batch 65/269 - Train Accuracy: 0.447, Validation Accuracy: 0.468, Loss: 1.874\n", "Epoch 0 Batch 66/269 - Train Accuracy: 0.456, Validation Accuracy: 0.466, Loss: 1.807\n", "Epoch 0 Batch 67/269 - Train Accuracy: 0.457, Validation Accuracy: 0.487, Loss: 1.854\n", "Epoch 0 Batch 68/269 - Train Accuracy: 0.445, Validation Accuracy: 0.465, Loss: 1.836\n", "Epoch 0 Batch 69/269 - Train Accuracy: 0.428, Validation Accuracy: 0.485, Loss: 1.952\n", "Epoch 0 Batch 70/269 - Train Accuracy: 0.459, Validation Accuracy: 0.472, Loss: 1.782\n", "Epoch 0 Batch 71/269 - Train Accuracy: 0.414, Validation Accuracy: 0.459, Loss: 1.870\n", "Epoch 0 Batch 72/269 - Train Accuracy: 0.466, Validation Accuracy: 0.471, Loss: 1.714\n", "Epoch 0 Batch 73/269 - Train Accuracy: 0.463, Validation Accuracy: 0.485, Loss: 1.749\n", "Epoch 0 Batch 74/269 - 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Train Accuracy: 0.912, Validation Accuracy: 0.925, Loss: 0.052\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3 Batch 264/269 - Train Accuracy: 0.898, Validation Accuracy: 0.921, Loss: 0.056\n", "Epoch 3 Batch 265/269 - Train Accuracy: 0.920, Validation Accuracy: 0.920, Loss: 0.048\n", "Epoch 3 Batch 266/269 - Train Accuracy: 0.917, Validation Accuracy: 0.922, Loss: 0.049\n", "Epoch 3 Batch 267/269 - Train Accuracy: 0.918, Validation Accuracy: 0.916, Loss: 0.058\n", "Model Trained and Saved\n" ] } ], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import time\n", "\n", "def get_accuracy(target, logits):\n", " \"\"\"\n", " Calculate accuracy\n", " \"\"\"\n", " max_seq = max(target.shape[1], logits.shape[1])\n", " if max_seq - target.shape[1]:\n", " target = np.pad(\n", " target,\n", " [(0,0),(0,max_seq - target.shape[1])],\n", " 'constant')\n", " if max_seq - logits.shape[1]:\n", " logits = np.pad(\n", " logits,\n", " [(0,0),(0,max_seq - logits.shape[1]), (0,0)],\n", " 'constant')\n", "\n", " return np.mean(np.equal(target, np.argmax(logits, 2)))\n", "\n", "train_source = source_int_text[batch_size:]\n", "train_target = target_int_text[batch_size:]\n", "\n", "valid_source = helper.pad_sentence_batch(source_int_text[:batch_size])\n", "valid_target = helper.pad_sentence_batch(target_int_text[:batch_size])\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(epochs):\n", " for batch_i, (source_batch, target_batch) in enumerate(\n", " helper.batch_data(train_source, train_target, batch_size)):\n", " start_time = time.time()\n", " \n", " _, loss = sess.run(\n", " [train_op, cost],\n", " {input_data: source_batch,\n", " targets: target_batch,\n", " lr: learning_rate,\n", " sequence_length: target_batch.shape[1],\n", " keep_prob: keep_probability})\n", " \n", " batch_train_logits = sess.run(\n", " inference_logits,\n", " {input_data: source_batch, keep_prob: 1.0})\n", " batch_valid_logits = sess.run(\n", " inference_logits,\n", " {input_data: valid_source, keep_prob: 1.0})\n", " \n", " train_acc = get_accuracy(target_batch, batch_train_logits)\n", " valid_acc = get_accuracy(np.array(valid_target), batch_valid_logits)\n", " end_time = time.time()\n", " print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.3f}, Validation Accuracy: {:>6.3f}, Loss: {:>6.3f}'\n", " .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_path)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save Parameters\n", "Save the `batch_size` and `save_path` parameters for inference." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params(save_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n", "load_path = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sentence to Sequence\n", "To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n", "\n", "- Convert the sentence to lowercase\n", "- Convert words into ids using `vocab_to_int`\n", " - Convert words not in the vocabulary, to the `<UNK>` word id." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tests Passed\n" ] } ], "source": [ "def sentence_to_seq(sentence, vocab_to_int):\n", " \"\"\"\n", " Convert a sentence to a sequence of ids\n", " :param sentence: String\n", " :param vocab_to_int: Dictionary to go from the words to an id\n", " :return: List of word ids\n", " \"\"\"\n", " \n", " sentence = sentence.lower()\n", " word_list = [vocab_to_int.get(word, vocab_to_int['<UNK>'])\n", " for word in sentence.split(' ')]\n", " return word_list\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_sentence_to_seq(sentence_to_seq)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Translate\n", "This will translate `translate_sentence` from English to French." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input\n", " Word Ids: [111, 228, 135, 180, 71, 145, 108]\n", " English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n", "\n", "Prediction\n", " Word Ids: [127, 326, 156, 194, 295, 157, 137, 1]\n", " French Words: ['il', 'a', 'vu', 'un', 'vieux', 'camion', '.', '<EOS>']\n" ] } ], "source": [ "translate_sentence = 'he saw a old yellow truck .'\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n", "\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_path + '.meta')\n", " loader.restore(sess, load_path)\n", "\n", " input_data = loaded_graph.get_tensor_by_name('input:0')\n", " logits = loaded_graph.get_tensor_by_name('logits:0')\n", " keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n", "\n", " translate_logits = sess.run(logits, {input_data: [translate_sentence], keep_prob: 1.0})[0]\n", "\n", "print('Input')\n", "print(' Word Ids: {}'.format([i for i in translate_sentence]))\n", "print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n", "\n", "print('\\nPrediction')\n", "print(' Word Ids: {}'.format([i for i in np.argmax(translate_logits, 1)]))\n", "print(' French Words: {}'.format([target_int_to_vocab[i] for i in np.argmax(translate_logits, 1)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imperfect Translation\n", "You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n", "\n", "You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n", "## Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
naoyak/Agile_Data_Code_2
ch07/Predicting flight delays with sklearn.ipynb
1
53524
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting Flight Delays with sklearn\n", "\n", "In this notebook, we will be using features we've prepared in PySpark to predict flight delays via regression and classification." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imports loaded...\n" ] } ], "source": [ "import sys, os, re\n", "sys.path.append(\"lib\")\n", "import utils\n", "\n", "import numpy as np\n", "import sklearn\n", "import iso8601\n", "import datetime\n", "print(\"Imports loaded...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and Inspect our JSON Training Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original JSON file size: 1,676,709,758 Bytes\n", "Training items: 5,714,008\n", "Data loaded...\n" ] } ], "source": [ "# Load and check the size of our training data. May take a minute.\n", "print(\"Original JSON file size: {:,} Bytes\".format(os.path.getsize(\"../data/simple_flight_delay_features.jsonl\")))\n", "training_data = utils.read_json_lines_file('../data/simple_flight_delay_features.jsonl')\n", "print(\"Training items: {:,}\".format(len(training_data))) # 5,714,008\n", "print(\"Data loaded...\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of training data in RAM: 50,897,424 Bytes\n", "{'DayOfWeek': 4, 'ArrDelay': 13.0, 'Dest': 'DFW', 'FlightDate': '2014-12-31T16:00:00.000-08:00', 'Carrier': 'AA', 'Distance': 569.0, 'FlightNum': '1024', 'CRSArrTime': '2015-01-01T10:10:00.000-08:00', 'CRSDepTime': '2015-01-01T07:30:00.000-08:00', 'DayOfMonth': 1, 'DayOfYear': 1, 'DepDelay': 14.0, 'Origin': 'ABQ'}\n" ] } ], "source": [ "# Inspect a record before we alter them\n", "print(\"Size of training data in RAM: {:,} Bytes\".format(sys.getsizeof(training_data))) # 50MB\n", "print(training_data[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample our Data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sampled items: 1,000,000 Bytes\n", "Data sampled...\n" ] } ], "source": [ "# We need to sample our data to fit into RAM\n", "training_data = np.random.choice(training_data, 1000000) # 'Sample down to 1MM examples'\n", "print(\"Sampled items: {:,} Bytes\".format(len(training_data)))\n", "print(\"Data sampled...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vectorize the Results (y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results vectorized size: 8,000,096\n", "Results vectorized...\n" ] } ], "source": [ "# Separate our results from the rest of the data, vectorize and size up\n", "results = [record['ArrDelay'] for record in training_data]\n", "results_vector = np.array(results)\n", "print(\"Results vectorized size: {:,}\".format(sys.getsizeof(results_vector))) # 45,712,160 bytes\n", "print(\"Results vectorized...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare Training Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ArrDelay and FlightDate removed from training data...\n" ] } ], "source": [ "# Remove the two delay fields and the flight date from our training data\n", "for item in training_data:\n", " item.pop('ArrDelay', None)\n", " item.pop('FlightDate', None)\n", "print(\"ArrDelay and FlightDate removed from training data...\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CRSArr/DepTime converted to unix time...\n" ] } ], "source": [ "# Must convert datetime strings to unix times\n", "for item in training_data:\n", " if isinstance(item['CRSArrTime'], str):\n", " dt = iso8601.parse_date(item['CRSArrTime'])\n", " unix_time = int(dt.timestamp())\n", " item['CRSArrTime'] = unix_time\n", " if isinstance(item['CRSDepTime'], str):\n", " dt = iso8601.parse_date(item['CRSDepTime'])\n", " unix_time = int(dt.timestamp())\n", " item['CRSDepTime'] = unix_time\n", "print(\"CRSArr/DepTime converted to unix time...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vectorize Training Data with `DictVectorizer`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sampled dimensions: [1,000,000]\n", "Size of DictVectorized vectors: 87,539,400 Bytes\n", "Training data vectorized...\n" ] } ], "source": [ "# Use DictVectorizer to convert feature dicts to vectors\n", "from sklearn.feature_extraction import DictVectorizer\n", "\n", "print(\"Sampled dimensions: [{:,}]\".format(len(training_data)))\n", "vectorizer = DictVectorizer()\n", "training_vectors = vectorizer.fit_transform(training_data)\n", "print(\"Size of DictVectorized vectors: {:,} Bytes\".format(training_vectors.data.nbytes))\n", "print(\"Training data vectorized...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare Experiment by Splitting Data into Train/Test" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(900000, 7436) (100000, 7436)\n", "(900000,) (100000,)\n", "Test train split performed...\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " training_vectors,\n", " results_vector,\n", " test_size=0.1,\n", " random_state=43\n", ")\n", "print(X_train.shape, X_test.shape)\n", "print(y_train.shape, y_test.shape)\n", "print(\"Test train split performed...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train our Model(s) on our Training Data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Regressor library and metrics imported...\n" ] } ], "source": [ "# Train a regressor\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import median_absolute_error, r2_score\n", "print(\"Regressor library and metrics imported...\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Regressor instantiated...\n" ] } ], "source": [ "regressor = LinearRegression()\n", "print(\"Regressor instantiated...\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Swapped gradient boosting trees for linear regression!\n", "Swapped back to linear regression!\n" ] } ], "source": [ "from sklearn.ensemble import GradientBoostingRegressor\n", "\n", "regressor = GradientBoostingRegressor\n", "print(\"Swapped gradient boosting trees for linear regression!\")\n", "\n", "# Lets go back for now...\n", "regressor = LinearRegression()\n", "print(\"Swapped back to linear regression!\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Regressor fitted...\n" ] } ], "source": [ "regressor.fit(X_train, y_train)\n", "print(\"Regressor fitted...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict Using the Test Data" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions made for X_test...\n" ] } ], "source": [ "predicted = regressor.predict(X_test)\n", "print(\"Predictions made for X_test...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluate and Visualize Model Accuracy" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median absolute error: 9.68\n", "r2 score: 0.828\n" ] } ], "source": [ "from sklearn.metrics import median_absolute_error, r2_score\n", "\n", "# Median absolute error is the median of all absolute differences between the target and the prediction.\n", "# Less is better, more indicates a high error between target and prediction.\n", "medae = median_absolute_error(y_test, predicted)\n", "print(\"Median absolute error: {:.3g}\".format(medae))\n", "\n", "# R2 score is the coefficient of determination. Ranges from 1-0, 1.0 is best, 0.0 is worst.\n", "# Measures how well future samples are likely to be predicted.\n", "r2 = r2_score(y_test, predicted)\n", "print(\"r2 score: {:.3g}\".format(r2))" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ys6XCQmsqRbTv7U/ca2bIzV0okBG1xYsXO90F1yEzM+RmH5mZiXVuGRndv56Z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\nNT/WXGFhIQDg+eefb/AcyJLxJkmyHzOTh7lJx8zkYW7SMTN5mJt0hYW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"text/plain": [ "<matplotlib.figure.Figure at 0x1169fd550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot outputs\n", "import matplotlib.pyplot as plt\n", "\n", "# Cleans up the appearance\n", "plt.rcdefaults()\n", "\n", "plt.scatter(\n", " y_test,\n", " predicted,\n", " color='blue',\n", " linewidth=1\n", ")\n", "plt.grid(True)\n", "\n", "plt.xticks()\n", "plt.yticks()\n", "\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
vallis/libstempo
demo/libstempo-demo.ipynb
2
330734
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## libstempo tutorial: basic functionality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Michele Vallisneri, [email protected]; latest revision: 2016/10/12 for v2.3 revision" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "import sys, math, numpy as N, matplotlib.pyplot as P" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the libstempo Python extension. It requires a source installation of tempo2, as well as current Python and compiler, and the numpy and Cython packages.\n", "\n", "(Both Python 2.7 and 3.4 are supported; this means that in Python 2.7 all returned strings will be unicode strings, while in Python 3 all function arguments should be default unicode strings rather than `bytes`. This should work transparently, although there are limitations to what characters can be passed to tempo2; you should probably restrain yourself to ASCII." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: AstropyDeprecationWarning: The private astropy._erfa module has been made into its own package, pyerfa, which is a dependency of astropy and can be imported directly using \"import erfa\" [astropy._erfa]\n" ] } ], "source": [ "from libstempo.libstempo import *" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import libstempo" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['/Users/jaellis/Packages/libstempo/libstempo']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "libstempo.__path__" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import libstempo as T\n", "\n", "T.data = T.__path__[0] + '/data/' # example files" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version : 3.8.8\n", "libstempo version: 2.3.5\n", "Tempo2 version : 2020.11.1\n" ] } ], "source": [ "print(\"Python version :\",sys.version.split()[0])\n", "print(\"libstempo version:\",T.__version__)\n", "print(\"Tempo2 version :\",T.libstempo.tempo2version())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We load a single-pulsar object. Doing this will automatically run the tempo2 fit routine once." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "psr = T.tempopulsar(\n", " parfile=T.data + \"/J1909-3744_NANOGrav_dfg+12.par\", \n", " timfile=T.data + \"/J1909-3744_NANOGrav_dfg+12.tim\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start simple: what is the name of this pulsar? (You can change it, by the way.)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1909-3744'" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's look at observations: there are `psr.nobs` of them; we can get numpy arrays of the site TOAs [in MJDs] with `psr.stoas`, of the TOA measurement errors [in microseconds] with `psr.toaerrs`, and of the measurement frequencies with `psr.freqs`. These arrays are *views* of the tempo2 data, so you can write to them (but you cannot currently change the number of observations)." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1001" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.nobs" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([53292.01653553, 53292.04810963, 53355.83359728, ...,\n", " 54641.17334037, 54706.99327092, 54764.83484499], dtype=float128)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.stoas" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.022" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.toaerrs.min()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.231, 4.668, 0.453, ..., 0.158, 1.336, 0.316])" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.toaerrs" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1372., 1372., 1372., ..., 884., 884., 884.])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.freqs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By contrast, barycentric TOAs and frequencies are computed on the basis of current pulsar parameters, so you get them by calling `psr` *methods* (with parentheses), and you get a *copy* of the current values. Writing to it has no effect on the tempo2 data." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([53292.01663801, 53292.04820909, 53355.82916309, ...,\n", " 54641.1796227 , 54706.99748746, 54764.83381846], dtype=float128)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.toas()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.37213133e+09, 1.37213155e+09, 1.37204346e+09, ...,\n", " 8.83982927e+08, 8.84066115e+08, 8.84080037e+08])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.ssbfreqs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Residuals (in seconds) are returned by residuals(). The method takes a few options... I'll let its docstring help describe them. libstempo is fully documented in this way (try `help(T.tempopulsar)`). " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function residuals:\n", "\n", "residuals(...) method of libstempo.libstempo.tempopulsar instance\n", " tempopulsar.residuals(updatebats=True,formresiduals=True,removemean=True)\n", " \n", " Returns residuals as a numpy.longdouble array (a copy of current values).\n", " Will update TOAs/recompute residuals if `updatebats`/`formresiduals` is True\n", " (default for both). Will remove residual mean if `removemean` is True;\n", " first residual if `removemean` is 'first'; weighted residual mean\n", " if `removemean` is 'weighted'.\n", "\n" ] } ], "source": [ "help(psr.residuals)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1.9495577521407410551e-05" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.residuals().min()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-8.90375027e-08, 7.20177874e-06, 1.09278937e-06, ...,\n", " 3.90245100e-07, 1.40247001e-06, -5.17387541e-07], dtype=float128)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.residuals()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot TOAs vs. residuals, but we should first sort the arrays; otherwise the array follow the order in the tim file, which may not be chronological." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 258, "width": 370 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# get sorted array of indices\n", "i = N.argsort(psr.toas())\n", "# use numpy fancy indexing to order residuals \n", "P.errorbar(psr.toas()[i],psr.residuals()[i],yerr=1e-6*psr.toaerrs[i],fmt='.',alpha=0.2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also see what flags have been set on the observations, and what their values are. The latter returns a numpy vector of strings. *Flags are not currently writable.*" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['fe', 'be', 'B', 'bw', 'tobs', 'pta', 'proc', 'chanid']" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.flags()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['gasp_1372', 'gasp_1372', 'gasp_1372', ..., 'gasp_884', 'gasp_884',\n", " 'gasp_884'], dtype='<U32')" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.flagvals('chanid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, there's a commodity routine in `libstempo.plot` to plot residuals, taking flags into account." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 278, "width": 419 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import libstempo.plot as LP\n", "\n", "LP.plotres(psr,group='pta',alpha=0.2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Timing-model parameters can be accessed by using psr as a Python dictionary. Each parameter is a special object with properties `val`, `err` (as well as `fit`, which is true is the parameter is currently being fitted, and `set`, which is true if the parameter was assigned a value)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5.0169080674060326785, 7.753759525058565179e-10, True, True)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr['RAJ'].val, psr['RAJ'].err, psr['RAJ'].fit, psr['RAJ'].set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The names of all fitted parameters, of all set parameters, and of *all* parameters are returned by `psr.pars(which='fit')`. We show only the first few." ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "82 158 4487\n", "('RAJ', 'DECJ', 'F0', 'F1', 'PMRA', 'PMDEC', 'PX', 'SINI', 'PB', 'A1')\n" ] } ], "source": [ "fitpars = psr.pars() # defaults to fitted parameters\n", "setpars = psr.pars(which='set')\n", "allpars = psr.pars(which='all')\n", "\n", "print(len(fitpars),len(setpars),len(allpars))\n", "print(fitpars[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of fitting parameters is psr.ndim." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "82" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.ndim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the parameter values results in different residuals." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1215cf4f0>]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 260, "width": 372 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# look +/- 3 sigmas around the current value\n", "x0, dx = psr['RAJ'].val, psr['RAJ'].err\n", "xs = x0 + dx * N.linspace(-3,3,20) \n", "\n", "res = []\n", "for x in xs:\n", " psr['RAJ'].val = x\n", " res.append(psr.rms()/1e-6)\n", "psr['RAJ'].val = x0 # restore the original value\n", "\n", "P.plot(xs,res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also call a least-squares fitting routine, which will fit around the current parameter values, replacing them with their new best values. Individual parameters can be included or excluded in the fitting by setting their 'fit' field. (Note: as of version 2.3.0, libstempo provides its own fit, although it does call tempo2 to compute the design matrix.)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr['DM'].fit" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.39468\n" ] } ], "source": [ "psr['DM'].fit = True\n", "print(psr['DM'].val)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-28-9573468db8f6>:1: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.9 it will stop working\n", " ret = psr.fit()\n" ] } ], "source": [ "ret = psr.fit()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12.44929217501132257 2.4420277109658732329\n" ] } ], "source": [ "print(psr['DM'].val,psr['DM'].err)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit returns a tuple consisting of best-fit vector, standard errors, covariance matrix, and linearized chisq. Note that these vectors and matrix are (ndim+1)- or (ndim+1)x(ndim+1)-dimensional, with the first row/column corresponding to a constant phase offset referenced to the first TOA (even if that point is not used).\n", "\n", "The exact chisq can be recomputed by `psr.chisq()` (which evaluates `N.sum(psr.residuals()**2 / (1e-12 * psr.toaerrs**2))`). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pulsar parameters can be read in bulk by calling `psr.vals(which='fit')`, which will default to fitted parameters, but can also be given `'all'`, `'set'`, or even a list of parameter names." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.01690807e+00 -6.58640325e-01 3.39315693e+02 -1.61479637e-15\n", " 1.24492922e+01 -9.61134885e+00 -3.55610623e+01 1.09376204e-01\n", " 9.98802510e-01 1.53344945e+00 1.89799103e+00 5.31139506e+04\n", " -4.23101754e-08 -2.76196797e-07 2.12686298e-01 -4.61493364e-04\n", " -1.33469060e-03 -1.54959068e-03 -1.56166022e-03 -1.53631872e-03\n", " -9.14443521e-04 -8.34872184e-04 -8.34769792e-04 -1.28675206e-03\n", " -1.31237732e-03 -1.77392514e-03 -1.93187424e-03 -1.11677453e-03\n", " -1.34125578e-03 -1.90583738e-03 -1.94116513e-03 -1.93439054e-03\n", " -1.83287537e-03 -1.37034067e-03 -1.31187932e-03 -1.50103444e-03\n", " -1.93739065e-03 -2.12021949e-03 -2.27831135e-03 -2.38561327e-03\n", " -2.32023428e-03 -2.26177402e-03 -2.11145472e-03 -1.81339103e-03\n", " -1.52561830e-03 -1.68029389e-03 -2.61732757e-05 -5.22638778e-05\n", " -7.82207213e-05 -1.03892764e-04 -1.29251007e-04 -1.54430070e-04\n", " -1.79441398e-04 -2.04096982e-04 -2.28617873e-04 -2.52966254e-04\n", " -2.77139592e-04 -3.01055318e-04 -3.24722586e-04 -3.48284300e-04\n", " -3.71647866e-04 9.06449976e-03 8.79400003e-03 8.66175892e-03\n", " 8.53142184e-03 8.40308958e-03 8.15198315e-03 8.02933281e-03\n", " 7.90829660e-03 7.78897788e-03 7.67132065e-03 7.55544076e-03\n", " 7.44119653e-03 7.32853224e-03 7.21731299e-03 7.10741712e-03\n", " 6.99977935e-03 6.89334945e-03 6.78835151e-03 6.68460406e-03\n", " 6.58242347e-03 6.48157123e-03 6.38234313e-03]\n" ] } ], "source": [ "fitvals = psr.vals()\n", "print(fitvals)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 5.01690807, -0.65864032, -9.61134885], dtype=float128)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "psr.vals(which=['RAJ','DECJ','PMRA'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To set parameter values in bulk, you give a first argument to `vals`. Or call it with a dictionary. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5.1 -0.6 -9.5]\n" ] } ], "source": [ "psr.vals([5.1,-0.6],which=['RAJ','DECJ','PMRA'])\n", "psr.vals({'PMRA': -9.5})\n", "\n", "print(psr.vals(which=['RAJ','DECJ','PMRA']))\n", "\n", "# restore original values\n", "psr.vals(fitvals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be careful about loss of precision; `tempopar.val` is a numpy longdouble, so you should be careful about assigning it a regular Python double. By contrast, doing arithmetics with numpy longdoubles will preserve their nature and precision." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can access errors in a similar way with `psr.errs(...)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's also possible to obtain the design matrix computed at the current parameter values, which has shape `psr.nobs * (len(psr.pars) + 1)`, since a constant offset is always included among the fitting parameters." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "d = psr.designmatrix()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These, for instance, are the derivatives with respect to RAJ and DECJ, evaluated at the TOAs." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1215845b0>]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 248, "width": 383 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# we need the sorted-index array compute above\n", "P.plot(psr.toas()[i]/365.25,d[i,1],'-x'); \n", "P.plot(psr.toas()[i]/365.25,d[i,2],'-x')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to save the current timing-model to a new par file. Omitting the argument will overwrite the original parfile." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "psr.savepar('./foo.par')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PSRJ 1909-3744\r\n", "RAJ 19:09:47.4380323 1 0.00000763036274628952 \r\n", "DECJ -37:44:14.31899 1 0.00032627056123570196 \r\n", "F0 339.31569275867968827 1 0.00000000000272673004 \r\n", "F1 -1.6147963690678677477e-15 1 2.3035693452634613346e-20\r\n", "PEPOCH 53000 \r\n", "POSEPOCH 53000 \r\n", "DMEPOCH 53000 \r\n", "DM 12.44929217501132257 1 2.44202771096587323285 \r\n", "PMRA -9.6113488454687274753 1 0.02203021686405922872 \r\n" ] } ], "source": [ "!head foo.par" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same for writing tim files." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "psr.savetim('./foo.tim')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FORMAT 1\r\n", "MODE 1\r\n", " 53292.000004.1.000.000.tsum 1372.00000000 53292.01653552588140172 1.23100 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 901.322 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53292.000010.1.000.000.tsum 1372.00000000 53292.04810962983469835 4.66800 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 901.322 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53355.000005.1.000.000.tsum 1372.00000000 53355.83359727578050169 0.45300 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 1081.587 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53800.000018.1.000.000.tsum 1372.00000000 53800.48353665754979858 0.14800 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 3424.969 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53838.000020.1.000.000.tsum 1372.00000000 53838.37506136744340068 0.54300 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 3064.446 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53858.000028.1.000.000.tsum 1372.00000000 53858.31918408581089963 0.08900 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 1982.877 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53889.000025.1.000.000.tsum 1372.00000000 53889.23934070044100153 0.07300 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 2703.923 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n", " 53979.000023.1.000.000.tsum 1372.00000000 53979.98924810684179931 0.16500 gbt -fe Rcvr1_2 -be GASP -B L -bw 4.0 -tobs 2703.923 -pta NANOGrav -proc dfg+12 -chanid gasp_1372 \r\n" ] } ], "source": [ "!head foo.tim" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With libstempo, it's easy to replicate some of the \"toasim\" plugin functionality. By subtracting the residuals from the site TOAs (psr.stoas, vs. the barycentered psr.toas) and refitting, we can create a \"perfect\" timing solution. (Note that 1 ns is roughly tempo2's claimed accuracy.)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7845169510588166\n" ] } ], "source": [ "print(math.sqrt(N.mean(psr.residuals()**2)) / 1e-6)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "psr.stoas[:] -= psr.residuals() / 86400.0\n", "ret = psr.fit(iters = 4)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.001511491882618748\n" ] } ], "source": [ "print(math.sqrt(N.mean(psr.residuals()**2)) / 1e-6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can add, e.g., homoskedastic white measurement noise at 100 ns (remember the tempo units: days for TOAs, us for errors, s for residuals)." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "psr.stoas[:] += 0.1e-6 * N.random.randn(psr.nobs) / 86400.0\n", "psr.toaerrs[:] = 0.1\n", "ret = psr.fit()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<ErrorbarContainer object of 3 artists>" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "image/png": { "height": 258, "width": 370 }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "i = N.argsort(psr.toas())\n", "P.errorbar(psr.toas()[i],psr.residuals()[i],yerr=1e-6*psr.toaerrs[i],fmt='.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }
mit
eds-uga/csci1360-fa16
assignments/A0/A0_header.ipynb
1
2759
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_header", "locked": true, "solution": false } }, "source": [ "# Assignment 0\n", "CSCI 1360E: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_dates", "locked": true, "solution": false } }, "source": [ "## Important Dates\n", "\n", " - Released: 2016-08-18 at 12:00am [EDT](http://www.timeanddate.com/worldclock/usa/athens)\n", " - Deadline: 2016-08-24 at 11:59:59pm [EDT](http://www.timeanddate.com/worldclock/usa/athens)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_grades", "locked": true, "solution": false } }, "source": [ "## Grading Breakdown\n", "\n", " - Q1: 25pts\n", " - Q2: 25pts\n", " - Q3: 25pts\n", " - Q4: 25pts\n", " \n", "Total: 100pts (but not really)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "a0_overview", "locked": true, "solution": false } }, "source": [ "## Overview\n", "\n", "This assignment will introduce you to the basics of the online infrastructure we'll be using this semester and show you how to use them. In particular, we'll walk through how to\n", "\n", " 1. Log into JupyterHub and interact with any released assignments.\n", " 2. Open up a basic Python shell through JupyterHub.\n", " 3. Run the lecture notebooks on your own.\n", " 4. Install Python on your local machine (OPTIONAL).\n", "\n", "Note the fourth point above: installing Python on your local machine is **optional**. However, it is nonetheless **highly recommended**. JupyterHub is great and can suffice if you don't want to bother yourself with installing Python, but if you don't have an internet connection and want/need to do some coding, you'll be out of luck if you don't have it already installed.\n", "\n", "Post any questions in `#questions`, or any technical issues in `#techprobs` on the Slack team." ] } ], "metadata": { "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
griffinfoster/fundamentals_of_interferometry
0_Introduction/0_introduction.ipynb
1
38281
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fundamentals of Radio Interferometry for Aperture Synthesis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interferometry is a fundamental technique used in radio astronomy but is often presented in an overly complicated way. It is true that the technique is rarely used in other fields of astronomy and physics. Many of our intutions about imaging, which comes from cameras and out own eyes, hide the underlying transformations which are central to aperture synthesis theory. With this book we hope to present a useful entry point into the topic by sticking to the fundamentals, this work is intended for a masters or doctoral student who is starting on the subject.\n", "\n", "As this is meant to be a living and evolving document with a number of contributors, any help in regards to content, editing, or presentation are welcome. We are striving to create the book we did not have when first learning interferometry and aperture synthesis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Outline <a id='preface:sec:outline'></a>\n", "\n", "* [Outline](#preface:sec:outline)\n", "* [A Note on Software](#preface:sec:software)\n", "* [Style Guide](#preface:sec:style)\n", "* [Known Issues](#preface:sec:issues)\n", "* [Glossary](1_glossary.ipynb#preface:sec:glossary)\n", "* [Mathematical Symbols](1_glossary.ipynb#preface:sec:symbols)\n", "* [Editing Guide](editing_guide.ipynb)\n", "* [Appendix](2_Appendix.ipynb#preface:sec:glossary)\n", "\n", "#### Colour Status\n", "\n", "There is a colour next to each section link which provides a quick status update on that particular section. The colours indicate:\n", "\n", "* <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span> : Status unknown.\n", "* <span style=\"background-color:red\">&nbsp;&nbsp;&nbsp;&nbsp;</span> : Need significant edits and rewrites.\n", "* <span style=\"background-color:orange\">&nbsp;&nbsp;&nbsp;&nbsp;</span> : For the ambitious to edit. Needs language rewrites or clarification/has major comments. Missing major content.\n", "* <span style=\"background-color:yellow\">&nbsp;&nbsp;&nbsp;&nbsp;</span> : Could be edited again. Needs minor edits/has minor comments. Missing minor content.\n", "* <span style=\"background-color:green\">&nbsp;&nbsp;&nbsp;&nbsp;</span> : Ready for another edit, just needs to be checked for style, notation, code, and figures.\n", "\n", "#### Content\n", "\n", "1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Radio Science using Interferometric Arrays](../1_Radio_Science/1_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Remarks on Basic Astrophysics](../1_Radio_Science/01_01_a_brief_introduction_to_basic_astrophysics.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Electromagnetic Radiation and Astronomical Quantities](../1_Radio_Science/01_02_electromagnetic_radiation_and_astronomical_quantities.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Radiation Transport](../1_Radio_Science/01_03_radiation_transport.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Radio Regime](../1_Radio_Science/01_04_radio_regime.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Black-body Radiation](../1_Radio_Science/01_05_black_body_radiation.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Synchrotron Emission](../1_Radio_Science/01_06_synchrotron_emission.ipynb)\n", " 7. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Line Emission](../1_Radio_Science/01_07_line_emission.ipynb)\n", " 8. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Astronomical Radio Sources](../1_Radio_Science/01_08_astronomical_radio_sources.ipynb)\n", " 9. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[A Brief Introduction to Interferometry](../1_Radio_Science/01_09_a_brief_introduction_to_interferometry.ipynb)\n", " 10. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Limits of Single Dishes](../1_Radio_Science/01_10_limits_of_single_dishes.ipynb)\n", " 11. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Modern Interferometric Arrays](../1_Radio_Science/01_11_modern_interferometric_arrays.ipynb)\n", " 12. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../1_Radio_Science/01_x_further_reading_and_references.ipynb)\n", "2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Mathematical Groundwork](../2_Mathematical_Groundwork/2_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Complex Numbers](../2_Mathematical_Groundwork/2_1_complex_numbers.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Field of Complex Numbers](../2_Mathematical_Groundwork/2_1_complex_numbers.ipynb#math:sec:the_field_of_complex_numbers)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Euler's Formula](../2_Mathematical_Groundwork/2_1_complex_numbers.ipynb#math:sec:eulers_formula)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Periodic Functions and Complex Numbers](../2_Mathematical_Groundwork/2_2_special_functions.ipynb#math:sec:periodic_functions_and_complex_numbers)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Important Functions](../2_Mathematical_Groundwork/2_2_important_functions.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Gaussian Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:gaussian_function)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Sinc Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:sinc_function)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Dirac Delta Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:dirac_delta_function)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Shah Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:shah_function)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Heavyside (step) Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:heavyside_function)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Box Function](../2_Mathematical_Groundwork/2_2_important_functions.ipynb#math:sec:box_function)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Fourier Series](../2_Mathematical_Groundwork/2_3_fourier_series.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Fourier Transform](../2_Mathematical_Groundwork/2_4_the_fourier_transform.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Definition](../2_Mathematical_Groundwork/2_4_the_fourier_transform.ipynb#math:sec:fourier_transform_definition)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Examples](../2_Mathematical_Groundwork/2_4_the_fourier_transform.ipynb#math:sec:fourier_transform_examples)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Convolution](../2_Mathematical_Groundwork/2_5_convolution.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Auto-correlation and Cross-correlation](../2_Mathematical_Groundwork/2_6_auto_correlation_and_cross_correlation.ipynb)\n", " 7. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Fourier Theorems](../2_Mathematical_Groundwork/2_7_fourier_theorems.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Similarity Theorem](../2_Mathematical_Groundwork/2_7_fourier_theorems.ipynb#math:sec:similarity_theorem)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Shift Theorem](../2_Mathematical_Groundwork/2_7_fourier_theorems.ipynb#math:sec:shift_theorem)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Convolution Theorem](../2_Mathematical_Groundwork/2_7_fourier_theorems.ipynb#math:sec:convolution_theorem)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Auto-correlation Theorem](../2_Mathematical_Groundwork/2_7_fourier_theorems.ipynb#math:sec:auto_correlation_theorem)\n", " 8. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT)](../2_Mathematical_Groundwork/2_8_the_discrete_fourier_transform.ipynb)\n", " 9. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Sampling Theory](../2_Mathematical_Groundwork/2_9_sampling_theory.ipynb)\n", " 10. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Linear Algrebra](../2_Mathematical_Groundwork/2_10_linear_algebra.ipynb)\n", " 11. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Least-squares Minimization](../2_Mathematical_Groundwork/2_11_least_squares.ipynb)\n", " 12. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Solid Angle](../2_Mathematical_Groundwork/2_12_solid_angle.ipynb)\n", " 13. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Spherical Trigonometry](../2_Mathematical_Groundwork/2_13_spherical_trigonometry.ipynb)\n", " 14. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../2_Mathematical_Groundwork/2_x_further_reading_and_references.ipynb)\n", "3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Positional Astronomy](../3_Positional_Astronomy/3_0_Introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Equatorial Coordinates (RA, Dec)](../3_Positional_Astronomy/3_1_Equatorial_Coordinates.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Hour Angle (HA) and Local Sidereal Time (LST)](../3_Positional_Astronomy/3_2_Hour_Angle.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Horizontal Coordinates (ALT,AZ)](../3_Positional_Astronomy/3_3_Horizontal_Coordinates.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Direction Cosine Coordinates ($l$,$m$,$n$)](../3_Positional_Astronomy/3_4_Direction_Cosine_Coordinates.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../3_Positional_Astronomy/3_x_further_reading_and_references.ipynb)\n", "4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Visibility Space](../4_Visibility_Space/4_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Baseline and Its Representations in Space](../4_Visibility_Space/4_1_The_Baseline.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The 2-element Interferometer](../4_Visibility_Space/4_2_The_2-element_Interferometer.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Visibility Function](../4_Visibility_Space/4_3_The_Visibility_Function.ipynb)\n", " 4. UV Coverage\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[UV Tracks](../4_Visibility_Space/4_4_1_UV_Coverage_UV_Tracks.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Improving Your Coverage](../4_Visibility_Space/4_4_2_UV_Coverage_Improving_Your_Coverage.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Fourier Approximation & van Cittert-Zernike Theorem](../4_Visibility_Space/4_5_The_Fourier_Approximation_VanCittert-Zernike_Theorem.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../4_Visibility_Space/4_x_further_reading_and_references.ipynb)\n", "5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Imaging](../5_Imaging/5_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Spatial Frequencies](../5_Imaging/5_1_spatial_frequencies.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Sampling and Point Spread Functions](../5_Imaging/5_2_sampling_functions_and_psfs.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Gridding and Degridding for using the FFT](../5_Imaging/5_3_gridding_and_degridding.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Dirty Image and Visibility Weightings](../5_Imaging/5_4_imaging_weights.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Break Down of the Small Angle Approximation and the W-Term](../5_Imaging/5_5_widefield_effect.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../5_Imaging/5_x_further_reading_and_references.ipynb)\n", "6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Deconvolution in Imaging](../6_Deconvolution/6_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Sky Models](../6_Deconvolution/6_1_sky_models.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Interative Deconvolution with Point Sources (CLEAN)](../6_Deconvolution/6_2_clean.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[CLEAN Implementations](../6_Deconvolution/6_3_clean_flavours.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Residuals and Image Quality](../6_Deconvolution/6_4_residuals_and_iqa.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Source Finding and Detection](../6_Deconvolution/6_5_source_finding.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../6_Deconvolution/6_x_further_reading_and_references.ipynb)\n", "7. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Observing Systems](../7_Observing_Systems/7_0_introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Jones Notation](../7_Observing_Systems/7_1_jones_notation.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[The Measurement Equation (RIME)](../7_Observing_Systems/7_2_rime.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Analogue Electronics](../7_Observing_Systems/7_3_analogue.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Digital Correlators](../7_Observing_Systems/7_4_digital.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Primary Beam](../7_Observing_Systems/7_5_primary_beam.ipynb)\n", " 6. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Polarization and Antenna Feeds](../7_Observing_Systems/7_6_polarization.ipynb)\n", " 7. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Propagation Effects](../7_Observing_Systems/7_7_propagation_effects.ipynb)\n", " 8. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Radio Frequency Interference (RFI)](../7_Observing_Systems/7_8_rfi.ipynb)\n", " 9. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../7_Observing_Systems/7_x_further_reading_and_references.ipynb)\n", "8. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Calibration](../8_Calibration/8_0_Introduction.ipynb)\n", " 1. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Calibration as a Least Squares Problem](../8_Calibration/8_1_Calibration_Least_Squares_Problem.ipynb)\n", " 2. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[1GC calibration](../8_Calibration/8_2_1GC.ipynb)\n", " 3. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[2GC calibration](../8_Calibration/8_3_2GC.ipynb)\n", " 4. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[3GC calibration](../8_Calibration/8_4_3GC.ipynb)\n", " 5. <span style=\"background-color:gray\">&nbsp;&nbsp;&nbsp;&nbsp;</span>[Further Reading and References](../8_Calibration/8_x_further_reading_and_references.ipynb)\n", "9. Practical Advice for Reducing a Data Set and Recognizing Errors\n", " 1. Imaging\n", " 1. W-Term Correction\n", " 2. Residual Artefacts\n", " 3. Practical Settings for CLEAN\n", " 4. Primary Beam Correction\n", " 5. Image Quality Assessment\n", " 2. Calibration\n", " 1. Frequency and Time Solution Intervals\n", " 2. Visibility Flagging\n", " 3. Including Direction-Dependent Solutions\n", " 3. Observing\n", " 1. Accumulation interval and smearing\n", " 4. Line observations: calibration and imaging issues\n", " 1. Source finding\n", " 2. Normalization\n", " 3. What now? How do we get to the science?\n", " 5. Continuum observations: calibration and imaging issues\n", " 1. Dynamic Range\n", " 2. Multi-Frequency Synthesis (MFS)\n", " 3. Source Finding\n", " 4. What now? How do we get to the science?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A Note on Software <a id='preface:sec:software'></a>\n", "\n", "This book is developed and tested with the following software dependencies (a guide for setting up a virtual environment with the current versions is available in the git repository readme):\n", "\n", "* python 2.7.6\n", "* ipython 4.2.0\n", "* numpy 1.10.1\n", "* matplotlib 1.5.0\n", "* astropy 1.1.1\n", "* aplpy 1.0\n", "* ipywidgets 4.1.1\n", "* healpy 1.10.3\n", "* ephem 3.7.6.0\n", "\n", "The very first entry in a notebook will import our current standard modules:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from IPython.display import HTML \n", "HTML('../style/course.css') #apply general CSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Followed by an optional import of any section specific modules, e.g. :" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.image as mpimg\n", "from IPython.display import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a section contains a significant amount of code, for readability it might be useful to suppress the code and only show the output. To do this an additional code block should be added:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.display import HTML\n", "HTML('../style/code_toggle.html')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Style Guide <a id='preface:sec:style'></a>\n", "\n", "#### Mathematical Notation:\n", "\n", "A global glossary defines all mathematical notation and useful definitions. \n", "\n", "Adhere to the following general mathematical notation:\n", "\n", "1. Vector, scalar and matrix:\n", " * $a, A$ - Denotes a scalar quantity\n", " * $\\mathbf{A}$, $\\boldsymbol{\\mathcal{A}}$ - Denotes a matrix\n", " * $\\mathbf{a}$ - Denotes a vector\n", "\n", "2. $2\\times2$-Polarized vs. $N\\times N$-Unpolarized matrices: \n", " * $\\mathbf{A}$ - Denotes a $2\\times2$ polarized Jones matrix. Number a Jones matrix\n", " with any other subscript than $N$.\n", " * $\\boldsymbol{\\mathcal{A}}$ - Denotes a $N\\times N$ unpolarzed matrix (contain all the unpolarized quantities associated with an array in one matrix).\n", "\n", "3. Jones versus Jacobian:\n", " * Please use $\\mathbf{J}$ to denote a Jones matrix and $\\mathbb{J}$ to denote a\n", " Jacobian matrix.\n", "\n", "4. Fourier transform:\n", " * Please use $\\mathscr{F}\\{\\cdot\\}$ to denote the Fourier transform. \n", " \n", "5. Subscript to avoid ambiguity:\n", " * If one symbol is used to denote two quantities use a subscript to remove ambiguity. For instance $\\lambda$ can mean wavelength or the LM-damping factor. Add a subscript, for\n", "instance $\\lambda_{\\textrm{LM}}$ now refers to to the LM-damping factor, while $\\lambda$ still refers to wavelength. Please add any new subscripted symbol to the glossary. \n", "\n", "The general list of symbols can be found in the [glossary &#10142;](1_glossary.ipynb#preface:sec:glossary).\n", "\n", "\n", "If you want to include a specific definition to a word or phase, then italicize the text the first time you use it in a section or chapter and add the term to the glossary.\n", "\n", "#### Notebook and Directory Naming Conventions:\n", "\n", "Each chapter is contained in a seperate directory, the directory name has the convention of the chapter number and chapter name, with spaces replaced by underscores, e.g. `6_deconvolution_in_imaging`. The directory will only contain notebooks, any data or additional files will be in a seperate global data directory which includes a duplicately named directory for each chapter in which to store the data.\n", "\n", "Notebook naming should be prefixed with the chapter number and either a sequential number based on ordering in the chapter. Like directories, the notebook name should be the section name (with underscores to replace spaces), a shortened version of the section is also fine, e.g. the Sky Model section of Chapter 6 would be `6_1_sky_models.ipynb`.\n", "\n", "#### Notebook Breadth:\n", "\n", "Each chapter is made up of multiple sections, each of which is possibly made up of sub-section, et cetera. To keep notebooks a reasonable and consumable size, a notebook should only contain a single section. For long sections it may be reasonable to further break up a section into multiple notebooks.\n", "\n", "#### Notebook Header:\n", "\n", "The beginning of each notebook should with a set of navigation links including a link to the global outline (this notebook), glossary, the chapter specific introduction notebook, the previous section notebook, and the next section notebook. See example section 5.1.\n", "\n", "Following the navigation links the standard python modules and any section specific modules should be imported, see 'A Note on Software' above for the current standard module import command. Following these import commands the notebook should start with a heading entry for the notebook with text that corresponds to the outlien text above, see 'Section and Subsection Headings' for sizes below.\n", "\n", "#### Noteboook Footer:\n", "\n", "At the end of a notebook, include a link to the next section notebook. If at the end of a chapter, provide a link to the next chapter.\n", "\n", "#### Chapter Introduction:\n", "\n", "Each chapter should contain a short introduction notebook which will provide an overview of the topics in the chapter and an outline of the notebooks in the chapter. At the end of the introduction include a list of editors and contributors of the chapter (indicate specific sections).\n", "\n", "#### Chapter Conclusion:\n", "\n", "The final notebook of a chapter should contain a section on further reading which contains links to papers and books, it may be useful to write a sentence about why a link is useful. Additionally, there should be a section which contains a list of all the external references noted in the chapter. Further, the conclusion to a chapter must contain a review of the topics covered in the chapter.\n", "\n", "#### Section and Subsection Headings:\n", "\n", "In a notebook, section names, e.g. 5.1 Spatial Frequencies, should use the heading size 2. While each subsequent sub-section should increase the heading size, e.g. a sub-section will be size 3, a sub-sub-section will be size 4,...\n", "\n", "#### Emphasizing important points / key points / prerequisites: \n", "\n", "For clarity, it is possible to create emphasized point in the course of a paragraph, or a summary of important concepts at the end of a section/chapter. This relies on the use of a common CSS for every user. The CSS style is defined in course.css and will be applied to one notebook upon calling initcss.css_styling(). Those two files are located in the /style dir in the main course dir. \n", "\n", "First add these python lines to load the CSS file in the main style directory (might change after some housekeeping/discussion)\n", "```\n", "from IPython.display import HTML \n", "HTML('../style/course.css') ##apply general CSS\n", "```\n", "\n", "To write a \"warning\" text box, one can use in a markdown:\n", "\n", "```\n", "<div class=warn>\n", "<b>Warning:</b> This relation assumes this particular hypothesis \n", "</div>\n", "```\n", "\n", "To write a note \"note\" or a piece of advice, use:\n", "\n", "```\n", "<div class=advice>\n", "<b>Advice:</b> Check the homogeneity of your equations !!!\n", "</div>\n", "```\n", "\n", "To create a green summary block:\n", "\n", "\n", "```\n", "<p class=conclusion>\n", " <font size=4> <b>Take-away message</b></font>\n", " <br>\n", " <br>\n", "&bull; <b>Conclusion 1</b>: Important item to remember with a specific <em>emphasized</em> word <br><br>\n", "&bull; <b>Conclusion 2</b>: A second important item to remember with a specific <em>emphasized</em> word.\n", "</p>\n", "```\n", "\n", "To create a \"Prerequisites\"/\"To read\" header block:\n", "\n", "```\n", "<p class=prerequisites>\n", " <font size=4> <b>Prerequisites</b></font>\n", " <br>\n", " <br>\n", "&bull; <b>Definition of ($u$,$v$,$w$):</b> [Go to 4.1](4_1_The_Baseline.ipynb) <br><br>\n", "&bull; <b>The visibility function:</b> [Go to 4.3](4_3_The_Visibility_Function.ipynb)\n", "</p>\n", "```\n", "\n", "#### References, Internal and External:\n", "\n", "One of the limitations of the ipython notebook is the inability to render equation, figure, and table labels properly. For the moment, we have settled on a consistent, but inelegant standard.\n", "\n", "Linking to internal (i.e. within the same notebook) and external (i.e. other notebooks in the book) references will use a dual method of using the standard markdown HREF and a LaTeX style so that dynamic links will work in the notebooks and conversion to PDF via LaTeX will contain references.\n", "\n", "Links internal to a notebook references are created by adding the down arrow symbol &#10549; (HTML code `&#10549;`) as the link, e.g. `[hyperlink text &#10549;](#destination)`. A reference is created by including `<a id='destination'></a>` where the desired reference desition is to be placed. In addition to the ipython notebook dynamic links LaTeX references should be included with the `\\label{destination}` and `\\ref{destination}` tags. An example of a complete internal reference is\n", "\n", "```\n", "[hyperlink text &#10549;](#destination) <!--\\ref{destination}-->\n", "```\n", "\n", "renders as: [hyperlink text &#10549;](#destination) <!--\\ref{destination}-->\n", "\n", "And a complete internal label is\n", "\n", "```\n", "<a id='destination'></a> <!--\\label{destination}-->\n", "```\n", "\n", "External links are similar to internal links, but use the right arrow symbol &#10142; (HTML code `&#10142;`) for a link. An example of a reference to another ipython notebook is `[hyperlink text &#10142;](another_notebook.ipynb#destination)` with a LaTeX tag `\\ref{destination}`. An example of a complete external reference is\n", "\n", "```\n", "[hyperlink text &#10142;](another_notebook.ipynb#destination) <!--\\ref{destination}-->\n", "```\n", "\n", "renders as: [hyperlink text &#10142;](another_notebook.ipynb#destination) <!--\\ref{destination}-->\n", "\n", "Note, HTML comment tags are wrapped around the Latex label and ref tags to hide them in the notebook.\n", "\n", "#### Citations:\n", "\n", "Citations to published work is a little tricky in our setup, we want to create two links. One for if we convert the notebook to latex we should be able to auto-generate a `\\cite{}` tag, see the nbconvert [citation&#10548;](https://github.com/jupyter/nbconvert-examples/tree/master/citations) [example&#10548;](https://github.com/jupyter/nbconvert-examples/blob/master/citations/Tutorial.ipynb). And, the other as a hyperlink to a abstract or copy of the paper (e.g. a NASA ADS link). To do this we need to create a link and use the HTML `<cite data-cite='bibtexRef'>` tag where `bibtexRef` is the name of the reference in the bibtex file in the chapter directory. An up arrow symbol &#10548; (HTML code `&#10548;`) is used to denote an external to the book hyperlink. An example of a complete citation is\n", "\n", "```\n", "[<cite data-cite='1999ASPC..180.....T'>Synthesis Imaging in Radio Astronomy II</cite> &#10548;](http://adsabs.harvard.edu/abs/1999ASPC..180.....T)\n", "```\n", "\n", "which renders as:\n", "\n", "[<cite data-cite='1999ASPC..180.....T'>Synthesis Imaging in Radio Astronomy II</cite> &#10548;](http://adsabs.harvard.edu/abs/1999ASPC..180.....T)\n", "\n", "Where there is an entry in the bibtex file\n", "\n", "```\n", "@PROCEEDINGS{1999ASPC..180.....T,\n", " title = \"{Synthesis Imaging in Radio Astronomy II}\",\n", "booktitle = {Synthesis Imaging in Radio Astronomy II},\n", " year = 1999,\n", " series = {Astronomical Society of the Pacific Conference Series},\n", " volume = 180,\n", " editor = {{Taylor}, G.~B. and {Carilli}, C.~L. and {Perley}, R.~A.},\n", " adsurl = {http://adsabs.harvard.edu/abs/1999ASPC..180.....T},\n", " adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n", "}\n", "```\n", "\n", "#### Reference Naming Conventions:\n", "\n", "In order to maintain uniform and informative reference labels we will use a standard naming convention of the form `chapterStr:type:uniqueID` where `chapterStr` is a unqiue, descriptive string for a chapter or chapter subsection, `type` is the type of content beinging labelled, and `uniqueID` is a unique ID for the content. For example a table in the Imaging chapter which contains information on weights could have the label `imaging:tbl:weights`. The `chapterStr` for each chapter is to be defined by the authors. If a section of a chapter is sufficiently large it shoud have its own `chapterStr`, perhaps with the prefix including the chapter `chapterStr`. The following are valid strings for `type`: `tbl` (table), `fig` (figure), `sec` (section), `code` (code block), `eq` (equation). The `uniqueID` is left to the authors, but suggested to contain a simple descriptive string and information on location within the chapter.\n", "\n", "| Chapter | chapterStr |\n", "|------------|-------------:|\n", "| Preface | `preface` |\n", "| 1. Radio Science using Interferometers | `science` |\n", "| 2. Mathematical Groundwork | `math` |\n", "| 3. Positional Astronomy | `pos` |\n", "| 4. Visibility Space | `vis` |\n", "| 5. Imaging | `imaging` |\n", "| 6. Deconvolution in Imaging | `deconv` |\n", "| 7. Observing Systems | `instrum` |\n", "| 8. Calibration | `cal` |\n", "| 9. Putting it all together | `pract` |\n", "\n", "| type | value |\n", "|----------|--------:|\n", "| code | `code` |\n", "| equation | `eq` |\n", "| figure | `fig` |\n", "| section | `sec` |\n", "| table | `tbl` |\n", "\n", "#### Images and Pre-made Figures:\n", "\n", "Though, ideally any figure that is included in a notebook can be generated from the code included in the notebook, this is not always possible. The preferred image type is PDF or SVG because these can be rescaled without aliasing issues, but PNG and JPEG can be used. If a figure or image is generated by some set of code, please include a reference or notes to that code so that if it needs to be regenerated then that will be possible. If a figure or image is made with a graphics progams such as Inkscape, GIMP, et cetera please include the working file in the git repository.\n", "\n", "To display figures use the Image function, e.g.\n", "\n", "```\n", "Image(filename='figures/sidereal.png', width=300, height=100)\n", "```\n", "\n", "or in HTML in a markdown cell (will center the figure):\n", "```\n", "<img src='figures/sidereal.png' width=30%>\n", "```\n", "An include a description block below the figure, which can include a label for referencing.\n", "\n", "\n", "#### Figures and Code Blocks:\n", "\n", "Below each code block or figure include a cell which contains a description of what is presented, use italics, which in markdown means by starting and ending the text with stars, e.g. \\*this text would be italized in markdown\\*. In this block one can include a label for referencing the figure or code block.\n", "\n", "#### 3D Figures:\n", "\n", "For a 3D figure include\n", "\n", "```\n", "%matplotlib nbagg\n", "```\n", "\n", "in the block to embed the figure in the notebook but allow for interaction.\n", "\n", "#### Equation Blocks:\n", "\n", "Equations can be written inline or in individual blocks. If you would like to reference an equation block, follow the label standard defined in 'References, Internal and External' above.\n", "\n", "#### Coding style:\n", "\n", "The majority of the code in this notebook is python, so please follow standard [Python PEP 8&#10548;](https://www.python.org/dev/peps/pep-0008/)\n", "\n", "#### Committing to git repository:\n", "\n", "Notebooks can get very large in size when they contain a number of generated figures. In order to keep the size of the repository down to a reasonable size please clear the output before making a new commit. This is done by selecting Cell > All Output > Clear from the menu at the top of the notebook.\n", "\n", "Binary files should be stored in a directory in each chapter, for example images would be stored in a directory called figures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Crossing out in math mode\n", "In calculus, it might be interesting to show simplifications as follow:\n", "$\\require{cancel}$\n", "\n", "$B=\\frac{x \\cancel{a} y}{b \\cancel{a}}$\n", "\n", "To do that, you need to write this in a markdown:\n", "\n", "```\n", "$\\require{cancel}$\n", "\n", "$\\frac{x \\cancel{a} y}{b \\cancel{a}}$\n", "```\n", "Please report to the \"cancel\" package for other crossing-out styles\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Known Issues <a id='preface:sec:issues'></a>\n", "\n", "As this is a working project there are a number of known issues we would like to resolve. If you have a nicer or more efficient solution then please let us know. For the moment, here is a list of known unknowns.\n", "\n", "* Equation numbering does not render, this is due to a built-in setting to ipython notebook. The solution maybe to just hack the config files, see http://www.rbeesoft.com/blog/?p=6\n", "* There is no built-in spellchecker in the notebook environment. There is a broken aspell notebook extension, maybe it will work soon." ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
chongxi/spiketag
demo/GT/unit_test_spiketag.ipynb
1
90300
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:24.519803Z", "start_time": "2019-03-13T19:32:24.489777Z" } }, "outputs": [], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:24.905212Z", "start_time": "2019-03-13T19:32:24.901857Z" } }, "outputs": [], "source": [ "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:25.758527Z", "start_time": "2019-03-13T19:32:25.320488Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:26.059450Z", "start_time": "2019-03-13T19:32:26.033924Z" } }, "outputs": [], "source": [ "%gui qt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:29.388814Z", "start_time": "2019-03-13T19:32:26.915664Z" } }, "outputs": [], "source": [ "from spiketag.base import *" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:29.433417Z", "start_time": "2019-03-13T19:32:29.391398Z" } }, "outputs": [], "source": [ "from spiketag.view import *" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:29.479477Z", "start_time": "2019-03-13T19:32:29.435760Z" } }, "outputs": [], "source": [ "prb = probe(shank_no=1)\n", "prb[0] = np.array([0,1,2])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:29.804169Z", "start_time": "2019-03-13T19:32:29.754059Z" } }, "outputs": [], "source": [ "prb.mapping[0] = np.array([-10,0])\n", "prb.mapping[1] = np.array([10,0])\n", "prb.mapping[2] = np.array([0,10])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:32.930904Z", "start_time": "2019-03-13T19:32:32.883283Z" } }, "outputs": [], "source": [ "prb.fs = 20000.\n", "prb.n_ch = 3\n", "prb.reorder_by_chip=False" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:20:37.258382Z", "start_time": "2019-03-13T19:20:37.132239Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-03-13 15:20:37,243 - spiketag - INFO - ############# load data ###################\n", "2019-03-13 15:20:37,244 - spiketag - INFO - ./cell_0109.bin loaded, it contains: \n", "2019-03-13 15:20:37,245 - spiketag - INFO - 9280040.0 * 3 points (111360480 bytes) \n", "2019-03-13 15:20:37,245 - spiketag - INFO - 3 channels with sampling rate of 20000.0000 \n", "2019-03-13 15:20:37,246 - spiketag - INFO - 464.002 secs (7.733 mins) of data\n", "2019-03-13 15:20:37,247 - spiketag - INFO - #############################################\n", "2019-03-13 15:20:37,247 - spiketag - INFO - processing folder: ./\n", "2019-03-13 15:20:37,254 - spiketag - INFO - raw data have 9309 spks\n", "2019-03-13 15:20:37,255 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:20:37,255 - spiketag - INFO - \n" ] } ], "source": [ "mua = MUA(mua_filename='./cell_0109.bin', spk_filename='./cell_0109.spk.bin',probe=prb, binary_radix=11, scale=False,\n", " cutoff=[-100,100], time_segs=np.array([[0, 40], [100., 200.]]) )" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:20:40.433217Z", "start_time": "2019-03-13T19:20:40.378694Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[ 4131, 4458, 6466, ..., 9278006, 9278135, 9279424],\n", " [ 1, 1, 1, ..., 1, 1, 1]],\n", " dtype=int32)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.pivotal_pos" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:20:42.130052Z", "start_time": "2019-03-13T19:20:42.080031Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 4131, 4458, 6466, ..., 9278006, 9278135, 9279424],\n", " dtype=int32)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.pivotal_pos[0]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:20:54.296522Z", "start_time": "2019-03-13T19:20:44.239316Z" } }, "outputs": [], "source": [ "spks = np.fromfile('./cell_0109.spk.bin', dtype=np.int32)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:20:55.228700Z", "start_time": "2019-03-13T19:20:54.299159Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 4131, 4458, 6466, ..., 9278006, 9278135, 9279424],\n", " dtype=int32)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spks[::2]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:05.302189Z", "start_time": "2019-03-13T19:20:55.233175Z" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(mua.pivotal_pos[0], spks[::2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit test for individual views" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:12.616597Z", "start_time": "2019-03-13T19:21:09.358625Z" } }, "outputs": [], "source": [ "wview = wave_view(mua.data, spks=spks)\n", "wview.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:23.493674Z", "start_time": "2019-03-13T19:21:23.297235Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-03-13 15:21:23,346 - spiketag - INFO - mua.tospk() with time_cutoff=True, amp_cutoff=True, speed_cutoff=False\n", "2019-03-13 15:21:23,486 - spiketag - INFO - group 0 delete 100.0%(2884.0/2884.0) spks via cutoff\n", "2019-03-13 15:21:23,488 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:21:23,488 - spiketag - INFO - \n" ] } ], "source": [ "spk = mua.tospk()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:28.854022Z", "start_time": "2019-03-13T19:21:28.800765Z" } }, "outputs": [ { "data": { "text/plain": [ "array([], dtype=float64)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.spk_times[0]/mua.fs" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:29.490215Z", "start_time": "2019-03-13T19:21:29.438123Z" } }, "outputs": [ { "data": { "text/plain": [ "array([0.0000000e+00, 5.0000000e-05, 1.0000000e-04, ..., 4.6400185e+02,\n", " 4.6400190e+02, 4.6400195e+02])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.t" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:32.105289Z", "start_time": "2019-03-13T19:21:32.053884Z" } }, "outputs": [], "source": [ "time_segs = np.array([[0, 40], [100., 200.]])" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:34.958722Z", "start_time": "2019-03-13T19:21:34.906026Z" } }, "outputs": [ { "data": { "text/plain": [ "(0,)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.spk_times[0][np.logical_and(mua.spk_times[0]/mua.fs<time_segs[1][1], mua.spk_times[0]/mua.fs>time_segs[1][0])].shape" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:35.617365Z", "start_time": "2019-03-13T19:21:35.565187Z" } }, "outputs": [ { "data": { "text/plain": [ "(0,)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mua.spk_times[0].shape" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-07-27T03:23:49.243260Z", "start_time": "2018-07-27T03:23:49.165075Z" } }, "outputs": [], "source": [ "def find_spk_in_time_seg(spk_times, time_segs):\n", " spk_times_in_range = []\n", " for time_seg in time_segs:\n", " spk_in_time_seg = spk_times[logical_and(spk_times<time_seg[1], spk_times>time_seg[0])]\n", " spk_times_in_range.append(spk_in_time_seg)\n", " return np.hstack(np.array(spk_times_in_range))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2018-07-27T03:24:37.740381Z", "start_time": "2018-07-27T03:24:35.432606Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "31.7 µs ± 1.86 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], "source": [ "%%timeit \n", "spk_times = find_spk_in_time_seg(mua.spk_times[0]/mua.fs, time_segs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-07-24T21:13:09.184441Z", "start_time": "2018-07-24T21:13:09.116625Z" } }, "outputs": [ { "data": { "text/plain": [ "(2838, 19, 3)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spk.spk[0].shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2018-07-24T21:07:45.418248Z", "start_time": "2018-07-24T21:07:45.353060Z" } }, "outputs": [], "source": [ "s = np.delete(spk.spk[0], [0,1,2], axis=0)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2018-07-24T21:07:49.357199Z", "start_time": "2018-07-24T21:07:49.285277Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(2835, 19, 3)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:21:43.366443Z", "start_time": "2019-03-13T19:21:43.312669Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-03-13 15:21:43,361 - spiketag - INFO - spk._tofet(group_id=0, method=pca, ncomp=6, whiten=False)\n", "2019-03-13 15:21:43,362 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:21:43,363 - spiketag - INFO - \n" ] } ], "source": [ "fet = spk.tofet()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:23:28.742876Z", "start_time": "2019-03-13T19:23:28.435755Z" }, "scrolled": true }, "outputs": [ { "ename": "KeyError", "evalue": "0", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-30-a4daa586633d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoclu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'dpgmm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_comp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/base/FET.py\u001b[0m in \u001b[0;36mtoclu\u001b[0;34m(self, method, group_id, **kwargs)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu_status\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgroup_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgroup_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 0" ] } ], "source": [ "clu = fet.toclu(method='dpgmm', group_id=0, n_comp=8, max_iter=400)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:22:08.407061Z", "start_time": "2019-03-13T19:22:08.250143Z" } }, "outputs": [], "source": [ "spkview = spike_view()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:22:12.099543Z", "start_time": "2019-03-13T19:22:09.910103Z" } }, "outputs": [ { "ename": "NameError", "evalue": "name 'clu' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-27-189e5433e664>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mspkview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'clu' is not defined" ] } ], "source": [ "spkview.set_data(spk.spk[0], clu[0])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:54:30.451189Z", "start_time": "2018-06-06T22:54:30.327824Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Invoking <bound method BaseCanvas.on_draw of <spike_view (PyQt5) at 0x150027780>> repeat 2\n" ] } ], "source": [ "spkview.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:55:05.970177Z", "start_time": "2018-06-06T22:55:05.701687Z" } }, "outputs": [], "source": [ "fetview = scatter_3d_view()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:55:11.123362Z", "start_time": "2018-06-06T22:55:11.052868Z" } }, "outputs": [], "source": [ "fetview.set_data(fet.fet[0], clu[0])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:55:11.902626Z", "start_time": "2018-06-06T22:55:11.765860Z" } }, "outputs": [], "source": [ "fetview.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:55:38.601431Z", "start_time": "2018-06-06T22:55:38.394865Z" } }, "outputs": [], "source": [ "ampview = amplitude_view(fs=prb.fs, scale=1)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:57:17.938053Z", "start_time": "2018-06-06T22:57:17.863540Z" } }, "outputs": [], "source": [ "ampview.set_data(spk.spk[0], clu[0], mua.pivotal_pos[0])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:57:18.777267Z", "start_time": "2018-06-06T22:57:18.466670Z" } }, "outputs": [], "source": [ "ampview.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:00:50.924707Z", "start_time": "2018-06-06T23:00:50.757003Z" } }, "outputs": [], "source": [ "treeview = ctree_view()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:00:54.203004Z", "start_time": "2018-06-06T23:00:54.041638Z" }, "scrolled": false }, "outputs": [], "source": [ "treeview.set_data(clu[0])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:00:54.715240Z", "start_time": "2018-06-06T23:00:54.577475Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Traceback (most recent call last):\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n", " \"__main__\", mod_spec)\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 85, in _run_code\n", " exec(code, run_globals)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n", " app.launch_new_instance()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 505, in start\n", " self.io_loop.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n", " self.asyncio_loop.run_forever()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 427, in run_forever\n", " self._run_once()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 1440, in _run_once\n", " handle._run()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/events.py\", line 145, in _run\n", " self._callback(*self._args)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n", " ret = callback()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 1233, in inner\n", " self.run()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 1147, in run\n", " yielded = self.gen.send(value)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n", " yield gen.maybe_future(dispatch(*args))\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n", " yield gen.maybe_future(handler(stream, idents, msg))\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n", " user_expressions, allow_stdin,\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n", " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n", " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n", " raw_cell, store_history, silent, shell_futures)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n", " return runner(coro)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n", " coro.send(None)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n", " interactivity=interactivity, compiler=compiler, result=result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3191, in run_ast_nodes\n", " if (yield from self.run_code(code, result)):\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", " File \"<ipython-input-38-d18c418556e8>\", line 1, in <module>\n", " treeview.show()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/canvas.py\", line 429, in show\n", " self._backend._vispy_set_visible(visible)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 323, in _vispy_set_visible\n", " self.showNormal()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 657, in paintGL\n", " self._vispy_canvas.events.draw(region=None)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 455, in __call__\n", " self._invoke_callback(cb, event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 475, in _invoke_callback\n", " self, cb_event=(cb, event))\n", " << caught exception here: >>\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 471, in _invoke_callback\n", " cb(event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 207, in on_draw\n", " self._draw_scene()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 253, in _draw_scene\n", " self.draw_visual(self.scene)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 291, in draw_visual\n", " node.draw()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/visuals.py\", line 98, in draw\n", " self._visual_superclass.draw(self)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 385, in draw\n", " Visual.draw(self, *args, **kwds)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/visual.py\", line 432, in draw\n", " if self._prepare_draw(view=self) is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 380, in _prepare_draw\n", " if self._update_data() is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 306, in _update_data\n", " v = md.get_vertices(indexed='faces')\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/geometry/meshdata.py\", line 195, in get_vertices\n", " self._vertices[self.get_faces()]\n", "IndexError: arrays used as indices must be of integer (or boolean) type\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x154438c18>> for DrawEvent\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x154438c18>> repeat 2\n" ] } ], "source": [ "treeview.show()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:05.907536Z", "start_time": "2018-06-06T23:01:05.832103Z" } }, "outputs": [], "source": [ "corview = correlogram_view(fs=prb.fs)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:10.112927Z", "start_time": "2018-06-06T23:01:10.038276Z" } }, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-40-14462d2bbbcd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmua\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivotal_pos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/view/correlogram_view.py\u001b[0m in \u001b[0;36mset_data\u001b[0;34m(self, clu, spk_times)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;31m# Not rendering immedially now, waiting for shortcut\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/view/correlogram_view.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnclu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnclu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0mhists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_correlogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;31m# begin draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/view/correlogram_view.py\u001b[0m in \u001b[0;36m_correlogram\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_correlogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_correlate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_spike_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmembership\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwindow_bins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_window_bins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bin_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pair_clusters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/core/correlate.py\u001b[0m in \u001b[0;36mcorrelate\u001b[0;34m(spike_time, membership, cluster_ids, fs, window_bins, bin_size)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mwindow_bins\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mfs\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mspike_time\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmembership\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;31m# the offset within the array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAssertionError\u001b[0m: " ] } ], "source": [ "corview.set_data(clu[0], mua.pivotal_pos[0])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:10.908531Z", "start_time": "2018-06-06T23:01:10.795976Z" } }, "outputs": [], "source": [ "corview.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:23.624837Z", "start_time": "2018-06-06T23:01:23.171489Z" } }, "outputs": [], "source": [ "traceview = trace_view(fs=prb.fs, spklen=19)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:27.938103Z", "start_time": "2018-06-06T23:01:27.839190Z" } }, "outputs": [], "source": [ "traceview.set_data(mua.data, clu[0], mua.pivotal_pos[0])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T23:01:29.298225Z", "start_time": "2018-06-06T23:01:28.576548Z" } }, "outputs": [], "source": [ "traceview.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Unit test for QT5 Wrapper" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:39.970805Z", "start_time": "2019-03-13T19:32:39.816324Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda2/envs/py37/lib/python3.6/site-packages/numba/decorators.py:146: RuntimeWarning: Caching is not available when the 'parallel' target is in use. Caching is now being disabled to allow execution to continue.\n", " warnings.warn(msg, RuntimeWarning)\n" ] } ], "source": [ "from spiketag.mvc import MainModel, MainView" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:43.501969Z", "start_time": "2019-03-13T19:32:42.436088Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-03-13 15:32:42,482 - spiketag - INFO - load mua data\n", "2019-03-13 15:32:42,553 - spiketag - INFO - ############# load data ###################\n", "2019-03-13 15:32:42,554 - spiketag - INFO - ./cell_0109.bin loaded, it contains: \n", "2019-03-13 15:32:42,555 - spiketag - INFO - 9280040.0 * 3 points (111360480 bytes) \n", "2019-03-13 15:32:42,556 - spiketag - INFO - 3 channels with sampling rate of 20000.0000 \n", "2019-03-13 15:32:42,557 - spiketag - INFO - 464.002 secs (7.733 mins) of data\n", "2019-03-13 15:32:42,558 - spiketag - INFO - #############################################\n", "2019-03-13 15:32:42,560 - spiketag - INFO - processing folder: ./\n", "2019-03-13 15:32:42,565 - spiketag - INFO - raw data have 9309 spks\n", "2019-03-13 15:32:42,566 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:32:42,567 - spiketag - INFO - \n", "2019-03-13 15:32:42,568 - spiketag - INFO - extract spikes from pivital meta data\n", "2019-03-13 15:32:42,569 - spiketag - INFO - mua.tospk() with time_cutoff=True, amp_cutoff=True, speed_cutoff=True\n", "2019-03-13 15:32:43,470 - spiketag - INFO - group 0 delete 0.0%(0.0/9309.0) spks via cutoff\n", "2019-03-13 15:32:43,471 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:32:43,472 - spiketag - INFO - \n", "2019-03-13 15:32:43,472 - spiketag - INFO - grouping spike time\n", "2019-03-13 15:32:43,473 - spiketag - INFO - extract features with pca\n", "2019-03-13 15:32:43,493 - spiketag - INFO - spk._tofet(group_id=0, method=pca, ncomp=6, whiten=False)\n", "2019-03-13 15:32:43,494 - spiketag - INFO - ----------------success------------------\n", "2019-03-13 15:32:43,495 - spiketag - INFO - \n" ] } ], "source": [ "model = MainModel(mua_filename='./cell_0109.bin', spk_filename='./cell_0109.spk.bin', probe=prb, \n", " binary_radix=11, scale=False)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:32:56.815121Z", "start_time": "2019-03-13T19:32:56.590975Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2019-03-13 15:32:56,637 - spiketag - INFO - clustering with dpgmm\n", "2019-03-13 15:32:56,809 - spiketag - INFO - Model.spktag is generated, nspk:9309\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "<function cluster._dpgmm at 0x12ef85620>\n", "{'n_comp': 8, 'max_iter': 400}\n", "0 [2 4 1 ... 4 4 6]\n", "0 cluster finished\n" ] } ], "source": [ "model.sort(clu_method='dpgmm', group_id=0, n_comp=8, max_iter=400)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-03-13T19:33:04.205896Z", "start_time": "2019-03-13T19:33:01.230701Z" }, "scrolled": false }, "outputs": [ { "ename": "AttributeError", "evalue": "'MainView' object has no attribute '_model'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-14-9029b4f3268e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mview\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMainView\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/mvc/View.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, prb, model)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitUI\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Work/pydev/spiketag/spiketag/mvc/View.py\u001b[0m in \u001b[0;36minitUI\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu_view\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcluster_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprb_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitter_prb_cpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddWidget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprb_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitter_prb_cpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddWidget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnative\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'MainView' object has no attribute '_model'" ] } ], "source": [ "view = MainView(prb)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2018-06-08T01:45:50.536364Z", "start_time": "2018-06-08T01:45:49.515255Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x183217908>> repeat 4\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x183217908>> repeat 8\n" ] } ], "source": [ "view.show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2018-06-08T01:45:53.391057Z", "start_time": "2018-06-08T01:45:53.033387Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Traceback (most recent call last):\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n", " \"__main__\", mod_spec)\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 85, in _run_code\n", " exec(code, run_globals)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n", " app.launch_new_instance()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 505, in start\n", " self.io_loop.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n", " self.asyncio_loop.run_forever()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 427, in run_forever\n", " self._run_once()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 1440, in _run_once\n", " handle._run()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/events.py\", line 145, in _run\n", " self._callback(*self._args)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n", " ret = callback()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 306, in advance_eventloop\n", " eventloop(self)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/eventloops.py\", line 129, in loop_qt5\n", " return loop_qt4(kernel)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/eventloops.py\", line 122, in loop_qt4\n", " _loop_qt(kernel.app)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/eventloops.py\", line 106, in _loop_qt\n", " app.exec_()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 657, in paintGL\n", " self._vispy_canvas.events.draw(region=None)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 455, in __call__\n", " self._invoke_callback(cb, event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 475, in _invoke_callback\n", " self, cb_event=(cb, event))\n", " << caught exception here: >>\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 471, in _invoke_callback\n", " cb(event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 207, in on_draw\n", " self._draw_scene()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 253, in _draw_scene\n", " self.draw_visual(self.scene)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 291, in draw_visual\n", " node.draw()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/visuals.py\", line 98, in draw\n", " self._visual_superclass.draw(self)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 385, in draw\n", " Visual.draw(self, *args, **kwds)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/visual.py\", line 432, in draw\n", " if self._prepare_draw(view=self) is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 380, in _prepare_draw\n", " if self._update_data() is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 306, in _update_data\n", " v = md.get_vertices(indexed='faces')\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/geometry/meshdata.py\", line 195, in get_vertices\n", " self._vertices[self.get_faces()]\n", "IndexError: arrays used as indices must be of integer (or boolean) type\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x183217908>> for DrawEvent\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x183217908>> repeat 2\n" ] } ], "source": [ "view.set_data(0, model.mua, model.spk[0], model.fet[0], model.clu[0])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-01-21T22:46:11.686489Z", "start_time": "2019-01-21T22:46:11.671835Z" }, "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'model' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-2-f3a598cf406c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined" ] } ], "source": [ "model.clu[0].index_count" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.clu[0].select(np.arange(600))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:14:13.375102Z", "start_time": "2018-06-07T20:14:13.314317Z" } }, "outputs": [], "source": [ "thr = -31.00" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:14:13.982828Z", "start_time": "2018-06-07T20:14:13.914878Z" } }, "outputs": [], "source": [ "idx = np.where(model.spk[0].min(axis=1).min(axis=1)>thr)[0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:14:15.541818Z", "start_time": "2018-06-07T20:14:15.471294Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(5032,)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx.shape" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:26:42.618393Z", "start_time": "2018-06-07T20:26:42.556956Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4067,)\n", "(2589,)\n", "(2104,)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2018-06-07 16:28:50,182 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:50,532 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:50,760 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:50,946 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:51,118 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:51,289 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:51,453 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:51,632 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:51,843 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:52,147 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:52,437 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:52,671 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:52,932 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:53,445 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:53,680 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:53,938 - spiketag - DEBUG - no more undo\n", "2018-06-07 16:28:54,136 - spiketag - DEBUG - no more undo\n" ] } ], "source": [ "def delete_spk(model, spk_idx):\n", " model.mua.spk_times[0] = np.delete(model.mua.spk_times[0], spk_idx, axis=0)\n", " model.spk[0] = np.delete(model.spk[0], spk_idx, axis=0)\n", " model.fet[0] = model.spk._tofet(0, method='pca')\n", " model.cluster(group_id=0, method='hdbscan', fall_off_size=30) #, \n", " view.set_data(0, model.mua, model.spk[0], model.fet[0], model.clu[0])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:14:19.831997Z", "start_time": "2018-06-07T20:14:19.355815Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-06-07 16:16:50,296 - spiketag - DEBUG - ctree view expand clustier 4279 here\n" ] } ], "source": [ "delete_spk(model, idx)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T20:26:01.053840Z", "start_time": "2018-06-07T20:26:00.988589Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Traceback (most recent call last):\n", " File \"/anaconda2/lib/python2.7/runpy.py\", line 174, in _run_module_as_main\n", " \"__main__\", fname, loader, pkg_name)\n", " File \"/anaconda2/lib/python2.7/runpy.py\", line 72, in _run_code\n", " exec code in run_globals\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py\", line 16, in <module>\n", " app.launch_new_instance()\n", " File \"/anaconda2/lib/python2.7/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py\", line 478, in start\n", " self.io_loop.start()\n", " File \"/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\n", " super(ZMQIOLoop, self).start()\n", " File \"/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py\", line 832, in start\n", " self._run_callback(self._callbacks.popleft())\n", " File \"/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n", " ret = callback()\n", " File \"/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py\", line 263, in enter_eventloop\n", " self.eventloop(self)\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel/eventloops.py\", line 123, in loop_qt5\n", " return loop_qt4(kernel)\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel/eventloops.py\", line 111, in loop_qt4\n", " _loop_qt(kernel.app)\n", " File \"/anaconda2/lib/python2.7/site-packages/ipykernel/eventloops.py\", line 95, in _loop_qt\n", " app.exec_()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 413, in keyPressEvent\n", " self._keyEvent(self._vispy_canvas.events.key_press, ev)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 428, in _keyEvent\n", " func(native=ev, key=key, text=text_type(ev.text()), modifiers=mod)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 455, in __call__\n", " self._invoke_callback(cb, event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 475, in _invoke_callback\n", " self, cb_event=(cb, event))\n", " << caught exception here: >>\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 471, in _invoke_callback\n", " cb(event)\n", " File \"/Users/laic/Work/pydev/spiketag/spiketag/view/scatter_2d_view.py\", line 136, in on_key_press\n", " self.clip.emit('clip', thres=self.amp)\n", " File \"/Users/laic/Work/pydev/spiketag/spiketag/utils/utils.py\", line 160, in emit\n", " res.append(callback(*args, **kwargs))\n", " File \"<ipython-input-16-cbf95aa6a25a>\", line 5, in on_clip\n", " delete_spk(model=model, spk_idx=idx)\n", " File \"<ipython-input-15-6615856d65ba>\", line 2, in delete_spk\n", " model.mua.spk_times[0] = np.delete(model.mua.spk_times[0], idx, axis=0)\n", "NameError: global name 'idx' is not defined\n", "ERROR: Invoking <bound method amplitude_view.on_key_press of <amplitude_view (PyQt5) at 0x1c3e6a3dd0>> for KeyEvent\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(4817,)\n" ] } ], "source": [ "@view.ampview.clip.connect\n", "def on_clip(thres):\n", " idx = np.where(model.spk[0].min(axis=1).min(axis=1)>thres)[0]\n", " print(idx.shape)\n", " delete_spk(model=model, spk_idx=idx)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:34:05.667494Z", "start_time": "2018-06-07T15:34:05.606767Z" } }, "outputs": [ { "data": { "text/plain": [ "(1678, 19, 3)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.spk[0].shape" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:33:46.753331Z", "start_time": "2018-06-07T15:33:46.681524Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-06-07 11:33:46,742 - spiketag - INFO - spk._tofet(group_id=0, method=weighted-pca, ncomp=6, whiten=False)\n", "2018-06-07 11:33:46,746 - spiketag - INFO - ----------------success------------------\n", "2018-06-07 11:33:46,747 - spiketag - INFO - \n" ] } ], "source": [ "model.fet = model.spk.tofet()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:33:48.057949Z", "start_time": "2018-06-07T15:33:47.998725Z" } }, "outputs": [ { "data": { "text/plain": [ "(1678, 6)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fet[0].shape" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:33:49.759503Z", "start_time": "2018-06-07T15:33:49.633825Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-06-07 11:33:49,754 - spiketag - INFO - clustering finished, used 0.066253900528 sec\n" ] } ], "source": [ "model.clu = model.fet.toclu()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:29:29.853518Z", "start_time": "2018-06-07T15:29:29.781272Z" } }, "outputs": [ { "data": { "text/plain": [ "(4740,)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.mua.spk_times[0].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:14:41.979657Z", "start_time": "2018-06-07T15:14:41.920523Z" } }, "outputs": [ { "data": { "text/plain": [ "-146.53759765625" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "view.ampview.poses[:, 1].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2018-06-08T02:30:39.627217Z", "start_time": "2018-06-08T02:30:39.523733Z" } }, "outputs": [], "source": [ "from spiketag.mvc.Control import controller" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2018-06-08T02:30:45.245388Z", "start_time": "2018-06-08T02:30:40.257465Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-12-28 20:20:37,338 - spiketag - INFO - load mua data\n", "2018-12-28 20:20:37,401 - spiketag - INFO - ############# load data ###################\n", "2018-12-28 20:20:37,402 - spiketag - INFO - ./cell_0109.bin loaded, it contains: \n", "2018-12-28 20:20:37,403 - spiketag - INFO - 9280040.0 * 3 points (111360480 bytes) \n", "2018-12-28 20:20:37,404 - spiketag - INFO - 3 channels with sampling rate of 20000.0000 \n", "2018-12-28 20:20:37,404 - spiketag - INFO - 464.002 secs (7.733 mins) of data\n", "2018-12-28 20:20:37,405 - spiketag - INFO - #############################################\n", "2018-12-28 20:20:37,470 - spiketag - INFO - processing folder: ./\n", "2018-12-28 20:20:37,473 - spiketag - INFO - raw data have 9309 spks\n", "2018-12-28 20:20:37,474 - spiketag - INFO - ----------------success------------------\n", "2018-12-28 20:20:37,475 - spiketag - INFO - \n", "WARNING: QLayout: Attempting to add QLayout \"\" to MainView \"\", which already has a layout\n" ] } ], "source": [ "ctrl = controller(probe=prb, mua_filename='./cell_0109.bin', spk_filename='./cell_0109.spk.bin')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-12-28 20:20:46,070 - spiketag - INFO - extract spikes from pivital meta data\n", "2018-12-28 20:20:46,071 - spiketag - INFO - mua.tospk()\n", "2018-12-28 20:20:46,378 - spiketag - INFO - group 0 delete 0.0%(0.0/9309.0) spks via cutoff\n", "2018-12-28 20:20:46,381 - spiketag - INFO - ----------------success------------------\n", "2018-12-28 20:20:46,381 - spiketag - INFO - \n", "2018-12-28 20:20:46,382 - spiketag - INFO - grouping spike time\n", "2018-12-28 20:20:46,382 - spiketag - INFO - extrat features with weighted-pca\n", "2018-12-28 20:20:46,404 - spiketag - INFO - spk._tofet(group_id=0, method=weighted-pca, ncomp=6, whiten=False)\n", "2018-12-28 20:20:46,404 - spiketag - INFO - ----------------success------------------\n", "2018-12-28 20:20:46,405 - spiketag - INFO - \n", "2018-12-28 20:20:46,406 - spiketag - INFO - clustering with hdbscan\n", "2018-12-28 20:20:46,406 - spiketag - INFO - clustering start with 24 cpus\n", "2018-12-28 20:20:50,116 - spiketag - INFO - clustering finished, used 3.7082719802856445 sec\n", "2018-12-28 20:20:50,123 - spiketag - INFO - Model.spktag is generated, nspk:9309\n" ] } ], "source": [ "ctrl.sort()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2018-06-08T02:30:51.514407Z", "start_time": "2018-06-08T02:30:49.845881Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Traceback (most recent call last):\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n", " \"__main__\", mod_spec)\n", " File \"/anaconda2/envs/py37/lib/python3.6/runpy.py\", line 85, in _run_code\n", " exec(code, run_globals)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n", " app.launch_new_instance()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n", " app.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 505, in start\n", " self.io_loop.start()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n", " self.asyncio_loop.run_forever()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 427, in run_forever\n", " self._run_once()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/base_events.py\", line 1440, in _run_once\n", " handle._run()\n", " File \"/anaconda2/envs/py37/lib/python3.6/asyncio/events.py\", line 145, in _run\n", " self._callback(*self._args)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/ioloop.py\", line 758, in _run_callback\n", " ret = callback()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 1233, in inner\n", " self.run()\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 1147, in run\n", " yielded = self.gen.send(value)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 357, in process_one\n", " yield gen.maybe_future(dispatch(*args))\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 267, in dispatch_shell\n", " yield gen.maybe_future(handler(stream, idents, msg))\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 534, in execute_request\n", " user_expressions, allow_stdin,\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/tornado/gen.py\", line 326, in wrapper\n", " yielded = next(result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 294, in do_execute\n", " res = shell.run_cell(code, store_history=store_history, silent=silent)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n", " return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2819, in run_cell\n", " raw_cell, store_history, silent, shell_futures)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2845, in _run_cell\n", " return runner(coro)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/async_helpers.py\", line 67, in _pseudo_sync_runner\n", " coro.send(None)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3020, in run_cell_async\n", " interactivity=interactivity, compiler=compiler, result=result)\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3191, in run_ast_nodes\n", " if (yield from self.run_code(code, result)):\n", " File \"/anaconda2/envs/py37/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 3267, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", " File \"<ipython-input-56-9a9adb335bc5>\", line 1, in <module>\n", " ctrl.show()\n", " File \"/Users/laic/Work/pydev/spiketag/spiketag/mvc/Control.py\", line 264, in show\n", " self.view.show()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/app/backends/_qt.py\", line 657, in paintGL\n", " self._vispy_canvas.events.draw(region=None)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 455, in __call__\n", " self._invoke_callback(cb, event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 475, in _invoke_callback\n", " self, cb_event=(cb, event))\n", " << caught exception here: >>\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/util/event.py\", line 471, in _invoke_callback\n", " cb(event)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 207, in on_draw\n", " self._draw_scene()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 253, in _draw_scene\n", " self.draw_visual(self.scene)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/canvas.py\", line 291, in draw_visual\n", " node.draw()\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/scene/visuals.py\", line 98, in draw\n", " self._visual_superclass.draw(self)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 385, in draw\n", " Visual.draw(self, *args, **kwds)\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/visual.py\", line 432, in draw\n", " if self._prepare_draw(view=self) is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 380, in _prepare_draw\n", " if self._update_data() is False:\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/visuals/mesh.py\", line 306, in _update_data\n", " v = md.get_vertices(indexed='faces')\n", " File \"/Users/laic/Work/pydev/vispy_cus/vispy/vispy/geometry/meshdata.py\", line 195, in get_vertices\n", " self._vertices[self.get_faces()]\n", "IndexError: arrays used as indices must be of integer (or boolean) type\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x199482cc0>> for DrawEvent\n", "ERROR: Invoking <bound method SceneCanvas.on_draw of <ctree_view (PyQt5) at 0x199482cc0>> repeat 2\n" ] } ], "source": [ "ctrl.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:01:42.102331Z", "start_time": "2018-06-07T15:01:42.042265Z" } }, "outputs": [ { "data": { "text/plain": [ "array([-19.429688, -18.166504, -17.284668, ..., -17.5 , -19.528809,\n", " -28.05664 ], dtype=float32)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:01:30.791411Z", "start_time": "2018-06-07T15:01:30.727739Z" } }, "outputs": [ { "data": { "text/plain": [ "(9309,)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.mua.spk_times[0].shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:01:31.285766Z", "start_time": "2018-06-07T15:01:31.221275Z" } }, "outputs": [ { "data": { "text/plain": [ "(9309, 19, 3)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.spk[0].shape" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-06-07T15:01:31.610442Z", "start_time": "2018-06-07T15:01:31.546623Z" } }, "outputs": [ { "data": { "text/plain": [ "(9309, 6)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fet[0].shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:53:52.572743Z", "start_time": "2018-06-06T22:53:52.462496Z" } }, "outputs": [], "source": [ "import sys\n", "from PyQt5.QtGui import *\n", "from PyQt5.QtCore import *\n", "from PyQt5.QtWidgets import *\n", "\n", "class sorter(QWidget):\n", "\n", " def __init__(self):\n", " super(sorter, self).__init__()\n", " self.initUI()\n", "\n", " def initUI(self):\n", "\n", " hbox = QHBoxLayout(self)\n", " self.splitter1 = QSplitter(Qt.Horizontal)\n", "# textedit = QTextEdit()\n", "# self.splitter1.addWidget(self.topleft)\n", "# self.splitter1.addWidget(textedit)\n", "# self.splitter1.setSizes([100,200])\n", " self.splitter2 = QSplitter(Qt.Horizontal)\n", " self.splitter_fet = QSplitter(Qt.Vertical)\n", "\n", " self.splitter3 = QSplitter(Qt.Vertical)\n", " self.splitter3.addWidget(self.splitter1)\n", " self.splitter3.addWidget(self.splitter2)\n", "# self.splitter2.addWidget(self.bottom)\n", "\n", " hbox.addWidget(self.splitter3)\n", "\n", " self.setLayout(hbox)\n", " QApplication.setStyle(QStyleFactory.create('Cleanlooks'))\n", "\n", " self.setGeometry(300, 300, 300, 200)\n", " self.setWindowTitle('spiketag')\n", "# self.show()\n", "\n", " def set_data(self, mua, spk, fet, clu):\n", " ### init view and set_data\n", " self.spkview = spike_view()\n", " self.spkview.set_data(spk, clu)\n", " self.fetview0 = scatter_3d_view()\n", " self.fetview0.set_data(fet[:,[0,1,2]].copy(), clu)\n", " self.fetview1 = scatter_3d_view()\n", " self.fetview1.set_data(fet[:,[1,3,4]].copy(), clu)\n", " self.ampview = amplitude_view(fs=fs, scale=1)\n", " self.ampview.set_data(spk, clu, mua.pivotal_pos[0])\n", " self.treeview = ctree_view()\n", " self.treeview.set_data(clu)\n", " self.corview = correlogram_view(fs=fs)\n", " self.corview.set_data(clu, mua.pivotal_pos[0])\n", " self.traceview = trace_view(fs=mua.fs, spklen=spk.shape[1])\n", " self.traceview.set_data(mua.data, clu, mua.pivotal_pos[0])\n", "# self.traceview.locate_buffer = 2000\n", " \n", " ### put views into splitter\n", " self.splitter1.addWidget(self.traceview.native)\n", " self.splitter1.addWidget(self.splitter_fet)\n", " self.splitter_fet.addWidget(self.fetview0.native)\n", " self.splitter_fet.addWidget(self.fetview1.native)\n", " self.splitter1.addWidget(self.spkview.native)\n", " \n", " self.splitter2.addWidget(self.corview.native)\n", " self.splitter2.addWidget(self.treeview.native)\n", " self.splitter2.addWidget(self.ampview.native)\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:53:53.590297Z", "start_time": "2018-06-06T22:53:53.528248Z" } }, "outputs": [], "source": [ "%gui qt" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:53:54.106316Z", "start_time": "2018-06-06T22:53:54.034948Z" }, "scrolled": true }, "outputs": [], "source": [ "ex = sorter()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:53:55.860314Z", "start_time": "2018-06-06T22:53:54.844399Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2018-06-06 18:53:54,900 - spiketag - INFO - mua.tospk()\n", "2018-06-06 18:53:54,909 - spiketag - INFO - ----------------success------------------\n", "2018-06-06 18:53:54,910 - spiketag - INFO - \n", "2018-06-06 18:53:54,943 - spiketag - INFO - spk._tofet(groupNo=0, method=pca, ncomp=9, whiten=False)\n", "2018-06-06 18:53:54,945 - spiketag - INFO - ----------------success------------------\n", "2018-06-06 18:53:54,946 - spiketag - INFO - \n", "2018-06-06 18:53:55,855 - spiketag - INFO - clustering finished, used 0.906870126724 sec\n" ] } ], "source": [ "spk = mua.tospk()\n", "fet = spk.tofet(method='pca', ncomp=9, whiten=False)\n", "clu = fet.toclu(method='hdbscan')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2018-06-06T22:53:57.807949Z", "start_time": "2018-06-06T22:53:57.724631Z" }, "scrolled": true }, "outputs": [ { "ename": "KeyError", "evalue": "1", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-32-c8b5c2bc0fae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmua\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mKeyError\u001b[0m: 1" ] } ], "source": [ "ex.set_data(mua, spk.spk[1], fet.fet[1], clu[1])" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2018-05-02T01:18:57.015770Z", "start_time": "2018-05-02T01:18:56.850123Z" }, "scrolled": false }, "outputs": [], "source": [ "ex.show()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2018-05-02T01:19:23.597561Z", "start_time": "2018-05-02T01:19:23.542644Z" }, "scrolled": true }, "outputs": [], "source": [ "ex.traceview.locate_buffer = 300" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR: Invoking <bound method trace_view.update_cursor of <trace_view (PyQt5) at 0x1c4774b690>> repeat 8192\n", "ERROR: Invoking <bound method trace_view.update_cursor of <trace_view (PyQt5) at 0x1c4a0aa110>> repeat 8192\n" ] } ], "source": [ "ex.fetview0.set_data(fet.fet[1][:,[1,3,4]], clu[1])\n", "ex.update()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "gclu = clu[1]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([ 848, 2681, 2795, 2899, 2951, 3162, 3521, 4018, 4031, 4074, 4169,\n", " 4224, 4237, 4486, 4489, 4648, 5064, 5078, 5823, 5966, 5967, 6047,\n", " 6049, 6070, 6610, 7207, 7319, 7919, 8057, 8167, 8378, 8602, 8679,\n", " 9240])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2018-03-11 15:08:33,804 - spiketag - DEBUG - ctree view expand clustier 9311 here\n", "2018-03-11 15:08:51,809 - spiketag - DEBUG - ctree view expand clustier 9313 here\n" ] } ], "source": [ "gclu.selectlist" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['spiketag.view.spike_view.on_select_id121320795744',\n", " 'spiketag.view.scatter_3d_view.on_select_id121444857928',\n", " 'spiketag.view.scatter_3d_view.on_select_id121449105600',\n", " 'spiketag.view.amplitude_view.on_select_id121452650320',\n", " 'spiketag.view.trace_view.on_select_id121529220464']" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "2018-03-11 14:14:28,666 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n", "2018-03-11 14:14:29,401 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n", "2018-03-11 14:14:31,183 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n", "2018-03-11 14:14:32,756 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:34,080 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:34,932 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n", "2018-03-11 14:14:35,486 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:36,014 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:36,999 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n", "2018-03-11 14:14:37,535 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:39,278 - spiketag - DEBUG - ctree view expand clustier 9312 here\n", "2018-03-11 14:14:43,798 - spiketag - DEBUG - ctree view collapse cluster 9311 here\n", "2018-03-11 14:14:44,643 - spiketag - DEBUG - ctree view collapse cluster 9309 here\n" ] } ], "source": [ "gclu._registered_func_name('select')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
mne-tools/mne-tools.github.io
0.12/_downloads/plot_read_and_write_raw_data.ipynb
1
2438
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Reading and writing raw files\n\n\nIn this example, we read a raw file. Plot a segment of MEG data\nrestricted to MEG channels. And save these data in a new\nraw file.\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nfname = data_path + '/MEG/sample/sample_audvis_raw.fif'\n\nraw = mne.io.read_raw_fif(fname)\n\n# Set up pick list: MEG + STI 014 - bad channels\nwant_meg = True\nwant_eeg = False\nwant_stim = False\ninclude = ['STI 014']\nraw.info['bads'] += ['MEG 2443', 'EEG 053'] # bad channels + 2 more\n\npicks = mne.pick_types(raw.info, meg=want_meg, eeg=want_eeg, stim=want_stim,\n include=include, exclude='bads')\n\nsome_picks = picks[:5] # take 5 first\nstart, stop = raw.time_as_index([0, 15]) # read the first 15s of data\ndata, times = raw[some_picks, start:(stop + 1)]\n\n# save 150s of MEG data in FIF file\nraw.save('sample_audvis_meg_raw.fif', tmin=0, tmax=150, picks=picks,\n overwrite=True)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "Show MEG data\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "raw.plot()" ], "outputs": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.11", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
bsd-3-clause
empet/Plotly-plots
Chord-diagram.ipynb
1
210208
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotly plot of chord diagrams ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Circular layout or [Chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) is a method of visualizing data that describe relationships. It was intensively promoted through [Circos](http://circos.ca/), a software package in Perl that was initially designed for displaying genomic data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This 2013 [stackoverflow](http://stackoverflow.com/questions/19105801/chord-diagram-in-python) question, whether there is a Python library for plotting chord diagrams, was closed as being *off topic*.\n", "Two years later I presented in the initial version of this Jupyter Notebook a method to generate a chord diagram via Python Plotly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Jupyter Notebook is an updated ersion to be run using Python 3.7+, and Plotly 4.+." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We illustrate the method of generating a chord diagram from data recorded in a square matrix. The rows and columns represent the same entities." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Suppose that for a community of 5 friends on Facebook we record the number of comments posted by each member on other friends wall. The data table is given in the next cell: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='Data/Data-table.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The aim of our visualization is to illustrate the total number of posts by each community member, and the \n", "flows of posts between pairs of friends." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "import platform, plotly\n", "import numpy as np\n", "from numpy import pi" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 3.7.3\n", "Plotly version: 4.5.4\n" ] } ], "source": [ "print(f'Python version: {platform.python_version()}')\n", "print(f'Plotly version: {plotly.__version__}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the array of data:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "matrix = np.array([[16, 3, 28, 0, 18],\n", " [18, 0, 12, 5, 29],\n", " [ 9, 11, 17, 27, 0], \n", " [19, 0, 31, 11, 12],\n", " [23, 17, 10, 0, 34]], dtype=int)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def check_data(data_matrix):\n", " L, M = data_matrix.shape\n", " if L != M:\n", " raise ValueError('Data array must have a (n,n) shape')\n", " return L" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "L = check_data(matrix)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " A chord diagram encodes information in two graphical objects:\n", " - ideograms, represented by distinctly colored arcs of circles;\n", " - ribbons, that are planar shapes bounded by two quadratic Bezier curves and two arcs of circle, that can degenerate to a point;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ideograms ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Summing up the entries on each matrix row, one gets a value (in our example this value is equal to the number of posts by a community member).\n", "Let us denote by `total_comments` the total number of posts recorded in this community.\n", "\n", "Theoretically the interval `[0, total_comments)` is mapped linearly onto the unit circle, identified with the interval $[0,2\\pi)$. \n", "\n", "For a better looking plot one proceeds as follows: starting from the angular position $0$, in counter-clockwise direction, one draws succesively, around the unit circle, two parallel arcs of length equal to a mapped row sum value, minus a fixed gap. Click the image below:\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from IPython.display import IFrame" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"377\"\n", " height=\"420\"\n", " src=\"https://plot.ly/~empet/12234/\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x227dfb1d3c8>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IFrame('https://plot.ly/~empet/12234/', width=377, height=420)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are defining functions that process data in order to get the ideogram ends.\n", "\n", "As we pointed out, the unit circle is oriented counter-clockwise.\n", "In order to get an arc of circle of end angular\n", "coordinates $\\theta_0<\\theta_1$, we define the function, `moduloAB`, that resolves the case when an arc contains\n", "the point of angular coordinate $0$ (for example $\\theta_0=2\\pi-\\pi/12$, $\\theta_1=\\pi/9$). The function corresponding to $a=-\\pi, b=\\pi$ allows to map the interval $[0,2\\pi)$ onto $[-\\pi, \\pi)$. Via this transformation we have:\n", "\n", "$\\theta_0\\mapsto \\theta'_0=-\\pi/12$, and \n", "\n", "$ \\theta_1=\\mapsto \\theta'_1=\\pi/9$,\n", "\n", "and now $\\theta'_0<\\theta'_1$." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def moduloAB(x, a, b): #maps a real number onto the unit circle identified with \n", " #the interval [a,b), b-a=2*PI\n", " if a>= b:\n", " raise ValueError('Incorrect interval ends')\n", " y = (x-a) % (b-a)\n", " return y+b if y < 0 else y+a" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def test_2PI(x):\n", " return 0 <= x < 2*pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the row sums and the lengths of corresponding ideograms:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "row_sum = [matrix[k,:].sum() for k in range(L)]\n", "\n", "#set the gap between two consecutive ideograms\n", "gap = 2*pi*0.005\n", "ideogram_length = 2*pi * np.asarray(row_sum) / sum(row_sum) - gap*np.ones(L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next function returns the list of end angular coordinates for each ideogram arc:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "def get_ideogram_ends(ideogram_len, gap):\n", " ideo_ends = []\n", " left = 0\n", " for k in range(len(ideogram_len)):\n", " right = left + ideogram_len[k]\n", " ideo_ends.append([left, right]) \n", " left = right + gap\n", " return ideo_ends " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "ideo_ends = get_ideogram_ends(ideogram_length, gap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `make_ideogram_arc` returns equally spaced points on an ideogram arc, expressed as complex\n", "numbers in polar form:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def make_ideogram_arc(R, phi, a=50):\n", " # R is the circle radius\n", " # phi is the list of angle coordinates of an arc ends\n", " # a is a parameter that controls the number of points to be evaluated on an arc\n", " if not test_2PI(phi[0]) or not test_2PI(phi[1]):\n", " phi = [moduloAB(t, 0, 2*pi) for t in phi]\n", " length = (phi[1]-phi[0]) % 2*pi \n", " nr = 5 if length <= pi/4 else int(a*length/pi)\n", "\n", " if phi[0] < phi[1]: \n", " theta = np.linspace(phi[0], phi[1], nr)\n", " else:\n", " phi = [moduloAB(t, -pi, pi) for t in phi]\n", " theta = np.linspace(phi[0], phi[1], nr)\n", " return R * np.exp(1j*theta) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The real and imaginary parts of these complex numbers will be used to define the ideogram as a [Plotly\n", "shape](https://plot.ly/python/shapes/) bounded by a SVG path." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.12583302-0.65j , 1.14814501-0.60972373j,\n", " 1.16901672-0.5686826j , 1.18842197-0.5269281j ,\n", " 1.20633642-0.48451259j, 1.22273759-0.44148929j,\n", " 1.23760491-0.39791217j, 1.25091973-0.3538359j ,\n", " 1.26266534-0.30931575j, 1.27282702-0.26440759j,\n", " 1.28139202-0.21916775j, 1.28834958-0.17365297j,\n", " 1.29369099-0.12792036j, 1.29740954-0.08202728j,\n", " 1.29950058-0.0360313j , 1.29996146+0.01000988j,\n", " 1.29879163+0.0560385j , 1.29599253+0.10199682j,\n", " 1.2915677 +0.1478272j , 1.28552267+0.19347214j,\n", " 1.27786503+0.23887437j])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "make_ideogram_arc(1.3, [11*pi/6, pi/17])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set ideograms labels and colors:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "labels=['Emma', 'Isabella', 'Ava', 'Olivia', 'Sophia']\n", "ideo_colors=['rgba(244, 109, 67, 0.75)',\n", " 'rgba(253, 174, 97, 0.75)',\n", " 'rgba(254, 224, 139, 0.75)',\n", " 'rgba(217, 239, 139, 0.75)',\n", " 'rgba(166, 217, 106, 0.75)']#brewer colors with alpha set on 0.75" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ribbons in a chord diagram ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While ideograms illustrate how many comments posted each member of the Facebook community, ribbons\n", "give a comparative information on the flows of comments from one friend to another.\n", "\n", "To illustrate this flow we map data onto the unit circle. More precisely, for each matrix row, $k$, the application:\n", "\n", "`t`$\\mapsto$ `t*ideogram_length[k]/row_sum[k]`\n", "\n", "maps the interval `[0, row_sum[k]]` onto\n", "the interval `[0, ideogram_length[k]]. Hence each entry `matrix[k][j]` in the $k^{th}$ row is mapped to `matrix[k][j] * ideogram_length[k] / row_value[k]`.\n", "\n", "The function `map_data` maps all matrix entries to the corresponding values in the intervals associated to ideograms:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def map_data(data_matrix, row_value, ideogram_length):\n", " mapped = np.zeros(data_matrix.shape)\n", " for j in range(L):\n", " mapped[:, j] = ideogram_length * data_matrix[:,j] / row_value\n", " return mapped " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.27949818, 0.05240591, 0.48912181, 0. , 0.31443545],\n", " [0.31429952, 0. , 0.20953301, 0.08730542, 0.50637144],\n", " [0.15714976, 0.19207193, 0.29683843, 0.47144927, 0. ],\n", " [0.33291045, 0. , 0.54316969, 0.19273763, 0.21025923],\n", " [0.40429305, 0.2988253 , 0.17577959, 0. , 0.5976506 ]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mapped_data = map_data(matrix, row_sum, ideogram_length)\n", "mapped_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- To each pair of values `(mapped_data[k][j], mapped_data[j][k])`, $k<=j$, one associates a ribbon, that is a curvilinear filled rectangle (that can be degenerate), having as opposite sides two subarcs of the $k^{th}$ ideogram, respectively $j^{th}$ ideogram, and two arcs of quadratic B&eacute;zier curves." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we illustrate the ribbons associated to pairs `(mapped_data[0][j], mapped_data[j][0])`, $j=\\overline{0,4}$,\n", "that illustrate the flow of comments between Emma and all other friends, and herself:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"420\"\n", " height=\"420\"\n", " src=\"https://plot.ly/~empet/12519/\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x227dfaf3c18>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IFrame('https://plot.ly/~empet/12519/', width=420, height=420)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- For a better looking chord diagram, \n", "[Circos documentation](http://circos.ca/presentations/articles/vis_tables1/) recommends to sort increasingly each row of the mapped_data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The array `idx_sort`, defined below, has on each row the indices that sort the corresponding row in `mapped_data`:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3, 1, 0, 4, 2],\n", " [1, 3, 2, 0, 4],\n", " [4, 0, 1, 2, 3],\n", " [1, 3, 4, 0, 2],\n", " [3, 2, 1, 0, 4]], dtype=int64)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx_sort = np.argsort(mapped_data, axis=1)\n", "idx_sort" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following we call ribbon ends, the lists `l=[l[0], l[1]]`, `r=[r[0], r[1]]` having as elements the angular coordinates\n", "of the ends of arcs that are opposite sides in a ribbon. These arcs are sub-arcs in the internal boundaries of\n", "the ideograms, connected by the ribbon\n", "(see the image above)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Compute the ribbon ends and store them as tuples \n", "in a list of lists ($L\\times L$):" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "def make_ribbon_ends(mapped_data, ideo_ends, idx_sort):\n", " L = mapped_data.shape[0]\n", " ribbon_boundary = np.zeros((L,L+1))\n", " for k in range(L):\n", " start = ideo_ends[k][0]\n", " ribbon_boundary[k][0] = start\n", " for j in range(1,L+1):\n", " J = idx_sort[k][j-1]\n", " ribbon_boundary[k][j] = start + mapped_data[k][J]\n", " start = ribbon_boundary[k][j]\n", " return [[(ribbon_boundary[k][j], ribbon_boundary[k][j+1] ) for j in range(L)] for k in range(L)] " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ribbon ends starting from the ideogram[2]\n", " [(2.31580258464619, 2.31580258464619), (2.31580258464619, 2.472952342161697), (2.472952342161697, 2.6650242680139837), (2.6650242680139837, 2.9618626988766086), (2.9618626988766086, 3.43331197142313)]\n" ] } ], "source": [ "ribbon_ends = make_ribbon_ends(mapped_data, ideo_ends, idx_sort)\n", "print ('ribbon ends starting from the ideogram[2]\\n', ribbon_ends[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We note that `ribbon_ends[k][j]` corresponds to `mapped_data[i][idx_sort[k][j]]`, i.e. the length of the arc of ends in `ribbon_ends[k][j]` is equal to `mapped_data[i][idx_sort[k][j]]`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define a few functions that compute the control points for B&eacute;zier ribbon sides." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `control_pts` returns the cartesian coordinates of the control points, $b_0, b_1, b_2$, supposed as being initially located on the unit circle, and thus defined only by their angular coordinate. The angular coordinate\n", "of the point $b_1$ is the mean of angular coordinates of the points $b_0, b_2$.\n", "\n", "Since for a B&eacute;zier ribbon side only $b_0, b_2$ are placed on the unit circle, one gives `radius` as a parameter that controls position of $b_1$. `radius` is the distance of $b_1$ to the circle center." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def control_pts(angle, radius):\n", " #angle is a 3-list containing angular coordinates of the control points b0, b1, b2\n", " #radius is the distance from b1 to the origin O(0,0) \n", "\n", " if len(angle) != 3:\n", " raise InvalidInputError('angle must have len =3')\n", " b_cplx = np.array([np.exp(1j*angle[k]) for k in range(3)])\n", " b_cplx[1] = radius * b_cplx[1]\n", " return list(zip(b_cplx.real, b_cplx.imag))" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def ctrl_rib_chords(l, r, radius):\n", " # this function returns a 2-list containing control poligons of the two quadratic Bezier\n", " #curves that are opposite sides in a ribbon\n", " #l (r) the list of angular variables of the ribbon arc ends defining \n", " #the ribbon starting (ending) arc \n", " # radius is a common parameter for both control polygons\n", " if len(l) != 2 or len(r) != 2:\n", " raise ValueError('the arc ends must be elements in a list of len 2')\n", " return [control_pts([l[j], (l[j]+r[j])/2, r[j]], radius) for j in range(2)]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Each ribbon is colored with the color of one of the two ideograms it connects. \n", "We define an L-list of L-lists of colors for ribbons. Denote it by `ribbon_color`.\n", "\n", "`ribbon_color[k][j]` is the Plotly color string for the ribbon associated to `mapped_data[k][j]` and `mapped_data[j][k]`, i.e. the ribbon connecting two subarcs in the $k^{th}$, respectively, $j^{th}$ ideogram. Hence this structure is symmetric." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initially we define:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "ribbon_color = [L * [ideo_colors[k]] for k in range(L)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then eventually we are changing the color in a few positions.\n", "\n", "For our example we are perfotming the following color change:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "ribbon_color[0][4]=ideo_colors[4]\n", "ribbon_color[1][2]=ideo_colors[2]\n", "ribbon_color[2][3]=ideo_colors[3]\n", "ribbon_color[2][4]=ideo_colors[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The symmetric locations are not modified, because we do not access \n", "`ribbon_color[k][j]`, $k>j$, when drawing the ribbons." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions that return the Plotly SVG paths that are ribbon boundaries:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def make_q_bezier(b):# defines the Plotly SVG path for a quadratic Bezier curve defined by the \n", " #list of its control points\n", " if len(b) != 3:\n", " raise valueError('control poligon must have 3 points')\n", " A, B, C = b \n", " return f'M {A[0]}, {A[1]} Q {B[0]}, {B[1]} {C[0]}, {C[1]}'" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "b=[(1,4), (-0.5, 2.35), (3.745, 1.47)]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'M 1, 4 Q -0.5, 2.35 3.745, 1.47'" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "make_q_bezier(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`make_ribbon_arc` returns the Plotly SVG path corresponding to an arc represented by its end angular coordinates, `theta0, theta1`." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def make_ribbon_arc(theta0, theta1):\n", "\n", " if test_2PI(theta0) and test_2PI(theta1):\n", " if theta0 < theta1:\n", " theta0 = moduloAB(theta0, -pi, pi)\n", " theta1 = moduloAB(theta1, -pi, pi)\n", " if theta0 *theta1 > 0:\n", " raise ValueError('incorrect angle coordinates for ribbon')\n", " \n", " nr = int(40 * (theta0 - theta1) / pi)\n", " if nr <= 2: nr = 3\n", " theta = np.linspace(theta0, theta1, nr)\n", " pts=np.exp(1j*theta)# points in polar complex form, on the given arc\n", " \n", " string_arc = ''\n", " for k in range(len(theta)):\n", " string_arc += f'L {pts.real[k]}, {pts.imag[k]} '\n", " return string_arc \n", " else:\n", " raise ValueError('the angle coordinates for an arc side of a ribbon must be in [0, 2*pi]')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'L 0.5000000000000001, 0.8660254037844386 L 0.5877852522924732, 0.8090169943749473 L 0.6691306063588582, 0.7431448254773941 L 0.7431448254773944, 0.6691306063588581 L 0.8090169943749475, 0.5877852522924731 L 0.8660254037844387, 0.49999999999999994 '" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "make_ribbon_arc(np.pi/3, np.pi/6)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Finally we are ready to define data and layout for the Plotly plot of the chord diagram." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objects as go" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def plot_layout(title, plot_size):\n", "\n", " return dict(title=title,\n", " xaxis=dict(visible=False),\n", " yaxis=dict(visible=False),\n", " showlegend=False,\n", " width=plot_size,\n", " height=plot_size,\n", " margin=dict(t=25, b=25, l=25, r=25),\n", " hovermode='closest',\n", " ) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function that returns the Plotly shape of an ideogram:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def make_ideo_shape(path, line_color, fill_color):\n", " #line_color is the color of the shape boundary\n", " #fill_collor is the color assigned to an ideogram\n", " \n", " return dict(line=dict(color=line_color, \n", " width=0.45),\n", " path=path,\n", " layer='below',\n", " type='path',\n", " fillcolor=fill_color) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We generate two types of ribbons: a ribbon connecting subarcs in two distinct ideograms, respectively\n", "a ribbon from one ideogram to itself (it corresponds to `mapped_data[k][k]`, i.e. it gives the flow of comments\n", "from a community member to herself)." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def make_ribbon(l, r, line_color, fill_color, radius=0.2):\n", " #l=[l[0], l[1]], r=[r[0], r[1]] represent the opposite arcs in the ribbon \n", " #line_color is the color of the shape boundary\n", " #fill_color is the fill color for the ribbon shape\n", " \n", " poligon = ctrl_rib_chords(l,r, radius)\n", " b, c = poligon \n", " \n", " return dict(line=dict(color=line_color, \n", " width=0.5),\n", " path=make_q_bezier(b) + make_ribbon_arc(r[0], r[1])+\n", " make_q_bezier(c[::-1]) + make_ribbon_arc(l[1], l[0]),\n", " type='path',\n", " layer='below',\n", " fillcolor = fill_color, \n", " )\n", "\n", "def make_self_rel(l, line_color, fill_color, radius):\n", " #radius is the radius of Bezier control point b_1\n", " \n", " b = control_pts([l[0], (l[0]+l[1])/2, l[1]], radius) \n", " \n", " return dict(line = dict(color=line_color, \n", " width=0.5),\n", " path = make_q_bezier(b)+make_ribbon_arc(l[1], l[0]),\n", " type = 'path',\n", " layer = 'below',\n", " fillcolor = fill_color \n", " )" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "def invPerm(perm):\n", " # function that returns the inverse of a permutation, perm\n", " inv = [0] * len(perm)\n", " for i, s in enumerate(perm):\n", " inv[s] = i\n", " return inv" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "layout=plot_layout('Chord diagram', 400) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us explain the key point of associating ribbons to right data:\n", " \n", "From the definition of `ribbon_ends` we notice that `ribbon_ends[k][j]` corresponds to data stored in\n", "`matrix[k][sigma[j]]`, where `sigma` is the permutation of indices $0, 1, \\ldots L-1$, that sort the row k in `mapped_data`.\n", "If `sigma_inv` is the inverse permutation of `sigma`, we get that to `matrix[k][j]` corresponds the\n", "`ribbon_ends[k][sigma_inv[j]]`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ribbon_info` is a list of dicts setting the information that is displayed when hovering the mouse over the ribbon ends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the radius of B&eacute;zier control point, $b_1$, for each ribbon associated to a diagonal data entry:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "radii_sribb = [0.4, 0.30, 0.35, 0.39, 0.12]# these value are set after a few trials " ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "ribbon_info = []\n", "shapes = []\n", "for k in range(L):\n", " \n", " sigma = idx_sort[k]\n", " sigma_inv = invPerm(sigma)\n", " for j in range(k, L):\n", " if matrix[k][j] == 0 and matrix[j][k]==0: continue\n", " eta = idx_sort[j]\n", " eta_inv = invPerm(eta)\n", " l = ribbon_ends[k][sigma_inv[j]] \n", " \n", " if j == k:\n", " shapes.append(make_self_rel(l, 'rgb(175,175,175)' ,\n", " ideo_colors[k], radius=radii_sribb[k])) \n", " z = 0.9*np.exp(1j*(l[0]+l[1])/2)\n", " \n", " #the text below will be displayed when hovering the mouse over the ribbon\n", " text = f'{labels[k]} commented on {int(matrix[k][k])} of herself Fb posts'\n", "\n", " ribbon_info.append(go.Scatter(x=[z.real],\n", " y=[z.imag],\n", " mode='markers',\n", " marker=dict(size=0.5, color=ideo_colors[k]),\n", " text=text,\n", " hoverinfo='text'\n", " )\n", " )\n", " else:\n", " r = ribbon_ends[j][eta_inv[k]]\n", " zi = 0.9 * np.exp(1j*(l[0]+l[1])/2)\n", " zf = 0.9 * np.exp(1j*(r[0]+r[1])/2)\n", " #texti and textf are the strings that will be displayed when hovering the mouse \n", " #over the two ribbon ends\n", " texti = f'{labels[k]} commented on {int(matrix[k][j])} of {labels[j]} Fb posts'\n", " textf = f'{labels[j]} commented on {int(matrix[j][k])} of {labels[k]} Fb posts'\n", " \n", " ribbon_info.append(go.Scatter(x=[zi.real],\n", " y=[zi.imag],\n", " mode='markers',\n", " marker=dict(size=0.5, color=ribbon_color[k][j]),\n", " text=texti,\n", " hoverinfo='text'\n", " )\n", " ),\n", " ribbon_info.append(go.Scatter(x=[zf.real],\n", " y=[zf.imag],\n", " mode='markers',\n", " marker=dict(size=0.5, color=ribbon_color[k][j]),\n", " text=textf,\n", " hoverinfo='text'\n", " )\n", " )\n", " r = (r[1], r[0]) # IMPORTANT!!! Reverse these arc ends because otherwise you get\n", " # a twisted ribbon\n", " #append the ribbon shape\n", " shapes.append(make_ribbon(l, r, 'rgb(175,175,175)' , ribbon_color[k][j]))\n", " \n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ideograms` is a list of dicts that set the position, and color of ideograms, as well as the information associated to each ideogram.\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "ideograms = []\n", "for k in range(len(ideo_ends)):\n", " z = make_ideogram_arc(1.1, ideo_ends[k])\n", " zi = make_ideogram_arc(1.0, ideo_ends[k])\n", " m = len(z)\n", " n = len(zi)\n", " ideograms.append(go.Scatter(x=z.real,\n", " y=z.imag,\n", " mode='lines',\n", " line=dict(color=ideo_colors[k], shape='spline', width=0.25),\n", " text=f'{labels[k]} <br>{int(row_sum[k])} comments', \n", " hoverinfo='text'\n", " )\n", " )\n", " \n", " \n", " path = 'M '\n", " for s in range(m):\n", " path += f'{z.real[s]}, {z.imag[s]} L '\n", " \n", " Zi = np.array(zi.tolist()[::-1]) \n", "\n", " for s in range(m):\n", " path += f'{Zi.real[s]}, {Zi.imag[s]} L '\n", " path += f'{z.real[0]} ,{z.imag[0]}' \n", " \n", " shapes.append(make_ideo_shape(path,'rgb(150,150,150)' , ideo_colors[k]))\n", " " ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ " <script type=\"text/javascript\">\n", " window.PlotlyConfig = {MathJaxConfig: 'local'};\n", " if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n", " if (typeof require !== 'undefined') {\n", " require.undef(\"plotly\");\n", " requirejs.config({\n", " paths: {\n", " 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n", " }\n", " });\n", " require(['plotly'], function(Plotly) {\n", " window._Plotly = Plotly;\n", " });\n", " }\n", " </script>\n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { 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= gd.closest('#notebook-container');\n", "if (notebookContainer) {{\n", " x.observe(notebookContainer, {childList: true});\n", "}}\n", "\n", "// Listen for the clearing of the current output cell\n", "var outputEl = gd.closest('.output');\n", "if (outputEl) {{\n", " x.observe(outputEl, {childList: true});\n", "}}\n", "\n", " })\n", " };\n", " });\n", " </script>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = ideograms + ribbon_info\n", "layout['shapes'] = shapes\n", "fig = go.Figure(data=data, layout=layout)\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot, plot\n", "init_notebook_mode(connected=True)\n", "iplot(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a chord diagram associated to a community of 8 Facebook friends:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"500\"\n", " height=\"500\"\n", " src=\"https://plot.ly/~empet/12148/chord-diagram-of-facebook-comments-in-a-community/\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x227e41b7e80>" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IFrame('https://plot.ly/~empet/12148/chord-diagram-of-facebook-comments-in-a-community/',\n", " width=500, height=500)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", " /*body {\n", " background-color: #F5F5F5;\n", " }*/\n", " div.cell{\n", " width: 900px;\n", " margin-left: 13% !important;\n", " margin-right: auto;\n", " }\n", " #notebook li { /* More space between bullet points */\n", " margin-top:0.8em;\n", " }\n", "\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 40pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0em;\n", " margin-top: 0em;\n", " display: block;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " text-indent:1em;\n", " text-align:center;\n", " }\n", " .text_cell_render h2 {\n", " font-weight: 200;\n", " font-size: 28pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 1.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " h3 {\n", " font-family: 'Fenix', serif;\n", " %margin-top:12px;\n", " %margin-bottom: 3px;\n", " }\n", " .text_cell_render h3 {\n", " font-weight: 300;\n", " font-size: 18pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0.5em;\n", " margin-top: 2em;\n", " display: block;\n", " }\n", " h4 {\n", " font-family: 'Fenix', serif;\n", " }\n", " .text_cell_render h4 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-style: normal;\n", " font-size: 16pt;\n", " margin-bottom: 0em;\n", " margin-top: 1.5em;\n", " display: block;\n", " }\n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:900px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " %text-align:justify;\n", " %text-justify:inter-word;\n", " }\n", " \n", " \n", " code{\n", " font-size: 78%;\n", " }\n", " .rendered_html code{\n", " background-color: transparent;\n", " white-space: inherit; \n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .rendered_html code{\n", " background-color: transparent;\n", " }\n", "\n", " blockquote{\n", " display:block;\n", " background: #f3f3f3;\n", " font-family: \"Open sans\",verdana,arial,sans-serif;\n", " width:610px;\n", " padding: 15px 15px 15px 15px;\n", " text-align:justify;\n", " text-justify:inter-word;\n", " }\n", " blockquote p {\n", " margin-bottom: 0;\n", " line-height: 125%;\n", " font-size: 100%;\n", " }\n", " /* element.style {\n", " } */\n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ [\"$\",\"$\"], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ [\"$$\",\"$$\"], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: \"center\", // Change this to \"center\" to center equations.\n", " \"HTML-CSS\": {\n", " styles: {\".MathJax_Display\": {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"./custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
Kaggle/learntools
notebooks/ml_intermediate/raw/tut3.ipynb
1
13711
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial, you will learn what a **categorical variable** is, along with three approaches for handling this type of data.\n", "\n", "\n", "# Introduction\n", "\n", "A **categorical variable** takes only a limited number of values. \n", "\n", "- Consider a survey that asks how often you eat breakfast and provides four options: \"Never\", \"Rarely\", \"Most days\", or \"Every day\". In this case, the data is categorical, because responses fall into a fixed set of categories.\n", "- If people responded to a survey about which what brand of car they owned, the responses would fall into categories like \"Honda\", \"Toyota\", and \"Ford\". In this case, the data is also categorical.\n", "\n", "You will get an error if you try to plug these variables into most machine learning models in Python without preprocessing them first. In this tutorial, we'll compare three approaches that you can use to prepare your categorical data.\n", "\n", "# Three Approaches\n", "\n", "### 1) Drop Categorical Variables\n", "\n", "The easiest approach to dealing with categorical variables is to simply remove them from the dataset. This approach will only work well if the columns did not contain useful information.\n", "\n", "### 2) Ordinal Encoding\n", "\n", "**Ordinal encoding** assigns each unique value to a different integer.\n", "\n", "![tut3_ordinalencode](https://i.imgur.com/tEogUAr.png)\n", "\n", "This approach assumes an ordering of the categories: \"Never\" (0) < \"Rarely\" (1) < \"Most days\" (2) < \"Every day\" (3).\n", "\n", "This assumption makes sense in this example, because there is an indisputable ranking to the categories. Not all categorical variables have a clear ordering in the values, but we refer to those that do as **ordinal variables**. For tree-based models (like decision trees and random forests), you can expect ordinal encoding to work well with ordinal variables. \n", "\n", "### 3) One-Hot Encoding\n", "\n", "**One-hot encoding** creates new columns indicating the presence (or absence) of each possible value in the original data. To understand this, we'll work through an example.\n", "\n", "![tut3_onehot](https://i.imgur.com/TW5m0aJ.png)\n", "\n", "In the original dataset, \"Color\" is a categorical variable with three categories: \"Red\", \"Yellow\", and \"Green\". The corresponding one-hot encoding contains one column for each possible value, and one row for each row in the original dataset. Wherever the original value was \"Red\", we put a 1 in the \"Red\" column; if the original value was \"Yellow\", we put a 1 in the \"Yellow\" column, and so on. \n", "\n", "In contrast to ordinal encoding, one-hot encoding *does not* assume an ordering of the categories. Thus, you can expect this approach to work particularly well if there is no clear ordering in the categorical data (e.g., \"Red\" is neither _more_ nor _less_ than \"Yellow\"). We refer to categorical variables without an intrinsic ranking as **nominal variables**.\n", "\n", "One-hot encoding generally does not perform well if the categorical variable takes on a large number of values (i.e., you generally won't use it for variables taking more than 15 different values). \n", "\n", "# Example\n", "\n", "As in the previous tutorial, we will work with the [Melbourne Housing dataset](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot/home). \n", "\n", "We won't focus on the data loading step. Instead, you can imagine you are at a point where you already have the training and validation data in `X_train`, `X_valid`, `y_train`, and `y_valid`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_kg_hide-input": true, "_kg_hide-output": true }, "outputs": [], "source": [ "#$HIDE$\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Read the data\n", "data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')\n", "\n", "# Separate target from predictors\n", "y = data.Price\n", "X = data.drop(['Price'], axis=1)\n", "\n", "# Divide data into training and validation subsets\n", "X_train_full, X_valid_full, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2,\n", " random_state=0)\n", "\n", "# Drop columns with missing values (simplest approach)\n", "cols_with_missing = [col for col in X_train_full.columns if X_train_full[col].isnull().any()] \n", "X_train_full.drop(cols_with_missing, axis=1, inplace=True)\n", "X_valid_full.drop(cols_with_missing, axis=1, inplace=True)\n", "\n", "# \"Cardinality\" means the number of unique values in a column\n", "# Select categorical columns with relatively low cardinality (convenient but arbitrary)\n", "low_cardinality_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and \n", " X_train_full[cname].dtype == \"object\"]\n", "\n", "# Select numerical columns\n", "numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]\n", "\n", "# Keep selected columns only\n", "my_cols = low_cardinality_cols + numerical_cols\n", "X_train = X_train_full[my_cols].copy()\n", "X_valid = X_valid_full[my_cols].copy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We take a peek at the training data with the `head()` method below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "X_train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we obtain a list of all of the categorical variables in the training data.\n", "\n", "We do this by checking the data type (or **dtype**) of each column. The `object` dtype indicates a column has text (there are other things it could theoretically be, but that's unimportant for our purposes). For this dataset, the columns with text indicate categorical variables." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Get list of categorical variables\n", "s = (X_train.dtypes == 'object')\n", "object_cols = list(s[s].index)\n", "\n", "print(\"Categorical variables:\")\n", "print(object_cols)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define Function to Measure Quality of Each Approach\n", "\n", "We define a function `score_dataset()` to compare the three different approaches to dealing with categorical variables. This function reports the [mean absolute error](https://en.wikipedia.org/wiki/Mean_absolute_error) (MAE) from a random forest model. In general, we want the MAE to be as low as possible!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#$HIDE$\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import mean_absolute_error\n", "\n", "# Function for comparing different approaches\n", "def score_dataset(X_train, X_valid, y_train, y_valid):\n", " model = RandomForestRegressor(n_estimators=100, random_state=0)\n", " model.fit(X_train, y_train)\n", " preds = model.predict(X_valid)\n", " return mean_absolute_error(y_valid, preds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Score from Approach 1 (Drop Categorical Variables)\n", "\n", "We drop the `object` columns with the [`select_dtypes()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html) method. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_kg_hide-input": false }, "outputs": [], "source": [ "drop_X_train = X_train.select_dtypes(exclude=['object'])\n", "drop_X_valid = X_valid.select_dtypes(exclude=['object'])\n", "\n", "print(\"MAE from Approach 1 (Drop categorical variables):\")\n", "print(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Score from Approach 2 (Ordinal Encoding)\n", "\n", "Scikit-learn has a [`OrdinalEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html) class that can be used to get ordinal encodings. We loop over the categorical variables and apply the ordinal encoder separately to each column." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import OrdinalEncoder\n", "\n", "# Make copy to avoid changing original data \n", "label_X_train = X_train.copy()\n", "label_X_valid = X_valid.copy()\n", "\n", "# Apply ordinal encoder to each column with categorical data\n", "ordinal_encoder = OrdinalEncoder()\n", "label_X_train[object_cols] = ordinal_encoder.fit_transform(X_train[object_cols])\n", "label_X_valid[object_cols] = ordinal_encoder.transform(X_valid[object_cols])\n", "\n", "print(\"MAE from Approach 2 (Ordinal Encoding):\") \n", "print(score_dataset(label_X_train, label_X_valid, y_train, y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code cell above, for each column, we randomly assign each unique value to a different integer. This is a common approach that is simpler than providing custom labels; however, we can expect an additional boost in performance if we provide better-informed labels for all ordinal variables.\n", "\n", "### Score from Approach 3 (One-Hot Encoding)\n", "\n", "We use the [`OneHotEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) class from scikit-learn to get one-hot encodings. There are a number of parameters that can be used to customize its behavior. \n", "- We set `handle_unknown='ignore'` to avoid errors when the validation data contains classes that aren't represented in the training data, and\n", "- setting `sparse=False` ensures that the encoded columns are returned as a numpy array (instead of a sparse matrix).\n", "\n", "To use the encoder, we supply only the categorical columns that we want to be one-hot encoded. For instance, to encode the training data, we supply `X_train[object_cols]`. (`object_cols` in the code cell below is a list of the column names with categorical data, and so `X_train[object_cols]` contains all of the categorical data in the training set.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import OneHotEncoder\n", "\n", "# Apply one-hot encoder to each column with categorical data\n", "OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n", "OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))\n", "OH_cols_valid = pd.DataFrame(OH_encoder.transform(X_valid[object_cols]))\n", "\n", "# One-hot encoding removed index; put it back\n", "OH_cols_train.index = X_train.index\n", "OH_cols_valid.index = X_valid.index\n", "\n", "# Remove categorical columns (will replace with one-hot encoding)\n", "num_X_train = X_train.drop(object_cols, axis=1)\n", "num_X_valid = X_valid.drop(object_cols, axis=1)\n", "\n", "# Add one-hot encoded columns to numerical features\n", "OH_X_train = pd.concat([num_X_train, OH_cols_train], axis=1)\n", "OH_X_valid = pd.concat([num_X_valid, OH_cols_valid], axis=1)\n", "\n", "print(\"MAE from Approach 3 (One-Hot Encoding):\") \n", "print(score_dataset(OH_X_train, OH_X_valid, y_train, y_valid))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Which approach is best?\n", "\n", "In this case, dropping the categorical columns (**Approach 1**) performed worst, since it had the highest MAE score. As for the other two approaches, since the returned MAE scores are so close in value, there doesn't appear to be any meaningful benefit to one over the other.\n", "\n", "In general, one-hot encoding (**Approach 3**) will typically perform best, and dropping the categorical columns (**Approach 1**) typically performs worst, but it varies on a case-by-case basis. \n", "\n", "# Conclusion\n", "\n", "The world is filled with categorical data. You will be a much more effective data scientist if you know how to use this common data type!\n", "\n", "# Your Turn\n", "\n", "Put your new skills to work in the **[next exercise](#$NEXT_NOTEBOOK_URL$)**!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
analysiscenter/dataset
examples/experiments/squeeze_and_excitation/se_different_ratio.ipynb
1
67858
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import torch.nn as nn\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colors as mcolors\n", "\n", "\n", "import sys\n", "sys.path.append('../../..')\n", "from batchflow.opensets import Imagenette160\n", "from batchflow import Pipeline, B, V, C, W\n", "\n", "from batchflow.models.torch import ResNet34, ResBlock, SelfAttention\n", "from batchflow.models.torch.layers import ConvBlock\n", "\n", "from batchflow.models.metrics import ClassificationMetrics\n", "from batchflow.research import Research, Option, Results, KV, RP, REU, RI\n", "from batchflow.utils import plot_results_by_config, show_research, print_results" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Global constants\n", "NUM_ITERS = 50000 # number of iterations to train each model for\n", "N_REPS = 5 # number of times to repeat each model train\n", "RESEARCH_NAME = 'research' # name of Research object\n", "DEVICES = [3, 4, 5, 6, 7] # devices to use\n", "WORKERS = len(DEVICES) # number of simultaneously trained models\n", "TEST_FREQUENCY = 150\n", "\n", "dataset = Imagenette160() # dataset to train models on" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "domain = (Option('se', [True, True, False]) @ Option('ratio', [4, 16, None]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "config = {\n", " 'inputs/images/shape': (3, 160, 160),\n", " 'initial_block/inputs': 'images',\n", " 'inputs/labels/classes': 10,\n", " 'body/encoder/blocks/se': C('se'),\n", " 'body/encoder/blocks/ratio': C('ratio'),\n", " 'head/layout': 'Vf',\n", " 'head/units': 10,\n", " \"decay\": dict(name='exp', gamma=0.15),\n", " \"n_iters\": 10000,\n", " 'device': C('device'),\n", "}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "train_root = (dataset.train.p \n", " .crop(shape=(160, 160), origin='center')\n", " .to_array(channels='first', dtype=np.float32)\n", " .multiply(multiplier=1/255)\n", " .run_later(64, n_epochs=None, drop_last=True,\n", " shuffle=True, prefetch=5)\n", " )\n", "\n", "train_pipeline = (Pipeline()\n", " .init_variable('loss')\n", " .init_model('dynamic', ResNet34, 'my_model', config=config) \n", " .train_model('my_model', B('images'), B('labels'), \n", " fetches='loss', save_to=V('loss'))\n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def acc(iteration, import_from):\n", " pipeline = (dataset.test.p\n", " .import_model('my_model', import_from)\n", " .init_variable('true', [])\n", " .update(V('true', mode='a'), B.labels) \n", " .init_variable('predictions', [])\n", " .crop(shape=(160, 160), origin='center')\n", " .to_array(channels='first', dtype=np.float32)\n", " .multiply(multiplier=1/255)\n", " .predict_model('my_model', B('images'), fetches='predictions',\n", " save_to=V('predictions', mode='a'))\n", " )\n", " pipeline.run(128, n_epochs=1, drop_last=False, shuffle=True)\n", " pred = np.concatenate(pipeline.v('predictions'))\n", " true = np.concatenate(pipeline.v('true'))\n", " accuracy = ClassificationMetrics(true, pred, fmt='logits',\n", " num_classes=10, axis=1).accuracy()\n", " return accuracy" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "research = (Research()\n", " .init_domain(domain, n_reps=N_REPS)\n", " .add_pipeline(root=train_root, branch=train_pipeline, variables='loss',\n", " name='train_ppl', logging=True)\n", " .add_callable(acc, returns='acc_vall', name='acc_fn', execute=TEST_FREQUENCY,\n", " iteration=RI(), import_from=RP('train_ppl')))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Research research is starting...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Domain updated: 0: 100%|██████████| 750000/750000.0 [20:01:01<00:00, 10.41it/s] \n" ] }, { "data": { "text/plain": [ "<batchflow.research.research.Research at 0x7fc69fbeaac8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!rm -rf research\n", "research.run(NUM_ITERS, name=RESEARCH_NAME,\n", " devices=DEVICES, workers=WORKERS,\n", " bar=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 32min 58s, sys: 24 ms, total: 32min 58s\n", "Wall time: 32min 58s\n" ] } ], "source": [ "%%time\n", "results = Results(path=RESEARCH_NAME, concat_config=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1296x504 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "show_research(results.df, layout=['train_ppl/loss', 'acc_fn/acc_vall'], average_repetitions=True, \n", " color=list(mcolors.TABLEAU_COLORS.keys()), log_scale=False, rolling_window=10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>acc_fn_0</th>\n", " <th>acc_fn_4</th>\n", " <th>acc_fn_2</th>\n", " <th>acc_fn_1</th>\n", " <th>acc_fn_3</th>\n", " <th>acc_fn_mean</th>\n", " <th>acc_fn_std</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>ratio_16-se_True</th>\n", " <td>0.878850</td>\n", " <td>0.870637</td>\n", " <td>0.866530</td>\n", " <td>0.860370</td>\n", " <td>0.866530</td>\n", " <td>0.868583</td>\n", " <td>0.006091</td>\n", " </tr>\n", " <tr>\n", " <th>ratio_4-se_True</th>\n", " <td>0.872690</td>\n", " <td>0.882957</td>\n", " <td>0.878850</td>\n", " <td>0.866530</td>\n", " <td>0.878850</td>\n", " <td>0.875975</td>\n", " <td>0.005749</td>\n", " </tr>\n", " <tr>\n", " <th>ratio_None-se_False</th>\n", " <td>0.856263</td>\n", " <td>0.848049</td>\n", " <td>0.835729</td>\n", " <td>0.848049</td>\n", " <td>0.854209</td>\n", " <td>0.848460</td>\n", " <td>0.007160</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " acc_fn_0 acc_fn_4 acc_fn_2 acc_fn_1 acc_fn_3 \\\n", "ratio_16-se_True 0.878850 0.870637 0.866530 0.860370 0.866530 \n", "ratio_4-se_True 0.872690 0.882957 0.878850 0.866530 0.878850 \n", "ratio_None-se_False 0.856263 0.848049 0.835729 0.848049 0.854209 \n", "\n", " acc_fn_mean acc_fn_std \n", "ratio_16-se_True 0.868583 0.006091 \n", "ratio_4-se_True 0.875975 0.005749 \n", "ratio_None-se_False 0.848460 0.007160 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print_results(results.df, 'acc_fn/acc_vall', False, ascending=True, n_last=100)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
radu941208/DeepLearning
Intro_to_Neural_Networks/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.ipynb
1
56732
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building your Deep Neural Network: Step by Step\n", "\n", "Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!\n", "\n", "- In this notebook, you will implement all the functions required to build a deep neural network.\n", "- In the next assignment, you will use these functions to build a deep neural network for image classification.\n", "\n", "**After this assignment you will be able to:**\n", "- Use non-linear units like ReLU to improve your model\n", "- Build a deeper neural network (with more than 1 hidden layer)\n", "- Implement an easy-to-use neural network class\n", "\n", "**Notation**:\n", "- Superscript $[l]$ denotes a quantity associated with the $l^{th}$ layer. \n", " - Example: $a^{[L]}$ is the $L^{th}$ layer activation. $W^{[L]}$ and $b^{[L]}$ are the $L^{th}$ layer parameters.\n", "- Superscript $(i)$ denotes a quantity associated with the $i^{th}$ example. \n", " - Example: $x^{(i)}$ is the $i^{th}$ training example.\n", "- Lowerscript $i$ denotes the $i^{th}$ entry of a vector.\n", " - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the $l^{th}$ layer's activations).\n", "\n", "Let's get started!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 - Packages\n", "\n", "Let's first import all the packages that you will need during this assignment. \n", "- [numpy](www.numpy.org) is the main package for scientific computing with Python.\n", "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n", "- dnn_utils provides some necessary functions for this notebook.\n", "- testCases provides some test cases to assess the correctness of your functions\n", "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work. Please don't change the seed. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n", "/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import numpy as np\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "from testCases_v3 import *\n", "from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "np.random.seed(1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 2 - Outline of the Assignment\n", "\n", "To build your neural network, you will be implementing several \"helper functions\". These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps. Here is an outline of this assignment, you will:\n", "\n", "- Initialize the parameters for a two-layer network and for an $L$-layer neural network.\n", "- Implement the forward propagation module (shown in purple in the figure below).\n", " - Complete the LINEAR part of a layer's forward propagation step (resulting in $Z^{[l]}$).\n", " - We give you the ACTIVATION function (relu/sigmoid).\n", " - Combine the previous two steps into a new [LINEAR->ACTIVATION] forward function.\n", " - Stack the [LINEAR->RELU] forward function L-1 time (for layers 1 through L-1) and add a [LINEAR->SIGMOID] at the end (for the final layer $L$). This gives you a new L_model_forward function.\n", "- Compute the loss.\n", "- Implement the backward propagation module (denoted in red in the figure below).\n", " - Complete the LINEAR part of a layer's backward propagation step.\n", " - We give you the gradient of the ACTIVATE function (relu_backward/sigmoid_backward) \n", " - Combine the previous two steps into a new [LINEAR->ACTIVATION] backward function.\n", " - Stack [LINEAR->RELU] backward L-1 times and add [LINEAR->SIGMOID] backward in a new L_model_backward function\n", "- Finally update the parameters.\n", "\n", "<img src=\"images/final outline.png\" style=\"width:800px;height:500px;\">\n", "<caption><center> **Figure 1**</center></caption><br>\n", "\n", "\n", "**Note** that for every forward function, there is a corresponding backward function. That is why at every step of your forward module you will be storing some values in a cache. The cached values are useful for computing gradients. In the backpropagation module you will then use the cache to calculate the gradients. This assignment will show you exactly how to carry out each of these steps. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 3 - Initialization\n", "\n", "You will write two helper functions that will initialize the parameters for your model. The first function will be used to initialize parameters for a two layer model. The second one will generalize this initialization process to $L$ layers.\n", "\n", "### 3.1 - 2-layer Neural Network\n", "\n", "**Exercise**: Create and initialize the parameters of the 2-layer neural network.\n", "\n", "**Instructions**:\n", "- The model's structure is: *LINEAR -> RELU -> LINEAR -> SIGMOID*. \n", "- Use random initialization for the weight matrices. Use `np.random.randn(shape)*0.01` with the correct shape.\n", "- Use zero initialization for the biases. Use `np.zeros(shape)`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: initialize_parameters\n", "\n", "def initialize_parameters(n_x, n_h, n_y):\n", " \"\"\"\n", " Argument:\n", " n_x -- size of the input layer\n", " n_h -- size of the hidden layer\n", " n_y -- size of the output layer\n", " \n", " Returns:\n", " parameters -- python dictionary containing your parameters:\n", " W1 -- weight matrix of shape (n_h, n_x)\n", " b1 -- bias vector of shape (n_h, 1)\n", " W2 -- weight matrix of shape (n_y, n_h)\n", " b2 -- bias vector of shape (n_y, 1)\n", " \"\"\"\n", " \n", " np.random.seed(1)\n", " \n", " ### START CODE HERE ### (≈ 4 lines of code)\n", " W1 = np.random.randn(n_h, n_x)*0.01\n", " b1 = np.zeros(shape=(n_h, 1))\n", " W2 = np.random.randn(n_y, n_h)*0.01\n", " b2 = np.zeros(shape=(n_y, 1))\n", " ### END CODE HERE ###\n", " \n", " assert(W1.shape == (n_h, n_x))\n", " assert(b1.shape == (n_h, 1))\n", " assert(W2.shape == (n_y, n_h))\n", " assert(b2.shape == (n_y, 1))\n", " \n", " parameters = {\"W1\": W1,\n", " \"b1\": b1,\n", " \"W2\": W2,\n", " \"b2\": b2}\n", " \n", " return parameters " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = [[ 0.01624345 -0.00611756 -0.00528172]\n", " [-0.01072969 0.00865408 -0.02301539]]\n", "b1 = [[ 0.]\n", " [ 0.]]\n", "W2 = [[ 0.01744812 -0.00761207]]\n", "b2 = [[ 0.]]\n" ] } ], "source": [ "parameters = initialize_parameters(3,2,1)\n", "print(\"W1 = \" + str(parameters[\"W1\"]))\n", "print(\"b1 = \" + str(parameters[\"b1\"]))\n", "print(\"W2 = \" + str(parameters[\"W2\"]))\n", "print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", " \n", "<table style=\"width:80%\">\n", " <tr>\n", " <td> **W1** </td>\n", " <td> [[ 0.01624345 -0.00611756 -0.00528172]\n", " [-0.01072969 0.00865408 -0.02301539]] </td> \n", " </tr>\n", "\n", " <tr>\n", " <td> **b1**</td>\n", " <td>[[ 0.]\n", " [ 0.]]</td> \n", " </tr>\n", " \n", " <tr>\n", " <td>**W2**</td>\n", " <td> [[ 0.01744812 -0.00761207]]</td>\n", " </tr>\n", " \n", " <tr>\n", " <td> **b2** </td>\n", " <td> [[ 0.]] </td> \n", " </tr>\n", " \n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 3.2 - L-layer Neural Network\n", "\n", "The initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and bias vectors. When completing the `initialize_parameters_deep`, you should make sure that your dimensions match between each layer. Recall that $n^{[l]}$ is the number of units in layer $l$. Thus for example if the size of our input $X$ is $(12288, 209)$ (with $m=209$ examples) then:\n", "\n", "<table style=\"width:100%\">\n", "\n", "\n", " <tr>\n", " <td> </td> \n", " <td> **Shape of W** </td> \n", " <td> **Shape of b** </td> \n", " <td> **Activation** </td>\n", " <td> **Shape of Activation** </td> \n", " <tr>\n", " \n", " <tr>\n", " <td> **Layer 1** </td> \n", " <td> $(n^{[1]},12288)$ </td> \n", " <td> $(n^{[1]},1)$ </td> \n", " <td> $Z^{[1]} = W^{[1]} X + b^{[1]} $ </td> \n", " \n", " <td> $(n^{[1]},209)$ </td> \n", " <tr>\n", " \n", " <tr>\n", " <td> **Layer 2** </td> \n", " <td> $(n^{[2]}, n^{[1]})$ </td> \n", " <td> $(n^{[2]},1)$ </td> \n", " <td>$Z^{[2]} = W^{[2]} A^{[1]} + b^{[2]}$ </td> \n", " <td> $(n^{[2]}, 209)$ </td> \n", " <tr>\n", " \n", " <tr>\n", " <td> $\\vdots$ </td> \n", " <td> $\\vdots$ </td> \n", " <td> $\\vdots$ </td> \n", " <td> $\\vdots$</td> \n", " <td> $\\vdots$ </td> \n", " <tr>\n", " \n", " <tr>\n", " <td> **Layer L-1** </td> \n", " <td> $(n^{[L-1]}, n^{[L-2]})$ </td> \n", " <td> $(n^{[L-1]}, 1)$ </td> \n", " <td>$Z^{[L-1]} = W^{[L-1]} A^{[L-2]} + b^{[L-1]}$ </td> \n", " <td> $(n^{[L-1]}, 209)$ </td> \n", " <tr>\n", " \n", " \n", " <tr>\n", " <td> **Layer L** </td> \n", " <td> $(n^{[L]}, n^{[L-1]})$ </td> \n", " <td> $(n^{[L]}, 1)$ </td>\n", " <td> $Z^{[L]} = W^{[L]} A^{[L-1]} + b^{[L]}$</td>\n", " <td> $(n^{[L]}, 209)$ </td> \n", " <tr>\n", "\n", "</table>\n", "\n", "Remember that when we compute $W X + b$ in python, it carries out broadcasting. For example, if: \n", "\n", "$$ W = \\begin{bmatrix}\n", " j & k & l\\\\\n", " m & n & o \\\\\n", " p & q & r \n", "\\end{bmatrix}\\;\\;\\; X = \\begin{bmatrix}\n", " a & b & c\\\\\n", " d & e & f \\\\\n", " g & h & i \n", "\\end{bmatrix} \\;\\;\\; b =\\begin{bmatrix}\n", " s \\\\\n", " t \\\\\n", " u\n", "\\end{bmatrix}\\tag{2}$$\n", "\n", "Then $WX + b$ will be:\n", "\n", "$$ WX + b = \\begin{bmatrix}\n", " (ja + kd + lg) + s & (jb + ke + lh) + s & (jc + kf + li)+ s\\\\\n", " (ma + nd + og) + t & (mb + ne + oh) + t & (mc + nf + oi) + t\\\\\n", " (pa + qd + rg) + u & (pb + qe + rh) + u & (pc + qf + ri)+ u\n", "\\end{bmatrix}\\tag{3} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Implement initialization for an L-layer Neural Network. \n", "\n", "**Instructions**:\n", "- The model's structure is *[LINEAR -> RELU] $ \\times$ (L-1) -> LINEAR -> SIGMOID*. I.e., it has $L-1$ layers using a ReLU activation function followed by an output layer with a sigmoid activation function.\n", "- Use random initialization for the weight matrices. Use `np.random.rand(shape) * 0.01`.\n", "- Use zeros initialization for the biases. Use `np.zeros(shape)`.\n", "- We will store $n^{[l]}$, the number of units in different layers, in a variable `layer_dims`. For example, the `layer_dims` for the \"Planar Data classification model\" from last week would have been [2,4,1]: There were two inputs, one hidden layer with 4 hidden units, and an output layer with 1 output unit. Thus means `W1`'s shape was (4,2), `b1` was (4,1), `W2` was (1,4) and `b2` was (1,1). Now you will generalize this to $L$ layers! \n", "- Here is the implementation for $L=1$ (one layer neural network). It should inspire you to implement the general case (L-layer neural network).\n", "```python\n", " if L == 1:\n", " parameters[\"W\" + str(L)] = np.random.randn(layer_dims[1], layer_dims[0]) * 0.01\n", " parameters[\"b\" + str(L)] = np.zeros((layer_dims[1], 1))\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: initialize_parameters_deep\n", "\n", "def initialize_parameters_deep(layer_dims):\n", " \"\"\"\n", " Arguments:\n", " layer_dims -- python array (list) containing the dimensions of each layer in our network\n", " \n", " Returns:\n", " parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n", " Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])\n", " bl -- bias vector of shape (layer_dims[l], 1)\n", " \"\"\"\n", " \n", " np.random.seed(3)\n", " parameters = {}\n", " L = len(layer_dims) # number of layers in the network\n", "\n", " for l in range(1, L):\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) * 0.01\n", " parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\n", " ### END CODE HERE ###\n", " \n", " assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))\n", " assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))\n", "\n", " \n", " return parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = [[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]\n", " [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]\n", " [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]\n", " [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]\n", "b1 = [[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]\n", "W2 = [[-0.01185047 -0.0020565 0.01486148 0.00236716]\n", " [-0.01023785 -0.00712993 0.00625245 -0.00160513]\n", " [-0.00768836 -0.00230031 0.00745056 0.01976111]]\n", "b2 = [[ 0.]\n", " [ 0.]\n", " [ 0.]]\n" ] } ], "source": [ "parameters = initialize_parameters_deep([5,4,3])\n", "print(\"W1 = \" + str(parameters[\"W1\"]))\n", "print(\"b1 = \" + str(parameters[\"b1\"]))\n", "print(\"W2 = \" + str(parameters[\"W2\"]))\n", "print(\"b2 = \" + str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", " \n", "<table style=\"width:80%\">\n", " <tr>\n", " <td> **W1** </td>\n", " <td>[[ 0.01788628 0.0043651 0.00096497 -0.01863493 -0.00277388]\n", " [-0.00354759 -0.00082741 -0.00627001 -0.00043818 -0.00477218]\n", " [-0.01313865 0.00884622 0.00881318 0.01709573 0.00050034]\n", " [-0.00404677 -0.0054536 -0.01546477 0.00982367 -0.01101068]]</td> \n", " </tr>\n", " \n", " <tr>\n", " <td>**b1** </td>\n", " <td>[[ 0.]\n", " [ 0.]\n", " [ 0.]\n", " [ 0.]]</td> \n", " </tr>\n", " \n", " <tr>\n", " <td>**W2** </td>\n", " <td>[[-0.01185047 -0.0020565 0.01486148 0.00236716]\n", " [-0.01023785 -0.00712993 0.00625245 -0.00160513]\n", " [-0.00768836 -0.00230031 0.00745056 0.01976111]]</td> \n", " </tr>\n", " \n", " <tr>\n", " <td>**b2** </td>\n", " <td>[[ 0.]\n", " [ 0.]\n", " [ 0.]]</td> \n", " </tr>\n", " \n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4 - Forward propagation module\n", "\n", "### 4.1 - Linear Forward \n", "Now that you have initialized your parameters, you will do the forward propagation module. You will start by implementing some basic functions that you will use later when implementing the model. You will complete three functions in this order:\n", "\n", "- LINEAR\n", "- LINEAR -> ACTIVATION where ACTIVATION will be either ReLU or Sigmoid. \n", "- [LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID (whole model)\n", "\n", "The linear forward module (vectorized over all the examples) computes the following equations:\n", "\n", "$$Z^{[l]} = W^{[l]}A^{[l-1]} +b^{[l]}\\tag{4}$$\n", "\n", "where $A^{[0]} = X$. \n", "\n", "**Exercise**: Build the linear part of forward propagation.\n", "\n", "**Reminder**:\n", "The mathematical representation of this unit is $Z^{[l]} = W^{[l]}A^{[l-1]} +b^{[l]}$. You may also find `np.dot()` useful. If your dimensions don't match, printing `W.shape` may help." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: linear_forward\n", "\n", "def linear_forward(A, W, b):\n", " \"\"\"\n", " Implement the linear part of a layer's forward propagation.\n", "\n", " Arguments:\n", " A -- activations from previous layer (or input data): (size of previous layer, number of examples)\n", " W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n", " b -- bias vector, numpy array of shape (size of the current layer, 1)\n", "\n", " Returns:\n", " Z -- the input of the activation function, also called pre-activation parameter \n", " cache -- a python dictionary containing \"A\", \"W\" and \"b\" ; stored for computing the backward pass efficiently\n", " \"\"\"\n", " \n", " ### START CODE HERE ### (≈ 1 line of code)\n", " Z = np.dot(W,A)+b\n", " ### END CODE HERE ###\n", " \n", " assert(Z.shape == (W.shape[0], A.shape[1]))\n", " cache = (A, W, b)\n", " \n", " return Z, cache" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Z = [[ 3.26295337 -1.23429987]]\n" ] } ], "source": [ "A, W, b = linear_forward_test_case()\n", "\n", "Z, linear_cache = linear_forward(A, W, b)\n", "print(\"Z = \" + str(Z))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", "\n", "<table style=\"width:35%\">\n", " \n", " <tr>\n", " <td> **Z** </td>\n", " <td> [[ 3.26295337 -1.23429987]] </td> \n", " </tr>\n", " \n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4.2 - Linear-Activation Forward\n", "\n", "In this notebook, you will use two activation functions:\n", "\n", "- **Sigmoid**: $\\sigma(Z) = \\sigma(W A + b) = \\frac{1}{ 1 + e^{-(W A + b)}}$. We have provided you with the `sigmoid` function. This function returns **two** items: the activation value \"`a`\" and a \"`cache`\" that contains \"`Z`\" (it's what we will feed in to the corresponding backward function). To use it you could just call: \n", "``` python\n", "A, activation_cache = sigmoid(Z)\n", "```\n", "\n", "- **ReLU**: The mathematical formula for ReLu is $A = RELU(Z) = max(0, Z)$. We have provided you with the `relu` function. This function returns **two** items: the activation value \"`A`\" and a \"`cache`\" that contains \"`Z`\" (it's what we will feed in to the corresponding backward function). To use it you could just call:\n", "``` python\n", "A, activation_cache = relu(Z)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more convenience, you are going to group two functions (Linear and Activation) into one function (LINEAR->ACTIVATION). Hence, you will implement a function that does the LINEAR forward step followed by an ACTIVATION forward step.\n", "\n", "**Exercise**: Implement the forward propagation of the *LINEAR->ACTIVATION* layer. Mathematical relation is: $A^{[l]} = g(Z^{[l]}) = g(W^{[l]}A^{[l-1]} +b^{[l]})$ where the activation \"g\" can be sigmoid() or relu(). Use linear_forward() and the correct activation function." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: linear_activation_forward\n", "\n", "def linear_activation_forward(A_prev, W, b, activation):\n", " \"\"\"\n", " Implement the forward propagation for the LINEAR->ACTIVATION layer\n", "\n", " Arguments:\n", " A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)\n", " W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n", " b -- bias vector, numpy array of shape (size of the current layer, 1)\n", " activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n", "\n", " Returns:\n", " A -- the output of the activation function, also called the post-activation value \n", " cache -- a python dictionary containing \"linear_cache\" and \"activation_cache\";\n", " stored for computing the backward pass efficiently\n", " \"\"\"\n", " \n", " if activation == \"sigmoid\":\n", " # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " Z, linear_cache = linear_forward(A_prev, W, b)\n", " A, activation_cache = sigmoid(Z)\n", " ### END CODE HERE ###\n", " \n", " elif activation == \"relu\":\n", " # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " Z, linear_cache = linear_forward(A_prev, W, b)\n", " A, activation_cache = relu(Z)\n", " ### END CODE HERE ###\n", " \n", " assert (A.shape == (W.shape[0], A_prev.shape[1]))\n", " cache = (linear_cache, activation_cache)\n", "\n", " return A, cache" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "With sigmoid: A = [[ 0.96890023 0.11013289]]\n", "With ReLU: A = [[ 3.43896131 0. ]]\n" ] } ], "source": [ "A_prev, W, b = linear_activation_forward_test_case()\n", "\n", "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"sigmoid\")\n", "print(\"With sigmoid: A = \" + str(A))\n", "\n", "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"relu\")\n", "print(\"With ReLU: A = \" + str(A))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", " \n", "<table style=\"width:35%\">\n", " <tr>\n", " <td> **With sigmoid: A ** </td>\n", " <td > [[ 0.96890023 0.11013289]]</td> \n", " </tr>\n", " <tr>\n", " <td> **With ReLU: A ** </td>\n", " <td > [[ 3.43896131 0. ]]</td> \n", " </tr>\n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note**: In deep learning, the \"[LINEAR->ACTIVATION]\" computation is counted as a single layer in the neural network, not two layers. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### d) L-Layer Model \n", "\n", "For even more convenience when implementing the $L$-layer Neural Net, you will need a function that replicates the previous one (`linear_activation_forward` with RELU) $L-1$ times, then follows that with one `linear_activation_forward` with SIGMOID.\n", "\n", "<img src=\"images/model_architecture_kiank.png\" style=\"width:600px;height:300px;\">\n", "<caption><center> **Figure 2** : *[LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID* model</center></caption><br>\n", "\n", "**Exercise**: Implement the forward propagation of the above model.\n", "\n", "**Instruction**: In the code below, the variable `AL` will denote $A^{[L]} = \\sigma(Z^{[L]}) = \\sigma(W^{[L]} A^{[L-1]} + b^{[L]})$. (This is sometimes also called `Yhat`, i.e., this is $\\hat{Y}$.) \n", "\n", "**Tips**:\n", "- Use the functions you had previously written \n", "- Use a for loop to replicate [LINEAR->RELU] (L-1) times\n", "- Don't forget to keep track of the caches in the \"caches\" list. To add a new value `c` to a `list`, you can use `list.append(c)`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: L_model_forward\n", "\n", "def L_model_forward(X, parameters):\n", " \"\"\"\n", " Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation\n", " \n", " Arguments:\n", " X -- data, numpy array of shape (input size, number of examples)\n", " parameters -- output of initialize_parameters_deep()\n", " \n", " Returns:\n", " AL -- last post-activation value\n", " caches -- list of caches containing:\n", " every cache of linear_relu_forward() (there are L-1 of them, indexed from 0 to L-2)\n", " the cache of linear_sigmoid_forward() (there is one, indexed L-1)\n", " \"\"\"\n", "\n", " caches = []\n", " A = X\n", " L = len(parameters) // 2 # number of layers in the neural network\n", " \n", " # Implement [LINEAR -> RELU]*(L-1). Add \"cache\" to the \"caches\" list.\n", " for l in range(1, L):\n", " A_prev = A \n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " A, cache = linear_activation_forward(A_prev, parameters['W'+str(l)], parameters['b'+str(l)], activation='relu')\n", " caches.append(cache)\n", " ### END CODE HERE ###\n", " \n", " # Implement LINEAR -> SIGMOID. Add \"cache\" to the \"caches\" list.\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " AL, cache = linear_activation_forward(A, parameters['W'+str(L)], parameters['b'+str(L)], activation='sigmoid')\n", " caches.append(cache)\n", " ### END CODE HERE ###\n", " \n", " assert(AL.shape == (1,X.shape[1]))\n", " \n", " return AL, caches" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AL = [[ 0.03921668 0.70498921 0.19734387 0.04728177]]\n", "Length of caches list = 3\n" ] } ], "source": [ "X, parameters = L_model_forward_test_case_2hidden()\n", "AL, caches = L_model_forward(X, parameters)\n", "print(\"AL = \" + str(AL))\n", "print(\"Length of caches list = \" + str(len(caches)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<table style=\"width:50%\">\n", " <tr>\n", " <td> **AL** </td>\n", " <td > [[ 0.03921668 0.70498921 0.19734387 0.04728177]]</td> \n", " </tr>\n", " <tr>\n", " <td> **Length of caches list ** </td>\n", " <td > 3 </td> \n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now you have a full forward propagation that takes the input X and outputs a row vector $A^{[L]}$ containing your predictions. It also records all intermediate values in \"caches\". Using $A^{[L]}$, you can compute the cost of your predictions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5 - Cost function\n", "\n", "Now you will implement forward and backward propagation. You need to compute the cost, because you want to check if your model is actually learning.\n", "\n", "**Exercise**: Compute the cross-entropy cost $J$, using the following formula: $$-\\frac{1}{m} \\sum\\limits_{i = 1}^{m} (y^{(i)}\\log\\left(a^{[L] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[L](i)}\\right)) \\tag{7}$$\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: compute_cost\n", "\n", "def compute_cost(AL, Y):\n", " \"\"\"\n", " Implement the cost function defined by equation (7).\n", "\n", " Arguments:\n", " AL -- probability vector corresponding to your label predictions, shape (1, number of examples)\n", " Y -- true \"label\" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)\n", "\n", " Returns:\n", " cost -- cross-entropy cost\n", " \"\"\"\n", " \n", " m = Y.shape[1]\n", "\n", " # Compute loss from aL and y.\n", " ### START CODE HERE ### (≈ 1 lines of code)\n", " cost = (-1/m)*(np.dot(np.log(AL), Y.T)+np.dot(np.log(1-AL), (1-Y).T))\n", " ### END CODE HERE ###\n", " \n", " cost = np.squeeze(cost) # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).\n", " assert(cost.shape == ())\n", " \n", " return cost" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cost = 0.41493159961539694\n" ] } ], "source": [ "Y, AL = compute_cost_test_case()\n", "\n", "print(\"cost = \" + str(compute_cost(AL, Y)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "<table>\n", "\n", " <tr>\n", " <td>**cost** </td>\n", " <td> 0.41493159961539694</td> \n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6 - Backward propagation module\n", "\n", "Just like with forward propagation, you will implement helper functions for backpropagation. Remember that back propagation is used to calculate the gradient of the loss function with respect to the parameters. \n", "\n", "**Reminder**: \n", "<img src=\"images/backprop_kiank.png\" style=\"width:650px;height:250px;\">\n", "<caption><center> **Figure 3** : Forward and Backward propagation for *LINEAR->RELU->LINEAR->SIGMOID* <br> *The purple blocks represent the forward propagation, and the red blocks represent the backward propagation.* </center></caption>\n", "\n", "<!-- \n", "For those of you who are expert in calculus (you don't need to be to do this assignment), the chain rule of calculus can be used to derive the derivative of the loss $\\mathcal{L}$ with respect to $z^{[1]}$ in a 2-layer network as follows:\n", "\n", "$$\\frac{d \\mathcal{L}(a^{[2]},y)}{{dz^{[1]}}} = \\frac{d\\mathcal{L}(a^{[2]},y)}{{da^{[2]}}}\\frac{{da^{[2]}}}{{dz^{[2]}}}\\frac{{dz^{[2]}}}{{da^{[1]}}}\\frac{{da^{[1]}}}{{dz^{[1]}}} \\tag{8} $$\n", "\n", "In order to calculate the gradient $dW^{[1]} = \\frac{\\partial L}{\\partial W^{[1]}}$, you use the previous chain rule and you do $dW^{[1]} = dz^{[1]} \\times \\frac{\\partial z^{[1]} }{\\partial W^{[1]}}$. During the backpropagation, at each step you multiply your current gradient by the gradient corresponding to the specific layer to get the gradient you wanted.\n", "\n", "Equivalently, in order to calculate the gradient $db^{[1]} = \\frac{\\partial L}{\\partial b^{[1]}}$, you use the previous chain rule and you do $db^{[1]} = dz^{[1]} \\times \\frac{\\partial z^{[1]} }{\\partial b^{[1]}}$.\n", "\n", "This is why we talk about **backpropagation**.\n", "!-->\n", "\n", "Now, similar to forward propagation, you are going to build the backward propagation in three steps:\n", "- LINEAR backward\n", "- LINEAR -> ACTIVATION backward where ACTIVATION computes the derivative of either the ReLU or sigmoid activation\n", "- [LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID backward (whole model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 - Linear backward\n", "\n", "For layer $l$, the linear part is: $Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}$ (followed by an activation).\n", "\n", "Suppose you have already calculated the derivative $dZ^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial Z^{[l]}}$. You want to get $(dW^{[l]}, db^{[l]} dA^{[l-1]})$.\n", "\n", "<img src=\"images/linearback_kiank.png\" style=\"width:250px;height:300px;\">\n", "<caption><center> **Figure 4** </center></caption>\n", "\n", "The three outputs $(dW^{[l]}, db^{[l]}, dA^{[l]})$ are computed using the input $dZ^{[l]}$.Here are the formulas you need:\n", "$$ dW^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial W^{[l]}} = \\frac{1}{m} dZ^{[l]} A^{[l-1] T} \\tag{8}$$\n", "$$ db^{[l]} = \\frac{\\partial \\mathcal{L} }{\\partial b^{[l]}} = \\frac{1}{m} \\sum_{i = 1}^{m} dZ^{[l](i)}\\tag{9}$$\n", "$$ dA^{[l-1]} = \\frac{\\partial \\mathcal{L} }{\\partial A^{[l-1]}} = W^{[l] T} dZ^{[l]} \\tag{10}$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Use the 3 formulas above to implement linear_backward()." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: linear_backward\n", "\n", "def linear_backward(dZ, cache):\n", " \"\"\"\n", " Implement the linear portion of backward propagation for a single layer (layer l)\n", "\n", " Arguments:\n", " dZ -- Gradient of the cost with respect to the linear output (of current layer l)\n", " cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer\n", "\n", " Returns:\n", " dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n", " dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n", " db -- Gradient of the cost with respect to b (current layer l), same shape as b\n", " \"\"\"\n", " A_prev, W, b = cache\n", " m = A_prev.shape[1]\n", "\n", " ### START CODE HERE ### (≈ 3 lines of code)\n", " dW = np.dot(dZ, cache[0].T)/m\n", " db = ((np.sum(dZ, axis=1, keepdims=True))/m)\n", " dA_prev = np.dot(cache[1].T, dZ)\n", " ### END CODE HERE ###\n", " \n", " assert (dA_prev.shape == A_prev.shape)\n", " assert (dW.shape == W.shape)\n", " assert (db.shape == b.shape)\n", " # print (b.shape, db.shape)\n", " return dA_prev, dW, db" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dA_prev = [[ 0.51822968 -0.19517421]\n", " [-0.40506361 0.15255393]\n", " [ 2.37496825 -0.89445391]]\n", "dW = [[-0.10076895 1.40685096 1.64992505]]\n", "db = [[ 0.50629448]]\n" ] } ], "source": [ "# Set up some test inputs\n", "dZ, linear_cache = linear_backward_test_case()\n", "\n", "dA_prev, dW, db = linear_backward(dZ, linear_cache)\n", "print (\"dA_prev = \"+ str(dA_prev))\n", "print (\"dW = \" + str(dW))\n", "print (\"db = \" + str(db))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**: \n", "\n", "<table style=\"width:90%\">\n", " <tr>\n", " <td> **dA_prev** </td>\n", " <td > [[ 0.51822968 -0.19517421]\n", " [-0.40506361 0.15255393]\n", " [ 2.37496825 -0.89445391]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td> **dW** </td>\n", " <td > [[-0.10076895 1.40685096 1.64992505]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td> **db** </td>\n", " <td> [[ 0.50629448]] </td> \n", " </tr> \n", " \n", "</table>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2 - Linear-Activation backward\n", "\n", "Next, you will create a function that merges the two helper functions: **`linear_backward`** and the backward step for the activation **`linear_activation_backward`**. \n", "\n", "To help you implement `linear_activation_backward`, we provided two backward functions:\n", "- **`sigmoid_backward`**: Implements the backward propagation for SIGMOID unit. You can call it as follows:\n", "\n", "```python\n", "dZ = sigmoid_backward(dA, activation_cache)\n", "```\n", "\n", "- **`relu_backward`**: Implements the backward propagation for RELU unit. You can call it as follows:\n", "\n", "```python\n", "dZ = relu_backward(dA, activation_cache)\n", "```\n", "\n", "If $g(.)$ is the activation function, \n", "`sigmoid_backward` and `relu_backward` compute $$dZ^{[l]} = dA^{[l]} * g'(Z^{[l]}) \\tag{11}$$. \n", "\n", "**Exercise**: Implement the backpropagation for the *LINEAR->ACTIVATION* layer." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# GRADED FUNCTION: linear_activation_backward\n", "\n", "def linear_activation_backward(dA, cache, activation):\n", " \"\"\"\n", " Implement the backward propagation for the LINEAR->ACTIVATION layer.\n", " \n", " Arguments:\n", " dA -- post-activation gradient for current layer l \n", " cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently\n", " activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n", " \n", " Returns:\n", " dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n", " dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n", " db -- Gradient of the cost with respect to b (current layer l), same shape as b\n", " \"\"\"\n", " linear_cache, activation_cache = cache\n", " \n", " if activation == \"relu\":\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " dZ = relu_backward(dA, activation_cache)\n", " dA_prev, dW, db = linear_backward(dZ, linear_cache)\n", " ### END CODE HERE ###\n", " \n", " elif activation == \"sigmoid\":\n", " ### START CODE HERE ### (≈ 2 lines of code)\n", " dZ = sigmoid_backward(dA, activation_cache)\n", " dA_prev, dW, db = linear_backward(dZ, linear_cache)\n", " ### END CODE HERE ###\n", " \n", " return dA_prev, dW, db\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sigmoid:\n", "dA_prev = [[ 0.11017994 0.01105339]\n", " [ 0.09466817 0.00949723]\n", " [-0.05743092 -0.00576154]]\n", "dW = [[ 0.10266786 0.09778551 -0.01968084]]\n", "db = [[-0.05729622]]\n", "\n", "relu:\n", "dA_prev = [[ 0.44090989 0. ]\n", " [ 0.37883606 0. ]\n", " [-0.2298228 0. ]]\n", "dW = [[ 0.44513824 0.37371418 -0.10478989]]\n", "db = [[-0.20837892]]\n" ] } ], "source": [ "AL, linear_activation_cache = linear_activation_backward_test_case()\n", "\n", "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"sigmoid\")\n", "print (\"sigmoid:\")\n", "print (\"dA_prev = \"+ str(dA_prev))\n", "print (\"dW = \" + str(dW))\n", "print (\"db = \" + str(db) + \"\\n\")\n", "\n", "dA_prev, dW, db = linear_activation_backward(AL, linear_activation_cache, activation = \"relu\")\n", "print (\"relu:\")\n", "print (\"dA_prev = \"+ str(dA_prev))\n", "print (\"dW = \" + str(dW))\n", "print (\"db = \" + str(db))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output with sigmoid:**\n", "\n", "<table style=\"width:100%\">\n", " <tr>\n", " <td > dA_prev </td> \n", " <td >[[ 0.11017994 0.01105339]\n", " [ 0.09466817 0.00949723]\n", " [-0.05743092 -0.00576154]] </td> \n", "\n", " </tr> \n", " \n", " <tr>\n", " <td > dW </td> \n", " <td > [[ 0.10266786 0.09778551 -0.01968084]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td > db </td> \n", " <td > [[-0.05729622]] </td> \n", " </tr> \n", "</table>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output with relu:**\n", "\n", "<table style=\"width:100%\">\n", " <tr>\n", " <td > dA_prev </td> \n", " <td > [[ 0.44090989 0. ]\n", " [ 0.37883606 0. ]\n", " [-0.2298228 0. ]] </td> \n", "\n", " </tr> \n", " \n", " <tr>\n", " <td > dW </td> \n", " <td > [[ 0.44513824 0.37371418 -0.10478989]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td > db </td> \n", " <td > [[-0.20837892]] </td> \n", " </tr> \n", "</table>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3 - L-Model Backward \n", "\n", "Now you will implement the backward function for the whole network. Recall that when you implemented the `L_model_forward` function, at each iteration, you stored a cache which contains (X,W,b, and z). In the back propagation module, you will use those variables to compute the gradients. Therefore, in the `L_model_backward` function, you will iterate through all the hidden layers backward, starting from layer $L$. On each step, you will use the cached values for layer $l$ to backpropagate through layer $l$. Figure 5 below shows the backward pass. \n", "\n", "\n", "<img src=\"images/mn_backward.png\" style=\"width:450px;height:300px;\">\n", "<caption><center> **Figure 5** : Backward pass </center></caption>\n", "\n", "** Initializing backpropagation**:\n", "To backpropagate through this network, we know that the output is, \n", "$A^{[L]} = \\sigma(Z^{[L]})$. Your code thus needs to compute `dAL` $= \\frac{\\partial \\mathcal{L}}{\\partial A^{[L]}}$.\n", "To do so, use this formula (derived using calculus which you don't need in-depth knowledge of):\n", "```python\n", "dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL)) # derivative of cost with respect to AL\n", "```\n", "\n", "You can then use this post-activation gradient `dAL` to keep going backward. As seen in Figure 5, you can now feed in `dAL` into the LINEAR->SIGMOID backward function you implemented (which will use the cached values stored by the L_model_forward function). After that, you will have to use a `for` loop to iterate through all the other layers using the LINEAR->RELU backward function. You should store each dA, dW, and db in the grads dictionary. To do so, use this formula : \n", "\n", "$$grads[\"dW\" + str(l)] = dW^{[l]}\\tag{15} $$\n", "\n", "For example, for $l=3$ this would store $dW^{[l]}$ in `grads[\"dW3\"]`.\n", "\n", "**Exercise**: Implement backpropagation for the *[LINEAR->RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID* model." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: L_model_backward\n", "\n", "def L_model_backward(AL, Y, caches):\n", " \"\"\"\n", " Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group\n", " \n", " Arguments:\n", " AL -- probability vector, output of the forward propagation (L_model_forward())\n", " Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat)\n", " caches -- list of caches containing:\n", " every cache of linear_activation_forward() with \"relu\" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)\n", " the cache of linear_activation_forward() with \"sigmoid\" (it's caches[L-1])\n", " \n", " Returns:\n", " grads -- A dictionary with the gradients\n", " grads[\"dA\" + str(l)] = ... \n", " grads[\"dW\" + str(l)] = ...\n", " grads[\"db\" + str(l)] = ... \n", " \"\"\"\n", " grads = {}\n", " L = len(caches) # the number of layers\n", " m = AL.shape[1]\n", " Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL\n", " \n", " # Initializing the backpropagation\n", " ### START CODE HERE ### (1 line of code)\n", " dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n", " ### END CODE HERE ###\n", " \n", " # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: \"AL, Y, caches\". Outputs: \"grads[\"dAL\"], grads[\"dWL\"], grads[\"dbL\"]\n", " ### START CODE HERE ### (approx. 2 lines)\n", " current_cache = caches[-1]\n", " grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, activation='sigmoid')\n", " ### END CODE HERE ###\n", " \n", " for l in reversed(range(L-1)):\n", " # lth layer: (RELU -> LINEAR) gradients.\n", " # Inputs: \"grads[\"dA\" + str(l + 2)], caches\". Outputs: \"grads[\"dA\" + str(l + 1)] , grads[\"dW\" + str(l + 1)] , grads[\"db\" + str(l + 1)] \n", " ### START CODE HERE ### (approx. 5 lines)\n", " current_cache = caches[l]\n", " dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\"+str(l+2)], current_cache, activation='relu')\n", " grads[\"dA\" + str(l + 1)] = dA_prev_temp\n", " grads[\"dW\" + str(l + 1)] = dW_temp\n", " grads[\"db\" + str(l + 1)] = db_temp\n", " ### END CODE HERE ###\n", "\n", " return grads" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dW1 = [[ 0.41010002 0.07807203 0.13798444 0.10502167]\n", " [ 0. 0. 0. 0. ]\n", " [ 0.05283652 0.01005865 0.01777766 0.0135308 ]]\n", "db1 = [[-0.22007063]\n", " [ 0. ]\n", " [-0.02835349]]\n", "dA1 = [[ 0.12913162 -0.44014127]\n", " [-0.14175655 0.48317296]\n", " [ 0.01663708 -0.05670698]]\n" ] } ], "source": [ "AL, Y_assess, caches = L_model_backward_test_case()\n", "grads = L_model_backward(AL, Y_assess, caches)\n", "print_grads(grads)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**\n", "\n", "<table style=\"width:60%\">\n", " \n", " <tr>\n", " <td > dW1 </td> \n", " <td > [[ 0.41010002 0.07807203 0.13798444 0.10502167]\n", " [ 0. 0. 0. 0. ]\n", " [ 0.05283652 0.01005865 0.01777766 0.0135308 ]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td > db1 </td> \n", " <td > [[-0.22007063]\n", " [ 0. ]\n", " [-0.02835349]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td > dA1 </td> \n", " <td > [[ 0.12913162 -0.44014127]\n", " [-0.14175655 0.48317296]\n", " [ 0.01663708 -0.05670698]] </td> \n", "\n", " </tr> \n", "</table>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.4 - Update Parameters\n", "\n", "In this section you will update the parameters of the model, using gradient descent: \n", "\n", "$$ W^{[l]} = W^{[l]} - \\alpha \\text{ } dW^{[l]} \\tag{16}$$\n", "$$ b^{[l]} = b^{[l]} - \\alpha \\text{ } db^{[l]} \\tag{17}$$\n", "\n", "where $\\alpha$ is the learning rate. After computing the updated parameters, store them in the parameters dictionary. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Implement `update_parameters()` to update your parameters using gradient descent.\n", "\n", "**Instructions**:\n", "Update parameters using gradient descent on every $W^{[l]}$ and $b^{[l]}$ for $l = 1, 2, ..., L$. \n" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GRADED FUNCTION: update_parameters\n", "\n", "def update_parameters(parameters, grads, learning_rate):\n", " \"\"\"\n", " Update parameters using gradient descent\n", " \n", " Arguments:\n", " parameters -- python dictionary containing your parameters \n", " grads -- python dictionary containing your gradients, output of L_model_backward\n", " \n", " Returns:\n", " parameters -- python dictionary containing your updated parameters \n", " parameters[\"W\" + str(l)] = ... \n", " parameters[\"b\" + str(l)] = ...\n", " \"\"\"\n", " \n", " L = len(parameters) // 2 # number of layers in the neural network\n", "\n", " # Update rule for each parameter. Use a for loop.\n", " ### START CODE HERE ### (≈ 3 lines of code)\n", " for l in range(L):\n", " parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n", " parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n", " ### END CODE HERE ###\n", " return parameters" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W1 = [[-0.59562069 -0.09991781 -2.14584584 1.82662008]\n", " [-1.76569676 -0.80627147 0.51115557 -1.18258802]\n", " [-1.0535704 -0.86128581 0.68284052 2.20374577]]\n", "b1 = [[-0.04659241]\n", " [-1.28888275]\n", " [ 0.53405496]]\n", "W2 = [[-0.55569196 0.0354055 1.32964895]]\n", "b2 = [[-0.84610769]]\n" ] } ], "source": [ "parameters, grads = update_parameters_test_case()\n", "parameters = update_parameters(parameters, grads, 0.1)\n", "\n", "print (\"W1 = \"+ str(parameters[\"W1\"]))\n", "print (\"b1 = \"+ str(parameters[\"b1\"]))\n", "print (\"W2 = \"+ str(parameters[\"W2\"]))\n", "print (\"b2 = \"+ str(parameters[\"b2\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "<table style=\"width:100%\"> \n", " <tr>\n", " <td > W1 </td> \n", " <td > [[-0.59562069 -0.09991781 -2.14584584 1.82662008]\n", " [-1.76569676 -0.80627147 0.51115557 -1.18258802]\n", " [-1.0535704 -0.86128581 0.68284052 2.20374577]] </td> \n", " </tr> \n", " \n", " <tr>\n", " <td > b1 </td> \n", " <td > [[-0.04659241]\n", " [-1.28888275]\n", " [ 0.53405496]] </td> \n", " </tr> \n", " <tr>\n", " <td > W2 </td> \n", " <td > [[-0.55569196 0.0354055 1.32964895]]</td> \n", " </tr> \n", " \n", " <tr>\n", " <td > b2 </td> \n", " <td > [[-0.84610769]] </td> \n", " </tr> \n", "</table>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## 7 - Conclusion\n", "\n", "Congrats on implementing all the functions required for building a deep neural network! \n", "\n", "We know it was a long assignment but going forward it will only get better. The next part of the assignment is easier. \n", "\n", "In the next assignment you will put all these together to build two models:\n", "- A two-layer neural network\n", "- An L-layer neural network\n", "\n", "You will in fact use these models to classify cat vs non-cat images!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "coursera": { "course_slug": "neural-networks-deep-learning", "graded_item_id": "c4HO0", "launcher_item_id": "lSYZM" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
Saytiras/StalkerML
Generate Topic Models LDA.ipynb
2
9253
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import logging\n", "from os import path\n", "from pprint import pprint\n", "import pandas as pd\n", "from corputil import ListCorpus\n", "from corputil.utils import load_stopwords\n", "from gensim.models import LdaMulticore, TfidfModel\n", "from gensim.models.phrases import Phrases\n", "from gensim.corpora import Dictionary\n", "\n", "stopwords = load_stopwords(path.join('data', 'german.txt'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "num_topics = 45\n", "chunksize, iterations, passes = 200, 500, 10\n", "labels = ['2015-44', '2015-45', '2015-46', '2015-47', '2015-48', '2015-49', '2015-50', '2015-51', \n", " '2015-52', '2015-53', '2016-01', '2016-02', '2016-03', '2016-04', '2016-05', '2016-06']\n", "files = [path.join('data', 'CurrentNews', 's_{}.csv').format(label) for label in labels]\n", "output_model = [path.join('models', 'lda', '{}.lda').format(label) for label in labels]\n", "output_dict = path.join('models', 'lda', 'Words.dict')\n", "output_bigram = path.join('models', 'lda', 'Bigram.phrase')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dfs = [pd.read_csv(file, sep='|', encoding='utf-8') for file in files]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "corpora = [ListCorpus(list(df.loc[:, 'text'])) for df in dfs]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def create_phrase():\n", " sentences = [sentence for corpus in corpora for sentence in corpus.sentences_token(stopwords=stopwords)]\n", " bigram = Phrases(sentences)\n", " return bigram\n", "\n", "\n", "def create_dict():\n", " docs = [bigram[doc] for corpus in corpora for doc in corpus.doc_token(stopwords)]\n", " dictionary = Dictionary(docs)\n", " dictionary.filter_extremes()\n", " dictionary.compactify()\n", " return dictionary\n", "\n", "\n", "def train_lda(corpus):\n", " bow = [dictionary.doc2bow(bigram[doc]) for doc in corpus]\n", " tfidf = TfidfModel(bow)\n", " bow = tfidf[bow]\n", " lda = LdaMulticore(bow, id2word=dictionary, chunksize=chunksize, #batch=True,\n", " num_topics=num_topics, workers=2, passes=passes, iterations=iterations)\n", " return bow, lda" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bigram = create_phrase()\n", "dictionary = create_dict()\n", "\n", "models = []\n", "docs = []\n", "\n", "for i, corpus in enumerate(corpora):\n", " mmCorpus, model = train_lda(corpus.doc_token(stopwords=stopwords))\n", " models.append(model)\n", " docs.append(mmCorpus)\n", " model.save(output_model[i])\n", "\n", "bigram.save(output_bigram)\n", "dictionary.save(output_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Web Visualization Pre-Computation for GitHub Pages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "import json\n", "import numpy as np\n", "from gensim.matutils import sparse2full, cossim" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def permutations(coll, window):\n", " perms =[]\n", " for frame in range(len(coll) - (window - 1)):\n", " perm = [coll[frame + i] for i in range(window)]\n", " perms.append(tuple(perm))\n", " return perms\n", "\n", "def topic_cluster(model, label, threshold):\n", " data = []\n", " for i1 in range(model.num_topics):\n", " for i2 in range(model.num_topics):\n", " if i1 != i2:\n", " similarity = cossim(model.show_topic(i1), model-show_topic(i2))\n", " if similarity >= threshold:\n", " entry = {\n", " 'week': label,\n", " 's-topic': i1,\n", " 'e-topic': i2,\n", " 'sim': similarity\n", " }\n", " return data\n", "\n", "def topic_chains(models, threshold):\n", " data = []\n", " for i, (first, second) in enumerate(permutations(models, 2)):\n", " for i1 in range(first.num_topics):\n", " for i2 in range(second.num_topics):\n", " similarity = cossim(first.show_topic(i1), second.show_topic(i2))\n", " if similarity >= threshold:\n", " entry = {\n", " 's-week': labels[i],\n", " 's-topic': i1,\n", " 'e-week': labels[i + 1],\n", " 'e-topic': i2,\n", " 'sim': similarity\n", " }\n", " data.append(entry)\n", " return data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def topic_words(model):\n", " data = model.show_topics(-1, formatted=False)\n", " topics = []\n", " for i, c in data:\n", " words = []\n", " probs = []\n", " for word, prob in c:\n", " words.append(word)\n", " probs.append(prob)\n", " topics.append([words, probs])\n", " return topics\n", "\n", "def topic_allocation(corpus):\n", " acc = []\n", " for vec in corpus:\n", " t_id = -1\n", " t_prob = -1\n", " for topic, prob in vec:\n", " if prob > t_prob:\n", " t_id = topic\n", " acc.append(t_id)\n", " return acc\n", "\n", "def get_topics(df, model, doc):\n", " transform = model[doc]\n", " topics = topic_words(model)\n", " df['topic'] = topic_allocation(transform)\n", " d = []\n", " for i, (topic, prob) in enumerate(topics):\n", " dc = dict()\n", " dc['id'] = i\n", " dc['words'] = topic\n", " dc['probs'] = prob\n", " dc['articles'] = df[df['topic'] == i].count()['topic'].item() # Just pick a column... here topic\n", " if dc['articles'] > 0:\n", " d.append(dc)\n", " return d\n", "\n", "topicData = dict()\n", "for i, (model, doc) in enumerate(zip(models, docs)):\n", " df = dfs[i]\n", " topicData[labels[i]] = get_topics(df, model, doc)\n", "with open(path.join('data', 'Web', 'Topics.json'), 'w', encoding='utf-8') as f:\n", " json.dump(topicData, f, indent=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def order_data(d):\n", " return [\n", " d['LINKE'],\n", " d['SPD'],\n", " d['GRÜNE'],\n", " d['FDP'],\n", " d['CDU'],\n", " d['NPD']\n", " ]\n", "\n", "def to_array(df):\n", " for key in df.keys():\n", " df[key] = order_data(df[key])\n", " return df\n", "\n", "complete = pd.concat(dfs)\n", "complete = complete.loc[:, ['site', 'LINKE', 'SPD', 'GRÜNE', 'FDP', 'CDU', 'NPD']]\n", "grouped = complete.groupby('site').mean()\n", "json_data = to_array(grouped.to_dict('index'))\n", "json_data['All'] = order_data(grouped.mean())\n", "\n", "with open(path.join('data', 'Web', 'SiteSentiment.json'), 'w', encoding='utf-8') as f:\n", " json.dump(json_data, f, indent=4)\n", " \n", "time_data = dict()\n", "for i, (label, df) in enumerate(zip(labels, dfs)):\n", " sentiment = df.loc[:, ['site', 'LINKE', 'SPD', 'GRÜNE', 'FDP', 'CDU', 'NPD']]\n", " sentiment = sentiment.groupby('site').mean()\n", " json_data = to_array(sentiment.to_dict('index'))\n", " json_data['All'] = order_data(sentiment.mean())\n", " time_data[label] = json_data\n", " \n", "with open(path.join('data', 'Web', 'SiteSentimentTime.json'), 'w', encoding='utf-8') as f:\n", " json.dump(time_data, f, indent=4)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
cliburn/sta-663-2017
homework/05_Making_Python_Faster_Solutions.ipynb
1
226105
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.datasets import make_blobs \n", "from numba import jit, vectorize, float64, int64" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_context('notebook', font_scale=1.5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching package metadata ...........\n", "Solving package specifications: .\n", "\n", "Package plan for installation in environment /Users/cliburn/anaconda2:\n", "\n", "The following packages will be UPDATED:\n", "\n", " abstract-rendering: 0.5.1-np110py27_0 --> 0.5.1-np111py27_0 \n", " accelerate: 2.3.0-np110py27_3 --> 2.3.1-np111py27_0 \n", " astropy: 1.1.2-np110py27_0 --> 1.3-np111py27_0 \n", " bottleneck: 1.0.0-np110py27_0 --> 1.2.0-np111py27_0 \n", " conda: 4.3.8-py27_0 --> 4.3.13-py27_0 \n", " h5py: 2.6.0-np110py27_1 --> 2.6.0-np111py27_1 \n", " llvmlite: 0.11.0-py27_0 --> 0.15.0-py27_0 \n", " matplotlib: 1.5.1-np110py27_0 --> 1.5.1-np111py27_0 \n", " numba: 0.26.0-np110py27_0 --> 0.30.1-np111py27_0\n", " numexpr: 2.5.2-np110py27_1 --> 2.6.1-np111py27_1 \n", " numpy: 1.10.4-py27_2 --> 1.11.2-py27_0 \n", " pandas: 0.18.1-np110py27_0 --> 0.19.2-np111py27_1\n", " patsy: 0.4.0-np110py27_0 --> 0.4.1-py27_0 \n", " pytables: 3.2.2-np110py27_3 --> 3.2.2-np111py27_4 \n", " scikit-image: 0.12.3-np110py27_0 --> 0.12.3-np111py27_1\n", " scikit-learn: 0.17.1-np110py27_2 --> 0.18.1-np111py27_0\n", " scipy: 0.17.1-np110py27_1 --> 0.18.1-np111py27_0\n", " statsmodels: 0.6.1-np110py27_0 --> 0.8.0-np111py27_0 \n", "\n", "numpy-1.11.2-p 100% |################################| Time: 0:00:00 4.89 MB/s\n", "astropy-1.3-np 100% |################################| Time: 0:00:00 9.79 MB/s\n", "bottleneck-1.2 100% |################################| Time: 0:00:00 14.88 MB/s\n", "llvmlite-0.15. 100% |################################| Time: 0:00:00 9.43 MB/s\n", "numexpr-2.6.1- 100% |################################| Time: 0:00:00 16.68 MB/s\n", "scipy-0.18.1-n 100% |################################| Time: 0:00:01 9.97 MB/s\n", "numba-0.30.1-n 100% |################################| Time: 0:00:00 9.40 MB/s\n", "pandas-0.19.2- 100% |################################| Time: 0:00:01 7.28 MB/s\n", "scikit-learn-0 100% |################################| Time: 0:00:00 6.43 MB/s\n", "statsmodels-0. 100% |################################| Time: 0:00:00 7.82 MB/s\n", "conda-4.3.13-p 100% |################################| Time: 0:00:00 9.12 MB/s\n", "accelerate-2.3 100% |################################| Time: 0:00:00 5.84 MB/s\n" ] } ], "source": [ "! conda update --yes numba" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making Python faster\n", "\n", "This homework provides practice in making Python code faster. Note that we start with functions that already use idiomatic `numpy` (which are about two orders of magnitude faster than the pure Python versions)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions to optimize" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def logistic(x):\n", " \"\"\"Logistic function.\"\"\"\n", " return np.exp(x)/(1 + np.exp(x))\n", "\n", "def gd(X, y, beta, alpha, niter):\n", " \"\"\"Gradient descent algorihtm.\"\"\"\n", " n, p = X.shape\n", " Xt = X.T\n", " for i in range(niter):\n", " y_pred = logistic(X @ beta)\n", " epsilon = y - y_pred\n", " grad = Xt @ epsilon / n\n", " beta += alpha * grad\n", " return beta" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ifLIMIg+1ff8FGE1WPDwq6Y7KmYjcEwuayAPVNxnwbUEVwnqpMC49Tuw4ROQE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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1197f6dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(-6, 6, 100)\n", "plt.plot(x, logistic(x))\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data set for classification" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = 10000\n", "p = 2\n", "X, y = make_blobs(n_samples=n, n_features=p, centers=2, cluster_std=1.05, random_state=23)\n", "X = np.c_[np.ones(len(X)), X]\n", "y = y.astype('float')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using gradient descent for classification by logistic regression" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/v/1tZdj27WuxYIGLL77Q2LVL5Ywz/HTpIl9oROPSNm+i5YhTw+4veeIZ3Fdf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\niBi03XqiYwVBTJpneZnNDzXcIVJb5IczwWZYQib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"text/plain": [ "<matplotlib.figure.Figure at 0x1198185c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# initial parameters\n", "niter = 1000\n", "α = 0.01\n", "β = np.zeros(p+1)\n", "\n", "# call gradient descent\n", "β = gd(X, y, β, α, niter)\n", "\n", "# assign labels to points based on prediction\n", "y_pred = logistic(X @ β)\n", "labels = y_pred > 0.5\n", "\n", "# calculate separating plane\n", "sep = (-β[0] - β[1] * X)/β[2]\n", "\n", "plt.scatter(X[:, 1], X[:, 2], c=labels, cmap='winter')\n", "plt.plot(X, sep, 'r-')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1**. Rewrite the `logistic` function so it only makes one `np.exp` call. Compare the time of both versions with the input x given below using the `@timeit` magic. (10 points)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(123)\n", "n = int(1e7)\n", "x = np.random.normal(0, 1, n)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def logistic2(x):\n", " \"\"\"Logistic function.\"\"\"\n", " return 1/(1 + np.exp(-x))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 477 ms per loop\n" ] } ], "source": [ "%timeit logistic(x)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 395 ms per loop\n" ] } ], "source": [ "%timeit logistic2(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**2**. (20 points) Use `numba` to compile the gradient descent function. \n", "\n", "- Use the `@vectorize` decorator to create a ufunc version of the logistic function and call this `logistic_numba_cpu` with function signatures of `float64(float64)`. Create another function called `logistic_numba_parallel` by giving an extra argument to the decorator of `target=parallel` (5 points)\n", "- For each function, check that the answers are the same as with the original logistic function using `np.testing.assert_array_almost_equal`. Use `%timeit` to compare the three logistic functions (5 points)\n", "- Now use `@jit` to create a JIT_compiled version of the `logistic` and `gd` functions, calling them `logistic_numba` and `gd_numba`. Provide appropriate function signatures to the decorator in each case. (5 points)\n", "- Compare the two gradient descent functions `gd` and `gd_numba` for correctness and performance. (5 points)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@vectorize([float64(float64)], target='cpu')\n", "def logistic_numba_cpu(x):\n", " \"\"\"Logistic function.\"\"\"\n", " return 1/(1 + math.exp(-x))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@vectorize([float64(float64)], target='parallel')\n", "def logistic_numba_parallel(x):\n", " \"\"\"Logistic function.\"\"\"\n", " return 1/(1 + math.exp(-x))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.testing.assert_array_almost_equal(logistic(x), logistic_numba_cpu(x))\n", "np.testing.assert_array_almost_equal(logistic(x), logistic_numba_parallel(x))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 496 ms per loop\n" ] } ], "source": [ "%timeit logistic(x)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 195 ms per loop\n" ] } ], "source": [ "%timeit logistic_numba_cpu(x)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 72.2 ms per loop\n" ] } ], "source": [ "%timeit logistic_numba_parallel(x)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@jit(float64[:](float64[:]), nopython=True)\n", "def logistic_numba(x):\n", " return 1/(1 + np.exp(-x))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@jit(float64[:](float64[:,:], float64[:], float64[:], float64, int64), nopython=True)\n", "def gd_numba(X, y, beta, alpha, niter):\n", " \"\"\"Gradient descent algorihtm.\"\"\"\n", " n, p = X.shape\n", " Xt = X.T\n", " for i in range(niter):\n", " y_pred = logistic_numba(X @ beta)\n", " epsilon = y - y_pred\n", " grad = Xt @ epsilon / n\n", " beta += alpha * grad\n", " return beta" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta1 = gd(X, y, β, α, niter)\n", "beta2 = gd_numba(X, y, β, α, niter)\n", "np.testing.assert_almost_equal(beta1, beta2)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 515 ms per loop\n" ] } ], "source": [ "%timeit gd(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 456 ms per loop\n" ] } ], "source": [ "%timeit gd_numba(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ywIF+fve7qrDt399i/nwXX36psWuXymmn+enWTb7QiKalbd5E61Enh91f+sQz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\nfnwEI4tN+/bto6CggH379rW2BQIBDh48KNf0BTB27Fi2b98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"text/plain": [ "<matplotlib.figure.Figure at 0x12e56dc18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# initial parameters\n", "niter = 1000\n", "α = 0.01\n", "β = np.zeros(p+1)\n", "\n", "# call gradient descent\n", "β = gd_numba(X, y, β, α, niter)\n", "\n", "# assign labels to points based on prediction\n", "y_pred = logistic(X @ β)\n", "labels = y_pred > 0.5\n", "\n", "# calculate separating plane\n", "sep = (-β[0] - β[1] * X)/β[2]\n", "\n", "plt.scatter(X[:, 1], X[:, 2], c=labels, cmap='winter')\n", "plt.plot(X, sep, 'r-')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**3**. (30 points) Use `cython` to compile the gradient descent function. \n", "\n", "- Cythonize the logistic function as `logistic_cython`. Use the `--annotate` argument to the `cython` magic function to find slow regions. Compare accuracy and performance. The final performance should be comparable to the `numba` cpu version. (10 points)\n", "- Now cythonize the gd function as `gd_cython`. This function should use of the cythonized `logistic_cython` as a C function call. Compare accuracy and performance. The final performance should be comparable to the `numba` cpu version. (20 points)\n", "\n", "Hints: \n", "\n", "- Give static types to all variables\n", "- Know how to use `def`, `cdef` and `cpdef`\n", "- Use Typed MemoryViews\n", "- Find out how to transpose a Typed MemoryView to store the transpose of X\n", "- Typed MemoryVeiws are not `numpy` arrays - you often have to write explicit loops to operate on them\n", "- Use the cython boundscheck, wraparound, and cdivision operators" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.25.2 -->\n", "<html>\n", "<head>\n", " <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n", " <title>Cython: _cython_magic_4f23273c761a567ab741673dfe3336ee.pyx</title>\n", " <style type=\"text/css\">\n", " \n", "body.cython { font-family: courier; font-size: 12; }\n", "\n", ".cython.tag { }\n", ".cython.line { margin: 0em }\n", ".cython.code { font-size: 9; color: #444444; 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0.25.2</span></p>\n", "<p>\n", " <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n", " Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n", "</p>\n", "<div class=\"cython\"><pre class=\"cython line score-0\">&#xA0;<span class=\"\">01</span>: </pre>\n", "<pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">02</span>: <span class=\"k\">import</span> <span class=\"nn\">cython</span></pre>\n", "<pre class='cython code score-11 '> __pyx_t_1 = <span class='py_c_api'>PyDict_New</span>(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) __PYX_ERR(0, 2, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">03</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n", "<pre class='cython code score-8 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 3, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) __PYX_ERR(0, 3, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">04</span>: <span class=\"k\">cimport</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">05</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.math</span> <span class=\"k\">cimport</span> <span class=\"n\">exp</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">06</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">07</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">08</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">09</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">cdivision</span><span class=\"p\">(</span><span class=\"bp\">True</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-12\" onclick='toggleDiv(this)'>+<span class=\"\">10</span>: <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">:]</span> <span class=\"n\">logistic_</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">x</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-12 '>static __Pyx_memviewslice __pyx_f_46_cython_magic_ce8cae9e9a1375e5cb1615f94fb5bc17_logistic_(__Pyx_memviewslice __pyx_v_x) {\n", " int __pyx_v_i;\n", " int __pyx_v_n;\n", " __Pyx_memviewslice __pyx_v_s = { 0, 0, { 0 }, { 0 }, { 0 } };\n", " __Pyx_memviewslice __pyx_r = { 0, 0, { 0 }, { 0 }, { 0 } };\n", " <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n", " <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"logistic_\", 0);\n", "/* … */\n", " /* function exit code */\n", " __pyx_L1_error:;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_4);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_5);\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_t_6, 1);\n", " __pyx_r.data = NULL;\n", " __pyx_r.memview = NULL;\n", " <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ce8cae9e9a1375e5cb1615f94fb5bc17.logistic_\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n", "\n", " goto __pyx_L2;\n", " __pyx_L0:;\n", " if (unlikely(!__pyx_r.memview)) {\n", " <span class='py_c_api'>PyErr_SetString</span>(PyExc_TypeError, \"Memoryview return value is not initialized\");\n", " }\n", " __pyx_L2:;\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_v_s, 1);\n", " <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n", " return __pyx_r;\n", "}\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">11</span>: <span class=\"sd\">&quot;&quot;&quot;Logistic function.&quot;&quot;&quot;</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">12</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span></pre>\n", "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">13</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">n</span> <span class=\"o\">=</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_n = (__pyx_v_x.shape[0]);\n", "</pre><pre class=\"cython line score-49\" onclick='toggleDiv(this)'>+<span class=\"\">14</span>: <span class=\"k\">cdef</span> <span class=\"kt\">double</span> [<span class=\"p\">:]</span> <span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-49 '> __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_empty); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_n); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n", " __pyx_t_4 = NULL;\n", " if (CYTHON_UNPACK_METHODS &amp;&amp; unlikely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_3))) {\n", " __pyx_t_4 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_3);\n", " if (likely(__pyx_t_4)) {\n", " PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_3);\n", " <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_4);\n", " <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n", " <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_3, function);\n", " }\n", " }\n", " if (!__pyx_t_4) {\n", " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " } else {\n", " #if CYTHON_FAST_PYCALL\n", " if (<span class='py_c_api'>PyFunction_Check</span>(__pyx_t_3)) {\n", " PyObject *__pyx_temp[2] = {__pyx_t_4, __pyx_t_2};\n", " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " } else\n", " #endif\n", " #if CYTHON_FAST_PYCCALL\n", " if (<span class='pyx_c_api'>__Pyx_PyFastCFunction_Check</span>(__pyx_t_3)) {\n", " PyObject *__pyx_temp[2] = {__pyx_t_4, __pyx_t_2};\n", " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyCFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n", " } else\n", " #endif\n", " {\n", " __pyx_t_5 = <span class='py_c_api'>PyTuple_New</span>(1+1); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n", " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_4); <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_5, 0, __pyx_t_4); __pyx_t_4 = NULL;\n", " <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n", " <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_5, 0+1, __pyx_t_2);\n", " __pyx_t_2 = 0;\n", " __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_3, __pyx_t_5, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n", " }\n", " }\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n", " __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_t_1);\n", " if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 14, __pyx_L1_error)\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_v_s = __pyx_t_6;\n", " __pyx_t_6.memview = NULL;\n", " __pyx_t_6.data = NULL;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">15</span>: </pre>\n", "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">16</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">n</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_7 = __pyx_v_n;\n", " for (__pyx_t_8 = 0; __pyx_t_8 &lt; __pyx_t_7; __pyx_t_8+=1) {\n", " __pyx_v_i = __pyx_t_8;\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">17</span>: <span class=\"n\">s</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"mf\">1.0</span> <span class=\"o\">+</span> <span class=\"n\">exp</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">x</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]))</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_9 = __pyx_v_i;\n", " __pyx_t_10 = __pyx_v_i;\n", " *((double *) ( /* dim=0 */ (__pyx_v_s.data + __pyx_t_10 * __pyx_v_s.strides[0]) )) = (1.0 / (1.0 + exp((-(*((double *) ( /* dim=0 */ (__pyx_v_x.data + __pyx_t_9 * __pyx_v_x.strides[0]) )))))));\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">18</span>: <span class=\"k\">return</span> <span class=\"n\">s</span></pre>\n", "<pre class='cython code score-0 '> __PYX_INC_MEMVIEW(&amp;__pyx_v_s, 0);\n", " __pyx_r = __pyx_v_s;\n", " goto __pyx_L0;\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">19</span>: </pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">20</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">21</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">22</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">cdivision</span><span class=\"p\">(</span><span class=\"bp\">True</span><span class=\"p\">)</span></pre>\n", "<pre class=\"cython line score-101\" onclick='toggleDiv(this)'>+<span class=\"\">23</span>: <span class=\"k\">def</span> <span class=\"nf\">gd_cython</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:,</span> <span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">beta</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">alpha</span><span class=\"p\">,</span> <span class=\"nb\">int</span> <span class=\"n\">niter</span><span class=\"p\">):</span></pre>\n", "<pre class='cython code score-101 '>/* Python wrapper */\n", "static PyObject *__pyx_pw_46_cython_magic_ce8cae9e9a1375e5cb1615f94fb5bc17_1gd_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n", "static char __pyx_doc_46_cython_magic_ce8cae9e9a1375e5cb1615f94fb5bc17_gd_cython[] = \"Gradient descent algorihtm.\";\n", "static PyMethodDef __pyx_mdef_46_cython_magic_ce8cae9e9a1375e5cb1615f94fb5bc17_1gd_cython = 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onclick='toggleDiv(this)'>+<span class=\"\">39</span>: <span class=\"n\">beta</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">alpha</span> <span class=\"o\">*</span> <span class=\"n\">grad</span><span class=\"p\">[</span><span class=\"n\">k</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-0 '> __pyx_t_17 = __pyx_v_k;\n", " __pyx_t_18 = __pyx_v_k;\n", " *((double *) ( /* dim=0 */ (__pyx_v_beta.data + __pyx_t_18 * __pyx_v_beta.strides[0]) )) += (__pyx_v_alpha * (*((double *) ( /* dim=0 */ (__pyx_v_grad.data + __pyx_t_17 * __pyx_v_grad.strides[0]) ))));\n", " }\n", " }\n", "</pre><pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">40</span>: <span class=\"k\">return</span> <span class=\"n\">beta</span></pre>\n", "<pre class='cython code score-1 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n", " __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_beta, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 40, __pyx_L1_error)\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n", " __pyx_r = __pyx_t_3;\n", " __pyx_t_3 = 0;\n", " goto __pyx_L0;\n", "</pre></div></body></html>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%cython --annotate\n", "\n", "import cython\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport exp\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "@cython.cdivision(True)\n", "cdef double[:] logistic_(double[:] x):\n", " \"\"\"Logistic function.\"\"\"\n", " cdef int i\n", " cdef int n = x.shape[0]\n", " cdef double [:] s = np.empty(n)\n", " \n", " for i in range(n):\n", " s[i] = 1.0/(1.0 + exp(-x[i]))\n", " return s\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "@cython.cdivision(True)\n", "def gd_cython(double[:, ::1] X, double[:] y, double[:] beta, double alpha, int niter):\n", " \"\"\"Gradient descent algorihtm.\"\"\" \n", " cdef int n = X.shape[0]\n", " cdef int p = X.shape[1]\n", " cdef double[:] eps = np.empty(n)\n", " cdef double[:] y_pred = np.empty(n)\n", " cdef double[:] grad = np.empty(p) \n", " cdef int i, j, k\n", " cdef double[:, :] Xt = X.T\n", " \n", " for i in range(niter):\n", " y_pred = logistic_(np.dot(X, beta))\n", " for j in range(n):\n", " eps[j] = y[j] - y_pred[j]\n", " grad = np.dot(Xt, eps) / n\n", " for k in range(p):\n", " beta[k] += alpha * grad[k]\n", " return beta" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "niter = 1000\n", "alpha = 0.01\n", "beta = np.random.random(X.shape[1])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta1 = gd(X, y, β, α, niter)\n", "beta2 = gd_cython(X, y, β, α, niter)\n", "np.testing.assert_almost_equal(beta1, beta2)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 525 ms per loop\n" ] } ], "source": [ "%timeit gd(X, y, beta, alpha, niter)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 372 ms per loop\n" ] } ], "source": [ "%timeit gd_cython(X, y, beta, alpha, niter)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**4**. (40 points) Wrapping modules in C++.\n", "\n", "Rewrite the `logistic` and `gd` functions in C++, using `pybind11` to create Python wrappers. Compare accuracy and performance as usual. Replicate the plotted example using the C++ wrapped functions for `logistic` and `gd`\n", "\n", "- Writing a vectorized `logistic` function callable from both C++ and Python (10 points)\n", "- Writing the `gd` function callable from Python (25 points)\n", "- Checking accuracy, benchmarking and creating diagnostic plots (5 points)\n", "\n", "Hints:\n", "\n", "- Use the C++ `Eigen` library to do vector and matrix operations\n", "- When calling the exponential function, you have to use `exp(m.array())` instead of `exp(m)` if you use an Eigen dynamic template.\n", "- Use `cppimport` to simplify the wrapping for Python\n", "- See [`pybind11` docs](http://pybind11.readthedocs.io/en/latest/index.html)\n", "- See my [examples](http://people.duke.edu/~ccc14/cspy/18G_C++_Python_pybind11.html#) for help" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "if not os.path.exists('./eigen'):\n", " ! git clone https://github.com/RLovelett/eigen.git" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%file wrap.cpp\n", "<%\n", "cfg['compiler_args'] = ['-std=c++11']\n", "cfg['include_dirs'] = ['./eigen']\n", "setup_pybind11(cfg)\n", "%>\n", "\n", "#include <pybind11/pybind11.h>\n", "#include <pybind11/numpy.h>\n", "#include <pybind11/eigen.h>\n", "\n", "namespace py = pybind11;\n", "\n", "Eigen::VectorXd logistic(Eigen::VectorXd x) {\n", " return 1.0/(1.0 + exp((-x).array()));\n", "}\n", "\n", "Eigen::VectorXd gd(Eigen::MatrixXd X, Eigen::VectorXd y, Eigen::VectorXd beta, double alpha, int niter) {\n", " int n = X.rows();\n", " \n", " Eigen::VectorXd y_pred;\n", " Eigen::VectorXd resid;\n", " Eigen::VectorXd grad;\n", " Eigen::MatrixXd Xt = X.transpose();\n", " \n", " for (int i=0; i<niter; i++) {\n", " y_pred = logistic(X * beta);\n", " resid = y - y_pred;\n", " grad = Xt * resid / n;\n", " beta = beta + alpha * grad;\n", " }\n", " return beta;\n", "}\n", "\n", "PYBIND11_PLUGIN(wrap) {\n", " py::module m(\"wrap\", \"pybind11 example plugin\");\n", " m.def(\"gd\", &gd, \"The gradient descent fucntion.\");\n", " m.def(\"logistic\", &logistic, \"The logistic fucntion.\");\n", "\n", " return m.ptr();\n", "}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import cppimport\n", "cppimport.force_rebuild() \n", "funcs = cppimport.imp(\"wrap\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.testing.assert_array_almost_equal(logistic(x), funcs.logistic(x))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit logistic(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit funcs.logistic(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "β = np.array([0.0, 0.0, 0.0])\n", "gd(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "β = np.array([0.0, 0.0, 0.0])\n", "funcs.gd(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit gd(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%timeit funcs.gd(X, y, β, α, niter)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# initial parameters\n", "niter = 1000\n", "α = 0.01\n", "β = np.zeros(p+1)\n", "\n", "# call gradient descent\n", "β = funcs.gd(X, y, β, α, niter)\n", "\n", "# assign labels to points based on prediction\n", "y_pred = funcs.logistic(X @ β)\n", "labels = y_pred > 0.5\n", "\n", "# calculate separating plane\n", "sep = (-β[0] - β[1] * X)/β[2]\n", "\n", "plt.scatter(X[:, 1], X[:, 2], c=labels, cmap='winter')\n", "plt.plot(X, sep, 'r-')\n", "pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 2 }
mit
nravi89/nlphu
HW8.ipynb
1
5957
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "###Exercise from http://www.nltk.org/book_1ed/ch09.html\n", "###Author : Nirmal kumar Ravi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> What constraints are required to correctly parse word sequences like I am happy and she is happy but not *you is happy or *they am happy? Implement two solutions for the present tense paradigm of the verb be in English, first taking Grammar (8) as your starting point, and then taking Grammar (20) as the starting point." ] }, { "cell_type": "code", "collapsed": false, "input": [ "S -> NP_SG V\n", "S -> NP_SG2 V\n", "NP_SG -> N_SG Det_SG\n", "NP_PL -> N_SG2 Det_SG2 \n", "VP -> V\n", "\n", "\n", "Det_SG -> 'AM'\n", "Det_SG2 -> 'IS'\n", "N_SG -> 'I'\n", "N_SG2 -> 'HE' | 'SHE'\n", "V -> 'HAPPY'\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "\t\n", "S -> NP[AGR=?n] VP[AGR=?n]\n", "NP[AGR=?n] -> PropN[AGR=?n]\n", "VP[TENSE=?t, AGR=?n] -> Cop[TENSE=?t, AGR=?n] Adj\n", "\n", "Cop[TENSE=pres, AGR=[NUM=sg, PER=3]] -> 'is'\n", "PropN[AGR=[NUM=sg, PER=3]] -> 'He'\n", "Adj -> 'happy'\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Develop a variant of grammar in 9.1 that uses a feature count to make the distinctions shown below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The boy sings.\t\t\n", "* *Boy sings." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Det[NUM=sg] -> 'the'\n", "N[NUM=sg] -> 'boy'\n", "V[NUM=sg] -> 'runs'\n", "\n", "S -> NP[NUM=?n] VP[NUM=?n]\n", "NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n]\n", "VP[NUM=?n] -> V[NUM=?n]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The boys sing.\n", "* Boys sing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Det[NUM=pl] -> 'the'\n", "N[NUM=pl] -> 'boys'\n", "V -> 'sing'\n", "\n", "S -> NP[NUM=?n] VP\n", "NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n] | N[NUM=?n]\n", "VP -> V" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The boys sing.\n", "* Boys sing." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Det[NUM=pl] -> 'the'\n", "N[NUM=pl] -> 'boys'\n", "V -> 'sing'\n", "\n", "S -> NP[NUM=?n] VP\n", "NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n] | N[NUM=?n]\n", "VP -> V" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The water is precious.\n", "* Water is precious." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Det[NUM=pl] -> 'The'\n", "N[NUM=pl] -> 'water'\n", "V -> 'precious'\n", "\n", "S -> NP[NUM=?n] VP\n", "NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n] | N[NUM=?n]\n", "VP -> V" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Write a function subsumes() which holds of two feature structures fs1 and fs2 just in case fs1 subsumes fs2." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#this is a pseudocode\n", "def subsumes(fs1,fs2):\n", " var unification = fs2.unify(fs1)\n", " if unification is_more_specific than fs1 and fs2:\n", " return True\n", " else\n", " return False" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Modify the grammar illustrated in (30) to incorporate a bar feature for dealing with phrasal projections." ] }, { "cell_type": "code", "collapsed": false, "input": [ "\t\n", "VP[TENSE=?t, NUM=?n] -> V[SUBCAT=intrans, TENSE=?t, NUM=?n]\n", "VP[TENSE=?t, NUM=?n] -> V[SUBCAT=trans, TENSE=?t, NUM=?n] NP\n", "VP[TENSE=?t, NUM=?n] -> V[SUBCAT=clause, TENSE=?t, NUM=?n] SBar" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Modify the German grammar in 9.5 to incorporate the treatment of subcategorization presented in 9.3." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nltk.data.show_cfg('grammars/book_grammars/german.fcfg')\n", "% start S\n", " # Grammar Productions\n", " S -> NP[CASE=nom, AGR=?a] VP[AGR=?a]\n", " NP[CASE=?c, AGR=?a] -> PRO[CASE=?c, AGR=?a] N[CASE=?c, AGR=?a]\n", " NP[CASE=?c, AGR=?a] -> Det[CASE=?c, AGR=?a] N[CASE=?c, AGR=?a]\n", " VP[AGR=?a] -> IV[AGR=?a] PRO[CASE=?c, AGR=?a]\n", " VP[AGR=?a] -> TV[OBJCASE=?c, AGR=?a] NP[CASE=?c]" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
bsd-2-clause
GoogleCloudPlatform/vertex-ai-samples
community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb
1
69546
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ur8xi4C7S06n" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "zFWqF3Y4Gilg" }, "source": [ "# Guide to Building End-to-End Reinforcement Learning Application Pipelines using Vertex AI" ] }, { "cell_type": "markdown", "metadata": { "id": "JAPoU8Sm5E6e" }, "source": [ "<table align=\"left\">\n", "\n", " <td>\n", " <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/tree/master/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n", " </a>\n", " </td>\n", " <td>\n", " <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb\">\n", " <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n", " View on GitHub\n", " </a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "tvgnzT1CKxrO" }, "source": [ "## Overview\n", "\n", "This demo showcases the use of [TF-Agents](https://www.tensorflow.org/agents), [Kubeflow Pipelines (KFP)](https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/) and [Vertex AI](https://cloud.google.com/vertex-ai), particularly [Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines), in building an end-to-end reinforcement learning (RL) pipeline of a movie recommendation system. The demo is intended for developers who want to create RL applications using TensorFlow, TF-Agents and Vertex AI services, and those who want to build end-to-end production pipelines using KFP and Vertex Pipelines. It is recommended for developers to have familiarity with RL and the contextual bandits formulation, and the TF-Agents interface.\n", "\n", "### Dataset\n", "\n", "This demo uses the [MovieLens 100K](https://www.kaggle.com/prajitdatta/movielens-100k-dataset) dataset to simulate an environment with users and their respective preferences. It is available at `gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data`.\n", "\n", "### Objective\n", "\n", "In this notebook, you will learn how to build an end-to-end RL pipeline for a TF-Agents (particularly the bandits module) based movie recommendation system, using [KFP](https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/), [Vertex AI](https://cloud.google.com/vertex-ai) and particularly [Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) which is fully managed and highly scalable.\n", "\n", "This Vertex Pipeline includes the following components:\n", "1. *Generator* to generate MovieLens simulation data\n", "2. *Ingester* to ingest data\n", "3. *Trainer* to train the RL policy\n", "4. *Deployer* to deploy the trained policy to a Vertex AI endpoint\n", "\n", "After pipeline construction, you (1) create the *Simulator* (which utilizes Cloud Functions, Cloud Scheduler and Pub/Sub) to send simulated MovieLens prediction requests, (2) create the *Logger* to asynchronously log prediction inputs and results (which utilizes Cloud Functions, Pub/Sub and a hook in the prediction code), and (3) create the *Trigger* to trigger recurrent re-training.\n", "\n", "A more general ML pipeline is demonstrated in [MLOps on Vertex AI](https://github.com/ksalama/ucaip-labs).\n", "\n", "### Costs\n", "\n", "This tutorial uses billable components of Google Cloud:\n", "\n", "* Vertex AI\n", "* BigQuery\n", "* Cloud Build\n", "* Cloud Functions\n", "* Cloud Scheduler\n", "* Cloud Storage\n", "* Pub/Sub\n", "\n", "Learn about [Vertex AI\n", "pricing](https://cloud.google.com/vertex-ai/pricing), [BigQuery pricing](https://cloud.google.com/bigquery/pricing), [Cloud Build](https://cloud.google.com/build/pricing), [Cloud Functions](https://cloud.google.com/functions/pricing), [Cloud Scheduler](https://cloud.google.com/scheduler/pricing), [Cloud Storage\n", "pricing](https://cloud.google.com/storage/pricing), and [Pub/Sub pricing](https://cloud.google.com/pubsub/pricing), and use the [Pricing\n", "Calculator](https://cloud.google.com/products/calculator/)\n", "to generate a cost estimate based on your projected usage." ] }, { "cell_type": "markdown", "metadata": { "id": "ze4-nDLfK4pw" }, "source": [ "### Set up your local development environment\n", "\n", "**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n", "all the requirements to run this notebook. You can skip this step." ] }, { "cell_type": "markdown", "metadata": { "id": "gCuSR8GkAgzl" }, "source": [ "**Otherwise**, make sure your environment meets this notebook's requirements.\n", "You need the following:\n", "\n", "* The Google Cloud SDK\n", "* Git\n", "* Python 3\n", "* virtualenv\n", "* Jupyter notebook running in a virtual environment with Python 3\n", "\n", "The Google Cloud guide to [Setting up a Python development\n", "environment](https://cloud.google.com/python/setup) and the [Jupyter\n", "installation guide](https://jupyter.org/install) provide detailed instructions\n", "for meeting these requirements. The following steps provide a condensed set of\n", "instructions:\n", "\n", "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n", "\n", "1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n", "\n", "1. [Install\n", " virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n", " and create a virtual environment that uses Python 3. Activate the virtual environment.\n", "\n", "1. To install Jupyter, run `pip3 install jupyter` on the\n", "command-line in a terminal shell.\n", "\n", "1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n", "\n", "1. Open this notebook in the Jupyter Notebook Dashboard." ] }, { "cell_type": "markdown", "metadata": { "id": "i7EUnXsZhAGF" }, "source": [ "### Install additional packages\n", "\n", "Install additional package dependencies not installed in your notebook environment, such as the Kubeflow Pipelines (KFP) SDK." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2b4ef9b72d43" }, "outputs": [], "source": [ "import os\n", "\n", "# The Google Cloud Notebook product has specific requirements\n", "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n", "\n", "# Google Cloud Notebook requires dependencies to be installed with '--user'\n", "USER_FLAG = \"\"\n", "if IS_GOOGLE_CLOUD_NOTEBOOK:\n", " USER_FLAG = \"--user\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wyy5Lbnzg5fi" }, "outputs": [], "source": [ "! pip3 install {USER_FLAG} google-cloud-aiplatform\n", "! pip3 install {USER_FLAG} google-cloud-pipeline-components\n", "! pip3 install {USER_FLAG} --upgrade kfp\n", "! pip3 install {USER_FLAG} numpy\n", "! pip3 install {USER_FLAG} --upgrade tensorflow\n", "! pip3 install {USER_FLAG} --upgrade pillow\n", "! pip3 install {USER_FLAG} --upgrade tf-agents\n", "! pip3 install {USER_FLAG} --upgrade fastapi" ] }, { "cell_type": "markdown", "metadata": { "id": "hhq5zEbGg0XX" }, "source": [ "### Restart the kernel\n", "\n", "After you install the additional packages, you need to restart the notebook kernel so it can find the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EzrelQZ22IZj" }, "outputs": [], "source": [ "# Automatically restart kernel after installs\n", "import os\n", "\n", "if not os.getenv(\"IS_TESTING\"):\n", " # Automatically restart kernel after installs\n", " import IPython\n", "\n", " app = IPython.Application.instance()\n", " app.kernel.do_shutdown(True)" ] }, { "cell_type": "markdown", "metadata": { "id": "lWEdiXsJg0XY" }, "source": [ "## Before you begin\n", "\n", "### Select a GPU runtime\n", "\n", "**Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select \"Runtime --> Change runtime type > GPU\"**" ] }, { "cell_type": "markdown", "metadata": { "id": "BF1j6f9HApxa" }, "source": [ "### Set up your Google Cloud project\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n", "\n", "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n", "\n", "1. [Enable the Vertex AI API, BigQuery API, Cloud Build, Cloud Functions, Cloud Scheduler, Cloud Storage, and Pub/Sub API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,bigquery.googleapis.com,build.googleapis.com,functions.googleapis.com,scheduler.googleapis.com,storage.googleapis.com,pubsub.googleapis.com).\n", "\n", "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n", "\n", "1. Enter your project ID in the cell below. Then run the cell to make sure the\n", "Cloud SDK uses the right project for all the commands in this notebook.\n", "\n", "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands." ] }, { "cell_type": "markdown", "metadata": { "id": "WReHDGG5g0XY" }, "source": [ "#### Set your project ID\n", "\n", "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oM1iC_MfAts1" }, "outputs": [], "source": [ "import os\n", "\n", "PROJECT_ID = \"\"\n", "\n", "# Get your Google Cloud project ID from gcloud\n", "if not os.getenv(\"IS_TESTING\"):\n", " shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n", " PROJECT_ID = shell_output[0]\n", " print(\"Project ID: \", PROJECT_ID)" ] }, { "cell_type": "markdown", "metadata": { "id": "qJYoRfYng0XZ" }, "source": [ "Otherwise, set your project ID here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "riG_qUokg0XZ" }, "outputs": [], "source": [ "if PROJECT_ID == \"\" or PROJECT_ID is None:\n", " PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "06571eb4063b" }, "source": [ "#### Timestamp\n", "\n", "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append it onto the name of resources you create in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "697568e92bd6" }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")" ] }, { "cell_type": "markdown", "metadata": { "id": "dr--iN2kAylZ" }, "source": [ "### Authenticate your Google Cloud account\n", "\n", "**If you are using Google Cloud Notebooks**, your environment is already\n", "authenticated. Skip this step." ] }, { "cell_type": "markdown", "metadata": { "id": "sBCra4QMA2wR" }, "source": [ "**If you are using Colab**, run the cell below and follow the instructions\n", "when prompted to authenticate your account via oAuth.\n", "\n", "**Otherwise**, follow these steps:\n", "\n", "1. In the Cloud Console, go to the [**Create service account key**\n", " page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n", "\n", "2. Click **Create service account**.\n", "\n", "3. In the **Service account name** field, enter a name, and\n", " click **Create**.\n", "\n", "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n", "into the filter box, and select\n", " **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n", "\n", "5. Click *Create*. A JSON file that contains your key downloads to your\n", "local environment.\n", "\n", "6. Enter the path to your service account key as the\n", "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PyQmSRbKA8r-" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "# If you are running this notebook in Colab, run this cell and follow the\n", "# instructions to authenticate your GCP account. This provides access to your\n", "# Cloud Storage bucket and lets you submit training jobs and prediction\n", "# requests.\n", "\n", "# The Google Cloud Notebook product has specific requirements\n", "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n", "\n", "# If on Google Cloud Notebooks, then don't execute this code\n", "if not IS_GOOGLE_CLOUD_NOTEBOOK:\n", " if \"google.colab\" in sys.modules:\n", " from google.colab import auth as google_auth\n", "\n", " google_auth.authenticate_user()\n", "\n", " # If you are running this notebook locally, replace the string below with the\n", " # path to your service account key and run this cell to authenticate your GCP\n", " # account.\n", " elif not os.getenv(\"IS_TESTING\"):\n", " %env GOOGLE_APPLICATION_CREDENTIALS ''" ] }, { "cell_type": "markdown", "metadata": { "id": "zgPO1eR3CYjk" }, "source": [ "### Create a Cloud Storage bucket\n", "\n", "**The following steps are required, regardless of your notebook environment.**\n", "\n", "In this tutorial, a Cloud Storage bucket holds the MovieLens dataset files to be used for model training. Vertex AI also saves the trained model that results from your training job in the same bucket. Using this model artifact, you can then create Vertex AI model and endpoint resources in order to serve online predictions.\n", "\n", "Set the name of your Cloud Storage bucket below. It must be unique across all\n", "Cloud Storage buckets.\n", "\n", "You may also change the `REGION` variable, which is used for operations\n", "throughout the rest of this notebook. Make sure to [choose a region where Vertex AI services are\n", "available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions). You may\n", "not use a Multi-Regional Storage bucket for training with Vertex AI. Also note that Vertex\n", "Pipelines is currently only supported in select regions such as \"us-central1\" ([reference](https://cloud.google.com/vertex-ai/docs/general/locations))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MzGDU7TWdts_" }, "outputs": [], "source": [ "BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}\n", "REGION = \"[your-region]\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cf221059d072" }, "outputs": [], "source": [ "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n", " BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP" ] }, { "cell_type": "markdown", "metadata": { "id": "-EcIXiGsCePi" }, "source": [ "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NIq7R4HZCfIc" }, "outputs": [], "source": [ "! gsutil mb -l $REGION $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "ucvCsknMCims" }, "source": [ "Finally, validate access to your Cloud Storage bucket by examining its contents:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vhOb7YnwClBb" }, "outputs": [], "source": [ "! gsutil ls -al $BUCKET_NAME" ] }, { "cell_type": "markdown", "metadata": { "id": "XoEqT2Y4DJmf" }, "source": [ "### Import libraries and define constants" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pRUOFELefqf1" }, "outputs": [], "source": [ "import os\n", "import sys\n", "\n", "from google.cloud import aiplatform\n", "from google_cloud_pipeline_components import aiplatform as gcc_aip\n", "from kfp.v2 import compiler, dsl\n", "from kfp.v2.google.client import AIPlatformClient" ] }, { "cell_type": "markdown", "metadata": { "id": "eIMBwJpVVaU3" }, "source": [ "#### Fill out the following configurations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PhWoA2uCVaU3" }, "outputs": [], "source": [ "# BigQuery parameters (used for the Generator, Ingester, Logger)\n", "BIGQUERY_DATASET_ID = f\"{PROJECT_ID}.movielens_dataset\" # @param {type:\"string\"} BigQuery dataset ID as `project_id.dataset_id`.\n", "BIGQUERY_LOCATION = \"us\" # @param {type:\"string\"} BigQuery dataset region.\n", "BIGQUERY_TABLE_ID = f\"{BIGQUERY_DATASET_ID}.training_dataset\" # @param {type:\"string\"} BigQuery table ID as `project_id.dataset_id.table_id`." ] }, { "cell_type": "markdown", "metadata": { "id": "fuN1uU27VaU3" }, "source": [ "#### Set additional configurations\n", "\n", "You may use the default values below as is." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "895ac243c125" }, "outputs": [], "source": [ "# Dataset parameters\n", "RAW_DATA_PATH = \"gs://[your-bucket-name]/raw_data/u.data\" # @param {type:\"string\"}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "62bfb9a820f6" }, "outputs": [], "source": [ "# Download the sample data into your RAW_DATA_PATH\n", "! gsutil cp \"gs://cloud-samples-data/vertex-ai/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/u.data\" $RAW_DATA_PATH" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "H3530hdGGilo" }, "outputs": [], "source": [ "# Pipeline parameters\n", "PIPELINE_NAME = \"movielens-pipeline\" # Pipeline display name.\n", "ENABLE_CACHING = False # Whether to enable execution caching for the pipeline.\n", "PIPELINE_ROOT = f\"{BUCKET_NAME}/pipeline\" # Root directory for pipeline artifacts.\n", "PIPELINE_SPEC_PATH = \"metadata_pipeline.json\" # Path to pipeline specification file.\n", "OUTPUT_COMPONENT_SPEC = \"output-component.yaml\" # Output component specification file.\n", "\n", "# BigQuery parameters (used for the Generator, Ingester, Logger)\n", "BIGQUERY_TMP_FILE = (\n", " \"tmp.json\" # Temporary file for storing data to be loaded into BigQuery.\n", ")\n", "BIGQUERY_MAX_ROWS = 5 # Maximum number of rows of data in BigQuery to ingest.\n", "\n", "# Dataset parameters\n", "TFRECORD_FILE = (\n", " f\"{BUCKET_NAME}/trainer_input_path/*\" # TFRecord file to be used for training.\n", ")\n", "\n", "# Logger parameters (also used for the Logger hook in the prediction container)\n", "LOGGER_PUBSUB_TOPIC = \"logger-pubsub-topic\" # Pub/Sub topic name for the Logger.\n", "LOGGER_CLOUD_FUNCTION = \"logger-cloud-function\" # Cloud Functions name for the Logger." ] }, { "cell_type": "markdown", "metadata": { "id": "E6ppE7imft-y" }, "source": [ "## Create the RL pipeline components\n", "\n", "This section consists of the following steps:\n", "1. Create the *Generator* to generate MovieLens simulation data\n", "2. Create the *Ingester* to ingest data\n", "3. Create the *Trainer* to train the RL policy\n", "4. Create the *Deployer* to deploy the trained policy to a Vertex AI endpoint\n", "\n", "After pipeline construction, create the *Simulator* to send simulated MovieLens prediction requests, create the *Logger* to asynchronously log prediction inputs and results, and create the *Trigger* to trigger re-training.\n", "\n", "Here's the entire workflow:\n", "1. The startup pipeline has the following components: Generator --> Ingester --> Trainer --> Deployer. This pipeline only runs once.\n", "2. Then, the Simulator generates prediction requests (e.g. every 5 mins), and the Logger gets invoked immediately at each prediction request and logs each prediction request asynchronously into BigQuery. The Trigger runs the re-training pipeline (e.g. every 30 mins) with the following components: Ingester --> Trainer --> Deploy.\n", "\n", "You can find the KFP SDK documentation [here](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/)." ] }, { "cell_type": "markdown", "metadata": { "id": "dxTLuuWEGilo" }, "source": [ "### Create the *Generator* to generate MovieLens simulation data\n", "\n", "Create the Generator component to generate the initial set of training data using a MovieLens simulation environment and a random data-collecting policy. Store the generated data in BigQuery.\n", "\n", "The Generator source code is [`src/generator/generator_component.py`](src/generator/generator_component.py)." ] }, { "cell_type": "markdown", "metadata": { "id": "ay1ztxwIGilo" }, "source": [ "#### Run unit tests on the Generator component\n", "\n", "Before running the command, you should update the `RAW_DATA_PATH` in [`src/generator/test_generator_component.py`](src/generator/test_generator_component.py)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R9FQacKtGilo" }, "outputs": [], "source": [ "! python3 -m unittest src.generator.test_generator_component" ] }, { "cell_type": "markdown", "metadata": { "id": "1gpYFPPBOWQP" }, "source": [ "### Create the *Ingester* to ingest data\n", "\n", "Create the Ingester component to ingest data from BigQuery, package them as `tf.train.Example` objects, and output TFRecord files.\n", "\n", "Read more about `tf.train.Example` and TFRecord [here](https://www.tensorflow.org/tutorials/load_data/tfrecord).\n", "\n", "The Ingester component source code is in [`src/ingester/ingester_component.py`](src/ingester/ingester_component.py)." ] }, { "cell_type": "markdown", "metadata": { "id": "ZQkLU7wyOWQP" }, "source": [ "#### Run unit tests on the Ingester component" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ej4rNnnEOWQP" }, "outputs": [], "source": [ "! python3 -m unittest src.ingester.test_ingester_component" ] }, { "cell_type": "markdown", "metadata": { "id": "KFdSpGAWWkL9" }, "source": [ "### Create the *Trainer* to train the RL policy\n", "\n", "Create the Trainer component to train a RL policy on the training dataset, and then submit a remote custom training job to Vertex AI. This component trains a policy using the TF-Agents LinUCB agent on the MovieLens simulation dataset, and saves the trained policy as a SavedModel.\n", "\n", "The Trainer component source code is in [`src/trainer/trainer_component.py`](src/trainer/trainer_component.py). You use additional Vertex AI platform code in pipeline construction to submit the training code defined in Trainer as a custom training job to Vertex AI. (The additional code is similar to what [`kfp.v2.google.experimental.run_as_aiplatform_custom_job`](https://github.com/kubeflow/pipelines/blob/master/sdk/python/kfp/v2/google/experimental/custom_job.py) does. You can find an example notebook [here](https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/ai-platform-unified/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb) for how to use that first-party Trainer component.)\n", "\n", "The Trainer performs off-policy training, where you train a policy on a static set of pre-collected data records containing information including observation, action and reward. For a data record, the policy in training might not output the same action given the observation in that data record.\n", "\n", "If you're interested in pipeline metrics, read about [KFP Pipeline Metrics](https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/) here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fh5S-bcCcHn3" }, "outputs": [], "source": [ "# Trainer parameters\n", "TRAINING_ARTIFACTS_DIR = (\n", " f\"{BUCKET_NAME}/artifacts\" # Root directory for training artifacts.\n", ")\n", "TRAINING_REPLICA_COUNT = 1 # Number of replica to run the custom training job.\n", "TRAINING_MACHINE_TYPE = (\n", " \"n1-standard-4\" # Type of machine to run the custom training job.\n", ")\n", "TRAINING_ACCELERATOR_TYPE = \"ACCELERATOR_TYPE_UNSPECIFIED\" # Type of accelerators to run the custom training job.\n", "TRAINING_ACCELERATOR_COUNT = 0 # Number of accelerators for the custom training job." ] }, { "cell_type": "markdown", "metadata": { "id": "MH3UOVU8WkL9" }, "source": [ "#### Run unit tests on the Trainer component" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CEJ7_ymvWkL9" }, "outputs": [], "source": [ "! python3 -m unittest src.trainer.test_trainer_component" ] }, { "cell_type": "markdown", "metadata": { "id": "5cz7h6V4ibYb" }, "source": [ "### Create the *Deployer* to deploy the trained policy to a Vertex AI endpoint\n", "\n", "Use [`google_cloud_pipeline_components.aiplatform`](https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#google-cloud-components) components during pipeline construction to:\n", "1. Upload the trained policy\n", "2. Create a Vertex AI endpoint\n", "3. Deploy the uploaded trained policy to the endpoint\n", "\n", "These 3 components formulate the Deployer. They support flexible configurations; for instance, if you want to set up traffic splitting for the endpoint to run A/B testing, you may pass in your configurations to [google_cloud_pipeline_components.aiplatform.ModelDeployOp](https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-0.1.3/google_cloud_pipeline_components.aiplatform.html#google_cloud_pipeline_components.aiplatform.ModelDeployOp)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "E7dbkbDMcR-m" }, "outputs": [], "source": [ "# Deployer parameters\n", "TRAINED_POLICY_DISPLAY_NAME = (\n", " \"movielens-trained-policy\" # Display name of the uploaded and deployed policy.\n", ")\n", "TRAFFIC_SPLIT = {\"0\": 100}\n", "ENDPOINT_DISPLAY_NAME = \"movielens-endpoint\" # Display name of the prediction endpoint.\n", "ENDPOINT_MACHINE_TYPE = \"n1-standard-4\" # Type of machine of the prediction endpoint.\n", "ENDPOINT_REPLICA_COUNT = 1 # Number of replicas of the prediction endpoint.\n", "ENDPOINT_ACCELERATOR_TYPE = \"ACCELERATOR_TYPE_UNSPECIFIED\" # Type of accelerators to run the custom training job.\n", "ENDPOINT_ACCELERATOR_COUNT = 0 # Number of accelerators for the custom training job." ] }, { "cell_type": "markdown", "metadata": { "id": "Ldr0yDs6ibYb" }, "source": [ "### Create a custom prediction container using Cloud Build\n", "\n", "Before setting up the Deployer, define and build a custom prediction container that serves predictions using the trained policy. The source code, Cloud Build YAML configuration file and Dockerfile are in `src/prediction_container`.\n", "\n", "This prediction container is the serving container for the deployed, trained policy. See a more detailed guide on building prediction custom containers [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/step_by_step_sdk_tf_agents_bandits_movie_recommendation/step_by_step_sdk_tf_agents_bandits_movie_recommendation.ipynb)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cKZd3Q_Pcfzo" }, "outputs": [], "source": [ "# Prediction container parameters\n", "PREDICTION_CONTAINER = \"prediction-container\" # Name of the container image.\n", "PREDICTION_CONTAINER_DIR = \"src/prediction_container\"" ] }, { "cell_type": "markdown", "metadata": { "id": "jkReLZUAibYb" }, "source": [ "#### Create a Cloud Build YAML file using Kaniko build\n", "\n", "Note: For this application, you are recommended to use E2_HIGHCPU_8 or other high resouce machine configurations instead of the standard machine type listed [here](https://cloud.google.com/build/docs/api/reference/rest/v1/projects.builds#Build.MachineType) to prevent out-of-memory errors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RUvEWUucibYc" }, "outputs": [], "source": [ "cloudbuild_yaml = \"\"\"steps:\n", "- name: \"gcr.io/kaniko-project/executor:latest\"\n", " args: [\"--destination=gcr.io/{PROJECT_ID}/{PREDICTION_CONTAINER}:latest\",\n", " \"--cache=true\",\n", " \"--cache-ttl=99h\"]\n", " env: [\"AIP_STORAGE_URI={ARTIFACTS_DIR}\",\n", " \"PROJECT_ID={PROJECT_ID}\",\n", " \"LOGGER_PUBSUB_TOPIC={LOGGER_PUBSUB_TOPIC}\"]\n", "options:\n", " machineType: \"E2_HIGHCPU_8\"\n", "\"\"\".format(\n", " PROJECT_ID=PROJECT_ID,\n", " PREDICTION_CONTAINER=PREDICTION_CONTAINER,\n", " ARTIFACTS_DIR=TRAINING_ARTIFACTS_DIR,\n", " LOGGER_PUBSUB_TOPIC=LOGGER_PUBSUB_TOPIC,\n", ")\n", "\n", "with open(f\"{PREDICTION_CONTAINER_DIR}/cloudbuild.yaml\", \"w\") as fp:\n", " fp.write(cloudbuild_yaml)" ] }, { "cell_type": "markdown", "metadata": { "id": "kByjVm5yibYc" }, "source": [ "#### Run unit tests on the prediction code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tzLU1V6fibYc" }, "outputs": [], "source": [ "! python3 -m unittest src.prediction_container.test_main" ] }, { "cell_type": "markdown", "metadata": { "id": "RelRBSFvibYc" }, "source": [ "#### Build custom prediction container" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9uHbODeXibYd" }, "outputs": [], "source": [ "! gcloud builds submit --config $PREDICTION_CONTAINER_DIR/cloudbuild.yaml $PREDICTION_CONTAINER_DIR" ] }, { "cell_type": "markdown", "metadata": { "id": "nW154IyqGilq" }, "source": [ "## Author and run the RL pipeline\n", "\n", "You author the pipeline using custom KFP components built from the previous section, and [create a pipeline run](https://cloud.google.com/vertex-ai/docs/pipelines/run-pipeline#kubeflow-pipelines-sdk) using Vertex Pipelines. You can read more about whether to enable execution caching [here](https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#caching). You can also specifically configure the worker pool spec for training if for instance you want to train at scale and/or at a higher speed; you can adjust the replica count, machine type, accelerator type and count, and many other specifications.\n", "\n", "Here, you build a \"startup\" pipeline that generates randomly sampled training data (with the Generator) as the first step. This pipeline runs only once." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cvXJzhSSGilq" }, "outputs": [], "source": [ "from google_cloud_pipeline_components.experimental.custom_job import utils\n", "from kfp.components import load_component_from_url\n", "\n", "generate_op = load_component_from_url(\n", " \"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/62a2a7611499490b4b04d731d48a7ba87c2d636f/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/generator/component.yaml\"\n", ")\n", "ingest_op = load_component_from_url(\n", " \"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/62a2a7611499490b4b04d731d48a7ba87c2d636f/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/ingester/component.yaml\"\n", ")\n", "train_op = load_component_from_url(\n", " \"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/62a2a7611499490b4b04d731d48a7ba87c2d636f/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/trainer/component.yaml\"\n", ")\n", "\n", "\n", "@dsl.pipeline(pipeline_root=PIPELINE_ROOT, name=f\"{PIPELINE_NAME}-startup\")\n", "def pipeline(\n", " # Pipeline configs\n", " project_id: str,\n", " raw_data_path: str,\n", " training_artifacts_dir: str,\n", " # BigQuery configs\n", " bigquery_dataset_id: str,\n", " bigquery_location: str,\n", " bigquery_table_id: str,\n", " bigquery_max_rows: int = 10000,\n", " # TF-Agents RL configs\n", " batch_size: int = 8,\n", " rank_k: int = 20,\n", " num_actions: int = 20,\n", " driver_steps: int = 3,\n", " num_epochs: int = 5,\n", " tikhonov_weight: float = 0.01,\n", " agent_alpha: float = 10,\n", ") -> None:\n", " \"\"\"Authors a RL pipeline for MovieLens movie recommendation system.\n", "\n", " Integrates the Generator, Ingester, Trainer and Deployer components. This\n", " pipeline generates initial training data with a random policy and runs once\n", " as the initiation of the system.\n", "\n", " Args:\n", " project_id: GCP project ID. This is required because otherwise the BigQuery\n", " client will use the ID of the tenant GCP project created as a result of\n", " KFP, which doesn't have proper access to BigQuery.\n", " raw_data_path: Path to MovieLens 100K's \"u.data\" file.\n", " training_artifacts_dir: Path to store the Trainer artifacts (trained policy).\n", "\n", " bigquery_dataset: A string of the BigQuery dataset ID in the format of\n", " \"project.dataset\".\n", " bigquery_location: A string of the BigQuery dataset location.\n", " bigquery_table_id: A string of the BigQuery table ID in the format of\n", " \"project.dataset.table\".\n", " bigquery_max_rows: Optional; maximum number of rows to ingest.\n", "\n", " batch_size: Optional; batch size of environment generated quantities eg.\n", " rewards.\n", " rank_k: Optional; rank for matrix factorization in the MovieLens environment;\n", " also the observation dimension.\n", " num_actions: Optional; number of actions (movie items) to choose from.\n", " driver_steps: Optional; number of steps to run per batch.\n", " num_epochs: Optional; number of training epochs.\n", " tikhonov_weight: Optional; LinUCB Tikhonov regularization weight of the\n", " Trainer.\n", " agent_alpha: Optional; LinUCB exploration parameter that multiplies the\n", " confidence intervals of the Trainer.\n", " \"\"\"\n", " # Run the Generator component.\n", " generate_task = generate_op(\n", " project_id=project_id,\n", " raw_data_path=raw_data_path,\n", " batch_size=batch_size,\n", " rank_k=rank_k,\n", " num_actions=num_actions,\n", " driver_steps=driver_steps,\n", " bigquery_tmp_file=BIGQUERY_TMP_FILE,\n", " bigquery_dataset_id=bigquery_dataset_id,\n", " bigquery_location=bigquery_location,\n", " bigquery_table_id=bigquery_table_id,\n", " )\n", " \n", " # Run the Ingester component.\n", " ingest_task = ingest_op(\n", " project_id=project_id,\n", " bigquery_table_id=generate_task.outputs[\"bigquery_table_id\"],\n", " bigquery_max_rows=bigquery_max_rows,\n", " tfrecord_file=TFRECORD_FILE,\n", " )\n", "\n", " # Run the Trainer component and submit custom job to Vertex AI.\n", " # Convert the train_op component into a Vertex AI Custom Job pre-built component\n", " custom_job_training_op = utils.create_custom_training_job_op_from_component(\n", " component_spec=train_op,\n", " replica_count=TRAINING_REPLICA_COUNT,\n", " machine_type=TRAINING_MACHINE_TYPE,\n", " accelerator_type=TRAINING_ACCELERATOR_TYPE,\n", " accelerator_count=TRAINING_ACCELERATOR_COUNT,\n", " )\n", "\n", " train_task = custom_job_training_op(\n", " training_artifacts_dir=training_artifacts_dir,\n", " tfrecord_file=ingest_task.outputs[\"tfrecord_file\"],\n", " num_epochs=num_epochs,\n", " rank_k=rank_k,\n", " num_actions=num_actions,\n", " tikhonov_weight=tikhonov_weight,\n", " agent_alpha=agent_alpha,\n", " project=PROJECT_ID,\n", " location=REGION,\n", " )\n", "\n", " # Run the Deployer components.\n", " # Upload the trained policy as a model.\n", " model_upload_op = gcc_aip.ModelUploadOp(\n", " project=project_id,\n", " display_name=TRAINED_POLICY_DISPLAY_NAME,\n", " artifact_uri=train_task.outputs[\"training_artifacts_dir\"],\n", " serving_container_image_uri=f\"gcr.io/{PROJECT_ID}/{PREDICTION_CONTAINER}:latest\",\n", " )\n", " # Create a Vertex AI endpoint. (This operation can occur in parallel with\n", " # the Generator, Ingester, Trainer components.)\n", " endpoint_create_op = gcc_aip.EndpointCreateOp(\n", " project=project_id, display_name=ENDPOINT_DISPLAY_NAME\n", " )\n", " # Deploy the uploaded, trained policy to the created endpoint. (This operation\n", " # has to occur after both model uploading and endpoint creation complete.)\n", " gcc_aip.ModelDeployOp(\n", " endpoint=endpoint_create_op.outputs[\"endpoint\"],\n", " model=model_upload_op.outputs[\"model\"],\n", " deployed_model_display_name=TRAINED_POLICY_DISPLAY_NAME,\n", " traffic_split=TRAFFIC_SPLIT,\n", " dedicated_resources_machine_type=ENDPOINT_MACHINE_TYPE,\n", " dedicated_resources_accelerator_type=ENDPOINT_ACCELERATOR_TYPE,\n", " dedicated_resources_accelerator_count=ENDPOINT_ACCELERATOR_COUNT,\n", " dedicated_resources_min_replica_count=ENDPOINT_REPLICA_COUNT,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "icYK0WoRGilr" }, "outputs": [], "source": [ "# Compile the authored pipeline.\n", "compiler.Compiler().compile(pipeline_func=pipeline, package_path=PIPELINE_SPEC_PATH)\n", "\n", "# Create a pipeline run job.\n", "job = aiplatform.PipelineJob(\n", " display_name=f\"{PIPELINE_NAME}-startup\",\n", " template_path=PIPELINE_SPEC_PATH,\n", " pipeline_root=PIPELINE_ROOT,\n", " parameter_values={\n", " # Pipeline configs\n", " \"project_id\": PROJECT_ID,\n", " \"raw_data_path\": RAW_DATA_PATH,\n", " \"training_artifacts_dir\": TRAINING_ARTIFACTS_DIR,\n", " # BigQuery configs\n", " \"bigquery_dataset_id\": BIGQUERY_DATASET_ID,\n", " \"bigquery_location\": BIGQUERY_LOCATION,\n", " \"bigquery_table_id\": BIGQUERY_TABLE_ID,\n", " },\n", " enable_caching=ENABLE_CACHING,\n", ")\n", "\n", "job.run()" ] }, { "cell_type": "markdown", "metadata": { "id": "_YDDhAx5i1UL" }, "source": [ "## Create the *Simulator* to send simulated MovieLens prediction requests\n", "\n", "Create the Simulator to [obtain observations](https://github.com/tensorflow/agents/blob/v0.8.0/tf_agents/bandits/environments/movielens_py_environment.py#L118-L125) from the MovieLens simulation environment, formats them, and sends prediction requests to the Vertex AI endpoint.\n", "\n", "The workflow is: Cloud Scheduler --> Pub/Sub --> Cloud Functions --> Endpoint\n", "\n", "In production, this Simulator logic can be modified to that of gathering real-world input features as observations, getting prediction results from the endpoint and communicating those results to real-world users.\n", "\n", "The Simulator source code is [`src/simulator/main.py`](src/simulator/main.py)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Sxz6T0yjcoX2" }, "outputs": [], "source": [ "# Simulator parameters\n", "SIMULATOR_PUBSUB_TOPIC = (\n", " \"simulator-pubsub-topic\" # Pub/Sub topic name for the Simulator.\n", ")\n", "SIMULATOR_CLOUD_FUNCTION = (\n", " \"simulator-cloud-function\" # Cloud Functions name for the Simulator.\n", ")\n", "SIMULATOR_SCHEDULER_JOB = (\n", " \"simulator-scheduler-job\" # Cloud Scheduler cron job name for the Simulator.\n", ")\n", "SIMULATOR_SCHEDULE = \"*/5 * * * *\" # Cloud Scheduler cron job schedule for the Simulator. Eg. \"*/5 * * * *\" means every 5 mins.\n", "SIMULATOR_SCHEDULER_MESSAGE = (\n", " \"simulator-message\" # Cloud Scheduler message for the Simulator.\n", ")\n", "# TF-Agents RL configs\n", "BATCH_SIZE = 8\n", "RANK_K = 20\n", "NUM_ACTIONS = 20" ] }, { "cell_type": "markdown", "metadata": { "id": "6JkcpQmpi1UL" }, "source": [ "### Run unit tests on the Simulator" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GoNH1VS_i1UL" }, "outputs": [], "source": [ "! python3 -m unittest src.simulator.test_main" ] }, { "cell_type": "markdown", "metadata": { "id": "0g8bl_pCi1UL" }, "source": [ "### Create a Pub/Sub topic\n", "\n", "- Read more about creating Pub/Sub topics [here](https://cloud.google.com/functions/docs/tutorials/pubsub)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5apj2cEri1UL" }, "outputs": [], "source": [ "! gcloud pubsub topics create $SIMULATOR_PUBSUB_TOPIC" ] }, { "cell_type": "markdown", "metadata": { "id": "jA8gMvqXi1UM" }, "source": [ "### Set up a recurrent Cloud Scheduler job for the Pub/Sub topic\n", "\n", "- Read more about possible ways to create cron jobs [here](https://cloud.google.com/scheduler/docs/creating#gcloud).\n", "- Read about the cron job schedule format [here](https://man7.org/linux/man-pages/man5/crontab.5.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "snTyjFkDi1UM" }, "outputs": [], "source": [ "scheduler_job_args = \" \".join(\n", " [\n", " SIMULATOR_SCHEDULER_JOB,\n", " f\"--schedule='{SIMULATOR_SCHEDULE}'\",\n", " f\"--topic={SIMULATOR_PUBSUB_TOPIC}\",\n", " f\"--message-body={SIMULATOR_SCHEDULER_MESSAGE}\",\n", " ]\n", ")\n", "\n", "! echo $scheduler_job_args" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4pY_Gs_Di1UM" }, "outputs": [], "source": [ "! gcloud scheduler jobs create pubsub $scheduler_job_args" ] }, { "cell_type": "markdown", "metadata": { "id": "HjyV2Arei1UM" }, "source": [ "### Define the *Simulator* logic in a Cloud Function to be triggered periodically, and deploy this Function\n", "\n", "- Specify dependencies of the Function in [`src/simulator/requirements.txt`](src/simulator/requirements.txt).\n", "- Read more about the available configurable arguments for deploying a Function [here](https://cloud.google.com/sdk/gcloud/reference/functions/deploy). For instance, based on the complexity of your Function, you may want to adjust its memory and timeout.\n", "- Note that the environment variables in `ENV_VARS` should be comma-separated; there should not be additional spaces, or other characters in between. Read more about setting/updating/deleting environment variables [here](https://cloud.google.com/functions/docs/env-var).\n", "- Read more about sending predictions to Vertex endpoints [here](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-custom-models)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0LJ2C_pdibYg" }, "outputs": [], "source": [ "endpoints = ! gcloud ai endpoints list \\\n", " --region=$REGION \\\n", " --filter=display_name=$ENDPOINT_DISPLAY_NAME\n", "print(\"\\n\".join(endpoints), \"\\n\")\n", "\n", "ENDPOINT_ID = endpoints[2].split(\" \")[0]\n", "print(f\"ENDPOINT_ID={ENDPOINT_ID}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V4xwXBBgi1UM" }, "outputs": [], "source": [ "ENV_VARS = \",\".join(\n", " [\n", " f\"PROJECT_ID={PROJECT_ID}\",\n", " f\"REGION={REGION}\",\n", " f\"ENDPOINT_ID={ENDPOINT_ID}\",\n", " f\"RAW_DATA_PATH={RAW_DATA_PATH}\",\n", " f\"BATCH_SIZE={BATCH_SIZE}\",\n", " f\"RANK_K={RANK_K}\",\n", " f\"NUM_ACTIONS={NUM_ACTIONS}\",\n", " ]\n", ")\n", "\n", "! echo $ENV_VARS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0yiHxUMBi1UM" }, "outputs": [], "source": [ "! gcloud functions deploy $SIMULATOR_CLOUD_FUNCTION \\\n", " --region=$REGION \\\n", " --trigger-topic=$SIMULATOR_PUBSUB_TOPIC \\\n", " --runtime=python37 \\\n", " --memory=512MB \\\n", " --timeout=200s \\\n", " --source=src/simulator \\\n", " --entry-point=simulate \\\n", " --stage-bucket=$BUCKET_NAME \\\n", " --update-env-vars=$ENV_VARS" ] }, { "cell_type": "markdown", "metadata": { "id": "213JWEcLxAhN" }, "source": [ "## Create the *Logger* to asynchronously log prediction inputs and results\n", "\n", "Create the Logger to get environment feedback as rewards from the MovieLens simulation environment based on prediction observations and predicted actions, formulate trajectory data, and store said data back to BigQuery. The Logger closes the RL feedback loop from prediction to training data, and allows re-training of the policy on new training data.\n", "\n", "The Logger is triggered by a hook in the prediction code. At each prediction request, the prediction code messages a Pub/Sub topic, which triggers the Logger code.\n", "\n", "The workflow is: prediction container code (at prediction request) --> Pub/Sub --> Cloud Functions (logging predictions back to BigQuery)\n", "\n", "In production, this Logger logic can be modified to that of gathering real-world feedback (rewards) based on observations and predicted actions.\n", "\n", "The Logger source code is [`src/logger/main.py`](src/logger/main.py)." ] }, { "cell_type": "markdown", "metadata": { "id": "gIuPCKRjxAhN" }, "source": [ "### Run unit tests on the Logger" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-eVdF88gxAhN" }, "outputs": [], "source": [ "! python3 -m unittest src.logger.test_main" ] }, { "cell_type": "markdown", "metadata": { "id": "hs56EW17xAhO" }, "source": [ "### Create a Pub/Sub topic\n", "\n", "- Read more about creating Pub/Sub topics [here](https://cloud.google.com/functions/docs/tutorials/pubsub)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ydoCTizJxAhO" }, "outputs": [], "source": [ "! gcloud pubsub topics create $LOGGER_PUBSUB_TOPIC" ] }, { "cell_type": "markdown", "metadata": { "id": "FnlsMxfjxAhO" }, "source": [ "### Define the *Logger* logic in a Cloud Function to be triggered by a Pub/Sub topic, which is triggered by the prediction code at each prediction request.\n", "\n", "- Specify dependencies of the Function in [`src/logger/requirements.txt`](src/logger/requirements.txt).\n", "- Read more about the available configurable arguments for deploying a Function [here](https://cloud.google.com/sdk/gcloud/reference/functions/deploy). For instance, based on the complexity of your Function, you may want to adjust its memory and timeout.\n", "- Note that the environment variables in `ENV_VARS` should be comma-separated; there should not be additional spaces, or other characters in between. Read more about setting/updating/deleting environment variables [here](https://cloud.google.com/functions/docs/env-var)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DwrukBPHxAhO" }, "outputs": [], "source": [ "ENV_VARS = \",\".join(\n", " [\n", " f\"PROJECT_ID={PROJECT_ID}\",\n", " f\"RAW_DATA_PATH={RAW_DATA_PATH}\",\n", " f\"BATCH_SIZE={BATCH_SIZE}\",\n", " f\"RANK_K={RANK_K}\",\n", " f\"NUM_ACTIONS={NUM_ACTIONS}\",\n", " f\"BIGQUERY_TMP_FILE={BIGQUERY_TMP_FILE}\",\n", " f\"BIGQUERY_DATASET_ID={BIGQUERY_DATASET_ID}\",\n", " f\"BIGQUERY_LOCATION={BIGQUERY_LOCATION}\",\n", " f\"BIGQUERY_TABLE_ID={BIGQUERY_TABLE_ID}\",\n", " ]\n", ")\n", "\n", "! echo $ENV_VARS" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OykKRkScxAhO" }, "outputs": [], "source": [ "! gcloud functions deploy $LOGGER_CLOUD_FUNCTION \\\n", " --region=$REGION \\\n", " --trigger-topic=$LOGGER_PUBSUB_TOPIC \\\n", " --runtime=python37 \\\n", " --memory=512MB \\\n", " --timeout=200s \\\n", " --source=src/logger \\\n", " --entry-point=log \\\n", " --stage-bucket=$BUCKET_NAME \\\n", " --update-env-vars=$ENV_VARS" ] }, { "cell_type": "markdown", "metadata": { "id": "n0YSz3xtJcci" }, "source": [ "## Create the *Trigger* to trigger re-training\n", "\n", "Create the Trigger to recurrently re-run the pipeline to re-train the policy on new training data, using `kfp.v2.google.client.AIPlatformClient.create_schedule_from_job_spec`. You create a pipeline for orchestration on Vertex Pipelines, and a Cloud Scheduler job that recurrently triggers the pipeline. The method also automatically creates a Cloud Function that acts as an intermediary between the Scheduler and Pipelines. You can find the source code [here](https://github.com/kubeflow/pipelines/blob/v1.7.0-alpha.3/sdk/python/kfp/v2/google/client/client.py#L347-L391).\n", "\n", "When the Simulator sends prediction requests to the endpoint, the Logger is triggered by the hook in the prediction code to log prediction results to BigQuery, as new training data. As this pipeline has a recurrent schedule, it utlizes the new training data in training a new policy, therefore closing the feedback loop. Theoretically speaking, if you set the pipeline scheduler to be infinitely frequent, then you would be approaching real-time, continuous training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-YmLQ-ykJcci" }, "outputs": [], "source": [ "TRIGGER_SCHEDULE = \"*/30 * * * *\" # Schedule to trigger the pipeline. Eg. \"*/30 * * * *\" means every 30 mins." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ay1x-rgIJcci" }, "outputs": [], "source": [ "ingest_op = load_component_from_url(\n", " \"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/62a2a7611499490b4b04d731d48a7ba87c2d636f/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/ingester/component.yaml\"\n", ")\n", "train_op = load_component_from_url(\n", " \"https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/62a2a7611499490b4b04d731d48a7ba87c2d636f/community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/trainer/component.yaml\"\n", ")\n", "\n", "\n", "@dsl.pipeline(pipeline_root=PIPELINE_ROOT, name=f\"{PIPELINE_NAME}-retraining\")\n", "def pipeline(\n", " # Pipeline configs\n", " project_id: str,\n", " training_artifacts_dir: str,\n", " # BigQuery configs\n", " bigquery_table_id: str,\n", " bigquery_max_rows: int = 10000,\n", " # TF-Agents RL configs\n", " rank_k: int = 20,\n", " num_actions: int = 20,\n", " num_epochs: int = 5,\n", " tikhonov_weight: float = 0.01,\n", " agent_alpha: float = 10,\n", ") -> None:\n", " \"\"\"Authors a re-training pipeline for MovieLens movie recommendation system.\n", "\n", " Integrates the Ingester, Trainer and Deployer components.\n", "\n", " Args:\n", " project_id: GCP project ID. This is required because otherwise the BigQuery\n", " client will use the ID of the tenant GCP project created as a result of\n", " KFP, which doesn't have proper access to BigQuery.\n", " training_artifacts_dir: Path to store the Trainer artifacts (trained policy).\n", "\n", " bigquery_table_id: A string of the BigQuery table ID in the format of\n", " \"project.dataset.table\".\n", " bigquery_max_rows: Optional; maximum number of rows to ingest.\n", "\n", " rank_k: Optional; rank for matrix factorization in the MovieLens environment;\n", " also the observation dimension.\n", " num_actions: Optional; number of actions (movie items) to choose from.\n", " num_epochs: Optional; number of training epochs.\n", " tikhonov_weight: Optional; LinUCB Tikhonov regularization weight of the\n", " Trainer.\n", " agent_alpha: Optional; LinUCB exploration parameter that multiplies the\n", " confidence intervals of the Trainer.\n", " \"\"\"\n", " # Run the Ingester component.\n", " ingest_task = ingest_op(\n", " project_id=project_id,\n", " bigquery_table_id=bigquery_table_id,\n", " bigquery_max_rows=bigquery_max_rows,\n", " tfrecord_file=TFRECORD_FILE,\n", " )\n", "\n", " # Run the Trainer component and submit custom job to Vertex AI.\n", " # Convert the train_op component into a Vertex AI Custom Job pre-built component\n", " custom_job_training_op = utils.create_custom_training_job_op_from_component(\n", " component_spec=train_op,\n", " replica_count=TRAINING_REPLICA_COUNT,\n", " machine_type=TRAINING_MACHINE_TYPE,\n", " accelerator_type=TRAINING_ACCELERATOR_TYPE,\n", " accelerator_count=TRAINING_ACCELERATOR_COUNT,\n", " )\n", "\n", " train_task = custom_job_training_op(\n", " training_artifacts_dir=training_artifacts_dir,\n", " tfrecord_file=ingest_task.outputs[\"tfrecord_file\"],\n", " num_epochs=num_epochs,\n", " rank_k=rank_k,\n", " num_actions=num_actions,\n", " tikhonov_weight=tikhonov_weight,\n", " agent_alpha=agent_alpha,\n", " project=PROJECT_ID,\n", " location=REGION,\n", " )\n", "\n", " # Run the Deployer components.\n", " # Upload the trained policy as a model.\n", " model_upload_op = gcc_aip.ModelUploadOp(\n", " project=project_id,\n", " display_name=TRAINED_POLICY_DISPLAY_NAME,\n", " artifact_uri=train_task.outputs[\"training_artifacts_dir\"],\n", " serving_container_image_uri=f\"gcr.io/{PROJECT_ID}/{PREDICTION_CONTAINER}:latest\",\n", " )\n", " # Create a Vertex AI endpoint. (This operation can occur in parallel with\n", " # the Generator, Ingester, Trainer components.)\n", " endpoint_create_op = gcc_aip.EndpointCreateOp(\n", " project=project_id, display_name=ENDPOINT_DISPLAY_NAME\n", " )\n", " # Deploy the uploaded, trained policy to the created endpoint. (This operation\n", " # has to occur after both model uploading and endpoint creation complete.)\n", " gcc_aip.ModelDeployOp(\n", " endpoint=endpoint_create_op.outputs[\"endpoint\"],\n", " model=model_upload_op.outputs[\"model\"],\n", " deployed_model_display_name=TRAINED_POLICY_DISPLAY_NAME,\n", " dedicated_resources_machine_type=ENDPOINT_MACHINE_TYPE,\n", " dedicated_resources_accelerator_type=ENDPOINT_ACCELERATOR_TYPE,\n", " dedicated_resources_accelerator_count=ENDPOINT_ACCELERATOR_COUNT,\n", " dedicated_resources_min_replica_count=ENDPOINT_REPLICA_COUNT,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9yPjcm75Jcci" }, "outputs": [], "source": [ "# Compile the authored pipeline.\n", "compiler.Compiler().compile(pipeline_func=pipeline, package_path=PIPELINE_SPEC_PATH)\n", "\n", "# Createa Vertex AI client.\n", "api_client = AIPlatformClient(project_id=PROJECT_ID, region=REGION)\n", "\n", "# Schedule a recurring pipeline.\n", "response = api_client.create_schedule_from_job_spec(\n", " job_spec_path=PIPELINE_SPEC_PATH,\n", " schedule=TRIGGER_SCHEDULE,\n", " parameter_values={\n", " # Pipeline configs\n", " \"project_id\": PROJECT_ID,\n", " \"training_artifacts_dir\": TRAINING_ARTIFACTS_DIR,\n", " # BigQuery config\n", " \"bigquery_table_id\": BIGQUERY_TABLE_ID,\n", " },\n", ")\n", "response[\"name\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "TpV-iwP9qw9c" }, "source": [ "## Cleaning up\n", "\n", "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n", "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n", "\n", "Otherwise, you can delete the individual resources you created in this tutorial (you also need to clean up other resources that are difficult to delete here, such as the all/partial of data in BigQuery, the recurring pipeline and its Scheduler job, the uploaded policy/model, etc.):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sx_vKniMq9ZX" }, "outputs": [], "source": [ "# Delete endpoint resource.\n", "! gcloud ai endpoints delete $ENDPOINT_ID --quiet --region $REGION\n", "\n", "# Delete Pub/Sub topics.\n", "! gcloud pubsub topics delete $SIMULATOR_PUBSUB_TOPIC --quiet\n", "! gcloud pubsub topics delete $LOGGER_PUBSUB_TOPIC --quiet\n", "\n", "# Delete Cloud Functions.\n", "! gcloud functions delete $SIMULATOR_CLOUD_FUNCTION --quiet\n", "! gcloud functions delete $LOGGER_CLOUD_FUNCTION --quiet\n", "\n", "# Delete Scheduler job.\n", "! gcloud scheduler jobs delete $SIMULATOR_SCHEDULER_JOB --quiet\n", "\n", "# Delete Cloud Storage objects that were created.\n", "! gsutil -m rm -r $PIPELINE_ROOT\n", "! gsutil -m rm -r $TRAINING_ARTIFACTS_DIR" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "mlops_pipeline_tf_agents_bandits_movie_recommendation.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
pbnator18/labs
Spring Oscillation Lab.ipynb
1
32077
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spring Oscillation Relationship of Time and Mass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hussain Hassan, Paul Nator, Esteven Velazquez" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Hooke's law states the force needed to extend a spring a some distance is proportional to that distance. Knowing this information we can test to see if this is true by measuring oscillation times in relation to weight. \n", "\n", "For this simple harmonic motion we can expect time to be linearly proportional when they are equally displaced at different weights. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ F_{net}=-kx$$\n", "[//]: # ($$ T= 2\\pi \\sqrt{\\frac{m}{n}} $$)\n", "[//]: # ($$ f= \\frac{\\omega}{2\\pi} $$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Procedure" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "For this lab, we used a spring apparatus and masses of 100g each. We applied each weight in 100g increments up to 300g. Afterwards the spring apparatus was pulled down about 1 cm from its equilibrium so that the weight and spring would oscillate. The amount of time in between each oscillation was recorded in slow motion and then measured. With the data collected we averaged the amount of time it takes it complete one oscillation for each weight. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data & Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### <center>100 Grams grams</center>\n", "| Oscillations \t| Time \t| Time In between Oscillation \t| \n", "|:------------:\t|:----:\t|:---------------------------:\t| \n", "| 1 \t| 0 \t| - \t| \n", "| 2 \t| .41 \t| .41 \t| \n", "| 3 \t| .85 \t| .44 \t| \n", "| 4 \t| 1.29 \t| .44 \t| \n", "| 5 \t| 1.8 \t| .51 \t| \n", "| 6 \t| 2.2 \t| .4 \t| \n", "| 7 \t| 2.61 \t| .41 \t| \n", "| 8 \t| 3.09 \t| .48 \t| \n", "| 9 \t| 3.55 \t| .46 \t| \n", "| 10 \t| 3.98 \t| .43 \t| \n", "| Average Oscillation time \t| 0.4422 \t|\n", "\n", "\n", "\n", "\n", "\n", "#### <center>200 Grams grams</center>\n", "| Oscillations \t| Time \t| Time In between Oscillation \t|\n", "|:------------:\t|:----:\t|:---------------------------:\t|\n", "| 1 \t| 0 \t| - \t|\n", "| 2 \t| .56 \t| .56 \t|\n", "| 3 \t| 1.18 \t| .62 \t|\n", "| 4 \t| 1.77 \t| .59 \t|\n", "| 5 \t| 2.37 \t| .60 \t|\n", "| 6 \t| 2.97 \t| .59 \t|\n", "| 7 \t| 3.57 \t| .61 \t|\n", "| 8 \t| 4.20 \t| .63 \t|\n", "| 9 \t| 4.76 \t| .56 \t|\n", "| 10 \t| 5.38 \t| .62 \t|\n", "| Average Oscillation time \t| 0.597|\n", "\n", "\n", "\n", "#### <center>300 Grams grams</center>\n", "\n", "| Oscillations \t| Time \t| Time In between Oscillation \t|\n", "|:------------:\t|:----:\t|:---------------------------:\t|\n", "| 1 \t| 0 \t| - \t|\n", "| 2 \t| .80 \t| .8 \t|\n", "| 3 \t| 1.51 \t| .72 \t|\n", "| 4 \t| 2.23 \t| .72 \t|\n", "| 5 \t| 3.02 \t| .79 \t|\n", "| 6 \t| 3.67 \t| .65 \t|\n", "| 7 \t| 4.40 \t| .73 \t|\n", "| 8 \t| 5.13 \t| .73 \t|\n", "| 9 \t| 5.83 \t| .70 \t|\n", "| 10 \t| 6.62 \t| .79 \t|\n", "| Average Oscillation time \t| 0.7355|\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "0.0132 x + 2.687\n" ] }, { "data": { "image/png": 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RIunQRKpVlOSiEWX31WgINK5aOCIidcOkSZPYf//9Wb58OVOmTGHatGnstdde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\nem0AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x74000d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "plt.plot([100,200,300], [3.98,5.38,6.62], 'r.')\n", "plt.ylabel('Time to complete 10 Oscillations ')\n", "plt.xlabel('Time (s)')\n", "plt.title('Oscillations in relation to weight')\n", "plt.axis([50, 350, 0.2, 1])\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "x = [100,200,300]\n", "y = [3.98,5.38,6.62]\n", "\n", "fit = np.polyfit(x,y,1)\n", "fit_fn = np.poly1d(fit) \n", "\n", "plt.plot(x,y, 'bo', x, fit_fn(x), '--k')\n", "plt.xlim(95, 305)\n", "plt.ylim(3.5, 6.7)\n", "\n", "print(fit_fn)\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equation of line: $$y = 0.0132x + 2.687$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "With our data we made a model that shows how the oscillation time is linearly proportional to the amount of weight on the spring. When we added more weight the period of oscillation would proportionaly increase. We can conclude that our data collected by our experiment does follow Hooke's law. \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
Unidata/unidata-python-workshop
notebooks/Surface_Data/Surface Data with Siphon and MetPy.ipynb
1
16315
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"top\"></a>\n", "<div style=\"width:1000 px\">\n", "\n", "<div style=\"float:right; width:98 px; height:98px;\">\n", "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_static/unidata_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n", "</div>\n", "\n", "<h1>Working with Surface Observations in Siphon and MetPy</h1>\n", "<h3>Unidata Python Workshop</h3>\n", "\n", "<div style=\"clear:both\"></div>\n", "</div>\n", "\n", "<hr style=\"height:2px;\">\n", "\n", "<div style=\"float:right; width:250 px\"><img src=\"http://weather-geek.net/images/metar_what.png\" alt=\"METAR\" style=\"height: 200px;\"></div>\n", "\n", "## Overview:\n", "\n", "* **Teaching:** 20 minutes\n", "* **Exercises:** 20 minutes\n", "\n", "### Questions\n", "1. What's the best way to get surface station data from a THREDDS data server?\n", "1. What's the best way to make a station plot of data?\n", "1. How can I request a time series of data for a single station?\n", "\n", "### Objectives\n", "1. <a href=\"#ncss\">Use the netCDF Subset Service (NCSS) to request a portion of the data</a>\n", "2. <a href=\"#stationplot\">Download data for a single time across stations and create a station plot</a>\n", "3. <a href=\"#timeseries\">Request a time series of data and plot</a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"ncss\"></a>\n", "## 1. Using NCSS to get point data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from siphon.catalog import TDSCatalog\n", "\n", "# copied from the browser url box\n", "metar_cat_url = ('http://thredds.ucar.edu/thredds/catalog/'\n", " 'irma/metar/catalog.xml?dataset=irma/metar/Metar_Station_Data_-_Irma_fc.cdmr')\n", "\n", "# Parse the xml\n", "catalog = TDSCatalog(metar_cat_url)\n", "\n", "# what datasets are here?\n", "print(list(catalog.datasets))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "metar_dataset = catalog.datasets['Feature Collection']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we've grabbed the \"Feature Collection\" dataset, we can request a subset of the data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Can safely ignore the warnings\n", "ncss = metar_dataset.subset()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What variables do we have available?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ncss.variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"#top\">Top</a>\n", "<hr style=\"height:2px;\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"stationplot\"></a>\n", "## 2. Making a station plot\n", " * Make new NCSS query\n", " * Request data closest to a time" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "query = ncss.query()\n", "query.lonlat_box(north=34, south=24, east=-80, west=-90)\n", "query.time(datetime(2017, 9, 10, 12))\n", "query.variables('temperature', 'dewpoint', 'altimeter_setting',\n", " 'wind_speed', 'wind_direction', 'sky_coverage')\n", "query.accept('csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Get the data\n", "data = ncss.get_data(query)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to pull apart the data and perform some modifications, like converting winds to components and convert sky coverage percent to codes (octets) suitable for plotting." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "import metpy.calc as mpcalc\n", "from metpy.units import units\n", "\n", "# Since we used the CSV data, this is just a dictionary of arrays\n", "lats = data['latitude']\n", "lons = data['longitude']\n", "tair = data['temperature']\n", "dewp = data['dewpoint']\n", "alt = data['altimeter_setting']\n", "\n", "# Convert wind to components\n", "u, v = mpcalc.wind_components(data['wind_speed'] * units.knots, data['wind_direction'] * units.degree)\n", "\n", "# Need to handle missing (NaN) and convert to proper code\n", "cloud_cover = 8 * data['sky_coverage'] / 100.\n", "cloud_cover[np.isnan(cloud_cover)] = 10\n", "cloud_cover = cloud_cover.astype(np.int)\n", "\n", "# For some reason these come back as bytes instead of strings\n", "stid = np.array([s.tostring().decode() for s in data['station']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the map using cartopy and MetPy!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way to create station plots with MetPy is to create an instance of `StationPlot` and call various plot methods, like `plot_parameter`, to plot arrays of data at locations relative to the center point.\n", "\n", "In addition to plotting values, `StationPlot` has support for plotting text strings, symbols, and plotting values using custom formatting.\n", "\n", "Plotting symbols involves mapping integer values to various custom font glyphs in our custom weather symbols font. MetPy provides mappings for converting WMO codes to their appropriate symbol. The `sky_cover` function below is one such mapping." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import matplotlib.pyplot as plt\n", "\n", "from metpy.plots import StationPlot, sky_cover\n", "\n", "# Set up a plot with map features\n", "fig = plt.figure(figsize=(12, 12))\n", "proj = ccrs.Stereographic(central_longitude=-95, central_latitude=35)\n", "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", "ax.add_feature(cfeature.STATES, edgecolor='black')\n", "ax.coastlines(resolution='50m')\n", "ax.gridlines()\n", "\n", "# Create a station plot pointing to an Axes to draw on as well as the location of points\n", "stationplot = StationPlot(ax, lons, lats, transform=ccrs.PlateCarree(),\n", " fontsize=12)\n", "stationplot.plot_parameter('NW', tair, color='red')\n", "\n", "# Add wind barbs\n", "stationplot.plot_barb(u, v)\n", "\n", "# Plot the sky cover symbols in the center. We give it the integer code values that\n", "# should be plotted, as well as a mapping class that can convert the integer values\n", "# to the appropriate font glyph.\n", "stationplot.plot_symbol('C', cloud_cover, sky_cover)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how there are so many overlapping stations? There's a utility in MetPy to help with that: `reduce_point_density`. This returns a mask we can apply to data to filter the points." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Project points so that we're filtering based on the way the stations are laid out on the map\n", "proj = ccrs.Stereographic(central_longitude=-95, central_latitude=35)\n", "xy = proj.transform_points(ccrs.PlateCarree(), lons, lats)\n", "\n", "# Reduce point density so that there's only one point within a 200km circle\n", "mask = mpcalc.reduce_point_density(xy, 200000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we just plot with `arr[mask]` for every `arr` of data we use in plotting." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set up a plot with map features\n", "fig = plt.figure(figsize=(12, 12))\n", "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", "ax.add_feature(cfeature.STATES, edgecolor='black')\n", "ax.coastlines(resolution='50m')\n", "ax.gridlines()\n", "\n", "# Create a station plot pointing to an Axes to draw on as well as the location of points\n", "stationplot = StationPlot(ax, lons[mask], lats[mask], transform=ccrs.PlateCarree(),\n", " fontsize=12)\n", "stationplot.plot_parameter('NW', tair[mask], color='red')\n", "stationplot.plot_barb(u[mask], v[mask])\n", "stationplot.plot_symbol('C', cloud_cover[mask], sky_cover)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More examples for MetPy Station Plots:\n", "- [MetPy Examples](https://unidata.github.io/MetPy/latest/examples/index.html)\n", "- [MetPy Symbol list](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.StationPlot.html#metpy.plots.StationPlot.plot_symbol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Modify the station plot (reproduced below) to include dewpoint, altimeter setting, as well as the station id. The station id can be added using the `plot_text` method on `StationPlot`.</li>\n", " <li>Re-mask the data to be a bit more finely spaced, say: 75km</li>\n", " <li>Bonus Points: Use the `formatter` argument to `plot_parameter` to only plot the 3 significant digits of altimeter setting. (Tens, ones, tenths)</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Use reduce_point_density\n", "\n", "# Set up a plot with map features\n", "fig = plt.figure(figsize=(12, 12))\n", "ax = fig.add_subplot(1, 1, 1, projection=proj)\n", "ax.add_feature(cfeature.STATES, edgecolor='black')\n", "ax.coastlines(resolution='50m')\n", "ax.gridlines()\n", "\n", "# Create a station plot pointing to an Axes to draw on as well as the location of points\n", "\n", "# Plot dewpoint\n", "\n", "# Plot altimeter setting--formatter can take a function that formats values\n", "\n", "# Plot station id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/reduce_density.py\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"#top\">Top</a>\n", "<hr style=\"height:2px;\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"timeseries\"></a>\n", "## 3. Time Series request and plot\n", "* Let's say we want the past days worth of data...\n", "* ...for Boulder (i.e. the lat/lon)\n", "* ...for the variables mean sea level pressure, air temperature, wind direction, and wind_speed" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from datetime import timedelta\n", "\n", "# define the time range we are interested in\n", "end_time = datetime(2017, 9, 12, 0)\n", "start_time = end_time - timedelta(days=2)\n", "\n", "# build the query\n", "query = ncss.query()\n", "query.lonlat_point(-80.25, 25.8)\n", "query.time_range(start_time, end_time)\n", "query.variables('altimeter_setting', 'temperature', 'dewpoint',\n", " 'wind_direction', 'wind_speed')\n", "query.accept('csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's get the data!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = ncss.get_data(query)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(list(data.keys()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What station did we get?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "station_id = data['station'][0].tostring()\n", "print(station_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That indicates that we have a Python `bytes` object, containing the 0-255 values corresponding to `'K', 'M', 'I', 'A'`. We can `decode` those bytes into a string:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "station_id = station_id.decode('ascii')\n", "print(station_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get the time into datetime objects. We see we have an array with byte strings in it, like station id above." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data['time']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we can use a list comprehension to turn this into a list of date time objects:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "time = [datetime.strptime(s.decode('ascii'), '%Y-%m-%dT%H:%M:%SZ') for s in data['time']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Now for the obligatory time series plot..." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib.dates import DateFormatter, AutoDateLocator\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "ax.plot(time, data['wind_speed'], color='tab:blue')\n", "\n", "ax.set_title(f'Site: {station_id} Date: {time[0]:%Y/%m/%d}')\n", "ax.set_xlabel('Hour of day')\n", "ax.set_ylabel('Wind Speed')\n", "ax.grid(True)\n", "\n", "# Improve on the default ticking\n", "locator = AutoDateLocator()\n", "hoursFmt = DateFormatter('%H')\n", "ax.xaxis.set_major_locator(locator)\n", "ax.xaxis.set_major_formatter(hoursFmt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>:\n", " <ul>\n", " <li>Pick a different location</li>\n", " <li>Plot temperature and dewpoint together on the same plot</li>\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Your code goes here\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %load solutions/time_series.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a href=\"#top\">Top</a>\n", "<hr style=\"height:2px;\">" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:devel]", "language": "python", "name": "conda-env-devel-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
Cyb3rWard0g/HELK
docker/helk-jupyter/notebooks/sigma/win_susp_procdump.ipynb
1
3589
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Suspicious Use of Procdump\n", "Detects suspicious uses of the SysInternals Procdump utility by using a special command line parameter in combination with the lsass.exe process. This way we're also able to catch cases in which the attacker has renamed the procdump executable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: Suspicious Use of Procdump\n", " id: 5afee48e-67dd-4e03-a783-f74259dcf998\n", " description: Detects suspicious uses of the SysInternals Procdump utility by using\n", " a special command line parameter in combination with the lsass.exe process. This\n", " way we're also able to catch cases in which the attacker has renamed the procdump\n", " executable.\n", " status: experimental\n", " references:\n", " - Internal Research\n", " author: Florian Roth\n", " date: 2018/10/30\n", " modified: 2019/10/14\n", " tags:\n", " - attack.defense_evasion\n", " - attack.t1036\n", " - attack.credential_access\n", " - attack.t1003\n", " - car.2013-05-009\n", " logsource:\n", " category: process_creation\n", " product: windows\n", " service: null\n", " detection:\n", " selection1:\n", " CommandLine:\n", " - '* -ma *'\n", " selection2:\n", " CommandLine:\n", " - '* lsass*'\n", " selection3:\n", " CommandLine:\n", " - '* -ma ls*'\n", " condition: ( selection1 and selection2 ) or selection3\n", " falsepositives:\n", " - Unlikely, because no one should dump an lsass process memory\n", " - Another tool that uses the command line switches of Procdump\n", " level: medium\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-*', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='((process_command_line.keyword:(*\\ \\-ma\\ *) AND process_command_line.keyword:(*\\ lsass*)) OR process_command_line.keyword:(*\\ \\-ma\\ ls*))')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }
gpl-3.0
calico/basenji
manuscripts/akita/tutorial.ipynb
1
55506
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Required inputs for Akita are:\n", "* binned Hi-C or Micro-C data stored in cooler format (https://github.com/mirnylab/cooler)\n", "* Genome FASTA file\n", "\n", "First, make sure you have a FASTA file available consistent with genome used for the coolers. Either add a symlink for a the data directory or download the machine learning friendly simplified version in the next cell." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import json\n", "import os\n", "import shutil\n", "import subprocess" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "if not os.path.isfile('./data/hg38.ml.fa'):\n", " print('downloading hg38.ml.fa')\n", " subprocess.call('curl -o ./data/hg38.ml.fa.gz https://storage.googleapis.com/basenji_barnyard/hg38.ml.fa.gz', shell=True)\n", " subprocess.call('gunzip ./data/hg38.ml.fa.gz', shell=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download a few Micro-C datasets, processed using distiller (https://github.com/mirnylab/distiller-nf), binned to 2048bp, and iteratively corrected. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists('./data/coolers'):\n", " os.mkdir('./data/coolers')\n", "if not os.path.isfile('./data/coolers/HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool'):\n", " subprocess.call('curl -o ./data/coolers/HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool'+\n", " ' https://storage.googleapis.com/basenji_hic/tutorials/coolers/HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool', shell=True)\n", " subprocess.call('curl -o ./data/coolers/H1hESC_hg38_4DNFI1O6IL1Q.mapq_30.2048.cool'+\n", " ' https://storage.googleapis.com/basenji_hic/tutorials/coolers/H1hESC_hg38_4DNFI1O6IL1Q.mapq_30.2048.cool', shell=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H1hESC_hg38_4DNFI1O6IL1Q.mapq_30.2048.cool\n", "HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool\n" ] } ], "source": [ "ls ./data/coolers/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write out these cooler files and labels to a samples table." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lines = [['index','identifier','file','clip','sum_stat','description']]\n", "lines.append(['0', 'HFF', './data/coolers/HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool', '2', 'sum', 'HFF'])\n", "lines.append(['1', 'H1hESC', './data/coolers/H1hESC_hg38_4DNFI1O6IL1Q.mapq_30.2048.cool', '2', 'sum', 'H1hESC'])\n", "\n", "samples_out = open('data/microc_cools.txt', 'w')\n", "for line in lines:\n", " print('\\t'.join(line), file=samples_out)\n", "samples_out.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we want to choose genomic sequences to form batches for stochastic gradient descent, divide them into training/validation/test sets, and construct TFRecords to provide to downstream programs.\n", "\n", "The script [akita_data.py](https://github.com/calico/basenji/blob/master/bin/akita_data.py) implements this procedure.\n", "\n", "The most relevant options here are:\n", "\n", "| Option/Argument | Value | Note |\n", "|:---|:---|:---|\n", "| --sample | 0.1 | Down-sample the genome to 10% to speed things up here. |\n", "| -g | data/hg38_gaps_binsize2048_numconseq10.bed | Dodge large-scale unmappable regions determined from filtered cooler bins. |\n", "| -l | 1048576 | Sequence length. |\n", "| --crop | 65536 | Crop edges of matrix so loss is only computed over the central region. |\n", "| --local | True | Run locally, as opposed to on a SLURM scheduler. |\n", "| -o | data/1m | Output directory |\n", "| -p | 8 | Uses multiple concourrent processes to read/write. |\n", "| -t | .1 | Hold out 10% sequences for testing. |\n", "| -v | .1 | Hold out 10% sequences for validation. |\n", "| -w | 2048 | Pool the nucleotide-resolution values to 2048 bp bins. |\n", "| fasta_file| data/hg38.ml.fa | FASTA file to extract sequences from. |\n", "| targets_file | data/microc_cools.txt | Target table with cooler paths. |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: make sure to export BASENJIDIR as outlined in the basenji installation tips \n", "(https://github.com/calico/basenji/tree/master/#installation). " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "if os.path.isdir('data/1m'):\n", " shutil.rmtree('data/1m')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Contigs divided into\n", " Train: 413 contigs, 2078450861 nt (0.8036)\n", " Valid: 47 contigs, 254228224 nt (0.0983)\n", " Test: 48 contigs, 253678336 nt (0.0981)\n", "writing sequences to BED\n", "akita_data_read.py --crop 65536 -k 0 -w 2048 --clip 2.000000 --as_obsexp ./data/coolers/HFF_hg38_4DNFIP5EUOFX.mapq_30.2048.cool ./data/1m/sequences.bed ./data/1m/seqs_cov/0.h5\n", "akita_data_read.py --crop 65536 -k 0 -w 2048 --clip 2.000000 --as_obsexp ./data/coolers/H1hESC_hg38_4DNFI1O6IL1Q.mapq_30.2048.cool ./data/1m/sequences.bed ./data/1m/seqs_cov/1.h5\n", "/home/drk/code/cooltools/cooltools/lib/numutils.py:1317: RuntimeWarning: invalid value encountered in true_divide\n", " val_cur = ar_cur / armask_cur\n", "/home/drk/code/cooltools/cooltools/lib/numutils.py:1317: RuntimeWarning: invalid value encountered in true_divide\n", " val_cur = ar_cur / armask_cur\n", "/home/drk/code/cooltools/cooltools/lib/numutils.py:1317: RuntimeWarning: invalid value encountered in true_divide\n", " val_cur = ar_cur / armask_cur\n", "/home/drk/code/cooltools/cooltools/lib/numutils.py:1317: RuntimeWarning: invalid value encountered in true_divide\n", " val_cur = ar_cur / armask_cur\n", "basenji_data_write.py -s 0 -e 128 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/train-0.tfr\n", "basenji_data_write.py -s 128 -e 256 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/train-1.tfr\n", "basenji_data_write.py -s 256 -e 350 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/train-2.tfr\n", "basenji_data_write.py -s 350 -e 478 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/valid-0.tfr\n", "basenji_data_write.py -s 478 -e 606 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/valid-1.tfr\n", "basenji_data_write.py -s 606 -e 667 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/valid-2.tfr\n", "basenji_data_write.py -s 667 -e 795 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/test-0.tfr\n", "basenji_data_write.py -s 795 -e 923 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/test-1.tfr\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n", "basenji_data_write.py -s 923 -e 981 ./data/hg38.ml.fa ./data/1m/sequences.bed ./data/1m/seqs_cov ./data/1m/tfrecords/test-2.tfr\n", "/home/drk/code/basenji/bin/basenji_data_write.py:182: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " values = values.flatten().tostring()\n" ] } ], "source": [ "! akita_data.py --sample 0.05 -g ./data/hg38_gaps_binsize2048_numconseq10.bed -l 1048576 --crop 65536 --local -o ./data/1m --as_obsexp -p 8 -t .1 -v .1 -w 2048 --snap 2048 --stride_train 262144 --stride_test 32768 ./data/hg38.ml.fa ./data/microc_cools.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data for training is now saved in data/1m as tfrecords (for training, validation, and testing), where *contigs.bed* contains the original large contiguous regions from which training sequences were taken, and *sequences.bed* contains the train/valid/test sequences." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 314 test\n", " 350 train\n", " 317 valid\n" ] } ], "source": [ "! cut -f4 data/1m/sequences.bed | sort | uniq -c" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chr6\t27002880\t28051456\ttrain\n", "chr3\t61059072\t62107648\ttrain\n", "chr4\t133545984\t134594560\ttrain\n" ] } ], "source": [ "! head -n3 data/1m/sequences.bed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now train a model!\n", "\n", "(Note: for training production-level models, please remove the --sample option when generating tfrecords)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# specify model parameters json to have only two targets\n", "params_file = './params.json'\n", "with open(params_file) as params_file:\n", " params_tutorial = json.load(params_file) \n", "params_tutorial['model']['head_hic'][-1]['units'] =2\n", "with open('./data/1m/params_tutorial.json','w') as params_tutorial_file:\n", " json.dump(params_tutorial,params_tutorial_file) \n", " \n", "### note that training with default parameters requires GPU with >12Gb RAM ###" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-09-09 10:41:15.238450: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA\n", "2020-09-09 10:41:15.247633: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1999995000 Hz\n", "2020-09-09 10:41:15.249360: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55f79eed2ec0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n", "2020-09-09 10:41:15.249407: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n", "2020-09-09 10:41:15.249630: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n", "Model: \"model_1\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "sequence (InputLayer) [(None, 1048576, 4)] 0 \n", "__________________________________________________________________________________________________\n", "stochastic_reverse_complement ( ((None, 1048576, 4), 0 sequence[0][0] \n", "__________________________________________________________________________________________________\n", "stochastic_shift (StochasticShi (None, 1048576, 4) 0 stochastic_reverse_complement[0][\n", "__________________________________________________________________________________________________\n", "re_lu (ReLU) (None, 1048576, 4) 0 stochastic_shift[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d (Conv1D) (None, 1048576, 96) 4224 re_lu[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization (BatchNorma (None, 1048576, 96) 384 conv1d[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d (MaxPooling1D) (None, 524288, 96) 0 batch_normalization[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_1 (ReLU) (None, 524288, 96) 0 max_pooling1d[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 524288, 96) 46080 re_lu_1[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_1 (BatchNor (None, 524288, 96) 384 conv1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1D) (None, 262144, 96) 0 batch_normalization_1[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_2 (ReLU) (None, 262144, 96) 0 max_pooling1d_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 262144, 96) 46080 re_lu_2[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_2 (BatchNor (None, 262144, 96) 384 conv1d_2[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_2 (MaxPooling1D) (None, 131072, 96) 0 batch_normalization_2[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_3 (ReLU) (None, 131072, 96) 0 max_pooling1d_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 131072, 96) 46080 re_lu_3[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_3 (BatchNor (None, 131072, 96) 384 conv1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_3 (MaxPooling1D) (None, 65536, 96) 0 batch_normalization_3[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_4 (ReLU) (None, 65536, 96) 0 max_pooling1d_3[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 65536, 96) 46080 re_lu_4[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_4 (BatchNor (None, 65536, 96) 384 conv1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_4 (MaxPooling1D) (None, 32768, 96) 0 batch_normalization_4[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_5 (ReLU) (None, 32768, 96) 0 max_pooling1d_4[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_5 (Conv1D) (None, 32768, 96) 46080 re_lu_5[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_5 (BatchNor (None, 32768, 96) 384 conv1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_5 (MaxPooling1D) (None, 16384, 96) 0 batch_normalization_5[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_6 (ReLU) (None, 16384, 96) 0 max_pooling1d_5[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_6 (Conv1D) (None, 16384, 96) 46080 re_lu_6[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_6 (BatchNor (None, 16384, 96) 384 conv1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_6 (MaxPooling1D) (None, 8192, 96) 0 batch_normalization_6[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_7 (ReLU) (None, 8192, 96) 0 max_pooling1d_6[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_7 (Conv1D) (None, 8192, 96) 46080 re_lu_7[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_7 (BatchNor (None, 8192, 96) 384 conv1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_7 (MaxPooling1D) (None, 4096, 96) 0 batch_normalization_7[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_8 (ReLU) (None, 4096, 96) 0 max_pooling1d_7[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_8 (Conv1D) (None, 4096, 96) 46080 re_lu_8[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_8 (BatchNor (None, 4096, 96) 384 conv1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_8 (MaxPooling1D) (None, 2048, 96) 0 batch_normalization_8[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_9 (ReLU) (None, 2048, 96) 0 max_pooling1d_8[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_9 (Conv1D) (None, 2048, 96) 46080 re_lu_9[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_9 (BatchNor (None, 2048, 96) 384 conv1d_9[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_9 (MaxPooling1D) (None, 1024, 96) 0 batch_normalization_9[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_10 (ReLU) (None, 1024, 96) 0 max_pooling1d_9[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_10 (Conv1D) (None, 1024, 96) 46080 re_lu_10[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_10 (BatchNo (None, 1024, 96) 384 conv1d_10[0][0] \n", "__________________________________________________________________________________________________\n", "max_pooling1d_10 (MaxPooling1D) (None, 512, 96) 0 batch_normalization_10[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_11 (ReLU) (None, 512, 96) 0 max_pooling1d_10[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_11 (Conv1D) (None, 512, 48) 13824 re_lu_11[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_11 (BatchNo (None, 512, 48) 192 conv1d_11[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_12 (ReLU) (None, 512, 48) 0 batch_normalization_11[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_12 (Conv1D) (None, 512, 96) 4608 re_lu_12[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_12 (BatchNo (None, 512, 96) 384 conv1d_12[0][0] \n", "__________________________________________________________________________________________________\n", "dropout (Dropout) (None, 512, 96) 0 batch_normalization_12[0][0] \n", "__________________________________________________________________________________________________\n", "add (Add) (None, 512, 96) 0 max_pooling1d_10[0][0] \n", " dropout[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_13 (ReLU) (None, 512, 96) 0 add[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_13 (Conv1D) (None, 512, 48) 13824 re_lu_13[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_13 (BatchNo (None, 512, 48) 192 conv1d_13[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_14 (ReLU) (None, 512, 48) 0 batch_normalization_13[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_14 (Conv1D) (None, 512, 96) 4608 re_lu_14[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_14 (BatchNo (None, 512, 96) 384 conv1d_14[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 512, 96) 0 batch_normalization_14[0][0] \n", "__________________________________________________________________________________________________\n", "add_1 (Add) (None, 512, 96) 0 add[0][0] \n", " dropout_1[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_15 (ReLU) (None, 512, 96) 0 add_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_15 (Conv1D) (None, 512, 48) 13824 re_lu_15[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_15 (BatchNo (None, 512, 48) 192 conv1d_15[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_16 (ReLU) (None, 512, 48) 0 batch_normalization_15[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_16 (Conv1D) (None, 512, 96) 4608 re_lu_16[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_16 (BatchNo (None, 512, 96) 384 conv1d_16[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 512, 96) 0 batch_normalization_16[0][0] \n", "__________________________________________________________________________________________________\n", "add_2 (Add) (None, 512, 96) 0 add_1[0][0] \n", " dropout_2[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_17 (ReLU) (None, 512, 96) 0 add_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_17 (Conv1D) (None, 512, 48) 13824 re_lu_17[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_17 (BatchNo (None, 512, 48) 192 conv1d_17[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_18 (ReLU) (None, 512, 48) 0 batch_normalization_17[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_18 (Conv1D) (None, 512, 96) 4608 re_lu_18[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_18 (BatchNo (None, 512, 96) 384 conv1d_18[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_3 (Dropout) (None, 512, 96) 0 batch_normalization_18[0][0] \n", "__________________________________________________________________________________________________\n", "add_3 (Add) (None, 512, 96) 0 add_2[0][0] \n", " dropout_3[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_19 (ReLU) (None, 512, 96) 0 add_3[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_19 (Conv1D) (None, 512, 48) 13824 re_lu_19[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_19 (BatchNo (None, 512, 48) 192 conv1d_19[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_20 (ReLU) (None, 512, 48) 0 batch_normalization_19[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_20 (Conv1D) (None, 512, 96) 4608 re_lu_20[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_20 (BatchNo (None, 512, 96) 384 conv1d_20[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_4 (Dropout) (None, 512, 96) 0 batch_normalization_20[0][0] \n", "__________________________________________________________________________________________________\n", "add_4 (Add) (None, 512, 96) 0 add_3[0][0] \n", " dropout_4[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_21 (ReLU) (None, 512, 96) 0 add_4[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_21 (Conv1D) (None, 512, 48) 13824 re_lu_21[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_21 (BatchNo (None, 512, 48) 192 conv1d_21[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_22 (ReLU) (None, 512, 48) 0 batch_normalization_21[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_22 (Conv1D) (None, 512, 96) 4608 re_lu_22[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_22 (BatchNo (None, 512, 96) 384 conv1d_22[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_5 (Dropout) (None, 512, 96) 0 batch_normalization_22[0][0] \n", "__________________________________________________________________________________________________\n", "add_5 (Add) (None, 512, 96) 0 add_4[0][0] \n", " dropout_5[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_23 (ReLU) (None, 512, 96) 0 add_5[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_23 (Conv1D) (None, 512, 48) 13824 re_lu_23[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_23 (BatchNo (None, 512, 48) 192 conv1d_23[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_24 (ReLU) (None, 512, 48) 0 batch_normalization_23[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_24 (Conv1D) (None, 512, 96) 4608 re_lu_24[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_24 (BatchNo (None, 512, 96) 384 conv1d_24[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_6 (Dropout) (None, 512, 96) 0 batch_normalization_24[0][0] \n", "__________________________________________________________________________________________________\n", "add_6 (Add) (None, 512, 96) 0 add_5[0][0] \n", " dropout_6[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_25 (ReLU) (None, 512, 96) 0 add_6[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_25 (Conv1D) (None, 512, 48) 13824 re_lu_25[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_25 (BatchNo (None, 512, 48) 192 conv1d_25[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_26 (ReLU) (None, 512, 48) 0 batch_normalization_25[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_26 (Conv1D) (None, 512, 96) 4608 re_lu_26[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_26 (BatchNo (None, 512, 96) 384 conv1d_26[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_7 (Dropout) (None, 512, 96) 0 batch_normalization_26[0][0] \n", "__________________________________________________________________________________________________\n", "add_7 (Add) (None, 512, 96) 0 add_6[0][0] \n", " dropout_7[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_27 (ReLU) (None, 512, 96) 0 add_7[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d_27 (Conv1D) (None, 512, 64) 30720 re_lu_27[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_27 (BatchNo (None, 512, 64) 256 conv1d_27[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_28 (ReLU) (None, 512, 64) 0 batch_normalization_27[0][0] \n", "__________________________________________________________________________________________________\n", "one_to_two (OneToTwo) (None, 512, 512, 64) 0 re_lu_28[0][0] \n", "__________________________________________________________________________________________________\n", "concat_dist2d (ConcatDist2D) (None, 512, 512, 65) 0 one_to_two[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_29 (ReLU) (None, 512, 512, 65) 0 concat_dist2d[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d (Conv2D) (None, 512, 512, 48) 28080 re_lu_29[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_28 (BatchNo (None, 512, 512, 48) 192 conv2d[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d (Symmetrize2D) (None, 512, 512, 48) 0 batch_normalization_28[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_30 (ReLU) (None, 512, 512, 48) 0 symmetrize2d[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_1 (Conv2D) (None, 512, 512, 24) 10368 re_lu_30[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_29 (BatchNo (None, 512, 512, 24) 96 conv2d_1[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_31 (ReLU) (None, 512, 512, 24) 0 batch_normalization_29[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_2 (Conv2D) (None, 512, 512, 48) 1152 re_lu_31[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_30 (BatchNo (None, 512, 512, 48) 192 conv2d_2[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_8 (Dropout) (None, 512, 512, 48) 0 batch_normalization_30[0][0] \n", "__________________________________________________________________________________________________\n", "add_8 (Add) (None, 512, 512, 48) 0 symmetrize2d[0][0] \n", " dropout_8[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_1 (Symmetrize2D) (None, 512, 512, 48) 0 add_8[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_32 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_3 (Conv2D) (None, 512, 512, 24) 10368 re_lu_32[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_31 (BatchNo (None, 512, 512, 24) 96 conv2d_3[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_33 (ReLU) (None, 512, 512, 24) 0 batch_normalization_31[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_4 (Conv2D) (None, 512, 512, 48) 1152 re_lu_33[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_32 (BatchNo (None, 512, 512, 48) 192 conv2d_4[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_9 (Dropout) (None, 512, 512, 48) 0 batch_normalization_32[0][0] \n", "__________________________________________________________________________________________________\n", "add_9 (Add) (None, 512, 512, 48) 0 symmetrize2d_1[0][0] \n", " dropout_9[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_2 (Symmetrize2D) (None, 512, 512, 48) 0 add_9[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_34 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_2[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_5 (Conv2D) (None, 512, 512, 24) 10368 re_lu_34[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_33 (BatchNo (None, 512, 512, 24) 96 conv2d_5[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_35 (ReLU) (None, 512, 512, 24) 0 batch_normalization_33[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_6 (Conv2D) (None, 512, 512, 48) 1152 re_lu_35[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_34 (BatchNo (None, 512, 512, 48) 192 conv2d_6[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_10 (Dropout) (None, 512, 512, 48) 0 batch_normalization_34[0][0] \n", "__________________________________________________________________________________________________\n", "add_10 (Add) (None, 512, 512, 48) 0 symmetrize2d_2[0][0] \n", " dropout_10[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_3 (Symmetrize2D) (None, 512, 512, 48) 0 add_10[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_36 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_3[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_7 (Conv2D) (None, 512, 512, 24) 10368 re_lu_36[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_35 (BatchNo (None, 512, 512, 24) 96 conv2d_7[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_37 (ReLU) (None, 512, 512, 24) 0 batch_normalization_35[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_8 (Conv2D) (None, 512, 512, 48) 1152 re_lu_37[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_36 (BatchNo (None, 512, 512, 48) 192 conv2d_8[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_11 (Dropout) (None, 512, 512, 48) 0 batch_normalization_36[0][0] \n", "__________________________________________________________________________________________________\n", "add_11 (Add) (None, 512, 512, 48) 0 symmetrize2d_3[0][0] \n", " dropout_11[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_4 (Symmetrize2D) (None, 512, 512, 48) 0 add_11[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_38 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_4[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_9 (Conv2D) (None, 512, 512, 24) 10368 re_lu_38[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_37 (BatchNo (None, 512, 512, 24) 96 conv2d_9[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_39 (ReLU) (None, 512, 512, 24) 0 batch_normalization_37[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_10 (Conv2D) (None, 512, 512, 48) 1152 re_lu_39[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_38 (BatchNo (None, 512, 512, 48) 192 conv2d_10[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_12 (Dropout) (None, 512, 512, 48) 0 batch_normalization_38[0][0] \n", "__________________________________________________________________________________________________\n", "add_12 (Add) (None, 512, 512, 48) 0 symmetrize2d_4[0][0] \n", " dropout_12[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_5 (Symmetrize2D) (None, 512, 512, 48) 0 add_12[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_40 (ReLU) (None, 512, 512, 48) 0 symmetrize2d_5[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_11 (Conv2D) (None, 512, 512, 24) 10368 re_lu_40[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_39 (BatchNo (None, 512, 512, 24) 96 conv2d_11[0][0] \n", "__________________________________________________________________________________________________\n", "re_lu_41 (ReLU) (None, 512, 512, 24) 0 batch_normalization_39[0][0] \n", "__________________________________________________________________________________________________\n", "conv2d_12 (Conv2D) (None, 512, 512, 48) 1152 re_lu_41[0][0] \n", "__________________________________________________________________________________________________\n", "batch_normalization_40 (BatchNo (None, 512, 512, 48) 192 conv2d_12[0][0] \n", "__________________________________________________________________________________________________\n", "dropout_13 (Dropout) (None, 512, 512, 48) 0 batch_normalization_40[0][0] \n", "__________________________________________________________________________________________________\n", "add_13 (Add) (None, 512, 512, 48) 0 symmetrize2d_5[0][0] \n", " dropout_13[0][0] \n", "__________________________________________________________________________________________________\n", "symmetrize2d_6 (Symmetrize2D) (None, 512, 512, 48) 0 add_13[0][0] \n", "__________________________________________________________________________________________________\n", "cropping2d (Cropping2D) (None, 448, 448, 48) 0 symmetrize2d_6[0][0] \n", "__________________________________________________________________________________________________\n", "upper_tri (UpperTri) (None, 99681, 48) 0 cropping2d[0][0] \n", "__________________________________________________________________________________________________\n", "dense (Dense) (None, 99681, 2) 98 upper_tri[0][0] \n", "__________________________________________________________________________________________________\n", "switch_reverse_triu (SwitchReve (None, 99681, 2) 0 dense[0][0] \n", " stochastic_reverse_complement[0][\n", "==================================================================================================\n", "Total params: 751,506\n", "Trainable params: 746,002\n", "Non-trainable params: 5,504\n", "__________________________________________________________________________________________________\n", "None\n", "model_strides [2048]\n", "target_lengths [99681]\n", "target_crops [-49585]\n", "Train for 175 steps, validate for 158 steps\n", "Epoch 1/10000\n", "2020-09-09 10:42:04.614121: I tensorflow/core/profiler/lib/profiler_session.cc:225] Profiler session started.\n", " 61/175 [=========>....................] - ETA: 34:18 - loss: 0.4550 - pearsonr: -0.0013 - r2: -0.2714" ] } ], "source": [ "! akita_train.py -k -o ./data/1m/train_out/ ./data/1m/params_tutorial.json ./data/1m/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See explore_model.ipynb for tips on investigating the output of a trained model. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
jts/nanocorrect
nicks_notebooks/2014-12-08-try-r7-data-again-for-more-coverage.ipynb
1
96499
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Currently the assemblies are topping out at around N50 ~100kb. Not bad, but not where we need to be.\n", "\n", "Although nanocorrect could perhaps be improved by playing with DALIGN settings, lack of coverage may well be a problem. PacBio suggest 20x is the basic minimum for doing this stage (50-100x required for Quiver correction more than overlapping). We have around 10x.\n", "\n", "Two possible ways forward:\n", "* Change DALIGN settings to find more overlaps\n", "* Add R7 coverage -- this wasn't very successful previously but perhaps better now that nanocorrect has been improved?\n", "\n", "The problem with R7 data is that it is generally much worse quality. One compromise might be to use the R7 data to find overlaps but not as 'seed' reads, in order to rescue more of the R7.3 data.\n", "\n", "Ultimately we need to generate some more full 2D data from R7.3 for E. coli. We might be able to do that as early as Friday." ] }, { "cell_type": "code", "collapsed": false, "input": [ "!cat FC20.wf1.9.2D.pass.fasta ../Ecoli_R7_2D.fasta > R7R73.fasta\n", "!make -f pipeline.make INPUT=R7R73.fasta NAME=R7R73" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is promising, the .las file is ~5 times bigger than the R7.3 only data.\n", "\n", "Just correct the R7.3 reads in the file (this works becuse the R7.3 reads occur first):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!grep -n \">\" R7R73.pp.fasta | grep \"R7.3\" | wc -l" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "8451\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "!python makerange_num.py 8451 > ranges.txt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "!samtools faidx R7R73.fasta\n", "!cat ranges.txt | parallel python nanocorrect.py R7R73 > R7R73_corrected.fasta" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from Bio import SeqIO\n", "import numpy\n", "\n", "lengths = [len(rec) for rec in SeqIO.parse(open(\"R7R73_corrected.fasta\"), \"fasta\")]\n", "print numpy.mean(lengths) \n", "lengths = [len(rec) for rec in SeqIO.parse(open(\"FC20_iter2_corrected.fasta\"), \"fasta\")]\n", "print numpy.mean(lengths) \n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6566.03661017\n", "6145.30460544" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "!bwa mem -t16 -x ont2d ../refs/NC_000913.fna R7R73_corrected.fasta | samtools view -bS - | samtools sort - R7R73_corrected.sorted\n", "!python mappingstats.py R7R73_corrected.sorted.bam > R7R73_corrected.mapstats.txt" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext rmagic " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The rmagic extension is already loaded. To reload it, use:\n", " %reload_ext rmagic\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "stats=read.table(\"R7R73_corrected.mapstats.txt\", header=T)\n", "ggplot(stats, aes(x=QueryLen)) + geom_histogram()\n", "ggsave(\"querylen.png\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "Saving 6.67 x 6.67 in image\n", "stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image \n", "Image(filename='querylen.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XnH9Pf+XF/SOhw/wzuin2NvzJ93r\nJx+8K/qsM7374WrfeumnKLvr+aUfoez5nl/6IeoePLie1+BDd6v90KUfouyu52+59DOUPez5U5d+\niLK3P3jv2T+3U8Or7/nTDy7oqYdP8P7op9jb8+jD3KOH/6Tz3Psu/RQ/JT19t9lnL/0QPzV95G61\nH7n0Q/zU9Mylf7G/N50c6nmHnsfoeYyex+h5jJ5XXH3Pn534L0l28+/bY/z79hj/vj3Gv2+P8e/b\nK66+534/3Is96V5+P1yM3w8X4/fDpfj9cDF+P9y5Jh7Cbnoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j\n5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cx\neh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoe\no+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHn\nHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6\nXqHnHXoeo+cxeh6j5zF6XqHnHXoeo+cxeh6j5zF6XqHnHXoes0zPZx7AGvQ8Rs9j9LxCzzv0PEbP\nY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0\nPEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxC\nzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j\n9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8\nRs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbP\nY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0\nPEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxC\nzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j\n9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8Rs9j9LxCzzv0PEbPY/Q8\nRs9j9LxCzzv0POYwPU/ff6TnMXoeo+cVet6h5zF6Pun+Iz2P0fMYPa/Q8w49j9HzSfcf6XmMnsfo\neYWed+h5jJ5Puv9Iz2P0PEbPK/S8Q89j9HzS/Ud6HqPnMXpeoecdeh6j55PuP9LzGD2P0fMKPe/Q\n8xg9n3T/kZ7H6HmMnlfoeYeex+j5pPuP9DxGz2P0vELPO/Q8Rs8n3X+k5zF6HqPnFXreoecxej7p\n/iM9j9HzGD2v0PMOPY/R80n3H+l5jJ7H6HmFnnfoeYyeT7r/SM9j9DxGzyv0vEPPY/R80v1Heh6j\n5zF6XqHnHXoeo+eT7j/S8xg9j9HzCj3v0PMYPZ90/5Gex+h5jJ5X6HmHnsfo+aT7j/Q8Rs9j9LxC\nzzv0PEbPJ91/pOcxeh6j5xV63qHnMXo+6f4jPY/R8xg9r9DzDj2P0fNJ9x/peYyex+h5hZ536HmM\nnk+6/0jPY/Q8Rs8r9LxDz2P0fNL9R3oeo+cxel6h5x16HqPnk+4/0vMYPY/R8wo979DzGD2fdP+R\nnsfoeYyeV+h5h57H6Pmk+4/0PEbPY/S8Qs879DxGzyfdf6TnMXoeo+cVet6h5zF6Pun+Iz2P0fMY\nPa/Q8w49j9HzSfcf6XmMnsfoeYWed+h5jJ5Puv9Iz2P0PEbPK/S8Q89j9HzS/Ud6HqPnMXpeoecd\neh6j55PuP9LzGD2P0fMKPe/Q8xg9n3T/kZ7H6HmMnlfoeYeex+j5pPuP9DxGz2P0vELPO/Q8Rs8n\n3X+k5zF6HqPnFXreoecxej7p/iM9j9HzGD2v0PMOPY/R80n3H+l5jJ7H6HmFnnfoeYyeT7r/SM9j\n9DxGzyv0vEPPY/R80v1Heh6j5zF6XqHnHXoeo+eT7j/S8xg9j9HzCj3v0PMYPZ90/5Gex+h5jJ5X\n6HmHnsfo+aT7j/Q8Rs9j9LxCzzv0PEbPJ91/pOcxeh6j5xV63qHnMXo+6f4jPY/R8xg9r9DzDj2P\n0fNJ9x/peYyex+h5hZ536HmMnk+6/0jPY/Q8Rs8r9LxDz2P0fNL9R3oeo+cxel6h5x16HqPnk+4/\n0vMYPY/R8wo979DzGD2fdP+RnsfoeYyeV+h5h57H6Pmk+4/0PEbPY/S8Qs879DxGzyfdf6TnMXoe\no+cVet6h5zF6Pun+Iz2P0fMYPa/Q8w49j9HzSfcf6XmMnsfoeYWed+h5jJ5Puv9Iz2P0PEbPK/S8\nQ89j9HzS/Ud6HqPnMXpeoecdeh6j55PuP9LzGD2P0fMKPe/Q8xg9n3T/kZ7H6HmMnlfoeYeex+j5\npPuP9DxGz2P0vELPO/Q8Rs8n3X+k5zF6HqPnFXreoecxej7p/iM9j9HzGD2v0PMOPY/R80n3H+l5\njJ7H6HmFnnfoeYyeT7r/SM9j9DxGzyv0vEPPY/R80v1Heh6j5zF6XqHnHXoeo+eT7j/S8xg9j9Hz\nCj3v0PMYPZ90/5Gex+h5jJ5X6HmHnsfo+aT7j/Q8Rs9j9LxCzzv0PEbPJ91/pOcxeh6j5xV63qHn\nMXo+6f4jPY/R8xg9r9DzDj2P0fNJ9x/peYyex+h5hZ536HmMnk+6/0jPY/Q8Rs8r9LxDz2P0fNL9\nR3oeo+cxel6h5x16HqPnk+4/0vMYPY/R8wo979DzGD2fdP+RnsfoeYyeV+h5x4I9v/88ZOh57MD0\nPEbPY/S8Qs879DxGz2MHpucxeh6j5xV63qHnMXoeOzA9j9HzGD2v0PMOPY/R89iB6XmMnsfoeYWe\nd+h5jJ7HDkzPY/Q8Rs8r9LxDz2P0PHZgeh6j5zF6XqHnHXoeo+exA9PzGD2P0fMKPe/Q8xg9jx2Y\nnsfoeYyeV+h5h57H6HnswPQ8Rs9j9LxCzzv0PEbPYwem5zF6HqPnFXreoecxeh47MD2P0fMYPa/Q\n8w49j9Hz2IHpeYyex+h5hZ536HmMnscOTM9j9DxGzyv0vEPPY/Q8dmB6HqPnMXpeoecdeh6j57ED\n0/MYPY/R8wo979DzGD2PHZiex+h5jKSAmkwAACAASURBVJ5X6HmHnsfoeezA9DxGz2P0vELPO/Q8\nRs9jB6bnMXoeo+cVet6h5zF6HjswPY/R8xg9r9DzDj2P0fPYgel5jJ7H6HmFnnfoeYyexw5Mz2P0\nPEbPK/S8Q89j9Dx2YHoeo+cxel5xrz3/u3/myz7pypu/6dUvf/mrXv+mytXHmHgIu+l5jJ7HDkzP\nY/Q8Rs8r7rPnH33F7Ze86MIH/tbtx33d+7euPtbEQ9hNz2P0PHZgeh6j5zF6XnGfPf/h2xf3/IN/\n8fZfec0Hn3z18SYewm56Pt9qA6Sf5/7n1fMYPY/R84p77PmHv+yTev66u2K/8jt/9A3f+cq7H7zu\nyVcfb+Ih7Kbn8602QPp57n9ePY/R8xg9r7i3nj/7xr96++Kev/HhP4A/9fBH73v13Q9/7ElXn2Di\nIeym5/OtNkD6ee5/Xj2P0fMYPa+4n56/8Xu+8dXP/wv0T+z5a29vX/bOj/3w7S+7vX3tk64+wcRD\n2E3P51ttgPTz3P+8eh6j5zF6XnE/PX/FC/9B/BN6/uDuw9e/8ME33n3w4PFXn2TiIeym5/OtNkD6\nee5/Xj2P0fMYPa+4XM+/9+7DH3/hgzfdffB9j7/6JBMPYTc9n2+1AdLPc//z6nmMnsfoecX99PwH\nvv+hv/Kinn/t7e3Ln3nhg2e+6Pb2ax5/9UkmHsJuej7fagOkn+f+59XzGD2P0fOK+/zzan/zRT1/\n9e3tVzz66Mtvb1/9+KtPMvEQdtPz+VYbIP089z+vnsfoeYyeV1ys5x992e3tNzz6a19/e/uFzzzu\n6hNNPITd9Hy+1QZIP8/9z6vnMXoeo+cVF+v5u29vb7/50V/75rsP3/O4q4986A2f7F3vvKDnn+29\n0U+x95f7vaIPf47VBkg/z/3P+4G7l/aZ/m3uy/vff9G/x3d5+m61H770Q5T95DWt9uE/133w0g9R\n9u73P3X2z+009mI9f9tdqr/j0V/7jrsP3/64q4+85faTfVv8sS9r7y/3e116vsFqA6SfZ7V5gWt1\nsZ7/xF2Lv/fRX/u+uw/f/Lirj+j5bJeeb7DaAOnnWW1e4FpdrOdvuGvxDz76az909+EbH3f1ET2f\n7dLzDVYbIP08q80LXKuL9vyHHv21f3b34Y887uojej7bpecbrDZA+nlWmxe4Vhfr+Y+/+GvFfPzr\nyLz01Uc++pOf7ANvvaDnf+vCu6KfYu8v93tFH/4cqw2Qfp77n/fhd0d4pn+b+/LUU2+/9COUfehu\ntU9f+iHK3vHUU2+79DOUPfz97e+/9EOUvfOpB2f/3E5jL9bzt96l+jsf/bWHv/PtrY+7+kQT/5DB\nbv682nyrDZB+nvuf159Xi/Hn1WL8ebWKi/55tW959Nce/sm0dz/u6hNNPITd9Hy+1QZIP8/9z6vn\nMXoeo+cVF+v5My/+yjHfcHv7smced/WJJh7Cbno+32oDpJ/n/ufV8xg9j9Hzist9vddX3d5+1aOP\nvur29lWPv/okEw9hNz2fb7UB0s9z//PqeYyex+h5xeV6/jW3t6949oUPnn3Fx7/zyktffZKJh7Cb\nns+32gDp57n/efU8Rs9j9Lzicj3/x7e3t2974YO3333wTx5/9UkmHsJuej7fagOkn+f+59XzGD2P\n0fOKy/X8PXex/tYXPvi2uw/e+/irTzLxEHbT8/lWGyD9PPc/r57H6HmMnldcrufPfcXt7Z956mM/\nfP+fvb39a0+6+gQTD2E3PZ9vtQHSz3P/8+p5jJ7H6HnFBXv+o3f/9P3XH57Scx/96rsf/tiTrj7B\nxEPYTc/nW22A9PPc/7x6HqPnMXpeccGeP/u1d71+7Y8+/cEf+at3P/i6J159gomHsJuez7faAIml\nd/Qn0vMYPY/R84oL9vy5D7360Zdhf83TT776eBMPYTc9n2+1ARJL7+hPpOcxeh6j5xWX7PlzH3zd\nC+H+ug9tXX2siYewm57Pt9oAiaV39CfS8xg9j9Hzivvs+ejZH/v6V7385a/6hjc9u331cSYewm56\nPt9qAySW3tGfSM9j9DxGzysu2/MZJh7Cbno+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPp\neYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi\n6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz\n+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5\nhZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8\nRs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0\njv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8\nqw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxC\nzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j\n5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpH\nfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7V\nBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHn\nHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9Hz\nGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/\nkZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oD\nJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMO\nPZ9vtQESS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmM\nnlfoeYeez7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9I\nz2P0PEbPK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQES\nS+/oT6TnMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYee\nz7faAImld/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbP\nK/S8Q8/nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6Tn\nMXoeo+cVet6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAIml\nd/Qn0vMYPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/n\nW22AxNI7+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cV\net6h5/OtNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMY\nPY/R8wo979Dz+VYbILH0jv5Eeh6j5zF6XqHnHXo+32oDJJbe0Z9Iz2P0PEbPK/S8Q8/nW22AxNI7\n+hPpeYyex+h5hZ536Pl8qw2QWHpHfyI9j9HzGD2v0PMOPZ9vtQESS+/oT6TnMXoeo+cVet6h5/Ot\nNkBi6R39ifQ8Rs9j9LxCzzv0fL7VBkgsvaM/kZ7H6HmMnlfoeYeez7faAImld/Qn0vMYPY/R8wo9\n79Dz+VYbILH0ezVMpOcxeh6j5xV63qHn8602QGLp92qYSM9j9DxGzyv0vEPP51ttgMTS79UwkZ7H\n6HmMnlfoeYeez7faAIml36thIj2P0fMYPa/Q8w49n2+1ARJLv1fDRHoeo+cxel6h5x16Pt9qAySW\nfq+GifQ8Rs9j9LxCzzv0fL7VBkgs/V4NE+l5jJ7H6HmFnnfo+XyrDZBY+r0aJtLzGD2P0fMKPe/Q\n8/lWGyCx9Hs1TKTnMXoeo+cVet6h5/OtNkBi6fdqmEjPY/Q8Rs8r9LxDz+dbbYDE0u/VMJGex+h5\njJ5X6HmHns+32gCJpd+rYSI9j9HzGD2v0PMOPZ9vtQESS79Xw0R6HqPnMXpeoecdej7fagMkln6v\nhon0PEbPY/S8Qs879Hy+1QZILP1eDRPpeYyex+h5hZ536Pl8qw2QWPq9GibS8xg9j9HzCj3v0PP5\nVhsgsfR7NUyk5zF6HqPnFXreoefzrTZAYun3aphIz2P0PEbPK/S8Q8/nW22AxNLv1TCRnsfoeYye\nV+h5h57Pt9oAiaXfq2EiPY/R8xg9r9DzDj2fb7UBEku/V8NEeh6j5zF6XqHnHXo+32oDJJZ+r4aJ\n9DxGz2P0vELPO/R8vtUGSCz9Xg0T6XmMnsfoeYWed+j5fKsNkFj6vRom0vMYPY/R8wo97/gp0PPd\notOeMfBqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R\n8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8\nRs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7Oc\nYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+8\n2vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0\n/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v\n0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0\nPEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm\n0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9\nz3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88v\nP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo9\n79Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j\n9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9\nj9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMs\nZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMOPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PID\nr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbPK/S8Q88vP/Bqz7OcYSI9j9HzGD2v0PMO\nPb/8wKs9z3KGifQ8Rs9j9LxCzzv0/PIDr/Y8yxkm0vMYPY/R8wo979Dzyw+82vMsZ5hIz2P0PEbP\nK/S8Q88vP3D6/ldv2ICex+h5jJ5X6HmHnl9+4PT9r96wAT2P0fMYPa/Q8w49v/zA6ftfvWEDeh6j\n5zF6XqHnHXp++YHT9796wwb0PEbPY/S8Qs879PzyA6fvf/WGDeh5jJ7H6HmFnnfo+eUHTt//6g0b\n0PMYPY/R8wo979Dzyw+cvv/VGzag5zF6HqPnFXreoeeXHzh9/6s3bEDPY/Q8Rs8r9LxDzy8/cPr+\nV2/YgJ7H6HmMnlfoeYeeX37g9P2v3rABPY/R8xg9r9DzDj2//MDp+1+9YQN6HqPnMXpeoecden75\ngdP3v3rDBvQ8Rs9j9LxCzzv0/PIDp+9/9YYN6HmMnsfoeYWed+j55QdO3//qDRvQ8xg9j9HzCj3v\n0PPLD5y+/9UbNqDnMXoeo+cVet6h55cfOH3/qzdsQM9j9DxGzyv0vEPPLz9w+v5Xb9iAnsfoeYye\nV+h5h55ffuD0/a/esAE9j9HzGD2v0PMOPb/8wOn7X71hA3oeo+cxel6h5x16fvmB0/e/esMG9DxG\nz2P0vELPO/T88gOn73/1hg3oeYyex+h5hZ536PnlB07f/+oNG9DzGD2P0fMKPe/Q88sPnL7/1Rs2\noOcxeh6j5xV63qHnlx84ff+rN2xAz2P0PEbPK/S8Q88vP3D6/ldv2ICex+h5jJ5X6HmHnl9+4PT9\nr96wAT2P0fMYPa/Q8w49v/zA6ftfvWEDeh6j5zF6XqHnHXp++YHT9796wwb0PEbPY/S8Qs879Pzy\nA6fvf/WGDeh5jJ7H6HmFnnfo+eUHTt//6g0b0PMYPY/R8wo979Dzyw+cvv/VGzag5zF6HqPnFXre\noeeXHzh9/6s3bEDPY/Q8Rs8r9LxDzy8/cPr+V2/YgJ7H6HmMnlfoeYeeX37g9P2v3rABPY/R8xg9\nr9DzDj2//MDp+1+9YQN6HqPnMXpeoecden75gdP3v3rDBvQ8Rs9j9LxCzzuO2PO90gMfbqF7DRvQ\n8xg9j9HzCj3v0PNt6YEPt9C9hg3oeYyex+h5hZ536Pm29MCHW+hewwb0PEbPY/S8Qs879HxbeuDD\nLXSvYQN6HqPnMXpeoecder4tPfDhFrrXsAE9j9HzGD2v0PMOPd+WHvhwC91r2ICex+h5jJ5X6HmH\nnm9LD3y4he41bEDPY/Q8Rs8r9LxDz7elBz7cQvcaNqDnMXoeo+cVet6h59vSAx9uoXsNG9DzGD2P\n0fMKPe/Q823pgQ+30L2GDeh5jJ7H6HmFnnfo+bb0wIdb6F7DBvQ8Rs9j9LxCzzv0fFt64MMtdK9h\nA3oeo+cxel6h5x16vi098OEWutewAT2P0fMYPa/Q8w4935Ye+HAL3WvYgJ7H6HmMnlfoeYeeb0sP\nfLiF7jVsQM9j9DxGzyv0vEPPt6UHPtxC9xo2oOcxeh6j5xV63qHn29IDH26hew0b0PMYPY/R8wo9\n79DzbemBD7fQvYYN6HmMnsfoeYWed+j5tvTAh1voXsMG9DxGz2P0vELPO/R8W3rgwy10r2EDeh6j\n5zF6XqHnHXq+LT3w4Ra617ABPY/R8xg9r9DzDj3flh74cAvda9iAnsfoeYyeV+h5h55vSw98uIXu\nNWxAz2P0PEbPK/S8Q8+3pQc+3EL3Gjag5zF6HqPnFXreoefb0gMfbqF7DRvQ8xg9j9HzCj3v0PNt\n6YEPt9C9hg3oeYyex+h5hZ536Pm29MCHW+hewwb0PEbPY/S8Qs879HxbeuDDLXSvYQN6HqPnMXpe\noecder4tPfDhFrrXsAE9j9HzGD2v0PMOPd+WHvhwC91r2ICex+h5jJ5X6HmHnm9LD3y4he41bEDP\nY/Q8Rs8r9LxDz7elBz7cQvcaNqDnMXoeo+cVet6h59vSAx9uoXsNG9DzGD2P0fMKPe/Q823pgQ+3\n0L2GDeh5jJ7H6HmFnnfo+bb0wIdb6F7DBvQ8Rs9j9LxCzzv0fFt64MMtdK9hA3oeo+cxel6h5x16\nvi098OEWutewAT2P0fMYPa/Q8w4935Ye+HAL3WvYgJ7H6HmMnlfoeYeeb0sPfLiF7jVsQM9j9DxG\nzyv0vEPPt6UHPtxC9xo2oOcxeh6j5xV63qHn29IDH26hew0b0PMYPY/R8wo979DzbemBD7fQvYYN\n6HmMnsfoeYWed+j5tvTAh1voXsMG9DxGz2P0vELPO/R8W3rgwy10r2EDeh6j5zF6XqHnHXq+LT3w\n4Ra617ABPY/R8xg9r9DzDj3flh74cAvda9iAnsfoeYyeV+h5h55vSw98uIXuNWxAz2P0PEbPK/S8\nQ8+3pQc+3EL3Gjag5zF6HqPnFXreoefb0gMfbqF7DRvQ8xg9j9HzCj3v0PNt6YEPt9C9hg3oeYye\nx+h5hZ536Pm29MCHW+hewwb0PEbPY/S8Qs879HxbeuDDLXSvYQN6HqPnMXpeoecder4tPfDhFrrX\nsAE9j9HzGD2v0PMOPd+WHvhwC91r2ICex+h5jJ5X6HmHnm9LD3y4he41bEDPY/Q8Rs8r9LxDz7el\nBz7cQvcaNqDnMXoeo+cVet6h59vSAx9uoXsNG9DzGD2P0fMKPe/Q823pgQ+30L2GDeh5jJ7H6HmF\nnnfo+bb0wIdb6F7DBvQ8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbP\nK/S8Q8+3HW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v\n0PMOPd92uIFXM2xYz2P0PEbPK/S8Q8+3HW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxC\nzzv0fNvhBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbPK/S8Q8+3HW7g1Qwb1vMYPY/R8wo9\n79DzbYcbeDXDhvU8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbPK/S8\nQ8+3HW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v0PMO\nPd92uIFXM2xYz2P0PEbPK/S8Q8+3HW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxCzzv0\nfNvhBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbPK/S8Q8+3HW7g1Qwb1vMYPY/R8wo979Dz\nbYcbeDXDhvU8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbPK/S8Q8+3\nHW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxCzzv0fNvhBl7NsGE9j9HzGD2v0PMOPd92\nuIFXM2xYz2P0PEbPK/S8Q8+3HW7g1Qwb1vMYPY/R8wo979DzbYcbeDXDhvU8Rs9j9LxCzzv0fNvh\nBl7NsGE9j9HzGD2v0PMOPd92uIFXM2xYz2P0PEbPK66+58+8/YJ+8uETvDv6KS5dg7bDDbyaYcPv\nv/TfNvs8ePCOSz9C2dN3q3360g9R9s5rWu3Dnn/g0g9R9s4H7z3753ZyePU9f/rBBT318AneH/0U\nl65B2+EGXs2w4YfReXbKy8kn+cjdaj9y6Yf4qemZS/9if286Obz+nr/3gp7v+VPRT3HpGrQdbuDV\nDBv+0N1L++yUl/NePHhw6Seoe77nl36Iumta7TOX/sV+n8ZqOzm8+p777+eLO9zAqxk27L+fx/jv\n5zH++3mFnnfo+bbDDbyaYcN6HqPnMXpeoecder7tcAOvZtiwnsfoeYyeV+h5h55vO9zAqxk2rOcx\neh6j5xV63qHn2w438GqGDet5jJ7H6HmFnnfo+bbDDbyaYcN6HqPnMXpeoecder7tcAOvZtiwnsfo\neYyeV+h5h55vO9zAqxk2rOcxeh6j5xV63qHn2w438GqGDet5jJ7H6HmFnnfo+bbDDbyaYcN6HqPn\nMXpeoecder7tcAOvZtiwnsfoeYyeV+h5h55vO9zAqxk2rOcxeh6j5xV63qHn2w438GqGDet5jJ7H\n6HmFnnfo+bbDDbyaYcN6HqPnMXpeoecder7tcAOvZtiwnsfoeYyeV+h5h55vO9zAqxk2rOcxeh6j\n5xV63qHn2w438GqGDet5jJ7H6HmFnnfo+bbDDbyaYcN6HqPnMXpeoecder7tcAOvZtiwnsfoeYye\nV+h5h55vO9zAqxk2rOcxeh6j5xV63qHn2w438GqGDet5jJ7H6HmFnnfo+bbDDbyaYcN6HqPnMXpe\ncXbPP//zP/+bXuLyj91d/4eN59lv4iHspufbDjfwaoYN63mMnsfoecXZPb+5uXnNS1x++931L208\nz34TD2E3Pd92uIFXM2x4o+ft+0+m5yl6HvNTpecfvLv+xxrPs9/EQ9hNz7cdbuDVDBvW8xg9j9Hz\nitk9//6767+/8Tz7TTyE3fR82+EGXs2wYT2P0fMYPa/Y3fP3v+Zj7rr9e1/zyV59+4vurv/RzgPt\nNvEQdtPzbYcbeDXDhvU8Rs9j9Lxid8/fcrPpVZ0H2m3iIeym59sON/Bqhg3reYyex+h5RaDn/9pb\nOg+028RD2E3Ptx1u4NUMG9bzGD2P0fOK+T3/lL/QeZ79Jh7Cbnq+7XADr2bYsJ7H6HmMnlfs7vm7\nPu9j7sr9737e6Lf9kX/UeZwzTDyE3fR82+EGXs2wYT2P0fMYPa+Y/fvb79/EQ9hNz7cdbuDVDBvW\n8xg9j9HzCj3v0PNthxt4NcOGOEVo+gAAIABJREFU9TxGz2P0vELPO/R82+EGXs2wYT2P0fMYPa84\nu+evf/3rf6zziaeZeAi76fm2ww28mmHDeh6j5zF6XuH7q3Xo+bbDDbyaYcN6HqPnMXpeoecder7t\ncAOvZtiwnsfoeYyeV+h5h55vO9zAqxk2rOcxeh6j5xWtnr/hW776Jbytc8v9Jh7Cbnq+7XADr2bY\nsJ7H6HmMnlec3/Nn/8aveukvEPftnefZb+Ih7Kbn2w438GqGDet5jJ7H6HnF2T1/5g8/7gu+fnvn\nefabeAi76fm2ww28mmHDeh6j5zF6XnF2z7/4sV/A/ds7z7PfxEPYTc+3HW7g1Qwb1vMYPY/R84pz\ne/6Oz/hYvH/x+Pf3P+k8z34TD2E3Pd92uIFXM2xYz2P0PEbPK87t+Z97GPPPed37Op97jomHsJue\nbzvcwKsZNqznMXoeo+cV5/b8t9/l/Be8s/OZZ5l4CLvp+bbDDbyaYcN6HqPnMXpecW7P/627nn9D\n5xNPM/EQdtPzbYcbeDXDhvU8Rs9j9Lzi3J5/+s3NZz3b+cTTTDyE3fR82+EGXs2wYT2P0fMYPa84\nt+efeXPzmzqfd56Jh7Cbnm873MCrGTas5zF6HqPnFef2/Jff3Pznnc87z8RD2E3Ptx1u4NUMG9bz\nGD2P0fOKc3v+O25ufm3n884z8RB20/Nthxt4NcOG9TxGz2P0vOLcnn/lzc2nvLXziaeZeAi76fm2\nww28mmHDeh6j5zF6XnFuzz/wc25uvqDziaeZeAi76fm2ww28mmHDeh6j5zF6XnH213v9+pubT/3W\nzmeeZeIh7Kbn2w438GqGDet5jJ7H6HnF+d9f7WU3N5/xVxf4I2sTD2E3Pd92uIFXM2xYz2P0PEbP\nKxrfL/Uvf/rNza//8nv+buejiYewm55vO9zAqxk2rOcxeh6j5xVn9/yrv/qrv+CnPfwi7r/wN/72\nz3+RH+w8z34TD2E3Pd92uIFXM2xYz2P0PEbPK87u+WO/XarvlzpV4lf4e3W4gVczbFjPY/Q8Rs8r\n9LxDz7cdbuDVDBvW8xg9j9HzCj3v0PNthxt4NcOG9TxGz2P0vOLsnt8+1hs7z7PfxEPYTc+3HW7g\n1Qwb1vMYPY/R84rzf3/7KiYewm56vu1wA69m2LCex+h5jJ5X6HmHnm873MCrGTas5zF6HqPnFXre\noefbDjfwaoYN63mMnsfoeYWed+j5tsMNvJphw3oeo+cxel6h5x16vu1wA69m2LCex+h5jJ5X6HmH\nnm873MCrGTas5zF6HqPnFWf3/C2P9XTnefabeAi76fm2ww28mmHDeh6j5zF6XuHryXTo+bbDDbya\nYcN6HqPnMXpeoecder7tcAOvZtiwnsfoeYyeV+h5h55vO9zAqxk2rOcxeh6j5xWTe/4p/8Xv+T3/\nvPM8+008hN30fNvhBl7NsGE9j9HzGD2vOLvn73uxt//w6/+nX3IX9P/4HZ2nOcfEQ9hNz7cdbuDV\nDBvW8xg9j9Hzipl/Xu2Zr/ncm5tf/OMT71gx8RB20/Nthxt4NcOG9TxGz2P0vGLunz8//bKbm9/0\n4am33P6cF6Tn2w438GqGDet5jJ7H6HnF5K8n88OffnPzp+becsvEQ9hNz7cdbuDVDBvW8xg9j9Hz\nitlfH+6/u7n53I9MvueTTTyE3fR82+EGXs2wYT2P0fMYPa+Y3fPvurm5+buT7/lkEw9hNz3fdriB\nVzNsWM9j9DxGzytm9/wddz1/xeR7PtnEQ9hNz7cdbuDVDBvW8xg9j9Hzitk9f89dz//I5Hs+2cRD\n2E3Ptx1u4NUMG9bzGD2P0fOK2T3/v+56/l9OvueTTTyE3fR82+EGXs2wYT2P0fMYPa+Y3fM/d9fz\nPzz5nk828RB20/Nthxt4NcOG9TxGz2P0vGJyz9/6r9/1/M/NveeGiYewm55vO9zAqxk2rOcxeh6j\n5xVze/6Dv+bhF3H/7qn33DLxEHbT822HG3g1w4b1PEbPY/S84uyev2bwZX/88z/tYc5/6TOdB9pt\n4iHspufbDjfwaoYN63mMnsfoeUXg+6V+Zed59pt4CLvp+bbDDbyaYcN6HqPnMXpeMb/nv//ZzvPs\nN/EQdtPzbYcbeDXDhvU8Rs9j9Lxids9/+p+833/bruerO9zAqxk2rOcxeh6j5xVn9/zzXsJv/b1f\n/KbOw5xl4iHspufbDjfwaoYN63mMnsfoecXsP39+/yYewm56vu1wA69m2LCex+h5jJ5X6HmHnm87\n3MCrGTas5zF6HqPnFXreoefbDjfwaoYN63mMnsfoeYWed+j5tsMNvJphw3oeo+cxel6h5x16vu1w\nA69m2LCex+h5jJ5XtHv+4X/6N/7KK7/oy173f36ge6czTTyE3fR82+EGXs2wYT2P0fMYPa/o9fwn\n//xv/owX/uT5p/2GV76rdbMzTTyE3fR82+EGXs2wYT2P0fMYPa/o9Pyp//4zXvzFZD7jj76/8yzn\nmXgIu+n5tsMNvJphw3oeo+cxel7R6Pk//iXjl4f7Zf+08zBnmXgIu+n5tsMNvJphw3oeo+cxel5x\nfs9/4LNeiPjP+vm/6Gf/tI//+LP/n87TnGPiIeym59sON/Bqhg3reYyex+h5xdk9f9fnPN/vX/lF\n3/Xg4Ycf+J5X/MrnL/z893Ye5wwTD2E3Pd92uIFXM2xYz2P0PEbPK87u+R95GO9f/Lc/4ZupPfu3\nfsHDa1/QeZwzTDyE3fR82+EGXs2wYT2P0fMYPa84t+f/8lPv0v3r3/Hii2/91Q+/w9rbOs+z38RD\n2E3Ptx1u4NUMG9bzGD2P0fOKc3v+l+7K/XPe8slXf/xn3V1+bed59pt4CLvp+bbDDbyaYcN6HqPn\nMXpecW7Pf9dduF85Xv7Td5d/d+d59pt4CLvp+bbDDbyaYcN6HqPnMXpecW7Pf8VduH9ivPzGh79F\nrvM8+008hN30fNvhBl7NsGE9j9HzGD2vOLfnn31z85kvcfnZn3Fz89md59lv4iHspufbDjfwaoYN\n63mMnsfoecW5Pf/Um5uf91LXf97Nzac1HucMEw9hNz3fdriBVzNsWM9j9DxGzyvO7fnn3nX7mfHy\nM4/rfM7EQ9hNz7cdbuDVDBvW8xg9j9HzinN7/h/c3Nx8z3j5e+4u/9rO8+w38RB20/Nthxt4NcOG\n9TxGz2P0vOLcnv9Xd+H+r8fLf+Du8h/sPM9+Ew9hNz3fdriBVzNsWM9j9DxGzyvO7fnXP/xScP/g\nk6/+Hw+vvr7zPPtNPITd9Hzb4QZezbBhPY/R8xg9rzi350/9G3fl/pl//8UXv+1n3l38nA90nme/\niYewm55vO9zAqxk2rOcxeh6j5xVnf/32v/L8d1/5A294dOUN/83z32Ttnr88nJ4v7nADr2bYsJ7H\n6HmMnlec3fOP/taPfYPU3/jHX/cdP/AD3/G6P/6bPvbxb3uJ3/UeNfEQdtPzbYcbeDXDhvU8Rs9j\n9Lzi/O9//r7/6OYl/Mb3dZ7mHBMPYTc933a4gVczbFjPY/Q8Rs8rzu/5cw/+0JjzP/ig8zBnmXgI\nu+n5tsMNvJphw3oeo+cxel7R6Plzz33bf/bimv+Wb+3c7UwTD2E3Pd92uIFXM2xYz2P0PEbPK1o9\nf+65//dP/86f+7GWf/bveNm/6N3rTBMPYTc933a4gVczbFjPY/Q8Rs8rmj2/8+xTP/FDP/QT73u2\nfaMzTTyE3fR82+EGXs2wYT2P0fMYPa/o9/zSJh7CbnrO8oZXSs9j9DxGzyv0vEPPWd7wSul5jJ7H\n6HlFr+cf+oGvfOFrvr75P/1DX/6u1s3ONPEQdtNzlje8Unoeo+cxel7R6fmb/ttPv7n5nz/+wRtv\nbm4+9fe9sfMs55l4CLvpOcsbXik9j9HzGD2vaPT8L3zqw9/X/ok9v7n59K/sPMxZJh7CbnrO8oZX\nSs9j9DxGzyvO7/n/+rE/p/bint/c/PnO05xj4iHspucsb3il9DxGz2P0vOLsnn//pzyf78964duv\nvO8LftXzFz71uzuPc4aJh7CbnrO84ZXS8xg9j9HzinN7/uzzX739N3/XRz7h2g9/3sNr/+E9/0n0\niYewm56zvOGV0vMYPY/R84pze/79D9P9P3zkxRef+ZMPr/7DzvPsN/EQdtNzlje8Unoeo+cxel5x\nbs//xF24f91HP/nqR3/D3eX/sfM8+008hN30nOUNr5Sex+h5jJ5XnNvzh9/t/FvGy9/28F+4d55n\nv4mHsJues7zhldLzGD2P0fOKc3v+C+/C/RJfPuZdd5d/Xud59pt4CLvpOcsbXik9j9HzGD2vOLfn\nn35z83Nf6vq/eXPz0xuPc4aJh7CbnrO84ZXS8xg9j9HzinN7/lk3Nz/7pa7/rJubz2o8zhkmHsJu\nes7yhldKz2P0PEbPK87t+a+4ubl5x3j5LXeXf3nnefabeAi76TnLG14pPY/R8xg9rzi357/7Ltx/\nYbz8v91d/p2d59lv4iHspucsb3il9DxGz2P0vOLcnn/VXbg/d/gH9Hd+zt3lv9R5nv0mHsJues7y\nhldKz2P0PEbPK87t+Xt+5sMvD/dJv8P9vf/J3cVPe2vnefabeAi76TnLG14pPY/R8xg9rzj767d/\n8cMvBfeL/sYnfnHXb/33Hl77Y53HOcPEQ9hNz1ne8ErpeYyex+h5xdk9//Cvf/7br/zSP/H33vzM\nc889+57vesWvef7CL3jQeZwzTDyE3fSc5Q2vlJ7H6HmMnlec//1S3/mrP/4dUm9+2mf+nE974cef\n88OdpznHxEPYTc9Z3vBK6XmMnsfoecX5PX/uHb/rZvDv//POw5xl4iHspucsb3il9DxGz2P0vKLR\n8+ee/ev/zotr/pl/8kOdZznPxEPYTc9Z3vBK6XmMnsfoeUWn588998y3/L5/+4WY/4zf8ur7/k/n\nz5t4CLvpOcsbXik9j9HzGD2v6PX8oTf//de95ku/4hv/6Ue2/68REw9hNz1necMrpecxeh6j5xX9\nnl/axEPYTc9Z3vBK6XmMnsfoeYWed+g5yxteKT2P0fMYPa/Q8w49Z3nDK6XnMXoeo+cVet6h5yxv\neKX0PEbPY/S8Qs879JzlDa+UnsfoeYyeV+h5h56zvOGV0vMYPY/R8wo979Bzlje8Unoeo+cxel6h\n5x16zvKGV0rPY/Q8Rs8r9LxDz1ne8ErpeYyex+h5hZ536DnLG14pPY/R8xg9r9DzDj1necMrpecx\neh6j5xV63qHnLG94pfQ8Rs9j9LxCzzv0nOUNr5Sex+h5jJ5X6HmHnrO84ZXS8xg9j9HzCj3v0HOW\nN7xSeh6j5zF6XqHnHXrO8oZXSs9j9DxGzyv0vEPPWd7wSul5jJ7H6HmFnnfoOcsbXik9j9HzGD2v\n0PMOPWd5wyul5zF6HqPnFXreoecsb3il9DxGz2P0vELPO/Sc5Q2vlJ7H6HmMnlfoeYees7zhldLz\nGD2P0fMKPe/Qc5Y3vFJ6HqPnMXpeoecdes7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5\nJh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2vi\nIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7C\nbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym\n5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO\n4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe\n9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf\n/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/\n6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe\n5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmO\nnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5\njJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo\n+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6f\na+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkm\nHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih\n7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJu\nes7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7Kbn\nHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7h\nRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF70\n9T/peY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//\nk57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/p\neY6ex+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n\n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6e\nx+j5uSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmM\nnp9r4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5\nuSYewm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r\n4iHspuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYe\nwm56zuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHs\npuccXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56\nzuFFX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspucc\nXvT1P+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFF\nX/+TnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1\nP+l5jp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+T\nnufoeYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5\njp7H6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+Tnufo\neYyen2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H\n6Pm5Jh7CbnrO4UVf/5Oe5+h5jJ6fa+Ih7KbnHF709T/peY6ex+j5uSYewm56zuFFX/+TnufoeYye\nn2viIeym5xxe9PU/6XmOnsfo+bkmHsJues7hRV//k57n6HmMnp9r4iHspuccXvT1P+l5jp7H6Pm5\nJh7CbnrO4UVf/5Oe5+h5zBF7/rW3L/L0v/oLb/6mV7/85a96/ZtKd5l4CLvpOYcXff1Pep6j5zFH\n7Plfeumef+BvvXDl695fuMvEQ9hNzzm86Ot/0vMcPY85Ys//7Ev2/IN/8dGl13xw+y4TD2E3Pefw\noq//Sc9z9DzmgD3/wF2w/+a3PPKRj11+3d3lV37nj77hO19594PXbd9m4iHspuccXvT1P+l5jp7H\nHLDnp7tev3e4+saH/1j+1MMfve/Vdz/8se3bXJCec3jR1/+k5zl6HnPAnv/ft7df+Oxw9bW3ty97\n58d++PaX3d6+dvM2Ew9hNz3n8KKv/0nPc/Q85oA9/67b21cNFx/c/TP561/44BvvPniwdZuJh7Cb\nnnN40df/pOc5eh5zwJ7f1fp/Hy5+713Cf/yFD95098H3bd1m4iHspuccXvT1P+l5jp7HHLDnf+32\n9u8NF7/29vblz7zwwTNfdHv7NVu3mXgIu+k5hxd9/U96nqPnMQfs+Zfe3n7PcPHVt7df8eijL7+9\nffXWbSYewm56zuFFX/+Tnufoeczxev7R/+X29oc/+O1f9SUv++LXftcLv8/9oy+7vf2GR/+fr7+9\n/cJnXvJnPzLxEHbTcw4v+vqf9DxHz2OO1/N33d7e/p0XvqTMF/6Dj2X73Xc//uZH/59vvvvwPZ/4\nkx586yf7/953Qc9//boPRD/FpX+1hieLvv53nn76qfSnmObh19D46KUfouypp5++9CPUPQzEhy/9\nEGXvf/pDZ//cTlUv1/MfefFXh/uqDz+8+La7H33Ho//Pd9x9+PZP/Elvuf1k33a/T33vLv2rNTzZ\npf8OAT7ucj3/xw9r/PJv+hc/8Ybvfv4rvP7thxd/4u4H3/vo//N9dx+++RN/kp7DWi79dwjwcZfr\n+d+/i/FffvfzP3z2e//U3Qc/evejN9z97w8++v/80N2Hb/zEn6TnsJZL/x0CfNzlev53vuRLXvuv\nvt3Kd9+V+a8997Ge/9Cj/88/u/vwRz7xJ739Sz/ZP/roBT3/H/2fiX6KS/9qDU8Wff3vPJP9G2yq\nh1/v8tlLP0TdNa32/2/v3oNkze/6vh/J4WYgJLYxxvgCrsQmAQps7FSgXLELx8ZgJ+VyMJCYOOBL\nSIzthEqVwZcWK2ul5WJhtCvJRiwCGXSLYI0kLBmIjBACgTCWBAZZQtoFRjeklbTS3nfPVKV75vRc\nzs70+X2f3++z3c/26/XHbvfTM/30Pr/n+7x3zpnpOdybQ9tT1W3+frUzrj5rsXjKfYeHv37+HWTO\nvbvMJQZ+U2KZ729n70VP/wPf357j+9tj9u/728973bLcv3p4+J7lv157unX1/XDvucFnDlyEMj1n\n70VP/wM9z9HzmH3v+V3H3wi3+nm1V59uXf282gdv8JkDF6FMz9l70dP/QM9z9Dxm33t+97Lcrzk8\nfPT8+8ncsVjc5P1kYIdFT/8DPc/R85h97/nq3WVev/z3rYvFC063vuCiX8F2nYGLUKbn7L3o6X+g\n5zl6HrN3PX/k4YcfPnP3rcuev2n57xcvFrec/FL0q7f4fSzbvlrDZtHT/0DPc/Q8Zu96vvoGuLtP\n765+YO23Do/fZea9643vW955442eaOAilOk5ey96+h/oeY6ex+xdzw+Wrf6J07vPWyy+Y/V1+YeW\nm39svfHHl3c+fMHnnn+iLdJz9l709D/Q8xw9j9m7nj96y2Lx9Pev77355H3bn7/c/NHjjfc+4/hN\nZjYbuAhles7ei57+B3qeo+cxe9fzw59eJvzW4z9xv/qWpy4Wtxy/Wdw7lpt/8Ogtch554fLmnTd8\nnoGLUKbn7L3o6X+g5zl6HrN/PX/4tmWub3rFm+565xtuX956ytuON199yfLO7e948P63P29546U3\nfp6Bi1Cm5+y96Ol/oOc5eh6zfz0/vOe7zvxalW/5xfXmB2473fqcB2/8NAMXoUzP2XvR0/9Az3P0\nPGYPe3543yuechLuu0433/+i9daXPtDwLAMXoUzP2XvR0/9Az3P0PGYfe354+IHXfe8zn/r0Z93x\nq+feBO7qnT98680333rHXVcv+7yzBi5CmZ6z96Kn/4Ge5+h5zH72fISBi1Cm5+y96Ol/oOc5eh6j\n51MNXIQyPWfvRU//Az3P0fMYPZ9q4CKU6Tl7L3r6H+h5jp7H6PlUAxehTM/Ze9HT/0DPc/Q8Rs+n\nGrgIZXrO3oue/gd6nqPnMXo+1cBFKNNz9l709D/Q8xw9j9HzqQYuQpmes/eip/+BnufoeYyeTzVw\nEcr0nL0XPf0P9DxHz2P0fKqBi1Cm5+y96Ol/oOc5eh6j51MNXIQyPWfvRU//Az3P0fMYPZ9q4CKU\n6Tl7L3r6H+h5jp7H6PlUAxehTM/Ze9HT/0DPc/Q8Rs+nGrgIZXrO3oue/gd6nqPnMXo+1cBFKNNz\n9l709D/Q8xw9j9HzqQYuQpmes/eip/+BnufoeYyeTzVwEcr0nL0XPf0P9DxHz2P0fKqBi1Cm5+y9\n6Ol/oOc5eh6j51MNXIQyPWfvRU//Az3P0fMYPZ9q4CKU6Tl7L3r6H+h5jp7H6PlUAxehTM/Ze9HT\n/0DPc/Q8Rs+nGrgIZXpy0Q8JAAAgAElEQVTO3oue/gd6nqPnMXo+1cBFKNNz9l709D/Q8xw9j9Hz\nqQYuQpmes/eip/+BnufoeYyeTzVwEcr0nL0XPf0P9DxHz2P0fKqBi1Cm5+y96Ol/oOc5eh6j51MN\nXIQyPWfvRU//Az3P0fMYPZ9q4CKU6Tl7L3r6H+h5jp7H6PlUAxehTM/Ze9HT/0DPc/Q8Rs+nGrgI\nZXrO3oue/gd6nqPnMXo+1cBFKNNz9l709D/Q8xw9j9HzqQYuQpmes/eip/+BnufoeYyeTzVwEcom\n9HzbV18YKzRbJ/Q8Rc9j9HyqgYtQpufsvdBsndDzFD2P0fOpBi5CmZ6z90KzdULPU/Q8Rs+nGrgI\nZXrO3gvN1gk9T9HzGD2fauAilOk5ey80Wyf0PEXPY/R8qoGLUKbn7L3QbJ3Q8xQ9j9HzqQYuQpme\ns/dCs3VCz1P0PEbPpxq4CGV6zt4LzdYJPU/R8xg9n2rgIpTpOXsvNFsn9DxFz2P0fKqBi1Cm5+y9\n0Gyd0PMUPY/R86kGLkKZnrP3QrN1Qs9T9DxGz6cauAhles7eC83WCT1P0fMYPZ9q4CKU6Tl7LzRb\nJ/Q8Rc9j9HyqgYtQpufsvdBsndDzFD2P0fOpBi5CmZ6z90KzdULPU/Q8Rs+nGrgIZXrO3gvN1gk9\nT9HzGD2fauAilOk5ey80Wyf0PEXPY/R8qoGLUKbn7L3QbJ3Q8xQ9j9HzqQYuQpmes/dCs3VCz1P0\nPEbPpxq4CGV6zt4LzdYJPU/R8xg9n2rgIpTpOXsvNFsn9DxFz2P0fKqBi1Cm5+y90Gyd0PMUPY/R\n86kGLkKZnrP3QrN1Qs9T9DxGz6cauAhles7eC83WCT1P0fMYPZ9q4CKU6Tl7LzRbJ/Q8Rc9j9Hyq\ngYtQpufsvdBsndDzFD2P0fOpBi5CmZ6z90KzdULPU/Q8Rs+nGrgIZXrO3gvN1gk9T9HzGD2fauAi\nlOk5ey80Wyf0PEXPY/R8qoGLUKbn7L3QbJ3Q8xQ9j9HzqQYuQpmes/dCs3VCz1P0PEbPpxq4CGV6\nzt4LzdYJPU/R8xg9n2rgIpTpOXsvNFsn9DxFz2P0fKqBi1Cm5+y90Gyd0PMUPY/R86kGLkKZnrP3\nQrN1Qs9T9DxGz6cauAhles7eC83WCT1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPz\nFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6ca\nuAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRV\nh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMY\nPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm\n51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8\nRc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kG\nLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXV\nIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxG\nz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTp\nORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P\n0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqB\ni1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1\nyPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R\n86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6\nDkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T\n9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rg\nIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQd\nMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0\nfKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpme\nQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MU\nPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4\nCGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWH\nTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9\nn2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbn\nUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxF\nz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYu\nQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh\n0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbP\npxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5\nFFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R\n8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGL\nUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI\n9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9Hz\nqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoO\nRdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0\nPEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAi\nlOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0y\nPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8\nqoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5D\nUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9\nj9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgI\nZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdM\nz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2f\nauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQ\nVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXP\nY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5C\nmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT\n8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+n\nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkU\nVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOpBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9Hz\nGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQ\npudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8Rs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0\nPEXPY/R8qoGLUKbnUFQdMj1P0fMYPZ9q4CKU6TkUVYdMz1P0PEbPpxq4CGV6DkXVIdPzFD2P0fOp\nBi5CmZ5DUXXI9DxFz2P0fKqBi1Cm51BUHTI9T9HzGD2fauAilOk5FFWHTM9T9DxGz6cauAhleg5F\n1SHT8xQ9j9HzqQYuQpmeQ1F1yPQ8Rc9j9HyqgYtQpudQVB0yPU/R8xg9n2rgIpTpORRVh0zPU/Q8\nRs+nGrgIZXoORdUh0/MUPY/R86kGLkKZnkNRdcj0PEXPY/R8qoGLUKbnUFQdsvTzj6TnMXreQs97\n6DkUVYcs/fwj6XmMnrfQ8x56DkXVIUs//0h6HqPnLfS8h55DUXXI0s8/kp7H6HkLPe+h51BUHbL0\n84+k5zF63kLPe+g5FFWHLP38I+l5jJ630PMeeg5F1SFLP/9Ieh6j5y30vIeeQ1F1yNLPP5Kex+h5\nCz3voedQVB2y9POPpOcxet5Cz3voORRVhyz9/CPpeYyet9DzHnoORdUhSz//SHoeo+ct9LyHnkNR\ndcjSzz+SnsfoeQs976HnsGNCs95Ez2P0vIWe99Bz2DGhWW+i5zF63kLPe+g57JjQrDfR8xg9b6Hn\nPfQcdkxo1pvoeYyet9DzHnoOOyY06030PEbPW+h5Dz2HHROa9SZ6HqPnLfS8h57DjgnNehM9j9Hz\nFnreQ89hx4RmvYmex+h5Cz3voeewY0Kz3kTPY/S8hZ730HPYMaFZb6LnMXreYvY9f+T9W/Sh1Sv4\ncOlTtn21gye20Kw3eXB5PXhomy+g5AP33POBbb+GZo8uD+39234Rze6+p5aFs3pyOPueP3jPFn10\n9QruLX3Ktq928MQWmvUmDy+vBw9v8wU8cT267Yv946Ynh7Pv+cMf2KLjr89Ln7Ltqx08sYVmvclD\ny+vBQ9t8ASV333PP3dt+Dc2Ovj7f9ototjy0kz+3J4ez77m/PwdOhWa9ib8/j/H35y30vIeew44J\nzXoTPY/R8xZ63kPPYceEZr2JnsfoeQs976HnsGNCs95Ez2P0vIWe99Bz2DGhWW+i5zF63kLPe+g5\n7JjQrDfR8xg9b6HnPfQcdkxo1pvoeYyet9DzHnoOOyY06030PEbPW+h5Dz2HHROa9SZ6HqPnLfS8\nh57DjgnNehM9j9HzFnreQ89hx4RmvYmex+h5Cz3voeewY0Kz3kTPY/S8hZ730HPYMaFZb6LnMXre\nQs976DnsmNCsN9HzGD1voec99Bx2TGjWm+h5jJ630PMeeg47JjTrTfQ8Rs9b6HkPPYcdE5r1Jnoe\no+ct9LyHnsOOCc16Ez2P0fMWet5Dz2HHhGa9iZ7H6HkLPe+h57BjQrPeRM9j9LyFnvfQc9gxoVlv\noucxet5Cz3voOeyY0Kw30fMYPW+h5z30HHZMaNab6HmMnrfQ8x56DjsmNOtN9DxGz1voeQ89hx0T\nmvUmeh6j5y30vIeew44JzXoTPY/R8xZ63kPPYceEZr2JnsfoeQs976HnsGNCs95Ez2P0vIWe99Bz\n2DGhWW+i5zF63kLPe+g57JjQrDfR8xg9b6HnPfQcdkxo1pvoeYyet9DzHnoOOyY06030PEbPW+h5\nDz2HHROa9SZ6HqPnLfS8h57DjgnNehM9j9HzFnreQ89hx4RmvYmex+h5Cz3voeewY0Kz3kTPY/S8\nhZ730HPYMaFZb6LnMXreQs976DnsmNCsN9HzGD1voec99Bx2TGjWm+h5jJ630PMeeg47JjTrTfQ8\nRs9b6HkPPYcdE5r1Jnoeo+ct9LyHnsOOCc16Ez2P0fMWet5Dz2HHhGa9iZ7H6HkLPe+h57BjQrPe\nRM9j9LyFnvfQc9gxoVlvoucxet5Cz3voOeyY0Kw30fMYPW+h5z30HHZMaNab6HmMnrfQ8x56Djsm\nNOtN9DxGz1voeQ89hx0TmvUmeh6j5y30vIeew44JzXoTPY/R8xZ63kPPYceEZr2JnsfoeQs976Hn\nsGNCs95Ez2P0vIWe99Bz2DGhWW+i5zF63kLPe+g57JjQrDfR8xg9b6HnPfQcdkxo1pvoeYyet9Dz\nHnoOOyY06030PEbPW+h5Dz2HHROa9SZ6HqPnLfS8h57DjgnNehM9j9HzFnreQ89hx4RmvYmex+h5\nCz3voeewY0Kz3kTPY/S8hZ730HPYMaFZb6LnMXreQs976DnsmNCsN9HzGD1voec99Bx2TGjWm+h5\njJ630PMeeg47JjTrTfQ8Rs9b6HkPPYcdE5r1Jnoeo+ct9LyHnsOOCc16Ez2P0fMWet5Dz2HHhGa9\niZ7H6HkLPe+h57BjQrPeRM9j9LyFnvfQc9gxoVlvoucxet5Cz3voOeyY0Kw30fMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYeZG3lB0PMYPW+h5z30HGZu\n5AVBz2P0vIWe99BzmLmRFwQ9j9HzFnreQ89h5kZeEPQ8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2z\nabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNp\nuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m6\n9TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1\nPEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8\nRs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxG\nz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbP\nW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b\n6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1vo\neQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5\nDz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkP\nPYc9s2m69TxGz1voeQ89hz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPYc9s2m69TxGz1voeQ89\nhz2zabr1PEbPW+h5Dz2HPbNpuvU8Rs9b6HkPPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmqEt/Bmp7H\n6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgIZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+gzU9j9Hz\nqQYuQpmeAxuVryrxHazpeYyeTzVwEcr0HNiofFWJ72BNz2P0fKqBi1Cm58BG5atKfAdreh6j51MN\nXIQyPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgI\nZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+gzU9j9HzqQYuQpmeAxuVryrxHazpeYyeTzVwEcr0\nHNiofFWJ72BNz2P0fKqBi1Cm58BG5atKfAdreh6j51MNXIQyPQc2Kl9V4jtY0/MYPZ9q4CKU6Tmw\nUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgIZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPy\nVSW+gzU9j9HzqQYuQpmeAxuVryrxHazpeYyeTzVwEcr0HNiofFWJ72BNz2P0fKqBi1Cm58BG5atK\nfAdreh6j51MNXIQyPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO\n1vQ8Rs+nGrgIZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+gzU9j9HzqQYuQpmeAxuVryrxHazp\neYyeTzVwEcr0HG+9DvsAAB+DSURBVNiofFWJ72BNz2P0fKqBi1Cm58BG5atKfAdreh6j51MNXIQy\nPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgIZXoO\nbFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+gzU9j9HzqQYuQpmeAxuVryrxHazpeYyeTzVwEcr0HNio\nfFWJ72BNz2P0fKqBi1Cm58BG5atKfAdreh6j51MNXIQyPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmq\nEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgIZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+\ngzU9j9HzqQYuQpmeAxuVryrxHazpeYyeTzVwEcr0HNiofFWJ72BNz2P0fKqBi1Cm58BG5atKfAdr\neh6j51MNXIQyPQc2Kl9V4jtY0/MYPZ9q4CKU6TmwUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8\nRs+nGrgIZXoObFS+qsR3sKbnMXo+1cBFKNNzYKPyVSW+gzU9j9HzqQYuQpmeAxuVryrxHazpeYye\nTzVwEcr0HNiofFWJ72BNz2P0fKqBi1Cm58BG5atKfAdreh6j51MNXIQyPQc2Kl9V4jtY0/MYPZ9q\n4CKU6TmwUfmqEt/Bmp7H6PlUAxehTM+BjcpXlfgO1vQ8Rs+nGrgIZXoObFS+qsR3sKbnMXo+1cBF\nKNNzYLsmX74ae/64vZ6N9LyFnvfQc2C7Jl++9DxGz6cauAhleg5s1+TLl57H6PlUAxehTM+B7Zp8\n+dLzGD2fauAilOk5sF2TL196HqPnUw1chDI9B7Zr8uVLz2P0fKqBi1Cm58B2Tb586XmMnk81cBHK\n9BzYrsmXLz2P0fOpBi5CmZ4D2zX58qXnMXp+xm++8rabb7715Xc1ffDARSjTc2C7Jl++9DxGz0/c\n90OLa156b8OHD1yEMj0Htmvy5UvPY/R87f7nLk485/4bf/zARSjTc2C7Jl++9DxGz9detOz4t732\nHb/22m9b3njRjT9+4CKULXueGFCAkJPL1470fNeef8T/kOj5Ne9cfVn+0dWtj9y2vHnnDT9hwMGf\nTM+BeTm5fOn5mB1cQM+vuX2xuOn9xzffd9NicfsNP2HAwZ9Mz4F5Obl86fmYHVxAz4/ds/ya/OXr\nOz+yvHPPjT5jwMGfTM+BeTm5fOn5mB1cQM+PvWGZ8F9f37lreefnbvQZAw7+ZHoOzMvJ5UvPx+zg\nAnp+7CWLxc2Pru88+rTF4sU3+owBB38yPQfm5eTypedjdnABPT9222Lx/NN737tY3Hajzxhw8CfT\nc2BeTi5fej5mBxfQ8yOP3LRY3HF694cXi6c+evlHHxlw8CfTc2BeTi5fej5mBxfQ8yMfXCwWrzq9\n+6rl3Q/d4FMGHPzJ9ByYl5PLl56P2cEF9PzIe5cB/8nTuz+5vPu+s4+//59f7w0PbdHDeg7Myun1\n65FHWq5y05+/za49f3kHF3i47dBeqCegO9bz31gG/A2nd39uefc3zz7+7sX1fvzxfonXKZ8sANuT\nvsTN/fnLO9ghO9bzX1sW+s2nd9+yvPvOs4/vXs8BYAfsYM/fcnr3Tcu7bz/7uJ4DwAV2rOe/fv4d\nZM69u8zKh37kev/h3i06+vVv92/zFZQ88NB8XuuDq0N737ZfRbMHZ3RoV39Bd3XbL6LdQw9t+xW0\nW/0SsEe2/SKa3TenQ3t1eWgf3vaLaHbfQw9O/tyegO5Yz9+zDPhrT++uvh/uPTf4lAHfjDjZhN+X\nuk0fuOf9234Jze5eHdp3b/tVNPvQPe/b9kto9pHlkX1k2y+i3T33zOc0WP0P/gPbfhHNGr+/fTf4\nfaktdqznq59Xe/Xp3dXPq33wBp8ycBHK9DxGz2P0PEbPY/S8xY71/NHz7ydzx2Jx046/n4yeZ+h5\njJ7H6HmMnrfYsZ4f3rpYvOD03gsWi1tv9BkDF6FMz2P0PEbPY/Q8Rs9b7FrPX7xY3HJ1fefqLbv/\n+1j0PEPPY/Q8Rs9j9LzFrvX85xeLxXvXd963vPPGG33GwEUo0/MYPY/R8xg9j9HzFrvW8w8tE/5j\n6zs/vrzz4Rt9xsBFKNPzGD2P0fMYPY/R8xa71vPD5y8WT//o8c17n7FYfN8NP2HgIpTpeYyex+h5\njJ7H6HmLnev5O5Zfk//gau0OH3nh8uadN/yEgYtQpucxeh6j5zF6HqPnLXau51dfsqz47e948P63\nP29546U3/oSBi1Cm5zF6HqPnMXoeo+ctdq7nhw/cdvrm7M958MYfP3ARyvQ8Rs9j9DxGz2P0vMXu\n9fzw/hetc/7SBxo+fOAilOl5jJ7H6HmMnsfoeYsd7Pnh1Tt/+Nabb771jruu3vhj9bxAz2P0PEbP\nU/Q8Rs+nGrgIZXoeo+cxeh6j5zF63kLPe+h5jJ7H6HmMnsfoeQs976HnMXoeo+cxeh6j5y30vIee\nx+h5jJ7H6HmMnrfQ8x56HqPnMXoeo+cxet5Cz3voeYyex+h5jJ7H6HkLPe+h5zF6HqPnMXoeo+ct\n9LyHnsfoeYyex+h5jJ630PMeeh6j5zF6HqPnMXreQs976HmMnsfoeYyex+h5Cz3voecxeh6j5zF6\nHqPnLfS8h57H6HmMnsfoeYyet9DzHnoeo+cxeh6j5zF63kLPe+h5jJ7H6HmMnsfoeQs976HnMXoe\no+cxeh6j5y30vIeex+h5jJ7H6HmMnrfQ8x56HqPnMXoeo+cxet5Cz3voeYyex+h5jJ7H6HkLPe+h\n5zF6HqPnMXoeo+ct9LyHnsfoeYyex+h5jJ630PMeeh6j5zF6HqPnMXreQs976HmMnsfoeYyex+h5\nCz3voecxeh6j5zF6HqPnLfS8h57H6HmMnsfoeYyet9DzHnoeo+cxeh6j5zF63kLPe+h5jJ7H6HmM\nnsfoeQs976HnMXoeo+cxeh6j5y30vIeex+h5jJ7H6HmMnrfQ8x56HqPnMXoeo+cxet5Cz3voeYye\nx+h5jJ7H6HkLPe+h5zF6HqPnMXoeo+ct9LyHnsfoeYyex+h5jJ630PMeeh6j5zF6HqPnMXreQs97\n6HmMnsfoeYyex+h5Cz3voecxeh6j5zF6HqPnLfS8h57H6HmMnsfoeYyet9DzHnoeo+cxeh6j5zF6\n3kLPe+h5jJ7H6HmMnsfoeQs976HnMXoeo+cxeh6j5y30vIeex+h5jJ7H6HmMnrfQ8x56HqPnMXoe\no+cxet5Cz3voeYyex+h5jJ7H6HkLPe+h5zF6HqPnMXoeo+ct9LyHnsfoeYyex+h5jJ630PMeeh6j\n5zF6HqPnMXreQs976HmMnsfoeYyex+h5Cz3voecxeh6j5zF6HqPnLfS8h57H6HmMnsfoeYyet9Dz\nHnoeo+cxeh6j5zF63kLPe+h5jJ7H6HmMnsfoeQs976HnMXoeo+cxeh6j5y30vIeex+h5jJ7H6HmM\nnrfQ8x56HqPnMXoeo+cxet5Cz3voeYyex+h5jJ7H6HkLPe+h5zF6HqPnMXoeo+ct9LyHnsfoeYye\nx+h5jJ630PMeeh6j5zF6HqPnMXreQs976HmMnsfoeYyex+h5Cz3voecxeh6j5zF6HqPnLfS8h57H\n6HmMnsfoeYyet9DzHnoeo+cxeh6j5zF63kLPe+h5jJ7H6HmMnsfoeQs976HnMXoeo+cxeh6j5y30\nvIeex+h5jJ7H6HmMnrfQ8x56HqPnMXoeo+cxet5i/j3fpnt+bOmD234VzR5+8JFtv4Rm71od2oe2\n/SqaPfTgo9t+Cc3etjyyr932i2j34INXt/0Smv3i8tD+wrZfRLOrDz647ZfQ7nXLQ/vWbb+IZo8+\nu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+ceu2W55dOO/6h89a1rH3v3+oHLe75xD4uzX+Ov6DlMpucwV6vvNPv0y95O7ervXT76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\nAAAASUVORK5CYII=\n", "prompt_number": 14, "text": [ "<IPython.core.display.Image at 0x7f66217a2e50>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "%%R\n", "stats=read.table(\"R7R73_corrected.mapstats.txt\", header=T)\n", "library(ggplot2)\n", "stats=cbind(stats, \"Frac\" = stats$AlignLen / stats$QueryLen)\n", "stats=cbind(stats, \"idy\" = stats$NumMismatches / stats$AlignLen)\n", "ggplot(stats, aes(x=Frac)) + geom_histogram()\n", "ggsave(\"histo.png\", width=4, height=4)\n", "ggplot(stats, aes(x=idy)) + geom_histogram()\n", "ggsave(\"idy.png\", width=4, height=4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "text": [ "stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.\n", "stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import Image \n", "Image(filename='idy.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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X1w8POjqU93upvKxBlv3b5xYvaxBN78saLi0j9eySrUVxffmfw90XMNzZu1H1AoZXBx0d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\n", "prompt_number": 11, "text": [ "<IPython.core.display.Image at 0x7f66217a2cd0>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "!cat ranges.txt | parallel python nanocorrect.py R7R73_iter1 > R7R73_iter1_corrected.fast" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!bwa mem -t16 -x ont2d ../refs/NC_000913.fna R7R73_iter1_corrected.fasta | samtools view -bS - | samtools sort - R7R73_iter1_corrected.sorted\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": "*" }, { "cell_type": "code", "collapsed": false, "input": [ "!python mappingstats.py R7R73_iter1_corrected.sorted.bam > R7R73_iter1_corrected.mapstats.txt" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
balmandhunter/jupyter-tips-and-tricks
notebooks/04-More_basics.ipynb
4
748309
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter Notebook Basics " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = ['alice', 'jonathan', 'bobby']\n", "ages = [24, 32, 45]\n", "ranks = ['kinda cool', 'really cool', 'insanely cool']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alice 24 kinda cool\n", "jonathan 32 really cool\n", "bobby 45 insanely cool\n" ] } ], "source": [ "for (name, age, rank) in zip(names, ages, ranks):\n", " print name, age, rank" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 alice 24 kinda cool\n", "1 jonathan 32 really cool\n", "2 bobby 45 insanely cool\n" ] } ], "source": [ "for index, (name, age, rank) in enumerate(zip(names, ages, ranks)):\n", " print index, name, age, rank" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# return, esc, shift+enter, ctrl+enter\n", "# text keyboard shortcuts -- cmd > (right), < left,\n", "# option delete (deletes words)\n", "# type \"h\" for help\n", "# tab\n", "# shift-tab\n", "# keyboard shortcuts\n", "# - a, b, y, m, dd, h, ctrl+shift+-" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format='retina'\n", "\n", "import matplotlib.pyplot as plt\n", "# no pylab\n", "import seaborn as sns\n", "sns.set_context('talk')\n", "sns.set_style('darkgrid') \n", "plt.rcParams['figure.figsize'] = 12, 8 # plotsize \n", "\n", "import numpy as np\n", "# don't do `from numpy import *`\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If you have a specific function that you'd like to import\n", "from numpy.random import randn" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10c020290>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3TIsbkp5Lq+GYaWm+gcW5BhoibmLOGc1uS8dBw50gKmZinhRJEATq3dJsVLG8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\nac58yx0rQFlP+7vP0ir2lyZaaZdaw2ta2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s0BZm3R4oVSq3qo1UIAbXst4d8mktQCdXQF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"text/plain": [ "<matplotlib.figure.Figure at 0x10695e6d0>" ] }, "metadata": { "image/png": { "height": 487, "width": 727 } }, "output_type": "display_data" } ], "source": [ "x = np.arange(100)\n", "y = np.sin(x)\n", "plt.plot(x, y)#;" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/jonathan/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n", " \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n" ] } ], "source": [ "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " fig.waiting = false;\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.send_message('closing', {});\n", " fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Close figure', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10ced5d50>]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(10)\n", "y = np.sin(x)\n", "plt.plot(x, y)#;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Magics!\n", "\n", " - % and %% magics\n", " - interact\n", " - embed image\n", " - embed links, youtube\n", " - link notebooks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check out http://matplotlib.org/gallery.html select your favorite." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 00-Overview.html\n", "1 00-Overview.ipynb\n", "1 00-Overview.py\n", "1 01-Initial_Demo.html\n", "1 01-Initial_Demo.ipynb\n", "1 01-Initial_Demo.py\n", "1 02-Exploratory_(Interactive)_Data_Analysis.html\n", "1 02-Exploratory_(Interactive)_Data_Analysis.ipynb\n", "1 02-Exploratory_(Interactive)_Data_Analysis.py\n", "1 03-Some_basics.html\n", "1 03-Some_basics.ipynb\n", "1 03-Some_basics.py\n", "1 04-More_basics.html\n", "1 04-More_basics.ipynb\n", "1 04-More_basics.py\n", "1 05-Pandas_Plotting_and_SQL.html\n", "1 05-Pandas_Plotting_and_SQL.ipynb\n", "1 05-Pandas_Plotting_and_SQL.py\n", "1 06-Extras.html\n", "1 06-Extras.ipynb\n", "1 06-Extras.py\n", "1 07-github-integration.html\n", "1 07-github-integration.ipynb\n", "1 07-github-integration.py\n", "1 Data_Cleaning.ipynb\n", "1 ex1.png\n", "1 example.txt\n", "1 github-integration.ipynb\n", " 347 918 8952 github-integration.ipynb\n", "2 00-Overview.html\n", "2 00-Overview.ipynb\n", "2 00-Overview.py\n", "2 01-Initial_Demo.html\n", "2 01-Initial_Demo.ipynb\n", "2 01-Initial_Demo.py\n", "2 02-Exploratory_(Interactive)_Data_Analysis.html\n", "2 02-Exploratory_(Interactive)_Data_Analysis.ipynb\n", "2 02-Exploratory_(Interactive)_Data_Analysis.py\n", "2 03-Some_basics.html\n", "2 03-Some_basics.ipynb\n", "2 03-Some_basics.py\n", "2 04-More_basics.html\n", "2 04-More_basics.ipynb\n", "2 04-More_basics.py\n", "2 05-Pandas_Plotting_and_SQL.html\n", "2 05-Pandas_Plotting_and_SQL.ipynb\n", "2 05-Pandas_Plotting_and_SQL.py\n", "2 06-Extras.html\n", "2 06-Extras.ipynb\n", "2 06-Extras.py\n", "2 07-github-integration.html\n", "2 07-github-integration.ipynb\n", "2 07-github-integration.py\n", "2 Data_Cleaning.ipynb\n", "2 ex1.png\n", "2 example.txt\n", "2 github-integration.ipynb\n", " 347 918 8952 github-integration.ipynb\n", "3 00-Overview.html\n", "3 00-Overview.ipynb\n", "3 00-Overview.py\n", "3 01-Initial_Demo.html\n", "3 01-Initial_Demo.ipynb\n", "3 01-Initial_Demo.py\n", "3 02-Exploratory_(Interactive)_Data_Analysis.html\n", "3 02-Exploratory_(Interactive)_Data_Analysis.ipynb\n", "3 02-Exploratory_(Interactive)_Data_Analysis.py\n", "3 03-Some_basics.html\n", "3 03-Some_basics.ipynb\n", "3 03-Some_basics.py\n", "3 04-More_basics.html\n", "3 04-More_basics.ipynb\n", "3 04-More_basics.py\n", "3 05-Pandas_Plotting_and_SQL.html\n", "3 05-Pandas_Plotting_and_SQL.ipynb\n", "3 05-Pandas_Plotting_and_SQL.py\n", "3 06-Extras.html\n", "3 06-Extras.ipynb\n", "3 06-Extras.py\n", "3 07-github-integration.html\n", "3 07-github-integration.ipynb\n", "3 07-github-integration.py\n", "3 Data_Cleaning.ipynb\n", "3 ex1.png\n", "3 example.txt\n", "3 github-integration.ipynb\n", " 347 918 8952 github-integration.ipynb\n", "4 00-Overview.html\n", "4 00-Overview.ipynb\n", "4 00-Overview.py\n", "4 01-Initial_Demo.html\n", "4 01-Initial_Demo.ipynb\n", "4 01-Initial_Demo.py\n", "4 02-Exploratory_(Interactive)_Data_Analysis.html\n", "4 02-Exploratory_(Interactive)_Data_Analysis.ipynb\n", "4 02-Exploratory_(Interactive)_Data_Analysis.py\n", "4 03-Some_basics.html\n", "4 03-Some_basics.ipynb\n", "4 03-Some_basics.py\n", "4 04-More_basics.html\n", "4 04-More_basics.ipynb\n", "4 04-More_basics.py\n", "4 05-Pandas_Plotting_and_SQL.html\n", "4 05-Pandas_Plotting_and_SQL.ipynb\n", "4 05-Pandas_Plotting_and_SQL.py\n", "4 06-Extras.html\n", "4 06-Extras.ipynb\n", "4 06-Extras.py\n", "4 07-github-integration.html\n", "4 07-github-integration.ipynb\n", "4 07-github-integration.py\n", "4 Data_Cleaning.ipynb\n", "4 ex1.png\n", "4 example.txt\n", "4 github-integration.ipynb\n", " 347 918 8952 github-integration.ipynb\n", "5 00-Overview.html\n", "5 00-Overview.ipynb\n", "5 00-Overview.py\n", "5 01-Initial_Demo.html\n", "5 01-Initial_Demo.ipynb\n", "5 01-Initial_Demo.py\n", "5 02-Exploratory_(Interactive)_Data_Analysis.html\n", "5 02-Exploratory_(Interactive)_Data_Analysis.ipynb\n", "5 02-Exploratory_(Interactive)_Data_Analysis.py\n", "5 03-Some_basics.html\n", "5 03-Some_basics.ipynb\n", "5 03-Some_basics.py\n", "5 04-More_basics.html\n", "5 04-More_basics.ipynb\n", "5 04-More_basics.py\n", "5 05-Pandas_Plotting_and_SQL.html\n", "5 05-Pandas_Plotting_and_SQL.ipynb\n", "5 05-Pandas_Plotting_and_SQL.py\n", "5 06-Extras.html\n", "5 06-Extras.ipynb\n", "5 06-Extras.py\n", "5 07-github-integration.html\n", "5 07-github-integration.ipynb\n", "5 07-github-integration.py\n", "5 Data_Cleaning.ipynb\n", "5 ex1.png\n", "5 example.txt\n", "5 github-integration.ipynb\n", " 347 918 8952 github-integration.ipynb\n" ] } ], "source": [ "%%bash\n", "for num in {1..5}\n", "do\n", " for infile in *;\n", " do\n", " echo $num $infile\n", " done\n", " wc $infile\n", "done" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hi\n", "/Users/jonathan/github/jupyter-best-practices/notebooks\r\n" ] } ], "source": [ "print \"hi\"\n", "!pwd" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PING google.com (216.58.192.46): 56 data bytes\n", "64 bytes from 216.58.192.46: icmp_seq=0 ttl=57 time=14.025 ms\n", "64 bytes from 216.58.192.46: icmp_seq=1 ttl=57 time=14.696 ms\n", "64 bytes from 216.58.192.46: icmp_seq=2 ttl=57 time=16.200 ms\n", "64 bytes from 216.58.192.46: icmp_seq=3 ttl=57 time=14.233 ms\n", "^C\n", "--- google.com ping statistics ---\n", "4 packets transmitted, 4 packets received, 0.0% packet loss\n", "round-trip min/avg/max/stddev = 14.025/14.788/16.200/0.850 ms\n", "\n" ] } ], "source": [ "!ping google.com" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "this_is_magic = \"Can you believe you can pass variables and strings like this?\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hey = !echo $this_is_magic" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Can you believe you can pass variables and strings like this?']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Numpy \n", "\n", "If you have arrays of numbers, use `numpy` or `pandas` (built on `numpy`) to represent the data. Tons of very fast underlying code." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 1 2 ..., 9997 9998 9999]\n" ] } ], "source": [ "x = np.arange(10000)\n", "\n", "print x # smart printing" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "9999\n", "[0 1 2 3 4]\n", "[ 0 1 2 ..., 9997 9998 9999]\n" ] } ], "source": [ "print x[0] # first element \n", "print x[-1] # last element\n", "print x[0:5] # first 5 elements (also x[:5])\n", "print x[:] # \"Everything\"" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9995 9996 9997 9998 9999]\n" ] } ], "source": [ "print x[-5:] # last five elements" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9995 9996 9997]\n" ] } ], "source": [ "print x[-5:-2]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9995 9996 9997 9998]\n" ] } ], "source": [ "print x[-5:-1] # not final value -- not inclusive on right" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.random.randint(5, 5000, (3, 5))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1674, 2256, 3525, 3811, 2539],\n", " [2051, 2165, 3020, 859, 4961],\n", " [3581, 3649, 3196, 2494, 1693]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "41474" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(x)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "41474" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.sum()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "41474" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(x)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([7306, 8070, 9741, 7164, 9193])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(x, axis=0)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([13805, 13056, 14613])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(x, axis=1)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([13805, 13056, 14613])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.sum(axis=1)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([3525, 3020, 3196])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Multi dimension array slice with a comma\n", "x[:, 2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = np.linspace(10, 20, 11)\n", "y" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.linspace?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.linspace()\n", "# shift-tab; shift-tab-tab\n", "np." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def does_it(first=x, second=y):\n", " \"\"\"This is my doc\"\"\"\n", " pass" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 13., 15., 17.])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[[3, 5, 7]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "does_it()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "num = 3000\n", "x = np.linspace(1.0, 300.0, num)\n", "y = np.random.rand(num)\n", "z = np.sin(x)\n", "np.savetxt(\"example.txt\", np.transpose((x, y, z)))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%less example.txt" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 3000 9000 226497 example.txt\r\n" ] } ], "source": [ "!wc example.txt" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.000000000000000000e+00 6.755524804082274626e-01 8.414709848078965049e-01\r\n", "1.099699899966655625e+00 2.573566361522948709e-01 8.910711957212333889e-01\r\n", "1.199399799933311028e+00 1.668969491698735208e-02 9.318214309529510020e-01\r\n", "1.299099699899966653e+00 2.776170095289725026e-01 9.633169657262274921e-01\r\n", "1.398799599866622279e+00 2.211580773928156773e-01 9.852449914594796354e-01\r\n", "1.498499499833277682e+00 2.385799302492821461e-01 9.973877225308434014e-01\r\n", "1.598199399799933307e+00 3.891476454005263763e-01 9.996245592899137833e-01\r\n", "1.697899299766588932e+00 6.537744236852294222e-02 9.919332858340597081e-01\r\n", "1.797599199733244557e+00 9.270871277442276348e-01 9.743902906531928254e-01\r\n", "1.897299099699899960e+00 9.065700223682380265e-01 9.471698079515821211e-01\r\n" ] } ], "source": [ "!head example.txt" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1', '1', '1', '1', '1', '1', '1', '1', '1', '1']" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Not a good idea\n", "a = []\n", "b = []\n", "for line in open(\"example.txt\", 'r'):\n", " a.append(line[0])\n", " b.append(line[2])\n", " \n", "a[:10] # Whoops! " ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1.000000000000000000e+00',\n", " '1.099699899966655625e+00',\n", " '1.199399799933311028e+00',\n", " '1.299099699899966653e+00',\n", " '1.398799599866622279e+00',\n", " '1.498499499833277682e+00',\n", " '1.598199399799933307e+00',\n", " '1.697899299766588932e+00',\n", " '1.797599199733244557e+00',\n", " '1.897299099699899960e+00']" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = []\n", "b = []\n", "for line in open(\"example.txt\", 'r'):\n", " line = line.split()\n", " a.append(line[0])\n", " b.append(line[2])\n", " \n", "a[:10] # Strings! " ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1.0,\n", " 1.0996998999666556,\n", " 1.199399799933311,\n", " 1.2990996998999667,\n", " 1.3987995998666223,\n", " 1.4984994998332777,\n", " 1.5981993997999333,\n", " 1.697899299766589,\n", " 1.7975991997332446,\n", " 1.8972990996999]" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = []\n", "b = []\n", "for line in open(\"example.txt\", 'r'):\n", " line = line.split()\n", " a.append(float(line[0]))\n", " b.append(float(line[2]))\n", " \n", "a[:10] # Lists!" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Do this!\n", "a, b = np.loadtxt(\"example.txt\", unpack=True, usecols=(0,2))" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.0996999, 1.1993998, ..., 299.8006002,\n", " 299.9003001, 300. ])" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Matplotlib and Numpy \n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy.random import randn" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10da07850>" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Zvd6f/fsnyx4DAAAAoBDHZQAAAAAAUJjIDAAAAABAYSIzAAAAAACFicwAAAAA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\ns5jkziQ92aot7kqns5ipqfm022vlhgRgR0pmAAA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"text/plain": [ "<matplotlib.figure.Figure at 0x10cf97550>" ] }, "metadata": { "image/png": { "height": 487, "width": 716 } }, "output_type": "display_data" } ], "source": [ "num = 50\n", "x = np.linspace(2.5, 300, num)\n", "y = randn(num)\n", "plt.scatter(x, y)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, False, False, False, False, False, True, True,\n", " False, False, False, False, False, False, True, False, False,\n", " False, False, False, False, True, False, False, False, False,\n", " True, False, True, False, False, False, False, False, False,\n", " False, False, False, False, True, False, False, True, False,\n", " False, False, True, False, False], dtype=bool)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y > 1" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1.83893164, 1.74924294, 1.63759596, 1.61812317, 1.0952195 ,\n", " 1.65513944, 1.55212009, 1.34197357, 1.16727989])" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[y > 1]" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.43218211, 0.53655612, 0.19196851, 0.18948493, 0.82077996,\n", " -0.22307154, 0.96933913, 0.6985783 , 0.38431027, 0.1857278 ,\n", " -0.31456629, -0.44862121, -0.36135728, -0.00888057, -0.39866509,\n", " -0.91453153, -0.02413912, -0.65570203, -0.490552 , -0.19144114,\n", " 0.19980678, -0.44656607, -0.03864452, 0.09292263, -0.52100933,\n", " 0.64195428, -0.29391021, -0.7878031 , -0.03254576, -0.03610955,\n", " 0.53898577, 0.09182785])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[(y < 1) & (y > -1)]" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10da8ee50>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10695ea90>" ] }, "metadata": { "image/png": { "height": 487, "width": 716 } }, "output_type": "display_data" } ], "source": [ "plt.scatter(x, y, c='b', s=50)\n", "plt.scatter(x[(y < 1) & (y > -1)], y[(y < 1) & (y > -1)], c='r', s=50)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10db42150>" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MtLe3J0mmT5+ez3/+82ltbc3999+fBx54YI/n3RFeH3HEEdUvGniRrq6pmTdv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\nAAAAAMURrwEAAAAAKI54DQAAAABAccR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"text/plain": [ "<matplotlib.figure.Figure at 0x10db425d0>" ] }, "metadata": { "image/png": { "height": 487, "width": 727 } }, "output_type": "display_data" } ], "source": [ "y[~((y < 1) & (y > -1))] = 1.0\n", "plt.scatter(x, y, c='b')\n", "plt.scatter(x, np.clip(y, -0.5, 0.5), color='red')" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x1112edb10>" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AALCgzW3X8LIkNyS5NseHmMm1Of/8A/O2Z3chdQgfXVGxdOnPpqnpx5Pcm3L5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\noyH3AgAAKIKaNgMEA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"text/plain": [ "<matplotlib.figure.Figure at 0x10f906a10>" ] }, "metadata": { "image/png": { "height": 481, "width": 717 } }, "output_type": "display_data" } ], "source": [ "num = 350\n", "slope = 0.3\n", "x = randn(num) * 50. + 150.0 \n", "y = randn(num) * 5 + x * slope\n", "plt.scatter(x, y, c='b')" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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eepXtZ70annoVPLRKZlglamcYZBE1kSRJqBYlVFdLqBZF28+iBFGUYBUlVFeL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LAAAg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sEPPTPp318/5dA1YIrHC5FWWzqWNiNBUEAQAAgCZCkhUibOGGJv60p/pcmhRw/ZkKghFq38ZO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\n/VY5OTm+ZXv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"text/plain": [ "<matplotlib.figure.Figure at 0x10326b510>" ] }, "metadata": { "image/png": { "height": 424, "width": 428 } }, "output_type": "display_data" } ], "source": [ "# plt.scatter(x[(y < 1) & (y > -1)], y[(y < 1) & (y > -1)], c='r')\n", "# np.argsort, np.sort, complicated index slicing\n", "dframe = pd.DataFrame({'x': x, 'y': y})\n", "g = sns.jointplot('x', 'y', data=dframe, kind=\"reg\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grab Python version of ggplot http://ggplot.yhathq.com/" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ggplot import ggplot, aes, geom_line, stat_smooth, geom_dotplot, geom_point" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AApYOUaPR6EXbDORKa2urli5dOupE+6efftrVvqXpMLWrq2vEa13O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TN/Db3+nOLjxUvOdaRKovc4qLGxETk5OZg4cSI6Ojqwbds2tLe3hz53Op34zne+A7fbDYBT+ImI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\nw6U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"text/plain": [ "<matplotlib.figure.Figure at 0x11457dd10>" ] }, "metadata": { "image/png": { "height": 501, "width": 681 } }, "output_type": "display_data" }, { "data": { "text/plain": [ "<ggplot: (289766805)>" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ggplot(aes(x='x', y='y'), data=dframe) + geom_point() + stat_smooth(colour='blue', span=0.2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
tensorflow/docs
site/en/tutorials/images/cnn.ipynb
2
12130
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "x4HI2mpwlrcn" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "679Lmwt3l1Bk" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "DSPCom-KmApV" }, "source": [ "# Convolutional Neural Network (CNN)" ] }, { "cell_type": "markdown", "metadata": { "id": "klAltGp8ycek" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/images/cnn\">\n", " <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n", " View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/cnn.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n", " Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/cnn.ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n", " View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/images/cnn.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "qLGkt5qiyz4E" }, "source": [ "This tutorial demonstrates training a simple [Convolutional Neural Network](https://developers.google.com/machine-learning/glossary/#convolutional_neural_network) (CNN) to classify [CIFAR images](https://www.cs.toronto.edu/~kriz/cifar.html). Because this tutorial uses the [Keras Sequential API](https://www.tensorflow.org/guide/keras/overview), creating and training your model will take just a few lines of code.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "m7KBpffWzlxH" }, "source": [ "### Import TensorFlow" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iAve6DCL4JH4" }, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "from tensorflow.keras import datasets, layers, models\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "id": "jRFxccghyMVo" }, "source": [ "### Download and prepare the CIFAR10 dataset\n", "\n", "\n", "The CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes are mutually exclusive and there is no overlap between them." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JWoEqyMuXFF4" }, "outputs": [], "source": [ "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n", "\n", "# Normalize pixel values to be between 0 and 1\n", "train_images, test_images = train_images / 255.0, test_images / 255.0" ] }, { "cell_type": "markdown", "metadata": { "id": "7wArwCTJJlUa" }, "source": [ "### Verify the data\n", "\n", "To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "K3PAELE2eSU9" }, "outputs": [], "source": [ "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n", " 'dog', 'frog', 'horse', 'ship', 'truck']\n", "\n", "plt.figure(figsize=(10,10))\n", "for i in range(25):\n", " plt.subplot(5,5,i+1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.grid(False)\n", " plt.imshow(train_images[i])\n", " # The CIFAR labels happen to be arrays, \n", " # which is why you need the extra index\n", " plt.xlabel(class_names[train_labels[i][0]])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "Oewp-wYg31t9" }, "source": [ "### Create the convolutional base" ] }, { "cell_type": "markdown", "metadata": { "id": "3hQvqXpNyN3x" }, "source": [ "The 6 lines of code below define the convolutional base using a common pattern: a stack of [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) and [MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D) layers.\n", "\n", "As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure your CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument `input_shape` to your first layer.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "L9YmGQBQPrdn" }, "outputs": [], "source": [ "model = models.Sequential()\n", "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n", "model.add(layers.MaxPooling2D((2, 2)))\n", "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n", "model.add(layers.MaxPooling2D((2, 2)))\n", "model.add(layers.Conv2D(64, (3, 3), activation='relu'))" ] }, { "cell_type": "markdown", "metadata": { "id": "lvDVFkg-2DPm" }, "source": [ "Let's display the architecture of your model so far:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8-C4XBg4UTJy" }, "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "_j-AXYeZ2GO5" }, "source": [ "Above, you can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer." ] }, { "cell_type": "markdown", "metadata": { "id": "_v8sVOtG37bT" }, "source": [ "### Add Dense layers on top\n", "\n", "To complete the model, you will feed the last output tensor from the convolutional base (of shape (4, 4, 64)) into one or more Dense layers to perform classification. Dense layers take vectors as input (which are 1D), while the current output is a 3D tensor. First, you will flatten (or unroll) the 3D output to 1D, then add one or more Dense layers on top. CIFAR has 10 output classes, so you use a final Dense layer with 10 outputs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mRs95d6LUVEi" }, "outputs": [], "source": [ "model.add(layers.Flatten())\n", "model.add(layers.Dense(64, activation='relu'))\n", "model.add(layers.Dense(10))" ] }, { "cell_type": "markdown", "metadata": { "id": "ipGiQMcR4Gtq" }, "source": [ "Here's the complete architecture of your model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8Yu_m-TZUWGX" }, "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "xNKXi-Gy3RO-" }, "source": [ "The network summary shows that (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers." ] }, { "cell_type": "markdown", "metadata": { "id": "P3odqfHP4M67" }, "source": [ "### Compile and train the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MdDzI75PUXrG" }, "outputs": [], "source": [ "model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", "\n", "history = model.fit(train_images, train_labels, epochs=10, \n", " validation_data=(test_images, test_labels))" ] }, { "cell_type": "markdown", "metadata": { "id": "jKgyC5K_4O0d" }, "source": [ "### Evaluate the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gtyDF0MKUcM7" }, "outputs": [], "source": [ "plt.plot(history.history['accuracy'], label='accuracy')\n", "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.ylim([0.5, 1])\n", "plt.legend(loc='lower right')\n", "\n", "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0LvwaKhtUdOo" }, "outputs": [], "source": [ "print(test_acc)" ] }, { "cell_type": "markdown", "metadata": { "id": "8cfJ8AR03gT5" }, "source": [ "Your simple CNN has achieved a test accuracy of over 70%. Not bad for a few lines of code! For another CNN style, check out the [TensorFlow 2 quickstart for experts](https://www.tensorflow.org/tutorials/quickstart/advanced) example that uses the Keras subclassing API and `tf.GradientTape`." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "cnn.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
dsteurer/cs4814fa15
turing/turing.ipynb
1
28866
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/david/anaconda3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n" ] }, { "data": { "text/html": [ "\n", "<style type=\"text/css\">\n", " .state {\n", " width: 110px;\n", " height: 50px;\n", " background: pink;\n", " font-weight: bold;\n", " }\n", " .box {\n", " display: flex;\n", " justify-content: center;\n", " align-items: center;\n", " border: 3px solid grey;\n", " margin: 5px;\n", " padding: 5px;\n", " }\n", " .tape {\n", " width: 50px;\n", " height: 50px;\n", " }\n", " .head {\n", " background: yellow;\n", " font-weight: bold;\n", " }\n", " .configuration {\n", " display: flex;\n", " flex-wrap: wrap;\n", " flex-direction: row;\n", " }\n", "</style>\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%run -i turing.py\n", "init()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Turing machine computation\n", "\n", "## Tape\n", "\n", "We will represent the tape as a list of *tape symbols* and we will represent tape symbols as Python strings.\n", "The string `' '` represents the *blank symbol*.\n", "The string `'|>'` represents the *start symbol*, which indicates the beginning of the tape.\n", "\n", "## States\n", "\n", "We will also encode states as Python strings. \n", "The string `'start'` represents that start state.\n", "The strings `'accept'`, `'reject'`, and `'halt'` represent final states of the machine, that indicate acceptance, rejection, and halting, respectively. \n", "\n", "## Simulation\n", "\n", "The following function simulates a given Turing machine for a given number of steps on a given input" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run(transitions, input, steps):\n", " \"\"\"simulate Turing machine for the given number of steps and the given input\"\"\"\n", "\n", " # convert input from string to list of symbols\n", " # we use '|>' as a symbol to indicate the beginning of the tape\n", " input = ['|>'] + list(input) + [' ']\n", "\n", " # sanitize transitions for 'accept' and 'reject' states and for symbol '|>'\n", " transitions = sanitize_transitions(transitions)\n", "\n", " # create initial configuration\n", " c = Configuration(state='start', head=1, tape=input)\n", "\n", " for i in range(0, steps):\n", " # read tape content under head\n", " current = c.state\n", " read = c.tape[c.head]\n", "\n", " # lookup transition based on state and read symbol\n", " next, write, move = transitions(current, read)\n", "\n", " # update configuration\n", " c.state = next\n", " c.tape[c.head] = write\n", " c.head += move\n", " if c.head >= len(c.tape):\n", " c.tape += [' ']\n", "\n", " # return final configuration\n", " return c\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following function checks that the transition functions satisfies some simple syntactic requirements (don't move to the left of the start symbol, don't remove or add start symbols, don't change state after accepting, rejecting, or halting.)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def check_transitions(transitions, states, alphabet):\n", "\n", " transitions = sanitize_transitions(transitions)\n", "\n", " for current in states:\n", " for read in alphabet:\n", " next, write, move = transitions(current, read)\n", "\n", " # we either stay in place or move one position\n", " # to the left or right\n", " assert(move in [-1,0,1])\n", "\n", " # if we read the begin symbol,\n", " if read == '|>':\n", " # we need to write it back\n", " assert(write == '|>')\n", " # we need to move to the right\n", " assert(move == 1)\n", " else:\n", " # we cannot write the begin symbol\n", " assert(write != '|>')\n", "\n", " # if we are in one of the final states\n", " if current in ['accept', 'reject', 'halt']:\n", " # we cannot change to a different state\n", " assert(next == current)\n", "\n", " print(\"transition checks passed\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "## Copy machine\n", "\n", "The following Turing machine copies its input, i.e., it computes the function $f(x)=xx$. \n", "The actual implementation uses different versions of the `'0'` and `'1'` symbol (called `'0-read'`, `'0-write'` and `'1-read'`, `'1-write'`) in the two copies of the string $x$.\n", "We could replace those by regular `'0'` and `'1'` symbols by sweeping once more over the tape before the end of the computation. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def transitions_copy(current, read):\n", " if read == '|>':\n", " return 'start', read, 1\n", " elif current == 'start':\n", " if 'write' not in read:\n", " return read + '-write', read + '-read', 1\n", " else:\n", " return 'accept', read, 1\n", " elif 'write' in current:\n", " if read != ' ':\n", " return current, read, 1\n", " else:\n", " return 'rewind', current, -1\n", " elif current == 'rewind':\n", " if 'read' not in read:\n", " return current, read, -1\n", " else:\n", " return 'start', read, 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the full transitions function table of the machine:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "transition checks passed\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>current</th>\n", " <th>read</th>\n", " <th>next</th>\n", " <th>write</th>\n", " <th>move</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>start</td>\n", " <td>0</td>\n", " <td>0-write</td>\n", " <td>0-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>start</td>\n", " <td>1</td>\n", " <td>1-write</td>\n", " <td>1-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>start</td>\n", " <td>0-read</td>\n", " <td>0-read-write</td>\n", " <td>0-read-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>start</td>\n", " <td>1-read</td>\n", " <td>1-read-write</td>\n", " <td>1-read-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>start</td>\n", " <td>0-write</td>\n", " <td>accept</td>\n", " <td>0-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>start</td>\n", " <td>1-write</td>\n", " <td>accept</td>\n", " <td>1-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0-write</td>\n", " <td>0</td>\n", " <td>0-write</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0-write</td>\n", " <td>1</td>\n", " <td>0-write</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>0-write</td>\n", " <td>0-read</td>\n", " <td>0-write</td>\n", " <td>0-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>0-write</td>\n", " <td>1-read</td>\n", " <td>0-write</td>\n", " <td>1-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>0-write</td>\n", " <td>0-write</td>\n", " <td>0-write</td>\n", " <td>0-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>0-write</td>\n", " <td>1-write</td>\n", " <td>0-write</td>\n", " <td>1-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>1-write</td>\n", " <td>0</td>\n", " <td>1-write</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>1-write</td>\n", " <td>1</td>\n", " <td>1-write</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>1-write</td>\n", " <td>0-read</td>\n", " <td>1-write</td>\n", " <td>0-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>1-write</td>\n", " <td>1-read</td>\n", " <td>1-write</td>\n", " <td>1-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>1-write</td>\n", " <td>0-write</td>\n", " <td>1-write</td>\n", " <td>0-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>1-write</td>\n", " <td>1-write</td>\n", " <td>1-write</td>\n", " <td>1-write</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>rewind</td>\n", " <td>0</td>\n", " <td>rewind</td>\n", " <td>0</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>rewind</td>\n", " <td>1</td>\n", " <td>rewind</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>rewind</td>\n", " <td>0-read</td>\n", " <td>start</td>\n", " <td>0-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>rewind</td>\n", " <td>1-read</td>\n", " <td>start</td>\n", " <td>1-read</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>rewind</td>\n", " <td>0-write</td>\n", " <td>rewind</td>\n", " <td>0-write</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>rewind</td>\n", " <td>1-write</td>\n", " <td>rewind</td>\n", " <td>1-write</td>\n", " <td>-1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " current read next write move\n", "0 start 0 0-write 0-read 1\n", "1 start 1 1-write 1-read 1\n", "2 start 0-read 0-read-write 0-read-read 1\n", "3 start 1-read 1-read-write 1-read-read 1\n", "4 start 0-write accept 0-write 1\n", "5 start 1-write accept 1-write 1\n", "6 0-write 0 0-write 0 1\n", "7 0-write 1 0-write 1 1\n", "8 0-write 0-read 0-write 0-read 1\n", "9 0-write 1-read 0-write 1-read 1\n", "10 0-write 0-write 0-write 0-write 1\n", "11 0-write 1-write 0-write 1-write 1\n", "12 1-write 0 1-write 0 1\n", "13 1-write 1 1-write 1 1\n", "14 1-write 0-read 1-write 0-read 1\n", "15 1-write 1-read 1-write 1-read 1\n", "16 1-write 0-write 1-write 0-write 1\n", "17 1-write 1-write 1-write 1-write 1\n", "18 rewind 0 rewind 0 -1\n", "19 rewind 1 rewind 1 -1\n", "20 rewind 0-read start 0-read 1\n", "21 rewind 1-read start 1-read 1\n", "22 rewind 0-write rewind 0-write -1\n", "23 rewind 1-write rewind 1-write -1" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transitions_table(transitions_copy, \n", " ['start', '0-write', '1-write', 'rewind'],\n", " ['0', '1', '0-read', '1-read', '0-write', '1-write'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an interactive simulation of the copy Turing machine (requires that ipython notebook is run locally).\n", "You can either click on the `simulate` button to view the computation during a given range of steps or you can drag the `current step` slider to view the configuration of the machine at a particular step. (If you click on the `current step` slider, you can also change it using the arrow keys.)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div class='configuration'><span class='box state'>start</span><span class='box tape'>|></span><span class='box tape head'>1</span><span class='box tape'>0</span><span class='box tape'>0</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>&nbsp;</span></div>" ], "text/plain": [ "<__main__.Configuration at 0x7f30e8f18978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simulate(transitions_copy, input='10011', unary=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Power-of-2 machine\n", "\n", "The following Turing machine determines if the input is the unary encoding of a power of 2.\n", "Furthermore, given any string $1^n$, it outputs a string of the form $\\{0,1\\}^n2^i$, where $i$ is the largest number such that $2^i$ divides $n$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def transitions_power(current,read):\n", " if read == '|>':\n", " return 'start', read, 1;\n", " elif current == 'rewind':\n", " return current, read, -1\n", " elif read == 'x':\n", " return current, read, 1 \n", " elif current == 'start':\n", " if read != '1':\n", " return 'reject', read, 1\n", " else: \n", " return 'start-even', read, 1\n", " elif 'even' in current and read == '1':\n", " return 'odd', 'x', 1\n", " elif current == 'odd' and read == '1':\n", " return 'even', read, 1\n", " elif current == 'odd':\n", " if read == ' ':\n", " return 'rewind', '2', -1\n", " else:\n", " return current, read, 1\n", " elif current == 'start-even' and read != '1':\n", " return 'accept', read, -1\n", " elif current == 'even' and read != '1':\n", " return 'reject', read, -1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the full transition function table of the Turing machine:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "transition checks passed\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>current</th>\n", " <th>read</th>\n", " <th>next</th>\n", " <th>write</th>\n", " <th>move</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>start</td>\n", " <td>0</td>\n", " <td>reject</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>start</td>\n", " <td>1</td>\n", " <td>start-even</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>start</td>\n", " <td>x</td>\n", " <td>start</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>start</td>\n", " <td></td>\n", " <td>reject</td>\n", " <td></td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>start-even</td>\n", " <td>0</td>\n", " <td>accept</td>\n", " <td>0</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>start-even</td>\n", " <td>1</td>\n", " <td>odd</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>start-even</td>\n", " <td>x</td>\n", " <td>start-even</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>start-even</td>\n", " <td></td>\n", " <td>accept</td>\n", " <td></td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>start-even</td>\n", " <td>|&gt;</td>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>even</td>\n", " <td>0</td>\n", " <td>reject</td>\n", " <td>0</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>even</td>\n", " <td>1</td>\n", " <td>odd</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>even</td>\n", " <td>x</td>\n", " <td>even</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>even</td>\n", " <td></td>\n", " <td>reject</td>\n", " <td></td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>even</td>\n", " <td>|&gt;</td>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>odd</td>\n", " <td>0</td>\n", " <td>odd</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>odd</td>\n", " <td>1</td>\n", " <td>even</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>odd</td>\n", " <td>x</td>\n", " <td>odd</td>\n", " <td>x</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>odd</td>\n", " <td></td>\n", " <td>rewind</td>\n", " <td>2</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>odd</td>\n", " <td>|&gt;</td>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>rewind</td>\n", " <td>0</td>\n", " <td>rewind</td>\n", " <td>0</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>rewind</td>\n", " <td>1</td>\n", " <td>rewind</td>\n", " <td>1</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>rewind</td>\n", " <td>x</td>\n", " <td>rewind</td>\n", " <td>x</td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>rewind</td>\n", " <td></td>\n", " <td>rewind</td>\n", " <td></td>\n", " <td>-1</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>rewind</td>\n", " <td>|&gt;</td>\n", " <td>start</td>\n", " <td>|&gt;</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " current read next write move\n", "0 start 0 reject 0 1\n", "1 start 1 start-even 1 1\n", "2 start x start x 1\n", "3 start reject 1\n", "4 start |> start |> 1\n", "5 start-even 0 accept 0 -1\n", "6 start-even 1 odd x 1\n", "7 start-even x start-even x 1\n", "8 start-even accept -1\n", "9 start-even |> start |> 1\n", "10 even 0 reject 0 -1\n", "11 even 1 odd x 1\n", "12 even x even x 1\n", "13 even reject -1\n", "14 even |> start |> 1\n", "15 odd 0 odd 0 1\n", "16 odd 1 even 1 1\n", "17 odd x odd x 1\n", "18 odd rewind 2 -1\n", "19 odd |> start |> 1\n", "20 rewind 0 rewind 0 -1\n", "21 rewind 1 rewind 1 -1\n", "22 rewind x rewind x -1\n", "23 rewind rewind -1\n", "24 rewind |> start |> 1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transitions_table(transitions_power, \n", " ['start', 'start-even', 'even', 'odd', 'rewind'], \n", " ['0', '1', 'x', ' ', '|>'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an interactive simulation of the power Turing machine (requires that ipython notebook is run locally).\n", "You can either click on the `simulate` button to view the computation during a given range of steps or you can drag the `current step` slider to view the configuration of the machine at a particular step.\n", "(If you click on the `current step` slider, you can also change it using the arrow keys.)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div class='configuration'><span class='box state'>start</span><span class='box tape'>|></span><span class='box tape head'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>1</span><span class='box tape'>&nbsp;</span></div>" ], "text/plain": [ "<__main__.Configuration at 0x7f310c5df320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simulate(transitions_power, input_unary=16, step_to=200, unary=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mne-tools/mne-tools.github.io
stable/_downloads/64e3b6395952064c08d4ff33d6236ff3/evoked_whitening.ipynb
1
4928
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Whitening evoked data with a noise covariance\n\nEvoked data are loaded and then whitened using a given noise covariance\nmatrix. It's an excellent quality check to see if baseline signals match\nthe assumption of Gaussian white noise during the baseline period.\n\nCovariance estimation and diagnostic plots are based on\n:footcite:`EngemannGramfort2015`.\n\n## References\n.. footbibliography::\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Authors: Alexandre Gramfort <[email protected]>\n# Denis A. Engemann <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import mne\n\nfrom mne import io\nfrom mne.datasets import sample\nfrom mne.cov import compute_covariance\n\nprint(__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set parameters\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_path = sample.data_path()\nmeg_path = data_path / 'MEG' / 'sample'\nraw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'\nevent_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif'\n\nraw = io.read_raw_fif(raw_fname, preload=True)\nraw.filter(1, 40, n_jobs=1, fir_design='firwin')\nraw.info['bads'] += ['MEG 2443'] # bads + 1 more\nevents = mne.read_events(event_fname)\n\n# let's look at rare events, button presses\nevent_id, tmin, tmax = 2, -0.2, 0.5\nreject = dict(mag=4e-12, grad=4000e-13, eeg=80e-6)\n\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=('meg', 'eeg'),\n baseline=None, reject=reject, preload=True)\n\n# Uncomment next line to use fewer samples and study regularization effects\n# epochs = epochs[:20] # For your data, use as many samples as you can!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute covariance using automated regularization\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "method_params = dict(diagonal_fixed=dict(mag=0.01, grad=0.01, eeg=0.01))\nnoise_covs = compute_covariance(epochs, tmin=None, tmax=0, method='auto',\n return_estimators=True, verbose=True, n_jobs=1,\n projs=None, rank=None,\n method_params=method_params)\n\n# With \"return_estimator=True\" all estimated covariances sorted\n# by log-likelihood are returned.\n\nprint('Covariance estimates sorted from best to worst')\nfor c in noise_covs:\n print(\"%s : %s\" % (c['method'], c['loglik']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the evoked data:\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evoked = epochs.average()\n\nevoked.plot(time_unit='s') # plot evoked response" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then show whitening for our various noise covariance estimates.\n\nHere we should look to see if baseline signals match the\nassumption of Gaussian white noise. we expect values centered at\n0 within 2 standard deviations for 95% of the time points.\n\nFor the Global field power we expect a value of 1.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "evoked.plot_white(noise_covs, time_unit='s')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
gutouyu/cs231n
cs231n/assignment/assignment2/TensorFlow.ipynb
3
31003
{ "nbformat_minor": 1, "nbformat": 4, "cells": [ { "source": [ "## What's this TensorFlow business?\n", "\n", "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n", "\n", "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)\n", "\n", "#### What is it?\n", "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n", "\n", "#### Why?\n", "\n", "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n", "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n", "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n", "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. " ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## How will I learn TensorFlow?\n", "\n", "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n", "\n", "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "## Load Datasets\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import math\n", "import timeit\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "from cs231n.data_utils import load_CIFAR10\n", "\n", "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\n", " \"\"\"\n", " Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n", " it for the two-layer neural net classifier. These are the same steps as\n", " we used for the SVM, but condensed to a single function. \n", " \"\"\"\n", " # Load the raw CIFAR-10 data\n", " cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", " X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", " # Subsample the data\n", " mask = range(num_training, num_training + num_validation)\n", " X_val = X_train[mask]\n", " y_val = y_train[mask]\n", " mask = range(num_training)\n", " X_train = X_train[mask]\n", " y_train = y_train[mask]\n", " mask = range(num_test)\n", " X_test = X_test[mask]\n", " y_test = y_test[mask]\n", "\n", " # Normalize the data: subtract the mean image\n", " mean_image = np.mean(X_train, axis=0)\n", " X_train -= mean_image\n", " X_val -= mean_image\n", " X_test -= mean_image\n", "\n", " return X_train, y_train, X_val, y_val, X_test, y_test\n", "\n", "\n", "# Invoke the above function to get our data.\n", "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n", "print('Train data shape: ', X_train.shape)\n", "print('Train labels shape: ', y_train.shape)\n", "print('Validation data shape: ', X_val.shape)\n", "print('Validation labels shape: ', y_val.shape)\n", "print('Test data shape: ', X_test.shape)\n", "print('Test labels shape: ', y_test.shape)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "## Example Model\n", "\n", "### Some useful utilities\n", "\n", ". Remember that our image data is initially N x H x W x C, where:\n", "* N is the number of datapoints\n", "* H is the height of each image in pixels\n", "* W is the height of each image in pixels\n", "* C is the number of channels (usually 3: R, G, B)\n", "\n", "This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### The example model itself\n", "\n", "The first step to training your own model is defining its architecture.\n", "\n", "Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. \n", "\n", "In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. \n", "\n", "Make sure you understand why the parameters of the Linear layer are 5408 and 10.\n", "\n", "### TensorFlow Details\n", "In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# clear old variables\n", "tf.reset_default_graph()\n", "\n", "# setup input (e.g. the data that changes every batch)\n", "# The first dim is None, and gets sets automatically based on batch size fed in\n", "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n", "y = tf.placeholder(tf.int64, [None])\n", "is_training = tf.placeholder(tf.bool)\n", "\n", "def simple_model(X,y):\n", " # define our weights (e.g. init_two_layer_convnet)\n", " \n", " # setup variables\n", " Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n", " bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n", " W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n", " b1 = tf.get_variable(\"b1\", shape=[10])\n", "\n", " # define our graph (e.g. two_layer_convnet)\n", " a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1\n", " h1 = tf.nn.relu(a1)\n", " h1_flat = tf.reshape(h1,[-1,5408])\n", " y_out = tf.matmul(h1_flat,W1) + b1\n", " return y_out\n", "\n", "y_out = simple_model(X,y)\n", "\n", "# define our loss\n", "total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)\n", "mean_loss = tf.reduce_mean(total_loss)\n", "\n", "# define our optimizer\n", "optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate\n", "train_step = optimizer.minimize(mean_loss)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). \n", "\n", "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n", "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers\n", "* BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Training the model on one epoch\n", "While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a `tf.Session` object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow [Getting started](https://www.tensorflow.org/get_started/get_started) guide.\n", "\n", "Optionally we can also specify a device context such as `/cpu:0` or `/gpu:0`. For documentation on this behavior see [this TensorFlow guide](https://www.tensorflow.org/tutorials/using_gpu)\n", "\n", "You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "def run_model(session, predict, loss_val, Xd, yd,\n", " epochs=1, batch_size=64, print_every=100,\n", " training=None, plot_losses=False):\n", " # have tensorflow compute accuracy\n", " correct_prediction = tf.equal(tf.argmax(predict,1), y)\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # shuffle indicies\n", " train_indicies = np.arange(Xd.shape[0])\n", " np.random.shuffle(train_indicies)\n", "\n", " training_now = training is not None\n", " \n", " # setting up variables we want to compute (and optimizing)\n", " # if we have a training function, add that to things we compute\n", " variables = [mean_loss,correct_prediction,accuracy]\n", " if training_now:\n", " variables[-1] = training\n", " \n", " # counter \n", " iter_cnt = 0\n", " for e in range(epochs):\n", " # keep track of losses and accuracy\n", " correct = 0\n", " losses = []\n", " # make sure we iterate over the dataset once\n", " for i in range(int(math.ceil(Xd.shape[0]/batch_size))):\n", " # generate indicies for the batch\n", " start_idx = (i*batch_size)%Xd.shape[0]\n", " idx = train_indicies[start_idx:start_idx+batch_size]\n", " \n", " # create a feed dictionary for this batch\n", " feed_dict = {X: Xd[idx,:],\n", " y: yd[idx],\n", " is_training: training_now }\n", " # get batch size\n", " actual_batch_size = yd[idx].shape[0]\n", " \n", " # have tensorflow compute loss and correct predictions\n", " # and (if given) perform a training step\n", " loss, corr, _ = session.run(variables,feed_dict=feed_dict)\n", " \n", " # aggregate performance stats\n", " losses.append(loss*actual_batch_size)\n", " correct += np.sum(corr)\n", " \n", " # print every now and then\n", " if training_now and (iter_cnt % print_every) == 0:\n", " print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\"\\\n", " .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))\n", " iter_cnt += 1\n", " total_correct = correct/Xd.shape[0]\n", " total_loss = np.sum(losses)/Xd.shape[0]\n", " print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\"\\\n", " .format(total_loss,total_correct,e+1))\n", " if plot_losses:\n", " plt.plot(losses)\n", " plt.grid(True)\n", " plt.title('Epoch {} Loss'.format(e+1))\n", " plt.xlabel('minibatch number')\n", " plt.ylabel('minibatch loss')\n", " plt.show()\n", " return total_loss,total_correct\n", "\n", "with tf.Session() as sess:\n", " with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n", " sess.run(tf.global_variables_initializer())\n", " print('Training')\n", " run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n", " print('Validation')\n", " run_model(sess,y_out,mean_loss,X_val,y_val,1,64)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "## Training a specific model\n", "\n", "In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. \n", "\n", "Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:\n", "\n", "* 7x7 Convolutional Layer with 32 filters and stride of 1\n", "* ReLU Activation Layer\n", "* Spatial Batch Normalization Layer (trainable parameters, with scale and centering)\n", "* 2x2 Max Pooling layer with a stride of 2\n", "* Affine layer with 1024 output units\n", "* ReLU Activation Layer\n", "* Affine layer from 1024 input units to 10 outputs\n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# clear old variables\n", "tf.reset_default_graph()\n", "\n", "# define our input (e.g. the data that changes every batch)\n", "# The first dim is None, and gets sets automatically based on batch size fed in\n", "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n", "y = tf.placeholder(tf.int64, [None])\n", "is_training = tf.placeholder(tf.bool)\n", "\n", "# define model\n", "def complex_model(X,y,is_training):\n", " pass\n", "\n", "y_out = complex_model(X,y,is_training)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Now we're going to feed a random batch into the model \n", "# and make sure the output is the right size\n", "x = np.random.randn(64, 32, 32,3)\n", "with tf.Session() as sess:\n", " with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n", " tf.global_variables_initializer().run()\n", "\n", " ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n", " %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n", " print(ans.shape)\n", " print(np.array_equal(ans.shape, np.array([64, 10])))" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "You should see the following from the run above \n", "\n", "`(64, 10)`\n", "\n", "`True`" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### GPU!\n", "\n", "Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "try:\n", " with tf.Session() as sess:\n", " with tf.device(\"/gpu:0\") as dev: #\"/cpu:0\" or \"/gpu:0\"\n", " tf.global_variables_initializer().run()\n", "\n", " ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n", " %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n", "except tf.errors.InvalidArgumentError:\n", " print(\"no gpu found, please use Google Cloud if you want GPU acceleration\") \n", " # rebuild the graph\n", " # trying to start a GPU throws an exception \n", " # and also trashes the original graph\n", " tf.reset_default_graph()\n", " X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n", " y = tf.placeholder(tf.int64, [None])\n", " is_training = tf.placeholder(tf.bool)\n", " y_out = complex_model(X,y,is_training)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Train the model.\n", "\n", "Now that you've seen how to define a model and do a single forward pass of some data through it, let's walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).\n", "\n", "Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.\n", "\n", "First, set up an **RMSprop optimizer** (using a 1e-3 learning rate) and a **cross-entropy loss** function. See the TensorFlow documentation for more information\n", "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n", "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Inputs\n", "# y_out: is what your model computes\n", "# y: is your TensorFlow variable with label information\n", "# Outputs\n", "# mean_loss: a TensorFlow variable (scalar) with numerical loss\n", "# optimizer: a TensorFlow optimizer\n", "# This should be ~3 lines of code!\n", "mean_loss = None\n", "optimizer = None\n", "pass\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "# batch normalization in tensorflow requires this extra dependency\n", "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n", "with tf.control_dependencies(extra_update_ops):\n", " train_step = optimizer.minimize(mean_loss)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### Train the model\n", "Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "sess = tf.Session()\n", "\n", "sess.run(tf.global_variables_initializer())\n", "print('Training')\n", "run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### Check the accuracy of the model.\n", "\n", "Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "print('Validation')\n", "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "## Train a _great_ model on CIFAR-10!\n", "\n", "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves ** >= 70% accuracy on the validation set** of CIFAR-10. You can use the `run_model` function from above." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Things you should try:\n", "- **Filter size**: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n", "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n", "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n", "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n", "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n", " - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", " - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n", " - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n", "- **Use TensorFlow Scope**: Use TensorFlow scope and/or [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) to make it easier to write deeper networks. See [this tutorial](https://www.tensorflow.org/tutorials/layers) for how to use `tf.layers`. \n", "- **Use Learning Rate Decay**: [As the notes point out](http://cs231n.github.io/neural-networks-3/#anneal), decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the [Tensorflow documentation](https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate) for learning rate decay.\n", "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n", "- **Regularization**: Add l2 weight regularization, or perhaps use [Dropout as in the TensorFlow MNIST tutorial](https://www.tensorflow.org/get_started/mnist/pros)\n", "\n", "### Tips for training\n", "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n", "\n", "- If the parameters are working well, you should see improvement within a few hundred iterations\n", "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n", "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n", "- You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.\n", "\n", "### Going above and beyond\n", "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n", "\n", "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n", "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n", "- Model ensembles\n", "- Data augmentation\n", "- New Architectures\n", " - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n", " - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n", " - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n", "\n", "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n", "\n", "### What we expect\n", "At the very least, you should be able to train a ConvNet that gets at **>= 70% accuracy on the validation set**. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n", "\n", "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.\n", "\n", "Have fun and happy training!" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Feel free to play with this cell\n", "\n", "def my_model(X,y,is_training):\n", " pass\n", "\n", "tf.reset_default_graph()\n", "\n", "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n", "y = tf.placeholder(tf.int64, [None])\n", "is_training = tf.placeholder(tf.bool)\n", "\n", "y_out = my_model(X,y,is_training)\n", "mean_loss = None\n", "optimizer = None\n", "\n", "\n", "pass\n", "\n", "# batch normalization in tensorflow requires this extra dependency\n", "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n", "with tf.control_dependencies(extra_update_ops):\n", " train_step = optimizer.minimize(mean_loss)" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "# Feel free to play with this cell\n", "# This default code creates a session\n", "# and trains your model for 10 epochs\n", "# then prints the validation set accuracy\n", "sess = tf.Session()\n", "\n", "sess.run(tf.global_variables_initializer())\n", "print('Training')\n", "run_model(sess,y_out,mean_loss,X_train,y_train,10,64,100,train_step,True)\n", "print('Validation')\n", "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "# Test your model here, and make sure \n", "# the output of this cell is the accuracy\n", "# of your best model on the training and val sets\n", "# We're looking for >= 70% accuracy on Validation\n", "print('Training')\n", "run_model(sess,y_out,mean_loss,X_train,y_train,1,64)\n", "print('Validation')\n", "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### Describe what you did here\n", "In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "_Tell us here_" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Test Set - Do this only once\n", "Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "print('Test')\n", "run_model(sess,y_out,mean_loss,X_test,y_test,1,64)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "## Going further with TensorFlow\n", "\n", "The next assignment will make heavy use of TensorFlow. You might also find it useful for your projects. \n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# Extra Credit Description\n", "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable." ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "3.5.2", "pygments_lexer": "ipython3", "codemirror_mode": { "version": 3, "name": "ipython" } } } }
mit
pfschus/fission_bicorrelation
methods/build_det_df_angles_pairs.ipynb
1
471349
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 id=\"tocheading\">Table of Contents</h1>\n", "<div id=\"toc\"></div>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Chi-Nu Array Detector Angles\n", "\n", "Author: Patricia Schuster \n", "Date: Fall 2016/Winter 2017 \n", "Institution: University of Michigan NERS \n", "Email: [email protected]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What are we doing today?\n", "\n", "Goal: Import and analyze the angles between all of the detector pairs in the Chi-Nu array.\n", "\n", "As a reminder, this is what the Chi-Nu array looks like:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<img src=\"fig/setup.png\",width=80%,height=80%>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "<img src=\"fig/setup.png\",width=80%,height=80%>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 45 detectors in this array, making for 990 detector pairs:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "990.0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "45*44/2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to characterize the angular distribution of the neutrons and gamma-rays emitted in a fission interaction, we are going to analyze the data from pairs of detectors at different angles from one another. \n", "\n", "In this notebook I am going to import the detector angle data that Matthew provided me and explore the data. \n", "\n", "1) Import the angular data to a dictionary \n", "2) Visualize the angular data \n", "3) Find detector pairs in a given angular range \n", "4) Generate pairs vs. angle ranges" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import packages\n", "import os.path\n", "import time\n", "import numpy as np\n", "np.set_printoptions(threshold=np.nan) # print entire matrices\n", "import sys\n", "import inspect\n", "import matplotlib.pyplot as plt\n", "import scipy.io as sio\n", "from tqdm import *\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import seaborn as sns\n", "sns.set_palette('spectral')\n", "sns.set_style(style='white')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sys.path.append('../scripts/')\n", "import bicorr as bicorr" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 1: Initialize pandas DataFrame with detector pairs\n", "\n", "The detector pair angles are stored in a file `lanl_detector_angles.mat`. Write a function to load it as an array and then generate a pandas DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was done before in `bicorr.build_dict_det_pair()`. Replace with a pandas dataFrame.\n", "\n", "Columns will be:\n", "\n", "* Detector 1\n", "* Detector 2\n", "* Index in `bicorr_hist_master`\n", "* Angle between detectors\n", "\n", "We can add more columns later very easily." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load channel lists\n", "\n", "Use the function `bicorr.built_ch_lists()` to generate numpy arrays with all of the channel numbers:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function build_ch_lists in module bicorr:\n", "\n", "build_ch_lists(print_flag=False)\n", " Generate and return lists of the channel numbers that correspond to the fission chamber and detector channels. This is built for the Chi-Nu array measurements. If the detector array changes, we need to modify this function.\n", " \n", " Note: In the Chi-Nu array, the fission chamber channels correspond to detector channels as follows:\n", " - fc 0 det 1-15\n", " - fc 16 det 17-31\n", " - fc 32 det 33-47\n", " \n", " Run with: chList, fcList, detList, num_dets, num_det_pairs = bicorr.build_ch_lists()\n", " \n", " Parameters\n", " ----------\n", " print_flag : bool, optional\n", " Print the values of all vectors?\n", " \n", " Returns\n", " -------\n", " chList : ndarray\n", " All channels measurements\n", " fcList : ndarray\n", " Fission chamber channels\n", " detList : ndarray\n", " Detector channels\n", " num_dets : int\n", " Number of detector channels\n", " num_det_pairs : int\n", " Number of pairs of detectors\n", "\n" ] } ], "source": [ "help(bicorr.build_ch_lists)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fission chamber channels: [ 0 16 32]\n", "Detector channels: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 18 19 20 21 22 23 24 25 26\n", " 27 28 29 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]\n", "Number of detectors: 45\n", "Number of detector pairs: 990\n" ] } ], "source": [ "chList, fcList, detList, num_dets, num_det_pairs = bicorr.build_ch_lists(print_flag = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize dataFrame with detector channel numbers" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "det_df = pd.DataFrame(columns=('d1', 'd2', 'd1d2', 'angle'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pandas dataFrame should have 990 entries, one for each detector pair. Generate this. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Fill pandas dataFrame with d1, d2, and d1d2 \n", "count = 0\n", "det_pair_chs = np.zeros(num_det_pairs,dtype=np.int)\n", "\n", "# Loop through all detector pairs\n", "for i1 in np.arange(0,num_dets):\n", " det1ch = detList[i1]\n", " for i2 in np.arange(i1+1,num_dets):\n", " det2ch = detList[i2]\n", " det_df.loc[count,'d1' ] = det1ch\n", " det_df.loc[count,'d2' ] = det2ch\n", " det_df.loc[count,'d1d2'] = 100*det1ch+det2ch\n", " count = count+1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>102</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>103</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>105</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>106</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "0 1 2 102 NaN\n", "1 1 3 103 NaN\n", "2 1 4 104 NaN\n", "3 1 5 105 NaN\n", "4 1 6 106 NaN" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5s8bRaQQ/RpoBAECdkpiYaBstriy3+Pnnn712/ri4OJWWlnrtfAgOjDQDAIA6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\nTaWmUPw7VvgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb66fe10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(det_df['d1d2'],det_df.index,'.k')\n", "plt.xlabel('Detector pair (100*det1ch+det2ch)')\n", "plt.ylabel('Index in det_df')\n", "plt.title('Mapping between detector pair and index')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the dataFrame so far\n", "\n", "Try using the built-in `pandas.DataFrame.plot` method." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ISIBKpQIAuLq6IjExUWgrLCxEWloa3NzcoFAo4OLigoSEBKE9KSkJzZo1g5OT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\nvOpopQNUAYDFJ3gBQAszaizLqBEAoBCNVx21Wm3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"text/plain": [ "<matplotlib.figure.Figure at 0xb823160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = det_df.plot('d1','d2',kind='scatter', marker = 's',edgecolor='none',s=13, c='d1d2')\n", "plt.xlim([0,50])\n", "plt.ylim([0,50])\n", "ax.set_aspect('equal')\n", "plt.xlabel('Detector 1 channel')\n", "plt.ylabel('Detector 2 channel')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some problems with displaying the labels, so instead I will use `matplotlib` directly. I am writing a function to generate this plot since I will likely want to view it a lot." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tU0Q1f3edoP7efbf49+a8j/D35sMDIcKCY8CpdqGCXa1/Ff+O/tJS2F+djweKanY2Cxds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"text/plain": [ "<matplotlib.figure.Figure at 0xc0f7e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bicorr.plot_det_df(det_df, which=['index'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 2: Fill `angles` column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `lanl_detector_angles.mat` file is located in my `measurements` folder:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['detector_positions.xlsx',\n", " 'det_df.csv',\n", " 'det_df.pkl',\n", " 'det_df_pairs_angles.csv',\n", " 'det_df_pairs_angles.pkl',\n", " 'lanl_detector_angles.mat',\n", " 'lanl_detector_angles_note.md']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir('../meas_info/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What does this file look like? Import the `.mat` file and take a look." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(57, 57)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det2detAngle = sio.loadmat('../meas_info/lanl_detector_angles.mat')['det2detAngle']\n", "det2detAngle.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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yqDrKhY5JzeLOO+/Eyy+/jLlz5+LPf/4zdu7ciZ07d2Ljxo0AgKSkJKxZswarVq3CzTff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"text/plain": [ "<matplotlib.figure.Figure at 0xbc1d390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pcolormesh(det2detAngle, cmap = \"viridis\")\n", "cbar = plt.colorbar()\n", "cbar.set_label('Angle (degrees)')\n", "plt.xlabel('Detector 1')\n", "plt.ylabel('Detector 2')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The array currently is `ndets x ndets` with an angle at every index. This is twice as many entries as we need because pairs are repeated at `(d1,d2)` and `(d2,d1)`. Loop through the pairs and store the angle to the dataframe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill the `'angle'` column of the DataFrame:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for pair in det_df.index:\n", " det_df.loc[pair,'angle'] = det2detAngle[int(det_df.loc[pair,'d1'])][int(det_df.loc[pair,'d2'])]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>102</td>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>103</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>45</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>105</td>\n", " <td>60</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>106</td>\n", " <td>75</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "0 1 2 102 15\n", "1 1 3 103 30\n", "2 1 4 104 45\n", "3 1 5 105 60\n", "4 1 6 106 75" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the angular data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9HzVyHy6t2XhslNIO+PBraU1s8kyl3SCP/A7kzY/VO50rr40wW/9HE3VZ2XkT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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc30ff28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bicorr.plot_det_df(det_df,which=['angle'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Verify accuracy of `pandas` method\n", "\n", "Make use of git to checkout old versions.\n", "\n", "Previously, I generated a dictionary that mapped the detector pair `d1d2` index to the angle. Verify that the new method using pandas is producing the same array of angles. \n", "\n", "** Old version using channel lists, dictionary **" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dict_pair_to_index, dict_index_to_pair = bicorr.build_dict_det_pair()\n", "\n", "dict_pair_to_angle = bicorr.build_dict_pair_to_angle(dict_pair_to_index,foldername='../../measurements/')\n", "\n", "det1ch_old, det2ch_old, angle_old = bicorr.unpack_dict_pair_to_angle(dict_pair_to_angle)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** New method using pandas det_df **" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "det_df = bicorr.load_det_df()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "det1ch_new = det_df['d1'].values\n", "det2ch_new = det_df['d2'].values\n", "angle_new = det_df['angle'].values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Compare the two **" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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a2hrZXCePiIiIaiIhgK1bgZAQ4N694jZue6wXJjESXJYHDx5gx44duH//Pj77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\nRRw9elQI8fjflZUrV+p8z+bn54tOnTppX2/OKuqTHTt2iFdffVXn3Nm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"text/plain": [ "<matplotlib.figure.Figure at 0xc6aa9e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot([0,180],[0,180],'r')\n", "plt.plot(angle_old, angle_new, '.k')\n", "plt.xlabel('Angle old (degrees)')\n", "plt.ylabel('Angle new (degrees)')\n", "plt.title('Compare angles from new and old method')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are the angle vectors within 0.001 degrees of each other? If so, then consider the two equal. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "990" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum((angle_old - angle_new) < 0.001)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Yes, consider them the same. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 3: Extract information from `det_df`\n", "\n", "I need to exact information from `det_df` using the pandas methods. What are a few things I want to do?" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>102</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>103</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>45.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>105</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>106</td>\n", " <td>75.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "0 1 2 102 15.0\n", "1 1 3 103 30.0\n", "2 1 4 104 45.0\n", "3 1 5 105 60.0\n", "4 1 6 106 75.0" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Return rows that meet a given condition\n", "\n", "There are two primary methods for accessing rows in the dataFrame that meet certain conditions. In our case, the conditions may be which detector pairs or which angle ranges we want to access.\n", "\n", "* Return a True/False mask indicating which entries meet the conditions\n", "* Return a pandas Index structure containing the indices of those entries\n", "\n", "As an example, I will look for rows in which `d2=8`. As a note, this will not be all entries in which channel 8 was involved because there are other pairs in which `d1=8` that will not be included.\n", "\n", "** Return the rows **\n", "\n", "Start with the mask method, which can be used to store our conditions." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d = 8\n", "ind_mask = (det_df['d2'] == d)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "Name: d2, dtype: bool" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a glimpse of the mask's first five elements\n", "ind_mask.head()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6 True\n", "49 True\n", "91 True\n", "132 True\n", "172 True\n", "211 True\n", "249 True\n", "Name: d2, dtype: bool" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View the mask entries that are equal to true\n", "ind_mask[ind_mask]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other method is to use the `.index` method to return a pandas index structure. Pull the indices from `det_df` using the mask." ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Int64Index([6, 49, 91, 132, 172, 211, 249], dtype='int64')\n" ] } ], "source": [ "ind = det_df.index[ind_mask]\n", "print(ind)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Count the number of rows **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the mask" ] }, { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 212, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(ind_mask)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the index structure" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 213, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ind)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract information for a single detector\n", "\n", "** Find indices for that detector **" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A single detector, may be d1 or d2\n", "d = 8\n", "ind_mask = (det_df['d1']==d) | (det_df['d2']==d)\n", "ind = det_df.index[ind_mask]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These lines can be accessed in `det_df` directly." ] }, { "cell_type": "code", "execution_count": 242, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>108</td>\n", " <td>105</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>208</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>3</td>\n", " <td>8</td>\n", " <td>308</td>\n", " <td>75</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>408</td>\n", " <td>60</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>508</td>\n", " <td>45</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "6 1 8 108 105\n", "49 2 8 208 90\n", "91 3 8 308 75\n", "132 4 8 408 60\n", "172 5 8 508 45" ] }, "execution_count": 242, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df[ind_mask].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Return a list of the other detector pair **\n", "\n", "Since the detector may be `d1` or `d2`, I may need to return a list of the *other* pair, regardless of the order. How can I generate an array of the other detector in the pair?" ] }, { "cell_type": "code", "execution_count": 243, "metadata": { "collapsed": false }, "outputs": [], "source": [ "det_df_this_det = det_df.loc[ind,['d1','d2']]" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>3</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>4</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>5</td>\n", " <td>8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2\n", "6 1 8\n", "49 2 8\n", "91 3 8\n", "132 4 8\n", "172 5 8" ] }, "execution_count": 217, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df_this_det.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a really stupid method, but I can multiply the two detectors together and then divide by 8 to divide out that channel. " ] }, { "cell_type": "code", "execution_count": 218, "metadata": { "collapsed": true }, "outputs": [], "source": [ "det_df_this_det['dN'] = det_df_this_det.d1 * det_df_this_det.d2 / d" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>dN</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>1</td>\n", " <td>8</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>3</td>\n", " <td>8</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>132</th>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>172</th>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 dN\n", "6 1 8 1\n", "49 2 8 2\n", "91 3 8 3\n", "132 4 8 4\n", "172 5 8 5" ] }, "execution_count": 219, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df_this_det.head()" ] }, { "cell_type": "code", "execution_count": 221, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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CLgAAgIMwCMJ5CLkAAAAOwiAI52EYBAAAgIMwCMJ5WMkFAACAxyHkAgAAwOMQ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\niIicDpNcIiIiInI6THKJiIiIyOn8DxtodrHM/tgsAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc5a4ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(det_df_this_det['dN'],'.k')\n", "plt.xlabel('Array in dataFrame')\n", "plt.ylabel('dN (other channel)')\n", "plt.title('Other channel for pairs including ch '+str(d))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Return the angles **" ] }, { "cell_type": "code", "execution_count": 222, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AAACUxFN9zedmggEAAABPowQDAADA71CCAQAA4HcowQAAAPA7lGAAAAAvYuc3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\nscckSU1NTcrLy5Pb7ZbH41F1dbXF1caWzs5Oeb1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"text/plain": [ "<matplotlib.figure.Figure at 0xc3ed7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(det_df.loc[ind,'angle'],'.k')\n", "plt.xlabel('Index')\n", "plt.ylabel('Angle between pairs')\n", "plt.title('Angle for pairs including ch '+ str(d))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 227, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lSGRkpKxWq7tjwMU8bqYXAAAAcDVKLwAAALwepRcAAABej9ILAAAAr0fpBQDA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\nAADDI/QCAADA8P4HcCZ2WpKYjAcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0xc697828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(det_df_this_det['dN'],det_df.loc[ind,'angle'],'.k')\n", "plt.axvline(d,color='r')\n", "plt.xlabel('dN (other channel)')\n", "plt.ylabel('Angle between pairs')\n", "plt.title('Angle for pairs including ch '+ str(d))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract information for a given pair\n", "\n", "** Find indices for that pair **" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d1 = 1\n", "d2 = 4\n", "\n", "if d2 < d1:\n", " print('Warning: d2 < d1. Channels inverted')\n", "\n", "ind_mask = (det_df['d1']==d1) & (det_df['d2']==d2)\n", "ind = det_df.index[ind_mask]" ] }, { "cell_type": "code", "execution_count": 259, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>45</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "2 1 4 104 45" ] }, "execution_count": 259, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df[ind_mask]" ] }, { "cell_type": "code", "execution_count": 260, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2 45\n", "Name: angle, dtype: object" ] }, "execution_count": 260, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df[ind_mask]['angle']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will write a function that returns the index." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: d2 < d1. Channels inverted\n" ] }, { "data": { "text/plain": [ "2" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bicorr.d1d2_index(det_df,4,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compare to speed of dictionary\n", "\n", "For a large number of detector pairs, which is faster for retrieving the indices?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3611942,)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bicorr_data = bicorr.load_bicorr(bicorr_path = '../2017_01_09_pfs_build_bicorr_hist_master/1/bicorr1')\n", "bicorr_data.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "det_df = bicorr.load_det_df()\n", "dict_pair_to_index, dict_index_to_pair = bicorr.build_dict_det_pair()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "132\n", "132\n" ] } ], "source": [ "d1 = 4\n", "d2 = 8\n", "print(dict_pair_to_index[100*d1+d2])\n", "print(bicorr.d1d2_index(det_df,d1,d2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Loop through `bicorr_data` and generate the index for all pairs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Using the dictionary method **" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|##########| 3611942/3611942 [00:11<00:00, 308280.87it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "11.716399908065796\n" ] } ], "source": [ "start_time = time.time()\n", "for i in tqdm(np.arange(bicorr_data.size),ascii=True):\n", " d1 = bicorr_data[i]['det1ch']\n", " d2 = bicorr_data[i]['det2ch']\n", " index = dict_pair_to_index[100*d1+d2]\n", "print(time.time()-start_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Using the pandas dataFrame method **" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "start_time = time.time()\n", "for i in tqdm(np.arange(bicorr_data.size),ascii=True):\n", " d1 = bicorr_data[i]['det1ch']\n", " d2 = bicorr_data[i]['det2ch']\n", " index = bicorr.d1d2_index(det_df,d1,d2)\n", "print(time.time()-start_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm not going to run this because `tqdm` says it will take approximately 24 minutes. So instead I should go with the dict method. But I would like to produce the dictionary from the pandas array directly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Produce dictionaries from det_df **\n", "\n", "Instead of relying on `dict_pair_to_index` all the time, I will generate it on the fly when filling `bicorr_hist_master` in `build_bicorr_hist_master` since that function requires generating the index so many times.\n", "\n", "The three dictionaries that I need are:\n", "\n", "* `dict_pair_to_index`\n", "* `dict_index_to_pair`\n", "* `dict_pair_to_angle`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RangeIndex(start=0, stop=990, step=1)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df.index" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>102</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>103</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>45.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>105</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>106</td>\n", " <td>75.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "0 1 2 102 15.0\n", "1 1 3 103 30.0\n", "2 1 4 104 45.0\n", "3 1 5 105 60.0\n", "4 1 6 106 75.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1d2</th>\n", " <th>d2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>102</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>103</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>104</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>105</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>106</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1d2 d2\n", "0 102 2\n", "1 103 3\n", "2 104 4\n", "3 105 5\n", "4 106 6" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df[['d1d2','d2']].head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dict_index_to_pair = det_df['d1d2'].to_dict()\n", "dict_pair_to_index = {v: k for k, v in dict_index_to_pair.items()}" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dict_pair_to_angle = pd.Series(det_df['angle'].values,index=det_df['d1d2']).to_dict()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functionalize these dictionaries so I can produce them on the fly." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function build_dict_det_pair in module bicorr:\n", "\n", "build_dict_det_pair(det_df)\n", " Build the dictionary that converts from detector pair to index and angle\n", " \n", " Parameters\n", " ----------\n", " det_df : pandas dataFrame\n", " dataFrame of detector pair indices and angles \n", " \n", " Returns\n", " -------\n", " dict_pair_to_index : dict\n", " keys: detector pair indices (100*det1ch+det2ch)\n", " values: index of pair in bicorr_hist_master (along first axis)\n", " dict_index_to_pair : dict\n", " Reverse version of dict_pair_to_index\n", " dict_pair_to_angle : dict\n", " keys: detector pair indices (100*det1ch+det2ch)\n", " values : angle of pair\n", "\n" ] } ], "source": [ "help(bicorr.build_dict_det_pair)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dict_pair_to_index, dict_index_to_pair, dict_pair_to_angle = bicorr.build_dict_det_pair(det_df)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Instructions: Save, load `det_df` file\n", "\n", "I'm going to store the dataFrame using `to_pickle`. At this point, it only contains information on the pairs and angles. No `bin` column has been added." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "det_df.to_pickle('../meas_info/det_df_pairs_angles.pkl')\n", "det_df.to_csv('../meas_info/det_df_pairs_angles.csv',index = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Revive the dataFrame from the `.pkl` file. Write a function to do this automatically. Option to display plots." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function load_det_df in module bicorr:\n", "\n", "load_det_df(filepath='../meas_info/det_df_pairs_angles.csv', plot_flag=False)\n", " Load pandas dataFrame containing detector pair information and angles. This was created in the notebook `detector_pair_angles`.\n", " \n", " Parameters\n", " ----------\n", " filepath : str, optional\n", " Path (absolute or relative) to det_df file. May be `det_df.csv` or `det_df.csv`.\n", " Default location is specific to pfschus folder structure\n", " plot_flag : bool, optional\n", " Option to produce plots displaying basic structure of det_df\n", " Plots will be displayed but not stored\n", " \n", " Returns\n", " -------\n", " det_df : pandas dataFrame\n", " dataFrame of detector pair indices and angles\n", "\n" ] } ], "source": [ "help(bicorr.load_det_df)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>d1</th>\n", " <th>d2</th>\n", " <th>d1d2</th>\n", " <th>angle</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>102</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>103</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>104</td>\n", " <td>45.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>105</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>106</td>\n", " <td>75.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " d1 d2 d1d2 angle\n", "0 1 2 102 15.0\n", "1 1 3 103 30.0\n", "2 1 4 104 45.0\n", "3 1 5 105 60.0\n", "4 1 6 106 75.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "det_df = bicorr.load_det_df()\n", "det_df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YEGp4DP4zPPax4bGPDYv9mz9jB4RmPr2Dnsvek7TdTYNSUKGUHQNCixoTExNE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\nvlbzAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xb6530f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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zPU59HzX0GCat2XBkpNIO/PBraU1cygylXT+P/A7kTY/UO50rrYk0W/9HY3VZ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"text/plain": [ "<matplotlib.figure.Figure at 0xbaf0d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "det_df = bicorr.load_det_df()\n", "bicorr.plot_det_df(det_df, show_flag = True, which = ['index'])\n", "bicorr.plot_det_df(det_df, show_flag = True, which = ['angle'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
OzFlux/Peters-iPython-notebooks
compare_OzFluxQC_EddyPro.ipynb
1
10324
{ "metadata": { "name": "", "signature": "sha256:74a9d0b68127f1e40ead95526b68137ae493073dd11c07e5a8aa3cee3bf7f1f0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run basics\n", "%matplotlib" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Using matplotlib backend: Qt4Agg\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "def round_datetime(ds,mode=\"nearest_timestep\"):\n", " \"\"\"\n", " Purpose:\n", " Round the series of Python datetimes to the nearest time based on mode\n", " Usage:\n", " qcutils.round_datetime(ds,mode=mode)\n", " where;\n", " mode = \"nearest_second\" rounds to the nearesy second\n", " mode = \"nearest_timestep\" rounds to the nearest time step\n", " Author: PRI\n", " Date: February 2015\n", " \"\"\"\n", " # local pointer to the datetime series\n", " ldt = ds.series[\"DateTime\"][\"Data\"]\n", " # check which rounding option has been chosen\n", " if mode.lower()==\"nearest_timestep\":\n", " # get the time step\n", " if \"time_step\" in ds.globalattributes:\n", " ts = int(ds.globalattributes[\"time_step\"])\n", " else:\n", " ts = numpy.mean(qcutils.get_timestep(ds)/60)\n", " ts = roundtobase(ts,base=30)\n", " ds.globalattributes[\"time_step\"] = ts\n", " # round to the nearest time step\n", " rldt = [rounddttots(dt,ts=ts) for dt in ldt]\n", " elif mode.lower()==\"nearest_second\":\n", " # round to the nearest second\n", " rldt = [rounddttoseconds(dt) for dt in ldt]\n", " else:\n", " # unrecognised option for mode, return original datetime series\n", " log.error(\" round_datetime: unrecognised mode (\"+str(mode)+\")\"+\" ,returning original time series\")\n", " rldt = ds.series[\"DateTime\"][\"Data\"]\n", " # replace the original datetime series with the rounded one\n", " ds.series[\"DateTime\"][\"Data\"] = rldt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def rounddttots(dt,ts=30):\n", " dt += datetime.timedelta(minutes=int(ts/2))\n", " dt -= datetime.timedelta(minutes=dt.minute % int(ts),seconds=dt.second,microseconds=dt.microsecond)\n", " return dt \n", "\n", "def rounddttoseconds(dt):\n", " dt += datetime.timedelta(seconds=0.5)\n", " dt -= datetime.timedelta(seconds=dt.second % 1,microseconds=dt.microsecond)\n", " return dt\n", "\n", "def roundtobase(x,base=5):\n", " return int(base*round(float(x)/base))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def read_eddypro_full(csvname):\n", " ds = qcio.DataStructure()\n", " csvfile = open(csvname,'rb')\n", " csvreader = csv.reader(csvfile)\n", " n = 0\n", " adatetime = []\n", " us_data_list = []\n", " us_flag_list = []\n", " Fh_data_list = []\n", " Fh_flag_list = []\n", " Fe_data_list = []\n", " Fe_flag_list = []\n", " Fc_data_list = []\n", " Fc_flag_list = []\n", " for row in csvreader:\n", " if n==0:\n", " header=row\n", " elif n==1:\n", " varlist=row\n", " us_data_col = varlist.index('u*')\n", " us_flag_col = varlist.index('qc_Tau')\n", " Fh_data_col = varlist.index('H')\n", " Fh_flag_col = varlist.index('qc_H')\n", " Fe_data_col = varlist.index('LE')\n", " Fe_flag_col = varlist.index('qc_LE')\n", " Fc_data_col = varlist.index('co2_flux')\n", " Fc_flag_col = varlist.index('qc_co2_flux')\n", " elif n==2:\n", " unitlist=row\n", " else:\n", " adatetime.append(datetime.datetime.strptime(row[1]+' '+row[2],'%Y-%m-%d %H:%M'))\n", " us_data_list.append(float(row[us_data_col]))\n", " us_flag_list.append(float(row[us_flag_col]))\n", " Fh_data_list.append(float(row[Fh_data_col]))\n", " Fh_flag_list.append(float(row[Fh_flag_col]))\n", " Fe_data_list.append(float(row[Fe_data_col]))\n", " Fe_flag_list.append(float(row[Fe_flag_col]))\n", " Fc_data_list.append(float(row[Fc_data_col]))\n", " Fc_flag_list.append(float(row[Fc_flag_col]))\n", " n = n + 1\n", " nRecs = len(adatetime)\n", " ds.series['DateTime'] = {}\n", " ds.series['DateTime']['Data'] = adatetime\n", " round_datetime(ds,mode=\"nearest_timestep\")\n", " ds.series['ustar'] = {}\n", " ds.series['ustar']['Data'] = numpy.array(us_data_list,dtype=numpy.float64)\n", " ds.series['ustar']['Flag'] = numpy.array(us_flag_list,dtype=numpy.int32)\n", " ds.series['Fh'] = {}\n", " ds.series['Fh']['Data'] = numpy.array(Fh_data_list,dtype=numpy.float64)\n", " ds.series['Fh']['Flag'] = numpy.array(Fh_flag_list,dtype=numpy.int32)\n", " ds.series['Fe'] = {}\n", " ds.series['Fe']['Data'] = numpy.array(Fe_data_list,dtype=numpy.float64)\n", " ds.series['Fe']['Flag'] = numpy.array(Fe_flag_list,dtype=numpy.int32)\n", " ds.series['Fc'] = {}\n", " ds.series['Fc']['Data'] = numpy.array(Fc_data_list,dtype=numpy.float64)\n", " ds.series['Fc']['Flag'] = numpy.array(Fc_flag_list,dtype=numpy.int32)\n", " ds.globalattributes[\"nc_nrecs\"] = nRecs\n", " return ds" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "epname = qcio.get_filename_dialog(title='Choose an EddyPro full output file')\n", "ofname = qcio.get_filename_dialog(title='Choose an L3 output file')\n", "ds_ep = read_eddypro_full(epname)\n", "ds_of = qcio.nc_read_series(ofname)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "dt_ep = ds_ep.series['DateTime']['Data']\n", "dt_of = ds_of.series['DateTime']['Data']\n", "si = dt_of.index(dt_ep[0])\n", "ei = dt_of.index(dt_ep[-1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "print dt_ep[0],dt_of[si]\n", "print dt_ep[-1],dt_of[ei]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2011-06-09 16:30:00 2011-06-09 16:30:00\n", "2011-08-05 15:00:00 2011-08-05 15:00:00\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "us_of,f,a = qcutils.GetSeriesasMA(ds_of,'ustar',si=si,ei=ei)\n", "us_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'ustar')\n", "Fh_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fh',si=si,ei=ei)\n", "Fh_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fh')\n", "Fe_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fe',si=si,ei=ei)\n", "Fe_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fe')\n", "Fc_of,f,a = qcutils.GetSeriesasMA(ds_of,'Fc',si=si,ei=ei)\n", "Fc_ep,f,a = qcutils.GetSeriesasMA(ds_ep,'Fc')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "us_of.mask = numpy.ma.mask_or(us_of.mask,us_ep.mask)\n", "us_ep.mask = numpy.ma.mask_or(us_of.mask,us_ep.mask)\n", "Fh_of.mask = numpy.ma.mask_or(Fh_of.mask,Fh_ep.mask)\n", "Fh_ep.mask = numpy.ma.mask_or(Fh_of.mask,Fh_ep.mask)\n", "Fe_of.mask = numpy.ma.mask_or(Fe_of.mask,Fe_ep.mask)\n", "Fe_ep.mask = numpy.ma.mask_or(Fe_of.mask,Fe_ep.mask)\n", "Fc_of.mask = numpy.ma.mask_or(Fc_of.mask,Fc_ep.mask)\n", "Fc_ep.mask = numpy.ma.mask_or(Fc_of.mask,Fc_ep.mask)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(1,figsize=(8,8))\n", "qcplot.xyplot(us_ep,us_of,sub=[2,2,1],regr=1,xlabel='u*_EP (m/s)',ylabel='u*_OF (m/s)')\n", "qcplot.xyplot(Fh_ep,Fh_of,sub=[2,2,2],regr=1,xlabel='Fh_EP (W/m2)',ylabel='Fh_OF (W/m2)')\n", "qcplot.xyplot(Fe_ep,Fe_of,sub=[2,2,3],regr=1,xlabel='Fe_EP (W/m2)',ylabel='Fe_OF (W/m2)')\n", "qcplot.xyplot(Fc_ep,Fc_of,sub=[2,2,4],regr=1,xlabel='Fc_EP (umol/m2/s)',ylabel='Fc_OF (umol/m2/s)')\n", "plt.tight_layout()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "fig=plt.figure()\n", "plt.plot(dt_ep,Fh_ep,'b.')\n", "plt.plot(dt_of[si:ei+1],Fh_of,'r+')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
tchmiel/TJC-CarND-BehavioralTraining-P3
P3_PreprocessModel.ipynb
1
765611
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Behaviorial Cloning Project\n", "## Thomas J. Chmielenski\n", "### September 2017\n", "\n", "This notebook is a utility notebook to help with experimenting and preprocessing the training set data.\n", "Utility functions to create graphs, show images will be created and utilitized here.\n", "\n", "Final executing code will be pushed to the model.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read the driving_log.csv into memory" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of total samples = 19483\n", "Number of samples removed = 12144\n", "Number of usable samples = 7339\n" ] } ], "source": [ "import os\n", "import csv\n", "import numpy as np\n", "\n", "\n", "TEST_SIZE = 0.20\n", "BATCH_SIZE = 32\n", "NUM_EPOCHS = 3\n", "\n", "\n", "remove_zero_percentage = 0.95\n", "\n", "def relative_image_path (root_data_dir, image_path):\n", " # converts driving_log.csv image path to be relative image path\n", " image_path = image_path.strip()\n", " if image_path.startswith('IMG'):\n", " # from Udacity driving_csv.log\n", " relative_path = root_data_dir + image_path\n", " else: \n", " relative = image_path.strip().split('data')[-1]\n", " relative_path = './data' + relative\n", " \n", " relative_path = relative_path.replace('/', os.path.sep).replace('\\\\', os.path.sep)\n", " return relative_path\n", " \n", "def load_dataset(root_data_dir):\n", " ds_samples = []\n", " ds_images_removed = 0\n", " csvfilename = root_data_dir + 'driving_log.csv'\n", " with open(csvfilename) as csvfile:\n", " reader = csv.reader(csvfile)\n", " \n", " for line in reader:\n", " # filter out 70% of the near zero steering angles to avoid high bias \n", " center_angle = float(line[3])\n", " prob = np.random.rand()\n", " if (abs(center_angle) < 0.01 and prob < remove_zero_percentage):\n", " ds_images_removed += 1\n", " continue\n", " line[0] = relative_image_path(root_data_dir, line[0])\n", " line[1] = relative_image_path(root_data_dir, line[1])\n", " line[2] = relative_image_path(root_data_dir, line[2])\n", " ds_samples.append(line)\n", " return ds_samples, ds_images_removed\n", "\n", "datasets = []\n", "datasets.append(\"./data/Udacity/\") #Udacity Training Set\n", "datasets.append(\"./data/CenterLaneDriving/\") \n", "datasets.append(\"./data/TeenagerDriver/\")\n", "datasets.append(\"./data/CounterClockwise/\")\n", "\n", "samples = []\n", "images_removed = 0\n", "for ds in datasets:\n", " ds_samples, ds_images_removed = load_dataset(ds)\n", " samples.extend (ds_samples)\n", " images_removed += ds_images_removed\n", "\n", "print(\"Number of total samples =\", len(samples) + images_removed)\n", "print(\"Number of samples removed =\", images_removed)\n", "print(\"Number of usable samples =\", len(samples))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Debug Utilities" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPUs available\n", "['/cpu:0']\n", "GPUs available\n", "['/gpu:0']\n" ] } ], "source": [ "from tensorflow.python.client import device_lib\n", "\n", "def get_available_gpus():\n", " local_device_protos = device_lib.list_local_devices()\n", " return [x.name for x in local_device_protos if x.device_type == 'GPU']\n", "\n", "def get_available_cpus():\n", " local_device_protos = device_lib.list_local_devices()\n", " return [x.name for x in local_device_protos if x.device_type == 'CPU']\n", "\n", "print (\"CPUs available\")\n", "print(get_available_cpus())\n", "print (\"GPUs available\")\n", "print(get_available_gpus())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### File Utilties" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "\n", "def ensure_dir(file_path):\n", " directory = os.path.dirname(file_path)\n", " if not os.path.exists(directory):\n", " os.makedirs(directory)\n", " \n", "def create_subDir (dir, sub_dir):\n", " new_path = os.path.join(dir, sub_dir)\n", " ensure_dir(new_path)\n", " return (new_path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Image Plot utilities" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#importing some useful packages\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "import numpy as np\n", "import cv2\n", "%matplotlib inline\n", "\n", "\n", "def saveImage(imageFilename, image): \n", " RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", " ensure_dir(imageFilename)\n", " cv2.imwrite(imageFilename,RGB_img)\n", "\n", "def displayImage(image):\n", " plt.imshow(image)\n", " plt.show()\n", " \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Image Methods" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def flip_image (image, measurement):\n", " image_flipped = np.fliplr(image)\n", " measurement_flipped = -measurement\n", " return (image_flipped, measurement_flipped)\n", "\n", "def colorCorrect_image(image):\n", " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", " return image\n", "\n", "def grayscale(img):\n", " \"\"\"Applies the Grayscale transform\n", " This will return an image with only one color channel\n", " but NOTE: to see the returned image as grayscale\n", " (assuming your grayscaled image is called 'gray')\n", " you should call plt.imshow(gray, cmap='gray')\"\"\"\n", " return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n", " # Or use BGR2GRAY if you read an image with cv2.imread()\n", " # return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "train_samples, validation_samples = train_test_split(samples, test_size = TEST_SIZE)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### show sample data images" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['./data/Udacity/IMG/center_2016_12_01_13_39_58_081.jpg', './data/Udacity/IMG/left_2016_12_01_13_39_58_081.jpg', './data/Udacity/IMG/right_2016_12_01_13_39_58_081.jpg', '-0.4490258', '0.8894962', '0', '30.11551']\n" ] } ], "source": [ "print(train_samples[0])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./data/Udacity/IMG/center_2016_12_01_13_39_58_081.jpg\n", "./data/Udacity/IMG/left_2016_12_01_13_39_58_081.jpg\n", "./data/Udacity/IMG/right_2016_12_01_13_39_58_081.jpg\n" ] }, { "data": { "image/png": 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1einXb3wdgE987L0Yl8LtJYZyyQ0ceQmLgfzgrtfrtHblR/VuRfYbYow7GZvE\n4Pp+8onHaTZEXLz++g672zLxlcYJoS/OdtsddrZ2UeYJAzZwSinTOt7Qfe5hbZybkg9upphs9MWS\nayfjhG5q+ngj5KHqlPn2SbXTo83caYIHhGqnCfYPQDsNnj9p/hyRfdkojmV4TSm3bCeBMTR2RXt0\nuqJXmvU2L734IgBXL70Pt6wVJS8YtJAkpG6gw0sTfLJg10QCXgHryt90Oy2aLQnU6bU7+VhU4Pn0\nXeNJEtFqymBLudaj1xX71EoZnDc2t4gj0YTnzizxoQ+Jtrvy2Hl2dmVAaWurzdpdOYeo28vXiNjc\n3KTZbJI8iEFmpSAGCQAKBverSXK9ZMzwcFPA8CTv4CNg87Epb2jLyN5Up8y1T6qdHm3mThM8IFQ7\nTbA3o3VSvs09O/Zop9TInERu7/SXZ8meFIM2bP5b2/N9fM/97k48TF9srE2wrm3fH4w7LS4vsrkp\nE1z370sw88ZawvMf/iQAYcWnVJL20sQDN2bkexaTPedsRBJJIEfc69DryKTa/XuihS5fPMPmmvSx\nuvwU3Ug0V6UUEvpyXrtbdS6VxdedrU22t0XPPfOsLG+xsrjA7ds3Afj85z/P1asSZP3qq69ydvUs\nANd36+zuusk1fIybFNza3KHfiSkqnbT8oKIoiqIoiqIoiqIoiqIoiqIoijL3zEemFh4GKX/ju1Tu\nBH/PzKiXzW6amCx/z1gziLCxMcbLotGytHGfbEpz/yzsYTPC4xg3Sz1L+9P26aSv06gU8llfp1Hb\np+1j1mRZF0U5SkTP9Oc3nf207c/yeg+3ted7ocA9sMf+kPdh3DHGTPfePYjzLnLvn4T9YXbemOs0\nK5+8PNXbgklGHDP8XZ+VpCB/LS8zm1H9pXieK7/mBXhZRLPvkWRt2F7+sfG8FM9FqiwuVkgTycTq\n9STbKu5ZlpclgjfwF2g1XTRM0sXzJOLXC2L8xLj2Yjxfzsv3AmpmAYCN7dsAtDopt29LNpjxDVm4\ncWpSwlAe5VGSEMcS7dLp7LCzI2VxskSuUmio1cR2abHC2TNLAPzKL/8ScSz+x3EfzxtEZkUuMmdn\np06v10OZJwzGBHJfu8+flF6R1z4pSX6vJ0NRZYOceGvjPMg+/yyYGGuDvdty+9PXKaPsT9sn1U4n\ng2qn46HaSbXTg9BOqfsNj7H5c0YyX9zvcj/BSwZ9pzYrmRzQ7Mj2rS3JctreatJqSLb4tSvPUylL\nGZtuupMqtvofAAAgAElEQVQne3kpeKlolsD3KbsSY6Hn55V/jMuO77ebNBuujHO/S8ll5FeCMmks\nxkkMVy/LQuX19is0nHZ657MSSdzc7dJ0uu1+u8vlC+cB+PrLL9NsiOYqByFtV3onSTx6PTnH+2v3\n8f1wbAk15RQwBmtCYKCljU0wxpUZJMm3SyWhMD8u//638eDzkGe+D5VJUu1UyF6108mg2ul4qHZS\n7eTlxd4Ofjb2ZqQPaSd/kJE1fKyUax7Kmh/0AoCPye8VD7C+G+NJ+vk9Z72Ibk+yxxdWA9pteb25\n2XB/LeVyxfUa0e12nU99AtdGYCJC97wKqyXKZbdMU+SzUBGtterGrm688Sa/+W9lDOpLX/qTlF3l\nHmNNXqNwZaFG1O66tlOuPS6ZWLsui2x5ocYzTz0NQK1codtyPqWWfls02vbGFmlkXNslNtZFrzV2\ntymFC2Pf5/0ceVLLGPME8PeAy8i79GPW2r9pjDkL/CPgKeAt4LuttdsTGsOYAGM9rMneUJ/sRkgZ\nFC0whKT5TWEHosFL8lJK1okK49khIV7e77+zLf4lbsx06cNHsT9tn/Z/4Gft0yhxMevrNOpLa9o+\nHg3mW1yMex9nZT9q/1HaOK79UXyapf2s+hhXI31WPtlD6hdL6nc2YA+jkpDlmIFN/j0ybOPK1abW\nkrr07NR6+QPV81IwseuhR7sjAxg27dDtyetefzCplQmiJImJY5fuXR6U7bH08QPnsZ+QuDWP+nGP\nthMjnb489FvtiIuXr0iDniGr+R/HMX44KH4SuvKI5SCh5AI9uq0dZ5uwdk8mxtbXNlm/LwM3Z8+u\n0nADM/e+9jI3brwFwI0P3aTbEZ+SeLAumHJ0ZqqdMBhCGZfJA3/MUAmc4VVPLDYbmCHOBx6NDzbN\nSje74zwD9DJ/9/svral2GunDSfmk2mmeUO2k2ml2fTwM2gm31hXGkgeWmqEShjbF+pl2gjgWfRNW\nDGUXlNN3QTM729ssVK66dg2hLxMNkQXPZgMfcX7VfBvjOT3U7zeIIymT7LnyhFG/S+ommAIM1pVo\nLpmQ2K13kXoBpDJAFPcbhJ74urUhZX3u3d2itSuDRc2dCrvbUp4n6vWIuuJ3u9Og2xG99v73fYzU\nRZR0OzGbW5tEUXzg2irFma12AkMgY0h5icwy1t1Vnk0hW/ZiuPyg8XJ7Y9LB0hjGTWbZaNAee/Xy\nPOiUUfan7ZNqp0cJ1U6qnWbXx8lrp3j8vqFtB8vOZiX9h3+BJwe1k/XwU9mWJAnWBeIY3+A53WOC\nmHx5JRNhnI5qt+v5uFL2PXP58mXCQOY7enFM5CaeykQEJps88/IShiVj8Z226ycJoREttliWoOqX\n1t+A5KI7pV7uk5+mJH1pe2mhxqWqrO311muv0qrLWJLndNbLX/867a6c10c/8nF+49d/052jx86O\nTF7FPQNW/N7cqNPvuQk4b4G4HzFYM/xwjlN+MAb+nLX2vcAngD9jjPkG4C8Cv2itfRfwi+5/RVEU\nRVGURx3VToqiKIqiKMVR7aQoiqIoygGOnKllrb0L3HWvG8aYl4CrwBeBF5zZTwD/EvjBSe15JgDj\nkWYBM8YMRWF5g9lNk2Jw6eEmwrrIdoPBetlCsdnM6mDm1LPVYd9H+jBuVndW9g+ij+P6NPz6JHzy\nOHr7RY85zjmcFNNG6zyY6J75jpgZ18ae97fAMZPuh0l9nEYa+LxEC03Xh3fIvhn4NOJ9mK6P/aUg\nRpSTcBm9cZSQxq4UoA+BS3DxPItxzxxr+lRKcmyz0aFez7KhJCKl3Uvy0n3GBiwuStRmJ+xjXRme\n1HbzTC1jEnqRSyXf2WZ7WzKqokjOu9OzfPITn3B+eHn2sjUpkSt9GAQB3fau6zPmzMqC80lsm80O\njV3xs9/v02hIdtnNm7ep1RYBKJcWCEvyen1tl/v37wPQ6rTzVHbl6MxSOxkGpS2ySCbD8PeiwWYy\nz5Bn92EgyaOQU6yfvT4YSe7ZQbTxvOiUk+hDtZNqp+KodirSh2qnon28/bWTtRXXCORZK14/10tY\nk2cTe4AXyOsk7g2ijWNp18fnwx/6MACBCei1JRMqWAxxlZElgjrKopT79HptAJr1dXZ37wKQ2mwB\n9C7WrTQemBLZ0IdNwHNR0r5fpt2Ucjmt5haVkkRYb6zJAueloExaEn14Y32Df/yP/zEAH/noc/Sd\nGNvY2qXuykxXqiv85m+/CMDKyiLGq418H5TizHzcyUN00WC0iezeMMZKFrz8h81uPDzygSrPkmsm\n4yLsTURWzYF0cdj3kT6odlLtdFKodjoaqp2KtavaqUgfk7RTiudK3vZjm5dl9nyPsCS21kvBjRmZ\nMKK2IFrr5u3rdCMZk8nusWeeeTbXU4HnUyrLEhM2SQld6SCbRNhInldp0KXblmypre1N6rvy+v49\nKQW9VFvhO779P3CXokrTlQ4s1QJSl9XV63TY7cm4UtTrUnIlDNstGc+6cO48b751C4Af/dt/h09/\n5lsBWF49w83rawDEccCF81LSudWy9LuuJGJc4s231vJSzpOYyZpaxpingOeBfwNccsIDa+1dY8zF\nMcf8APADAGfOXcYzCWDJ07rx8g91amN890WYmsHgjTUeuDW4jEnye89m5QmJyetXJqNvOGNONlV7\nWvvT9mncB3NWPo2rKT7L63TYOUzbx8PNfIuLcYJxVvZFj3nYzvvErtOYNPBZ+TT4UTm9MCraR+AC\nJqyJJOUbCAJD6Geld/r5j1fPT0liKTWY2gTfDdKcO3dOjvO7VCoiPnod8P1BGnpW2tAzMWEoAyVh\n4JFmqerGJ3FrGvUj+WHdarW4cEFK8rTbPoEX5P55vth4ns/duzIJFUcJ1omYnhv8qe+2aLkaxjYN\nuHdfhIjvL9BquXUtdjr0ep47rsS1p54D4B3veAcXLlwA4N/8058eef2U6Tiudlo9dxnfZKUsh43k\nj00NqRlstNkz0Ph5pcHUGEy2Pls+qZWSlydU7VTIXrXTo4RqJ9VOqp32kJcfHKyHbTwGz5E0IjvU\nWI9qRTTL5naXbru9p6nV5TM88453AdCuV+nU3UBPZCCQRnzfz9cKC/yQNHH9twdBPll56HqrTT8f\nEwnywNgk9khj0UjlUokzZ1YAePPWTr7O5OqqlNXxTY2X70jA0PrmDmdWLwHQjTyy8ZbNzQ5eIINI\nj197N7t18e/DH/kQnh/z4q/9yuhrp0zNcbXTyrnHMH4fUU5Oj9sU8mcdDBZnGxprAmxeXtPk9/Rg\nW0I+qTWmZJJqp9H7Tson1U7zhGon1U4PkXayh08+Gm/wuR81uQXka3xKAKoL7AlSPDfWlBjwAheU\nU4KO0zWtVoMwlGOrNRlHunbtGl1Xui9J2wR+tl5W4tb6kt/5WUKQ56VEiTyvWp0+fRdE3XNtbO+0\nuXDxMgB377YplQZrS5ZKMr5l4pSXXnoJgMtXrtKot1x7rgyhX6belLGyeqND5NYx/c3fepG+019h\neZn1TbEplxM+88Lvzq/ZSy9dh6Eyjodx7LAhY8wi8H8B/4m1tl70OGvtj1lrP2Kt/cjC0pnjuqEo\niqIoivK2YCbaaVm1k6IoiqIojwaqnRRFURRFGeZYmVrGmBARFv/AWvtP3Ob7xpjHXLTMY8Da5IZS\nfC8ra5TNXnoYF+XimYA0i/zCy1PFU/yhuU6bR0Jki9RaM8j88tIxs7B2f2rg4Qz6KHbMtPan7dPY\n2eoZ+TQuYmaW1+mwc5i2j1kxbZ9pmk42OmYf8xgxM817aowhHXefjbN317VIH3m06QmngU/zeRyO\n+prmOo39PE5pf7hP5kR9Sl0pkJH72d+GS9sed53GLBycJaqEvo/nspyMFxO5jKwobuKHYlQNPV6/\n/qY7Os3To7OSg71ej8iljLd7ffruta14GBcG7PtgXAQOJiFxC5tb6+F5Urqk3ZaonJ2tHm+8fhuA\ni5feifGzNjzCsOL6Tmh1XZ/tLkuLEmXcc2V9erFPUJZtYTlgfVMiarygwr37svD5+laXS4+/G4AP\nffjTnLsgKeGeCdja2jpw3ZTpmZV2MqR43t7yA/La7fc8PBe7ZAnyLPcUQ0qmqWxeyjL/WJgk/+ek\nNcGs7E/bJ9VOJ4Nqp2Kodirmk2qnfftnpJ1SBlnmWRuYBOO5EswmybODIWB7exuAdrNJuyOZWnFf\ntEvUt3z9pVcBuHLpOaou471t+/hZZLQHnivdHJQsNpVzDMsh1mXNNzrybGw0O0SxDHekaQk/cCV1\nvSqm787HC9nadRHQnYigmp2bnNfLr73J1rpEQz9+7d187/f8UQC+9vXf4v6WlIq+cWeXs6uSTV9Z\neoyPfPIaALWFgJ3ddezx44gfeWapnQJPIsnzssyexWZy3HiQLXVhB1nukn2VfQZs/p6a/P6PyePF\nVTsVslftdDKodiqGaqdiPj2a2skfsT/7zjdDt/PQMkk22WefDtlnn7HsfQY/kW2hMRinXXwvpd+X\n8Zl+u5lnZJVrhhvXbwCyhEDiMtSbTdFQG+tbLK2cd37Y/N5LjZcvVRCbBNySA37JJ3X3WhT5JNlS\nTf4yAFvbN3nDlQ70/SWWVySbvd5pUPJEl+12G8SuslC7E/HGjTsAnL8g2ez/7re+yt37ovf+7o//\nPX78f/8/ADh38Ql+6Zf+lTvfkD/4B/8QICUU69tSRai2sMBf/m/+e/7yf/T7KMKRFZaRd+zvAi9Z\na//HoV0/A/wR9/qPAP/PUftQFEVRFEV5WFDtpCiKoiiKUhzVToqiKIqijOI4mVqfAv4w8BVjzG+7\nbX8J+GHgp4wxfwK4Afz+SQ0ZEoxX3+OQRBFni2T5GJvVnAxJnJVng9wmsYOgGJtFmsHIepfjZmsn\nLe64f5Z6WvtJx6hPh9s/CJ+U02X4PZomWuMo9kX+fxAcxafDznta+6P4cCo+ZU8HO8L+wLbsO2J/\ne+m+//eSxhK9EoQepVD2p9bS7ksEb7u7Tdmth16pLNDrSQaX7/u0skiZDYkw2dnq5O0uLi5SqUh0\ncDvu5t8/njHYRBYWr3cbbG1KxO/mRoNGUzLC2m15hu3sRPzmb0nd4s9+7p0Evvjn4RH6EkHcbjbo\nxu51P+b8gkTsLJ2XrK+Lj72HhUWJtFleOk+pIus/xImhFEqETj9O8oVLq4sL3Lsna3QtLZVZPvv4\ngWumTM3MtBMkGE/W+cjy1q21Q2s5BFi31klqy3nEvpeGkK9ZOvxs9F2rPUbFPKlOmW+fVDs9uqh2\nmhOdMo8+PQDtNNiSDtaRMAnGrd1gPB9SF/Vs4fU3XgOgFCSkLkO93RIttLnW4P+79QsAfOk7r7G6\nLNlPsUlwCfTYtEMvkUysuB8RJ6K3tpoNttyaDj23HmQ7MkSxGyugTBCKHvK8BUzooqj9Ei++9AYA\n9XaC6ct6FsvnRRe96z3P88L3y4Lpb7zSorQgyzY98+xH+Njv+l0AfOH3fg9OElJZvETUl7bvb25w\n/sLjeH7pwHVTpmJ2404mxvek8kAml2xqwM/GnUJslrliK1ib3Xhevk6pxWKcpgJXicGmqHZ6+/mk\n2unRRbXTnOiUufQpPLgvf2CAGV7rKZM9+GQpv8YYsopxMrPhjjUDPZXGojV8L8H3s9/xPXpdEROt\nVkqttgBAzZTZ2ZExptWzVe7flWfYKy/fk7b6v8YnP/HNAJRqhkqY+VfGK7nXaY/IjXWZOKXRlf63\nGj36XRl3unFLMqtWL1yltiqlegNvgX4qxyXWYN1a7q++eh0/Er9f/q2v8Z1f/CIAX33xFQD+2Pf/\naSpu3OnuRp1v/fy3A9DpxXzmc79XzrHZydeXjxKf2oqs355ay/2teu7vJI48qWWt/RUYm0f62aka\nMymB36BaLfPaa1Jy4PrNG5Qq8iY2m10CXwbfPvbxb6Hfkou3sHCBxA3CRLGPCeSCJG4CLEk7+CU3\n8Dc0uTXuQTPui2Xcw2xa+/3bDnvYTXr4jdp/2DHT2mf7j3MOo7YZe3DfpOs67XUatW3WD41pxcm0\n9r5fbFG84/Rx0unNs7jmkx46aYH3etqH2MFt0z0oi9x/09qP21bU/iiCYto+vH0LOU7qP0kOPqQO\n9cnK97vnk6dRZ9du7/dGmqdcV6tVdnfl4VypVAgCGVSoN3ZZWJDnSxBkZWlSyhVptx/1iPvycI/p\ncO/+TenHdHl8+bLzzeaLiN+7u07XiYHsR26adqhUsrKANi8j6JVqlMqB66eF57n08KRPq9Vxr31s\nIsf22tJep+VR3xXx0Wn2WFpwj2/Px3P36GJtiY9+0+ek7X7Mm29eB+DNVyUdPLUh3/otH5DzDmvg\nUsZ9PyByz0gTeCTuR3m9lVCuiaCJUkPSn+336KPILLWT51lq1R5g2XIlkDbX17lxQ8oTVMvL3Lq5\nDkjJyk98XJpvd1KMq69UqVSJncBOswFIQqK07/pQ7TSNfbZftdNoVDvN3r5oG6qdDvKoaaf9nw9r\n7dB1TPPv+yRJyL76gyAgjkXfRHGfcll0VN9N/IRhSFZbME5jqmUXMGFS3rr+FgCNnTrr90UD/Z4v\nfJEPPPd+AN548xWiSNpJEznu1q07vPOp53P/okgmu2w1pBvLRFY5DEhS56BvSN2kQ73boxPL82y7\nKbbtyOf8uSsArO+0efe7ngWg1Urouwk1Y2M+/kl5Pq7Xr2BD0XxvXv8yAK++ucatm5IA9Plv+z76\nPem7tnKJVk8m0Sw1wgV5rnb6Ji+JWFk6R7MXjfzMKcWZpXYyxAT+FtZavvKVFwFYW9tgoSaafn2j\nyWJNyid99rPfQcdNuJZKy9TrEsBWqZQo10Snd7rilu+FWFfH3Oq401T22X7VTqNR7TR7+6JtqHY6\nyKOgnbJlJYIgyO/dpSWZoNnd3WWhJsHK/X4/10hhGOZjPFHUI01le21hIddUnU6mGRKqFdEJ3X5C\nrSra6q0br7OxKZNXcb/MM+8UzeL7PtWqjF11e336bkymWl10PjU4e/YsACktmnUJSg6qj9PsyjPM\n9wwLq2Lfj1q8fl3KC+It0E/FZqcp98cT586zviVJR49dWSGN5PpVFxdo9US3Pf/RT7DiuaUuuhFf\nefFrAGy45+Sv/rsv8/FPfAqARrvHuXMSEJT4EV03GeaXq0SJvI76ca5Dfd9ncXkVzy82XaUFnhVF\nURRFURRFURRFURRFURRFUZS55zjlB2eHjSHd5rWv38wjXDxvi9Vlce/unTe4ePEpAP7VL/8cn/uc\nLBgWRQntpswU9uOEoCrRaGme3lcm8FzZQmMLRWjstykSNTJsU9Q+2zatT0WjcR5ln+aR4Wj3Ihxl\nwc5p+3hQC1FOw/5zmBjpMWV0yigmR5NM59M8RszM8rhx5zLtuz1tFE/2XZ6kMf2+RM9knxPP8whd\nmnUQhJgsargfEbpMrHKplGdlLS0s5m3Hrq1+v4+f7V8u03VRKL1Om15XIlW2tu/x7DNPujaWsPK4\nwjMVqhV5/rz5+telvV7C7q4sRH3u3AWslaiVqAf9xGXBBCmBL+dw8dJZlhcl6qfbSVhelGiWpUWX\nDXZvjcAtIrqwvJRndfUjCF10T61c4ed/4Vddn+cIXfZyw5XEKZVCllYl+rTdidjdkcjpxcVV0vw+\nHyy86vkenj9YZH7a7xjlZFlbu8uP/M8/xPd93x8iMHIf93q3ec97pOzk3dsbLC/LTeqbOvWduwBc\nefy9NFzGe6cX44Kl6MYSpbW4skC1JtnxSbOh2ukR8WkeUe1UDNVORX06nv1xjjst7eSb7Ke+JY3t\nATvfy2w9jPPGD0J6/SxzPMnvr9AP8szwMIueTS2JW9T87LkK7b5kDW/v3iOORAMZEq5eEe3R70WU\nwkzrxNxf2wTAJWzx3m/4AB9838cAuPrEFVxQM22i/D0vVSsQ9pwfHokr9/P4E0/zTZ/6jLQXuYyt\nrRZdSdqiWlllY0dK9va6Kb6fLcDu09gWo69+9XVKy2sA7LhShufOP8YLn/73AIg7FRJ3DQJS8Pru\nHAOM06nGhoPVD4wPJoUpvweUk8PaCBvdY3t7m8WKe4+fvcBbb0lVg+bOGteuPAZA1N2hUhEdvrqy\nTFZyqt3u0qjLPZN9qhaXl7LKzvR6PdVOj4hP84hqp2Kodirq0/Hsj3PcaWmnMyuS9WStzbO2um3R\nRR4+qcsK9/Aou/J5AHFfMpriKAGT5Meltu/ak22VSoWkI2NDadyi3ZIxmajfolGXKkNhcJYzZ6Ri\nTrO1S+oq7LTqLZoN0UBpItuS2A7KE54LeOKaVBbaaizS6Yj/5VqZ3bZUcDm7usQ3v/Atcmzfp9WQ\n8YLPvPBd4keUsOr67kUdGu7cq9UapZJkpVdri/z6v/6KnGO3T+rKM+463174tm/Kyw8un19gty7n\naPHySo6eZ3LtaTwvzxoNPANYjCn2Ts/FpFbgG86s+Gxt9LlzV8rmPH55ga987V8DUA4qBMibtH5v\nk37n0wD4oc/Kogy8BKUFfFfqqd6SAcjt3U36kbwBy9XqngfbOEbd3JO+JIePmcbeGDO1T0Xsj+NT\n0T6mPYfc1k7v07TX6UGIjOlTrk/+x8y0fai4KNjHAxYXx/V/lP0kn6YVIiOvwZiqIOOu1/Dnu4h9\nGmWl0aAcyMBBNsnjeR5ZjeI0TUkTN5DveVRCVxM5Sei7QfvA80icTTn03LaAcja4Y/s0G1KreGvn\nLr4rEdjtdvJ7tN3qceXyOwDY2XqFd7/3vQB84mPf4nwrkSTic6vVYHlZ0r3j3jJxIj+iY9vm9i1Z\nx6HXa9Goi3+ths3buX9Pnn2LCyvUFlzghu1Tq8jju1TysK5UXLfT51Pf/AIAX/3K17hxU9LKb9yQ\nH+rV2hKvv/EWAMvLq1y65Ca42m2CEW/f3vs0wSbz+wPuUcQzUCvBYhV++7d/A4Anrl1mZ1N0VNLf\npb4l4pU44qtfESG9uLBCWJZJ04VKFROKdtpxg4dRlNJxJZWWgul1Sva/aqfx9qqdiqHaqRiqnVQ7\njbMPXPtpaknTbKBW/nrGw9tzW8gzolwq0+u4dRc8j1JpEDSUldkpOw0SxzFpORukCWn3ZKC/369T\nCaSfRq/F+UuyNpaPT626DMD7nvsw73XayQ+y0kAetco5ADq9DlvuwVS5eCYfxSqVPXbq8lt/d+ce\nd+5KucCNjTVWVuXYyJXmWVg4w5lVGStIE484zbYH+TVL0y7LZyQYZPXsWW7ek/Ugbt+75a7BLt/4\nQZmg89IFLlx4Qs6rfc8NvID1YvJIJ/oM3o4+KR2G1yVTThkbY3vrXH/1y1x78mkANtbfhEgG3L7h\n2cfY3X4LgDR+DxtbosOT2JAk8ptiaWkZL5TPQL0p90avmxC7e6AUqnaaxr6oT6qdiqHaqRiqnVQ7\njbPvdeR5EATh4DnvHuNnlldyLRRHg/vI8w3Zs75SquK5NUZT2ydxk0+em7QplQKqngQlez2fVk+e\nI9VaiO/WTW82G5Tdmlr1Vp1veO8HnYcpwcdkssizMvkWdcssr8pkU715i0Zd5kB6vQqXLsk6Vddv\nfZXN7dsAvPbqNvfvSVDR57/t93H+gowLlEsyXtVoNCi78ohBKcELXVBRqUynLeNOuztb7Oy2nEcm\nn5DquzG2UrnK2oYEOl28GNBsim5bXFwkCLJ1VlOsm/DzrIzRgdxDaRTnunQSGnatKIqiKIqiKIqi\nKIqiKIqiKIqizD1zkall05R+p8X9u9dZu/cWAJ/97HexufE6APfub9Bpy2zeM+98nG5HFj5bDpeI\nUtm+sd6AQGY7S64M1JkzZ6hUZFvLpfHB3miNSTPdwzZF047H2e8/ZlxUy2n6tP+YST6Ni7QZa3+E\nczjOdZoXpo9mmT7C5qT7OI2on0nRKaM8mn3EzHQ+FemjaLTKrKJaHkTEjLXTXadRi9Ie+t55WanB\nQYZW9hdjiSK3OHMa4WXROCSUyr7zLyFy2V7VSo1Op5u/Buj1YkJfokH6UYeLFyWSuNW9R6ks0TCN\nxgqRS0FfWl6hUpIMl+feu8TFy48D0HWLcVqbsrAg0TX9uEPL1dAJoyrgFlgPYrZdxMz29jrNhrSd\n9Bc556KGtzbluMALaTakbE63t021Js+5anmRxJUUCvA597hEEHsm4PnnZbH1lRVJH4+ihMCVTomi\niF5bnovlIMAzWYQxeRZbamOyiKM4jvMF25X54MKF8/yp//BPcO/eDWol+Swsli3n3ynRVm+9+RXe\n+Y4rADTqHT75yfcBEJYiqhLMRb3To9WUz0Kml6I0xvNcBJUZREjNi05R7aTaaVb2D6IP1U5F7afz\nSbXT+GP2/O8yxn3PEjrNlEfMD5XmT9M4j0L3jE/gMqeq1RLlskTrRlFEuyVZ7BcuSOSvwZLE8gzZ\nWLtDqSqvS36MVxY/6rtbeMhv92tXP0CnLRpjefECXuDK+sSNvI+uq0UY+hUee1zKwG21NzGptB33\nIrY3pJzubn2DRl18ajdbVMqijbzsuqaGVkMirnu9KC9DvbhUybViHHdYW5eo5o9/9KM8j5SbK9e+\nFYBmPSH0JHrZJBW2N0S3lSoRQejKEfkJqcuaT+PeUDmvDjZtY+xAYymnTJpg+nX+wJe+wO27cl+u\n33mLqCOa+NUb17n02LsAOHc25B1PPw1As+1z957cS6kt4zs9HccuyjwMWKjJb4c4qufdzYtOUe2k\n2mlW9g+iD9VORe2n80m10/hjhv+PrYy/GMh/EyfuvAI/od9z4z2pJXTjQa12g1JJNEalFOYlB0n7\nVCrSdpqKttrZuQdGnheVmuGuy2gKK4alRfmRfvf2Ol1XOnBh8QypdWWhDfi+aJIQGWsyaUC73XY+\n9bG4Es0G7t2+DsA7rl2h05IM9Jubd1i7L33+0r/4l3zLZ75DjnVVjdqtJsZ03Xk3RSMClXBlUAUp\nCPn0p6WC3srKCvfXpb0PfkBslxar+fIgpSDlycdFN7bbbQI/ctcmppe4dTJIMK4qUNzr0ul0SN2S\nHSLwIREAACAASURBVJOYi0mtXq/Lm6++xvPv/wDf9In3A3DjzZc5syIXYXn5AqQiUn/mZ3+GrQ25\nyT7y0d/DtWvfCEAprNFLnUh3F68bdVnfEIGy6EoTQrG6usMUqatbxH7/tsPaOy2f9h8z7TlM8mm4\nLGbRc5j2Os1jGvi09kdZu2beBNZx19+Z9iE+64f+PPg0i4c+jH4vTltcTGu7sDAodZM9qNPE/R0q\nORiGHhX3fd/vdwnd+lrGs2DlAeqZDl3347VWlX52tu/z1usvS3s24jMvfAqAZmOLrS15SF+/fisf\njPn0N38bq67MzdLCMvUdVwLQTWr1+k3OXZTJsDjpg5s0qlXL9GOXqu318H15kAdBRJqKGKnv9rh7\nR0THY5efctcjxeu7dS1KIaWy+7zbdr6+Q7m8wJ0bIlxWV87k73tgXJr47gZJImLq2Xc+w927MihU\n9gJ8VyaIJCFKs8mrCOty7U3aJeo3UeaHqBdx+/pdbBoRubrcTz1xmVde/W0APvHhd3P7jpTNObty\nll/6xZ8G4P66xxe+/fsAuHjlWRJXunP5nJRoWt9aJ0mz9U0HMnFedIpqJ9VO41DtpNqpiG1R+4dB\nO/luosXzPPxAvuuHB3eyAZbUJoN1Sg2UQ3m9UIPEDTi0mlus3ZcJnTOrorO6nQ4796WEzdbuDd7/\noScBiOMGnU62LlfEzeuiab7xfRbflwCKXhc6bpkAPDdws1TD4CYLopTEBQFdPFum15PnUrO1ze6G\nlFVud3bw3KSCb1O210SvPfH4M64Nj8TpwLQc5loM2yKNnf7y+tSWZCIr7m1jfdFo9W1pq1o+S7cp\npXzPrZzFc2WHrGlSdmt7eUFE1JftEYbUrREepy3iqJGX11FOn7jfZ+3N61x/5RXe8cw7ASibiI98\n8FkAojTkJ/6+6KVeL+aFb5G13BdWnsB3JQcT28a9xfguqKhcLmOz0p5D3x3zolNUO6l2GodqJ9VO\nRWyL2j8M2unCOZk0SlPyNbVcVUC6nW3SfF11k6/fvljz82Bqz0+o1yUYOYrbLC1JgHE2DtOo3+Pe\nbVmC4hs/9i4iV/Z/t9EgjkUjJXFK1JfPWXVhGYtbhsKkRD3ps+t8C41PUBIHz5w7g3FBTEl7kaYr\npfjPf/5naXdlzdC01ybuSDDRRz70AZZdmUM3nMZStcTqsguETkweDB4QYnBlEw2E7pkYGqiV5fWl\n82fcddrNywLubNyn4hKPatUypcy/uIfBrbVlYwxOk8ZN4tZOvsTGJLT8oKIoiqIoiqIoiqIoiqIo\niqIoijL3zEWmViks89SVd7B29zpxKlHF1cWIC2ez0k09Ol2ZtfuuL30rnY5Ev188XyUIZPau3a7T\njmR2srYs+2uVgHJF2oh7gwVax0VgTEqhHrYxZvyCk4dFdExKoT5tnw47ZtpzGOXT8Bx40XM4znVS\nxlMkSuJB2hc5/kCEy5T2E9sbaX94pMhxI2aKtnmcfo6yf1p7a81U5zIuYm+cfZJIBEsU9ckWOa/V\npHRgWAqIItmWJDHWpYwvLZWou8XEW60GG5sSaZumKbu7kqn1wQ/Kopu7u3fBpXXXd7awiUSklCt+\nXurvzOoqu7sS1bJQWyXuSwRLrwupiyx+8pqULInTFs2ORPl2ezErq+KrbUgpGoBSOeED75co0ZXV\nRfp96WdzDZ5793MAvPy1N+UaeAmVmoTPVKspcequRz8h8OU5Vw4Dzrtyiru7awQuXT4rE3TlQo1m\nXfyvhB2I5XqEYYUwj3jq49F2ryNMlulm20TJoJyKcvo0Gy1+9Zd+nc//7k/z5OOSNfjil3+HhSV5\nz7a31lioupIJcYMXXvgwAK+8ss2VyxKB5pcj1raldJMNXSkAIs6ekZIIvfre8oMZqp1UOz2qqHZS\n7VSk7aL2J62dyq7ES6/XputKzWZR1JVqibIr1wd2KEM3xjo91Gn32a3Lb/PNzU3W1iTj6umnLwGw\ntbXO2h3Jmur0NkgT0Ru91m4epbxYrVDfkrZ9r8zigkTxWmMoVSXi1y+Jn6Wan2d4NbsdOk1XqrBd\np7YgvvoVeP79kom1vFLDpnLunU7EE0+8G4DXXxU/S36NalU0UqnsEbtyjP3eNmEoYwiVms/KomQq\nv/rml/mGD0nW1rrL0l+oQVCT/fXt+yy6+r2d9g5lz0Uv+xG+kbYNMamLNja2TprWMTZ7viqnTbVc\n4bl3vZfNzXUam/Jb4B1Xr9Jwkei7O+v8we/5IgCLy4/zC7/wfwPwwme/i0uPvQeAbmTpu2oR1ZqU\nYjJ+ytaW6KkVV34c5kenqHZS7XSaqHZS7VSk7aL2J62dWi0pTdvr9bBuuaMrV64CsLNdZ+nMotsf\n5WNUtVqZnsu4ajabrK2LDul0m/iBaKZyWTRPu73B9o5kKNWq7+XMqjwz1t64Sbdbcu0tUnPaI4qg\nG6Vue5VySXSI77K3KkGFlsu8ancabNdl/OiJpasErqTgd3zuM6ztSHbY8vIigecqCnWXsG6JicTN\nudBv4yVZFlYb6+ZcqkGH2GX1Gwury+Lf9evXuXBRxiKMy646t1whdpnti2EtX8YiSCOC1GU1p12w\nDXflY3y33EiS7uD11jFpMe00F5NagR+wsnSW9bXbGCtCu9nY5ZVX/x0AVx+/zPq6CONy9TIgF2Fr\n4zYpciGXFs6xUpHXibs3d1u7dPpy8RYr5yY+gEbd8IcdM2xT9CE33IfneVP7VMT+OD4V7WPac8j8\n8Ka8rtP0MSmtfZacdBr4UZi2j7ymfmH7+RMXjBG6RX0oJi68Q/fv3zZOfBfts5hPR7M/btuHHeN7\n/kS7UT8oivbT6cmgytbWFv2+PCzPnJGH8fnz5/P1tayJSNygRafb5a5bp7HVbnD/vpTb63a7RG7N\nhg+Hss5QnLSwrszNTn2d3/6yPH+azQYd11+/36ftyg9GfUviHtTnzj6ONfKj9vpbImAqNYMJZf/Z\nc6t0e/LAPlMrU0bK9qT+NovVSm6PW3OhWi5x+6asKfnUNVmra2PzLt2+/PhuddZotaTcz/LSKufP\nuQWS0g4LooP4zZd/h6eevibn1g1cuyGxGzhau/N1iKQ9r1LDVSUitT1M4soMpn08V5rO9neJO4O1\nKZXTx1qIOrBQWuLVV6Tk4JlzFe7efAmQckipE0SJTfmd3/k1AGq1p7lxQ0ptXrxquHzpLABNd5/j\n9ak3pTxB1bug2mmGfah2Uu00a/six6t2Osijop2CUAYo1jfXuX//vrORNi5evMjFi7J+Z6kcYmPR\nB2ma0mjKhE6702R9XY6Looh+JFomLIk2aLY2iSMZxOl1G9y88SoASdSl3RLbTrsCVnRKrbZIEouP\nUWJJ3LqNSV8mFKLdJtbpjsWFVRZdWUC7uUnNrVXR68WcvyJ+Vyol1tdFD8Um5cZrXwfg0hkZfOp2\nEppb687XbRJbd35YVs5Ie165RKMh+vDu7Vc5d94Fxy67CcHmBkEgAUP9VowXybnE0RrWPTeNSTBu\nbTETRXhuUitIGsTxtk5qzRMW6EGv2aXiyiXdu32HyA2eLS+f4f590fK3727w7LueAuD8+UW8wA38\n9fr03H1sjdPYpWUWltyabhzt+avaaTbnkPmh2unkUO2k2mmS/dtZO21tiyZYW9sgiuR5fu68zDXs\n1Ne5eEkmcDw/pdEQXXHn7j3W1l2QT7dJoynapNHY4f9n702jLMuu8sDv3OnNQ7wYMzIjx5qyRqGh\nqiRVoaE0WcJI6pJogYQYDKJpGpvG3SzbvdqLhd3uBtrLXo1X2013g5kRi9WrwUAbhMFIlKpQlrLG\nrKwcIjIyY37x5unO9/SPve9590VGZmVmVUICd/+JF/fdd+655557znf2+fa3+0M6XpsmUk+nuwND\nI1xxaXUZthfnUNfQbDa5zgdgmYw9PA/5HOEhQ9cQBjTvxHLNppkBWM15enoK5RphIG+9j0O8znf8\nDRycJXxjWQbWr2wDABbm51DfIrxW5LyQW50Oti4TScMLd2Fa1H4Ls0uwdCpD102064SvXnvpeTz1\nkQ8DgPKxhY6PMIjzivnwbWonK2/C4I2xIBwBPu3z6DJQOd6l3YQ32ER0g5taqfxgaqmlllpqqaWW\nWmqppZZaaqmlllpqqaWWWmqppZZaane83RGRWttb2/iZf/GzWL1yFl/64c8CAEZeBzM12snstndh\nmcRm73dbCDgqbtB/Htk8MclmF+7FwmGSIjCzHI5nCEyx5IDv7r9bu/fY9XaU936/XxlvVN7eYzdb\npzcq746vk3zjOt3IPVyvjDsxJPxmk1fGyZpv5zVuN2PmVp5D8h5Sxsz16nRz599snW7lGrjG+3oj\n/eZG6mQyIafdrGN1dRUAkMvTvHDs2DEsLi4AAMqVIrJ5Clc6d+4straIaZMv5FAs0PmGHiEOSikV\n6VgQDGE7xJLRNGC3QewVXdfRalGE0tZWE4UsSetUKlOAJKZKvzdEsUwsmFKJjtVmcnju+T8FACws\nljA9Q8wXbzhCJk/PQ+qAQQFecJ0hHFfjMqYAZuB0usSSabZ2AY0ZLtkQ+QL9cLpWVIzg5ZXX8doZ\nisZpNpv44PveAwCwbbqvVrOJSonmx53tOjyOHqsVdWgyDtWyIT26ThTYCMBM5WEbo24Tqd05FoUR\n+t0hfvEXfgU7u68BAD73nR9Q787rr53Bgw+TvOby8iV0u8QgfuDhu3Hq+a8DAA63bTz25FMAAI2T\n11YLRQwcjtbzUux0R9QpxU43ZCl2SrHT9et0c+f/TcBOgiPA+90W1q9cig8CAExDoMYSOpmMADia\nSEY++j3CHruNOup1wkOFQg4ZjpaKsRPgqmiX3VYfr5+lKN+Dh6fhuhS5tLHeAOQiXdO0EPh0vgx8\nJa2TLdFf27MwYizmeC76WxsAgLsqBnS+l16vgVKJpHxCP4LPUV5ztTm4NvU7z6Fj0peIOALN1CSq\nJcJi5apAGBK2293ewsZZwj0XLr2CVWYvf+rT7wMAGHoesRxjuVjExpXLAAArZyObobI1ESH06Jwo\niCDAbOOwDxn2AIylfFP7q7XNzTp+6if/FX7whz4HyYoLu9s70DK00Gh1e/Aky4sjj1BQn37xpW/A\nylO/WzpyP6aniQnf7lJ/9QIb2Rxh88hOsdMdUacUO92QpdgpxU7Xr9PNnf83ATtVyhQhtbLcxOXL\nhEPmZig669LKFRw8QH4nXddhmfTb4aCDZoMi2yV8mAYd1zQf9oh8LkGZpQVzBhrrhDv+9I+/ghK5\nl1AoFVSkVhgaCMMYr2UQMa4IQ0BGGl+f55woUr/LlgBNp+inmfwSWnWKEsuUHGRyVF7GENA1+txv\nN1ApzAEAfI9xoO/CzMZyh3nkCnQ9wwixvb4KANjdGaDRoKjmM2fO4APvf5KuOUV7ONs76+ox7dQ3\nEHA02okThyBCKlsLHEQ++Rxk6EAwVnKGDYx6O4jCv0byg770sOVdgj5VQL1DIGJm6l6UdQKbq41n\nEUW0eTVsA90WdYBiNQvMU8fol0IsZumzqxFg1fOH0OHyaoY5fsGkBsmzXCQSL54AIoxDjwEgSoSP\nCwBx2hEpJfZ7ZaLQURIDhmZBFwTSo4jqFmoeIoPqHxo9DB0CwDOVw+jt0MOtGHkYkhyxxcIGRsNz\ndH3NgpkhuYXekBYhQ78Ci98CDxHA2um23UbeZO3xyITm04spongRAkhQCL3UR4g0l28g8b2USN6k\njG/+qhvfM0kJADJxLAkCxHjSTLZr8tje4qWcnGj3a/fJY7d/Io9DPW+X3eQ8zr+5yR/Jmzz/ZsGF\ndmOT2ARITP5+n/P3HjNZ/z/564nfTOjt7lN/qe05X7uqDE8fjb+GljiH5e6knjhfS1wmGt+PCFTz\nSREiln4BgCDk91SnPmU7Dsplzmfj+nB4YT4zM4PhkCaDKJQwDBq+i6USGo2WqneBddwHA5ogpqam\n0O5TH8/lctAMuvag30GlShscnc54syMIfUhJE0qecxI6jquIBYOerXR8pRSwLDo+CsuAoLqGYQOF\nPH0uVyQuXTxDdc1TeZ3dAeqbVL93vv1JiCC+lzw6XR77pgxEIADwwkvPYb5Kk3Zz90VMT1GbHTtK\n+au2tl7GpfNfBQB87nOfR6fDUjmDXYBlC52gg0uXSNLvoYcexMYmzSnP/OnvUD3LZVwJSCt/p5PF\nJz793QCAF0+fwhe+QI6N0B8pnWAEbZSKVL9ufRkXV9cAABmTn22vjLcvUT1zmRGsgOYzx3Lgjcb9\n9o9/92sAgO/4ju9Ar0llHDriYmfnBQBAifOGTR3T0WjSwjqbtdDlRXRG9mCPGEB5lxB69GxEkMHP\n/dy/AQC8733v4noAwwHVL5uxIBkoXL6yjMBlzeNCGREDDcPIIV+gUPehHaHe6CC1O8dC2Ogar0H6\nGQxdeoc21g2cOHoPAOChu7JorxOQzfsGnBE9+1e++gx8jfqmcaSM09+gd+7YPe8EAGjWPdBdAqSZ\nPeO4lDE2Go9hEhKc0mRigRjts74MMTnH6wzKpRaqcSeSWUiWqook3Vek24DBID/agsUym85AIKsR\nEDdhQYLGwkKlAT+8AgCw1nzkeNPZZu3xwKygzZvIhdpBbO5S3y4WKqriugS0iOqkSUDjew/Z2QWh\nqbsRwk200V7sNMaY17XrfC8TuCbFTtcpP8VON4adlH69hrgX0LPhZaEUEGqJuE+/kNG4PBGpGghN\nAqB+4YnxPCcT5ShHIPb2hzGOkhNH9X3vQTNojIgxkmFoChf5vg+NMZppmipXVBAEKhdnFAUIeSO/\nUChge5s2MiqViqpnFNC5lmWhP2B5FF1DsZTlaw9h8YZOGPowmX3TarM8cLmMgKVXcrmCwmXFfEHl\nGRhkM4hCj8v2oQuP7ydEj3M6HZinDaELr12AxnipWpjF4gzJE1taBo0dOrdarqDCG0SnTv0Fckun\nqP0yIwSCpPmeeA8RGS6cv4A/+7OXAQAf//gngIjGseULr6PXp7HURIj5Gg24mYyJZoPaweLnbIYa\nNiJah/aFRLdO37/3Q5/Ho4/zuDsYopAlDKehA9NkLCM8wCeMvf3KKj07b4i5WcIdVkZDIUdjtt3p\nY3aexvrpbID/9zf/PQDgO7/ru6AHtK7WAgNxbtSZWcLRu9vbmJ/n3JK+pxxLBa2KkUPX1h0bbUkY\nLcoCoxG1X2uHSUA7qziyxHlW8xk0XcpJEXkaNvt0X7Zt49D8Afrc6+OR+2geXj13Fp1mhGA8RaT2\nV2yeBC67BobZORgak6OXqqhvEQZHoKHfov693YpQmOKNy3uKyPK4ZmTnMRrRQ52t0nt45dIAtQP0\n3B2tq66XxAQTficAe2fu5PdX+Z0SvhXNIswi/QykR+OUFmXVeCk0HvOMEcJ4bSYkMhb5jwbdEUzG\nNwVziLzO2CnbxKC/xvd4Em2eKjI5kjNv2xZElt7PvtNDvhyvhYcweE1hhBa0gMZIEWXjPXREXA+p\nOwBjphD5iXbai2vidrieXfd7mWKnGyo/xU43hJ3AaQqSv9UnesOkXyn5zvKRSRyjlgfj5+vpTuIa\nb73fSTfYT66Pr+n79O6GoVTrOCHGEpuFfFH1262tLczO0uZ+LpdTssY5zjVpmiZclgYMPBecHYII\nMYy5pPQRcH6lbDajzomv0ek0oVs09y8sLKDdofEuCAJkOXe47y3C4JyWQvSRjYm/uqtyXDWbNK5t\n1wc4dJjybZZnDqE/GKl2ymQ436few6BHm1dnznwTCxUmQqOJj3/o7QCAnS3CicGwC8GknanpKbg9\nOu4OVjFfoz5y9vVX8LGPfRwA8Lu/cwbZiPDB3PGj9P3qBaw7HMAzAL70nT8AAMhnDXzk47HPrQED\ndO/Vko6I76vfaWCLN5YqjEfdwMUDB1gS0YlQKrM/0H8OIeOsF549h16PcOi3fuu3Yr5Y4Hato1ik\nc1yXnksh66PTYZKzDJDnsVoPBqjyuRdeP4WNOvWRg4fn0O5T+736+n+me50tYarK+Ug7Z5HPUl1f\ne2kTNud4ffu3vBP1Lfo8Pz+P/oDut+9UUO/UEYQ3Nr6m8oOppZZaaqmlllpqqaWWWmqppZZaaqml\nllpqqaWWWmqp3fF2R0RqAbRb/IXv/V6MOrRTVymX0W4Se/jo0eNwbNq1c50mNm1iYZ1bPo1clVjI\nc8cOos07ph//1BcAANt1F8UisbrgeIkdfDnB1hgfTe62xzvaQYIooI3DTvds/AvBzBjoEMzw0KBB\nMgsGzECGiMa75SKCzp9D30MU8GfhA8x26XVa2Nq+DACwrCzCiJhk+TJFKNxz/7fglfPUBrlKCRqz\nFC3LgkiwL+MdffrLLMmYLaklWAPXZEVomOQzJI/vMaldkzYjJ0Jq+dhEiOrV4cO3wh65GbvZ8Gn+\n1Vtejzdrt5sxc9Pl7xsp/ObCg/f+r+3zHJJsFzHB2nnjSK0J5kv8aaJ/aInyx+988vv9TGgmYukR\nqtOYpaXr9M7GDOMoChBf0jR1RFE8TEcqkqFQzMO2iUmx26jDNIkpGkd4AUCG2bwju49SnpjHtt2F\nw2NLPm/C0Ki8YsFEyDRS1x2oaK9dDldeWFjEqW88DwB44L4HMVUhpl+v18eAJWoK1SklDWuZJiCJ\nxdHrdjA9TcyRrTUKUW43hxg5EbeHAclTkS+BkBMudwcjdPvE5q03ujg8RyzB+08+hFqNIlbXrtAc\n0e70MWTGZBAK1GoL3PCXkWNpm91GHboRJ+TcwLlzFAH7D37sRwFQ2PTaGknz5LIGZmaIPfP+9z8B\n16V7Cb0Bpqosaes42NmKk4H2kc1xtFmemEWmJeBz0nAZAX5A/aQ8U8NwSMzQy5cvq2fXarVUu/d6\nPdXvkrIXS0tLfG4Dhw9TeziureQYPc9VfXg4GmB+gdiUMQOrWikgwxEum5sbqDPjyHM8hC5dp1Kp\n4J3veAf/zkVM5DJNUzHYU7szTGgaLMtCsz1CnqWW7733JHY2XwEA3HPXIWQsZhuvdwCOyK7X6+g6\n9LK+uvI67nr4IQDAyCeW3WPvvhsFlvYMnUS0hQCFL2FypIsmRkGpjk0EwV6DcSuYpigxxkZCyDFk\n4kL0BHbRrSyyGY7CQBZmSP3SGbqIIVfgCHSH9P4V7RHiuzh3mRLaHnvgXSgUKLpgMOhjqkLvShiM\nMYcmk9huPGWKaIyhhIyxVXIe2IuV3rw0jZTJeSo+lmKnt8L+VmInGeMKkWAB61DRVDASDPdr4Zq4\nv4mxrFBCjUETyf6oXV2nfaO0Jq83gefE5DkGy71kGD9omjaeN6GP2cZaBERx9LEG36c1ZBAEihVs\nGBryBYvPp0FE13XYI46sKtZQKdP46PsuHE52rWmAZdL124MebJt+e+L4EQDAysqKmn/rO1uwOMR0\nulZGi6PFc5kZOJyEPCMMaIxb/VEfi7MUodVhtrE3tLG9SWPY3/3YQxgx4zbSXRw5Qed2201st/j8\naIBczHINBU7e9yCV16HfDfoOOh0KxdCEhVnGPVub26qlNzbWFF56+yNvx/YWXf9Xf+XLAIClQ0cQ\nBoRjzp17DYsHiI2MyMfsHJXX1gLYA7qOLiIELC/Ybm6ryKqIpfsKBUtFv0WRh5CjeTNZE2trFEGy\nu7urWOGe5ylmcbfbhpVj9YAR4TbTNDEzQxFjgeeim+h28bOxbVvJTA96fbgj+hz3oXw+r1jt3W4X\nA2Z5a4aJdpfawzAMGAcJox09ehTr64R3pZQ4ePAg4eLU7gg7dHAJ/90/+Ye4cO4UykVWlShVUWTp\nwNZuFx4nur90ZRPr9QsAgJcvrOCuh+gd6o4cfPSjTwMAXJv60dTUlOpTQpMI9/MZSZmIoIDy1ez3\nffy/KmMftRuA0m0AAAIBTcZKROyLkpH67Hkeuh16944vHceoS/1chCN4PrXDKy8+h/lp6vebrT4e\nfhvJSTkccVrIl2FztMXM7DRGNkWlGtp4jBdCTHyOb1HdqxCIeJwT0W3GTkix0+2yv43YSe73HIRQ\nSg57cYo6ZRxC9YZ10sTt9TuNGNfE/VBLvLuGYSm/hK7r6t1YW1/FgQMktzozOw3HZclV30aJZfoy\nGY6qtm2UCuR7GcGFwVFbQnqEx8DjRZz1IHAwcFnel/1YAhFcmzDD7q5EuUxrtCAylI8sFEL5AXUK\n/afyZKAwQRwd3+v1MD1Na76R78NkNbPZ2QU0WSWn1Rxgc50ijR55+J1wu+zjGUZKArDF2CoMJLKc\n7si2XeR4DV4p1/CN50mBZ6o6i+dPnQYATM/MYfki+fOnpwknHDtxF05dJN/a/Owclg4TdtpYW0Wp\nxJHtIoKVoc+dbhMaR9MPhj34fhzRR7gymxu3zWgwwIjbT+oh2m2KHO50OhiN6HdRFCkMbNu2mrti\nf+T0dA159gW4no2IFQ8Ggz7abZo7PM+Lg+9Qr7dUu2c5IsuyLHS7XfUsQr6GOwoQ+dSmGxsbOHHi\nBADC5aORpsoolUrQb3C8vGM2taKQcnwEvCDoNDfxwfffDwA4f+EZaJx4xDQslMvUUPpOT8lObG3t\nYMQd7sRJAt8zcw/A5NDFgMFybGK/ENXE5xBXO26kDMZbXlfptvJLZWrQYqmMAErGJlQDiwR4wWTo\nEtksL8Z0oMD65AUzAzNisO7osG0Cz0J66A1YZzygwaKyuw0rfophgJBdN6VcEb7jcs00aNp4IIzr\nrhZ/mgYppDp3Ujt47Fid1BLef4OAL3JNe6Pp5I30pW+H3ZoW8u0FF7dyz3cauLje6W8GZNzIb5O6\nzfuec1U4+LXL1bWko0fHuBcnj+3XH/SJDez4d2JiYRJB8oQiGHBoQir9WEPXIFjjHdJXGv2QPiyW\nudM1DcVijsvz1QRu8ALcsixIngSFHOHIYQoZ39hYRylHm1PS6ytQsrG2geUOTdpve/hb6PtgiHyG\n6n/+3Kt4mI+bhoZDB4k40Ov56jpmVqiJ1XVDHDrI17x8hetsAizhUapUMGA5vlDoyBQJuMDMu+oI\n1wAAIABJREFUocD3ePzkI1hZJkf9cDhEIU+bWp02jYel4jT+zkf/CwBALlseO7YjE0FEc0e358Pm\n+b9YKmJm/igA4E/+7BsAaLI9duxtAIAnnngC2Qxd2w5DhH4sZ1OBKXiOarXRbJHjR0obWYva27To\nd0HowvdY2gYSGZbHlV0dy8skg7i6uqrAqW3bapze3d1VUpO9Xk89r2/7ux9X57ZZ3kgiQsj9ZXt7\nG902TQimqeO+eyjUfmaG2tTzeghZ8sj3fczN0bMb9EZoNYk4srm5jcx7s3zNNbh8D6PRQMkopXZn\nmK7pKJUqsLQplExy2r3wwguYKlMf3d3dVYD/2InjCEJ6xs2WxID1q+3hENsb5HzzohcBANMzJ3Hy\n/ifoe88fy4VdRebhTabEsRggJwXEZMJJI/Y6DxAvqiLEiy4ZRdD0+DNvegkBxNIbkYDvsixgoEMI\nere0SIdpVbltXHRaDJ4bmzCzJf4t/a7baUL4fO3cNEx+96BDpT4RQkLjsVrIBO7RY8f5GC/pYi92\nSt775P+3Ylc5vvZYip3IUux0Y+cLmZQW5GcitcTmrEg4TvZ7ZkLhGCo2/qyrG9BxjbpOOH/2u4fE\nBpjci+USUlIsBayrzbUIsWqUrgG6LtRxjedty7Lgs7M6Y+lKTlUgVBIp8bpSExFKWSrD7m8rucCp\nqSoclx3XQkJyvqZRv6EW+gZLCM7Wyjj1jW8CoM2Qb//2TwIA2u0Wjh0m8k2/nwe4vIKhQ9fo2fTc\nIabnaVy/cp4wQ+S5KPCGj2FKROD1oZlF3yY8Ykd91A6QY2h5fYjRkMrutIeYmyWZNHtEDVUsTCHL\nY6aAhV6P7r3dGWGTSS9CWDh2/CQA4PULVzBVI8fL+jp9P3/gBByHnBZvf/sD+PznvhMAYBo6tjcv\n8/UGivBTKRRw7jXaJOh06ijlCaNVyvR9IZ+Bx/NTu91Clhe587NzSoJ5Y2NTreM3NjaQ4Y0sz/Pg\n8vN49dVXAQD5TAYPPEj1FzJCv0d11RCh3yenT7PZVE5HXRcKd8UbWfl8XvXJGGMDhMVi21jfwvYa\nOcQ++OQTcLtURjlr4vz583DcVH/wTjHXc7G6uoper4d+hzaXn3jvvWg3OWdbd6DyZQmxo9YzjuMp\nOaaXXngZ995LBLADc9S/qtUqRvQ1Iv3aUnqTJB+WXY7JNHt+cy2/UxgTqDXJpB+QbCwXFInY7xSB\nuZM4NDuP+g6NUWHkIQhZUlMHIo/PlxGqnA9lo9HH8gXK17p0/FG6BHw1Rm2tr+H+B48BAJZXXkOF\nN5qF2N/vBD4mdU3NE1f7nRL3/hZgpzfyPKXYiSzFTjd2vsT+5ITkRpN+vfLlZLki8Xl8yu31O8V5\nNGP55fhv/H3IhFzXGRN/FuanEYU03xULBeRz/C4n+rLrMgmokIUdp34Y9lEtl/g6HkzWGTR1DUPO\n3z3sddVm0fHjxwEA/W4DvSGV564FePTRx6h2oY/ApzpFugddi6UDx0R3GUo1PkYJn/eA/emZ3DSc\nARPD6x1ETJw5cfQk1i9dBADUN3vY3SSZYU1kYBqET7pdwgxZq4IgllIMJLa2SYKx0eyiOkM+r0aj\nrnBjNlPCfQ88DABo9ci/s7F9BnffRQSoj37sw1hdOQsAOHrkIHod8vHoRqiIP+cvvIpyPpaCFihX\naLy12D83GvZQZ+ykIVJE6Xang3abnsdwOEQQsHS0aapnT2Qd+m29TjjrHe94u8LRfuDC5Y3HVquJ\nrW3COrZto5gj4vr25hokb3zFpCNN0xTOoudHf0ejEXZYMvvihRXUnv4sXXtnC3a8GYcInuepZ/hG\ndudt+6eWWmqppZZaaqmlllpqqaWWWmqppZZaaqmlllpqqaWW2h67QyK1BIQw8Qd/8B+hh7QDfvTw\nLB5+gFhstdoMTp/+OgBARiUwCQtLS0fggthZrVEfGxu0a/h//fz/CQD4x//Dv0abd35ZVYKuJgQk\nrmaF0Oc4XPvqXcGJxJ57vo9zPmqapnboZRgqpozJbC9pSIS8me/4DjQOGfecPgze5Q98B+DdcHs0\nlvdCAIScDLS+RTJZl6708IlPfQ8AoO/6aA9o19PIFxEytUjXdcXKFMJQxMdQ44bUNcWeiRMQ73eP\n+0ZnAftGvVxzv1QmpIwSzJz9WDp/WXZr17y9+8G3lET0pu/jL4cx80ZJOm/ku2ueL4x9v3+juu4b\nqXmN32jaWIZnklkcM2YSTOeryrxaeoIuM5bqCUKOSFCsO6E+G6YOjaMCwshDhiM7hRZBcmLxUrkA\nl8PAh8OhChuXzP7TDQP+gBgfrZ11WBozaTI6Ao/GlowRotOkMOthbwd9Tsg5UyN2zde+9ufQNWKp\njIZD9Ls0zhw6dBi9bpNvZQYma4ZlIFW9oevotlmqh8c4w9CwtrZK5TmeYj5Jw1IMwEa3h9U1khfs\n9ZuYMznRZOCqRMdTVZojVlev4OgRikp6/ex51FmqJ2tV0W4TG6fRGCkmZMm3UKmSVM/GFt3rwYMH\n8clPf5SfkYBtE5uxVMzg+bPEnvEWZ/DASUoC/Y3nziGf50hb6SOMQ7hZVjEKXZX8PWPoMDiSuNfr\nodkkuZpcLqcisiqVCkajMes3TlofM2Zse6RCuKUMFSO8UMwrNk4ul8NUlebEza02XnyJwt4LBYpA\ng/BRLmX4segIma3TarfR5me0uryC3V16piurlyCjmJEeJuSmUrszTEATJnzfwxpLhV4428H7n3wA\nAFCbPoqt9fMAgPpOD/1+zNoTSt7pkUcewuo2vWdnzrwEAOj2MziwcB8AIFssqatp2IsLru4PatxP\nniX3/wwAgjGBFBIxVV6KSCllqJlQQEVqCd1EwEAw8gQsPY7kzyHL418UDTDscvTIqIndFr1zUwdI\n4uD86y8jXzsKAHj3B06i1aV6aFJXkb5CBwTTypLTQyTHNyl5TNOugw9vnWGcsAQkSLHTtS3FTjd6\nvpn4fiwzKBKf93tW+17jqtNiBv54PTHxO3V/e3+4j+zgBHbSJ872A2LdxlIpyffMNE2A119RFCns\nBETQDTovm80oFq3r2tB1jqaWNLYEgcQUS6+cPv2yYrG+91vfg1KeKjYcDjDs0hxuD5rod2icOfQY\nzblra2solahceyTw/Clayz744MPo92gOzwYHIEOWjwkEshaPP0YenW2a/4M+S+kJAddmxY5RB5Vq\nns8d48aMLtB3aQ7vew1EXcI4llmAIeh+pqqEoXa3L8P3eNyNTFgWYQlIE/edpIj8F198EadPv8y/\nm0WOGdpBSFjiqQ99OwKLsM7m5iZMk9q029qFwSzf48cX0Nqle3n263+MfC4esyPkCzGuZWmb4RC+\nT/fo2gNYJtW13W6PpXKcIWyObvuLbzyHD334IwCI/bvO8kHr6yQpVMjmsHiQsKIGiTB2IkRjdrqu\n64r1HAQjxUiOo9ODIFD4azQaKWkdMwPUL9F1VlZWcGyJZKEty8KZFYquq2RMeK59S2NTarfHmo0m\nfvEXfhnFgo9HHqTIw1ari8YurW2Wlo7ij75CslCHD59AsEnv6uKxBfRY+WdtbQ1f/o3fBAD88Jf+\nMQBABEN4HkcoFfdcVKm1RlcDIew/M17P7xTLoGlSQEFzHYgjv4wYNBhRrE6FZnMT2Sy949tblzE/\nRVGatVIZly90uX4BXn2V3veBk8PMNLXPf/qj/wAA+ORnfhgW++qy+Xm88hKde/ddhzCKJUZhQoul\n8zVDKZGEShJWg4j9Tsno29uAnaIUO92QpdjpBs+X+r7fT0ZzXb+8+H39K/M78RwYy81JKZV0oGFq\nqi9YGV1FK+dyOWxsrHEZvsJOmqYhnycc4nocJaSH2NmiKO1hr4+p0l0AAB0BMgbLPEvCTACpwihF\nEBlHNHswWKpwt9tUamaaLQH2E7V9D4bBazRDQPpUJ8cdoctj0dCh8TqUEX7912i8/vwXfwhHj1JE\nWKvVALtq4A59GBqNjzsbTUxVKFK+XJrB1g7hl3dxxNiLL7yOSo0ilDY3N7HdoDnCyhcxatCaut7s\nYTik6y8dLuP0y+RL+vH/luaLw0tHcYnbtFLNws4wdups48plihK75+5jOHXqGQBAIWPA8Wictows\nTI7Qitvddnpw+fmXiwWYPO53u130B/S7IAjUswPGkeeNRkP5mxoNmgfvuedu1Z/CyJ9Q64n7xdTU\nFHoDVsXzXYVDQ4/6VrvdV36uTCajZDpHjoMO+50yRkZFcy2vrCBgdSQro8O2h0rR6o0sjdRKLbXU\nUksttdRSSy211FJLLbXUUksttdRSSy211FJL7Y63OyJSyzAMTNdm8c3XzuH4CdL7ru80YXA+rPpO\nFwcXif300ukV+D4xh61MXjH2ZcaA3aadxXaDWPqGrkOPqTGJ7TuJhH5uQgseSW3Tfep5PcZMEI53\nPdXGuyaV1rvGbLVICxGCk5cXDOicYHjYCVFgXXHYAfyAdjXDyIXkPDqd1g4CSQnRW5zkd6u5hc01\n2s1dPHY3bIe2ZT3HhcFRLLowAGYyS6EpOddY7zjSNcSC0Vo0uc85vs89TJpkdNZVjXXtvVIpr+5y\nb8TMuVmmTsyyvL12e/eDbyWJ6J2mbXzd3Go3G1l1DRbNZLLM/c6P9jl2rWvsTRQbjxFG4l7EVWxh\nyESS9KuMIwW0JKsmSrD+NZhWnJSc30HdTDDIJHyfWCuO46BapTwJmYyFnToxXDJZHRublKsqikKc\nPEna7jEzwnH7CB1i6q5fOYcrl0kf/emnP42dOjFaZ2ZqePZZitKwdKDbIbZGl7XmDy7OY5XZqKPB\nCN/4C2KNzNSmkeNcUaELCD1uGxdaPKxKgW9yosw4wmE4CFT7GpaJONeljEIVreEEETwuw8yWoQ2p\nHWSg4+yrF7ld6Xq+B6wsU16gE3ffj5XV/0S18AJwakEcPHQXXnudWDJSdLC7S4zqf/kv/1c6JqWK\nzpqdncUOM1acYYAyJ+w89Y2vor5N7NupqRxF1QIIQweBRwyWOPpJ1yQMPR7/JSIelwa9AFqscy0p\nKTkArCwvY36edJijIITHFRc8h33i4x+HzzkZer0ODINzcQ1HWF+n55/P52FaVHYuZ+H114hB+fhj\nFLkz6Lfg2Bw1HEYJppWBSoXmlmptWj2bfK6IKKJ79H0bgyEnC0jtjrAwpOSvnYaHrcvUn48szuN3\nfoeS1D75xH2YmqH8c77bRbvNSW/dSDHNS6USplyKAugOicnV7bRgxIES+ni8o7lfjj+zCSkTuTqv\nrufEFL6HcCXjZOFizJqnmPk454SiNAJ8rmmYKDCrMNKLMCJiCfqBjoDz5SAyUcgQC7mQ19BiffLt\nDYpyv7iyi/ICvZN33beJTJHevTAIVbSoJuOxmyJfFdE6juSSUs0NWpjANmIPbhFxO9w663i/gPgU\nO+1Teoqd3vB7IQSEtK4+We7FN+PIzrFFie8S+RowxlxjPJR8FuIaddkHw12VR2v/MgyTzrM451IU\njXGFaWqKIRsEASoVWjcmcyBBBBiOeMzrduHxfF4sjvNNjPo0rnaam+h0aW0ZeQ9Dj3Noaj58ZsnK\n0Ibn0Xzeasb5FeoQzDLNmDpefpnm5Pvvvx8G54rOehEsM85r5iEbt2XGwKnnKeenVPlxBIbMQLYs\nS/X3lUsrOH4XMY8RCJz6JkWZGNkM9JCYx7qZRbNJ96sLetc9J8TWNt1jqVhVzO1MtoyNdTre67tY\nWDgKAHj88Sfw5888BwDIF4mlDK2ArEnteujAFDRB7Vir5fD8Kar/yy8MkM9xTq1KBuCxRsgQHkdl\n9VghANJHucxJ16tF9ex2NtsqUXkYhuj36V7OX7yAJ558EgDh3phlHGOa0PPVfUWBD7CigD0cQPJn\nx/bgc45F33dVX3A5KscZDtAfUP1CSJhZupd2u4fpOUo+f7d3Lx58gPB3Nl9UzHc3cJHP5a6zTkjt\nL9ukBHw/BKSBV1+jaPYPP/VOHFyk/CaXLlxBtUp5pfq9EfKck3NqqoYR53XLmlmlEBSrJYhQwOLP\nI72756LxBw0Ta9PrTNMx650+7w1z54hYKYAwzoseQsSLL/4rjAhSp/5vmZHKhRN5GWic29i1HUSc\nRycKfHQ5r3KxMoev/uffp3aw6X1ynQ6ckPBjuTaN40fJb9frtJGxaF7RpQYtViiKNEQizp3KUZ5J\nv9Mev9B+uOUtiXa/Tnkpdkqx041cWwiBSO7vd4rDr5Lv87WuIfeJpkqer+H2+p3iFMJhQgpDZ+Ww\nMPRVXlFd11EoUATzcNhFsUTvfTZr4fz5VQBAEPh429soMj1fIEzQ7/dVFFar1YClH6VqaxIWu7rD\nIAA4KivwhyqiMlb/iaOlAWA46qu5OgxDNc4YQoNhxGvPca7lbr+noo46bcJLjgMUS7RWlEGIRn2X\njzvIsXLJc1//OrImzdvlwgw6HVLM0fQ8fM45+OKLL/I1fGxtUUTWu9/zHrzwCh0vTxUhJZVhZsqY\nLhE+6A1D5CvUPg6P15u7beQyVG4pL5Tf/oXTp6Hzevf3f++38fBD99O9uwOV28tzB7Btaj+N9wlK\nxZzKWaYhQrNFONT3x8/U8zwVnTUYDFQEum3b6pwc5yg1TRODAfl9/MDFaES/03VtItqry4pyvu8i\n4mi5IZ/b7zVUTkchAceLc9rmMD1NbfPYo49jhn0VhUIBg76n6i2EuOEAzztiUysMI/S7fRw4VMP6\nGnWgn/rJn0CzsQoAyOeLaDaoU2ayBZRBjop6YwCNZRqK0yWU/NhBQWGOgeuhkCVQIo29Ce7jN1lO\nhD7Hto86GaTUVMioFOHEd74KeZUqzFoiVDIXsXPTlx5CTqQndRuSB8dR38f8Iicl1QQigwaRQraE\ng4vslBrY2N6l+xgO6GXXohp+/dd+EQDwIz/2EyiypIPveyq5m5AaBC8SpW4iDk0PlRNdH0sVRomE\nntcLCY/2W+y+0YQo1GJioqzrbBZe69j1LIquvsZfN7sVAHfzUeC3FtZ9w+df9Xux7+drX++Nz5eJ\niX5iI2u8s5w49+rw1aTk6F4NHfUW7DmugETseBF4w8UqDcr7b7DFYEXJBeq6WoDbtq02XwaDHkol\nktgLIz8x6RgIAprYOp2OctIUirSw8n0f6+zwLpcyatNrNOigWiLnjes5qE5VVBlHD9PC7rXXaAPM\ncQKVHBPQ8dSHSOrFDwMEEbVUxhjLSYQS0NhhY0RZ9dt8IXYWSZVE0jI1hPEmnq7BY7k7GQSQsTRj\nFKHHYcrHDh/D2to6n0/XGPRHSnb2p/7Zv8AOO2lq03MwTWqni8tnUanSWDozP4ef+mc/Q2Xzo2g2\nmygWqf7d7jYOzNM8s3z+HM69TmBlOOggY5JsYX/QQZ4dW5CRAhVm7KjKGYjz1EeBr8CA45hqQ2Fz\ncwXNJgG4P/zDP8TTTz9N1+91kGUH0PZOnJizAY/BgKYBayytI4TAaDTk37VxaZmuk8uZaoMhm6E6\nbW8NsLU93rgzNDp+78kH4Dsen1tAt09lFAoFjGy6L9/HBIhJ7a/ewjDCoO8gmy2gXCKsE4Uapmeo\n7+w2WzB5ns8WiqhUSUrB8T1IlulbXrmIhcPklHBYUmZ+/ihqVXLi9KPEYiqxqSUS86wExg4WbbyI\nUhYl5vikhKWQY5kOMXb0SCWsA0g1PkfKSdNp11Gr0vsZ+CaEJCBu6kX4AdUraxm46y6SGhyc+SqW\nlgg8v/Q6bewZGnB55RwA4Fd+6RfwPX/vRwEAhdz0WJUqEuqagK6S1cZDvwZdbXXpEDeEX25Z8mYf\nSJBip6stxU43iJ2SUn9Jh0l8fmJjaaJFRYz9JcZyNpOVHq8gJh0xV9dF279+19zUmsRaQjlK6f8o\nGo8/YRgqmV/HcZBl8o1hahPnxMnJe70uuizT9+ijjwKgDZL6Ko0RlXIGukZjoqFLyIjmy3w+i/yI\nyrYsQ8n2PPvsswCAqekZ9Lo0n/YGNrJZcqrYto9jx2jc9TaGyJgsIxPYqg8bmo9Wm5wS+SLhJV0z\nUWD5sGHfwfETS1SeGyqSp65ZMHTWPpPAJss4z8zMQ9epnE6fcIA7stFpEWbM5QpwXWqPer2ODGPM\nwA9RZZmdM6+9ju/9/i8BAO46QRK1zUYHJrdHpZjFyKb7jfwAwx49g2GvD3fI6/WDi8gydrKDAIhi\ncg21XSFfQLlC9XeGAyW1LIRMYJBIbRoNh33ljBkM+rDYaxbvX/b7feXEQRQqScSdnR3lKBv2B2jZ\nnAi+3YTJfcRgTGuaJno9wqCV2hQcJh1Z2QwOLh2j+gtTrY3X19exsEBYcdhuotXYRhim+OlOMcMw\nMD09i2bzEn7s738BAMmvDftD/t5CLkfvWbvnwTRjknUDGcbvtVoN5SqRYeIN29CzoDNmsvU968IJ\nItDVdRofS25kjT1Ue+e2MJZdDSREFDt2I2iqIJYYC4bwAur/pUoZzQZhoKXF4+iy1Hi/38CBBcJI\nTq+EA/O01rx8ZR2mTn3d4M3qn/1ffhI/+CP/BADQinzkS/HYBJixpGA03rSC0BHPCm/sd0o211uD\nnfadYlLsdJWl2OnGzk+SqZN+p8lgifiE/fxO45OuKT+I2+t3yrP/xTDonZYJYmK/31d+CcI0NPeX\nK0VoYSzvHiL2JbdaDaxvrAIAFhdpztMNqchGoW8jF4+PfqA21DzXVxJ2hUIBm5vk/9+pEyklCCV8\nxlP9wQhD3sCxXR+CN3ZgjomRUhPQWdrQsLIwWHsvlqe2LA1Pvf9xAMDhIwvotInMaRkZzNXI/3XK\n85HJU9tsb24jx5KHzd0WSiVqh9g/5/kOfvlXf4nqCokBp49o97todMjn5UsD5QKtzVvdHn78H/4j\nAEA2S/gm8CNkBP1ua+sSjh05Sm2weRmSpfnn56Yw7NPzqFaK8BlfQQbIs6x2TKa2LAMhSwRGUaAw\nSyaTVbhXIlR4yHEc5VfKZCyU2Ce4uUlzxGDQU7KAYeRjc5N8b/l8XvUR2x6ivkVtqYtQ9bkMt3/d\ntlGtUvuORiMEHt3XoSNHEQZX+NoZXGa/nmVZmJomX0W/18TW9s4NY6eUNpRaaqmlllpqqaWWWmqp\npZZaaqmlllpqqaWWWmqppZbaHW93RqRWEKLTGcAZhrBYcnC33kIU0s7fYLCGXIHZxtkiRgPalXVs\nDwNOyluzAJN3fGM5J8cewjSIFZfJmteuwD4Mi8m98wQTV0nijJtOSql2m3UjhGDmlyYkdN6xjBPZ\nuY4Dl6XAttYvIOKIr8aWh2p2DgBQsUrQBO2cWoZAIUe7nWa2MA79YwlDhA5WV2hH1bP7sDipcT5r\nIIyjoRNbl1EklZyO0OPoLQkZHwuS8h7avswYKcdlXK/V9vte1/ZIGe2xtyYp6M0lu7y15L133n7w\nzSfsvLl7uGl21BuUcbOsmf3+j7Srw8DFRPRWsj/tcz0R7Tk/Pmf8O21PQs6r6nQNCUQyTiiqyfFn\nkRx2IxX9GQT0wbQMRMzUc10bbU4+2e93YZxkeRTHRa1S5eN95Jixut7r4FVO2vv444/yufaYrWHl\ncOgQsYPXtzZx4ACxDHu9HgRH7biuxE6dxqipGtXVNLOYnSMGzvOnX0JthpJuD4c2aszg7bQkAh50\nNMOCyWzZCAHuvoeiw2JWdBS66HUvUxndBrJ5YkDn8xa6fWKt2N0d7LK0aqO5A6tJbOMTx4+iUqbz\nh8yMsUc9xM+s2arj4UceBAD8yZ/+GUJmNv3sz/40/u3/8fMAgC9+3/fCdmkMjVnAMzMzsAS1Uylf\ngOfQPHL27EvggGDMT1exfOECAODhh05CsOREaAJg6VaTddsypqakcoauh9GAGKC54oxiGGcyGdWf\nms1dxZiJokglXg1DmiO+8pWv4MMffgoAsHhwAauXSQbRMDTMzdHcceHiOQxZIsf3NExxtE2nQ/dY\nKhfgelQPz/OwukHMmIxVRLtNbO3BaIjTp0ku8tiRQxiNRlyeq6J/U7szTNM0ZK0s+l0XkkMOu70+\nvvvznwIA1He2EEX0vFcvbaFSPMK/C9Dn51rf2kaxRrKmYJa56wzhOSyBrE1iJ8UO1KBkNCe+H2v0\njY9pSZbt+Fwp5ZhJmEiGLDRNRWhpSn4whGBc1G5swBsRS6yxE6Gao4isE0ceUlIUuhGgyNENQ/gq\nsqtcIDxlhwJrHLW4ttUHolieR4PHmFBLhOyHkIp4HEd3BpDqfkQkrsJJdFt7sNMtJiXXE5grxU5v\nrf2txE776FnSOdo+50fJk1Qtk6eMz9fVO63JvWPHDcr/XCNS62rsReXHrOYwCmGaLG8VBCq623Ec\nYInZo5kcTDOO5LHhMoaI/BC7SlaM5ued7jb6HAWtmxlAI8xgGBZ0lgsUho6BTeOS50togsaXfIGY\nqbuNHqocIdvqujBMmtenpw9gY5vGsJqVh86sZqH7CDTGFfBw9DgpeNSbLf5ex4svv0J1MgqwDJrj\nq1OzGLQ4IqzXQuBQO1xcvoAC6B4XD8whCjiS3CLW86svn8WhRcKEo2EHGquKdFq7MDJ0LxIBvu/7\nvgcAYBo5zM7T+csX1/heZiAYp/hOHxleW66sXIbBbO6MBdgjwlSFnIU8q4qIyEEmlnKsUGSMaeoQ\nzFImeR16dr47luPW9bFckmEYYzmlMFTPN8YuI3ugjpWLVRW5vruzDY8lhQzDwICxU7fbhsVyRLHM\nc76QVVFihUIBLWYyH7vrPly5TO2wvr4Ol/GwJSQESxeOui1kDR1vvEZO7S/LPNfDldU1HD0+D4+Z\n48vLl2Fq1B+215s4MEfRShtbl9Dt0ljSc31MH6J1U+D7KkJKRQJ6gOA+L8o69pPAl1JOrBuTcvNk\n4+/05PdaNPEbES8ehYCms69JE9D5mrGCh223MbAp+mFl+QzWrlBEBB71cYDln0yrhLxFv6sXM7D7\nNN6UCxoqJRoHtrapDESz+L9//ucAAP/9P/mn8Fmi3BQShpI+A+K5JIoERCwzHfudNDEc34S/AAAg\nAElEQVT2RYnbjJ30FDvdLvvbiJ20hHpFvBYgP1J09fn7XE8KeY06jfuELnXcTr/TgNVz4r5vWuPv\nfceHx5gmZ2VQLdP60NA1NFkiuNVqweVQaNceYv0yR9zwfDkYDCB5rWi7IQwrz/VwEXLf7/ZG6A9o\n7AhCAaHROBNj00wuh0GLxl0pLOQLVT5OKRMAwJEmoohTU0Co6FpregFaEK8huU59B6dOUeqMQrmE\n+TnCVjKMsLNDsvTOsIXtId37yvJ5NLoUqf+Zz3wHel2WInYJE/b7PfyP//SfU5uFkVIfGrZGWJin\nsj/56U8pRaFsvoDBkDBJm6Wsq5UaTPYT1cp5PPNVSp0xN1tFfYswhjPo4t67yKc17PfUPJHP51Hh\nKNkBSzFr0DGIJQm18fZGLNUdW+xX0vWxwoiU40j4ODorCAJUp+j5m6aO7e0Nbuvxvonruhhx6gLL\nNNRFS6WCOif2eXU6XRSLVF4YSHisxvTNF16E51D7lgpZZLJjNYV2u4kguLFI2De9qSXoTX4ewIaU\n8tuEEMcA/CaAGoDTAL5bSrlX++9qC8nhGnJj/Nqv/QZMgxrpIx97ZCy3J3T43FFnZ+fh1alj6BCY\nqVGI33PfJKfu0oEDcFx64L4xDl2bmMAiqUIXgbFDJnnOWAYn3Hfi1SBUbixNkxDsGIIWwRDxObzD\nFDjwWH7QdTpKM7TTcoCQF0x6AUYMVqIQOgOZxuYWopA6Ro7zpjT9Ib7lEcqXUqvk0GGt9VymCBl3\nAmElHEcy9lsl1qZjQDE5cEokhUQmbb+J4VrnJn+2/+SSBDF7AcbtnjRvzVJwcbP2VukZT9i+zo/k\nIuIa9yhi8LFXHkd9Use0xDB56+Bo/3wSgAaLxWYlawtnrJxCSp7rI2Tt3SgCSkUO4bUH0Pl358+f\nVwt23w9x6RJtFj300EMAgAsXlhExAFhZWcEF/v6zn/0sRg6VPTN3CL/7e18BAMwuzMMN6Pp+nCdG\n19Ft07iVL0xhbYskce699yRWLxGYqU4dhstSdRlLQ8Qb+aGr4a67aVPrL577cypP+rjnOOVJnKoY\n8Bwa66Vro8Da799yz0G87eQBaiUtQoUnYSmA5Qski7jL4c/TtTy+8MXvBwCcev45DEc05Rw6NIP+\niNrVC/r4/Bc+AwAwTImIw5mrUxRubWU0GJzE69Szz6DG4dIHZmtKxqixW8fmGt3vhz/4BDwev0PP\ng+8RWInYGSOjcZi664YYMkgMZB+7DWq/bDar5gAK56br2LarQEUsj9NoNFCr1dS5R47QBkW9vo0G\nO+OklCixs2hxaRGPv/ud/Mw4/0O3gd3d+Np51Gokx9ju9NBqEljNZDIY8kbf1Mw0Ot04r0ZHOZRS\ne/P2VmAnTdNQLBaxtbGJgPOB5EwTv/Xb/w8AIAx8fPrTjwAA7r33PqxcbPPFdSXXdPDgAUQsV6AJ\nlrzcWFcSTYEfTOCDWMJAF7r6LISAzmNmDJYBfYynEE46wBNyhGFyccb5HSChcmnFOR+kcCHYqSy0\nIdot6pdnz6xhcZoWGXcfOQwzQ+f0hptwoxaXF6LdIoeMyU7b0bCHGr/7d913L2osd+U6fRhanG8o\nOX9IhKoduLpUGdUGt9VS7HTbLMVO+52f/Cf5jXb19xN5tMbnaGL/a7xVMkCGltnzvYZ8nhbSRBDR\n1PFCnuZz3/dhmXGuF4HAj6+ho8H5mOM8Bevrm8qB1ez0sM1OXccHqiUqr9frYW2D5l/b9cFwA6vr\nRMh5+7vehRE7SYZOhM985rNUj8hEpULOccduITIYtxqAH+fRiSKcfBvhuM7X/wIAYOpZPP44Sei8\n/30fhiGYjDS0sTRHeKmfz2N+ijZXvu2pj8DSCC9FocAv/vtfBwDMzxH+CvwBfvS/+QGqhzPCqy+R\n1PKBA1PK8fJjP/ZfweSNJ8e10WwQGebIYXKI/2//5t/hR77/AwCA5547hdlZOr69cQU5JpTubrXR\nYOwx/6EPIBM/A99T+RZi2TTf8eA4LA1kZRUR6+t//gwMJktJKTEcssSP7yoMLBGqzaw4r6zrukry\neXp6Wm1q6bqu8nhEUaRkEB17gCxvmAWhx49FIPCY+GBZGA1pnnnhhZegs8R1pTKFtTXa4ApdF8cX\niWyUyxXQae8q/0Zqb87eCuxkWBZmFg9hd3cdv/orvwmAlIY/8XceBgAsLCzi3DnK3Ts7fQDNFSLV\nVSoVFJhwNnBd7OzQ8diZq2kZZEzaaG6JAbDP5pUGqDFVSnn1bCLGZMeJHPAJorEmMXYrirH0maFJ\nGIydfI8GI9fpoNejMSoMbZUn5pln/gQfez8R5WYrWbgsa3X0yBLOniHnZbO5hVKB1h21Ko2tnaGO\ngHFZtZzB2ga917MLNYzYVyekkfA7RYj2+JiEEIi0yWO3z1LsdLvsbyN2kvttNokIYzlmMe77Eykw\n4jVNsg3Gua6ixHEtur1+pxLnxlJSx4ahfBGeG6qUEZqmwWHfRblSVHkGm80mbAY72Wwex49T2p8j\nRwhXfO1rX0PARN/uwEfIBCTouspX2eqOUG8yWaA3wsCmNsuzX6pgmmj1eC63JQLOA6ubFlzOL57P\n5xFwHi1NhrAMui+jUIabJ79Si5vAd/t4+ulPUBlGFs6IxkErY2J6itr76ac/hBxvgnvuENkK/fhX\nfvlXEYb0Oc759bM/89PY5tz2L750Bi4TQaMowCc/STgvl8sB7H/zPAeFwiRZ2Xb6yAq6l9WL55S0\nYOg5yHNAy4Xzr+O9j78DADBXW1IbWM5wiIjJ8J5L43G5WEIhpE0j3/cwYt+aEdiw7aG6djLdScDr\n/36/j+1tapP4XE3TErmucnjttTN8jyFCJrE7jqNITboh4HGO7kKB5sEgCJTyrucFKPLm5Esvvopc\nhufS0RDlIn+2R2hxHrRCwYBhGPvtDe9rb8UI+Q8AnE38/9MA/pWU8m4AbQB/7y24RmqppZZaaqml\nltrfFEuxU2qppZZaaqmlltqNW4qdUksttdRSSy01ZW8qUksIcQjAJwD8TwB+XNCW8AcBfBef8ksA\nfhLAv71uJXQDtVoN7d0RzCwzyltb4IhHSCkwP0tyV88un0G3S9FXni9gcFK4arWKC5eIGTc3Qyyx\nbruFSpl2jSMRjZkYe1kZSeYGM1wsi8r1XRdhEO9kW2rXO/D9MWNG15X8YBg6KHJCvFavg+oM3USv\nQ7uls7NlbNTpcz6rq5C6fFYgn6PHkbOAc6+dp/sq1WGadJ1yuYxms8v3xmXklpDhsEIZekpax/cG\nKDMzsbHbwVSVd24jHUOXIwY4zLBSq2CnScy7ijlmrnmeh0qFymi1Wuqz74fq3n3fVyGXMdO/VCqp\n9ut0OirUUAgBl3fok4xvardQ/VWMozgJqpSJUMlxQlHP81RIo2EYsDkUdh9FuuuabtzkD7CvYuVf\nO7v5ZKs3y5iJE9dqE3+BPZGQ2jhxt+d5YylPXZ+Qa9j7OwCIc0Zr2jUSjiPJDNMT7BldfTeu33hc\niKJI9Tkhx+Xqug7DmGTmJ2UEhBDq/16vh6kpYrX0+31UWf6q1xso1qiu65ABlWexLA0gkMsRw+HC\nhWVMsXQNSQhylIRmwtCz6l7abXr3CvmSSlQdS96YZgbtLr13xcocOj0aO0Iti2KBohN6Qw8z8zRW\nPv6eJxXrNU6OHPhAlaOE2q0ufJ/aZnNrF7PMDh4GAjIOYTc0BBxhJHQDRszE5T5h99to7xJzMG86\nsFi+whk2MF2jdnLtAfJ5ur7nObBZBiiXy+GLX/wMH6cO4AchCmVqv0ffeRJfe5ZYzaNOE6uX1rjs\nOmo8j0BI1DlBcvyMzrx6CjN5Ypg8+Z7H8OUvfxkAMFOrqonyzMsv4djRo1yEhmlmLQ0GfTjcHwYD\nljzyHLgscXjk8HGcOnWK23KA6Slqy7Nnz6LK42oUAj6PzQcPLKDLyUq3WCLwvnvuHUuQRRKuzW1m\n23BiGcbhCG97hKLinvrIU1hZIabpc8/RtaWQKJfpelfW1iCZDdbXXNSY2T3o9dVz7/eHmJ8nNvni\n4jRCTgb/W7/2DFK7dXursBNA+GimNo2mTwwvy9IxGjGDygWyzISyMjn4HLm0sbGB6QVike90NlGo\n0XjjctTCaNhHqUD9z3ayalzM5XIq+jBwPRVNJWUEg8e0ODlsGMqxnGY4ZhsbQlM4KwxDZJmd5bg9\nZJhBPxrYyDGuiSUOW+0GjhylfrkiBrCZZXf4UAGrF0mOK5t/CrZL48ra2guYPUC/LRQKCFlWyPcp\nWi0KPID7fxQ6ECzpbGoGfJZEqBTL6LGskJXJosvSERZHg2iapiRwLS2JA0MVCRdFkcJDum6o+SEM\nQyUxGsuylkolhXtarZaaJ+I2jq85jsz14Tgc4ZvJKPnumMVoGLp6XmEYjqPs9plbAewfgH8dS7HT\n7TlfSZ7rMftfm8Ab+8kl+b6v+o5hjPvitbBTGOqqvBhHQ0Sq72iaQIbl5/zkekfhpcn7Svbr+FyK\nhBnjo2RkZ1ynWKJXCIFikfBIo9FQ641Op4Pp6Wl1DYXLhEDGmOzvumahxwxjIYSSiXdsH3/wB/8R\nAPDBD34QGkdirqycwWjkqHuM5QzzOVqzaMKCZAn7EENoJr33hfIM+hwNXijP4l2Pvw8AcPr0aTz5\n/o8CAH7jy78FAHjXu74VAY8nH/lYRa0nR64Lk3GeJwK4LEVv6KGSMi7kyyq6yePoVtcd4qWXXqD7\nFQY+9D6KtDg4X0O3HUvRD1AsUttkjRCjEWG+6docfugHPk/3E1DbfPpTH4dgucNevw3HIVy0vXkB\nJY5etQc7sBizLC4soNel9m7z9X74S59Ha5Pq9OS7H8eVVVIDCBwHzTqNt+5wgEGXcGoxm0GepfIR\nTit5bI+jszwvgMGMa0Qh/vSP/4zu14hw7MRRAMDy8rJa82VzlmIWW5aFqSnqO+fOUXnZbFaNwWEY\nosBMYQDY2qJIm+mpKtodisT72Mc+gruOUXRYyKzjldVltWZeXV6Bye9auVzDNie2F0KHxlJDjj/A\n0tFjVCeEmKlNIZM5g9TenL2V2Amg/sDdDwdmofwfpVIFQUB9o76+gUyG8NBOvY65w7TmcYYDGBa9\nwwVeq4y6LooV6ms9bxzNHoYhAlaHMHRDjc/9fl/N/wsLJPGuRYmIjYSSTvKzEAIBqM8XSyX06jGz\nPkLEMs3FPPXFytQiBq/SOmjQ72FpkcbTC6+volym98wZ9TDoEHYK3C0YHEVVyhewzFFqhk5jQLVc\nwPd+6e/z7zrI53n97nShMU7JWHklC1tv9lBmWU6Pxzkrk8eQsYmuC/VuaZoGyxrL1sYYUggo3BME\nY/WA5O/i713XVcfz+TyCYB9ZOABhOJ6z4ucRz6Gu66p50fd9haMsy5p4purZpH6nG7K/bL8TgAk8\nEj9fYNx3ks//RvxOYCWbbDarUsIYlqWwjuu6Cst4wThgVJUnoCTkdN1UamFJqVE9HGMnIYTyO8X3\ntRcHxvUnLDSu39hfGihf7Wg0gsO+kxhbXVlbVeovnncRBs9jly+v4bHHHuNrBgj5foZDR6036vU6\nFuZp7DpzhrgGmUwOO5s09+tmEUOaqiEAlEvko3/Ho4u48JsUJftffu678b//O0oP8c5HnwAALB0+\nimaLyjAyWWSyhL9sz4fG0dGRH8DlwhcXZ9DlKHLLBDav0Jjn8TNqbF/GH/1/vw0A+OCHnlIRSL43\nRMC+msgdIcfrslLBUFF5n/n0t8PiOcAwYtlrFzkrjpx14IxoHB8NhshxZHu1WFAS1oNOE8UKYcu5\nOWqD//D7v4en3ka+t0MHFrB+hWQQA9dDnyOyWrsNWHxNUzewtLgEALh48TwClhowTapbv2er+s3O\n1rC+TmN3Pm+pMUzKcCLSLR5jZ2ensbFBbRb7xeh+DXVuvK4VmlT9aTAY4IMfeBIAcM99d2OnTrjw\nhRfI/zQYDsYRYI6H+i7hpVKlil6H8Ho2m8dllnF+4r2PwmBMKoSHQ0sH8PLpbdyIvVn5wX8N4CcA\nlPj/aQAdKWWs9bcO4OB+PxRCfAnAlwBANzT0ewNUqzW4Qxq1Z2bm4Nh0E4ZuYcgL90p5Cq+8SA01\nPVPEbocAf3mmiCw7VAf8sOZmairPhBdEEwv2WN8X+t5gNfpfLeyEATM7Huxibc7IyEw4CIYMVnzP\nQSx/dmBhDoI3rVhpAb47UJtUwbAPBByG2dzFb/3GLwEAfvCLP4TNDVoU5I9FWLlMOVwqhg+ddVGX\nlmjwubg6wqBPL/2o30a9FXecATKsYfrg/Y/B5sXC1uY25lk/PV7U7u7uYp5z64iOgIj18DWoBSfd\narw4jRBPHKY5XjznGNSZlj4ObTSEGrxd11E69FJKJatFYCQGFFpioB7/jecnLRG3T+eyJJkcD/o3\n65i5pfDmNy/B/Fdu15Tmu+b5N9dOVma8iIz/7t3MAiY3LXO5nPpMIauT2tpJ544QAqaVlIOKAQj2\nTPaa+huD29ikjCa0WuM6GYYxXgT74++TMgURI8y9m1rx76amqrBteu+mp2sY8cbDzEwN3W6X6yRQ\nqYzDuQHA9XwFNIbDoZJh6XX7SoqwUCihXqd3vV6vq/PX1tbwmc/Qhs+LL5AM62jkIBAEAJ74wBOY\nm5vh4yM4PrXfy6+cg8uba/lCBS57tzWddXCbuwh5syyXK8DzaCI6cGBR5aTKVfPAiJ1WiJDPMegw\nctA4r0/I+ZxG/QamKzTx3ntsGkd4MScQotejCW9nO4KV4bFX5JCvHAdA4CzOhxU7NaTQUZuhNnjo\nwaN497tJsudL//WP4qf/539E9cuX0B+x88SPsLP5OgCg2yHwWcgDFXbkv/LCNzHDRIDtrXUlBVIr\nl/C+J94LACgVcpC8KGo327DYIW9wDqJQBMo5tr3VgOeyjGxjC+973/sBEHD4/9l7zwBJrus89Ktc\nncPksLM7m4HdBZHDYkESzCAJEAAzRQlUICmKT360JEvms/WUg/lsP0mWTYmkkmVKsklBlGQSFBPA\nAGABIm1Os7uzk2c6p+qu7B/n1K0eijJ3QUDC0+vzZ3t7qqvuvXXvueee853vPPjg5wAADzzwAFZX\naD+rVqtiXLdvp37/3Zcewl1vfC0ASit/GdMS/dEfPYl3v/vdAIiKcP40GUVPfftx5HK0Nbc5kGkk\nE4LWcNeuPTjF4InJiUmsrdGzfdcT10iShEuXaC/K5UwR1BrI9y0viO1kJHQkEgb8jAZ4DPBACD+g\nNZRKO2ix3nHcljBI19Y2UGagTbJoIGHQWi2VyOmYMHIiaDpcGBP2UKvVEAbuyHAe1WrEr14RwU+L\nqSpUVYWuMI2fovbpZGmTs77NhrYEDQHr/mw2C8+ioeBLMTVRwOoqBa+yGQntJv0ucHwkmHLwd37n\nl/HT/+oD1FZrGe4qzePpXoDFBTK0I5twZGQEpk39vuvuu5BiXXNs7gwaddJTu3fvQTZDDlBVDxEy\nPaPLfenajghqGbIm7BEA0KMaOX3AiSAI+pz4sX0VAyViJ7+uq0gkoqC+A0WKawUFrIsVRRLPoXp3\nAfcx2pui7wFN07E5mBAdmEWTv4MW5XvLwHa63OuvbJwMBvf1207/UA2OaF1lMplN10cH1f429L9/\nVTgAHYQRRVYIZDJ0biDHId27Z7eRSqX4t9EzNt87qnFgGH3gP8cT869/HfS3I+S6W5IkweLDv2ka\nIqCWzWbg8/kqckIB5FyM7JSILjCbTWNighzODz30kAj4hmGIRp32/onxSXG4n56eQa/n8D2qKDCw\nI6IhNE0Tp07R/vehD30I8/PzAADNzMIOyS574umjgjb4DXe9SfTt3e/6QR5SCcNcg9Tq9NBgivid\nO6YwN0cOh8zYBKwufd/ttSGzwZE1c0hlGRjAc259bQlXX002waHbrkE6yfW8uiu447arAQApQ8LC\nEtkBvttDINHzk8kMOl227ViXSbKOkJ3o22aK2Lubrt06uxUr66QzXd9DnelgLp1/Do0a6ds2gye2\nzuyAz/vF4vkLOH7sCADS3WNDDMqqliCD3t/qypJw6littqDKj2iOfN9HyNQ2hUIBQ0XaW9bLF9Bo\nkF104MA+PHb4SQDARz7yEcydp/5aliXofq6+msbjG488IoJotm0L+/u6665DJKqs4NDt9P5bjQrW\n19gXwO/l+PHjojZ3OpsVwclLly4hmc7zWKrCnm+USyJgZsoSVPm7B6MHcsXygthOmq5jdHQUQwUV\nvQ5RitfKXchSDCqJ9MTKSglKgt5rcXRE1I8ZHipg/hK9Y8+h+T8+sRWXzs8DAIzxgtCXEkJRn01V\n42BXOpmE0gf4AQBZkft0pQJwQNvzPLh9jng1Szqy1ewIB63TtpA0omA+rZVaaRlhwPTobhvJBFOu\nqyFktjeOHn8OWdPmvpTQanCgFjGQuM11xSqtHibG6XlPP/csqk3Sf47bxQ033Mi/60KR6TyVSRsI\nXF7bMgep1CD2oakSQkR7QgA/iByakggWhWEovg8RCId6VPuu1+vB5hp2iqpAZXCV4/agyJq4dyT9\ngQLhO0IcbNB1VfidDEMTPqp+keU4AOL5VxZxGthOl3v9lY2TyqVg/L730Q82i3wGQOysv1K/k6pG\n3KEeEmyrKYoi6mvpqkRcpgA0VRY1pPqdk8KbGgQi+CtrcbBb9ZV/wOaL2rb5W03029t0bXR2C4IA\nEvuB0ukk5IAD7xzR3759O9bX18U4RYC7lZUVxM2Qxf1c10W7bfH1gag5aPdojVqdHhQO+N//9jcg\nwfZjJpnAFx76PLVZUeG6DHAMFbzvfVRCwtDj4FsUHNTMBOoMWHIcD0NsP8g+kJboOY1SGSOcDWO3\ny9hgP3o+R/3+1O//Nrrsg5IUYK3EwXo5gZEC2Y0qQhHgqpQ2oJsEkEmbClIZaksENkxlcgg5jDI2\nmkO1RvfO5oZRqpKNcf7cCrZuI3tt1+wknnnuKWpreR4AcNWucTRrBCY4cuQI7E4MzvG4xpRv99Dh\nAFcxvwXnz1EMBGEIl4OTGgchs9msCCBVKjVcmica2de/4ZXi3T3yyCMCAPD44UeR4pr2juMI+/nh\nhx8GANx73z0icCtJAe54Ofm/Pv/5v8X+/VTnq1jMI2dwAk+jikp5g/tO9cjMZFKcDyRJwcoKtS+d\nysOOapoqgRjXWq0Gj32MquoimTI26e7/nTxv+kFJkt4MYCMMw6f7v/4ul35XNRyG4SfCMLwxDMMb\n5b8XWBrIQAYykIEMZCAD+eclL6Tt1F/cdyADGchABjKQgQzkn6O8kLZThGYfyEAGMpCBDGQg/9+X\n78cjcjuAeyRJeiMAE0AWhKDJS5KkMmpmGsDK92yEomJkaBjJRAH1kCKCnU4Xw8OMMrc6lGkFiqKn\nma5mbW0FOiPhl5eXYbkU4cwNEUjHUIASU0ypmc1F975bUWOi7SJbKJelqLLvBQIFWG/WBe2WrutI\nJNPic5SqGaoqXG5HJpNEp0VI5mSC6WJ8C60aIWAatQ3khwjhZTVrsJkOAoGDaS4wW2+cRI+jpHIo\no1qhtvgytS9ppHCO0xWTCRmeR9HyhYWz2LVjFwDg0W99Ebfd+kYAwDX7dmGNC/OFjLrfPrsVp88d\nBwBMpidEFpusSAgYZaTpKkTVd8mHqsQp2o4T0TPGtCQRwsk0dSQ4Bd71bKhaP2I5LiIqRYgdWd5E\nYwJQho0cxsWpBYJJVsU1QeiJQsYRivnFFOkK08H+MRB6/enWlyOSdGXp71eKmPnOPvfTDCqK8h3o\n9TjN+ruhY2JaHfU7UL5Rpl7Ql0EVCNQXUdDwNUEgCkRLfcV7+xE6oq2QBO2VtOmZ/fOW08TlsA/F\nAwRcn1hRFYyNM/Kz1UEiEaEQqkinM6IPJU7V7U/3jQpRKjLw6lfdyfdoIZvmIuhuD0W+/oPv/4Ao\n8Ll4zQGMjxPS9vgxytSq12pYKZFOeuOb3iwQ3Iqi4bHHHhN9n59fEG3KZFI8TiRbZ6bQZhqudEqH\nLBGSxnPbcBlR4VsSNEYn6XJA9F4A2o0ypkZJT7/nnfcCAPZsnwRcanPB1BCEpBPnL5yDwoiMyeE8\nEmZMf2QxMlqCi4Cp1uCzPgxDrK3QmKUyOYxP0B7w2T/9fWwwsvf0mXP4b39GlIJbpmcxPUPZrqNF\nQlqtrq7CYzqj/VftwH8/SoiapGEIfaYqIc6dpaLrTz91GGOjpKfX1laxd+9eAMDUFD07COJs3rNn\n5wTNUTabxcrKMl8TYt8+ogscHx/HwiVKnS+XywJBFaFXgiBGNjqOA5XT7++9914sLMxTH0+fRoYR\nirWygwsXCSkTZWydn7+InXvoectrq8gwfeLW7dvR5gLP1XJF7LcrKysx3ZwnA2E/Cmwgz1NeMNvJ\n8zxUKhUYag4jI4Rc63VbKJXpnQ2NGCKrMZMaRjpDqLNsvoAf+TEqO/ELv/IxdD3KWtRNQtVnMymk\nEmRnVaorYg7m0lpMZ9FtwlBJd44Ws5B8Wu9jIzSnyDaIspIUQdeEUBZzqtfrQk+SbgiDAD7fw0wm\nUHVJryhsn2WzJk6cmAcAlKpn0WB0W8IYQaNBn8vlDgyDbRbNhxfQPZaWOmg1SVek86QfO00Hp0/T\nWu44n8br7roHANBstJE0mQrWb4EBxmh3JGSLtN6jDDRZBWRGvAWWLbJ3gXjPkvtsGiAQdlI/9Vqc\n1RXCdaNMtziD3bZ9kfnsenYE3Iau65DkOCNMUJqo0X7li3YofeAx3w8g923n8V6MF10GttOVS3+W\nU7/t1J9x1z/PJEn6e3RK0f+j30UZ5WFgQ+F9u9PpwGTEpe/1BFo3k0oKSqjYzto8XyKbSpZkcb4K\nlXATvXP/86N2RlaGJIcCfpwyUwKVn86YAl1tmH2UPLKPHOuzNFP1BkEgqN5vvfkWfOWrXwIAvPlN\nb8Kf//mfAwC6VkdkIk9PTiGfjWjVbdG+pSXah0+fPIVUlnTi2kYFab7WdgMcfqkWyScAACAASURB\nVJyyhPzAFRQ1H//4x/GRj3yEx53pgXtdBGyn2L02Ehr1sVFtY3qCsh02ukCGi2onzAxanEnUbfeE\nLrrtJsqASBy8CU6HzpXX7d+JbVOkz3ynjsAjG873fUyMkr1kdZoIpWh8XPgajV9aUNWHWOOsf6sb\nZ3uuLVkiuyldSGP3JCF4h7ImajV6H0eP0L5RWT2LLKv3dDKJgDMmQjnExgrZdioCHDpINEbHjjyD\nW2+9GQDwxOEnREaVzawE+VxRZJ488cSTqNepv2ZKw/nz5wEAMzMz2LefbJlarSreXyaTwfmLl3js\n6R3Mz8/3rR8VGxu0X8xMTWPPnj0AgFajidoi/a5aq8B3qS0J1sH5bFqURwAg1ga0BDI5svPnFxaE\nUyUIJdQ4OzCpSdBlaVP2wECel7xgtlOIEJ7nIZVMYnmJ56sX7wWTE9MolUgHPXNkARZnJwaahlqX\n/TrNNDKcyR3RDzbqZUxOkJ3QknvC1pEQQmE/kSx5CH3SA4YiwYzOW+xX8bxA+Jo8zwbY/6FpGlKZ\nrPhc96ibkuQKfe7JXbg2nfeSCfru1JmLqFSYmtTtoV2mM1G3XUexQHonnUqiukF6r165hFyGs7Na\nNma30RrxQ9JLa9UqVpZorbTbVZEFtmW6gMrGPLVPySGXpT4WslNodpnNKHoBQShsQkkKha3TT6sm\nSRCU1P2MLrIsi/Ue9ds09b6zrRyXEHFlsU/5/uYyJHIfY0D0ffQMTVfF/TRNg6bG9G7RtZqmCZuv\nf399sWRgO12G9PmV6L/yJurZ6HtFUQR92iYaycvwO8lulK3Ui+/t2QCvX11TYfPcURUFqv4dNpgU\n2zFeELdJCkLEOVzKJr9TPG9jv2lkU4VhCNeL+P180a9EIiWGJQiCTVSdAV+fzdC1y4sLGB4hW2d9\nbQWHmJHm/Nw52BHj0HAB2QxT5U1NCP9bo9EQ5TVWuFzC/MWLeNPb6IxZLA7B5XMoIAv2o/XlVbQ7\ntEeqEiALG5P9LQiJegmAbwdgkwUp04DiU/uDXg9TI2QrNCsd6BGNvBbi//nVX6CR1CK94CFhMHtb\nYGG0GNmsIdot0mdu18bYEOnvmYkCSjXWWL4Fl7PcozOhLFnI5mjMtkykMTlGn8/NXcLSBSqHdOLk\nGTz0158BANx88CYYzAgScumkbruDcc5mv+PgTfjqV79Kf3d7kJmq9faDN+PJw49TH+s1LDB7zrZt\n28QcjXSVbdsolShz1vd9jIxQXx599FHMzhId8h133CH8VK7ro1wiZoKIlQcADhwgJqBcLif8S51O\nG9tm6f2/4x3vwCOPfI360O0iVaD9b2V1AbZH68Nke6lYLELidqYyBbgcp9ixezc6z1DsoVqtCu3W\n6/WQNKM16yDw/MvWZc87qBWG4UcBfBQAJEl6JYCfCcPwByRJ+gyAtwH4CwAPAPjr73Uv13WwvLwM\nt7sMTY1qFTh4/V3kzA3CVSRMUlQV1YHBB5iClEaOJ9+ZCyfx0Ne+AAB493tpId1379148C+/DADo\nhTVsSkzjPNFAkgUdTShJCHhYW5xKaRgJQTmg6VlIUU0bTd9U2ylK6wRix71lWWLCZZnSQ0IgHIPp\nlIke11/ZsX0rTj7D9DiBCzU6CALQo9pSroHpaeLS/PqjdJhIZWeQZQf50HAWC6vsEB7Lotelw0Sv\n6+Nv/5acuW9724+h2aAJP8GTc+HivEg5dKu2cGAZhgKJF5VhxNQ6khQiQoi7niMCX1HabxB48Nih\nbRiGcLTIctBHIygLKjXf9/uMfaUvQBCngffX8Io2AFVVxDVUL+L5GRXPZ+OXrvBZL03j4sVNA3f8\neAMFyDCINvX+2lP9QS1ZlsU1mqb9AzUYAvFZU2IKwCCMg6vxZh8fJD03gPDkCT5jBRobJdDjNR34\nIcIg4j+OHTOQJDY8YuNMVTVyyGCz4aAbmqAOdJweQo3XkqkJZW9ZFrbMkFOiXNngZuhIpalNe6/a\ngWSKDSstJQwX33dEIGt1bUnQqezdu1fUUbr22msAAI899hh+4sM/BABo1OpiLCcmxnDHHWS4/NWD\nn4Us0YZtqAFCL0or53cYqjD4753GIlSe/6qiY+cM177oOXC5TX6vi+ER+n4qP4bJEXbYqHSPrBJA\nViL9uQ6dg92zW/IxBZYkobxG6eHlchlTu/cBADLpBBIG/bbDdpLjOCjxQa26XoEm06Y6ObUFvk3B\nrv27p/F/fogogT727/4jihl6zkKDnpFOZ9GsRYe9CtIJ+nurVUWPrZgbbjggHCy7ds4KykjPc7C2\nRvp7eLgo3m10oG23m4KGac+uXcJ4aLc6mOQgpGc7gkLXNU30LHoHC/NkHP3yL/wiHn+calllsmnh\nkJ+amsA4H6INTcP0FFH1lCsVmDr1oValuXXVVXuwusEGT6hi31XX87U1EchaXFpBp0VGzsryedxx\n2w38OiSYJu29A3n+8kLaTpJEdW+WLi2gWSFnn6bKGB2mtXfnnbfCc0k3lKs1NBp8EAiA//qnnwYA\nXHv9LFbLtAb++A//EADwmle9Cx98//sBAJ/8k0+hy05KXddjHvX1MhQOoE5OTaDKNpNr0/pw3QA+\nzylZVRFyvRpJ1aCzrWCmdXhMg+G5NkJ23jhuD2pEm8cBGte2YLId2G7WsG0rGeVe14DTmwcAfOw3\nfh0yH4h0VUaTudYNJYPRUVqXi2zAX7iwJA5xFy6extTUDwMA7F4LYdjha47D4to5YajhwPUHAQC5\nIttLtgeF9wNDV4XuD8MQstJPp/P36z70H7DjjLsQnh/Xboj2qRCx7SRJYR8fvxSd+RCGARQlooCL\nnxEFs8IwrkcUhv3BhufPlDCwnS73+iuznSIqo+h3mqZtor/pr/XQbxtH/dB1fZOtFUn/7wLe43UV\nSDDoznMAk8FhXasHWWK6taQhAgSRna0pKiThyIvPUWQ70bf9tb2oX7yW+upexAG6QNBtGoaOpSUK\n0AwNDYngbyKRErZWt9tFt0fgluhAbxiG2J9HR0fx9rffD4Dqct1//1voHkkNzJQHSfaRzkT17hLI\nZMhO6fYo6LVlZgLHT13gZxvoMR1MJpvCK17JNCx/+9dYXyf9+bM//S/QbJJ+KXI9qtyWUUElnDI8\njIzQ/ry8vAyHXgEyqVmEHFOzbRtOk64308BIkZzYOY10uuJ3ccshOhvbbgMJcPBf76ET0u88uwVV\npTGRwzoCEL2z73gAUyanTKZVTSXg2TTuXduC1Y3qT6xgbIyAEo7cRWDzmRhdTBRozOzt1JdKuQaJ\nAQy21cB991BdsQcffBAyd+zOl9+K46fIZpmZmcZT3z5M93YtfOtbjwAAXv3qV/MY9HDyFDk7dF0T\ndSOWlpZw7fVEGbi4sIyey3pV0TA5RbT6Z86cg5mIazoDwMGDB/HkkxSEnBgbx/k5ol3+xsOP4O57\n3gwAOPLscyhViX5pZssEVpaovoPPtunU1JQIdi4sLCBboL5v23UVQg5WtNtt1NgmDG1LOJf2zG5F\nJmn8ozi+/znLC2k72T0b586dQzYLRKUrf+wDd6PTpDlz8uRJLCzQ+9u1fRahTuvQCj3UuHbKJz/x\ne/iRHyW64/d/gOyHj/36f0aSgQGm6kD2ozomobBlfEhworkrq6JeV4MpplRFh8FOvZSSQuS78gMI\n34pldeEzGGFoaATtJq1bQ9PQazEV4jjpM1NXBPXnUDGLnlXm72UsLxIo+tprrsZTjxHYzqqrMLTI\nL2bi+DECx7kBAZZM0xSAzZ7XwpnzdPZZW6uhzYABUxvGti3U7nx2DCY7cyNEjisT6AEAvG4clFMU\ndRPANNq+KPBE/U0mUyiXyzyuXP80lxPX0l4Vn/GjazRN2eR36KeLjn0G9H9dV0XgLAz9vn006GtT\nDIbtr9V0OTKwnS73+isM5PXZTJH0+5eigM932lFX4ndicwmB50FlG8kPPch8ja6qCPjso6gaXD86\nF3CfVAhbXVGU2EcV+MIvKkvxnJPkEAg3B09VVRUBrv6aYJZlC78TQPsrAORyBXStCLgTwkww9WKK\n+prJ6VC5NM49b7lLAGEfeN+7hV1UKq2L55gJQ4xfImnCY+BOZDPMzExjnCkCbacLlXXV8tIl/PgH\nqVzCL/7bX8X/8YH30Vj6For8zEjHua7XV1NPRr5ANossy6g2mCJaC7G2SPZXMZPEy2+mAHzghEjo\nNO49h20XRUKHyxp5cGFqEahQR9CNbEwZQ1xn0LMdpKMgmOXCY4Mt8sPJaApfU+ABqko239aJPObP\nMcjG8LHUJLsiKVkweG55Xa7pnjGxunSen53AKNe3PnHiBG69nc6btVocNOx2Gzh/nuyXdruBG264\nCUAMWrSsjtBJKysrYg7t279D2EPlckV83r17Lxy3J54fUVC6HJj6yle+giLXuW+1mvjLBylAd//9\n94qag0tLS0gxbe/K0iJSDNIY5jqKru/DcWgfLA5PYMuWAj8jQKNNZ+2L8wtQJNb7YRd7dnJykqkj\nlTLjevLfQ14M3r+fAxXvnANxHf/Bi/CMgQxkIAMZyEAGMpB/LjKwnQYykIEMZCADGchALl8GttNA\nBjKQgQxkIP8/lhekIEMYho8AeIQ/XwBw8xXdQCKqu1arCT+iEMybIiJcrzYBmSKCqVQKrRYhqFKJ\ngkDzmQkdrzr4CgBAYZJQaaaREYkZKiNvSWRR0E+BijBCCioyQo66pyI6i1ARUXYvkCBHKapqjHqV\npAAypxJ6VoBslqK1YbeNdJoivmWOfuaHFAxxunfSt0S2xcVzF5BO0bW/81v/L/7tv/4gAOBLX3pY\nZIp1WjImJwmNdmme0v4Uw8bMjt0AgMcf+wbaPXrO5GQeqyv0eX29jFyW0g7X1uYxPUmfA0ayjI+O\nwQsZhWmoAuUWBIGIkpumIYr4AgFkjrpnMqlNKa0AFzWPCp/LgM0FVBU1RtoQhQpnoPWl1ip9BVIj\n5EwQ+HFqvefGmTC6tumafkTPlcg/BprlH6M+8JUCrq+UEOdKs8A1RhUpfSn+EdrFdb1NVDkR9YAs\nx7Q5iqL0XR8XkI7bIwGMRpVkSSD7FSVGTUlQxHzxVF9kHoWioLoCVWV0jW5C5uyrQO5DtcMV6GAg\nFBk3EcWTokhwGXXX7XZEdk4ul8XXv/51AMC1116HM6cJ7faqV70Ga2sbfDcftkuICYfptoZHJgUq\nenrLCBwubJlIxijkYrGIySlCwZw5ewq5POmUWn0d6QzpkaEhQkkY5h2AT/crFExR8L1eWRNZoz/y\nwDsEXaBjW5B4nZpJGseHv/JF3HP3G6OBR6NKKJNcKoeNNdLBmXQWrD6haCGu3kZojJxpQAGn4kfv\nDi1ITERRrywKFF8ul6MXSHfB8Bj1YXhyHJCjbFjA5mLDjtWImoQ8F00GdGRS9G4WLpwQdB1LFy/C\ntqm/P/rA23Ds6CkARMUGAM3qEj0fgCz72LuXMlk1zRCI2/MXLuD++98GADh27BjOc6F3x+7g/AVC\nDm6bJR2dSCRgdQkhpBuyQGt3u11R/BSAKD6/c+duQbMUBB46HZoP0fztdFqbEIwRPU8YhkJPu66D\n+QuE4vGCADpnBDYY+V2q1bFRoTmUy49jzx6a47XyOtbXqY/tdhtdhrIrCATNTi5nYHgoi4G8cPL9\n2k6u62J1bRmeJwmqqFazjlKF3mu1XhOI/Vx2CMEEZysttmCxjlk9uxyZV3jrfW+lazNDsHm/bzYX\nkGZqrHZjA+D1NDpsolKjuVRv+EiZpFdaTGNpmiY0RrxBUtDl+1ndNjQttuccm773QgemyfQ7Tg+5\nHN1PZTTi4qUzSDOF68vvuB1nTh2j56hZZNO0hn/+3/wSXJ90wn/5xM/j81/6Y26Tj4ktlBHpLlGb\nFxdWse0qoq/SNRmLS5ThWq+to1Bg2tjGBhwGN2YL48gwunGc0YhyuQVJJVvRsdvQtIjCJKaI27xn\nhcJ2IrsnokrkTBnPE/QUuq4gCKIMFAUR8lhVlXh/k2KkMlHORRRbMv8u0ok0V6KMh+g+1Fb579lw\nlysD2+kyr7/CH6jS5iw6SYozzl3X67OXFUFd2Y827qdTit45FQqPKXa8gCa2qqqQZGaPyJjIclFt\nxzXgMDJVVfuyBqOMrMATWdWSBGE7yWo8mIHsirENw1BkuUd2O1EaRtTyQIOpggvFHCrVEg9eXNw9\nZaUwyec7TVOFvpiYpPXabDaRTEX7oosyU24lEgnwcQOe38XQcLSP+fA5k6LVbsB2KNthapoyn0fH\nCrj1NsrIyqZ0mFqE8u+i1yGk8Dvf9iaxlirlReTzdG+FM6jm584jxRnOI0NDqK0Sy8bWsRGBdF0t\nNcC3QCarwzEZDW0G2DFF+2+Wsz80+ADfO6f5aHXonOd0qxgdznO/ZLTabE8WC7A9uoecVYUeNkQm\nqY0ko5F1RYfM7yiVTEBnFLIp6whYRxTTOlaW6d0kwBRgvoX1Ktk/iqLg6afnAQA33ni1oMP+wkOf\nR2mNMkFedectcTb40iqaTS4KznZ2y+1ijG2/p556BiHbiomEKXTb0HABp8+Szt6xcyd6fJbWNAWl\nKs2jS0zNb6gGbriBMrwqlYqw8/btvQqnT9P7MAxDnMdzuQzOn6G5YPO89+welpep/dt27haMJvV6\nHRLvZ7KkYoMz4ZOqjBrP7ex1OYwNF6FpsT4eyPcnL4TfSVIkVMo1MLAc7XYbJaamHJ+Ygm6QLvn6\n14/AkcheWlpdQXaK7I33v/9H4bv0jjdqpGsOHLgaZ08SE4OZt6Ggj4KZM5MUP4TPzBeSpAt9WMxH\nficJnh/Rk9kieyMIJVEsTFZiyldNM9BlCkAzYSI9RO1bYZsml01CUkin9XoWhos0/0vLNXzyE78P\nAPi///XPIhnRTOdyWF+n7IHtu27EZz77bQDA7qvod6qio16l9dtslSHzeUzVHLRbpI+soANTJZ2V\nTo8iN8TnKSVOcwrZR+CpPmQlzqaJNlhiYGG/UwixB8lKgGQq9lMBnJHCf1c1CNvJtX1obKOR3cTP\n9Lz/rd9JkmL6LLouYg7SNmVBh2Fkiw1sp8uRF9t2grKZOtD3/b4sc1nobd/30emQjk+lUlfkd9J1\nnocJFVGCnq6pCNkGUlQJEvttpT5WqoBtjcDzIKmkFzTNiCmiQy0uuyL32+MxTXNUZkVRZNg27b+9\nXkfss81mA6USU406LjEUAbj++huhG0yPqOvwmFGjw4wwhWISbbYZcrkcKlVmDgtDrKzS3j4+Ph77\nIKTYP6yoMQPF1fvIHz0+MYxGlfwjQ8U8TGb4MJUsTjz3HADgZz7yAVTLlDmVz6XQqs5TH3mPLxay\n0PJ033q9jvo6XauqKkweM0328drXEK2xCqBTp/03nzIQ2Za9Jmegqg5SObqfKkkIwdmhUKCy3avL\nJjQpOrsFUNjGMbWY9cIP+IyWCKGC5lC314Xr0/h12i727SA/0ORIDpXVcwAAt7UBU6FxKK0SA8Cx\n9WW89k5iEahUKshmqX3vec99OHz4Cbq2XMVQgegAN9YWMTEeneMv4MabrqV7c/ZYp9NBnv2B5fLm\nMi8R7bbve2LOJZPJmO3Bs4XdvbpK7+51r3uNWA+NRh333Xef+CyyVDUFIX82dAUdZqALe/Ts85cW\nMD2zAwBg265g/JmbuwC7x1STqi6yfDudjmj30FABk+Mjgn7/e8lLosp44AVo1ZsYGR1Gu0GL5I47\nboPJ6de5XA7tDqf0h0k0+JryxkW4XD8pkdexa992AMDCEhkltu3jHW+nF/Dx3/vZmFoMkqARDGUF\nikqTWVZ0SDzh1BRtxrYXRmwNkBQdkPlg6dmIEt0UQ4LKRq2E2OEATRMv/fBholrI5kLYPrUvmfPQ\nadHLHy4U8O3jNPETahYpdlDt2LEDc3O0IFW1iGPHyUgxdHKKdr148v3FX3wa732A6kLYThN2j4yO\n7bNjCD2636Pfehh33U3Kz4l4YVVf5OylUkkxmWy7J5R+JpOOFbsb9PG6hptoccAjHAXzAFnU4ikU\nCmi2yeGkqDFvbYi4xpmqyvHhnYNhjUZDHGR1Q4YXUaIpcY0Zzw+FURQGL34a+IuT5Pj9yRWngffV\nlbqs66/Quuiv4fGd99C02DA0DENsiBGNHrCZWztaU5qmiWsVRYFr01oiQzRy4jjo8tyRoAhqh0w6\nJWhHIyPD8wKupQcgCBFG8zoMRQBbVfrpDDdT59C9XDSapJ/K5bJYj7IS12lYX1/D8goF4+fmzgqu\n40TCxOEniSI1CkKNjKXR4WBIMpmEw2nAqYwKVae1sVFeRAA6cG3dNinGrNlsCtqKtXV63tj4GGQp\nrhvlsjNmanoC33jkYQBAKZVAjekPU0ldOBc21kn3jA0XcO4kUd8dO/IcbriOqA0vnC7jAz9M9Bth\nYEOXmSfYtpDm2ja93jp8dqD5bCCosgurV+NxdKDpTGunyPA5AFerNmEkMtwmDUFUu8v30WQHSrtO\nfVENVaTIywrgdWkejRRTGMqTA/r8qeOC/z6XSOHW91EK/OIi9XH79u3wmW61UqlgaIh+1263hTHQ\ntV2Uy/TMrVvHMHeeOaCho2NRf+pM75pKT0UU28jl02h3Gtx+VxijQRCIWlxzc2fj53S7oqbWhz70\nIQDA448/iujc9MwzTwm6wx964L2CimN8fBy7t4zyNc9grUzvNJul/Wxlo4R3vetdNL71HpaXOW2/\nMIYKH1Iz+YKot3jrTTeLcc1mTXGfgbw0JAwDOI6Fbg/ocRo/HEDPxTV3PI/mdNmqQmGdUSqX4bCz\nW9IAQ4/oUGlvHR0polklXfhzP/V+/OZv/iYAIICM+Tkyxnfv2YvAozm9vLCE4RGyK6I6bR2rDtuh\ndqTSOWRztMaNlCwAMu1uFYrCASIlRCLJ+sPvwWDapy4byHPnzkCWmeZm4xh2zG4DAFyaW8XEKNEy\nP/vEN7BzL33utAA5JDspCGyUSmSHKOzgSOcKuMC8502riz/81McBAO/5gXfBcejA57lN+B5THjZK\neOZporDSTXLutC0JZiKqvRjvGUHgw+XxVRQFCa634fu+OChLkiSAERGrjBSEyDDoIAxDoQ9yuRx6\nNtMSGnJfXS4fEtukREfi8Xukg6osy8JxrmoygjDew1XBzhJA4roQEq7M6TqwnS7z+u/Tduqni9R1\nXdhDpmkKwFc0V6LrN9etItspulZVVfEWJIn47KPfRWAT33NhszMoM5wStKOBzza3GyLyb4RBPB6y\nJAvAmgOnL6jVb8fFVD8R9YntdHH2HAFNdu/eDctq8e88AQgJQg+jXLfATGgIOAjx6T/7SwDAO9/5\nTtEXRVEwNkH7fb1eFxSftt2BwQF413WhcR2HsWxejGG+QPtcpxOgkCG7bGV5UbTbTGhoVsm+Wl9s\niu/brRqyadIv0cHe0OLzSGd4GC0+8516LrZ1W/UO9uyhQ/+1r78TowXaw52gDU1mWtfovBZ4CLkm\nhZnOiBXb6fbQrJEdms3qSLGOCnwfHa7tVBwehxadzVgX1KtVYU9Jsg+HaziPjBbQapMetOEhmyG9\nnkomcKnDdIp87p0eLeK2W4nKWNMUjPBesLy2IKgjb77lZ2ExfWOpVML+fVTzeWOjjN/4zY9RW0F/\nL28sYZn13c5dW3GRa2Tt3nY1KhywMozYHrEsC10GWOi6Ls4R11xDduq9d9+DL32J6qu1Gk1Ru+vR\nb3wTb3kLOZQ83cXoGO2P7XZLgFOTPD/Onj0r7LZdu3ah3aV30LZdtJvUbvo7TfhkOo2t28ixVSwW\n4fuhODsM5J9ewiCEY9tIpXW85tXkGHR8T1CkNhoNpNNEM1wu15AaiiiTTTR5Dio9RVAs5wsUtLlu\n72585q//ju5RuYQgKoApSaKmVihpwq8kKzpcl3SGmaA554UK/CAukaGwX0pTNQIYAJBUBb5FN2m3\n2+Kc6zgOcgXSb597kOqz5PI2jATXxg09dHhde04XC1xLOZNKCF2tqaoAICwuVpBgOuPFBXIqS3oG\nH/3oRwEAb33Pm+AGXG85raJYZHBVtYeNDVq3tm3g5oNk54WMoNI0BQE/MJmN62H5vif2P6pZFdO+\nRXtZs1kXdFwxxXtbUMulUhnxfRAE6PWYilWJ6XAlOfY7KUpfnVfeUzrVjqhTK0lSXNfRMKCy3UYA\nbhqzqO7Z5crAdrrM66+UujniPmbpp/wmEBDXEu0b//5nXI7fKWzTudkwdLTZp6mquljT7XYTuRzt\nl17gYYhp82wGP7uuD8+PqMUBGfGcjEo7hHBFQC8MQxGwjb5zXRtVriu8vr4u6OJqtZrwHXQ6XQG4\nJvB1VKfPwdHjFDCJ/K2HDh1CVIKr2V7HCO+FCwsL2HMVBaqWl5c31WjN5ZNizKL1Aw7Wy4qPJPur\nUrovglfl0pqgJP7aQ49jdhvRzB178gwmx2m9+R7ZhEfrVdHvAwf24c7bbwMAZBP9fggbEkifKfCQ\nMpgaUAvQq9F7Urgee8KQELC96esSOt2oTrsKlUHTmpQUd27Wm3C4PqEmu8gwYDwIo4BpF91ORNdc\ngmvT95n0KDR+UdddvR3bP/LjAIDtO6bQZr9Xq/MyujZrotslvbWxsSHAypcuLeD2g7QvPfi5v0Gx\nSO169tk1dLg+e6m8jNCPuLS5PIhVR5L3kdGxAopMAej5NopFsp0dxxH+xlOnTqDVoj5aloUtW+j8\n/NM//dMAgC9/+e9QqZT53i088ST5AQ8dOohCgeZ4pVoWMYHx0WFsMOi9xCB8y7JEvxKJBCoMrPa9\nUACvjWQCnkvju3v3bmzdSjXvDSOEqsmXrQdeehpyIAMZyEAGMpCBDGQgAxnIQAYykIEMZCADGchA\nBjKQgQxkIAP5DnlJZGpt3TqDX/71/wu+p+KLn/8KAMAwZZw7x7QAZhNpppbSE1m89a1Eg/X6174L\njz1JadF/9tn/ioVL8wAg6GBMQxMIunp1AQz4gOsF8KKEDEmCzJlaimqIkkCfHQAAIABJREFUrK3V\nDYqmakYKo2OUCTE5tRU6089Y3R4Cjgmmkmm0OxQlzWaz8D2Keqp9kcXo04W5OXRcQq9kiz4yXEy4\n3WphdpZoAVtlS6TLeo4tIpTp9DCWlgkdODxCCCIPCSxvEHrZtRpwXIoam4qHbZzBsbHWxhNPfgsA\nMDq6H//pt/8jAOD9P/kRar+ZRINRkbKuo8XFStfX10XEf2RkiKlCCCkQdc3zvDi9UVRBDAS9VqfT\nEZQidI+oGKiyCQ0RRf/DMBQUaxtMA1AulwViJp1OC0SALMubiiaKAuzeix+rDcMrQ49cKdrk+ciV\nPuPFRsxIzKfSj7TqRwpH33e7XYGsTCQS4nsgpmXqL8IaoSU7nQ6yyWgOxSiUenVDUKkhVDAxQUiQ\nqcktApFg6IR6SBgJgSr23AB+BLWBBFlmSig1TrEPAojFHKHlbccXa2Z1dVmg/4vFPNIZes6lSxeh\nMWJ3bm4Ot99+O4+JBJmzly6epyLaG2uXcP31hHrttCw89dRT1A7Pw4EDhO44ceKEoK0bGxsTxR0P\nHbpDINiiMW01KqiUCL3c61m4/lqiYTn63BPIZegdldYXUGAODsuqQGNan5tfRmjaY0efxod++F8C\nAB64/w1ocqFkRZVw+gRRLOZ1Q6T2V8s1zMwQcjFh6lA5U8FjWruNjUV0e6RbduzejUgCWDjNmSCr\n61Xs209tzaRNyEzd4dhdpDmbIcpoVVVZ0LS6roNKjdp3YXEBT9coS3bHjl2iUOrs7AxSnBUy3yXd\nd+LZp+Fzdtnsth34wucfBACMjo+JrK2RoVFEDDLjE0Xs3k3ZwdV6Q1DTlCqk76xuC80WIVZkWcbM\nDOnjRCKBUinO7Nu/j9LKn332WYG2bDabYr1F+m52dhanzxCSfWhoCEeOPEvv8ehR7NpF70nTVCwt\nRujILl7xijsAAF9++BFqZ7WKz33ucwCA62+4XSBtrF4PTS40bRqaeObVV1+N0SEaaz9oCYTiQF4a\nMjU1iY/+2oeRTY3D7tJafurxw6hWiXZJkupif9Y0DapEk/etb307brqF2HqOnHgKH/+9PwYA5Jm+\nam1lHVJIn2vzi6hWCM2bSKZQY0qJc+cc9JjOM4CK5WWmkKrQfKnVW3A4k6NQHMHoONksiVQOOlda\nTqoJ2D2mcdUUkWmUTOnwOdMpQhImk0mcPk3tmJ4aRatBKLu11QryKcps2Hf1jXj/B94LAMimEjB1\nQqOlJwxcZPuwa9H9ctkCJEYgF0YLaHUi9JglChybqoRalZ5TrbZx5BLp4kOHXgcAKOZzkBXS8Z7c\nFRlS5XJZfM5ms4IaRNMMOE5E+xZnK8d7ZIAEUyy2221UGfGWz+c20aOIDHXPE3uqYRjodGh/i2yn\nRqMhnj0yEtOdBUGc5S5JUkwFfIU2wfORge30vSUUmeFxttU/ZDtF9nImk4kpk8NQ2Ez9tlOEhu92\nu1DB2cwjI5BCRvG7Ls6cuiCeGc2RrVu2oFal6yO7SJENaPxZVnQgZIRxKIl3rKiSoGsG4u9VzsIM\n4Yszxvr6GpaWlgBQsfGorUHoiWyuer0q7idJISSmNY7oWP72bz6Le+4htopHH/2moJmbm5sTxdNH\nR0dF5ler1RKZGfv27RPjF6FKLctCdZ3aNzM9iZER0ie1UgPw6X7pBNDlLG3frsFVaA0WMpyd6Vq4\nMEcsHHq4DR/+wIdpzBDA5uLqYW8Ny8uk25z601isMzWRIov1m+Asj8APoSdJx64tnofDOljTTYQe\nv2tlGJJL7+uZp7+NoWGygRNGEobBzCSc8aEpKpIJpoNNGgDT4+eGi0jWaKzPnj2LhR7NC1nSEPjU\nlr176MxaKI6iG9A7evbIczh6jM7lO3dtF+P+3NEnsH//fgBAs1GC7VA7RseK+M1/9ysAgKkpymx6\n+qkjqFZpXp+du4hanezb0V4Go6Ok648fP45LizRfts3uQJMzlU+fOoMco5OffZZspEO3HRQ2jevG\nmfKWZQlq6XQqib27qcj86sqyQAq7jOb2HFfcY25uDorOGexD42iuk71ZqdTEmUWWZWHnTw7nYLVa\nUJWXhMtlIAB27NyB//Cffxd/+KnfQhgSEr3ZbAo0e3WjBV0hPXDvfW/GLYdeAwA4t3gRn/iTTwAA\nUvkUGi2aHx0+A45t3YL1VfLJ+OFiTAMbhHA4ax6SBpXZKRTVRMiuuA2e88l0HoUi2SD5wgh0poGX\nZAndiBKt50Nlxp7Ai6llwzCE02PKLD6Pzc0dh27Sd1tmpmBxtqiuanjZtXT+cBwHihRn+Ec6sNJU\ncOCaGwAAR46RXbllcgrzy/MAgPHxYcg8rzdK55HLcOZwrYtqiWzFlaUubrudbCZJibLTVdjMCKHp\nGsqVkngHETtEoVCA1ucbivYjq9sRJS5cj2kBDU2wlFSrFeFDm52dRRCQbaoo8b7db0cFQSD20bW1\nNb5HVYyBaZrCnlNVVYy153mCnn3gd7rM619k20n4c7woQ0kR/kjP80Q2dv/3juNckd9pSKF1rKvA\nCpfAkSQJPT7w2I6H66+/iZ4jyahzBrVu0FwxjARMpir0/FAwB4WSBJkztYLQF+xXQSBBkmO7h37n\noMb3XVy8hJUV2guz2axgAlJtGZbliD5FayIIJPCWj4sXqf2q7OL02ePiHo8/SiVuLMvC+XPkm1pa\nWhJryHVd4ePRdUOcLYRPwbIwPU5/nz+3KKgXlcBBs0ZrXZMs1DdIp0yPZ+Hb1J8feCeVeChkMyjx\neXNpaQGr80RbWNY08W5SRROVdTrvbBmbQJrt5PJCCdUy6eEtW8mGMlQDiGICnQouzpOvZKg4jtkJ\noqIHEvCZEq9d7SBd4HOXbiKfZx8ZM400mh04UbZX0EXSYL2VNTA9uUP8rrRCttPRZxYwPMIsAT7p\n4COnLyHUqX07d+5Eh1np0hkNo2OUNfVT//LDcDhosW12Gr/9W/8JADAymhfMQBJTbySTOqpVGo9u\ntyvoW00ziXKZ7n3mzBlMTEyK9xRluJ4/f16sj8jWKZfLwvdvGIZgospms1hYoLmzf/9+bJwjFrkA\nkrifX+G+pNPCnnccR3xud3uoctZztVqFHNAzDxw4gJkp2odbrRIURbpsGtJBptZABjKQgQxkIAMZ\nyEAGMpCBDGQgAxnIQAYykIEMZCADGchAXvLykoANeb6PWq2GkeFp3HQTRbd1Q0IqczUAIAjXkclR\n9PXc6WXksoSmWlxcxM6dOwEAH/25n0OrxzVLQFHF//4XX8SFOYpYeq4FjyOdPduFzVFIL5AhRWhD\n1YTE6LUUZwJU6jWUmE/y5OlTsDkbRJJ1zMxuAwDs3XsVOh2KTG7bMoRGhRDvkiShyPUlbmcu0MOH\nGzhxhqLebtiB40SRcw1dRoJMTIzhr/7qrwAA6dQGOh2KnJZr69i+gxD5Tz79EACg60lQzLj436f/\n7L8BAN7+ttehI0Xc9wqGisRBeub0Cfzir1GUV2ZeeV+WkEkRYsG2O6jVKbq6tLQkkL/79+8XiBQg\nRk+bZlzk0HF4TD1PIDGtbhsrKxQtLxaLIjMtuo7GKa7L5TiuQB8sLMwDILSx617Dzyui21XF7yLU\ngizHPP2iRtJlyvNBswTB976mX/oRQi+WXClHs3SFMe0rHSeNERPRew7DUCBgwjAUCN2FhQUR/b/r\nrru+6/URoqrVaomMmPX1dbz8dloPqiYLFEq5XMa5c4SM9dxAIHCymTyOHiUkytgooRSmp2eQzVJG\ngiypov5Mf208K/TieaZQwV8AIvPK8x3R5k6nDcOIC9iurFC/UsmMWD/bd2wTCKFqtQ6fEa4JRuOt\nrS+i3iCEi23byGRJt1y6dAlr64TGqTfKUNSQr+mKue+6PbRahDiL2qyqEtbXKMMzlUphcYEQFcPF\nAkKuj1FIz6C0QW2dGiviPW99K93bI93zhtfcigTXm+p5Nchc+2B5fhFDBepXe2UNQxlC2gYJCU6L\nECQrF8sCXZ3MMno56MHQ4vlUq9Oz3VDC+BS9m7Hp7SikCMFSs9swXa63UI7vl+HssiD0YNtci891\nYXHBU1WRcWA/7SO9roM9u2m/6Founvo2cUpbzE+MUEaVa3RZVgsHrqHfra+vo9EgneR5jkAKl6t1\naFxTYs+eHXj5LYcAACfmKJvq9NmzQpetrq4I1PiZMzWkUoToKZVK+P3fp0LNhw4dErztjUZDoMmj\ntVEqrYs5pOuqQKQnkgY2NgjJPDw8jAgwmEoncOoE7TW2Rfdtt9tw+IIvfvGLuOOVr6d+uYpYa61W\nCzrP/eHhYbz+dS8HACyvnEPPiuu2DOSfXjzPQ7myAfgmVJn02Oj4GPbvp3WoaE0YJumBVt1F4JId\n0GzEKMTZ2Vn84i/9DADAMAkdFQZF/PuPfRIA8Fsf+yWAi51fmj+HTI6zuVbnUeVaLbPb96DBSGWX\nUXiBZCCU6BmLS/M4fpJ0L2QN23cSim3v3quhyKQLFUkS9lA+nYLdJjRXLkN/v/G667FwkbIuv/3t\nwxgdJiTznp3X4lsPX+DxyOCRhx+jdjy8jMIw15y4uIE0tztg1Nm3jx9Bg21GxZSQ5ppfn/qDP8JP\nfPB9dA87QIfnfKXSwOIl0g8REk3RkyjXyG7Lj6bEnra2tiYyACYnJzchfvszaCK7IK4n4Yu9rtfr\niVp5k5OT0I2oTml/Bk8IVY1qU2poNskGq3KNnDNnziAMSS9ms2nonF0gSTHyTZJInwBXjjYe2E6X\nef2Voo21uMh59PsoUwuIs7Dn5+fF/vDGN75x0z2+03bqdDp9e0kJckB706FDh1Ao0N4UhiFOnDgG\nYHO28P79+0XWS1SXcqg4hqEi7YXpVAYK19P0/VCctcK+OR6GIULEBdcBsm+ibMTl5WWx/m3bFjUi\nJFlHKkXrrd1Wxdrrdrs4dvQZAMAGs1XYto2zZ2nPy+UyKJWpv1a3iauvpv383LmzqFSi2lNZ9Jo2\nj5eMaKpFGclDQ3m4nIkxd+4kPJfssu3bpjGU54LfpWWYbKPNbhkStSPSKfr7ieNn8Cu/SPVnUrqJ\njkV7dTaZRYszkAx/DnCimqYpRAXOW60W1hZoLUe1gsNAR6NJ+mJ4aBIq16hJGinkhul9dKobokbm\nVXtuQpILhyMMUOcs8QjTatuuyOxIp0wk2H49/I2vYPsO6m/K0GFyjale18fU5GxfW4HS6gpOLhCz\niq7rIgv+oYf+J17z2lcBAHbt2IavPUy1hm644SaMsB3V7dqixs/qKrXplltvQFqneXb6/Fn8Lme+\ntNttzF+6xM8xhG3verY4q46Pj+PM3Hl+jzRm6+vrgu1DVzWoo6SPp8YnNiH0e1zzq1arIMVQ9o0V\nOm/064hnn30Wt95O/SqXywLd3G63obE+3tgoY2qKMuSmRwtIJsxNZ+iB/NOK67pYW1vDwYO3ophn\nPetXofJ+mdRzKK3RfAiVItp8pjhw4AB+8icp21LSJWHj/Jff+QMAlCVtsx4L0RHP6/ZstNr8vaQg\nkeQawKoJx4/qN3OmUW0Di8t01gtCGck0rYXxiUlMTBF6v1gcRqtLv8vlcmhXyfbIZ7PwuNb03Xe/\nCQDwqU8eQbtFemRpaRGcdALb6UEN6Zl//Vefgy6TrrNaZXTapMdCPYcKs0/InA1w4fw8uAQNfvWX\nfwkf/PDbAQDptCHOXnavjUaT7MZapQ1Tj5hPeB9TVXT5zN5ux36iUqkkzli6rgt9D8T7aKFQEOs2\nOvenUimxRo8cOSL2yPHxcWHfANiUzRzdz3F6KJVIF1+4QOdj27aFXyqZNNFucz1FUxfPlmUgwXrC\n6mze276XDGyny7z+CscpkaY9KbLF+22nTqcjsksURRH1g3K53BX5nd58B9kSqqpifZ1sDMdx0Oly\n5nWo4KabbgEAJBNpfPNRquc4zqxBM1tmkS3QWUvuq3UFxGssROzfDEJP1HWPfD1B6PVl7Fti/0sk\nEsjlIh9UG+12W1wfZVOlUhlRcz2qxfTVr30RuzlTuV6viJq7qgY4LukFP7ChqNG9PVF3UlFif5lt\n07WNRhUXTp4BAFxzzQEo7D8aGsliJMe6wE7j7ffcTfeAC4f9TQZnYbbaq2g3SJdNjWWFfdhut2Aa\n9Oy1lSVMjrKfqLyIRc52HR8ewvQk7fmuwwxNPlBhn0w6n8a2GTonZ9JDiGyujbUlhA71cWJiCs0e\n6WEJkqg13rVobjUbVfEO0ikTw0Okt/LZItZ5fMulGiz24deb68J2GxomW2DL9BgurFK/Tpw8gt3M\nXJRO59FzIrYSDylm8Ni7+1r81m//BwDAs88dwze/STWuKmXS0ZNbZuCwryyRMMT5r9VqiWzDZDK5\niR2tvx5vtFaOH6fz+tatW4VPs1zZwF133QWA1lKG33+9Xhcxk+XVFVE3tswZdI1eD088Qf62fft9\n5IbGaKw3NuJ1CkVk6O7YsUPY18PDSWydmUQq9Se4HHlJBLVCT4VfGUa15cFUaKFnjRQsdpi0Ojr8\nFm1sGWMUf/zJPwQA/MSHf1xM0MD3sWuSDPDTp8m4vvvl12HoPqIhaC6vQzWou7lMCqFOCqLRqKLR\noEW/UWpgeYU2/te+jhyGGc9DnhVBuVpCVudURFXBycfokDf/3EMo8uK4dD6JW28hygHL6qGtUlBL\nH6dF+sr734jg4WgyeTjHdFKhEeJs6SgA4HxlDa+YfC3dr5SAItMkf+AjP4kiU0P89me/AQAwki5C\nppkYT6dx8RwdZMxwFFaLC+YFBjo16pcKBR1e9Lt3kWOp5+hocqFbaWwDhw9TQKDRbiHPh9Y//8sH\n8Z53E62PbmbF7mpZFkw+gMsmHwTzKays08Zx9PjTqNfJuTMxfRNK87SQC8UEXNCE7/klnD33NACg\n22sikyLHm+qTUeW3HUzkydHdXgfSiW0AAA8+XIcUaCqZgc3FBTeWnsDsLPUtlDiNWDMR+NTOdqeH\n4jApwXKpJoITiUQC7SaNTTGfF0UYUwkTqkybS7RxyMlhkZ6pSBIihairGjxOs3W6FnK86EPfR5cV\nWyqVBCLDmR1VpdI6EkwVkE6nUeM05nw+LzZTRVFEcT/LsoQyjSgG+ttnmuYmJ3UchIyvDSRjU9HH\nfoUHkMO0v9hq5HDfsmULFhdJYReLxZgqs4+KMpFIwPJIybp+RM2ZQ4+p3lRVx+IKHTyPnzoh+hVI\nDowkb3ieA1WL5hnpgnReQ/UUPdsJOvjM54j6bnw0g4M3U/pwp3YKo2meW+0mjh1+ksZdfgN8m+bo\nKrMTnp5fxn3veBcAYHF1AROTpH/a3TIkTts9cfioGMsD+1+GLAek0jxPV9sWNJf+nlXS8CT6ndRy\nkGZbZTyVFO//yS/9DWaLdAgoX7iADI9fl4vZotpGlgPz6x0LbE/A1Atod2ge5vKTWFwifbFlsoAb\nriVawla9AlWm8Z7goptnTz2NtHuW/r7YQm6UqCSaSx1MTRElWKfVwk/80L3UboRQQhpjxaN37tpd\nhHxYz6TSiCweeaKIaomLjCd0bDTpnUMGWhatJTUbF7Nvc18bja7YBOcvlFAoUFtHCwWIbSlwYHdJ\nt5lBIOYZAFFw2XVpPLpdu28uq8JRns/lwWxfAFRRcNzzPCTZ6ZTJJvl3CqYNMjp7PRvVDdpbOvV4\nw1d8CS1+ds5ICoNxsVSFM0Zz9GU794h/xzl49bWvfU04FPMTO8VGPj42gVe+4k4AwJkz55DJkHPR\n8QK4AU2eP/2zPwUAvOLOVwijrjiUFcVKL8yfw+wsUeUEug05QYZ6zw+xukbPbLeZasjNIJuksbnh\n4A14w6uInvChL/xP/O5v/CsAVJB1lQ+YzWYTF87ROq3V6qiyo2wgLw1RAh357gzU0IQbMO2Vmofc\npbkddotYX6Q1lMvnBb3lX3zuE7jvbRS4luUsUqB5ajdoseiahR+4j4KZ82cnEfni0mYeAdP0ZSQZ\nZormYHnuKFoN0tXPPkNOgTff84PwwUXBfR+Ox8W6kz4W58l2OnXqYTR7tCdt37YVdzJdZstpguPF\nEIwrso/8BDlT9+XGkWKn0NLSCpoardGOV8ZdP/RTAID/8T8+i9Or7FRK7sMD734PAGByjPT2Zx55\nJbo26cqZ4SLqTJWTTqZh16hfPSuE2+Q12dLgW7S/yT3SVxqGkOGDTsM+gswI2xjH1tDskf7w1xzc\n/koKeLeaFhR+B7KqCh5nm/fWselxlNgpfvzsEaFDlcRVSHT28O8cuAHp3LRm4+nnvgwAUHVX7J2S\nS/tfPtnE+hLp/jvveCU6XFi+0wuQYP3rhyFctke6q98UxaxHx0kXjkxMo1qncbQ9CaNMKbK8sg5d\np3cnhYDCvLyh6wDRnp9MwHdpTnHtd6x7oaAo8V0X7TbpwtHhESo4DCCdMOGKAGcSFl/jeS70CLzE\nABTD0LDGunX79u2oRPso4sCOZVnClkkkEsJRFunvTDYjaIcMwxD7laZpgnaoUCgI+8oN4sCmbdvC\nFoucBo7jiGdLkiQcJrquCyeQbdvCHguCQBwsU6kUOnwIj/ZIQzdRq/FeaCaRL9L4XfrGt8Q1nh9i\ndju9s+eOnBLAmaF8RDHbQrlGfYQSwrJpHfzl5/4OP/a++wEAX/7Cp6G5tPdnpSbqTer7M4968Fwe\nM95nNy646J6lA//BOw4hm2OnixHAZjtPsXycYGDFwYMH0eZ9D2EENEpgyaW+Dqd0yEVaa2tzZzHM\nFHKypKDToPtlXAkmz+FTz56EzLSEOaYeLndq+OZXCfD3ute9BnMbZDvrvo2gwzToaQNWieZIRg7h\n+EzfZ7uC6kq1aK6MjhVxvkxnNKtcQn43ta+2XEPkJ02FHrodejeOr+H976GxVPhM4L/5Vrg2jbvd\n68FkQE7obGCcfNXo2NsxrNIZp1aribmgaVkRiJZZV/R6PaipSOe0AdAc7gQtOAqNR6FQwHSBdKVt\n24Abrc0AUTCrwza1pikoMsVwz21grUQ6Z+feq8V8h56Gxw73zHAGTYf01QqDfTptCxqf/5IpHdks\nza03zdyFBttPpVIJk6Nkm6wvb6BZpXtPTU1heozmbRTEP3fiJCYmyDbdvmUM//7Xfh4A8PUnjuPz\nn/+8GIc2n939nockv7tKvQaHHesKj9nG+opYaxElOAAsra5g9widrxc31rE3Q8+cGB4Xfa/znri8\nUoGm0vresmUrLHb057N5/Oy/+TnuyyRaPMfb7aagXgz9EKdPnt5kww7kn1aUwEOuW4HfC4AKvdd6\nI43JadrfyuttSEwF9kd/8Md493t/EADgN3xsyxM4zgt8YRP/iwc+AIB0f33lBABgdLgmqMXyqg5J\npvffbHWxujQPALAcD9t2MNiOqURzuTx0k3RQvdWEy5RUSxcu4uQxXu+dDmb2Ewj89ttuATJMk5nW\nIHn02fXpeUOzI9iWIn2wtrYBI0P68suPfQGTk/Rs009ibY101tjYq/HeH/9h+t428baQ2vWed7+F\nBk9tQGdg40iuiOYyPU/JJ1GtMOe0bcJhG6jeLIExRShVaLzS+hgQ0prU9DbcBq39oNvC/Bzp0/Nz\nF/DGe+iZipGEJEeUgx741cDg/S8MfczPkRO919wAeuwg95swQX0Pwh580PfPnXwMskrjMzM7AVOn\nvSKdIP0XuhJGeQ8qb3SR0snH5/cUGOxgHsqPoF5n+qz1p7BrF9loiRTtEyurG1Bk2jfHxqdw8jTZ\nxjNbZkWwxrIsSGx/SWEo6MJ0VYHB4PAI9NTTYophTVHQ73dy2b7utlvIRmBPz4GuRqCWNtIMDvFs\nutZ1bQyz76XVaiHks2G32xXn6X6bJWovEPuSNE0T+1UmkxFn23w+L3xKsiyLz46vCIBnqVTC9DSN\na6T7TdMUfQQgQFutVkuUVtA0TQRBh4eHxf2WlpbgBtRfPcFUmgFg9UiX54sFNI4eAQC0WxZuuJEo\n2BuNhrDXCoWsKOcQgWlyhTQePcz0akGAb3yTgHOy4kLSaL7Um/NI5uiZ9VYDX32C5r6EAspNGj8t\nSfvi+UvP4u63kL8llDxApbWkaA5sh+536ttnBI34vffeI8ZE5mSFi/+LvfcMk+yszkXfHWvvXTl1\n7pmerFFCCITACLAIApMMGDAmCTDghG2QDcgcn2ODOeAgw8GYYOyDDDaYYCyCkQkCISQBytKg0aSe\nzqG6unLced8fa+2vep57Hp/7z3ruU98flWq6dvjCiu9619kltGv0txP5CWwOyW6TwwgmxzRTpoo8\nA4kN2YPK/tptt30dSU6QJnXaK6ce/DkuO0xUpNlkESt9slnqtQHSGaZATuSQYf250TwPlwGMc9Nl\ngGNFJdb9nXNLyNtk+9V+fgJXXHEFAOC5Vz0XZorsgMAdYtgmWZnKpqAxeDLiJH/SkKBO0Np2Oh0M\n+yT7fNdBq0H7aaowA7/LLxOaUNkOqbRtaEw1HydLgr6JQn6C59qEqZOMHfS7aPG8h9EQvsQglbAH\nQx9RstaZijoMac6K6cPiPPi+j8BmYHp3S+h6VYkwOUFzsrB/UvgKMWi5stpE6HPiPopQWSXZNz1t\nIWnS73pBD51duvej1YfxpCc9mdb08itx+SGKe/7HfxBg6KEHT4jk9/zCtNhDUioPnwH0smKhukv3\nl6CJ+GqrXoMeA+oYzOn1erBkmr+JVBLrTNmYlRUkWB7nkwm0FPobPanh3MPU7iihkz0VNrcQMM33\nXCEPx6H7fe1zH8dgMEq6xiCCTqctntsPPLjLNdHq4P82xvSD4zEe4zEe4zEe4zEe4zEe4zEe4zEe\n4zEe4zEe4zEe4zEe4zEe4/G4H4+LSi0oEYKMD08l5AAA9LodWElChu0/dARZpk9ot9vYd8klAIDA\nyiAhMSI5kqDlKStY3M9lwr0BUpyhLVst9Dlb73kOBtzcbTj0wEA9qKoB3aC/X9+gDHQuN4XKDtP/\n1TtQ9LiBoIykRSV0yZSJrWXK7O9UdlFdo4xvqTiBa5/5LADAZIadClw8AAAgAElEQVT+tt1poMsZ\nV9NQcGQ/VRQ9/PDDKDK6Y2V5C7d+/ZsAgG998ztwbMo2Nz1fNM+99dtUzjrs7cBI0L9/9Us3w6Jq\nczTbXcRMH+sbFUzM0NzU25t44pMoY77Mz9wbRChwpUSzVcdLX/QCAMAPb78DJ08RyjcMZeSZBqg7\nGIKLCFDMpxEwKtcZchVGv42tNcrmDjpt0Wh3Y3UJh+fj0sVdUboatgGPm59WtjcxzHAD5hRXDOUN\n+IzatZJZGNxkfnN7B4lk3EBVhqbSQlYqVTFPQ6Yku/bZz4Pvx3RsqsiuW5YxokqJglE5uu/A4Kaj\nkCKBSkkwInSn64+QLAkVCLgcXQISBmXdrYQOmcuHNV2Dxhl/KQzQ6/bF/QGiGEtnKPsPOYLtM9qg\nUhENi1utlkBrOI4j0MR33UVZ8UsuuQRHjhAd39ramkAK5PN58btkMikQMaqqCnRKu90W6ErRODmV\nEu+9Fz3TbrcFYmY4HIrPuq4LNHS320U2TdcTFW2ygpCvYaVMsV+OHTmKU6cIDZuykqJhay6XGTVB\n571y//33YtintdNVDT5XKSIMUMxm+HsFNt/H9xx4TCmys7UJO6S/mcwSOsjxInz5S/8CAHjNa1+N\nbp/evZjLwnZiKqlt8Q6apony9Ze/9OUAgG/++zdg8ZqHfoDhkNb2uuc/F7kcnenPf+5mUaZeq1YE\n1cGJEw9jpsCUfIzoG9hDtDuEXpBl9YK1k/jgDQY9zM0QeqbRrMHcoN8eP3Iw7oOJAVO6aCowDLny\nL5vGr/3ar9K1EQoEVuC5GA640iOVQacfNxLmPavrGDBKZqdSQZepxjqdjjhLoRcJdG2pVBrRNw4G\nAhG/t1nk3ias8V7WdV18tm1boGVd14Us0RkLw1CsR3xe430c/y7+93q9vuesWwIVZlnWCIXMI5PJ\nwMzQevV6fTE3/X5fPH+32xVofEXWxN5PpUKsMi3OI49QBd2VV16JZz3rFwFQBcHNN98MANhutATV\nbC6bx9YGIQSnpibw0EOEIqtWq2hxCXcsJ48fuwhByFRJCRU+N5k/f/4MVldWAADDwQQyGq1BNpsV\nFBxxlVgQBKLK8s477xSfDx1cEGimarUm5vPQoUNi/+VyOezbtx/j8fgZgQR0pBCbtRE15b59+8Se\nl1Udl19G9lK/3xfr+uZ3vkPIds/z8Pf/8A8AgLe85a0AiNr50BOJ8tcZ+lBVOrcJ3YWikSxxHRe9\nKumSRnsXzQb9zdQcnaF0XsWJk1T1rZomoNF5qu5uoevEVAkTKJXJNtpZP49bvrLK91HxRH7uY4cO\n0rsEA3gD+l3K0NFukhzOpVSkTXqm0LcxXWL6rLtvxUc+8hEAgJW9Cr0uIYQ3t+h3P/zh9zDokxz+\nx//9CWTYlhgOejC5irPvdNF16AxFSggvYvRviunBejsIGTKcTVpwGU2WsSx0dTq/gWNje52fNVtA\nkteJqp9ZlzG8TAp8gbKUAg+VDTqf3/r6v+KNL7qUrhf50BjBWyzl0O0x7aPfwcRkib8v8BopWN/k\nCp+EinaLPqesNPJF0oW1Rh15Rvae/PESEiY9X4MrLMx0DgHbN5aVQZd1Qz6fFTpLkiRojCANXQmB\nO6JNiSlUJP73TCYtbBNNV0Z0cvZA2FyqqkJV6PtarYpCkaqNnMGIfyfNPkHkB2hxhXCn14YXjaj7\n4gopwzBERZPjOKL6PbZ1FhYWhG4AcEFFRXxm9uqdZCZ7QeP4uEI91l0xKhkg/RfrjGazKagoB4OB\n0IGe54n79/t9wSoRP4ciyUgl6bxmsxlR/VLIZwUzxTVPfxqWzlPl1MGF/YK+b8DrlctmkOC9tb6+\njso2zdnsVBY6U9skTQO9BlO6OS6kkHVgpw2bP5sp+ttmo4Zkjt5lbmYSnk/6tNHYRXmC9OI9996N\n5RWq9H3wofsFWveapxEdu2VZqFSYWq7TFXrqQ3/2Qdx0000AgEMHDsNnJHu9UcPP7iG6lWQyiZ1N\nQnPGuqvRbOOpV1P1gmFYMAzay4XJKbSZ+WN3dxdJpluXZRng99JUGYkErfXUNL3Xbbd9Fxcdogqq\n3coW7r6TGDKOHzuCd/zubwEAdEhY26B3XJibxcCObVK2UeAzqh5QZB2uE1MMt8T6NnuyQId3u11R\nZTcxMbGnufvI/omR8XvZFBRFEfaVqqrCNjGNJPrNhvj7eN/aw3iPaoIWPp1NoVAg2dHv95FgHeEH\nETpsk1a2a6Ip+dQk2RrHjx/HDlM9JhIJoYskKEImRlEk0MmDwUCwaWxvb6OQp/mOfZBWq4MzZ6jq\nolAo4OBB0gHPe9azcdWTrwQAvO1tv4Ff4H1Uq1XFnGxvbWHIvn58LqenJmAy3f7E1KSw7c6ePYPT\npx7juZ4cNUFP5wRSOP5b0zSF3/vRj34UicSoSjU+35VKBTrLi1KpJGRfFEUolUpj+sHH0fDCAJu9\nNpLZFMwU7TtragoW+635ffthM2PK0auuxPQx8q03NjZElbOW0JDnao4K75fJ+YKoBvRq9yHPVbW1\nWkPQ+CWMFFocA9ipNnDoKDFtlIp0Dtv9Aba2yX/r9gawOPZimBYUiWRdIZfHYw/RGRk2+pBZ3lx0\ndD8W5qkaocQV0QaS2N2gypGp0jROnKA9f/HCRdis0DN991vfxu23/xQAcHZpDSkuRXWHPnI5ep8v\nfemLAADT8PF3n/pLAEDot1As0/3WVs+JqpmJdAkrGyR3dMNEq0NysTQxx+/dQqlMnyXfBhjJL0U+\nIp/e5fzKOkrsQ69tbaPAlNORFECPZSrLqEZ9B12umnIGfWQsWpdcysKwx60xAgcprlzp9lroDUj/\nt7pVhGFcjcStBqQQNttnhqmJWE4kRegwJVomm8L5RZpLFX38x39QlfAvPvs6AOSDpVJUCVWp7Ahq\nMc/10ebqY1mGkBmKsoeeLwoRssBRmA5X10bUh5BCeDbH3sJA0CCmzLKoZh/0uoj4udPpJCL+rZmi\nudk+t0nxK1CMKo6baJom7tPr9S7wr1fY1zx/nnReTEcW/07I/j20gbqui//PWjmhg1KplJDbse1U\nKBSEXzo5OapsyWQyogrM933s20cVlcPhUNhzmUwGHp+DTCq29zxx7yjyhQ3kago21snfOH78uNC/\nnmML+ty4Kum2226DZYyq8NeWKS4qaz6ufhpVN0VSBzt1sr/atQbMBOnfQj6P4yw7ajXS5d12BxJX\nq0tyhIj1tqJFKDIV9OrSeeS5cv3Wf/8mnvOc54g5BoDN9WWh3+q7VYDX7pqnPhUbG+RvDLsdLJ6m\n2NrffOyjeNKTngiAqocirjquinigLmyN82trgubw0KFDo3n3HLSazACGCFOTdB7brTpKHKdo1Gge\n/+jGdyMRkj6FoiJgypx2uy1a6hiGIfRrtVIRbFCxXez7Pmo1OqM7OzvCni+VSpicJL+x2+2LGLTr\nuuIajuOIas54jxlGApddRuuFyIPD7xtF0QW2e58r77vdLiZZ5mi6Bl26kC7etm00OEYZx5wBouuO\ncwWALK5tmqZ4t0K5xHMa4O77iPI7CALx/N1uV1DEK4oi/PUgCHDnnXcCAI4cOYIZjgO+5jXEOLW5\nsS3anpw9d1qwYrRbDajMEjA5WRZVm9vb2+iwfeg4DhyOl8Z24JVPfILwZ6LQFz7T4uKioFdOGAZC\n0Nysra0KX+bRkxQLGwwGqGySnX/q7ElEGFWrxfSIqiaLvZ1OZy6gTjVNE7o2ol//z8bjI6ml+pAm\n6jBTKUyxQnRddxSwTLmo2TQhA2mIp1xHJaP1sIUs82QndAsVdvSUWKlOz6DZiDdnWwg7WR4JXM8P\nMLA5SOOFQEST+sB9xCf5nOfuR4KFfjozOoDNVgs+09m1233Mlqi8WU5nodpkFJ17cAObp6k3Vuws\nHT12CPvyZKC32lVsrZLwDtoBWlt0KA5MLmA4oGcKugGy7IC5uiYc27e99Y0AgM/f/Ek06yTA/EBB\nNkcKdGX5nKDnuerqq3HiJJXO1js1PO+FJBy/8rVbAQCWKwvFV4iScDgYroQeFmZJcKysbmB9iQT5\nwcNH4LAyDR0bEpemJ5nSUZcUcQ1dCrDDFJE/vu17mHkNrV2nt4tQoY19+sxJ1GtcIikBE+z0hz4L\n3c11fOJvPwoA+O3f/gN4Cj3rxEQKuTwdzHNLJ4UjrSgKNtk5sXltfd9Hgh1cP4pG/bciCTqvjaZp\nkGR6Jk2JkEpxiXR1C2HEpaQsZJKpCVEWrSgKPOakt50BJObYVhUZ7TbNg5HQEXDibnnxPBym77j6\nKiojbXVb2NyivXDmzCn8yuvfAgDY2tqC43BgQ5GFkFNVBaurKwCAnR0qE37Ri16IxUUKIh44cOAC\nPvl4v+/sVITgMA1dCKtsJiUcsEsuvggAGS1mLsO/2xac9IoMlMtF8XyN+igJtpf+MOAS9z4rlImJ\nSSRjzv/+EJ0GGaCu68Plnj+B48JghzmbTCFkTtlSjgSs0x+I69q9PhwO2Aw6EgzmfTVUBQOmQvIc\nV/QTWl1Zgu3RHsgVqPz51KOL0Pl8pQwFvktruru1hekZesd9+2eEY37y5AnUaqQA4sDi3NwM+l1y\n+DvOELU6KfWbbvoLvOuG3wcATE4W0e7QPJXLBawz9UShmEW9Qd87vD+6/QFqPDf5YlkYGplcVjgG\n3WZdBEea9QoUj+4/W05jc5X6y6jSiPf7L9//P2kdGzvotxv8/ahQN6FrwtjbrGyKJHLIyTDP8xCw\nowGMDFDLsoTROT0/J7jQM5mMSAQN+jZaTVqnOMkyHA6FstV1HfaQ6RDcQBjFexW853loteP5KyPH\nv42DP5Iso92KncAeYvek2RoFjiJQ/0aAlHDc8y0+G5ZlYZUVr67r4vkSicSo7xZGAQxN1YSx53mB\nCFbOzpJC73a7eOjh+wFQQui97yWKmh//9AHccss3AAA/vO076PbpHd/znhtx/KJjvB4G+uxQxXSm\nuUwKA4fpPS0dikLv/tIX/zJClsFLS4toV3viHfb2sAPIsbn99h8AAM6dOyeMuiAY0ag4jiN6LHY7\nffGOw+FQBJ3G4/ExNENB+VgexTArzqGekxEM6CyHoQ+dVDh2NzvCIJU0De94928DAN73vvfBmiE9\nqpb4LIQG2i7JoPlJC12m7uoNqggGtE/CoCuoXKxMCEWlc/voz8ke+ew/fgJPfxb3+dGB04sUfFeM\nCDP76aF2GzWEHealGaqQfHruRqWJu7YpeL08SYGb/fvmMZUj+WIldDS36F0Gdgd+j85KIghwLQc6\n77zrLnG2O1KEJPeOAAewnvnsZ+BrX6VE8+VXXIb1FbpPMm3g7DJ9nijP4OAxcp43Nuo4s7ZM75Bk\nI98bIp0h3VSpVoUMiBwHZQ7GLJ1fw9f+5QsAKBBrdzkxrSagS7HDHtPF2KhuMiCo3aBgD4D6dguG\nxaCHoY2BHcsdHTEFzXA4FIFYhfvs9Ho2wPSI3U4NpSI9qx9I2Kms0D1DH0GCZIyVSsJhSsaVFVrH\nJzzpKQKskMwW8CgDnWRZR4LpBxVFgcyyRtUVyBrtJ88ZQFMuJITwfEc4NaqqI8VJrXanKQIHUhQJ\nmWUkDagMrji9vCx6nF10lPtpqqoItNRqVTznxUT/5jiO0EGyLAl7bTDwsc09euJAxpkzp0WPQkVR\nRNAlkUjAZKohXdeIOhqA6wwFnWIqae7pfTVKkMSBJSOREQmGel0VzqSEUOhw13UBTiz6noNkinVT\nGAe4HKEjtzttnDxJgQpDAxLs+33pnz+HP/hDot6sVisCMJVk/+UHP/y+SOxF/hDTZdoLl19yBO99\nD/3uI3/+Ptz+fTrrj1RW0WX6wfWNBiKZ9OFLjpLN2un6uPd+onb+pec/GxnuSTdZzCGKe4wOOjhw\nYAEA0G43cd991BPvWc/4BQDAF774OYRMI6nqCiSZPv+3P34vPvCBDwAA/uULX0IE2pOFQgY+n4lK\npSn6Z8R2p67rMEz6bvH8MiwzxfPehsNzLUUyFs+TnaxIkuj/m7ISIhi0vESByvW1c7CbbN9MlPDu\ndxNFbzaZRpcpwWqdNsqlHK9pJIJV7TYncHpt0QNU1yT4HLz1HBcBIyodW0YcApyanMSxY2QHFItF\nAeyKE0JRFOF8fM4VRTj/pmnCZXu+Ua8LeygIAmjM4UoBGzo/sW0nKTmxf4MggscJ7MHQwSbbQ5VK\nFa4fU2yaSLMMjX/nur7wD3zfF8mfMPREMGZ+fh+mpqbFO1hMdbWXGjS2STOZjPhdp9PB/feTHZUt\nzgm/5mtf/BKe8oynAgB+8zd/SyQ2JUnCJp9vRR6Bcxrsizn2QPg9r/yVl4uz2e/3cfrRhxGPmHEr\n7rkbhWvIZkcAvhiUF0WR6OEryxIslmckWkaJSMuyRM/e8fivH4ouI7PPoh5JEVPvp0w0OGGR1goi\n1nDoyQtY75BfZUwmoZoxxZkGNaTzd3CeYkDdXhubDQpuXz4ziTNnaG8k01nM7ydbYnW1glyW5K/t\nyfjXr1Kc6Cm/QCBow8ggaZItIUseOmzfbG3uQmU/d3pqBlmJ9FF3M0CLW0y013s4k6Z7XnacqAXT\n8gym5xcAkN3vNUke7K41sW+GgGrtnbMYNEg2HJrah8oO2VeakcbQIfk6ZEo/SMDTryF66k5rC6ce\nIzrpIxddJgLI55fWkOJ4VM/rweMDNWDgemlqEpvcr24yOcQky1AzoePsEtlZkduHxjrhkqMHscYA\nQF3XAU44xjGbXnMXAwYu+cMuXJn+ffncaUzNEOW+pocwTPZdDRX9IV17p7KFOZ6fIseUzpw+gZv+\nivzmD3zgJqyvUUwpnU7j4AGyX3/+6ENIM8ijut3FFlMY//CHPwQAvOzlrxT+PRQI+njbdlGeoPXX\nNE1879q9OKcGz7WFjDY5QB4oEPEqRVHgsyy0nQGErIki7LCdXyoWcIaT9hura3ghA9b7DJjc2tnC\n2XNkl1933XNhWaP+ZbH9FYah8CMbjYboO1iv035LJi0Rl/J9T/R+ooRcxM8qw3WZ+pqppAFgarIs\ngATxPfq9DvZzO5e1tTWRJO20m0InqKom6KllKUKZQVye52E4ZP83zWApry/sgMHAhseAh167hrvv\noHUqZlMiRjtRzGGNCxP6baas1gFnQPt28cxJXHsFUb5tV9YwVWKKynoKGxuckGi3Ud2hM5HQ13HN\nNbS3VTAI1tSR4aKNEAGsBNksXbuG228jv+eKyy7GD35Avno+n8ePfsA047zmgesCSkzTpwv69APz\nM7j7jtvE2j3lSkqYb26uC1txd7cG+HFhBz1ztVrF0hLJuGhPEsV1hvCZ5nlleRGTRXqHxu4Wvvol\nao1w/Wtfhde9iuzugPs5KaEt7Ghd18VzZ7MjUJtt26NeZgiFvR4D3WRZEs9RKBRE3Gl6ehpTU5TM\ncexQxCh2drbFPfv9PjRuFzSiAjfw2GN0HizLEnR3URQJu6zf74tnLZcnYqZsJK20eNbYFtvc3BT2\nmaYlhP8nSZLom26aprBrHMdBh5PZewHbhw8fFp/j59B1XfxOkiRR8GPbDma57/zKyrIoUoj1ydt/\n463iu7NnFvHTn5LNnUmnRAGEqqq49CJar0svPibmzLX3FCmwz1VvNhAbdIqi4OlPp/ZKuVwW9913\nHwBg4NjwQprjyamyAHjHMYl9++bwvR9QIc7x48fEma/Vd0W/w0QiIZL3YRgK2QcAnU4X3p744382\nxvSD4zEe4zEe4zEe4zEe4zEe4zEe4zEe4zEe4zEe4zEe4zEe4zEe4/G4H4+LSi3dCDF/eIB+fxcO\n59mGgQOPM/uVZiAQg9lMGekSN6JrOxh69H23r8J2GEmmc1a+JYkm44rhoi9Q9S5CRhAkDMBkNGSn\n66LDVC5LK5RJvHS3DjXBlAjtPnymW/F9CRInDg0jhT4BRBD2TUQqZZm1IAenQ9nGDiNdLLkKVaes\n50VHn4C1JUL02B0fU0XKvn78bz6FJDcUdIYBBk1uZj2/gBpn43NcudLvOcimCb2cy09jncsO1UQG\nlRplkz3oaPXo/oqpC6RHpUmol2JpGu0OZZunUwru+iGVNq4uryLD2d/Kynn825cJbfzOd90Ai5ud\ndzo9gU5NZ7ihcUKGwtUFhbSFbc64nnn0hHj3QjmFMOLGfKGLHFcEVXf7Ihu+ukxoO9txoHPmXJZd\npGIGBznCY6fuAQA88cmXoLpL76PqOiQuEzUMphQaDmHynEqSK6gNW50uwjBu3OwhQly150FhhNDy\n8jkMmFIubvz71Gf9MlyuIgojH7rC1VmqLNABqVQSEVdQaKoMTSU0xj33VuDGVI1dmoNMNoUOf3Yd\nFye5ZPjgwYN7mlYmEUMLihNl/PCOHwEAEoyu+e9/+if46Eepou306dMC/Xjw4EGRGZ+emxUoiW63\nI5C799xzD57wBEJ01Bt0Pz0h46676R6NRgNPfSqhIkulEgZDOkvZXFKgIAzDgLdFKI3ZuUlx3tIW\n3cN1PVERaBk6SnneW5UqUkx55AyGonR1MOjB5fne3WEkecKAzWWozmCIAtOmSIGLgN8rbVpwmQ5w\no9dHhykJmk0HmfwCrQ2jta649CLcfichYzSEyDD9USk3i69+hfZ7uzMUSJBqZQfHjhFKJ8nI5J3q\nFnodpqVKJJDJ0rvc+Efvwec++7/pd7s7mORy49XNZRSYZkKSJIQyIxX4mXL5MiLek4qmCiRNLpcT\njdYl+NjZIjTE/GwZG1xl8MynXIIUN8q84feoSmw47GO3RmdDkSRILMNSVlqUUW+uLyPGCi8tLaHE\nyKcYBawnNLFXUqmUQLj0ej2BVJE1VVDmtXtdsf8iWUKe0UzxXh0MBiMKnSiE1Kf7SKqCgTNCwMZI\nD0mSkGCZUyyWkc4SajwegecKOodmsy0QPUePXiRQN7Is76lckpBmBG4MM/NdV1QkhGEozonnedD1\nUMxHXAFrWRYOHqBqgU63LdYprmbSNP0CNA6h6YDLLj2OffMk63/pBc9Dl2WzJCkjysNuC+lc3CCV\n5nFrcx1HjhBlz5lzZ5DNcvPOyRK++MV/pnkIAsxMEOrnscfOwmaqziwj2j3PE7JAkiRBg7iXRrSY\nL4nncF0XikJzlkppkOXHhckwHvGQXEj6GiZLJbiMXF1ZeUxQB/hBhIceJWSa6/qQVToXiqxAMejc\nJnMtPO9FRPW3tEFIrmw2C4l1dSQVoOqEEpOcAVyH7BFENhSucogAUVmY4Cq/xdVtlMpET3BuZQNH\njxL1WKW+hXPnCNFqpRNwfNbLQRIqy8CklIXdZvrXISNGe21ILKNcU4KJGPXfQ3eHmg3f8s1vocdn\nLuiN6E/6ahcao8BcrvR9whOeACNBsnpxeQVJM67CAfJMp+MiRKdLqLf7H70fPUY3wqT5UHwHDW7Q\nPFsqo8sVu1oYCiS/0+kI2ZRJJER1abPZhMFVQKrGMsgPsLtJ17MUYELQbgfY2iEU5ZEj+9Hnazc6\n25idpXlI1KnKh9aDkYbFPHYqhHr+Xx/5K7z516mZ/cFDR+Bx0cDkzCw2+Z7XXfcCfPVr/wZgRBv8\n1X/9V7z8VURZu3vmLI5fRnaCBAVdplAKggA+N6X3PQcRP8fq+UW4doxeZXvUKuEpT6GKfdvpIWnG\nFbsqJHlEARR5JHMLhRwctgM2KmuCxiKmCJmemsKAZWu9VccO05VMTk7Cj2GWID0EAIquoR8/E1Pu\neoGPVExfrOuQuWpZ0zRR7Td0HXGN2blJYZfd/ZM7BKIyphean5/Hj39MVHWrq6t4wQsIIa2qKio7\nZPMXi0VBVR0EukDBG6aKfrfJ707nWJIU6NzQHQAeuI9ooqJIgqbSO7ZaLYRcxbRvbgKVCsn5lEVy\nXUaA7c0VAMBEKYeHHyC5oKOHEsv+H932fbTr5MyokYw+2zX12hAz82S/rC1RFUCnH0GXSOYc3p/H\n+gbbqSkdPm+ugd3FgN/rgfvvFdQvhTJXv0QBHJfWIJlMCurmer2Of/qnzwFgihK2AxYXF7HB7/Dc\n5z4X956gSrGYimZjews2V7zLsioaYE9Oz2KHfaNh30aKbdJSIY/1dTofntvH1BQ91/e/+1OepwI+\ncdP/EM/UbdE7Lp15BEePkZ6dnMihyWwFrXoFIdMCx9/1+z1Bq5lOWQKZaiXTomogndeR5kq9IAjQ\nYX+sVquiXq/z+9AeKxQK4sz3+31R/d7tttFq0drlcrkR/aBpQldJzkCSoMWMBgbJoXK5LPZ+6Puw\n+zFaOomI/ShFS4jKvpnZOVGtHtOPdvs95DSSVUbCQkLnikso8Jkhpdfr7bGHfGE3lkuTosoq9v2C\nIBL3mJ2dFZVVfqRiukzvvrKxiFu+9mWehyG+/vWvAwCmpqawj32I+HcnHnlQzEcQRWK/mKaJj370\nrwDQmQ35/ru7NeyfIzaHBx4gxL1t2/jsZ/8Xr0sdCfZD4vkHgEMHDwq52WzWYVkjytMIo8qF8fiv\nH1oCmD4QodPZhcM2QaR24CBmwzgvKp5L04Djkc1iZvLYrpIc+Iv3fQwf+tCfAABOnCR2n/3796PI\ncuSh++8TNJXdoYutLbIlhg7geSQHKpVNDFi/xUwXnb6LBsdvHAcwk3Q+p6dy8OIYlCdDcegMK7qK\nabYV4LvYWiR9nnBJV6qKjLTCFItSGVpEMkULk/jgf6dqpANHjuLc4gpdI4gwkWe7LJPCkONik7NM\nndtpYHGZ7JFMWoOsWzwHi6KyJluYxqn7qPLR9lQYqTR/prn2FRtTC2Qfpvur2GFa6E63j4j1mKUC\nNzDN6/v/7IMoMA0jAMGOInHJtBp4sFi2pk0dba6Quu27t+JNv0FU2rquYGOT5H06l4Ibso/VbcJg\nmdVgGXX44H5srpM8HQwayOfp39MZDdUd0oGXXDyP9U1aj3pNw5EjVGG7V2bEvtT8/DyqOzTvCdMQ\nsR9dVUT1ea/ThMN2dKO+gybL87gK6+pnvnTkzyqAyc9sSNno/zcAACAASURBVDo0rqwwTQOeF8d4\n2qju0r41Ujo+9zliJnjd636N3mvYEzTOP/jR7bj2hS8Tz703rhPHCc4snhMVr7HOsNIpYS8NBgPE\nJceyLEHjyntZU+EzdXQ6bQi//mv/9mW87nWvAzCidLv11luFzksmk3jZy17G7xsiwbprb+sAXddF\nq5pqtYV8lmRuImZ/Cz1kmG4xocnQ2V6S4WNjk2yxiXJW2HmVrRVMT5Kui316KfLRbdPZvfiiw2hz\nlZrd6+Lkz4lizXb6iI3qtJnEgFs06IaC5i5Vf2YzJAt2K1sY8vonDAUGV37mMiV8n6tstqprWOA4\nQa/XQ61CMZzf/32K63zjG9/A1hbTHbbb6LVozkxNhs/yZHt7W1SitxpNHOIKuMi1keFWC2fPEY1k\nuTwpYm+PnT6DIdMFlkoFcR4NDdheI9YvKyHj9a99JQDgja96Bba2SB4k2M/y7CFKE7QWg0FP6GLH\ncUQlUa1WEzZQuVzGwsICgFEFuG0PxboYhrGn4mmIlRW6X7052BPjUVCcoIpxazhijtrl/bS9s4Mr\nmYJxL8Wm59qCatYPgTRXWZUnZiA2dOhik2nk2xzn2rdwAJexvPM8T+h+1wuQ4DhcwjAgMXOFngTS\nmdE8xO9aqdFzptNpET/WdV2wRCQSmrj2YDAQdlI2ewilItlD8Tk3DQuSRL5CPp+FYdLcRHoGV1x6\nMQDyQ+J573a7gmlA1ydQ3a7w+9JzJJOmiIVddsmlghr0a7f8m6jIOnjwIHSN1mzxzBmxfgrbO//w\nd58RNKcPP/ggShwPNPWEiCuqqizeK0QoZEoYhiQb/j+aTo+LCFXo2RjsnIJpmVBlDmoYMpJF2ixh\nIKHTYwGbSGB9jSZ1oTSDFpcBtrtDHJolJ2PAjury5gbmZqnc2+nVsbZJgd9KZQs6B0gzuTxk5u4f\neE00mrSgaS5BDiUbc/NcGhj46PSG/NQ6aqw0gyglFJFtu4g02nyJlAFLJKf4MIYd9AekjM8tt3Dt\n84j//bFTBi69lHom7Ducw+lTpDRnZ/ejoNI11nq2KMX8739MhpSiqOB8ClwPKE8sAAAefvh+0YdA\nTWRQa5MgUpMW/vIviKu+yBRyfhRAMWnH9FotJFlx7JuZxi7ToE0UC6K8XUEghGYhkxJUegoncALf\nFRyd5WIWZabxau7uom/T9eZmy9iqkKC3PReKElM5duBPxLRp9N3ERBbbm/Q7MyHDcblXBYa49BIq\n+V9bewx33X07AMAbjug0hjYpymTSFNydhqFj6MY0eaN+CEHoQ424PjcMEIR0kAdODx3u2RCXkqcz\no5LSXq+HBBtTaiDBd2NhpUNROTjsD2FxzzTHGWJzmxTUHT/+Eb2XaaLLtCCmmcAdd9C7TE1NiIRf\nIpHYQ8fmXEANR/MF1NmRnp2d3sOvLwun2rZHVB/FYkEkFqIoxHe/+x0AwHXXER/0iROPCOXTarUw\nw33ZgiDAv//7vwMggfjKV5JiGw4HggO61+tiyAolpiX53ne+LriP3/WuP0CVg0Lzc7P4Ofchuvdn\n96DwSzRPuq4jx4o3ftdep4sBX7dRrwPcN8LutXDn7VRK3m/vIGCF0Wv1EHEvtSD0cdVTiDpnm3uV\ntAY+LjpKiYJetwkjQYqomM5il4X7gaMXidLfmZkpYUjEfaUymRTm58k4v/Xb30LE9C1TUxPYv0Dy\np1BKixJeTZdR5p4NQRDAkGl9VTYiU5kCTA58KrIKnWmTTEtHNkXn/6lPvgRzr3oxAOCRB3+GS375\nefRMrV284+1ETeoyPY7T66DKFKX1el0EFB996H7IfG1ZlsVemJuZFAq3mCcZUSqVxH4fDAZosWHb\n642MFVlKXEAXGCfEUqmUoPKLr+H7/v8x2XSBoeGNuLA1TRM9pFLZDBymHtjbM2V6jtZganYGuvZ/\n6l3gi16KQRBg6FzYq8K2bfjB6N5xkiAMQyF3TdNEmmlJLdNCoxn3Xiwgk6Z3DJjuK4oiZJmabO9I\nRgFYX6M3XYamkeG6vLSKA4dI19hDR8zlBBscALDLxvEbXvNa3M/Uhuvrq/hvf/THAIAbbrgBSkBn\nDKEvelG+613vAkBJuTgY26jXRQl/vV5Hu9ni55bEvCqKglw6BgNIgh5gPB4fQ458WEENSz8/KQLG\nPbOPaQ7gGQkLy2u0Z8pzE4JG0/U8fOxD7wAA2HYNMwWSRzVOTAw7LREE3FrvIgiZ6rSxgZ0dMmQh\necjyWbAHEnaqMVUZB6lTJTSbnGSJkqhXOUkcWEiZdFZ9z4XtkG6KAh8hA4wyVgmKzH1vmHqs3fUg\nIe6LqWPfgYv5OXzYHsmJx84s4RLuueq6rqCx6HuRoFbRVLZHGj3oOgc6fQPZHAU6z559DJ0OvUN/\nOECOOdWdSMYMy/Md1rPpQh5TTCMTVZs48SDpscAN0Ob+GZqsYJZ7zzR2dxEzhactSyTfuz36W8sy\nRA/IciGPLlPF9jttfObmvwUA3PhHN0BVaE7uuPNH0NiZaLKjDQDpVI7fsSVAJZ7Tx3EOxPftATod\n2hfJNJBJk0C69bvfEWc8DrC97Fd+FSpTN6czObTZ5oYsC6pYz/ERSTGloEI91AB4/gA2J0GrTMu7\ntriK657/bPquWoXjjvpWxHZ0MpmExwAjL7ARsG2Zz2dFwP/8Etm0YRSgNEH7t91u4tZbyTZ5+ctf\nLhKIwIhmNpWyBBBrRHERololfb9v3z5BRWOa5h5wgyP20/r6mqAa8X1PgF5iquhUKomVlWVx39iJ\nC8MQDzxwP/+tg2uvvVZ8H9P2lEpFOANOpHGS5/bb7xBUak984hORYMd3Z2cXaQ7Y7N83C4epdSxT\nFY6qsA+lADonT5eXzmKKgSuh6+DKy0l2bK4vImOxLSup6He5X6YfCn2dZzCFHzmYnaY9slvtIMdA\nsV6vjm9/+1sAANu1RcLCMHTxPouLRGFpJQ1kOAh1z09/hhQ7uJ/6n5/Ed75N6zg7NyV006WXHsf6\nBsmf86tnRX/guN/TK1/9KgzYR0uaSSRn6d533nEHJngNnnb1L+DB+38CADhwYBbXPYeoEA1TxuI5\nSvTNz9PcvOn6N0AOmM6mtSN64CVUCTJTkm+trKHRqIn3qTAFVeyFp5ImCmz/pNNJsQ9lWRa0mo16\nV/Qvi/wArfqoB5bGjSHieUyZFhJM0T1ZKovkj2EYwuba2/8tCAKw6wPDMEQQWCTXUibiUICsSDA5\noKMoCo4eJRl79JiENNsBMhLwWE/EtNxQZPQ6ZG9Sz+IRrWZsx5BNIYtrx7aHZaYEEEgSIYkIPlMz\nqkoC4Hg9ICEA3Xthbg42Ux1psoR3/M5vACBQVmzjSExB9bSrn4SHH6YA+5Ejx/DTewkMORwk8Lcf\np0TVO9/5TmgSyeNiLo9zHOz78w9T76BsNi/8Idd2BWWP7/votuOeI6PvXdcWfW+pP2z3gl574/Ff\nO0LfgV1fRDBwkGM6TckdYibLvvLQE1xGISRo3INmcflRlPjvP/2X78FwSGf1qqMMLkkAm+sEsukM\nNuE02ZZWDXh8QTOdhtelvTv06nA5aOyFJB9z+RwUdhKq1RYcl+Sb50foMRXhwHExUyTftd/zkGM9\noGkStCT7WQw6cNwBNnbod/mJDF7/DOq/krAS2NiluJie9TF/hOS57bmIIvJjWj0XOoODXT5vybSF\nDAMLHbsHVSU9G8k+NIO+39qpodamv//oxz+NFNuYAUh2DW1f9Ll/+Md3YNgj/3N+bg6b9z9Az+85\nGDIwYXZ6At3BqOdO6LL84l5SkxMltJtk03iFHDocr1pZWoNqsN2oA3f/jEDbxVJGUC07to9qjeTX\nbpX8+nZrIPxB3+0LW0JVHAy5J2wQKVhaon44tm0Ln1FRR/TfsZ0wHPZhpRlcEEmQWZ+bloEiA/Wj\noIQO23yQHdQbpEt2uU/R5FRRJMlarZbwNcMoEK02kkkD6QzthWx2AvfeR7ou8FzYnOxKxPEg18ZQ\nUCDbI5pcXYXCeiKKAhH/qFYrQobF350/f04kIwxDFzoo2kMhrSiSoEMmGueYNriHj32M5O/b3vY2\nmmvfE8H8RqMudK7nuXjwwQf4+4agiw6CAFVOMheLRSisH3YqFKfZ2NgSsZdyuYyAY0ZGQkXSor3z\n+c99FjfcQBTMW5srmJ2J29rQc25vrQER7ZUTjzyAgwzgn5ktoxHTv+UTaNTITqlUd9Bo0t6eKh7C\nBMdRNAZCuIOeoPHTTGCL44GqagBMs7Zvdkb0b8/m0vA8mtc+x51MTUaPAdsIfeQY4N3vtHDVkwh4\n1usdErZJq9VCht/X7klwmQr3hS+kOFKr0xF+/759czh3lmgpt9dX8IynE6BdC4e4/PnXAADmp0p4\n2pPIbjxz6gEY3E7GY/q81eVFuOxvOI4jwB+dTkfYknOz0wJgsrffpB/3XrcduNzaRZIkcFteuM5I\nz87PHxLP7boufD+O+SYxMzNzwbUTCU3c27ZtEYs3zSQm2UfjCQVAlIhtbp+TzWaxwH1AY5sm3t8A\nkExloDGNpGv3xL8NbBs2A3hs2xbnJ447RaGEACMbLsvJYtM0IQlvMYTCSSFqszLqNwXQ9SYYiNlq\nN0WfrXpjFwsHKFHphjoG7Gfm8zlxZofDoWg9oaoqkgb5uHvBOpddRrmJH/3oDhGr+8N3vwcf/vCH\nAQCra+vQNW5bpEp4P1OHxzHoU6dPChuv0aijz8CpVqu1J6mlinWUZYiYmyRJcBwHnjumHxyP8RiP\n8RiP8RiP8RiP8RiP8RiP8RiP8RiP8RiP8RiP8RiP8RiP/5+Mx0Wl1u5WC596/y14zWuejf37qWll\nFEXwVC79VDUUmNqg3xrgaI6yr3JkYIqz3UpZhx9Q9tLnqp+pI4dFBjIxmQJkykBOTWfhcAl0vdFB\nhZsqVndb6DOtWrVG//7Ag3dD5yxvBAlc4AXdSKDDaMpQcWBOM/VhCCCie7ZtF/UWZ5yZJjGvp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pSRIcpnAgupg+X2NAFAYgoz1OcOVyOUGnWS6Xxfemwf3fkkmk0mQUHzigC8N6deUM2jy/ge+C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\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qaqjGb15Yibgo4gDN7Rub69i7agJKaZriiSdMcOfowMi+bGxs4BzJ/nzBS17ikm5z7eNa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9RtfPt73otz2zsAgI//m3+D3buaTbJ6aQO3b2oZwYN8Hz/20z+iv19dxesTjfr5uV/8L3H+G75d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76YJvfVcRzee5ZlaRkl1NdisXgCjlee69pZKiK0pc5i2zbLD8uyWJ+ktdBqtXhc5XKZn5V8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\nMYzgLw5rcfQFZXUUCgX25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rVaLWxvb/M770Yz+MEZzWhGM5rRjGY0oxnNaEYzmtGMZjSj\nGc1oRjOa0YxmNKMZ/dzTz0WmluM42Nzc1CInLMtiL7SERnJdlyOCJTwZAC3iF1BRdvQ7mZK3vr7O\n0RhbW1taYXNqf1ohc8dxOMOHvLf0bCIRe8ZlhgL324k9l4koxTuTiCMhXdeFL7ygBJNmGhZ/Hg6H\n7HEmr/jZs2e5vaOjI/ZSJxIJ9npK76rnefwMRQdks1n25u7vHYvx+sjZynst4Y1830egHLRIJ9NI\nWVFR+rSKrA08oFxUczR2XOSi71NmCgf7KnX1yuWLuHLlCgDg+eefR78XZegNunjpRQVv8r3vfRuA\nitwoFNQ7CoUCRwosLs7z2JLJJHvdrTDO9vFdtYZK+UKcKhkCw140f0GAdpSKvba8guVfVh71arXK\nPOl0OnGKtOoGhoM440em/k+uSeqHjBqanp5vIpEQ0HeU4XebDC4ACCaiEM2knvJ5O+L3BQEMisiS\nkYf0XgmHGIaw0nHWlB5RFmVCGYBpRu9IxHCFk+3erj8ATqStEt1b9KWMdo0iJBMGY5aYgVozepum\ntq49Ly6wS/N41KppfTXNWEYBgJWMCwMnEgl0uipCI53O8LsPDvbQaKj9Vi6XkUyp32bCJBIUtRJB\nTiYSSaQi+VM72GMIoGwmh09+4hPMJ9rfnhdnLO7v73NmDUcnmcBrr6kM1Pvvvx+vRoXe8/k8Uqk4\naolk3urqCmcskqytN46QzajFn81mhWyOo7wLpSLD3IQGUI6K36ZSKY76kRlUHJGSTiETZbEl0ykt\nldyN4GC8wOco6lwup8FTAGqPyr1GUf5BEGgZGvS9GruaM5mZQG1LeK7RaKRBMchCtCRLZWYftXHu\n3DmWsbu7uxxR4wcePwOA567f73NfDMPgeaT3ykil3d1dDa6BIAITiQRnO8vsK4oUs21by9ygqCp5\nbkkIFhk55rruiT09o39cokgnWYg3kUho2bMSflBCStC6klDFdJ5KnavRaGi6Fa3RXC7HnyUsIelI\nk1DMtHYm4bPo3RIScRKSlMY1mb0F6JmZMmJwfn6en5NFfml/eF6cCWoYhsYz+l7Co4zH4xPRkp7n\nsXwJjPhzGIYsn/P5vAa3Qdkt165dYx7TuC9evMj6zc7ODsvhcrmMzQiKJpPJaJHM1L9+v8/tk9yR\n0W/5fF7TTWQ0J0U9Tsso7/f7/DmRSMSQ2gKmtNfr8RysrKwwZMgnPvEJ/O3f/i0AMCRd/4c9Tc69\n+OKLAFTx79deew2A0tepr/1+n/knoWwpetk0TTz88MM8R7u3VJTt9vY2Z+oFQcBwL7lcDvfddx+A\n+PwYDAZcGDuRSOBv/uZvmE8kh9vtNsvter2uRboSfyij7L777uNM2+eee44z0G7cuMHrL5VK8Rh7\nvR5/HwQBnzEUMey6Lt9f5FkDgGX/YDDgPSihX2jPLC8v83m+tbUFw4xhfeh8Hg6H8INx1D8b83Mq\nG3h1dRWdjto/1I9Op4NGo86/G0Q6dRAEOH9eQQTC8NCuq4jPwC1g3FftWJZa7+fOrHMkbhAEcIaq\nzx/+yAfxgcc/GI3QxMBV89TqDBBAjSedSHMKUrer2s5ms7AsivxNw2RIlAyGUQb49tY2mpfU/Ods\nG8V8pEeNhhgNosy5lNrfp1bXcCOCVZ+k95K1dTu0hPiHt3/HvbZzpzZ+HPjB95yB9h6fl7LoTm1q\nSC1+rH/I7I/J5+6VP5PZU6a4f5yAKTOMOB1HfHZdV0Oo4LMN4MwX+iLELFPr54dCznyKkkI1uK5s\nNstlFKrzi5yptbt3CNdTGRfj/pCRPgyTYAgDBBGcoWnptoBpmX6TcEz0HZHMqpfP+L6PXm944nn5\nbGxPm7hXh3GWpFznlMkj9fxE8r1lak1DVZE2Euq/vJ/kCzm+By0uLXAGuGVZrGMMhn3kIzvQ977/\nDyATzm//F18EoM5yyoRJZ1KsB25ubsLOxZlusp8SCWQSHlHqgTLD59/8m3+DP/iDPwAALG1soBCd\nqTBMFKPz8uGS0rkNywCiPT9w+jioKV2i3mqyPeDSpSs4rCjdpJAvoRfpzy+/9Dr+v6cVNDPpMaZp\n8nlfqVRYp3LGY/T76uz8i7/4T3zXdByX9c1er4cRQWtGusHh4SHrGKVShfW2j370o5zt8cYbb7C+\nK2HBSS9KJpM8X7Zt89+HwyE/43ke67W5XE6DPqP7Ca2bXq+n6Xv0faPRYN0vn8+zbm8YBn9frVZP\n2MtGoxHrPb1ej9efRD45c+YMw5nbts3f053BcRzmUz6fZz3v1KlTGnIF6dq2bTPP8vk865mUvS9h\no1dXVxn68OzZs6wDHx8fMxrL/Pw880yimZGOt7+/z8/KrJ7J9U4ZTel0Gju7KpOMkHsajQaPRfLg\n3XffZb233W7zfeORRx5hG+4LL7zAdw7qx2g04v4Bum2P+GSaJj/T6XQ4Q4vmttFoMKLX4uIir/fD\nw0Neq9vb21OhDX3f18YL6PchAJotnPq3uLjImVWDwQDddof7JGEM6X0S7pDWYRiGWpYiZbpdu3aN\n710kT0qlklb2hH6XTqeZD/1+n+cylUrx3ePw8JDhIMk3kE6ndajZ6HeFXA7JyD5rGiaMyN489j34\nrhpPYuzGPhCy9yYtDQEC0TtSmTRSGdXXjJ1FYMhzJD6jppG8Y0qkMZLfBwcHzIdCoYCFhQWGxr0b\n/Vw4tXzfR6fTQT6f5wmXDiTP8zRnDhkCJJxGKpXSDihAbUzaYDJ9eGtriy+4zWaTmZrNZrXUTiCC\nlosm1HEcLa1Otm1ExuEgCDAYCZgdN07FVm3YsKOFX6/X0R+O4vYihcKyLOYDEiZ8xEZNWR8GUEKG\nDg7iIaCEII0rl8sxz7a2trjfdDmtVCosWBLJbCwIxyHGUf/7YZy2mkxYsGw13uJciYU9Cdtw7OH8\nxhn1vkSC0zBvbG6i11EX2K3Na/jrv/o680nC9nzyl58AEEP9fec738H6+ikeCwmDsTOC5ylep6wC\nwxLOFct8GNUO97kfUmGk+Q2CgHm2cfoUz3Wr1cLRcawwjhzFn2aEv58wpkHh6QYHecGZlgpPz0w+\naxhG7LAKwxjWAHe+wFmW3p9p2NFa+n3CAILpQmfaOxgn3I+VP9+PYQ3kOAOESCZ08XKnSzf9f6fT\n0XgyqSBPXgwSCTlm4RSOHE+q9lgyej6hKUIAkDCn1xWTgvnU6UVeF1KRoH8V5F+MC0zdMPIxvNWg\nN0CXHJ/ugA/hZDKJpQUlo0gp6PXaGDsx5KgbYbkP0h0EXoyVnkzGTg5aww9euYTHHn0EADSHSzqv\n9vzzzz/P0Eovvvgi3nrrLQBK/pDykEpleL8RpVIp3gPDUZz2nrXTiFgNy0qxfKzXm6Bloeo4KZnn\numos7XY7lnEweSyZjI1MRiki3W6XFSjXHcMdxVjepGSREiFhK2nMgI7N63key8HxeHwCIkQaaWWd\noSAImJf9fgy92O/3WbnY2NhgOUiK4UMPPcTvePvtt2M87WKeFZFMJoP771dQTMlkkpW2mzdvnoAB\nk87EdrvNZ9XKyop2KSbeTBqDASXr6e+u6/J4x+Mx97vX6/H7KpWKBsX7XqFIZvRTpkhOSXjOQqHA\n6y6ZTGp1OEnG0N8AtWZI15L1pqRhQzpz6KKgDMixvkbykGRAv9/n947HY035l+uIjNrSsCDhCmSg\nE/1dQpRIOD7p8On1eizfXGfM/aM9Ic9cyY9EIsHQCxIeBYjlLI1b1g70wtiAIKFMpZ4qZUmhUOD9\nR79rt9v42te+BkCH+7x+/TraDcXL06dP81z3ej2eU9/3eewSjkXCr0qeygAuGpfkN71jNBppBh+a\n3zAMtdqGJNMKhQIeeUSdQel0WrukA8D8QpUNHBcvXuSaWu+8844Gx0fvu//++3kdyeAqOf/URrFY\n5DW8sLCAhx5S0Hc7Ozushz7yyCNsXHjqqae4/9KJSncJWY82nU5r8MXSaUQyktZFs9lkJ+S1a9c0\nGGwJmyn3Cp1ZxWKR38MQ3b7P36VSKR7jzs6OBjdL39u2zYYmGfhFxq52u41Od8htsEFk0NVqi1Ag\nCSBrzSgePProoxhETqBnnnmG+1cqldjh+MhD5/kM/+Y3v4mHo3oh776rzrnPf/bTeOKJJwAA27d2\nsboS6flzVQAkfxLIpqIzaD6HMAIYUc4tgjqKanZaFkLQhTqIjIqq3lMuo5759Kc/jdq+Muhsvfs2\njMhRUsxlUSCI4+i7poBemqTb6dWTUIKTUHXTgtQmoft+HJjDn7RT66cNlSf5dK/jvZPDT97vpkG7\nTeP75Bi9cQyRc/KOovu06D4WBiC7NYK73Nd+ElCVM/pJkcG1qU3hbCQ5l0xYLFfsXAbpdFSbsNsH\nHYee5zKMq5GIziU/YOjU1B32/rT1OK10gdSFJt8l66LLNTZtn0jjoYH4fiz7QUGp2vveI3gp6Q/y\nHdNsBLIuuOM4fDbVajUOCDk4OGBbzrPPvoBRZIb6/K9+nO9bBPPW6/U0HZjsYsViESFiHU7KBlkb\netKpJevAXL9+ne1R77zzjmYYpw0/6g2QjoIzDWGA9aO6jqaVwerqGQCI/lU86Q17eOrb31V988ao\nRDrfxz/xKa6/8+YbKrgpk8ngoYeUbrW5uYmPfUzV4fZ9n+tjh0YCB4dK1/I8D+U5Nd5mu8uOPnJk\n2LaNbDbH76DgoGKxyPMk9RFZ4oT0dllvnfQg4jPph7ZtsxPi9ddf5zn1fZ/1TAm1RnzPZDLskOr3\n+5p9k2h+fp6dfkC8HqhPV69e5YClGzducB8rlQrzYTJoiPRXcuzJOqvEe0DpebQWMpkMlw+Q9a/f\nfvttXtukdzYaDX53p9OJg4AQBwVK3iwsLGjBicQvWsvVanVqMoV8hwwGNgwDXmT7I92vVqsxf2UA\nsHQmep7H471x4wbz+AMf+ADrzxSktre3xzyV9Ytt22b7dalUYl1bOkCoTxsbG8yPra0tbGxsMD+e\ne+45AGp9kk7dbrf5PbZt8xjoziWdmrIevIQqDMNQq3tMNnd5n5XOumn2Q/v/Z+9NgyS5jjPBLyIz\nIu/Myqysq6u7+iLRB3HzAkFSIEFS5CxASdQOMaS40mo03OHuSLY7Jq5MI2m1I6NJZiuJpsM0f7jS\n7ppJNhpBHGmHK85IpAQCPAQChAASfd/d1dXVdeV9Z0ZGxP544R7+qrMaXRhR6NkN/9PZWZHx3vPn\nz5+/5+6fp9Mst8VikWEau90u61vaZ7a2tpin8/PzPF+VSkWry0afZfJKoVDQkm6I1zTPskSGbdta\nORd5LqQ13W63eR5J3uT67vV6/D4Jq5lOp9HqKt5YlnWLL0YGKTqOw++zLIvP2o1Gg9uWNa5V8FwN\nIyeCH4wooogiiiiiiCKKKKKIIooooogiiiiiiCKKKKKIIoro/yN0V2RqxS0Ls7OzcBxHi6Ilb67M\noJLpiqZpciRAqVRizyI9K9/X7XY5sqDX67G3dGZmhj9XKhUumkeeWsdxtMhUGS1JHt/xeIxOV2UR\nyCLoiUSCPd/UT7geOgSvJYpgyiJ0qUwaSTtMe+Ysr1wYBUGZZs1mk73U0rspva+e57GnnFK8gRBa\nrFarcSRBr9/jPttWEm6Q5RI3wX2an5/H4UMHAAAvvPACZqdV1EQho3h2/fp1fOf5v2Ve24kgmtKy\nMT+n2nnkkUe4r71eT4MgyhdU+6+8oiBb3nL8CPdvMOjxnI6dECpn7IQRJLWNm1o0EP0rI8/Jw5xK\npTjDbHV1VYsslzBdNNfEm9EwLKi9PeJbFpHnzKVtEALbC5fKwrAAOK5o+3snRnqZlN4eerJvhRW4\nFdYACNucFCEmI0FuFyEpeSyjFgZtvWi9/LxTtKTMBonFYvDcnSMcFZTnrdGeMiU4Fosx9IJp+pzZ\nJ4tUm76AfZTzEnSr12nz91bMRLGgouFLUwXxuzDzgKKQer0ewxmWp6d4rbfbTXTa6plUKoVuMG0k\nWzPlohbVkAmiZGKxGMaOimQw4GHsqDU9HA4ZDnM07PPYqT3TNNEbqTXzwcc/wNEQD9x/L47co9Lo\n6/U6ajXVp2984xthVFLAm3KpxLqv2WyiH2SYthpNXjPFuTHr7EQioaXLy8w0QM8OMU2T9W0ul9NS\nxWWEmDcOs+xoDLLIqCziSWu9UChomVgk5/1+/5asvXg8zn2W2WD5fJ7b6XQ6vI80m03ux82bN3mM\npI9PnTrFkVmPPPIIZ2E54xFnECSTSY6o7/f7HIE2MzPDuprakCnoU1NTWqo7ZUHIbDPTDAth0+9o\n3yDehDAVQ23NhFmIA24/kUiE+1hEdw1RYVYJp0nz6nkeZ7ysrKxw5BwQRnY1m02We1orEpI4n89r\ncJT0u8FgwOuW+gFAy4iidei6rpYZKeF6iSzLmpg5RXIudYPv+6wvS6WSBnlCWTtaFrFvaP2mv9O4\nZT9M09SgmaUNJzO0qI/E6/JsWcugJ753O314gelWKpU42lBGT9NaTyQSePRd7wEAnDx5Es/8zbMA\nlK32+OOP87spum1ra4szkMj+kbzL5/Os52Rx4FwuhGNutVpsA6VSKV7jEuqReDcajXjsMzMz/LnT\n6bA9mc1muXh7s9nkiGoa99FjRzjj6dSpUyxbzzzzjKbX6X2vvvoqj6tUKjH8jiw2TmPPZrMsC+Vy\nmeEsms0mj6vZbHKkLen1arWqFSF/5JFHAKhMN+JZs9nkqM1Tp07xb8fjMf+W+r++vs48S6VS2h5D\n+rndbvMcyDUrbVXaJ0ajEcsTEK7TwWDA0dfZbFY7k2y3mWQkezwe57EcOXIE7Y5aB6urqxrcbCqA\nIJGQNrSHnDx5QsvqGwVh9Buba9i3pAqE1zZ7HKH9T3/8x/jd/80nPg5AyfvF8yqbctAfwRkq/qW3\ntlAIonWnijMwyJ6Hh4Gr5KU/HKEYRMZ7QSrSeOzBMGXGAhWbbqESrLG9e2bRrAewu5aNdMCTem0L\ng05gyxRCGKFao43tdDt7+HZZEsAOtq9IpboTqO1/CPjB3WZqGbtGK9zZtt+Jv74vz04AGekK3iw8\ny0i4VIBs9KCfIuPKNA1IRMCRH9qE8lwDBOcsalv0jWB6qJ+Tzl2E4BFlat1dRIg7iQC63zCMMDPb\nE5lGfpiNK+0U3/fhB5k4RnCdpmTn1qwpGcm/01qVWQT0/Xg85u8lzLQ8524/49963g/7YZrmRH2z\nHc4z7NTuZNYMYK1cz4NP/TYMxIJ+b8+IAgDXDyGkc7kcXnjhBQDA8vIqnntOZVP/63/9rzUYNNr3\nyJY4duwY222VSoX3+1arhcJUCPcu4Q8nZZIRSeg7Ccn+/ve/H+nANvH6fZiBXCSzYfYWQ2ZbccAI\noPtiFuuN3qCHdNA/HzG8I4DabTYb+OLTTwMAyqUiysEeSOglnjfGN7/5TQDKriAIuZWVFezbtw8A\n0O0NMF1Wdmin00EsuP+I2xZPuxvAyVQqFbYfu90uHnpIZSh94xvf4PNqKpViW2wwGPDzMouNbGTH\ncdiWldl3g8GAIZhXV1eZP+PxmO0Ksu2z2awmI3Qn22g0+BmyKWmeyM7r9Xp417sULyVCC2VC1Wo1\nLdOM2p6ZmeF7G9M0+UxONlej0eA2y+Uy218S5cC2bSwuKrtnNBppcIB0NpM2K509isUi34VcvnxZ\nQ3JgOM1EgueA9FAqleL7gqNHjzKfJI1GI+0+kvo9HA4ZIYayAYvFIrd98+ZN5lM+n+dMo3K5zHPw\n8MMPhyhcV6+yXfvWt76V2yf+ptNp7exGMtJut5l/s7OzPAf0LnleXVtb43lxHIdt42q1qiEl0Bgk\nshq1PTU1pZWPCBHUUqwDarUaz00qlYKXCm1tCd8vxwfoqB2SbNvm+7TFxUXWSzTn9Xqd+1yv15kf\n6XRau28heS6VSpyNaJom60SSj16vx3zsdDqswzY2NvjZTCbD30v0i9FoxHqYzpjJZJL5l0wmNeQX\niQRkp8L5ncQDifBC62F1dZX11tLSErct58BxnF3ZolGmVkQRRRRRRBFFFFFEEUUUUUQRRRRRRBFF\nFFFEEUUUUUR3Pd0VmVp+UGfANE3Otpqfn2cv9Hg8Zg/tzZs32RsuM7UKhYJW7BpQUQgUuZFIJNjb\nOD09zR5cWRyP/ka/pbbJuy0zZeT3vu8DiQCD07KRTCq2ZjIZ9k5Se5VaHY2q8m5PTeVhCw8o9T+Z\nTGpRzbJgN3nPySNsmiZHVMuaK/V6Xau/Igu90/f03dWrV9mTfOToYa2I3zDAzG80GlgPMIrXbl7H\nxXOnAACrqzfgBxFyzbrKYJjKF3DkqIrOPHrPEebTaDRCOqO8w71uU4vMabfCrLJmUz1z8MBenqPh\nQP290+lokQpegAs7GnbR6yrPbj4TRifLwvc0/7JeW6PRYI9wqVTSamqRJzudTvNn6rOqF0XeY5M/\ny2wf04yDCgW7rst41ervMtpQ1YOS0VUSPVTHvA4xoMMoRPVcLK5HDMrPE6MhoRc+lNHEgJ4lKN8p\n64bJbDQgjN5xHAd7l/Zp/d8+lklZW3KNua7La5n43u/3tUL1vje85X2y37J/hhHjtRLy2oHnTe4T\nZV8l7biYUz0bLeRv+Hlpr4og63Q6zI9YLMZjqVrxMKrec2EFRdNdqpflhhmI8ZiNWIDVPhh00ArW\niczUsS0Tw2FQX6TdYBnmGk65HMZBofXlq5dx+vRpAKo+4W/91u8CAP7oD/+Qo24efuB+5s/16zf4\nWYpUevXVV5FOTvEczc8GWV3JFEfYZLNZ1n2bm5usT0m3FItFzniS9ZxkcU+ZaaSiKZVcyrpmMjJd\nRufSHE1NTXFUjcwcketBZrhQxkK329XWgczWoM++73NEzNbWFveJsjVisRhjbGezWcbvdsYjlotM\nJsP43aZpcgTT8vIy6yjaE2U9QcuyuL1ut8t837NnD49BZhlPysSsVCoT6xlZlsXtjEYjzvL1PE/U\nQYvobiHDMJDJZFguu90uR1kCYQboAw88oBU1poi1wWDAepHsgEKhwPrj5s2bEyOMXdfV9PJ228R1\nXX6H1OWO42h7nSwULfXv9gi0XC7H8pdOp3l90HuJSF5lLSgDJo+X+rk98o6zcS2L9ZQs/pvNZrXa\ndYDaK6mfx44d4+i2arWq1QqQdihFjd64cUOrFwaoqFRZcJnqDXmeh29/+9sAVHQbrVvbtrl9uQ/L\nLAMZ2U19SiQSE7O3fd9n/tF+UKlUtIwDeresz3fu3DlNz1KEaTKZ5KLaJCsvv/wyR2AuLCzw98vL\ny5qNRm1OT0+znVIqlfhcQP3Y2NhgWeh0OpgqKHkZj8dcr2tjY4Pn7pVXXmGekK6Ox+P4mZ/5GQDA\nl770JXzve98DABw+fJjl88qVK1zrY2lpSYvmJL7KmnRyj6JsL9u2uf/Xr1/nMTiOw3LWbre5Hgid\ndba2tjQ9TJlmuVxOq21Bn9vttmZfA0ov0HeJRAJLB5R9duz4Ec6+arVa4n1h7YpcLseRwPfco2z7\nzc1NURvDQbOpPs/NzXG/j+4vc62PRx95BC+9pOogNOtKxqvVKrwgEyBXKGCqqPRQJp1EcVpFecMw\nMRwF6BwxG8mYkhE7bYN2NSsR6BnPR5xsZNMG1eW6dvkKvvvd7wIAkvGH0awFyBrdFoyssjeK+QKQ\nU7py0AtQH2o1eJhcR3KnDKPbZR7tlA1hGrHb/u61vqPvtyNAcJsTf3F7mpS9cDuSdYnuhF4rI203\ntcxUplaY/btdt+nnstCG931fP+OQDjUM+MH3Hul8H/DEeUlmz+zUz+2ZIFGm1t1DrqdquadSKRgZ\nte7lPdH2zEMrOH/E43HEglp9ZsyAz+dHOhObfCb3/bBOzE7rU5KM9Je2hpRjPfvv1prY8p4gbCeU\n0Vgsxn2+NZsQ/Dl8/vXHvsvssO3rZCSQimJ+mF3+//zF1/Av/oefAKCy1X/lV34FgNqb6Fxi2zbb\nGzJzgGwWwzDYlm21WvD8EX8/6X5jkt4ej8daXSmyGw3DAETGi6wjRWTGQ7SUAKwFZjwmakdnUQvQ\nWqZyU8jllY3xxS/+GS5eULWWUvffj1ZrmfuiOurgAx9QdbTOnj/H++9UoYSr19T+vG/ffrz3ve8F\nAPzpn/4p/OAO6tjxe3HzhrpHPXPmDI97aa+yAz760Y/i9GmFcnDixAktg4p4nUqlbkHEqtVqbCdI\n+9C2beZ1PB7XsmxojSUSiYlnVPo8GAy0Glh0zu31etodqUTh+su//EttLmq1Gq+lRqOhoULRniHv\nlQaDAds6JE/FYpHtvZmZGbZ7DcNguWg0GlqmFp332+02v49s/9FoxHbR1NQUjh8/m1KFgQAAIABJ\nREFUzryhOfV9n8/y8t5T1sQm2a9WqzwfEpFhOy/l+Yruc+jZer3O58Nyuaydje655x4A6i753Llz\nAFRWGc27bdtcS4ts8f379/MY0+k0Z+3F43Eeo0Qay+VybJ/SPXyn0+GspEKhwGt6eXmZ3zEej3lO\ny+Uyj0fWZCc5TCQSml0u93B5r0dy0ev10Gmp8Uh9QfJL80D9IJn8zne+wzX4yuUy/06ek+me68iR\nIywfW1tbbIuXy2UeV7vd1mruEmrHiRMnONOO3lcqlbTaufS+6WJRWzMS/Y74k8lkWEZI3qU8FYtF\n1omtVovnoNvtIp4IUeJCVKo285/uGGKxGOvxhx56iO+8Njc3+d179uzhesLtdhvNZhPxO6znflfc\nUJmmiVQqpRU4B8IUUym0R44c0aBGaIF7nsfMIaE1DIMhNjqdjgZFIotky2LNdCkhhVYqQSkIpPQ9\nz8OgN+TfWang4jGeuOVgkM/nuWBxs1lnYUoJp5bvu+j1+twn6p/jOFohTEAJS6+jlEJlc0uD1GKD\neuwC8cC4iBlYXFCX7k888QQA4MswOP2+221i9eY1xf+VGyy0MTM08uImMCDon2IGDwRpoEWC9Eva\nqG0FBem9IZyhmo9CLgc3MC688QDFQibgawyDAD4tn8/zIkwGRmTKFrBEqSTSAeTIaDRipZ/JZJAP\nDFN5IUL/rq6uas5BeXkilbos7k68HgwGPAekOPzQLwXfEMVUfR8GVw12YVIxYfgg0ArTMBgykJWM\n58H1Qpi0vjO5iPIkp9atBqxOO6VuJpIx7rf6l+AMA0PEsrULMSIJJSWdRo7jYDwIoGEGI7R7YXFI\nesdO8IPSgKc2LcuCGchlOhfIQjanv8ML4UrlBeF2xxMRQRlJp5eEFCVZkNADowE0fUHPSGgTyftm\nU8lkNptFLhfKkGWp54vFAstir9fDVk2tX5Llfr8fFv30XIwGlOqewL5FBb3U6XQQIBGybAKBfNqW\n9v2w10UigEi5ePECcsHFjWXF8ZnP/DMAwHNf/xpvOv1+H1/4wu8DADJp1f9f/MVfZGiCYrHIPLh2\n7RrriEPH78Ply5cBqA2P4CGmp6e5KCoZSr7v86bVbDZZp/d6IbyoPIDYts1rybIsNnpoDobDoWas\nEP/kM8lkUivSSZeE0gEuDUYylLfDNsmCnKRH+v0+7wfSQUdtDwYDhkVLpZOaw3GaISaSPGe2bTPM\nFr1PGrELCwtsQI/HY97bzp49y7+zbZvHS4ajLN5rWZbm9JcGHvGvWCxqhpWEfYjojSc3gPHs9Xoa\nXATZB4lEQjOoZcFcWi+ZTIYdD+RsaLVabCxLuRyNRhqM3+3WU2ybESrlSwbwSJhUGVghYQfpXynP\nskg6rZFms6kF7ZCsVys1XgM0bnmBJCEW0uk0ryF5sbH9MpSIvrty5QpDn5TLZTzzzDMAlP77kR/5\nEe4TObovXLjAh0Jpd1JR65WVFYbxG4/HMILAiUwmoxXKpnFRYBi1A+j72GAw4INKo9FgZ+fc3BzP\ne7vdZjua9F+xWOQ5kFDWhUKBebm4uIhTp07xuEg3zc7O8iUI7W+JZIL3iatXr7Kczc/Pa4FiMnCL\n9pvBYKBBmgB60EGv1+ODlGVZbEv2ej3uq+u6LFt00bJ//378/u//Pv+d9kIJDVIul3k+FhcXWeZs\n22bZoUsXGTgn+y/hYWWw1OrqqiaPBNVI+06lUsE73vEOfgc5PiVkzObmJuvnbrfLey3JirSFcrkc\ny9xgMNAg01yXzhAW86FUKuHipQA+NxD9wWCAZFLJWaPR44uPj33sY9zWoFfjoJdvPPcc7n9AFXSv\nVlX/DfhBgBiwuDCPWmUr+D6GmTnlcIQdQ8IiZ0McCFxZY3eE0Ui1kw7OXDHTgDMOIOc7LZw6qZyT\nX/4Pf4bnv6XmbjqXQdwMYBNzOfTaah6HhgcrHkBKBkFC6XQa7e6A+bYb58sk2tFRI9xOt4M2vN13\n2+1QHSJt97RbpxZ26dSS9Hr5OsmptZPDaZLzyTRNDY6MYCwNGAgQujhwcjwea/ugPP9PgqLfDksP\nqP06oruDxo6LylYNyVQInVooFHgvSdih7USOAQAMbwqouxpGOA6+cxE6usxt93Cv5cidFEDteZ4O\nyR806DgOnykBXb9P0iHad/6tzpztgaFh+7c6zm5HrwUVRetGBmlfvXgB7373uwEATz7xA6hWCcKu\nj5kZOusZfLbOZDIYBuUWSiVlX2xsrKFQCG3F69evAVB3OTQP8r5C2kbbSzvQv2TrJJNJtitefPFF\n/Pfk5JmaAqiESNxBLGiI/jUSFkL3AuAg1Ff9vpK5//gf/x08R/HEspPIF9WFtOu6SCTUvlapKDtl\nplzgO4xUJg3bUn/PFaZgB2f1/nCEP/n3/x4AELdtXLqiHAsxy8LahtrzM8F+aSfiHABnmibW15X9\n0G63+fw+HA7Z3pifn9fuPYinZINubW2xDMsgtEwmwzaS1J22bbNtOcnhm8lk2JZdX1/XdLy8i6U1\nu3fvXrbLyL7J5XIc0CLL2vT7fV5Le/fuxYMPPggA+OIXv8jOE7ITK5UKO6lu3LjB0H3nz59nPqVS\nKbaZp6am8P73vx+AOpPLewxAdzZIB9O5c+fw2GOPAVA2JJ3r19bW+HnagwqFAvP9woULDOMmg3e3\nlxCh8UxNTSEe3BNRgFQ8Hudz4MmTJ9lunJ6e5jNVNptlWSD7kvpE/5flD8iOlc4webcqobld1+Xv\nv/MdFfyUTqd5niuVCtu6rutq9jUFgY1GIw48O3ToEK9ZsvOXl5e1gEUpK3SemJubY73fbDbZVkyn\n09w/4m+z2dQcO/LsS/I3HA75rks6C/l+bjhkmZSycPnyZZaXTCbDMrS5ucmwmHv37mW4R2rPdV2W\n39nZWZbJtbU1dIIzUCqVYlnI5/N8RptbWOD1Tc7EWq0WnssMA+lgrcUsC/Ggf6PRCJ1ueO6SDlbq\nE509h8MhnwVrtRq3fejQIe0sQ3y1LAuLi4uwrPBO73YUwQ9GFFFEEUUUUUQRRRRRRBFFFFFEEUUU\nUUQRRRRRRBFFdNfTXZOplU6nEY/H2QMqs2Nk6pqMDpVFnGdnZxmmhrysjUaDn71w4UJYABTQIGXo\nd4DugQX0dG/HcTiyhDyXQJCWGPRjuljSUmQrQeQhRXwYng+KNMxkMnDY655jr2alUkfSDiI/82Gx\nwHjMRD+AA8wE2UqVelhQTaZ43n///VxQcqqQ1wpO0zPffv5v+b3Xl6+pcRlD9gJPF0vIZoK0w6kC\nHrhXvSOVTnDWhDceAx4V0lP9GPU9WHGKdhkinQ4iqt0BZyPFYyYG/QA+q+NyjF+n3UKeIED6YWQt\n8S8Wi4FC6DzPQzaITvE9n4s/r23V2BsuozUoattxfZiu6v/QcYEgMymRTGPoBKmmVgLNtuJ1MpmE\n4yoPshf4gQeDEcukzPYzDEOD7qoGfdrY2MCRI0dU/1ZX2etO41pYWOB5np6exjCI/srn81qBSBnV\nsB0SSmaibIfbmPQ9wdoRbY+w2p5NJWVe0k5RlnHcGnkjo0YmFYqWKbxa9KUXwBqOHQ3eyqIMRNOE\naQayFfe0TCw5jrfc98AtY6NoCFlkfnV1lSPI3X6Ln5mfn+doDZnpQHMkM/9oDNvHLlP0M5kMUhml\nLw4dXAqeNDmzaWtrK4xwGvbgjR3mEyw1Xluk0Q/63TCqjyNTPfT7QcFMO87RyL1uGwk7yIqLxdEP\non48z8Onf+qfqueDtpevXcHyNRXR80d/9G+5vc9//vM83p5v4vjxo8y/RqPGc7C0pDIBKGqk0aiJ\ndOkp7N+vooza7TbPTb/f16J7igUV0bE9gwtQewRlV3ieF2Z7ikyQXC7HkTuO4/A6pXmMxWK8R8is\njPF4zJElEnZWFgadVCQ0k8kwb2S2cSqV4v4Ph0OWM1lMczwecxQRvcN1Xf7OcRwN7pBgDY4dO8ZZ\nEDJ7TUY4kW6UayCfz2tZoNQPuac0m00NqjeiN57IdspkMrwfZbNZrcA1zb3neTx/vV6PIwwffPBB\njl4jGISNjQ2WAcdxOCLQdV2W416vx/Acvu/zOygqrdPp8LoZDAasC23b5nfEYjGWL7k3yWxbsrmG\nwyGvG4rMBJSeoH0UAK/r4XDIWUee67NOJf09NzfH/fM8j7OVSqWSlgFOY69UKnjooYcAhJF/siD0\n8ePHmb8nTpzgPeHJJ59knfHss8/ymlxaWuLxUERju93GSy+9BEBlCUlI7Ww6y22+5z3vAaAiKiVk\nB/WVdJ7ruhrcIumSXC7Hn2V2Wzqd5jGQDpV2eSqV0iJCie/STqnX68x3y7I44o/4riDnQqhYyvAx\nDIP1c6lUwrFjxwCouSa4HNd1GT6XssHq9Tr3w3VdFPJqn923bx/bXBKSptfrcTQkQb9ubGxw/8vl\nMkc62rbNz66trWk2C0UYzszMsO1GkCPz8/O8162srDA/FhYW2F6PxWK8v+bzeY6YPnDgAM87wdI8\n9dRTvDZXVlb4eyCE57xy5Qq/L5VK8fql/eD48eM85zdv3oQTwIZvbGzgyhW1NnK5DHq9cJ6I78lU\nCNsyPaXae+KJf4S/+Iu/AAD8+q//Om4E5wbbtnke4Tq81g8dOsT6QsokUa1Rhx2ce67fWEWlrp7d\nd+AwEilCSDAZzmkwdHDpiuLr+oaao1G/h2HwzngMGA+Dgt6jIQ4eUGssbphwAztqOOyxbA+dETwv\niHAOYCm6gz7c7ekWr0GvC2JuB9tb6kT5t0kFyW+X4eWLV0xCS5DweHJt7obuJNtqUobrTr+dRJ4H\nDT0ihBx04DiU5amg39X3t2ZsSWjxWCwuzvdjeMGVyPr6GssoRVFvbm5q+46Ea6ZnSqXSxLNFLBYL\nMkS/dkfjjOj7T+Oxg0qlomC9A70j4YZNhJmPpmly9k2320W7HUIzkc51KSp97CEu7H5Jk6D45WeS\nl+1ZU0Ty3G9Z1i3whjvR9vcN+mGWA60nx3FgmmHmEvXdGQ44a4PsnlgsxvtvrVbj/VfepfW7XbYJ\nR6MR2sG+99JLfwcA6HaBH//xjwEA5qZLWhYO2S9TU1MhVL/gz2g00jLQAR1SudVqaWsVRnhnJOHC\nJDzedgQWeXcwHo/Zjv72t7+N559/HoCylznTMzWNAwcOAADuve9+AEC+UGK5aLe7qDVC24oyXqqV\nOp595q8BKNSiMwEE4Fx5BlvB/RX1qdlssnwaiCERZFz1BgNux/MNJANb8dq1aziwT+17rU4HjwZ2\n41Re/T1uxvDy36msmHPnzmHfvkXmu8zkJlun1+sxkgAhDsjsfs/z+BxOmRaAyoQnu6dQKPDfJPIJ\nraNsNsttb25uskwVCgXt7Cqz3yW8Nz1DcyFh6Gq1Gmf12LbNa6ZarTLKwiOPPMI2ONk85XKZ7bnR\naMTZTbKvhUJBg+enbCN57qK74RMnTmhlXuhsksvl2O5+5JFH8NRTTwEA/viP/1hDgJlEhASzd+9e\n3pve8pa3MP/OnDnD67Tb7SLpKzmic+DGxgbzTs6XhIuXCA/S/q/Vanx+InvZcRz+XCwW+Xw6PT3N\nZ7Rarcbfv+1tb+O+fuUrX+F5ofaq1Sr/bmZmRitfwXdQvR7fX5umyWMjXt+8eZP7lEgkGMltbm6O\nx3vjxg2Wi2QyyShmEmqU+pzL5fhzpVLR7mckGgnxtVqt8vd0bsxms5pOkn4POpdJJBQ5/zdu3GA7\nn/WCYXB7lmXxGC3L4nXQbrd5DW5ubvIZ/NChQ3y+o/XT7XZZPi9fvqydX2UJI9n+9qx4uZ8QnCB9\nJp6VSiWep1KpxPqAICrvFDngrnBqGUFtrPX1dQ3Tkw67xWKRldaZM2d4o8lkMsy8mzdvsgDQpFcq\nFRZgecGYyWRYSWez2YnwPBJmht43HA5DDGABeZLJZGDaIW49LbZhP4QACfFpLWQCIYTvYmFBbQC+\n52I0Uv2fKU9zGvDGxgYrwrgJTE+HxjMADHotWIFzYn62xAKcyyTwxD/6EPeP+jRbnuJLVO7ncIhU\ngEnvxy184qmPBb8L02lnStOwg1T3zbV1pNIB3IdhAggWWSBzru/CDXDyTc2m8zB0dqpfdOsBi8iy\nLFhBvShZx6Ner2uXZrzY7TQfnmmBttttdAI4vGw2y8bZ3NwcBgTv6HqwgnTvuJ1g7ADLtlEOnGek\nVNvtNmqNJvdDOt1oMcoNbnpmFl7g5Hn4bW9nOaf+ra+v4+BhlfqbSqXQGbr8Drl5c70k4RihdrbX\nzWGeGkZ4aBafe4HTkJ6f9Fl+t9MhV0M14P8YMGj8Rvg3De7Q2P4bMHTndppUI8s0TfhOCDnHRrio\nJTQcDuE44QVfK8CxlpsSgt/VGg1uJz81hfuDdPTZXI7fbRgGKlVlxKyuKuOoUqmh2Wzz32m+VL2V\ncBMk2ZGOhfF4jOK02shpkzHNOA4fVpdjDzxwH4/3ypVrrAflO/p9vf4TwQeRD98QKcOu68JxCD7M\nFYc2AWcZM2HFlQzTWPr9HsplddH31FP/mC+tqtUtvrT8v/7dF7GxoS5Vl5ZK+LVf+zUASifShnfm\nTFBLKp3isbz9gx/A3/zN3wBQ65GM4lqtBuPokWDsV3hNu67Ll3rUtrzoSafTvCHLC1tV1y50Jko4\nhO3kOM4t+wmRdBbTJiwNPJrHXq/He0Qul2ODrSfmS+ImN5tNHqNt22yQ0UW1NI56vR7jVdfrdYZG\nWFhYwJvf/GbmH/GY9jUJuSH7LI0GwzC0S3EymiSmeUR3B8UDp1A8HmeZGo/HmuNffqZ9Z2lpiW2F\n9fV1PqSRrEuYm9XVVQ1GgNbFeDzWaujRWqALfrmu5HpLpVKsC2OxmBYQIiFkZT09QJfdeDwu9F9Y\nv2g0GmmwhPTbTrvLNpN0llGf77//fnZItFotPhjV63W+0Hn729/OfJIHD9KFzzzzNbZHXNfFhz/8\nYQDA/v0HuB7W+voGX4Bmszm+DCCeyRqfg8GAHUHz8wsYBPbLBz7wAXb4XL58mW2I8+fP80UT6Zqr\nV68yDxzHYX0gD6dSRiR/pK4k/lJ/ae6o5tfJkydZT1iWxfzLZrOsz8lBEouHelPa5cPhkN+xsLDA\nUCjr6+vMh/X1dYYXIZmcm5vjQ9f+/ftx71tU8NWBAwc4KELWTKC/AWBbeM+ePcyPmZkZHvvy8jKf\nIeQhudFoaLU0SJ6l7UeyKp+VenZ+fp7net++fSy3yWSSeUYXBYlEgvl3+vRp1tulUokvQQ4dOoSH\nH34YgJIFWdsA0GtPLC4uYnZetXHlyiXNfpVno1JRfW42m+zoq20p++fcuXOsQ65evcqOoFYrhELN\n5OI8T447xsgJodyoTxzs44YY/K4P9ANI961qjSGxHdfDODhbDPojwFJyxDBRtgUzGZxlXAcDJ3Rq\nUfBdt9MGvGB/G48wppoevgs/CJiLaYB9u3PuvB7afua5E3uc6LUgzYIf3PLMTsFmrwUfdid0Jw6u\n3cMbAhCOhp3qk8m2Ju0nlhXC2UtI1k6ngx5C24j0wUJQc2ZmfoH1qqyvMRqFAY62bbOtNRqNwiBE\n34fj+biN3yGiN4AIOjIWC6HMWP59IbO+i0qV6u12hBPYxdgN7g/G6tnReIx4nJypce2eg2inNTZp\nbd7+t3e6VsPnBoMBbFvtVzIQ1jRN9PthHR2ibDrNMi0DBynIQga6rKysaPcV5EB44YVToGuvj3zk\nB2/5e+w/X+XclkgPbK8hNukymdZ1q9XiPa/RaLAd8Nu//dtsZ8kL3FOXr+PrX38WAPBXX1WX8raV\nQCxwmGayeSSCeyR37LMtdvHixRB2Ej5+5Zf/VwDAf/pPX+b9qB0EWMdjPqyglEA8ZvMdlR+LcXC2\nYcdgGerzwUP34MxZBQv9qU9+AsUAnvEtQbDPyy99B93gHiiZTOLJJ58EADz//PN8tj58+DDbX9Ie\norNEIpFg+2ZtbY3nf2Zmhu0iGaycSoVn/263y99vhy8HlI1E7zAMQ7ufpblxXZfP3JVKhd9DslWt\nVrW7AZoveR+wvLzMSQBXr169pQbi1tYW7xPS8V2v1/l8DoSBW67rcpBSo9Fg+HFZF5fW2J49e3iM\nhUKB2/n617/OAVzS9qSzwiuvvMJ35IlEQgu+oPddu3aN7bmHH34Y3/jGN5gndjKEWwf0WkemaTKv\nZRmDdrvNNpqsJzY3N8dnI4JxXFlZ4T6vra2xjKeFPslkMrw2n3nmGQ4yo7mVUHXpdJplY2Njg59x\nHIfPJTJ4UkKGSkc18WNra4v7Pz8/z31NJBIsT1LXSrhK0hUy0UDa/JSkQzSpXpysaU0kIRhN09Tq\nFEtdRTQcDrV6d4C6a6LP0tYplUpakA3xptvt8lqvVCosc5QIs7i4yMGBuVyO752kbB05cgTXV5a5\nT7IEAKDOEqQXHMfhNToajVie19fX2Y6anZ1l+6tQKCCRSCAevzM7PIIfjCiiiCKKKKKIIooooogi\niiiiiCKKKKKIIooooogiiuiup7siU2swGODcuXNIJBIa1JosWE0e80wmw5GJFNUO6F5NiiAYDoea\n55m8hul0mj2ZiUSC3y3TUckr2u/32aPqOA57X+U74vE4e6ltO2Sp4zhhkbmc8or3ej1cv6Y8ndPl\nEhb3KKieBx98K774J0+r91kmHnvvewEo7zWNa2FxDxoN5bUm+L/73nKcIzElvJYsctlttzAIYAvh\nuUgFkR73HlfRvuPxmCNMrdSY2+s26yhP5QN+dNBuqPdNlwqwE2qczXoDVDfV5YytEFrO82Q0oAfD\nCKMuKEpCQjy6rgt3HH4mPpLnt1Zr8HzI9HDTjCOZVBEH1c4Ai3tVyvWNVRVVMj09zW2sb2zhniNq\n7MPhENlcIRjjiD3PjuNgq6Lki+QNCCNJC8WS5q3fs1fNwZ49ezjyQUKYeZ7H0cntdht79h0AAGwG\nkbNze/aF6b6eASNIeXUBOKMwBZXa3F48lsZC9FowB+rzrUWlt5P8niIE7rSQdTJu7fjc9sK1k6Iv\nfd/XID6AIB04EWYRUQafETdgWQGEQ9pA1pgcEkmFzWkso9GI+dbpdNBpNfl7Gvv1S5c5Wn9paQl7\nl1SkeK4wze9Np0O5JriftbU1tFoq4qNQKHBkSSaTYVkoTk9padSASv2ntjudFtwAKjOdTuL++1Wh\n9Xa7y7LYbrd5rbdbHY6qJ32Xz+fR6SidaJomy4ttxWCIKGSOYBu76IrCyQDge2MV4Qxg7+IejnDZ\n2txgONCf/sw/5wiXhYUFTGVVxInhjvHfffqnAQCplBrXH/zBH/D6PnPiVdwbZGRVq1WsBlCouVwO\n73nknQCA+tYmGu0e8/iFF14AAIZCqNfrvH5GoxHzcnp6WoPslBl3xAfOhI3HtQw/0oOu62pFbOkd\nEpJCZoXITBHqh4zySWfS3E6z2eQ9r1gsamn0NL/090QioUVMkdx0u13efyRcQyqV4rHRnlipVDQ5\no+ikTqfDelW+u91uc/TM1NQU67aI7g6izFDf97UIcQl1KrMiKAoMCPe1hshQJb3UbDY5IksWa6b3\nAEq/kGwkk0mWb1o3nufx75LJJLcto4M9z+PfSd0kM8+knUC0tbXF4+31ehq0TghJFUKFxuNx1uHU\nXrPZZF1Zq9X48/nz53lc+/bt47XzyiuvsJ6lTKnhcMgRoWY8zDrbu3cvR7T+4R/+IUe6fepTn8L5\n8+cBAC+88MItcJ7ZbJbXrNRRlmXBzivd0Ol0GE7P8zzOOur1erw3EjQMPU88oH4XCgXun4TbSKVS\nt9gVEg5IwkwWi0XWH3/913/N0X4HDhxgaI1Go8FzQ1k97U5Ly+qSEBtSbkkf/e3f/i0XQ5aZEDSP\n7373uzWdS309c+YMR5Mnk0mO/PN9n/tNkeedTocjBqWdv76+zv2wLIvna2pqinnd6XR4DARF0+v1\nWIaWlpY4I+z48eP8/fr6Oq+xj33sY5yVdfLkSY5kfvZZFfl9+PBhbm9hYYEz4RzHYRk/ePAgy47M\n0KP2ut0ur6VSqaRF1L/tbW8DoBAvKDJ2aWkfloII/FOnTqFWq3CbgIqmfNvbVWaYGQPGozHziSGa\nR50wy9MJ7VdnGPzrjOEHMIgww2j5/nDE8IgDZ6SQEwBYdpKj04fDIZKW+t40nGAsLttwg34XCGAG\n87kUSoUDAR/aMAi2y3cZwkudTwLIYYRZy7uFE3w98IOeG0by3g4GnGg7vAu1u1PWFsGKbX9m+3M7\nZYDdEU14/E7b2lUzQkdMep/8jvYimXksf9ftdnlNdzodHH3rO/kdMmuUnqU2hsOhBkPtt9rcpoR6\nltHajuPAfV3ZaRF9P8hHmKklYegYpccN5cQTe6DSb+rux4GHkUPyGGa50z2QRB/c6fwrP0+ydSRt\n//71ZP7Ztg3fo1IXFV4X2WxWs+9pf6hVtnjvlDDP9Kznebxn+H4I89ztdvHud78bAPCpTx3mvZjO\nNbK8QMzebXz9hIFL3tzCv1uj/LdnT2zPqJubm+NzEBDuo3v27GEb7r777uOMnGNH34z52SCbOUDx\nqVTr2Kwo3qyvr6PdCWHIKlu14JlNvpO7eu0y8tkAxcA0sHpT2VQkn4mEBTc4b9sJAwGKKmJGHKYV\nyHDMDOEWYaA8o2yc3/29f4P/5ed/HgDwf3/pSwCATCrB+3m5NK3BahPix4c+9CG2cdfX13muaT10\nu10tq4LvF2yb57fdbrO89Hoh5G82m9WyuQAdAWVmZkYrJUB3rhKNQmaxZDIZ5pU8h5DtPz09zXcU\ntVqN57rX67HNXK/XeTxyn5V2scyEIbssmUxqiBFyvb/44ou3vIPeXavVtDPVBz/4Qe4HraWNjQ3e\nV8i2lvdjq6urjESRSCT4rqnVauHVV18FoOx1kvmZmRnUmyEEJP2dxtXr9bSzE9H+/ft5XOfOnWN0\ni62tLV4fEumB+Pjggw/yWtra2mK7ezwe871NMpnkTDIai4Sqn5mZwdvf/nbsAhZfAAAgAElEQVQA\nap7pnm1zc5Nt536/z+eQS5cuaXcr1Cdpz5PcbGxsMF/n5uZYJldXV9HrdJnf8hy8vX8yk0yecX3f\nZ73Z6/VYr0ofhMwepHfIe2/5fCwWYzmTZzMJzSmz4jgrfTTEONijrISNnBlklycT2loZBshWp88q\nZIiTp08xP0qlEnIF9bvUeMz9q9ZrfKYaDAas42luNzY2eNxTU1PM60qlomVwkW5pNpusLyjz7E7t\n0bvCqWUgdGwQc33f58/S0DAMgwV0c3OTmadSp5XgEGNisRgzT9YrkZAnMhWu0+lwm6Tcx+MxK6Kp\nqSlWjvKAW6lUMBO04zhhzZ9kwgLdrRNWey6TRinAsz18+CCuBQ6uUydeQSnAqu90Wnjua88AAO67\n9xgrlR958gk2DKifyWSSld3GzRX+XCwW4TkhxNnRN6uL+OXlZeQzQaprVi2SVqsFO9gc0wkbTpCO\nbNlx7F1QynF9fR214GI8ZVtYu6k25HwmS6iDoVHgG6CB+6bBTi/DiME3QlhHvhx2XO0yOTwUEJxJ\nizcOx3EZLz2ZTPHi7Xb6GAX4+alsFltV1b9sPqhvNgjxUPcfPIR2V40xkUhgNFbvuHz1Go9ha2tL\nO7x/6EMKypHmv9nuTIQ8SiaTmiOL3u15HhrBwce2bdy4uRaMJ7jQGbvcv81KFbFkmIIabqwm4sEh\nXl4GjgM+mTHdiUSkG9Pie+2AtRPsiDw0xoJ/b2lgUqMYb4dGkBcEhsFQhKqV0MAnR6jnefAD/hmO\nkpXeyIFlhQfVUTcwsg1vYv/VgcHlz2SoDYdhym8mqxyZU6VpDbaE4ekadV5vG5tVvriizTGfz/LB\nt1AoYGZOXd4dOHSY9Y9phheNp06dYtmfn5/H3sCxLQ85MsU4lQohjcINMcH6bHZ2luWo0+mgXlPG\nCm0ovV4Po9GQx4XgHaaVgClwH8jJ6fsGRsHGRjUpkpaNduBQHw8HIaSQ47CBUu/0EJTSw+UL59Hv\nhIeXn/+flVOL+m+ZBj77L38WALC52UEup2Tr6aefxvKySmNut9v4zrcVdvnhA/tx4pwy8EajEX74\nh38YABjbnHgJqLVLa3O7Y1TWrCKiC7hUKsVzKiEGpANewp/2ej2tHhfJi1z/Ml2ddFyr3WSogFwu\nx7LVbrc1iFxZr4v+lXsYyZaELZQGsoTO3V4DkvpPfaJ30e+IhsMhv7vb7d5i1EX0xpJ0ZkkYGQmf\nzPuEOAhWq1Xes+TFnoQClIEEEo6D5FzCP1cqFe0CAFByJg14aZRTO/LwJ+UVCGV2EnxPvV7XIDJJ\npuUltGEYLK/DQQg9J7HOZUAD2YG2bbMeWFtb4wOWhFH97ne/C0A5vQiK5vkXvs2G/Wg04n3CMAx8\n9atfBaDsNnpHJpMJayEKWFHqx2Aw0HD3zWA/vX79Otu9H/3oR3Hq1CmNL0B4cSQvDeQ8ysuJpaUl\nhq178cUXNacgtS3rRNLfO50OPv/5zwNQh13aB1qtFkOQTE9P88GcdKWE7et0OhqkN81jpVLhw/js\n7Czrp3K5zGMj3riuy32yLIv3j5WVFXYmlkolDYqEHHpkz8XjcQ6ic12X9XO5XOb5dV2XnVONRkNz\nFtPYSd42NjZ4/5WXJBsbG9qlEMnq9evXmQ+JRILng6BAVldXGXaxXC7zO1qtFs/p9evXea7z+Tze\n8Y53aPw+f/48X06dOHECM3Oz/Hcar2EA9aCWVa/XE7X0HB4PBX696U1vQiqtdMFgMEB1q8K8ZqhH\n14PrUADREN44cI66BENtwDBvrUHkuh5DUlkxA6kAKic/NYVkQu2BQ2eEagDLNB4rx+2oP0a9puZx\n2O0iFfAjl0miGAToteoNtnVMw+B6U4a4wPKM8BL0H8KpJWH/7jRobDvdiTNM/nYnyMGd+vGatMPj\nO/V/kl6XNOl7OhNuf+92OHsiaZORDjNNU3Piy+DU6Tlli8sL704vCPLsDTR7SjrXRuPQCUq/jcfj\niFlh+2bcZqdpRHcPSWhKKRvS6eIhdHKWSlN8f+CaJhoMPR/olLgJy761JACgrzlp1xPdCUShpDt3\nDIfPua7LOhQI9/l4PM7j7ff7bNulUinNgQCoC2baq0+fPs0Xyb3eEB//+I8CUOdder7f77ONQ3yU\ntWScUVgK4ftBk6BOd6o1Tv/KWq25XI7Pib/8y7/MF/if+9zn+GJ/vbaJuXllK5Zn6DK9ENbcqTXR\nOKucYRfPn+MagLlcBrW62js/+dTHsXpDBaz43gizs+p9PpVAcUJb1zRNmLEAcjAegxlADrqGCQT1\nxfNTRWwEds3Y8fBbv/s7AICnflSVGHnxxRfxzneogJarl6/gy1/+MgA1R+QYuXz5MgeR3rhxQ9zF\nqTuHRqPBY5QQZ9JZK8/Qsk74wYMHma9kX8jgbMuy2M6Td67NZpNlrtvtarWw6d0kZ7Zta3Y0zW82\nm+W75Ha7zeehXC6n1Q0GlA1Kdo9MlnAch9uRtWeTyaRWo5XO0OR8uXHjBttzy8vLzKdXX32VzxtT\nU1P8zMrKCo+d2jt48CDfB5fLZc2RQTa3dOLUajW285rNpqbziL9Eko+SFhcX+W78kUce4bphsq4k\n2cgyYI3+BuglDcbjMbe/tLSEj3/84wDCmlqnT59m3tRqNebve97zHjz22GMA1BxJWEfisUyAoX9t\n2+Y+ydrTzWZTg7+Uus8bh3u+dNIC+nlYfnZdl2VO6s9Wq8XzQe0lEgmWre3lAibtEdLuyWQyfLag\nccmAaFlrOz9V0Nam1Om05iRMqCznQn8fDAbaXZgsVyBhIuk8xPflzSavb/lZ2l/y3N1qtdj5nEwm\nkU6HgeCvRZGFFVFEEUUUUUQRRRRRRBFFFFFEEUUUUUQRRRRRRBFFFNFdT3dFplYsFkOhUEC322Wo\nFNd12dNtWRZHedDfAT26olqtsjeTojoTiYSW5joJcrDb7XKEcbVa5fdT2zISem1tjaNAp6encfTo\nUQDAQw89hHNnVRbG+nqT+zRTmhaZXypqN5VKYelNyvt+5fJFHD58GABwYOlRvPzy3wEA+l3gfY+9\nB4DytJ48eVI9f+kiRxN4AazG+s2qlhafK6l+e57HhZHTyQTOnFLvOHToEEObGL7H7xqPVJ9/+/P/\nhrMgksk0TnzvJPcjlwzgW8Y+sikqMh5G3JGHVHE2hIMZi+ikQT/M3pDR2qNR6BUej3XvtGGYMA3l\nYU7YgOcGqZzd0HMbj9thYe6+Azuhoj/mguwNejcAtNodThlvtVpa1N773/9+AODoXEBFCPjBeE6e\nUoXHM4Up9nobcRM9gn5yhxx5YhgxDAMvfzKZDqPdTRsHDu/ldys+uhgHXu+p6Tm0Ay++hzARyjRN\njmo1TZ+jdzgy0IrvGF35WpGddxKVKVOx7zR6dMJDYfsTfhuPxydGs2lZTMErxmMfqWyev58U8e97\nLmekef4YF66o9RsWdzRhGGr993o9DIeK79lslqMM9pfLyE8pec8VprF0QBWwp8iW8XjEUQqVeoMj\nDFqtFuLxMAplbk5F8T8+v0eLgv/yf/gzADq8AfUvlUppWTQUyZBKpUDBG7I4ZqlUwsK8aoeiNTY3\nN9EbqHENh0ORadTRUswTiTClPpsLoQIAFb1kGCHcRDKZYl7TeBvtDhcMzmaSaLfqzGsZsQEAf/3V\nv8SPffIpAErXEkzFeDzGZ3/2s8H3OYZT+rmf+zkU5xZ5DDQ2jpJbX2dIpnQ6zbKwsrLCUUGyoK2E\noyG9MRiEkbgyMkYWUI3FYry+ZfaVhCuUUWMkIxIq1U5YzNdKpcI6fWFhgee63++zTMlCohL+liJ0\nisWiFulDOmUs0sPpdzKTZTwOoWb7/T7rMxnZZtt2GE25LR0+ojeefF/JqoTrkzrUcRwN2ldGpkko\nW5pXWVCZ1p7M8JLZV47jsKzJgskkO7JPlmVpGYwyG0jCg07q905wUyEPfGErhDahZVncvm0lWGdQ\nhJxEBmg2mxrcIemV69ev87rIZDKczUMZv+973/s4YvTRRx/lyEnXdTlq79KlS8ybe+65h3Xk+vr6\nLdArjUZDg8eQeoey/mOxGNsnsViMoUdv3LjBupiiPWXEZb1eZ8iOcrnMY8zn8xzJ2Ol0tGxQ+S/9\nnXT42toaw4E8+OCDOHHiBADg7Nmz3L9UKsV8pT2gVq9qmYQkTxL6aDAYcKbbcDhkXSehZpaWlphP\nMso3ZobrgPSi7/taJCHZ9CS/mUwmhIdNp7WC39S2jFyVkZjNZpPXCo3FdV1uo9FocOTi5uampn/p\nnCGLVsfjcX6eZKtaDTPEPc/j8cbj8RB6WBTx7na7nI1GGczD4ZD5PjMzw3LmeR6fPY4cOcI8u379\nOlZvKHk2TZP3prc++JDiR9IKzxKGoWXOjUYD5ocfmI2eEYdrENSgWv8mDD6n+L4PQ0R2cxZjr6/p\ngr6t+NTvD7HeUOuR4AyTto0YlC5L2CasACZiNOyh3QgyP+EzZLIvIrc9qIwMRQFkou8jvh1x4PtA\nu4XikxmtO2UbTLLvd4InvJPMrzulO8k0AyZnT7xWpophxIEJ82EYMZgECQJPyAs9a4JOqJ4HjAkB\nwjB57ZZKJdRFlgDpZJmNzzacbWs21aR+x2IxDRZ1+3MRvbFkwEAsZsG2bdbbhhELI+WdEL6YIN8A\ntVeUZ5QuHPkuCDnU84NsUTe0q00j3DO2ZxZKqGUiacd8v2gwGCBhq3NQKpXSoLEok1BCvXninJNJ\nqT36wrnz+N6rKhsklzXw3qBcRqPRQLOubJBiYYqzdDudDqaLyiaZKasMoHq9jo0A8rc0nf++jRfY\nWb9JBKjtkK6maXJpj83NTZHNHKIY/MIv/ALz5v/8t/8H1tcC+OG+2lsL+YyWyXPhbHDv2Gvz/Hfb\nYzz+2A8AAAb9HoYDJUfZdBJrq2r/LxaVjopZae5nLGYBMep/DAZlbflgFdntDVAM+L504CBqW4rf\nN9dVxvbBg4fx8ssvAwBSiSTbm9JGunDhAkMzJ5NJ5iXZmrZtsz2yuLjI9sNgMGA7KpFI8PO9Xo/t\nm6mpKX6fvA+kdVCv1zUkrUk2IWVy0NyQzUR8Go/HbFtlMhm242ZnZ3lOpf2VSCSYD9R2Pp/XkBwk\nMo7MCKOxJxIJlpHhcMj2ONlLlmXhh37ohwAA3/zmN3HmjIJ6y2aznBWXyWRwzz33qLlJpbS7BkCH\nd89kMrxPDQYDLStS3mlIWzGRUmOkudt+/yEh4omXjUYDn/70pwEAv/d7v4fHH38cgFofpMfoXvz6\n9euctfX8889zX1OpFCMGyPk4e/Ys95uQCkqlEqPx+L7Pv3v66adZto4ePcp3Z0eOHOGzzM2bN7lN\nmud+v6/Z7dJnQHyV0N2zs7NIJZLcPvFH2vby7lJCepMMSVjHXC7H/gY6j9RqNbGmQ7QK6bPo9/ts\n20uYecuyQvQjkYkusyg5A80da6gdkzL0Ju1FhUJBO6PLO0jK5szlclhfu8m8IVmVaFK0Nra2tpgH\n8g5NoifRfNIzrVZLs3lvR8brgUr4+6Zyueg/+dHHAyzisC6JZKS8dCHBln2Xly1ywklA8vk8M2U0\nGmkQLxLmidqUaan0u2KxyDBj8XicFf3NmzdheqqdVCrF9aZM00RcpDECQDqThB1sRP/1P/4YzMBJ\n8Ru/8RtIBfV+HnvsMeRyasHOzMzga1/7GgBgQdSHIQUsFbpcdJVKhQVOCm273ebvf+mXfgkA8NnP\nfpZ52WxVeCMfj8doNEJYIhL4VCrFcD5aWqSAFuSLtLGvKcpeP3Rayto0npBXghcML6cSPN7xeAzC\nCzFNU9SfiPGF095jx3g8hNFarVa1C/qPfvSjANRFi0x5pHlyHIeVyHA45EVGfU5ls9oGRgszHo/z\nZisvH3q9Hr73ve8BUCm1n/zkJwGAlczs7CxfTh04cACGqJNFsipTQyVMgkzxnkQ7rXHLSr3mc/I7\nqXR2ekYz3GO7TwTdjm19K4a47ugygi4po1R9lvBbhiGgVYzwMtV1b03xdV2Hn5U1YEbVKusRmW6+\ntF9dTiqs4lCH0GeZfr22vspOl8FggOlppV/m5+exMFfi3wJqAyMjp1ar8WVSKhWmGhuGwU4owzC0\nlP10KjQkiVrtsN4UraVOp4N+AK0yGo0EbF1o4NMaKJVKiMeUfpIwXKZp8rqykikN6ow2ro985COM\nKU3r5MCBA6zHbTt0SKfTaV6PpmniPe9Rzv0f//H/FrFMeFH7uc99jucDUPqf3vfiiy/y2jRNk+W2\nXq9rhhXpM5m+LY0W+W56Jp/Pa/VgyAgbDAbcb2l0ykMTyValuqXBslE/lFN1yPymNHoy0rZfUknH\nJ403m82yUS8Na4l/LuG+iB/tdls71EmdTXOTSqVYX33+N7/wsu/7b0NEbyjNzk77H3/qCc2BYxiG\nhrMtoQVIp8kAIsMw+HuSl06nw0Z3sVhk2Ugmk5q8EiSGfAfJVCKR0NbKJCjO8XisQSJIuIXbObXk\nxUIikWAZHY/HrI+63W64dzqhnpdO5+2Y6zQuiU8vDx+05n7yJ3+S+/bnf/7nAIBLV66yvhqNRgzT\nMTMzw/zrdrvsZHj++eeZPxKSTzrbNaiZ4KI9m81qWPqk37rdLvf1TW9SgRfxeFzbV+hAd+TIEW77\nwoULOHdOBeuUy2W2dSTfJZ4/HVpjsRg+/OEPAwC+9a1v4a/+6q8AqAPu8ePHuU3SJeToOn3mFNtt\nyWSSx0hjoDkgOWu32+xEvHHjBveb7NQf/dEf5YuAmzdvot1Scj0cDvndnU6H5b3ZbLJuJftMymeh\nUOC9s9/v82cJT5tOp7k+meM4fBagQ3wqldLWEvGyUCiwDiUHKD1Psur7PvOK9lDTNLn/cs8tFota\nYJ+cd5Ipqpd19uxZruF1/Phx/N0rypG1tLTEvD506BA7PldWVlRdKiibmfr9Y594isdF67hSqWBx\nQcm1PKT3Ri5DDo4GQ4wDZ5c7DmpRuuOwvpUX1iByhsNwvxO2rp1M8bs77R48OzzTAerSlWp7xWDA\nCi76ep0OYsH5RB7cYRoMn+4bgBusMU9g6SV2CRn3es7UhgBNuZPANAnNeidOLZghpOAkB5fWl9d5\noS7L2N5J0NukS4rb2f6AfmaRY5FQONKmlyUMaB3HYjGtXirpopmZGfTtDD8vZRtQth+917IsERgX\nytNOc0B9+t9+7fewvHwj8mzdBbQwV/T/2T95HPPzs9gX7AfZbJbh6l1nHNojCOGaPN9AP6hzXW+3\nUQ3g3utNZQvVGq0Q/tyzJ9ovALSLTHkBD+wMpwlsX0d3WqNN12GjYRikJC+vU6kwcJHk3jZNPP/8\nd4L+qd8/8cQHOEjl0qVLWk1W2lMlhLoMqCLnhmEYHNS9uXXzDscB/u3tvrudDnut3xLV63UOQGo2\nm2wDyaC/arXKtsTKxmU4IyU7997/AADgwz/4XyFXUPdtr3z3BL727DcBAKdPneH99IknnsDaTWUr\nLl+7gkKOzvVAIa/mg3gWs3OwAyheO5GBQc5YKwkz0I0+YhgF9xvtdhvJoD778rXLOH9GBR498jYV\nmOKNRsgFNbwW5ubhuiGEGelI27a1u7DtENf79u3jZ9/ylrewPrxy5Up475lO8/ODwYDP03v37mW7\nhmydTCajQXTLIGEZMEBtWpbFNqthGBxcJaHy6X0LCwtsP/b7fe3ynWw4IHSCyAAzGQBFsmBZFs+/\n7/v8zHA45N/6fliLidbA0aNH8VM/9VPMj9/8zd8EoNcT29zcZBtxcXGR+0pyI4N019fXNQhustGb\nzSY7AiWEWzabhRXUcZPnIZqj8XjM++VwONQC08ghubi4iC8FtdkeeOABnkdZQojOhK1Wi3kmS/Rk\nMhkNipB0B+3Jtm3zs1LvyXccOnSIz1rD4ZDnLplM8vtojPl8nmWo2+1qCQ18By5qLZumiWRQzzWf\nz98SvNbv9zX5pDHKkknyfkbWnCP52H4Wl3WU6bPv+1pwsYRKJJL2jYTVZ76744n10WWAjgw4lf8S\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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff229785da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import random\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "def showImagesHorizontally(data_row):\n", "\n", " labels =['left','center', 'right']\n", " plt.figure(figsize=(30,15))\n", " for i in range(3):\n", " plt.subplot(1, 3, i+1)\n", " name = data_row[i]\n", " print(name)\n", " assert (os.path.isfile(name))\n", " image = cv2.imread(name)\n", " image = colorCorrect_image(image)\n", " plt.imshow(image)\n", " plt.title(labels[i])\n", " plt.subplots_adjust(hspace=0.5)\n", " \n", "showImagesHorizontally(train_samples[0])\n", "plt.savefig('output/left_center_right_images.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "./data/Udacity/IMG/left_2016_12_01_13_39_58_081.jpg\n" ] }, { "data": { "image/png": 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ZyMq07Pen3/LKotx6Gdd73fWYV1kRwr3Tq5SztXyrFCurbS8LU+67d9ze9yIkWxQUgiBI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zjB6Krjx5/0rzFgAfNzHL1odNrevYREGB8j5N3JE4pgKBeozW+69Y3+tbvuuJPal6U+ToyPaZtn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2g4lNl0+YdASNUlX++glowxPnaybj8dTvvDQhJUk41vYPdg/9zlP7y5Ci6YKm6+uk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\n1WFqdwY0ypFIWeiVFGQrEongpnIapef7pk+2L/FolWm11imddwviyCPDp3k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Jf+lkRj3l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GdvQzDoI5HP0bpc6pgszP6+BEZvmrzeeI+p8L2+SOosWYBw2hWa/NNPWhqbW0RE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\ntbwNlTGDdbQhkqfDAc+nF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5PT9L2YbbF26MXEDmONYzl2ti1udZi+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\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DRESXLlzm4j/6GZZnsfs/93UcCxFtn40NZnSh6sP7fuYXiQgYKGWbk5dkIgHeKzVkHaJ2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\noFXHQODzwtrahv2L//IfasQrWl9PY3k8LuMSlql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h9CKg6JX9GtZM071hHKI8LKN+L8yy\np8Y0rqGsvO5CntiznHvubH0P/c3rKOxzkT7WfnyN2wFrVhX+ArKD8ii557qtr7fPsAB/jsQyp/Z9\nCBHg6Vb5mhrTisL9+eefN7PSH5w39tjYq6uzEL524cKFRh1VqA6WHbQd2pX79sE9zLftfLqoMVUb\neko+L+ucDz+n8vE0kiyrXWNU0XR6OswGbd9k0rjHv9WYQ33VHrIrtIenYuUyds1JDDyPZxrUfPuH\nrWuQD7Qdy6o/g+V7qk38/Mbw/dygQBT0g34uVmeY6twG6yd1RqrOW9FO3L5et7LO9PSnqr3UPOK/\nDZi1z4HVWYuaF6DLq4ops3Mf9SAvRO+Y6bfHxzyPgO65vf7t5e8wi3n6y99x5jnc1MKKTPdFW3ch\nPLUCgUAgEAgEAoFAIBAIBAKBQCAQCAQCJx4nxlNrabBq4zF96ZvA0rh8ERwM8LUwfYs7puCa+Ap9\ndDTOz5bvdeNx/rrYYwvd/CXc8GWz/fV3PGt/4YWlAVtM4KunCoKO3/jCq4LdKs8NFQSw9h4R1sjK\n4slboPGXWu/hoiy6+Xkf0FVZq8ymbUuGrudVuZA3LEb4OeUt5K2glIcTWyZ7qwXlNYS6KisaWO2Y\nFYsX5aHngxo+q9Wc6g9Vb2/Bwf2Da/hCz1/vlcWlvzfoP9mTSj2vLOlQBrYE8AEulYWYyhPpw2qJ\n04V1F/eV8rRD3gj6+gu/8Av1PVh/wSvAzOzmzZtmVjxQODCqtyB8WmBp/FZWorAuxF9uS9SNg2d7\nDy2WPVjMworq6LhYiinrEwTm9Za9Zu1gwj4wrFnT2gp9A4skFXQZ7cDeYrCuY30K3QSrFbaWVJ5w\n3lpZBblFPdiaD23X5YGirEpVMHdl8eO9OTgflF/pAtW3Vf/JAci9pxICAvM19DWXR3nzQNbY4hR9\no7zq4MGHtLgf0Q/KYhHtzPKIvNHmKpiumkeU9/TpzVRGZbWtPGu9flPBbgElLwxvscW6eTZtBm3l\ne5AJHlddltyqTbzFodLbqA/3y3DUXm90eah58D1l0eoDDKsg0t4C0ayMV5UWyq+86tCWrIdfeOGF\nRj5mZXxA//LYQZ24PyDf6BceC9C7SF9ZB3Na0JXsierRZRHKslBbfq+m8pV1La3PZk3LPTOzWW6K\n+YS9/VJ7vv/Rx41ympltnNpslOuQ2nLnoG0pv7z8ZK+yQODzQlX1bLC8YvukTyd7Sa+wPK5spP2b\n97owM/vsfpL31dWkcwY8Z+Tg2YNRm+0CVqyNvZqld7GU7vI24fvKYhzA3hO6yqzM07wegle2XyOb\nlXWc8jRFWmrdjLyV9yn2aqzTlde893BS3rfwkOF31d7AW4zzWqlXnw+0Pb1V3n4fI/uKzhqQPuty\nnxag9gZdc6zynFMsL/XeiDxyvXcCt4lap/j6cn261kNd/YK0eP3oPYjUGk6dc6DMzBKBtFTeyisJ\n6wek8XCr7KUwFvCXx4JfI5u1dcZv/dZv1fc8c4hZ2wO9a7+wmHV7p6BOyiPMe52qfBjew4XzQ/uq\nMyBv+W9Wxj7Kw/l5TwzWUWAA4X5EnpcuXWq8Z1b6Q7GQYO/JHqP4rfa2SIv3tH7s8PnThx9+aGZF\n93Gb4DyBx9fyKJURuozXd9DFau8FGWUdC+9cX06zttfI0zwg0eZdHiKQs2vXrtX34D3La92PP/64\nkSfPo8hTeQj7v2Zmjx9tNcql5L+LMYWB8uCe0r8KKs/j43ErDf+8OotU7AqeuUet3ZWHWpdXkvLM\nVGcHAPqD95zwSue2UXs0wO/NuY6YP5S3kPIG9l7gXH/ledTFHOLnpIZOy7qCdY2XBXUGDXBa6nzP\n9x+n7VluGL2cD8+7KIf3EuT01dkwxjanhTUbysBtCR3I8wjG8LDf9lAzS/oT61r+LgOPq6b+aXqa\nsS4YDXPbVbmugiVksERMe8P2OqsL4akVCAQCgUAgEAgEAoFAIBAIBAKBQCAQOPE4EZ5as9nCdvcn\nja+Mq2vpyzZ//dvZSdYT+/tty298OT19NlmpNb/EwkOiWCaMJ4f5WvurZNVLX0Q3N5PlhPqKryxM\nYBXEFg14F1boyiqGv2KiHviqyhYjtUWz4CdVVmDe20vF40A9mnzwVauO6AflnYJ311bXW9dUWrCa\nUZY/qh6eY5nby38lV1zpDP+1W8VoUd4gzxK/RHG+q9hHyJt5m336/FVdWfV7LyzFgazisilPD29N\nwXk/i3W+smzEX8XZWvOIC+t75Z2BtN55551WWsoSAu3Elne+LT766KP6nrI28lYqyjoCgLUW14Ot\naZXniQfucR+j/i+++GJ9DVZsqDeX2ccZYCsMWH+xhZT3jGAvG+QNKzNl8aQsforHbLseKBcs2cyK\nZwRbvsCTC2VmTzVYv3Hfok5oZ7ZM9jzSqh48zmH1h/S5fVF+ZXmoLNDQrmhDFV8O5VG6WVkfKwse\nb7XK7/lYcmZmX/7yl82stCtbEqr4DdBdsAbCXzOzt958q1Fv8LablX5kjxX0N9pCxYVUegtl4Db3\nbcjja7Tc5LLu0vOcvrIa8xaOyguT5cp7GjZih1jTU7grFheXS3mcqXgqnj9bxRpRcxnrCsA/x22C\nvJV1tLIoUx7RAOqtPFm7OOKVVZ6qN+DHr1mx6IWu4fWW5zdXabB3L/odnhLskQpZ5rGGOQht2BWD\njKE8t1Hf0SjNRUpORis5Ztsae0+dzvm0LQL39tsW7Fvbjxt583hcyWv2hlwdFV0cCHxRmC/mdnC0\n34w9N0zyyAwgkF/IOM+LVzaTRfr6Wtq/NcfXkz1dJpPskTtu6/LjPHcob1q1HlT7H/zGPMp65fr1\n67mOZb6GZzueV7E9VCwq7Ft5H449LfS70oVqH6esvf1crKyj1f5NzX0+boliseD29V7jqlxdHsm9\nqr1/6/IeV2sY1YZ+flMxhtS8VceVHA5a19B/yhuN6+33OLwf9XOMmn+VZ0i9dl0q6wG/PlNMCipG\nufKmxHyDv8zegXmXn8f8jL3j+ql2/ErPbMLlYa8ZrBXgUQNvFS6rkh3FJOC9TdS4Uqw+PhavWWkL\nlE/FdOG8/T6R5QSyA73InusqjhnWS8hTrc9xjffcSB/e7WalXdFezNABWUO9mYVE7Z2xzoI3lmca\nMtPnFpAh1ovIuy88K1SMM+gKyA6z++Aa6ggmGC4rX1Mxqj18nHh+j88mdvZ2G893efFxfdAPLAso\nK845eI2Ivm3EEnZsIiyj46PUFn6vw2VU86FiZ0K7qveALgYQ5ZWjvHu7YjGpmNDYSyhPKsX84tuC\n+wptiDSVlxyPQ/xW+z7IRxeDmJozus6yGp7xWRbUWSHaR+2T1d7L77/V2SLKxfpu/2Cvdc2fNSjP\nK3UWq2TUMzexbKt61EwpWSexLsd6C/lw26t5BO16+fLlxv+cpzozApR39s5OOstbzFk/ZA+qQZbf\nJfYCT7+XyUcKedfe0BSX2fL0sbYGBqAyJ9dnGuOir3f322cTXXiqp1ZVVX+lqqp7VVW9Sdf+m6qq\nflxV1Q+qqvrbVVVt0r1fr6rq/aqq3qmq6o/8RKUJBAKBQCAQCAQCgcA/14g9ZCAQCAQCgUAgEPii\n8Cz0g3/VzL7lrv1DM/vqYrH4eTN718x+3cysqqqvmNmfNLOfy+/8DxWTLwYCgUAgEAgEAoFA4Gcd\nf9ViDxkIBAKBQCAQCAS+ADyVfnCxWPy/VVW95K79P/Tvd8zs1/Lvf9vM/uZisTg2sw+rqnrfzH7Z\nzH7LOtDr9Wxled2mEwpkdpDc1mYzdnNM3+DgJr80IPfDpSZtw4ICqCINpi9Y7sPtbrn1PAA3UnZz\nVHQ7gHen5GsqoK2i5IE7M9x5FT3Rs1LSeBdZrr+vB7tnqyCpigLCl4vdxb3bLLs+4nm0BdMewD1b\n1UO5fuKaapu6Pw73W8/DJVcFIlTUWIrqyNPpMbybsXJd5rIyxZpPEy6c3CZwWVUUGIreEEAf8fOQ\ngbrfp+17iuoKcs5966nZlLzDvZ6pnuD2z20Od3fUdf+wjCtP2aQCicKV16z0B1y1OUgs+p3r0eX2\n7wM375KKQj24ndAGr776aquON2/ebNSH+wwywPoElBdoO6ZvQN7QIb/8y79c30O9mTIDFA0oH7s6\n+8DHXB/Q33C50Kd4TgXDhov0/l5bP3KbgKYC+XCb+IDBZoWSEWMIgW3NigwgfabOUzSVqBNTyAJI\nH/MC0+pBtjmoNdoAtCCgaDQrbQ/ZQ9tw+p9++ml9DdRh0I8s25BRtCXLNsA0Kp6+gXU6qC5ff/31\n+hrqCQoX1plXrlwxs9JH3JY//OEPzaypayCvcHvnOePll19upMF0F+g3pj5BPdBXPCd/9PGHjfxY\nXtQc7gPZsr4GRQPKxTLURfGkdMHhwVEjb5Z7RUEEQB+xnkO7cv/hvqKE8jSzjTkjD1dFY+uDh6v0\nOS3oa0VjhXqoINWeFofL06AVc5QWLI9+PuS2h37nOQllVPMV8uF5CmUEDSrTH0EPoTzQ7VwnbhOM\nHaUDvf5VtNLcJrUOGO818uPfhaKwyL2idEY/o26zWZkXUEa0ySHNyUdHs0b5zHTA6sAfLPxe7CEr\nM+v3e3Z8XMaQp40yM1tfX81/m/ooPZfktqZEI2r6rr0ddA3LOn6rPYuig0EailrHU60xtTXWFJw3\n5mLUkZ+H3oKO5nkRz/M1T13P9e+iQFf18BS/DQoqQdnkaeBZd6K+SEPRxqq1glrPexp5NQcuaMvd\nRUvldTPLnqLAhiyoPSfyQV+xrKL+K6tturd6XyLo1HndCFo09LcKN4D6cD08laNZkTHI10svvtx6\nHutS7he0BdZKZkXOUS5eI/J8a2b2ySef1L9BNcjrf6xnUb6790taqBPWulwGrHV5ze73+7ymhszx\nnOzXpSxX6GesH48Pi9yDwpvH9GeffdZ4XtEiQj7UuUIXhRinhd/oK+5jyALvVd54441G3jwOMZ6w\nJ2JaPbQT7+2wrkZ/8zhBG0Imnjtf9lloG5YryD7aiym+1N4DfYo9B+sajAvIENrbrMgXt+EP/ukP\nG3nyPvG9994zszIOma792rVrZtbsP0/vh2fMSpu///775oHyc97H4+benOcklLWmnhZrXhVCQ521\n4J46P8M4Ybmq8rBA//EYRx15L4y8sN5WZ34oP8s9ysp96yn2eP7pD3qN53lfjbZjKmAAba6oDFEG\n1s1Il88c/BkvnyugzIoCUIW/wO8uenfWW57iVQH5cD+uriQZ4jnf7yEZqAf6k8eoop5FPfBX7XsV\nxSLmSHWm7Pf9nC50DZdLjR1PPauokNUeVa1dUA7k3aB3z9eULodu7drb8Xyi5nBcW11pU/QWu7K8\njiJ/qNLHfK15HjadlP6YTI8b9d/ZKWvKfr9Npbqy3C5PF57FU+tp+I/N7B/k31fM7Cbdu5WvtVBV\n1Z+uqup7VVV97/CwfYgTCAQCgUAgEAgEAoGfSXwOe8hj9UggEAgEAoFAIBD4GcdTPbW6UFXVf2Fm\nUzP7G7gkHpMR5RaLxW+Y2W+YmZ0/f25xcHDkAsG2LZO9R4wKvgtrj6pqfyXn13t9fKHGF/R2kL7h\niANqN/NRFtb4y19qvcWesq7lL7WwFIFFgwq6p4K+4iurChCOPLsCQypPHy6rstYG8C4HpYTVBerG\n6XvLO64j2pDzxhddFVTWW/2p4LXoa05XeZD5QI+cD8Bfu30wYLZw9BbKXdbk/Dzal/vKe7aZlbZD\nm/AXfVhnQZY4b/QHt2/Lm2xR2sT3uwpEqMYCLADY0gIWAyiXsspU1pso18XLl+p7PhCoCtzIFjno\nG2/hyfmwFSrqVMtQh6X8yqhtQcl96+WXrehxD23x1ltv1fdwjS3QYKWCvNlCDBZ0sHxhCy6kxZZb\n3stCBbNEfdh6CtZl3IaQK/Qxlxm/8Xd1tW2RwzIEyyXICVsdQb+zRcrt27fNrG3ZynkiDR4LsIzi\nsfDuu++aWWlX5cWF9FlvqcCePgg030NZUR5uL1xTHqCQY9a13tqM64MyK686H3yZ84TFLecJC1i0\nt5nZjRs3zKxYL/KYg07iusFDDWVmnYlrKkg30uI2Rz2V5xw8tPA834P1LVsXev3AbQ85R1rsHQlr\nZx4L3uqa73nrbqVPGd4Lj3Umyqx0nwpk7D20+L258FT3loBqbaHmBdSb+x3XVLm8pRvPc8oCWnkq\n+bT8X7NSbx4LmJ984Hb+zXMr5BuyyWs3eGah/DxGMT5YByBvpMHyCBlD+bnM0EksV6XcqY4rK6UN\n19eb69im3oaubc9Jw+FSoz5c/1Onk87YPFPG6Hjc9nw9OvrJgvwG/mDh89tDnlmMJwe2vFLG6pmz\nSTaxlzQrOgAes1tbxRsY88h43J5HMQZGI/bkLLJvplkfFov2PgNgy2yMK+hAHu9YBymLZu9FbFbm\nWNSH9Res26FDec5R62CU23szpbo1g613sYTwu117NV4P1H2V68ZrRJ++anv2AvHPcd7KQ6tdx7aX\ngre+N2vvpZTVNrevT1MxwKh8MO8quUJ/o6/5XVUetcfx3l4sX2oPiXJj3uL1FuQVayX2iFKe8b4N\nWSawjvfeRlxvbl+MBfw9fabIBDylUQ9e86q9BOqNNRjv45AGj2lYqfv1uVnZx6B9ue0h50pG8ZxK\nC/fUvozHB97F+ozbC22Ncn3pS1+q7yFdXtdBN2GscT0gM9gvqPMqNW4ho9yW/syIywAZ4L7Cb8ga\nr62UF6U/k+A29O3Ksq3GLbx38DyPHXiV4R57IKHMnBbWjf5sioFrytuGPXwOj5v7Ee4Pz3ij1ry8\nDvR7L7VP5rKiDZEWj+m7n915YlooI58BeKYcdY6kGHwUGwPKpVgvlpaa3j/q3EaxRiFP3if6czrW\nd2qe9vOVOp9FfmrO4LKiPZWcqLNOL2v8vJ8/G/vebFjUtVfj+ce3F7c98uF2guwoRhP0A8Y5647Z\nvN1X/j111qvObdS6qesMWp3l+DNL1r+Qc9SVz91QN8XW4vf9Zm1dxu2r5jf8Hh+3mdrKOjCfFTbO\ns9vfELDuxT4ZTHpmZsNRydO/p9rw+HhiPwl+6o9aVVX9KTP7t8zsX1+UlcUtM7tGj101s0/9u4FA\nIBAIBAKBQCAQ+IOF2EMGAoFAIBAIBAKBf1b8VPSDVVV9y8z+rJn98cViwWZHf8fM/mRVVaOqql42\ns9fM7Lf/2YsZCAQCgUAgEAgEAoF/XhF7yEAgEAgEAoFAIPB54KmeWlVV/a9m9ofM7Lmqqm6Z2X9p\nZr9uZiMz+4fZ3e07i8XiP10sFj+qqupvmdlbligl/sxiIXhtHHq9nq2srkrqF3aZ826gYxEwzXrp\nmSVyMV1eHjbS5LQWizblz8KalD/KPU7RMHB9AE+TwC6TKDNT2CBPpKHcgOFgqCjUFJ1RV7A+lKcr\nQLxZcbv07rr8Ll/DuyizokjCM4ruohGw0VE2KdqvLqjA8+p/T0OhAhGyGztcRVFWdqmG2zfS4CCT\ncO1XbtOomyozt68P2M5B6X0AWJYhRb/haehAE8FpeVpBfo9l2o8ZpmBCm4ASgN34FWUV5MS783Od\n0B9MVQZ3bB6jkDHQDHC5VOBf/MY9FQwaf8+fK4FjfT5mhfoCtGfc77gGOeEy4zl2LwdlhgrEDRlD\nOy2syAvaleUK/QHaQi7XRx99ZGaFyvLll0vgZ7SdcqFHPbhvvbs16yi4iTNVDO6jzMr9m2kVEPAX\nz7Ougayh3TiwK+rLYwd0GxiPKpCmCtSK55QbO8B95YN3clq4xgGJt3eb9IkqILOipVWu+tA/qAcH\nK0YaP/rRj+prGGugU2CKwdHSqJEmB9b2tLxmTG3WpghVlBEA2h5UK/wc3OZZhh4+etB4RgWSZ50M\nmYa8sC73bvzcV6qsft5t0OtW7WDx/r0uGiSeAyDvXesmljkvt1yG8aRNu+LlSsm2olyGLuhKS9FQ\ndFHv8lzc1b5+fcLtpeh5FKUxoNYbnp6F7/l8WNeiHIoGCPLL5fKUQioAvaKS2tlL+rrXpzbpLzfS\n6g/KvdFypgfpF0oo5IXxwfPCo600ruYPp61y9fttmVayHPiDhd+LPeTCFjafT+3wsKwf8Xs2KzJY\naIOS3I6GRc976poGNfmsTfsEivs68PesjJNZpmRZWz+HNihldXQ1ZmX+VNSw0A88rwNK12JMqrWr\nz4/fQ/oqH0Wf7ymFFMUO61+kq6h01b7Ezztqz632qr7+/LzSw572VqU1XCrzqM9bUVB5qkX/G/Br\nYs7bp6/2vf1B2y7ZB6Ln33zNhxngNQ/mRazjed2FtS6v3TB2kP65s2XtirSwX+YyIE+um6eu5z2h\npzZjecTz3IYoI9au/aVyD/L35ptvNsppZvbCCy+YWXNNCao4zIsqbIKiBVR969cbvNZX6xSs47rW\njUq2sU7hvkUaKCuvEfGcold75ZVXWs9j7wVaPd73oTzQX7z3wj5BUbKjzZm+zY9fLhf26rwnxDpe\nhW7w9ItmZu+9914jjZdeeqm+h/qijjwWIAtM4wVqW8gmtxco3JWuZX0IgCpe0bEhXbzH5xfY7/IY\nvf/wwRPzVmFIADVf4UwD8sL5oBxcH0+1zWMNcnHr1i0za/YL0sIZhVnpW8gAZNCsyB/ucdv7cCF8\nH+OEaesePEwy2jUn89ka6uvDf3CZAXV+qs4A/Jxp1t73Nqj2spxAVs3aFKes09Afaq5Qey+vOxrh\nFvrtdYYvl9r/4C/fQ78oyl3MNdyPnj6yi67RjPZCImyEn7u5XGq/j/uKlhZ9ymXFGMaYUWfQSItp\nN5UO8N8XuB/9WO6ifjRj+c5rkl55fmmY10Gj9hleCa9S6l1TWee5XOktRZlY1/Ep66YuPPWj1mKx\n+PfF5f+p4/k/Z2Z/7icqRSAQCAQCgUAgEAgEfiYQe8hAIBAIBAKBQCDwReGnjqn1eaLf79vpzQ0Z\n1Hs2E9ZGs/xVtSLPoEEOLrmWvoiPRuXLIH9NB/AlFFYBbDlQrNsP6/IBKki5t0pTX0tVAGBYJHH6\n+IqOr9HKym4vf3nvCrbOv5UFuC+rsjZTgSGVxTigPCpgyfG0gMQAysGeNN5ij9vXW9Grtl9bL1Yk\nPsC9srr3luNm2ioPfYV24vaChQ3ki9OChQVbT3mPNvWFXnlqIX22FIKFE6xc2GIElkVswQJPDViT\nVMRKinzwnrKmUFZTKoCqt0xg6xPvwWHWtjIbLpe0vPyyhZjqP/xGmirQLKMrCKcPUHrz40/qe2iv\njz/+uL6G59BH7O0HizBc4/aFBRKPE2+ppyx46nFPBs6wBGQrEsgFLBVZvtAPL774opkVzy2z0nYs\nv96DQfUt8t56VCzdlKcdgLbgMiurHm/Zx2mhTrCuYzlBWVmXo9zIR8k25Jjly79nVtpJeYF4rxRl\nsciyd/wgpQ9Z6JpHlNcMAzL3zjvvmFnTUw16l/NG/8H6i8fOq9dfbTzP4xcWZCxX0IsIHs3BudEP\nCHDOlp2QX2W1i7bjNoFMoAw8v6MMbHHJOtKsKduwelTBcTGfsLUcxjnqw2NhXjWt8pSFHPef9+Bl\neYSVnfIkV5ZY6BsVMBdzpLKk815WnKdaDyhrNkCtU3BNBZjGWFOWjQCnhXbCNdWWyhJfrR9U4GqU\nA/mwroHMoXwsq8qrDmMGsgOLSv6tgqBDb3E/1p7RZ4oVZn1vBllrW0vCKm8xb/eH6kess4tMcMDg\ntmUjgjQHAl8kFouFjSfH1u+1LXubgctHzXv99h5H6fl6z0ne7yX9HHSb9hlAb9H2GlIB7qH7oK8U\nawDW4DyXqXUz1lRIi/UK1pRqfdPljaV0pw+y/jRvNJQbf9Vejb2n/b5KsbZ4byP/+0l1U2wqnhGE\nr82m7f2u8ozBnKH2bCgzr//9PobbxOthrpfaj3pvHvZOR7s2LOvd2gJrMrPmnGfWPEPBGhHrNL6P\nfHgt5vf07NWhvJQho1in8T2MhevXr7fKgHxYhtAfkKH3b7zfylt5Srz77rtm1lwjotyvv/66mTX3\nRIDypIcs8JoSqNk4nhu1rnFfeTlXnoaQVfbEUIweeBd9pLw1AZY5rP9Z/yBveNJgrWzWZqRR40R5\npwDKkxUyPVouz6Lf1TpN6Ro8x6wS2OciH+yNOH30H8scyswy16ua3h+KdUidL0C+eN+D51Bv7g9c\nQz24DGhrHrdnzp01hlrrq74CWL8jXbSzOq9SaaANeT07Pkp5Qi+yfKG+SmegPLz3QnuqMY2xwHLm\nvZ24va5eS7KMMwN4apqV9lL7RMgQ7239+p/nGOTJbYIxjPZlfYqxg36HnuQ0WK4gT3iP5wzkrbyF\n1L5PeYrWddw7aKXftX7wnoycNuqrvBeVTHjv1sZZ9LC9HvCeWurMRMmxakO1p/X31N55Pc9l3O+e\nvU0xzKhzY6SvvOWV1zzGmPJQW15O6XNfFRlNf9W5aJOxy3nD0rcasB5U0/Y+c5A9qYejMg67ZE7h\np4qpFQgEAoFAIBAIBAKBQCAQCAQCgUAgEAj8XuJEeGqZLWyxmDW+JBaeYP7aj5gb4Jhux1MCrz+n\npeKdeKuxyYQtTpvWuPxVHV/5+esinsdX3K54RU/j28RXW2Wh7L8gIP00YwAAIABJREFUKwtl5Z3i\nY5FxGZVXC8qjYlUo6wtVDzynYvngeW+pzPXn9vUc22y14GOQKb5c/rLtY3ApiyfFaY18mL8X95W1\nPvJUFnVoe+bZxXOeU5V/cxuiTbwnlVmxOgI/NFswof6cPryKbty4YWZm62vFStSXR1mJcl+h3soj\nCtZPsOrhNjl7NlkRMbc4ZB+WMufOl3ueS1Z5LXK5/LhiCzxYQygLP2/twPWurV2tbaHKcgX5UJ5H\nPlYZlwtyzla7sBJDmtyPSAN/i3WxjgEDXmyUR3H1em59zlPFmkE78zjz3MTgHDfTsQP9WGZ9irqx\nZ4331mO9iLZTVrh4jvOGLHj9aEYxDYV1sIoF6C2+WYa8RwnLBNLgvGFthTxZTjCefBxDzof14ltv\nvdWot7IU4vGEvJAG4g2YFZ2Mcc91xHtsbYT+Ux56kA+kyboW5VFxHNCG3PanTje96pSlKs8LKCus\n69jyEGMAlsYsL8qbx3uGNzy1Zs24Z12xOfma0nOom5orgGf1tj5zNtVNWWfhmrJOU9ZsaAset54X\nu8tzgeugyuq9s3ns+HHV5YXLv7u805VHgY8TwuVCuymv4y4PNR6HmLuVV7OKo1Nb/i43LVXTP1iD\nQF+Vtp9NkT7zobc9VUpaeU1ZxxFqezCwjl0ZtK3TA4HPG71e39bXNpznazuu3qCfx0APe6OSxmQy\nbVxjOa69swdsNYr0c55V26J5fNjeeykLc8Sx9esus6IfMMZZT3TFWvR7XLMyppW3p/JiAqDLFaOJ\nrxeXS+2F1f5VxbXFnK0YITxTSNMbr8kSYtYdt9F7XCnvqu098sRwcbCUd6+vA/9We5WufYyK1wSw\nJ6xndVHxjzl97Hew9lFeSWA/YOYUrLvYIwjtinx2d8qaD/WG7GDfYdb2UOS8fVxqLj9knL0nUC7F\nooM1Jcfm+eSTTxpl5nUb+pTHGtLF+pljT9dx3qn/UTf0H5fLMyLwfkaNDz82VbxOxQwAcL/7dROf\np/i4Ldwm6D/2/IRMYt3M95AnzjnUmQ6vkTyTAMs7yoi9xGxeytUVFwdyorzRuN5eLyjPDfQVywTe\nQxwlM7NBFlfoHx47SANlYC8b5eGDd1XMK99XrI/hxcP1+OTWzcbz6gwTfxXjBnuYolxoQ+53FXvY\ne1twf2DfjnHP+SD9Dz/8sL72wQcfmFnRP9wf+A1ZZR2ItPhMyjMXsa7Z22/G6OO2V3MM9BXqxjLh\nvRD5PdxTMSbVWICeVjKBOnK9vZcu9zvOFXie8ufG3I/e47khQ7P2nI9+QBk5H898w20CmVN9pero\nvbob8bZESFbvqaU8t/16hfPmudafvatzcBU3SsX783Gv1XzCazAf702df6t1nTqTQr+Nx7invsfM\nG89yvZtykuq7utpmySvPufWzlTi1vEdV69EuhKdWIBAIBAKBQCAQCAQCgUAgEAgEAoFA4MQjPmoF\nAoFAIBAIBAKBQCAQCAQCgUAgEAgETjxOBP3gYgFXPHZXTX97varxnBm7wBW3Ne/mxy6DCJDNbppV\n1XSjRAA0Mw7i2KTr4XQVrYKiH/Rufooail048S7TfXmoIG3KbVoF6/VQbqRdNBTKJRVQVGjeddus\nUM3BfZhdJpGucmPHc0zb4V1wuf6KlgppKFqfLpdfRXuG59j9GYALtQ9Gy+kyDYMP1Mn5eLolBugB\nfvVXf7W+Bnd/uLbfunWrvgd3XqaO8LQYB/vFrRcuwWhzroen1uTy4C/XA3mrIJsYC9yWvv9YJnzA\nUU+9we+blb6F6y5TD6D8XB70H3SBClpbAvq2KUCYRsW3Ibu4+75legxQayi6B9VeoAQAZcgHFBQZ\n9eW+wvOgk2MdAGoOBENWwWu5fVEu9LcKKF7nvSjvoT/Yzbyrjj6AtVlx+4fMsY71buycD8Cyg7GD\nfFhvoR+V2z+gKApUuTyVIcsjysi6CUGaoTtZB6IfkRZTooHCj9PHu/jL8oW8IUNcfvQt02NMckBQ\nPM/vod9Y16AcSIPTgj5RNIo+oK9Zm9aGAeoHJffol5dffrmVFnQmUx++/34aR9AJPC9CTrjNMXcr\nOpzppBl8l9NS48rPU7yuQb8o+qMuOiNFzQcZ4HGLNlHUeX6dpQIyK6oUQK030MeKSkvRx6i1GJ5T\nwWVVkF/8VoGMFTUzfqu2h6whH5ZfPM/19tRhSp+gD3iNhDzV88fj3Va5ZnVA3kUuSynXSqaHOLXO\nW4HUJmrdcXiY+mN3N8n7mKk1M1V4z0p7Vf2wmwt88ajMrFr0bD4t4346xnqeaLyWkpxvrKV5ZGWl\nTS9W5nymX8n0wuMy1xwdYXzkeWvQpgtbIypvwOscszbljdKdSqdhbCq6QkUx4yllWW+rOQa/lc5R\nVFWAmn9QfrRvF301l1vtYz0VH+toT+HD6NrTou3VnlBREiqKIPxGXblcqCP3lZ9juE18Pg2dDko7\nCsGAtRQo2nitrKj4kR7m2DfeeKO+h/U+6M65TbA2wprUrLSrWjdjHQTZ4zUcKKV4XY68IMcsX36d\nyXOsf9+svWZdGrUpRa9evWpmzX7BnpnlEXs61BHrQk6L90n43UURivItD8v5k6K5Q/qesph/K1kF\nGhSsWQ4hC9xXaDvcY3lE+/A17D1QVqbw97TKvEZGn3IdkS7GNsuEpzHje639pbXPclR4Db6G/e5L\nL71kZmavvvpqfQ/jBHXjcaX0CcI/oMxM6wk5VHSu6GM+A8C6vD03tWkHuVxKN/WXmmd9fEaBa4pO\nHO3KMoex7CkQOS2G71Muq5dDdVbG7QwZRVqcH84yFPWuomjzdL9cLr+vZr0CWeazNYTOeP31182s\nuRf+zd/8zUYanBbKr3Qg2oTHDvQp9CivC1T7QgZw72k0in7/pvaEKmyCovLzlKKKts9ThZoV2VTz\nCNqCz8gxTpBfoz7D9jm+XxsoGma/X+ayKqhQD13n5T5cCAN5sn701KVmRTeps2E/7yiaQ6aXLXLe\nDBdiVvp5b69NN1rWs08OfaTWQb0e5IXPFVB21uXttutC7DgDgUAgEAgEAoFAIBAIBAKBQCAQCAQC\nJx4nxFNrbuNxM7jlwtoWA/gaWQKala/EteVSf9h6Dx9J+Yv+ykrTowTvmZUvlpNp0+KN81SBOr0l\nWsqnafGirO2UZRygLLn913Iugyor2ou/FiNdZQWI38oqT1lH++DpnAa+uHOwTHgbKEt7fLXmL8je\nYo/bHlZpyjoNZVxeKf0B6wZvQcD1UNYLqAd7HbzyyitmViyy2Erp9u3bZlYssdg6Dd4gXA/UTX29\nV1b9sFq4cuVK6x4sGODpw+2LOrGc+MCTa6slUKf3OFOW6VzvLmtMWLL4AIZm2kIIMoA0emQJ4GVB\neWt0eW+xVQieU540yuoT/Vd7FPVLPZTnkR8zKvijDzDN17hNfPpsMYF+r61EB0UmVFo+MK2yuoHV\nGdffe+OZFXmF96EK4gnMyfICz2H8mrU9+ZQlMOsryC/GHMvo888/L/9y3mxxCtlEGhy0FjKB9zgf\n5emBOqkAuN4aittSeZ1euZbGOfofgYDNmuPPrKkLMG5hwWZW9ALKAB1iVmSILcMga8oSq181AyVz\nWyqPK/Ql5J4t1vAb6bNlIOSKZQG6GOORdfPj7aanM8u2spL0AWa5PzBfqbTQ7zwfYlxhrKF8ZqVv\nlRWYgvfi4fGrrBhRbh9AlvNSHuK7ezuta4Cff/k3ytBlKcblURZr3qqdx5C3UDZr61PO248drn+X\nB5nSzXhXtSHkRK2b1BrRr924PMoaEzoN+XTNTZz30lJaZ7Bs700OcvpJLrceFV3b76c0lgalXINB\nulav0yjv06eSLF+69HzOp4xReG/t7JZ6qHk5EPi8MZ+bHR/PGl6IF84ni/eNjTIv1PcXaUyDxSP9\nTnrk3t2H+f+i0ydTeDGVa71+0kkIhr08antmVlV7/Yh8eLz7dSDrFeifwgxQyqC8qwDl+ev3akpH\nNxlNmrqZdY73glDeTMoC2gdWV/lwemrP6fetan5Q3gZq3vUsGayz0Fcb66da1/CesmRXc4f3MOZy\nqPnKW3krr7f5vNQD8qQ8nND2ap8BPc9rHs9oojwB1Z4A+fBaDOtleHix3Hvv/Gbd2msGv7ZSeylu\nX7+eq/rt9RbWrryOOH/+vJk1127eov7LX/5yKw1el7O3v1nzDADp4wzh4f2H9T21v/LsOcpDCOt0\n3mf4/Qynpc4CsGdBm7M3BPbv3CZ4/saNG2ZW9hacjzqjAHj9j75FO7Ouwdipx4noR7UuR5n5LEd5\n8WCfizL84Ac/qO95GWIPJ6X7fvzjH5tZNxsQ3lPeELynx/kRyqB0M/pYMVuwDE1mqZ/Rrmr9iHbm\nvRf6g880IMt4Xp27MdD3nv3BzGw+bbI9cL/gefSPWZFXblcATBu4x7rDM5SYtfdvLENbj1Pbo514\nXEGeuKyQfT8HmJldv37dzMoZBZ93KE87/Pb7UrMy/qBHef+Pdv3617/eqjf00QcffFDfU2fJaN8u\nZgslq0eHqc1ZrtBOaDtuQ7+HYtlGu7JMY07x54JmZb+kzgN7/VQGtff0Hp1cDrWO8Ofm/Dz+qj2e\nnNfF2s172fK8qOYFvw9XZ/Zq3ajkCmMB61hur+Gw2a4HB0V+kT4vLWqGq1l7fkee6hyiXrMS28dP\nivDUCgQCgUAgEAgEAoFAIBAIBAKBQCAQCJx4nAhPrdl8Zju7j6XVWNPjqvmllb8u4wvfeJK+JPKX\nvuFQcRTnmDm99LWQv/rX3hAHj1v5Kgs079mjLJ4AtnbwVjGcBv6qL7wTwbuuLCw8t7riHVcxyJT3\nlu8P9UVYfYVHW/DXa88D2vwiPGzUx6wdB4mtHNBvKCtb5MA7jK32UW9YLyiecljisEUS6sGWSHge\nVkpsgQagPNzvsNZg/m3PK8sWEMqKEbKMv7BIMiteDWgvbktlPQSrEPSf8uhD3pwW0mfOaN8PLP9e\ndrpiBHD66P/BsFhmeN5xFauuq6xsbYXysKUMnkOZlVzh74cf3KjvwbKNrWdQHnAsg6ferPC5o335\nPWWFW6wixq1yecuH1bWV1nssh74fWHcASJ+9VTAu+Jq3LOJ8kAbqCIsezlPpXzyvuIC53uhnjpEE\nYEzC8o4tSK9du2ZmTU8lb9XCzyNP6B8Vn4tlyFu0dsWqYKjnvXcV61rEFMNY5fGodCa8tpCm0ivK\nIhD9wHru+PBYPmNW5JD71reZ8l6DbCuuaWVdiDHHXmLrG6ke0BNcR5SBn0edlBUndCvGJlsWQYa4\nXLAWhA5nPdTvNbnruf+VFTnKrWJfoq35mrek47b3ls9Ps5T3lvW8rvEWqiyraEtlwa48CrrWA8rS\nzbeJ8hpX9VFl8BaayhtArXVUrK+aLUDEPwBYpn0cMxVDA/3HYw51Y4tT/D59OqUxpBgda2tJh0Hv\n7u/T3H+AuEDcJqmOW1vboo7wTsiWt8s8J6c8L14ouvlpnoiBwOeB0WhoL734svPYSb93dsrcd//e\nVr6HeBHtWCCH9ZxW0gKTB68b19ay1fkK5hiOhZjG0/b2fi5DWdfht/J0gS5QMTa9fjHTXrQ+Fp7S\n6cqLS3mb+zgRPM/hObVO7donqzKruC1edyiLceXdi/d47ea9zNU8pDyvvGcFP4fyc5v4GIg8d6gY\nZwCeU+wdai5TcZ9RDuwpeJ+B527evFlfQ7pY3/CaB3tIrBuV54aKf4byY09s1o7Jye2l4jb6vaOK\n+YS/KjYnA2VF+UfE2gL58PGa+DdiLZkVWcD6mdfZeJ7PDLA/9BbzZmXuhydOz9qeAmr9q8YonoNe\nYZngfRKANbeP68vvoo/4XMF7ZZmVdQnq+tprr7XyA5TnFcN7WSiPffxl9h3l6QLZxPqc2wFtzl5o\nuMbnLgDkD/3Cz8i4wftNJg81dpRso1/UHhV15H0c9hk+TpdZWQ+yPL78SvIWgpzAu5DLpRgFFHsF\n8sS1rtho3AYqXvR0PGmkpWLCdZ1Fspz4uGc8x2DscJv4eUHtPdDmPGcoD0jUEWnx2ME48YxMnC7n\njfRVXOa33nrLzMqYY9lGuuyN5T2cMD+YFeYa1v0YFyrOMsqB+nCbwPtUsa8gfR47Xg/x2QF+c/v6\nswOWOR+vkudRxEHledfLjjpnR95qXaP2ifW52zOyaqD+SrYVY5diD+I28PX2ceK4zGhLtd5aW031\n4fE+Gq006shLGKTFbHuzeXMeYdn2ZyzqvEOxozwrwlMrEAgEAoFAIBAIBAKBQCAQCAQCgUAgcOIR\nH7UCgUAgEAgEAoFAIBAIBAKBQCAQCAQCJx4ngn6wssoG/WHTzc+ye2CHq36TFiX9hismu6wVWi6m\nKMhpzJJr3vGYXIRz8OtRlekVyK1ukfNeIbe/M6eTOytcDblcRwfJxa526yZXzgubZ3O5Sje0ArCL\nIJOz3CTsPg0XQ+U2rGh0aiqw7ALItBp4T1GCIX1Fy8XlQSDPpWGm4lkurqWewoddIFHWN954o74G\nmqz9B8kNeErBmuGGjnbbpwB2Dx6moK1jcrt88aWXzMzsWqYQ4DrWlAu5XA+zSzqXFe7gZmZwqJzk\n/t4h11q42cKFk9vrTKZtq+ib8nAptc+5szmwKQW8VgE019dSf50+lVyQ333nvfoe+mFvN5V5Qd7c\nx0dwTy3jY5Spig6zrK6cLXnDzRhUC9xXcMtuBGV09B5wkTZrByPl8a7oUFBvT8nJzyHI6MFecU1W\nLqzDQQ6Outamb4ALL49byEJNc9Erz4+P0rU7e4m+TNELsKsv3N1Bc8duvXCXR95cR0X5iLEDapHL\nly/X93zgX3b5hVvyiGipIKOgpVoelbxrKtV8jeXxVA68Pp+V9LcePW5cO0XB2VGehw/SeDo6Ku2M\nfjw6KnUcDBCQN7Vbk7o0/X30aIeupYtoE3b/LlNcMzgyp3VwUFy2z5xJtA1oL0UH44M2cz24P9C+\nigrOU9UqGjp2WYdcIc8GDVCepyaZxgH/m5mt5P5jepNx1gEXnrvQyI/Lc/X5QlEA+QblxHRcygpK\nTaTB7vCoNwfProOR5nyYwsVT9nD7ou2YfgOAfHCbzLI8LUZJP4x6RH1SZZmYFMU4zNcGOUjq5KD0\n1SSndXo16dxBv8zX924nChDuvzpoa9ZvByTbz13IlBRLbWpGT51h1qamY90xm+RAvlb6b+rWRmsr\nTEOX0qj78+6d+t7KqVTv8ZSoQbcyFe5+6hem0wDVBNqeddR03gzqzXmiv3ktgnIpesC9HdAYtOml\n8NhgUMb06mozDdbNNWUe6blTp5oB7hXdBVNm1H1TpXZa3ygUgMiznjO3i45S1NS9LEdLWe75+Xv3\nHzSe53kUaY0npay3bqc10s72RqM+/LyipEGZmR6t0KtmaiSm/cptaLkPxmOmiW7SW5ppKplA4PPG\nZDK1e/fuyTm5QfeWKQV7/UzxO2c9n2nUM3Ut07xg7Ejar+223oYe2dlr7gPMzNbWN1rP33+QqKCw\nlmYqHqxnFnk+XdDaff+gTVODdzfPnH1imfu9pCd5PYQ9oKL1QX14rV+omqtczqILPTWhmdl4DDrE\n9DxHEwDl6fqpNsWtos5Gn65tbOa82/ulU5tlT4u1AdLiNQnW+livNAKX53nnPs2VWPOcO7PZKlfd\nPvN2APoZ5sj5rPW8p5g0a+ttRWf74F6b+n1vmuTx470iE/W6q0/00KupbNOjJlWQmdl6Xgf1qvTM\ngVgr8ZpqMc5nEyIt1Emt4bBu4Lp52mOWOcyDuMbrDqxJeOygXdE2TLm2n/fH6E+WIcz5o6VSLqSB\n9TOvqbHO5rU31smoB6eFdwe99r4EdVLUoIpqDzKGevBZDuqhaNhUeAK8izbc2iprHyyNeJxXVaob\nzjd4bYH0QZPH4wr6cHm56FikdXyMOnLYhH6+tp3fK2khT95zok7Yl44uFb2C55gGf3mU2vNMPpNr\n0MNNs25aw760tK8Ks7CZzwMLnW1JC5Tn0DkNanJxbgE9hbQ4b5wn+P0Z58nj6s6nnzXS4jMN/B7l\nUC3PnStrfTzPeq6/1qROmxwT3dthGpusy7AOXM7pj5ZKe6Gsnh6d0+B9uKcY5zUmdAvmK76HNmd9\n4ik4WUaxJq7PTBZlPOIcjfsK520fvJ/oOR89LOehCNWAc7vppLSNp2flcg2zzuD64/wQ9bhHc5k6\nz13F/goXKJ9//Nu/bWbNOR8ys5LLwPs+3BtnesMt2r9DPliX+fZtUODls9rTm0mfXH6+0OW+/fbb\nZqYpSFHWAcnQPNM9j/NaZED7xfMX0lmGon5HezWp9przLL+H53hd40MvcF8hH26TWkfuNyk/zYgi\nM8vJ2fWiayHT3IaH2yl9zDGrVI9Tm6m9DoZJP/D5/P5RnhdJflHvw6OdfK/sL/uD5pkMf0sZDhHK\nqL7UotlUdVQ0zFVe2/ZoHuV3nwXhqRUIBAKBQCAQCAQCgUAgEAgEAoFAIBA48aj4q/jvF85fOLf4\n9q/90ca1n7RceB5f9vnLKH7zV3sVsBDAl8TxXvrC2QwOvNYqH7724hpb/MDaTwUi9FZaZuWrL74I\n8/P46vlod7t1D5BfPzuCwMPziK2RYaHAlgP4uoy6sbUVrJ/YWsVbSqhyAWzVhTRu3bpVX0O5cY/L\nhbarvZOoXJCFb3zzm61rPuAsA/2NIIpmxQqKrWHwxRyW2WwFBusZ1I2tPtG311+6Xl+DNR4s2eGd\nxmXm/kb7ok04fW9hrqwKVGBEFazYB+VkS3ZvocB1w1d/Hgve0kB5DnJaXV/0a2uj/FcFE+T2QhrK\nYkR5RPngoGy9geeVlTue47zxG/eUFT0sedhzBVZakC+zIk+QUW5fH6Sa64N6czuhn5E+1wNlRlrs\naQgdyJZYGH9IU1nOok0fPy5jFHLP+hoyhGtsWaUC+aJNoKNYhlA3BAJm+S0evKU/NjebFmgsoz64\nuAryy/XAuIOFmJITH+jTrOgOrve5c+caZeiyZlOyrcYVyqosKLm/ff9xGyLwONJnqy60L1sIeW83\nnvu8NTjrcmXx4z18GpguGs8r+eL+9Fba3C8lEGq7fQFuQ9Rb6a3jSaqHGo9+buI8Aa6/17VmzX42\n0zrW6wkzs0/vf9Z6HuVBPVgmII8qEPndu3fNrGm1C7lA2/A4xryJPm4ELj/utcqF9vc6iuumAvqq\n4PIoD/4qqzCly+HhwZ5gkN/ifVrGL/Q6l8evZ7hcaHOkyWMOz/Gcj98Iuq3GdB1knaz5UG/lIY17\nLIOoL/rqWDAJMJDuX/pLf/37i8XiG60HAoHPARcunFt8+9t/TO6JeEzXVshZb/E6xa+b1Jjg8Yvx\nreZrjNfBsBlsnn+rMY2/rO+85z2PR4xR9uT0e0fWBagvPLW4jmoNjTGNejc8cnO5lKc00lJzGe7x\n87UH+qjU2zM1YM4xK+tflAdrLLOy3rh48WKrPtBbvFfDGhFtzvKCcj1/vnjge8971nt+zm8ErM/P\ns5x4nanmMpRH7imW2vMv6qH6ioH2R55qzeP3mVwergfata7PoC1LyuNOnddgLoMcs/x6Vg11RsH1\n9l54zLTi51FOCzKhxjTqym2K9Lk/ffuyXPkzGZZHP18z1LkV9lBYY/F6EGVl/ePPPJS8Q754r6Y8\nBdCG2L/yGhFtgvGo1s18toZ6o8xKvrCHwj7NTPcfdDLanteiqC/LIfrb6yiz0r6oG48FtBOPTb9u\n4vZGubD+5TZB+vA4TPVMeh37V+UNjPSVLldy4s8v+Dnc47Ul6qNk258d+XIAeE6tZwHFwKXGlWcr\nYf2A9lW6Fmkpr0XJ7pP72Z8dcVk5LYw75fGM8ae8gdX5LM4e0f98dnDjoxuNuinPV9YdmDchXzzv\nqnNNnD2qsxbU0c9NZmbf++53W9cgRxh/3CZ+PcN9fPXq1cY9rpuCP2fm9kUavH5AuSAv6owC44V1\nFM47mixx7TMZXy4G+vts9hhVDFT+m4KZZmvx51QsC6ib/6bA11gHor+H623GOUCdW6HNWTf58xql\nE7qgnv/L/+PfeKY9ZHhqBQKBQCAQCAQCgUAgEAgEAoFAIBAIBE484qNWIBAIBAKBQCAQCAQCgUAg\nEAgEAoFA4MRj8PRHvngM+n3b3NyUFDPK/VDRnsGlEpQD7FbnqcTM2gEelRve6iDdY/ovuAFz3nBl\nhEueCszr6bnMiKKCXCXh3gf3WX4eLokrp9ZbZYCbYpdrP7s5wmXwzTffNA/cYzd2XEP6TLuD9mL3\nRkC5CPsgcuzminvs8undOpWrt3cR53tc1kuXUjBCyBDTVqDf4KbKlADf+c53GvfMOFByksueCLoH\nN2AuF+SF5QplhHyxOyz6jV3CPX2VylvRjyi5wm/Io6IMBLiP8RyXy1OacR3RTqgrj3fIsnLL9lRM\nDEUX5elaOK06YDLVA+OK00e6Kn1PnwiZMisUEKxrECgXeojLBblFW6qgyIq6UlEf1norp8V9jHqw\n67mnBGCqAnYl5mfMSp9yPdAWXbQ+GNOjUSmzolYB0M5cLuSjZBRyxW2P9PG8r5cH+gi6nMeVT5/b\nBO9x+shT0RnhGvqM84GMss7w41bR+ijaDrSrogRQ1IcoB1NgePoCnnfwW1G7eeoiLhs/B0Cu8Dy3\nZRcllKLyW1pOcg4d/jDrVVUfM7ORm7tYGmegmsj/D2gMQd55vvL0I1yPnb1mHbnfC+0mj51R4zme\nk9GW29tlPPlx2KQHTO0znaZ+GQxKLc+fu2Aenm6GA6MfH6a69atmUHczs5UcZH5/t8y7Nk/9ALna\nWCsBcOeZKnKpv9eog5nZ9nHWmfMy1noVKABTPQ72y/phL1Mzozys55dHTcoUM7PpJFNITXN9aJx4\nasJ0H/XN9BtVaRPIO8YE12Ntpa3fkRd0uKKHXhm1qTLRH3s7pd7bW4neEDKtxmEX/aIa0576kt9V\nVBgol6LmCAS+SFRVZUtLS3IMNcdvk2aI5yb8VsHpFfWsp1IM36woAAAgAElEQVRS97YzFavSK6vL\nRTfjvqJg6mW9jbUijymMP9zjuqHMau6f5ED1Tb3SrqOn1unzOqXWp+mZ3Z2y91TzW6ETz2s+0ulV\nL+uVlbLWxbsoM88Z03F698H9NK/s75e5BvPPglRPvZbM81bPSpuc3kjrbLVXBeZzpn5H280bf83M\n9vZSG2DO5/pjPXj2bHsPqagP0fZIk+eTmtZ+raxh/JkG9yPWmbwWQY38+sbMrA9aLtEWW5lKl/fH\naDOsrSbzkrenaFPnPApoG1DbmbWpO3n+Qbo8l4FeC+sm3r/79RnXB9Tvij4KZyAq1AGvQT2NuqKW\nVPTuqBuf5fgwAIpq77PPPmv8z3kr2nlFNYd8OG+PJbH+RVosX+g/9Dv3laeJ4zQAHjue3v3s2aIL\n6nAh4mxN0SSjLVjH8nkL58PPqTnAzxlmpU9RR64X9DTo2Hg9qM4moDMwfnm/j/KgbfgcAnny+Rbe\nVSFBPG05t40KJeHpaBX9oNKjPiwJP6f0gzqvQrlRHs7n8uXLjXxYHjFeeXz4eYrTwvO4ptbIvB/z\ndPBqb6fCUyAf7lsA8sEyBx3G+gdQVMigzMM5FJcZ55qvvfZafQ17NJSfZRvnWyg/0jQze+WVV1pl\n9VTmrIeQvtJR0GV8zVNHK52mwspwWwCeQlbpLa8TzIp8sYziXRUiAeNdlRXzDY93yBXajeuBs2GW\nUU+vynljfECXM8WkWhv60AB8PuS/baizUp6n8TxCEbBMeBpQFX5IUVM/K8JTKxAIBAKBQCAQCAQC\ngUAgEAgEAoFAIHDicSI8tSbTqd27d6/TksWsHYCdvzjjN77wqi/obH2Cr6T4isvPw2rmOFt/ndos\nVjT4AslfS2tLPVh0TMtXSZT1yrWrjXzNytdLtvLGF118JZ7OybtsLX15xZdqLrMKHo4vx/h6D+sj\nrje+snIgTXxxVV4KKpg73mXrfm/NpKwjlMUI+pjbxFs+sEWDDwj64osv1vdefvllMzM7JiuPGzdS\nkMXbt2+bmbYkhmUDf8VGm3A7+S/O/DxkAu38iDwF0IanN4pcwaIG7cZtgrTY6sZ7c/A9vOsDavI1\n5R2I/uC+9UFu1Vd1rre34OF6d8kQ5JbL5S1FlMeOCtiprP+gA1BWtsRCm7O1hrfQ67I+Zys75M0y\nirGM+ijrwrvCohdWHjzOfZrKkxPXuK9QN2V5qIKAo62Vd5yyKPOWdMo6ugQVLn2MejzO1p/8HNLk\nfoEMqWCyALcXyuO9esxKf/Acc3Cw07jH8uhlTVlms1UQZEDpax8kldtLBYruCu6MdoJcKq9jbiPo\nChXwGnVUQT9RLtW3aFceV9DhPD5g2YUy8LwA1F4qlJZaD6B9vJVhupbbcJzahq2u7j1MeU9obkUZ\n6wDZD8r4RXnmi7Y1dTXIv/tFX///7L1ZsJ3Xdee3zh2Bi4kgwQkEQRAEZxISNVmTI0p2Ik8tTx27\nu9PdcTtV/RJXnlJJpTpVyYsfXJ1UynlwV7eTVFceHDtJy/FIWbYmq2XJojgPIAmABEEQHIThggAu\n7nhOHvb3+/b/W9/iwQV5pSDyWlWoe3C+7+y99tprrz2t/1qjiUH418xsdnNZP1xaKvp+Sfrq0tJK\np63atm3NukNluavpB/XY87Y1Qr4ug2ycr2289rodvbZ5j+/IBvKd6gRJjnVO8gm41auUNj333HNm\nZnbkyJH22fRU6Uf1GsNTETmprcWO8p2OG8aJ2kA/pnUeiRJEV5vR11FkTZ06h+vaCKKvkEXkVYrs\ntR8Zh9ofLWqk0WlFKYwa9tca/V1aEa/Xpe5aQT9HdaMTEbJ6ohkL0zIWpgIEblLSRtNgMGGzmzd1\nxmrrkS92fnG56/kdefdjyyI7Ea2fWlu4JvaxqZM5UMeJ90zXz4ztKPoBdqWLvi2ktoN2RF7hrMeX\nl/se85Gn/LiIEHzGVqmtpW61e6xneU/3Bqz/vn+qen7jPb53795eu9m/0X/6DG9yjQCCTOirqD90\nDoN4/8zpc733p5o1xqTOGU10l7mt23t8VZRcXTeCTofXhcW+x3jUxq3NOkLXFr31hthh1in6Pm0b\nNnr71ql6PsDain7XiBuRB/v2baVts3ONF/1yP9F7lPydtkWIPkjrhi/6WNcRHtWin1mXHz9+vH1G\nP/BMz0c4p4n4Yn2t+58Ise+923Vc8V2E+IhQTBHy0b+PDdD1cLQO9ONc6/H7mLm57e/6O/0cRarw\nexbli/Ij++PReGY1agnnMJs29dsTIcgq2rGukaPILPRpO7ZFXsg3OleI9on+fdWT6LwNQl56vsB6\nFjlpWT4KlD6LECJ+rxWhmiGVjUccKq9+DClpf7R7jyDSiD9/iiLGaP/5SE9RVJiojYxH5StC2nn+\n6eMI1aLf+bHQidTQ1I0tU/kie+0r5kZkoUjsc000CtofRf6JIjVEEc747auvvtp7Pzp3Yp6+5ZZb\nzMzsQx/6UPtstin3lVdeab+jXMrUvoLXqN/5LjqT4rvoLJK9oe4Xsevax8gOnQaBpXVHUbNoT4Sm\nZEzrOBmHbLK1fhunGrz0zmtLWdq3zGXabtoLj9HabW5rkYXeY0RoYD6zHx8XjU3tF99p/7H/xm7r\nM49OV73nuwiRul5KpFZSUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlLSVU9XBVJrYmLCtmzZ0rld5fZT\nPVO8d12EzuAGUeNHRnmwiLXMrareDOKls6XJZ6BeCNxm6g09ZcC/3kZzY37y5Ekz695+clusvHrP\nmshbnbxh6gnNbXTkTR2hAfB4UY9AKIq3Sd3R7T084z1n1r+NVdlzE4y8Iq9E7Q9ux2kjt79mZgcO\nHOh8p15azzzzTPlOvDzwHNyzpyDn6BdtLx6C6u1AH6ku+HjYqr8esaM36Nxsnz9XeaWvat6hKmcv\nS8+b8q6fIw+QyFPRe6no/z3qS3mIPJ6853uECIOfyIsm8mryqBbl0cftfre6+czvojx5kQciPKhM\nvGdYFPtckUc8Z8ypNx9tYizreNcYtVDr0dqMhShWuNclsxjh5G1M5IVLH6veRwgZX4bKxMfRXl7W\nHATvnosqyi8R5VaCD3hUG0tfRTk0/Dgxq0jPKB+S5yEaj+pRBd9RXjb4ihBIlK/xqj0aS8ukn+kD\nHSfYH+0PdIgxp7rNZx2H/Bad0/J5L7Lzkb7736md855xKt/Ik87LtYsqLGXs2FHWA3NzdY6JPGHf\nfvtUp8wuerjrSbi4qIgaciHW8U7b+Nu1gaD2iP2t8fkbz+mh5ktb7Ty7dKk+w7vsxhurl5nPFane\nx37dpPKaP1fWPNp/8B/1B/VEMczxho6QrxFKlzkZj3x9dvilgtpaW6t8TU6W5zt3lnl369a63qLb\nmK+Vr/n5fkxy75mrzuRra++OUINHHe/oMnKO9DeKre5tQWlTd7xHXttKbX6uC+d7dUORx+k428R4\njPIp8t24PKJmMYoyKWmjaWQjW1tb68xNkZ1nXER5MnzehygnSDRXMl60LMbF9q3ber/z9ZnVscN4\njPJQRvuAKPeCR3NEHrRRdAna27UJzBV9W+hR/HNzdc3A/lqRNLS3ek4r0r3021133dV+d+utt3b4\n0r3a4cOHO2Vpm0E3qG32eS8iRG6d3+tat7XRw/6cEXm+M5f53CNm1Xa++Wbd7/ucP5F+TTT5xlg7\n6OfBoPYVa5HFxTO9doxbN9Pfy8v1/cXF+R4/0M6dxTtc7XzV5b5MkBfvR+tB1TnfHzoWPNpL92ys\nOxQB6JFg0f4dvnTNG+Uo8egqPYdgLa06x3tR7nCfbzbKoa3y9fmcVA68hw1RuxXtY3ykIx07nGWg\nx2++Wc+YIlSLR29F+6XIkz9C//u8YVqW39vOzPT3qtpuH5ElmhdUF3gvQsp6JFG0h45ydPs1slm1\nVzxTHWqjRMjZATKI9lLsndF31fuoP3xEFtWh6JzD86XzlY/UcLmoJb7/tB4/90WRbJR87lrtd/Y9\nEbI4mt/8/jVCU0b/j8ry+6pIlm3UD6mH9qvt8Kgk3b97RJC2kTK0r/gt84/2C3aLqFbKPxGrtB1E\n4aDdjz32WPtsWzNmNG/hhz/8YTOrOk2kLP2MrdEoWyDBougVtCeKSMN3upeK5mJPUR5R7KraYc+D\nti3KoUd/K7KJ8/s3Vsp5ueoC6zPGcrRO0TNC7Br9orYJfqJIVNE5EjLjfe1H9Ioy9FyBvbaeV7L+\ni+5qfLQPXSsgCz3DG5ffMaJEaiUlJSUlJSUlJSUlJSUlJSUlJSUlJSUlJSVd9ZSXWklJSUlJSUlJ\nSUlJSUlJSUlJSUlJSUlJSUlXPV0V4QdtNLLV1dUOBPuNN94wsy70DNgkkDmF4gIXJmQB8DezCvFV\nCDJwQ6CDCo8Dgj1/qoTk0SSFwOMUmge0EH4UOgc0jzIUhtjWI3Bj2gZEUWGUQCyRk4ZjAHarsFOg\npfClyfCAd+7fv7/Dp1mVvfaHD5MQhYJTKLUPNRcl4QUOq+2gXxSyDfwTiKzCQdEJoLIKxaX9NzSy\nNOuGNdQyzfrQRw0neN9995lZFz6LzgHNVCgn8vJJXJUmB33oJ3+jxHqadJnP8KxwUN8ehdbSb1FI\nJcaA6jbf+XAJWobC2H0CTYXKeuh5N8RXP/Gmfz9KyhmFdYqSvbZJwxuedYxGcHwfIjGCFNNXUbjG\nKPyGtzlm1S4Q8pRwmmZVJlo+v+V9JaD08B61UWlcgmzejyDbURgRvovCGHgo/ZkzNaRFFIrSQ+4V\nno3NBFJu1u9TtU2UG9VDu6OEzzxT2fDZJ/fWOnVcIRN4Vp3w9jQK66PfYbf4Lkr8zDPts2is7du3\nz8xiPcGe6vt+nGtoDuYzZKIhaEkYS/vN+vLVsnxYH20j/IwLG9QJTTNowmA240vbw3yguoB8ab/W\nTfjaKNH5kKTFGrKI5MlN36oODVebcJiDUv62rVU2LetB4tSV5VLmpYVqO9aashYu1rqpa36+9Mfy\nkoZKKfqHDFeWZYw2+rgs4Q35HI1pn7hcdZuQU9sk5CNyPfla0Y+XDx9tn6ED/NW+2rZlrmljXYMd\nPfySmZm9ebKUpXaI325tEtZPTYwPvYsNR25qa9rQCWsyVw6bubIJs3Rewthawwf6rmXpGhJifdEm\nz1b5NrrTzjWaBN2trcxqn27bsb1TplnfxujvosTznrTffXi0KGm8rnWuNMlvUtJ7oeHa0C5cuNDR\nVR9q0KzqO+Nd7VYUirNXTxD6z68Htdzhcn9t6UN2mZmtse9pxk5Uz5Zm7ozCsWyXeZQ2RuuUUVtu\nE35wSvYg0/2QjD5ss+5LVie6aypdR2EzX3/tePudDxEU2fnvS2i+t06W8MvYTrWh1MU+SOuG1+Ov\nvNp+50PC6pzs5ze1WW1Y5dkaGmpqssiHPtU59uyZIvPRcNDhxayGFNLQ563uzPRDSfm194SGGjxf\n1mwTU1WvqAv+wzDyoodLzXusU3S/y1yBXDU0VhTCfaXR23Y9N+rvpeAh2mdF7fZ7T7OqA+x3da6B\nH9UFzjmYk/VMw4fAUzsRhSTkO/jSswR41NBejBXW16q/vk4d7z6sp5mEM236IQpBRft1Pc8zlTnr\npagsH9JNn1FGFA7Sh3nXspBbFCZOxxp2gfKjPSFjaPfuer7HWFb7y2/HretUzv78RcvicxQyke9U\n5j4Fg+oE7yEbXbvCq8qJUKrUqfsl6o5C6dIPOnaoK9pzQn7saT2q79QV7dV8aEItL9qH+7DYKnvG\nn8oJ+UZnM9gDeIjm0XHnTjpOOGOJeB4XvjhK0cI8Rd16Xhed5VAGPHfC8C109xI6Rhlrasv9ukbt\nEOe/6JlZ1VFStKi8dP43M7vjjjvaz+y1VU84r0I22GOzenbAO6SLMevvy7RNtFfPm/15lbaZ82X9\njn6jHpUh55+cl+tdAue/es7Beblf3yhf2n9ez/UsC/nSHzp+tb0Q8xnn93q/QDtoq/Y7Oqd9i225\nZtd1vbrpI3hXmURhBBkfyEbrRk5RqosoDc169qhKidRKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpK\nuurpqkBqjUYjW1lZCZOk6u0fN4ncbOqtL7eSNRnrm+0zbj2jBPeQoidab57VwgOe7WZmd955p5l1\nvc+5maZOvXnn5hXklXpa+MSjZv0ki4qgwrvlYpOoXr0j8ARQNA91eW8a5fX55583s+5NqnqWQB79\not4IeFFookqf1Fjf51Y8SrhK+epRxXeRp5f34Nc2UrfKUPvNrNvv6AfeAdqPR44c6dVNWfxO+4O6\n6QNFl7XII/G8817UEaKk4/HflI++oxtafuStEiVV9Ukvtf99Ak1tv0/wqeX7xKBR3etNWM9f9abw\n3kaRl52OK8qIEhdG73skmL7vvXoi1KKS92xUTw4+M/6UB0hljt3ir5aFNwhyVu+xCFHhE8dGXvXI\nXGWPN0WEnKN8tXM+Affbb1c7gU5HSYThWW11NJ68N2KE9os8yiIPEzxx8HhRjyfeixK0RglzfcJN\n9XZFn+h3HaMRiom+ZMypTDxiMPQ0lja2XuSBJ3uUqB69oh6dY5APc7KiPPHIUbtFO5gr1Isce+rt\nvVnf00/f47uOV56Vz8hXxyX83CgIXvQJvnReYH6PvLroh8izEX3voHQbD25+p7Lxnv9aZ+SxFyWW\n9l5NOt/5cdtBB64UOUVoduSqY9QnII8S+qouMMfj4aXrDX6LnHX8btlS5DQzU2WCXM+eLfPz+fPV\nYxy5VhnWfpmexm63X9nqKgnYS9siNJrKBJlNT2/qvKPtiOwKfOl3rJui9QbrMz/uzaptipLSD6b7\nKGWejWtPhMj1HrFmVYc8SvJy5Scl/SBpZCXah+q9RwWb9fVWxzu6GnmGRhEnGFeRh32757L+GhE7\np3scbCvzEGhXs2pHIwRDtJ71NlltVEVeFRs6HNbxyeeVlT6KqSJq2ketPa3zQ50DLl4s87ruJZBv\nZB8Hg8K/rgf82lsTkfu9re7xsKPRWtz3mVm1rR7Fp+9PTfcR5ZQfRZOBhwgpECHXaUcHCef2hFES\neJuo/e7Xv5dD2fhoAdpu5muVOcR5iuqvR8YMrB95wEcU0O8idDrvKSKMPoIv9RinbYrqoB0e1aLt\nQA4qr2hM08ZoL+X3OPrcr+H0PXRI1/q8p7pAneiOzv2s/yIEJOVGa0qPyjLrRwPaubOu9aP9le/b\naM3j61O+dLwjk7rG6o9R+lERCdSj7fa2T3lGPyI7H62zvR1R/fUIUC3Xn52Y9ZEIEYJX5UQ/0I4I\nTRmdnUR7QR+ZRdsPH8w/+ozfqf76My8dO/Cj5w/+nEbXsy2K1CG29HO0p4/k69GUHTS0Oxcz60cm\niZCGUZSiSwGi2vMfIYMYv1EEGH3frzc6EY9G3chFyjP9onsJb0fVnoKk0fFEneie6qNHC+m8uznQ\naX/WF0VaidZp8BNFsGH9pGMUW0mZql/MFWqb+cw+VOs5duyYmZkdOnTIzLq2OZoPd+/e3Wmb7m1B\n5Okenb7Zsqm0W5Fa2HV/FmJW5RSta5jLtG9ZN7KW/MQnPtE+473Dhw+339Hut88U/lQnfL9H85ye\npyCnT37yk502m1WUW4TWjezWeiIoKCVSKykpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKemqp6sCqbW2\ntmbnz5/veN9zy6i3pD7Opt5YchPIDaTe9EVepdz2Rggfbku3NXWvDGs9Tz/3rJl1byrhA/7Vk50b\nS24nTxyu+aOoU9/n1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UNGyEEfGenYsWPts3ZODu4ZaL/qC321tdFRtfPInj26PoNHHWvI\njHGlZ568R9/qWODsQHVBdXM9lEitpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKSkpKue8lIrKSkpKSkp\nKSkpKSkpKSkpKSkpKSkpKSkp6aqnqyL84MrKqr355pudUDzALhV6BgwSyKtCd4Gw8Z1CDQmR8/zz\nz7ffAbXzcGAzCffWwPyulWdAV5VXygIyqdA5ygIC2Une28DwtgisF36mgbgLvHOpgRdvdmGXtFyt\nG6grPCtk3ZPCHIFDRuFwaKNCJuFDkxl6uSpElrLoxyj8oMIbPZRYw3jVxKGlbaoT9Ms10n8+RIOG\nYHr11RJ+glAAWhZ1K3TZh99TWC/1REliWyjy9hryxydgV/lGsH9kggwVBu0TtivslHK1fJ80Uevx\nyYojOLuSh8hGoSA8n1qWvu8TMiuE14eK1HFFX0Ww6ajuKBmyT5KpdcMreqhJEIHZqi5gywghoM/u\nvPNOM6tQ5K9+9au932loSdoNPwcPHmyfYWPQX2D62o4okTrtjsKTRkl+aa+WBT/8VdnDDzb69Olq\n09BbtQ8+3OaLL77YPouSZhPSK9JRb680BAY2Q8e0z60eweXR0cgGqp5QVxTKhO/4neoE7Vc758tS\neSFX2qG6zZjTkACEHKC/1XYAG1dIuA8ZoaFSohBoEHLSce9DU6ieaPgM3w5I5Quv2BydD3c24XOo\nT+cYIPrzEr5gkfAmzV+1mYQkmbAm5I2E3Zme7CZbN+vr4bIo1bnT3WTQUbisiUmxVcPy24UmPNHK\nUtXfCSv9NhjJPNV8PteE5uOvWZUddmX/vppYer4JO3huvvbthfOlTp+43azalq1bSqjA63dVHSJs\nkOo0+huFCKXd6PTZM5IUeWsTJkLk6+fDJQmN1Ib2DeyQ2vf3SxMT/YTaS4sLnb86NpDPjTfU9Sxj\nOQpbzHcRz3H4subZdH/OZ35jfGl4DMb0/Km6DvKhD3VuRc/pY7UTUTjeqG1JSRtOw5ENl5dsVfQ+\nGl8+wXkU/gk91vHFs9tvrWH+CJdE2OYo3BLhZpXGjV++i8J2RuOL71aW61rk3MpS53ebN9V1x7bG\nng6H/bX7e6XWDgf7UbXNE4SdaWyHzpnM02r74fueu+9seL6jfUbfMqfp2pWyNDSShs4yM3vrrbfa\nz6wfWSN0Et03f3WuRIdOnij7RV0Heh50bl5cKGuweVk/tba1meenJXoUfbS8eLHz+/Ks9PuO6+p8\n0oYva+aAaVlH+LW+WV3PWFPP22/WeQH9jfaL7LlvubnKhDUCc/7Zc3W9xRwchaunHVHoKearaN9L\nH0R7bm0jesFffca6l9CHulagfA2TxrqZtYzuG2i/foeOUqeGcPQhA3Wtjyy0bX7drDrq9zaRvPRs\nxq+ftCy/v5ierjaNdXy0h2JPoPsMyqKPo3DturejH6hbZenr0XNB3otCwUX6y3pe9wTIf1xYrmi/\nSJtUr7AtzA+a1gB9IjSd6hw86BnI5z73OTOrsj98uIaOhh/aE4WZ1bMJiPeilArYI7Wn9FV0rglF\n603VE59WQ+umb9iXqSyjszXVMS3TrPZHlGYjIr8PU9uEfMedmUWhDJGTluVTPUTnVSpfykJu2h/z\n78x3ylfZRGEXec+H0TSreqV7b/hgPGmqB/bc0ZxPv6husDZq5wXZc/vQkjoWeE/HIXzRL3pmT7/D\ng9q7cWH7KF/7Cv2lD6LxrraM8Y0soxRAKhPa+Wpzrqft5tx/OzZD+rENvSo6zdkCPKhe0aYtY0LM\n69mMDymqZXE2Tp9pWaR10jCNPv2Djh36Ebul52L0m+qJymc9lDvOpKSkpKSkpKSkpKSkpKSkpKSk\npKSkpKSkpKueLovUGgwG/5uZ/ZyZvT0ajR5wz/5LM/uXZnb9aDQ6NSjXc79tZj9jZgtm9muj0ejx\ny9WxtrbauZkzq7eGL7/8cmXW3fCptwY32aAh1MOGG9oHHqjsc+uJ943eCIOa4IZXb/vxwlCED7ed\n3ovVrN7y4rWhZfnbe7N6Aw7PekPPrSdJX/W2mM9668kNp0dZmdUbdlAgeosdJXT1Ho7ajgg9QR/h\nfaNeMciQW3W97Y+8DygDGWq7+S3eJ8oDslevPI8WUq9ldIcy9TaaPlVeffJL1SHvYR2hNNS7pU3u\n29yOqzcKt9yRJwfeFOolSh/xV2UJqfdB9crqetPoZ/jSG3f413Z7z2yVA3KKvHXoN2037Y284Hgv\n4st75ymv1KNeXZSheuWThKo3G2OBdmv7f/mXf9nMqi6ZmX3ta1/rtCdKYsn7eD2Ymd13331m1k04\nStJsdHoqQDBQfoT2U/3lMzxoX3kPSh1XeMiojUWG1BMlLK2eTHUMYqOiRJr0AV4iZlU+OqbRD/pF\nxzRjGI817JK2SW35nj3lPZUTxG/RvShpr45D6vbJVc3qWON99cDznkJmkuDdoSj0WSR7eNX5lfkt\nQrbhsaVl0EfolY4T+h2e1VswSiIcoSgh5qLIkz2aD+n3KGHuxKD8dnam6NJ119Z+t1HxMN55TfWO\npb30mcoeb1LGk3p1tWi3nXXOp0+9bMzMdsxNd8rQMYp+LF6qutd6+7bJ3OsYPfT802Zmdv/997ff\nfeqTnzGzOleq7fdzZHd+L33VkWEj8wg1MBp10XGrq5XnQ4ee7dRjVsc08t0p8qJOeFV5nTpd9FfH\nldedKGkvXu4Xxbt9HFL9SikaO/ARjdHpqS4azaz2bTQ/D5tk0KNhH2E7Ds2x+E4fMbqpafettxTb\ndtMN1/XaoWOaeRaZa0J1dJS6IwSo2kWzPhol6e8e/aD3kYPByKYmRmYT1T5u3150Wr3V/Zysa0rm\n99YjVuZrbKeOOY8oX7pU5z7s+qZgnotQmHxuvwtApZs2l3G8tlbtCnNMlOi8RecrMmbU33u8X8J+\n6ZphZrbY+w4yyO0NdI31zvliY6Ymq43G/uzYUfpRPYdZs779dlnrRVE18BIvPBbemDtuvrk+43M0\n31Hn5s113eHXYmo7mWNYg37ve99rnz33XJmvVR9XV4quXWrWSjqfwOv2bUVHozXy1Kaqv8gLvdR5\nFISL7tvXVpc7fx+4/972GfqLfVe+2AvrmqpFOjQoQZWJP39Y7xoR/b311ooIa5GJIAGD/biuEeHD\ny0bLYr7SsqIoJLQbG6L7Mr9/1zLadV2wd472l0QU0jUPET2iyCQeuaBzciQn1s20I2prjS7yZvsd\n/OgeCpmzdte6WYPyO92zgfTXvZ3fE6k98WcgOhYgtQF+ravrJ/b+iuSk3AhlxHs8U11ljOo+HP2D\nH62HfRjtP3DgQPuMfZlGlOJcgDJU9j4KyWc/+9n22Z49BVH8ne98p/3OI5f1DMSjChU5iCx17Pjo\nNtF+VO0PRBl6tgYxDrUfPfpJy6UfonMeeNC+Qs+js84omopHBkVjW/Wd97ATeg4Mz7RNbSDfqU4j\nV48eN4vHB8Q4j9Ca/q/Wo2t22uTPac3qmIa0HVEUM+SELHRO5rfMlXqe1CK35dyCMYnuaCQibB8y\n0THnI+yY9c+3osg01K18oWu6B+W76Iw42tvB42zT37pGQl7+HkDrUX3nM/N7hLxqEVuy1gVVxdm6\nWT1bo86jR4/26tm/f7+ZdeWFnPTMk3KxQ6qr8Prss892+DOrclVbczm0paf1ILX+rZn9lP9yMBjc\namb/oZkdl69/2szubP79czP7V1fETVJSUlJSUlJSUlJSUtKPAv1by31kUlJSUlJSUlJSUtIG02WR\nWqPR6K8Hg8G+4NH/ZGb/lZn9kXz382b2v4/KNeN3BoPBNYPB4ObRaPRG8PuWVlZX7e233+7cbPuY\nl9F3GjeT2z88DPQWl5tmvXnkpvGee+7p1cONIzeQ0c2r3iRye83tp8aWxOPlueeeM7PuzSu8drxq\nXSxVfZ/b27ebG3T9XXR7j2cBN51648kzbkb15hmetY3IwN8kK4+ac4ObcLyZNGYrniXc/uqtLzfn\nu3fv7pUfed1w+wyvURvV0wBUBp4TWha3y1F+Lt5XDzSfnyvynqKNeksOvTNfvSM8kiaKC679TX/R\n3+oF5RFRGntWb/khH0858maDv8jzJfJWj/KAIV/4Uu+QyJsAPvCwiOIQg9ZUD4UIvcVz5AbiSflR\nj0AfT1fbiD5F8c2/+MUvmpnZ3r172+8+//nPm5nZoUOHzKyLPsVzBZvx8MMPt8+wb0888UT7HXXi\nAaF141Ef5QhDlhECiTGnNpN+RK/Uw4axqV4ofGb8RXYrQuAgS30fncamaxvxclV0Ff2NF5uOW3QA\nW65jiLLU/rz11usdHtRDzHvPqKcbc1c0Fhhzqu/wde+9xTtWxxy8avl33FHySdDHOvd5RKaOdz7r\n3ErdyCuKda/jiT6K7K8fA9oOyo/yTUUoaHiNYvd7DzyzKs8oz9jF+aK3yFB1e8e2hueV6kmIns+f\nKfNUx6vpuiK706eLbObnxSN/oUEWz4mH+WyjOxNFTtOTVV6nmhxX2Ie77/5o+4z5U9vh8yVEeUS1\nP+iHk6+XM2K1mbzX5nNb7Hv6Ddf63ojYE/W89PZdebh1T7EPY/OMSQ4YclC1Xpxb6xx16VIpd0oQ\nGMPVLipU7Vyby2Sy1L0mv5ucCOAP75FAqm2aqX1FeyM71yLQF/vx1j2i3kzQHBN4wvb7Ksq3de21\nfRtY0XSlvoGgp8hzg7e+mdnCxdI28qtFuQTanHgzkm92ruv1ala9Kv/nf/Xverwm/d2hH/Q+ctu2\nbfbZh/+DzlofW6Pzu89vM3+26vbipTLvtPltZQyB/B2ZrH9Xu3tBEDVmFbm7dKmfbynKpeWRGxH6\n9J35Ln9m1d5Nzdb5x9sftQVtfpQNtIXk8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clRmADky3sKKYYf+k9hpxEEG6I/7r777t77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\nEfu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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff216566ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import random\n", "import numpy as np\n", "%matplotlib inline\n", "\n", "def show_image_flipped(image):\n", " plt.figure(figsize=(30,15))\n", " plt.subplot(1, 2, 1)\n", " plt.imshow(image)\n", " plt.title('original')\n", " plt.subplots_adjust(hspace=0.5)\n", " \n", " plt.subplot(1, 2, 2)\n", " measurement = 0 # not needed for display\n", " flipped_image, measurement = flip_image (image, measurement)\n", " plt.imshow(flipped_image)\n", " plt.title('flipped')\n", " plt.subplots_adjust(hspace=0.5)\n", " \n", " plt.savefig('output/image_flipped.png')\n", " plt.show() \n", " \n", "# show the center image flipped\n", "\n", "name = train_samples[0][1]\n", "print(name)\n", "image = cv2.imread(name)\n", "image = colorCorrect_image(image)\n", "show_image_flipped(image)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cv2\n", "import numpy as np\n", "import sklearn\n", "from sklearn.utils import shuffle\n", "\n", "def generator(samples, batch_size = BATCH_SIZE):\n", " steering_correction_factor = 0.25\n", " num_samples = len(samples)\n", " while 1: # Loop forever so the generator never terminates\n", " shuffle(samples)\n", " for offset in range(0, num_samples, batch_size):\n", " batch_samples = samples[offset:offset+batch_size]\n", "\n", " images = []\n", " angles = []\n", " for batch_sample in batch_samples:\n", " \n", " name = batch_sample[0]\n", " #print(name)\n", " assert (os.path.isfile(name))\n", " \n", " center_image = cv2.imread(name)\n", " center_image = colorCorrect_image(center_image)\n", " images.append(center_image)\n", " center_angle = float(batch_sample[3])\n", " angles.append(center_angle)\n", " \n", " #height, width, channels = center_image.shape\n", " #print (height, width, channels)\n", " \n", " # use left and right images, as well, applying a sterring_correction_factor\n", " # create adjusted steering measurements for the side camera images\n", " left_image = cv2.imread(name)\n", " left_image = colorCorrect_image(left_image)\n", " steering_left_angle = center_angle + steering_correction_factor\n", " images.append(left_image)\n", " angles.append(steering_left_angle)\n", "\n", " right_image = cv2.imread(name)\n", " right_image = colorCorrect_image(right_image)\n", " steering_right_angle = center_angle - steering_correction_factor\n", " images.append(right_image)\n", " angles.append(steering_right_angle)\n", " \n", " flipped_image, flipped_angle = flip_image (center_image, center_angle)\n", " images.append(flipped_image)\n", " angles.append(flipped_angle)\n", "\n", " # trim image to only see section with road\n", " X_train = np.array(images)\n", " y_train = np.array(angles)\n", " yield shuffle(X_train, y_train)\n", "\n", "# compile and train the model using the generator function\n", "train_generator = generator(train_samples, batch_size = BATCH_SIZE)\n", "validation_generator = generator(validation_samples, batch_size = BATCH_SIZE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. ..., 0.9322197 1. 0. ]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff216566e80>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 197. 86. 82. 98. 130. 222. 290. 563. 732. 1135.\n", " 1628. 1092. 347. 361. 114. 84. 45. 28. 24. 81.]\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# Visualizations will be shown in the notebook.\n", "%matplotlib inline\n", "\n", "steering_angles = np.array(samples)[:,3].astype(np.float)\n", "print (steering_angles)\n", "\n", "def plot_histogram(dataset, title, x_label, y_label, save_as_filename = None, color='green', num_bins=100):\n", " (n, bins, patches) = plt.hist(dataset, num_bins, color='green')\n", " plt.title(title)\n", " plt.xlabel(x_label)\n", " plt.ylabel(y_label)\n", " plt.grid(True)\n", " if save_as_filename:\n", " plt.savefig(save_as_filename)\n", " plt.show()\n", " print (n)\n", " \n", "plot_histogram(steering_angles, \"Training Dataset Steering Angles\", \"Steering Angles\", \"Num Images\", \n", " \"output/both_steering_angles_histogram.png\", num_bins=20)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "## Model Architecture" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from keras.models import Sequential\n", "from keras.layers import Convolution2D\n", "from keras.layers import Lambda, Cropping2D, Flatten, Dense, MaxPooling2D, Dropout" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# first attempt\n", "# Preprocess incoming data, centered around zero with small standard deviation \n", "#model = Sequential()\n", "#model.add(Lambda(lambda x: ((x / 255.0) - 0.5), input_shape=(160,320,3))) #normalize the data\n", "#model.add(Flatten())\n", "#model.add(Dense(1))\n", "\n", "#model.summary()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# second attempt - LeNet\n", "#model = Sequential()\n", "#model.add(Lambda(lambda x: ((x / 255.0) - 0.5), input_shape=(160,320,3))) #normalize the data\n", "#model.add(Cropping2D(cropping = ((55,25), (0,0)))) # ((PixelsFromTop. FromBottom), (FromLeft,FromRight))\n", "#model.add(Convolution2D(6,5,5, activation = \"relu\"))\n", "#model.add(MaxPooling2D())\n", "#model.add(Convolution2D(16,5,5, activation = \"relu\"))\n", "#model.add(MaxPooling2D())\n", "#model.add(Flatten())\n", "#model.add(Dense(120))\n", "#model.add(Dropout(0.5))\n", "#model.add(Dense(84))\n", "#model.add(Dropout(0.5))\n", "#model.add(Dense(1))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# third attempt - NVidia\n", "model = Sequential()\n", "model.add(Lambda(lambda x: ((x / 255.0) - 0.5), input_shape = (160,320,3))) #normalize the data\n", "model.add(Cropping2D(cropping = ((65,25), (0,0)))) # ((PixelsFromTop. FromBottom), (FromLeft,FromRight))\n", "model.add(Convolution2D(24,5,5, border_mode = \"valid\", subsample = (2,2), activation = 'relu'))\n", "model.add(Convolution2D(36,5,5, border_mode = \"valid\", subsample = (2,2), activation = 'relu'))\n", "model.add(Convolution2D(48,5,5, border_mode = \"valid\", subsample = (2,2), activation ='relu'))\n", "model.add(Convolution2D(64,3,3, activation = 'relu'))\n", "model.add(Convolution2D(64,3,3, activation = 'relu'))\n", "model.add(Flatten())\n", "model.add(Dense(100))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(50))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(10))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(1))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "lambda_1 (Lambda) (None, 160, 320, 3) 0 lambda_input_1[0][0] \n", "____________________________________________________________________________________________________\n", "cropping2d_1 (Cropping2D) (None, 70, 320, 3) 0 lambda_1[0][0] \n", "____________________________________________________________________________________________________\n", "convolution2d_1 (Convolution2D) (None, 33, 158, 24) 1824 cropping2d_1[0][0] \n", "____________________________________________________________________________________________________\n", "convolution2d_2 (Convolution2D) (None, 15, 77, 36) 21636 convolution2d_1[0][0] \n", "____________________________________________________________________________________________________\n", "convolution2d_3 (Convolution2D) (None, 6, 37, 48) 43248 convolution2d_2[0][0] \n", "____________________________________________________________________________________________________\n", "convolution2d_4 (Convolution2D) (None, 4, 35, 64) 27712 convolution2d_3[0][0] \n", "____________________________________________________________________________________________________\n", "convolution2d_5 (Convolution2D) (None, 2, 33, 64) 36928 convolution2d_4[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 4224) 0 convolution2d_5[0][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 100) 422500 flatten_1[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 100) 0 dense_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 50) 5050 dropout_1[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 50) 0 dense_2[0][0] \n", "____________________________________________________________________________________________________\n", "dense_3 (Dense) (None, 10) 510 dropout_2[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_3 (Dropout) (None, 10) 0 dense_3[0][0] \n", "____________________________________________________________________________________________________\n", "dense_4 (Dense) (None, 1) 11 dropout_3[0][0] \n", "====================================================================================================\n", "Total params: 559,419\n", "Trainable params: 559,419\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n", "Epoch 1/3\n", "44s - loss: 0.1095 - val_loss: 0.1031\n", "Epoch 2/3\n", "42s - loss: 0.0989 - val_loss: 0.0890\n", "Epoch 3/3\n", "42s - loss: 0.0959 - val_loss: 0.0838\n" ] } ], "source": [ "# print out model summary\n", "model.summary()\n", "\n", "model.compile(loss = 'mse', optimizer = 'adam')\n", "history_object = model.fit_generator(train_generator, \n", " samples_per_epoch = len(train_samples)*4, \n", " validation_data = validation_generator, \n", " nb_val_samples = len(validation_samples), \n", " nb_epoch = NUM_EPOCHS, verbose = 2)\n", "\n", "model.save('model_20170906_both_nvidia.h5')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['val_loss', 'loss'])\n" ] }, { "data": { "image/png": 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P5eQqny5LY/x3G/h1/1EGtI3nvuHt6NSkQUBe35jKKtjdfyuDY8eOERoaSlhYGAsWLOCW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\n16wQb0wV5LMrEnesrRuATjh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UeFNVHy+yvTYwEegJ7AZ+r6qpIhIOvAn0wLlqmqiq/3aP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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff1fd6da908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "### print the keys contained in the history object\n", "print(history_object.history.keys())\n", "\n", "### plot the training and validation loss for each epoch\n", "plt.plot(history_object.history['loss'])\n", "plt.plot(history_object.history['val_loss'])\n", "plt.title('model mean squared error loss')\n", "plt.ylabel('mean squared error loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['training set', 'validation set'], loc='upper right')\n", "plt.savefig('output/model_loss.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5871\n", "1468\n" ] } ], "source": [ "print(len(train_samples))\n", "print (len(validation_samples))\n", "\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
InsightSoftwareConsortium/SimpleITK-Notebooks
Python/02_Pythonic_Image.ipynb
2
9185
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pythonic Syntactic Sugar <a href=\"https://mybinder.org/v2/gh/InsightSoftwareConsortium/SimpleITK-Notebooks/master?filepath=Python%2F02_Pythonic_Image.ipynb\"><img style=\"float: right;\" src=\"https://mybinder.org/badge_logo.svg\"></a>\n", "\n", "The Image Basics Notebook was straight forward and closely follows ITK's C++ interface.\n", "\n", "Sugar is great it gives your energy to get things done faster! SimpleITK has applied a generous about of syntactic sugar to help get things done faster too." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "\n", "mpl.rc(\"image\", aspect=\"equal\")\n", "import SimpleITK as sitk\n", "\n", "# Download data to work on\n", "%run update_path_to_download_script\n", "from downloaddata import fetch_data as fdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us begin by developing a convenient method for displaying images in our notebooks." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = sitk.GaussianSource(size=[64] * 2)\n", "plt.imshow(sitk.GetArrayViewFromImage(img))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = sitk.GaborSource(size=[64] * 2, frequency=0.03)\n", "plt.imshow(sitk.GetArrayViewFromImage(img))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def myshow(img):\n", " nda = sitk.GetArrayViewFromImage(img)\n", " plt.imshow(nda)\n", "\n", "\n", "myshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-dimension slice indexing\n", "\n", "If you are familiar with numpy, sliced index then this should be cake for the SimpleITK image. The Python standard slice interface for 1-D object:\n", "\n", "<table>\n", " <tr><td>Operation</td>\t<td>Result</td></tr>\n", " <tr><td>d[i]</td>\t<td>i-th item of d, starting index 0</td></tr>\n", " <tr><td>d[i:j]</td>\t<td>slice of d from i to j</td></tr>\n", " <tr><td>d[i:j:k]</td>\t<td>slice of d from i to j with step k</td></tr>\n", "</table>\n", "\n", "With this convenient syntax many basic tasks can be easily done." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img[24, 24]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cropping" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[16:48, :])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[:, 16:-16])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[:32, :32])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Flipping" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_corner = img[:32, :32]\n", "myshow(img_corner)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img_corner[::-1, :])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(\n", " sitk.Tile(\n", " img_corner,\n", " img_corner[::-1, ::],\n", " img_corner[::, ::-1],\n", " img_corner[::-1, ::-1],\n", " [2, 2],\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slice Extraction\n", "\n", "A 2D image can be extracted from a 3D one." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "simpleitk_error_expected": "Invalid" }, "outputs": [], "source": [ "img = sitk.GaborSource(size=[64] * 3, frequency=0.05)\n", "\n", "# Why does this produce an error?\n", "myshow(img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[:, :, 32])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[16, :, :])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Subsampling" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img[:, ::3, 32])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mathematical Operators\n", "\n", "Most python mathematical operators are overloaded to call the SimpleITK filter which does that same operation on a per-pixel basis. They can operate on a two images or an image and a scalar.\n", "\n", "If two images are used then both must have the same pixel type. The output image type is usually the same.\n", "\n", "As these operators basically call ITK filter, which just use raw C++ operators, care must be taken to prevent overflow, and divide by zero etc.\n", "\n", "<table>\n", " <tr><td>Operators</td></tr>\n", " <tr><td>+</td></tr>\n", " <tr><td>-</td></tr>\n", " <tr><td>&#42;</td></tr>\n", " <tr><td>/</td></tr>\n", " <tr><td>//</td></tr>\n", " <tr><td>**</td></tr>\n", "</table>\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = sitk.ReadImage(fdata(\"cthead1.png\"))\n", "img = sitk.Cast(img, sitk.sitkFloat32)\n", "myshow(img)\n", "img[150, 150]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "timg = img**2\n", "myshow(timg)\n", "timg[150, 150]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Division Operators\n", "\n", "All three Python division operators are implemented `__floordiv__`, `__truediv__`, and `__div__`.\n", "\n", "The true division's output is a double pixel type.\n", "\n", "See [PEP 238](http://www.python.org/peps/pep-0238) to see why Python changed the division operator in Python 3." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bitwise Logic Operators\n", "\n", "<table>\n", " <tr><td>Operators</td></tr>\n", " <tr><td>&</td></tr>\n", " <tr><td>|</td></tr>\n", " <tr><td>^</td></tr>\n", " <tr><td>~</td></tr>\n", "</table>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = sitk.ReadImage(fdata(\"cthead1.png\"))\n", "myshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparative Operators\n", "<table>\n", " <tr><td>Operators</td></tr>\n", " <tr><td>&gt;</td></tr>\n", " <tr><td>&gt;=</td></tr>\n", " <tr><td>&lt;</td></tr>\n", " <tr><td>&lt;=</td></tr>\n", " <tr><td>==</td></tr>\n", "</table>\n", "\n", "These comparative operators follow the same convention as the reset of SimpleITK for binary images. They have the pixel type of ``sitkUInt8`` with values of 0 and 1. \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img = sitk.ReadImage(fdata(\"cthead1.png\"))\n", "myshow(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Amazingly make common trivial tasks really trivial" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img > 90)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow(img > 150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myshow((img > 90) + (img > 150))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
DAInamite/programming-humanoid-robot-in-python
kinematics/inverse_kinematics_2d_autograd.ipynb
1
428735
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inverse Kinematics (2D) \n", "with https://github.com/HIPS/autograd" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "from matplotlib import pylab as plt\n", "from numpy import random, pi\n", "from __future__ import division\n", "from IPython import display\n", "from ipywidgets import interact, fixed\n", "import autograd.numpy as np # Thinly-wrapped numpy\n", "from autograd import grad # The only autograd function you may ever need" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coordinate Transformation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def trans(x, y, a):\n", " '''create a 2D transformation'''\n", " s = np.sin(a)\n", " c = np.cos(a)\n", " return np.asarray([[c, -s, x],\n", " [s, c, y],\n", " [0, 0, 1]])\n", "\n", "def from_trans(m):\n", " '''get x, y, theta from transform matrix'''\n", " a = np.arctan2(m[1, 0], m[0, 0])\n", " return np.asarray([m[0, -1], m[1, -1], a])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1., -0., 0.],\n", " [ 0., 1., 0.],\n", " [ 0., 0., 1.]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trans(0., 0., 0.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parameters of robot arm" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "l = [0, 3, 2, 1]\n", "#l = [0, 3, 2, 1, 1]\n", "#l = [0, 3, 2, 1, 1, 1]\n", "l = [1] * 30\n", "N = len(l) - 1 # number of links\n", "max_len = sum(l)\n", "a = random.random_sample(N) # angles of joints\n", "T0 = trans(0, 0, 0) # base" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Forward Kinematics" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def forward_kinematics(T0, l, a):\n", " T = [T0]\n", " for i in range(len(a)):\n", " Ti = np.dot(T[-1], trans(l[i], 0, a[i]))\n", " T.append(Ti)\n", " Te = np.dot(T[-1], trans(l[-1], 0, 0)) # end effector\n", " T.append(Te)\n", " return T" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def show_robot_arm(T):\n", " plt.cla()\n", " x = [Ti[0,-1] for Ti in T]\n", " y = [Ti[1,-1] for Ti in T]\n", " plt.plot(x, y, '-or', linewidth=5, markersize=10)\n", " plt.plot(x[-1], y[-1], 'og', linewidth=5, markersize=10)\n", " plt.xlim([-max_len, max_len])\n", " plt.ylim([-max_len, max_len]) \n", " ax = plt.axes()\n", " ax.set_aspect('equal')\n", " t = np.arctan2(T[-1][1, 0], T[-1][0,0])\n", " ax.annotate('[%.2f,%.2f,%.2f]' % (x[-1], y[-1], t), xy=(x[-1], y[-1]), xytext=(x[-1], y[-1] + 0.5))\n", " plt.show\n", " return ax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inverse Kinematics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numerical Solution: autograd" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option);\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-6-d35263df4e47>:9: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", " ax = plt.axes()\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "865a195a44114e3ba328bfb734a7fd29", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(FloatSlider(value=1.5672579565188922, description='x_e', max=30.0, step=0.01), FloatSlid…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def error_func(theta, target):\n", " Ts = forward_kinematics(T0, l, theta)\n", " Te = Ts[-1]\n", " e = target - Te\n", " return np.sum(e * e)\n", "\n", "theta = random.random(N)\n", "def inverse_kinematics(x_e, y_e, theta_e, theta):\n", " target = trans(x_e, y_e, theta_e)\n", " func = lambda t: error_func(t, target)\n", " func_grad = grad(func)\n", " \n", " for i in range(1000):\n", " e = func(theta)\n", " d = func_grad(theta)\n", " theta -= d * 1e-2\n", " if e < 1e-4:\n", " break\n", " \n", " return theta\n", "\n", "T = forward_kinematics(T0, l, theta)\n", "show_robot_arm(T)\n", "Te = np.asarray([from_trans(T[-1])])\n", "\n", "@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\n", "def set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n", " theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n", " T = forward_kinematics(T0, l, theta)\n", " show_robot_arm(T)\n", " return theta\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:robocup]", "language": "python", "name": "conda-env-robocup-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "widgets": { "state": { "0065ff23834146a99efd42e25d3294dc": { "views": [] }, "00c59707f24e4d80b90a6ca59c255d84": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "1cfd9a6f210b4054bcb39b0429cc55de": { "views": [] }, "1d683e58238c426daa981843821bbe42": { "views": [] }, "1da0339e3c6e4a44884e772f18fa4e2b": { "views": [] }, "1dee2d42496b4c838041027fdf78954d": { "views": [] }, "1e92889027a541ddb44f454e7f14a387": { "views": [] }, "1f76e9cb47ce4bf9b0b3452b6bd854a0": { "views": [] }, "212aab5aa2bf4660a541d175db422584": { "views": [] }, "22df1d38fd2d43539cc7d9fccbaf577f": { "views": [] }, "22f22aafaf554defab70e6fe1bf02f58": { "views": [] }, "2310bd576c5b4f88a20f09a7d851a497": { "views": [] }, "236f20f3353a427796cd3e1e2d4475f9": { "views": [] }, "23cf28a2d9f5434696f27517edb1e019": { "views": [] }, "23e68019becd47078dbd546b0907a08f": { "views": [] }, "2494fdbd167046d8a7a0fe196ff53f54": { "views": [] }, "2600b6c694094bffb6043623d1819ea9": { "views": [] }, "26406e17a6ff431688684397e3d84ecd": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nq+rEJNdW1VVjjJv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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "26795a5634004f179a4433436959f69c": { "views": [] }, "28f6bbda5c7d487fa65a3ccfcb890b44": { "views": [] }, "28fbf1d342404c90b19e51eb06b656a6": { "views": [] }, "29291248cbaf449dad8c04aefea3c251": { "views": [] }, "29324c74ce44472d816ddb618e5e7154": { "views": [] }, "29d7ec8815be42d180e90f0867e537a5": { "views": [] }, "29ef4faa80594de0ab95b9e1e31a0500": { "views": [] }, "2aa04ae23f974820977ecc02dc8b6b43": { "views": [] }, "2ab47ef47437437c8cfaa90fb663193c": { "views": [] }, "2af796b503d34c988689c3b04df5de24": { "views": [ { "cell": { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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6lU2tdcmqFBJJUmvdcxULiXelOxpz9/LWJnFkAgCAR1rWkQl4NEcmAACAJmICAABoIiYA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\nAQAANBETAABAEzEBAAA0ERM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"text/plain": "<matplotlib.figure.Figure at 0x7f7e70534390>" }, "metadata": {}, "output_type": "display_data" } ], "source": "from numpy import sin, cos, pi, matrix\nfrom math import atan2, acos\n\nT0 = trans(0, 0, 0)\nlv = [0] + range(N, 0, -1) # length of link, l[0] is ingored\nbf = B\nfor i in range(N):\n bf = bf.subs(l[i + 1], lv[i + 1])\n\ndef inverse_kinematics(x_e, y_e, theta_e):\n b = bf.subs(x, x_e).subs(y, y_e).subs(theta, theta_e)\n b = (b.subs('I', 1).subs('pi', pi).tolist())\n b = [float(i[0]) for i in b]\n return b\n \n@interact(x_e=(0, max_len, 0.1), y_e=(-max_len, max_len, 0.1), theta_e=(-pi, pi, 0.1))\ndef set_end_effector(x_e=5, y_e=0, theta_e=0):\n b = inverse_kinematics(x_e, y_e, theta_e)\n T = forward_kinematics(T0, lv, b)\n show_robot_arm(T)" }, "cell_index": 31, "root": true } ] }, "2b08bea1b4f54c9ba077c00da9f32df4": { "views": [] }, "2b93cbb0c4224f89849f2dde2fc15157": { "views": [] }, "2bc083e7865744218921a220b1dcd2bd": { "views": [] }, 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "33b2e7d8d4c84e768423aa3803e2b9f5": { "views": [] }, "33d1a4a3e2984b5999adf4a82ed6d243": { "views": [] }, "34acdada4843453d86dc7812e4271c09": { "views": [] }, "356a4ddeac674b17a489dc73c02f2f57": { "views": [] }, "358e60da5d7c45278a2640a67c6af292": { "views": [] }, "36aa1b263e224022a6b374b09c44b6fb": { "views": [] }, "36ff2aeae7a14f91a7be43d3c47a0597": { "views": [] }, "374ce53463064914b8233dfced2d9d52": { "views": [] }, "37513503a13b45cfbd26e56238d8c949": { "views": [] }, "386c80b57d6a404992acaf6469506db3": { "views": [] }, "389ea982a40d46d591bebd0ee559801e": { "views": [] }, "38b47640815546939304ec2a6d71c3ad": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "38bc0f4ca75245689ea57a229c49472a": { "views": [] }, "38d0989572be494b80d396336d933313": { "views": [] }, "39240ffe45674b32aa64d099d6e61bd6": { "views": [] }, "3a5050c9cd9e457fae55d0a598eb4b4f": { "views": [] }, "3a80053831f94247947bda7171a375f5": { "views": [] }, "3b0b75b35ae34a6d9576ab19dea42ccd": { "views": [] }, "3b667c3b4fc4424d8893d82380ed312a": { "views": [] }, "3bd271b7ed304418a7d058b88a46817c": { "views": [] }, "3bfb2f44245f4631af2733821fc24f2d": { "views": [] }, "3c160aed9d4645a3867ab97e9d4428e8": { "views": [] }, "3c4064aa9a404ca0802954fe0baf05ad": { "views": [] }, "3c8d97fabeff4d4882982ae88559ca1d": { "views": [] }, "3ce5b9ab8fbc4af092b37669aef932a9": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nq+rEJNdW1VVjjJv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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "3cfd0856c6ad4d72957c378772dddceb": { "views": [] }, "3d5f1dddcb724d6f8776e5beb45145be": { "views": [] }, "3dc13517176b4d059867e0c5c14b333e": { "views": [] }, "3e69ea50d1bc4d33a564d6e3dc695803": { "views": [] }, "3e7ec1eec9bb432ab76638a80530882e": { "views": [] }, "3ec90899379e473a8a3d9537a9fd3e6a": { "views": [] }, "3ee2ee71c4df41fe8bcc0e36c035bdc9": { "views": [] }, "3f1c69d137514a16a98abd88055e502c": { "views": [] }, "3f419880ead047eda91f3d02a9958b64": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "5dbe6899de6941e4baa16dc072df40c9": { "views": [] }, "5e8769162a4140d5bc70b8aec03e7ad8": { "views": [] }, "5e87aee9f185402f8ff57a465fbe73e7": { "views": [] }, "5ef7e2e41c0043bc8a55756ce22df3e9": { "views": [] }, "5fef33cf88954c079bf52d5dbbb0339f": { "views": [] }, "61ac09cef9924de2a2146dcb2b2e852e": { "views": [] }, "621c02cb75364b558abc79df43f8b70e": { "views": [] }, "63511e5c5aa34ec3a58130ae0d8b6bdf": { "views": [] }, "63775a25bb2b4ee9b8446ceef21818fc": { "views": [] }, "639c23cf0db44536a5f78136ea5b449e": { "views": [] }, "63d34734668d4d4e8a9abd079cfffcc9": { "views": [] }, "647b63c63fed43daa91f41fe25b22482": { "views": [] }, "64976a06e0c24cbebbe7fe4ebe3ee1cd": { "views": [] }, "654d4b7dbb4a4a18bcb54cc292d2f4bb": { "views": [] }, "6628109154014be7a19c42fb56a91ef8": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "66b011dbaa5c41fcb4c71d72f55af266": { "views": [] }, "66ecc76b33ce4b9dbf080bae63d3d6d7": { "views": [] }, "67377e0ad39f42a19133ea575e166cfd": { "views": [] }, "674d2cf140c5448d9fe5b7a5cceac6ba": { "views": [] }, "67e0805befc644748271decde7bf1218": { "views": [] }, "697a98b6e58844049a81cc4b5da87be0": { "views": [] }, "69d28d94f5eb40a3879b21f9507e6121": { "views": [] }, "6a212e13893a4ab0a241a89a6007906c": { "views": [] }, "6abd33d635584ac3bbe8902f7ddd90d4": { "views": [] }, "6b17c7b15ea1410a93477b40977226b5": { "views": [] }, "6ca5197312004a9683d386408cc08392": { "views": [] }, "6d017c1c4b01499693c498de96d7f97a": { "views": [] }, 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "71fe3ab37df047f8af2367d8f9a79994": { "views": [] }, "72231c63ee8d4c2196df2b889e895eb7": { "views": [] }, "7224f8df15bf4c81b657da552ca9ed24": { "views": [] }, "730a7d996a3a4ff18f7d3dd9ec5f5ae8": { "views": [] }, "73736b6f6f69435d971a25fe39a572df": { "views": [] }, "73b62c199ef741e6b2e86487476ed81d": { "views": [] }, "745aea5ce26e4cbd9559786ae4f2c9a5": { "views": [] }, "74932e9cb8f64a30b15936223eb666e6": { "views": [] }, "74ec10ad98f44cae8ec7cb23bca2e7e8": { "views": [] }, "755577b3878e4659a9d79c14d1a8b663": { "views": [] }, "75a0ef9fd2144683a0c221172e75686c": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "9a19791b387e4c5a913a92790141f553": { "views": [] }, "9a3dd9ada08d4312b729bc1891e43f1a": { "views": [] }, "9a81582415584ce588f6a1c53b927d32": { "views": [] }, "9ae0d4a38f1e4b498b0bc4801df700ec": { "views": [] }, "9aef67ddda784eb7b93dc9c617ef03ec": { "views": [] }, "9b917897eeb24144930072d47c8a7571": { "views": [] }, "9bd57fc229d24f9aa65f8366028e3007": { "views": [] }, "9bdd3a0d16de4cd7801d67957b196382": { "views": [] }, "9c13a849122d47458c4ab77c56edf863": { "views": [] }, "9c9748ffe74d4a5cbc26aef71c6e4780": { "views": [] }, "9ce4a1bcfd694843a536393891af83be": { "views": [] }, "9d7f35226a3a4f6c9ae12362454a9716": { "views": [] }, "9dba4d8a3c4d420492b5b5ac732b5fa0": { "views": [] }, "9dd653229bd649df86e7ce3d5688e963": { "views": [] }, "9dddfa92060a4a4ab09253e94473d45e": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "b0939de0ddd64138a947665fc7d23e20": { "views": [] }, "b097623d1e7a4a48b11b1f405d98d4e8": { "views": [] }, "b1318684110d42d79687a6386cbae811": { "views": [ { "cell": { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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HZBAxO/0aq2plkj9M8u4dLDto+Pqfbg2aGlz17Q1JXjb7PJiqWrrdFdhekOSIJLdW1UGz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\nAACgO0IHAADojtABAAC6I3QAAIDu/P+zHD7CA7x3IAAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7e6b4d6ad0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "from numpy import asarray\ntheta = asarray([0.1] * N)\nlambda_ = 0.001\ndef inverse_kinematics(x_e, y_e, theta_e, theta):\n target = matrix([[x_e, y_e, theta_e]])\n while True:\n T = forward_kinematics(T0, lv, theta)\n Te = matrix([from_trans(T[-1])])\n e = target - Te\n T = matrix([from_trans(i) for i in T[1:-1]])\n J = Te - T\n J = J.T\n J[-1, :] = 1 # angular velocity\n JJT = J * J.T\n d_theta = lambda_ * J.T * JJT.I * e.T\n theta += asarray(d_theta.T)[0]\n if np.linalg.norm(d_theta) < 1e-4:\n break\n return theta\n\nT = forward_kinematics(T0, lv, theta)\nTe = matrix([from_trans(T[-1])])\n\n@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\ndef set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n T = forward_kinematics(T0, lv, theta)\n show_robot_arm(T)\n\n# NOTE\n# while numerical inverse kinematics is easy to implemente, two issues have to be keep in mind:\n# * stablility: the correction step (lambda_) has to be small, but it will take longer time to converage\n# * singularity: there are singularity poses (all 0, for example), the correction will be 0, so the algorithm won't work. That's why many robots bends its leg when walking" }, "cell_index": 33, "root": true } ] }, "b249266f72ce41b096bfeafd4789ed36": { "views": [] }, "b27d4b54be3c499bac6ce346de64e0bb": { "views": [] }, "b28e241d2ac14db39d89dc3952ff8974": { "views": [] }, "b402a799328c49e99abd5beb99ad63cb": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "b491570fd738421e971d2f01f6e5e025": { "views": [] }, "b4cd5a2f926e43be8c8028d2c071e2e5": { "views": [] }, "b579ef0051d945a9adc2c1c7a0387ab9": { "views": [] }, "b7203daacce34cedab333faf601f8080": { "views": [] }, "b74a7887033349f59d4dcce5df469ebe": { "views": [] }, "b7da1a920b6441d8b303aacc5472a743": { "views": [] }, "b7ebf419b324467196b93aec2ddfb2e5": { "views": [] }, "b7f65fb78b1f4963923e4b2b889ae71d": { "views": [] }, "b80d613ff47c4b5e8a92d15ce973c058": { "views": [] }, "b80d7b1129344995a0e08645bd02c43c": { "views": [] }, "b8af5204f7bd46e49556c9b9339a61d1": { "views": [] }, "b8f0724bbf9c4379af6c6b24598a2e45": { "views": [] }, "b94bcf1136124faa83d0d49f6ffd01ef": { "views": [] }, "b94d48ddfc0c4b9bbe799e23d02bdb23": { "views": [] }, "b9a4a36b6f0541d08765760f39d5979d": { "views": [] }, "b9b0e906c50b4fe194ba0561860a48d2": { "views": [] }, "b9b20e2b81ef469fbc2147fe4fd1fbcd": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nq+rEJNdW1VVjjJv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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "b9eee2008f4e4bbdb794dc096ad66eed": { "views": [] }, "bb317461e530495584bed769e3907371": { "views": [] }, "bb4935d65aa4419a903c269ee1c245c6": { "views": [] }, "bb5048ede7214dbc95ddd20b2860d967": { "views": [] }, "bc375a0bce7f422988cf8184f1b766e4": { "views": [] }, "bcdf58e3772d46bc85c99b311fca3597": { "views": [] }, "bd0e575537f84a4dbbe73055036d545f": { "views": [] }, "bda506ba1987430e8e52e2370d61f1a7": { "views": [] }, "bde65c86e6d1468fb60ae401c4c8b29a": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "be1acc039ba446ee8faa23f50a109cad": { "views": [] }, "bf5d09791dc84a05a89a788915401157": { "views": [] }, "c04926eeeee849949eb61738e5e66675": { "views": [] }, "c05c848c83c649bca51c6b180a176e4a": { "views": [] }, "c1778dbcd3454ee190824e9101a0b647": { "views": [] }, "c17e1418e17445ef8f2a0f8e1dad0c38": { "views": [] }, "c1eaf2eb5c684f2db956365800471e0d": { "views": [] }, "c205b755237d444eb87a20babac93ad5": { "views": [] }, "c229e9376d6a4e1b8243aa035744b4a8": { "views": [] }, "c296feae9fc241f6b7cc2155c35d2a21": { "views": [] }, "c298b40a15fc4980a8bec3c3c36e1cdf": { "views": [] }, "c2a9da2b5f9a441c86c7ca1d98bdfb5c": { "views": [] }, 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efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O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\nq+rEJNdW1VVjjJv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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "c39014455211404cae26ef0b4179cc29": { "views": [] }, "c3ae4ee5304c468f84b15b2f834369c3": { "views": [] }, "c40830ec69ab4c61882d3bb657705a29": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "dae71c9df74f476d9bdca7dbdc9c4813": { "views": [] }, "db36bc23e8224a13bd5bd39d59b341ea": { "views": [] }, "db74c7e7bc0445af81e1a1a8cd4c1b2a": { "views": [] }, "dd7767d3ee1e4fdf9457356dcb9d8856": { "views": [] }, "dd9547c6a0de46bfbb8766c95faad93a": { "views": [] }, "de9d33d2fd504e6591a42183a04245c4": { "views": [] }, "dea19948b29b4852a2fafd8eb5ed81ba": { "views": [] }, "df3f2a8c0b8d4450b9c81e9bb6bf5a48": { "views": [] }, "df69964a0a784d678379ef980433bac2": { "views": [] }, "dfef404d9e874f5ba4bcb626c407e772": { "views": [] }, "e07f7328f0cd427d9af41fac575919a2": { "views": [] }, "e0fbecdda5c24dd0b7ae34203dd4bd08": { "views": [] }, "e14cff4f97544aa2850e91e886f48705": { "views": [] }, "e172cd6cdcb1425f9686213f3e73af38": { "views": [] }, "e21b7ba964e54981ac5505b6a6d8e9d4": { "views": [] }, "e27ff57b6a15404a9f220a4f234d012f": { "views": [] }, "e35aa9674b9c4be1a8bf4a6b6c3f61b0": { "views": [] }, "e399e06d7f1d45e98f6db2a87d658e37": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "e3bbf63ca8f24f3f90c845609efaaa2a": { "views": [] }, "e3cc82f1d7704f12bdf1b63dd046c1e5": { "views": [] }, "e3d7d5bde7594823974275a142584fa6": { "views": [] }, "e3da4e2ae1d54776aa4c7cc721e5b154": { "views": [] }, "e404ccda48544b2caf82f95feb1a9bab": { "views": [] }, "e422e5c381a5484dad09cbaadcc77087": { "views": [] }, "e43c2bd292b24ab2816c0877b4738954": { "views": [] }, "e46fb472807547428ea7d4e8afa77dac": { "views": [] }, "e4cabad725b84230b67353f65ecabaae": { "views": [] }, "e536ce329e8a4dac80d16cc15a9733a7": { "views": [] }, "e54d65d577fb41fc93b093e342ebf57b": { "views": [] }, "e571d3e0777b4304baa3db684b3fdf4f": { "views": [] }, 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"execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>" }, "metadata": {}, "output_type": "display_data" } ], "source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n" }, "cell_index": 10, "root": true } ] }, "ebd1bb1a6e184910a6ea9194bb246247": { "views": [] }, "ec331fec5dc849a096d6fc4614f5c6a1": { "views": [] }, "ed2373b2c86e4249963f1f0c1539875b": { "views": [] }, "ed5515d5ada341d5bddc42285601a968": { "views": [] }, "ee6badce55564b0bb690ba653a5c311b": { "views": [] }, "ee6e242f9f5b4d1197ca01c6bd49119c": { "views": [] }, "eebf627136a94f8db9d06a66bc1f5ff7": { "views": [] }, "eef0260f78af4a21ad250a3641b7a8d8": { "views": [ { "cell": { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "trusted": true }, "outputs": [ { "data": { "image/png": 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gpl-2.0
WMD-group/SMACT
examples/Inverse_perovskites/Inverse_formate_perovskites.ipynb
1
36334
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formate perovskites\n", "\n", "Perovskites are an important class of inorganic materials, particularly for solar energy applications.\n", "Usually, the perovskite structure contains an A cation, a B cation and an C anion in the ratio 1:1:3 (black, blue and red in the figure below, respectively, [see wikipeidia for more information](https://en.wikipedia.org/wiki/Perovskite_(structure)).\n", "\n", "<img src=\"Perovskite.jpg\">\n", "\n", "\n", "Here we search for charge inverted perovskites, i.e. with an anion on the A site. This class of material is closely related to perovskites, and may represent another fruitful search space for new photovoltaic materials. \n", "\n", "In this example we assume a simple [formate moelcule](https://en.wikipedia.org/wiki/Formate) as the C-site and uses [Goldschmidt ratio rules](https://en.wikipedia.org/wiki/Goldschmidt_tolerance_factor) as part of the screening.\n", "These rules allow us to estimate whether or not a perovskite structure is likely to form based on data about ionic size alone. The tolerance factor is defined as a ratio of the radii of the A, B and C species\n", "\n", "\n", "\\begin{equation*}\n", "t = \\frac{r^A + r^C}{\\sqrt{2}(r^B + r^C)} ,\n", "\\end{equation*}\n", "\n", "Values of t > 1 imply a relatively large A site favoring a hexagonal structure, 0.9 < t < 1 predicts a cubic structure, and 0.7 < t < 0.9 means that the A site is small, preferring an orthorhombic structure. For t < 0.7, other (non-perovskite) structures are predicted.\n", "\n", "We also apply the standard charge neutrality and electronegativity tests as described [in the docs](https://smact.readthedocs.io/en/latest/examples.html#neutral-combinations).\n", "\n", "\n", "We outline 2 approaches for achieveing the same result:\n", "\n", "1. The methodology is written out explicitly for transparency, including accessing the `smact` data directory directly and storing element and species information as simple lists of strings, ints and floats.\n", "\n", "2. Fewer lines of code are used, making use of inbuilt `smact` functions. Element and species information is stored directly as Element and Species objects." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "### IMPORTS ###\n", "get_ipython().magic(u'matplotlib inline') # plots appear in the notebook\n", "import smact\n", "from smact import Species, Element\n", "import smact.lattice as lattice\n", "import smact.screening as screening\n", "\n", "from itertools import product\n", "import copy\n", "import os\n", "import csv\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "from smact import data_directory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we define the positions of the 3 sites in the perovskite structure and specify the allowed oxidation states at each site. Note that the A site is defined as an anion (i.e. with a -1 oxidation state)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "site_A = lattice.Site([0,0,0],[-1])\n", "site_B = lattice.Site([0.5,0.5,0.5],[+5,+4])\n", "site_C = lattice.Site([0.5,0.5,0.5],[-2,-1])\n", "perovskite = lattice.Lattice([site_A,site_B,site_C],space_group=221)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "### Approach 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now search through the elements of interest (Li-Fr) and find those that are allowed on each site. In this example, we use the F- anion with an increased Shannon radius to simulate the formate anion. We access the Shannon radii data directly from the smact data directory and are interested in the octahedral (6_n) Shannon radius. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "search = smact.ordered_elements(3,87) # Li - Fr\n", "\n", "A_list = [] # will be populated with anions\n", "B_list = [] # will be populated with cations\n", "C_list = [['F',-1,4.47]] # is always the \"formate anion\"\n", "for element in search:\n", " with open(os.path.join(data_directory, 'shannon_radii.csv'),'r') as f:\n", " reader = csv.reader(f)\n", " r_shannon=False\n", " for row in reader:\n", " if row[2] ==\"6_n\" and row[0]==element and int(row[1]) in site_A.oxidation_states:\n", " A_list.append([row[0],int(row[1]),float(row[4])])\n", " if row[2]==\"6_n\" and row[0]==element and int(row[1]) in site_B.oxidation_states:\n", " B_list.append([row[0],int(row[1]),float(row[4])])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*NB: We access the data directly from the data directory file here for transparency. However, reading the file multiple times would slow down the code if we were looping over many (perhaps millions to billions) of compositions. As such, reading all the data in once into a dictionary, then accessing that dictionary from within a loop, could be preferable, e.g.:*\n", "``` python\n", "for element in search:\n", " ...\n", " r_shannon = shannon_radii[element][coordination]\n", " ...\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We go through and apply the electronegativity order test (pauling_test) to each combo. Then, we use Goldschmidt tolernace factor to group into crystal structure types." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# We define the different categories of list we will populate\n", "charge_balanced = []\n", "goldschmidt_cubic = []\n", "goldschmidt_ortho = []\n", "a_too_large = []\n", "A_B_similar = []\n", "pauling_perov = []\n", "anion_stats = []\n", "\n", "# We recursively search all ABC combinations using nested for loops\n", "for C in C_list:\n", " anion_hex = 0\n", " anion_cub = 0\n", " anion_ort = 0\n", " for B in B_list:\n", " for A in A_list:\n", " # We check that we have 3 different elements\n", " if B[0] != A[0]: \n", " # Check for charge neutrality\n", " if int(A[1])+int(B[1])+3*int(C[1]) == 0:\n", " charge_balanced.append([A[0],B[0],C[0]])\n", " # We apply the pauling electronegativity test\n", " paul_a = smact.Element(A[0]).pauling_eneg\n", " paul_b = smact.Element(B[0]).pauling_eneg\n", " paul_c = smact.Element(C[0]).pauling_eneg\n", " electroneg_makes_sense = screening.pauling_test([A[1],B[1],C[1]], [paul_a,paul_b,paul_c])\n", " if electroneg_makes_sense:\n", " pauling_perov.append([A[0],B[0],C[0]])\n", " # We calculate the Goldschmidt tolerance factor\n", " tol = (float(A[2]) + C[2])/(np.sqrt(2)*(float(B[2])+C[2]))\n", " if tol > 1.0:\n", " a_too_large.append([A[0],B[0],C[0]])\n", " anion_hex = anion_hex+1\n", " if tol > 0.9 and tol <= 1.0:\n", " goldschmidt_cubic.append([A[0],B[0],C[0]])\n", " anion_cub = anion_cub + 1\n", " if tol >= 0.71 and tol < 0.9:\n", " goldschmidt_ortho.append([A[0],B[0],C[0]])\n", " anion_ort = anion_ort + 1\n", " if tol < 0.71:\n", " A_B_similar.append([A[0],B[0],C[0]])\n", "anion_stats.append([anion_hex,anion_cub,anion_ort])\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1, 40, 91]]\n", "Number of possible charge neutral perovskites from Li to Fr = 132\n", "Number of Pauling senseibe perovskites from Li to Fr = 132\n", "Number of possible cubic perovskites from Li to Fr = 40\n", "Number of possible ortho perovskites from Li to Fr = 91\n", "Number of possible hexagonal perovskites from Li to Fr = 1\n", "Number of possible non-perovskites from Li to Fr = 0\n", "----------------------------------------------------------------\n", "Structures identified with cubic tolerance factor 0.9 < t < 1.0 \n", "----------------------------------------------------------------\n", "Cl C (HCOO)3\n", "Br C (HCOO)3\n", "Cl Si (HCOO)3\n", "Br Si (HCOO)3\n", "I Si (HCOO)3\n", "Cl S (HCOO)3\n", "Br S (HCOO)3\n", "I S (HCOO)3\n", "I Ti (HCOO)3\n", "Br V (HCOO)3\n", "I V (HCOO)3\n", "Br Cr (HCOO)3\n", "I Cr (HCOO)3\n", "Br Mn (HCOO)3\n", "I Mn (HCOO)3\n", "I Fe (HCOO)3\n", "Br Co (HCOO)3\n", "I Co (HCOO)3\n", "Br Ni (HCOO)3\n", "I Ni (HCOO)3\n", "Br Ge (HCOO)3\n", "I Ge (HCOO)3\n", "Br Se (HCOO)3\n", "I Se (HCOO)3\n", "I Zr (HCOO)3\n", "I Nb (HCOO)3\n", "I Mo (HCOO)3\n", "I Tc (HCOO)3\n", "I Ru (HCOO)3\n", "I Rh (HCOO)3\n", "I Pd (HCOO)3\n", "I Sn (HCOO)3\n", "I Tb (HCOO)3\n", "I Hf (HCOO)3\n", "I Ta (HCOO)3\n", "I W (HCOO)3\n", "I Re (HCOO)3\n", "I Os (HCOO)3\n", "I Ir (HCOO)3\n", "I Pt (HCOO)3\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print (anion_stats)\n", "colours=['#991D1D','#8D6608','#857070']\n", "matplotlib.rcParams.update({'font.size': 22})\n", "plt.pie(anion_stats[0],labels=['Hex','Cubic','Ortho']\n", " ,startangle=90,autopct='%1.1f%%',colors=colours)\n", "plt.axis('equal')\n", "plt.savefig('Form-perovskites.png')\n", "\n", "print ('Number of possible charge neutral perovskites from', search[0], 'to', search[len(search)-1], '=', len(charge_balanced))\n", "print ('Number of Pauling sensible perovskites from', search[0], 'to', search[len(search)-1], '=', len(pauling_perov))\n", "print ('Number of possible cubic perovskites from', search[0], 'to', search[len(search)-1], '=', len(goldschmidt_cubic))\n", "print ('Number of possible ortho perovskites from', search[0], 'to', search[len(search)-1], '=', len(goldschmidt_ortho))\n", "print ('Number of possible hexagonal perovskites from', search[0], 'to', search[len(search)-1], '=', len(a_too_large))\n", "print ('Number of possible non-perovskites from', search[0], 'to', search[len(search)-1], '=', len(A_B_similar))\n", "\n", "\n", "#print goldschmidt_cubic\n", "print( \"----------------------------------------------------------------\")\n", "print( \"Structures identified with cubic tolerance factor 0.9 < t < 1.0 \")\n", "print( \"----------------------------------------------------------------\")\n", "for structure in goldschmidt_cubic:\n", " print( structure[0],structure[1],'(HCOO)3')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "### Approach 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Get list of Element objects\n", "search = [el for el in smact.ordered_elements(3,87) if \n", " Element(el).oxidation_states]\n", "\n", "# Covert to list of Species objects\n", "all_species = []\n", "for el in search:\n", " for oxi_state in Element(el).oxidation_states:\n", " all_species.append(Species(el,oxi_state,\"6_n\"))\n", " \n", "# Define lists of interest\n", "A_list = [sp for sp in all_species if \n", " (sp.oxidation == -1) and (sp.ionic_radius)]\n", "B_list = [sp for sp in all_species if \n", " (4 <= sp.oxidation <= 5) and (sp.ionic_radius)]\n", "C_list = [Species('F',-1,4.47)]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# We define the different categories of list we will populate\n", "charge_balanced = []\n", "goldschmidt_cubic = []\n", "goldschmidt_ortho = []\n", "a_too_large = []\n", "A_B_similar = []\n", "pauling_perov = []\n", "anion_stats = []\n", "\n", "for combo in product(A_list,B_list,C_list):\n", " A, B, C = combo[0], combo[1], combo[2]\n", " # Check for charge neutrality in 1:1:3 ratio\n", " if (1,1,3) in screening.neutral_ratios(\n", " [A.oxidation, B.oxidation, C.oxidation])[1]:\n", " charge_balanced.append(combo)\n", " # Check for pauling test\n", " if screening.pauling_test([A.oxidation, B.oxidation, C.oxidation],\n", " [A.pauling_eneg, B.pauling_eneg, C.pauling_eneg]):\n", " pauling_perov.append(combo)\n", " # Calculate tolerance factor\n", " tol = (float(A.ionic_radius) + 4.47)/(np.sqrt(2)*(float(B.ionic_radius)+4.47))\n", " if tol > 1.0:\n", " a_too_large.append(combo)\n", " if tol > 0.9 and tol <= 1.0:\n", " goldschmidt_cubic.append([combo])\n", " if tol >= 0.71 and tol < 0.9:\n", " goldschmidt_ortho.append(combo)\n", " if tol < 0.71:\n", " A_B_similar.append(combo)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of possible charge neutral perovskites from Li to Fr = 132\n", "Number of Pauling senseibe perovskites from Li to Fr = 132\n", "Number of possible cubic perovskites from Li to Fr = 40\n", "Number of possible ortho perovskites from Li to Fr = 91\n", "Number of possible hexagonal perovskites from Li to Fr = 1\n", "Number of possible non-perovskites from Li to Fr = 0\n", "----------------------------------------------------------------\n", "Structures identified with cubic tolerance factor 0.9 < t < 1.0 \n", "----------------------------------------------------------------\n", "Cl C (HCOO)3\n", "Cl Si (HCOO)3\n", "Cl S (HCOO)3\n", "Br C (HCOO)3\n", "Br Si (HCOO)3\n", "Br S (HCOO)3\n", "Br V (HCOO)3\n", "Br Cr (HCOO)3\n", "Br Mn (HCOO)3\n", "Br Co (HCOO)3\n", "Br Ni (HCOO)3\n", "Br Ge (HCOO)3\n", "Br Se (HCOO)3\n", "I Si (HCOO)3\n", "I S (HCOO)3\n", "I Ti (HCOO)3\n", "I V (HCOO)3\n", "I Cr (HCOO)3\n", "I Mn (HCOO)3\n", "I Fe (HCOO)3\n", "I Co (HCOO)3\n", "I Ni (HCOO)3\n", "I Ge (HCOO)3\n", "I Se (HCOO)3\n", "I Zr (HCOO)3\n", "I Nb (HCOO)3\n", "I Mo (HCOO)3\n", "I Tc (HCOO)3\n", "I Ru (HCOO)3\n", "I Rh (HCOO)3\n", "I Pd (HCOO)3\n", "I Sn (HCOO)3\n", "I Tb (HCOO)3\n", "I Hf (HCOO)3\n", "I Ta (HCOO)3\n", "I W (HCOO)3\n", "I Re (HCOO)3\n", "I Os (HCOO)3\n", "I Ir (HCOO)3\n", "I Pt (HCOO)3\n" ] } ], "source": [ "print ('Number of possible charge neutral perovskites from', search[0], 'to', search[len(search)-1], '=', len(charge_balanced))\n", "print ('Number of Pauling sensible perovskites from', search[0], 'to', search[len(search)-1], '=', len(pauling_perov))\n", "print ('Number of possible cubic perovskites from', search[0], 'to', search[len(search)-1], '=', len(goldschmidt_cubic))\n", "print ('Number of possible ortho perovskites from', search[0], 'to', search[len(search)-1], '=', len(goldschmidt_ortho))\n", "print ('Number of possible hexagonal perovskites from', search[0], 'to', search[len(search)-1], '=', len(a_too_large))\n", "print ('Number of possible non-perovskites from', search[0], 'to', search[len(search)-1], '=', len(A_B_similar))\n", "\n", "\n", "#print goldschmidt_cubic\n", "print( \"----------------------------------------------------------------\")\n", "print( \"Structures identified with cubic tolerance factor 0.9 < t < 1.0 \")\n", "print( \"----------------------------------------------------------------\")\n", "for structure in goldschmidt_cubic:\n", " print( structure[0][0].symbol,structure[0][1].symbol,'(HCOO)3')\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ultrareality/mat201a
hw1/samplingdemo.ipynb
1
20474
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5jihYVAGIyJ+KFIHhw60jtU4dGynkV++/bx3Y3bvb3gdB7btwSX0AIpKjf/0L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\nIiIiIiIicjj/D9caDHlE+QO+AAAAAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x104e7df10>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 6\n", "max_value = 2**(N-1) - 1\n", "f8 = (f*(max_value)).astype(int8)\n", "plot(f8)\n", "xlim((0,20))\n", "ylim((0,5))\n", "grid()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x104f83f10>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "N = 16\n", "20 * log10((2 ** (N-1))/1)\n", "\n", "def dynrange(N):\n", " return 20 * log10((2 ** (N-1))/1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "print(dynrange(8), dynrange(16), dynrange(24))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(42.144199392957368, 90.308998699194362, 138.47379800543135)\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ " " ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
StingraySoftware/notebooks
Simulator/Lag Analysis.ipynb
1
102867
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook analyses lag-frequency spectrums of the light curves simulated through impulse response approach. First, a simple case with delta impulse response is covered. Subsequently, an energy-dependent impulse response scenario is analysed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import some useful libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import relevant stingray libraries." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from stingray import Lightcurve, Crossspectrum, sampledata\n", "from stingray.simulator import simulator, models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initializing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a simulator object and define a variability signal." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "var = sampledata.sample_data()\n", "\n", "# Beware: set tstart here, or nothing will work!\n", "sim = simulator.Simulator(N=1024, mean=0.5, dt=0.125, rms=0.4, tstart=var.tstart)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For ease of analysis, define a simple delta impulse response with width 1. Here, `start` parameter refers to the lag delay, which we will soon see." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "delay = 10\n", "s_ir = sim.simple_ir(start=delay, width=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, simulate a `filtered` light curve. Here, filtered means that the initial lag delay portion is cut." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f860a4cf3a0>]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lc = sim.simulate(var.counts, s_ir)\n", "\n", "plt.plot(lc.time, lc.counts)\n", "plt.plot(var.time, var.counts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute crossspectrum." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cross = Crossspectrum(var, lc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rebin the crosss-spectrum for ease of visualization." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cross = cross.rebin(0.0050)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate time lag." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "lag = cross.time_lag()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot lag." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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TARgzawxTFk6pdF8gafvZy2czacQkAK6eejWzl8+udN92Ldolbb9u6zrGDhsLwKjJo1i4bmGl+/do1yNp+3Z7tuOWwbcAcNaEs1i3ZV2l+5/Q6YSk7U/odEL4LAEDxw2sdF9I+OxF25f9LFWl1p+9SNntkz5L066pcv+y29879F565vVkcvFkbpt9W5X7l92+7GepKvrspe+z99LFL6U8qcRd/DUOuBN4KGHZVcA0d7/VzK6Knv8mZa/YvXsYXPK112D4cHhmEpzXOBQRdexY+kWcKk2ahPqV73wP/msYrHoH/nER9BkACzbC4W2h5ZwQU3U1bQrf/jac9xMongxV/WM3ahTGPjv5fCjuA7PGwMhrYPZbMP32cCUUJZVPNn8CwP6t9q/lGxaRhiz24i8z6wpMSbhSKQYGuvtKM9sPmO7uPSs7Ro2KvyAkk0mTQvHQL34Rbm3b1vo9ZLXrr4cbboCvvoKmTb/+9VPy605Eclc6ir/ivlIpz77uvjJ6/Cmwb3kbmdkoYBRAly5davYKv/89HH88/OAHdWullQvy88P9unWh7khEpA4yMal8zd3dzMq9lHL3scBYCFcqNTpwnz7hJqVJZc0aJRURqbNM7Py4Kir2IrpfHXM8ua19+3C/Zk28cYhITsjEpPIUcFH0+CLgXzHGkvtKrlRWK3eLSN3FWvxlZn8HBgJ5ZrYcuA64FZhgZpcAHwEj4ouwAUgs/kIV9CJSN7EmFXc/r4JVg+o1kIasXbswsoCKv0QkBTKx+EvqU+PGoQVclFTGzBrDmFljYg5KRLKVkoqEIrAoqUxZOKVavZRFRMqjpCKhBZiKv0QkBZRUJOlKRUSkLpRUJCQVNSkWkRTI6B71Uk/y88PEYzt3smfTPeOORkSymJKKhKTiDuvW8cwFz8QdjYhkMRV/yW4dIEVEaktJRZKSyh9e+gN/eOkP8cYjIllLSUWSBpWctnQa05ZOizceEclaSiqi4i8RSRklFQnjf4GaFYtInSmpSJjzfu+9daUiInWmJsUSRL3q27VoF3ckIpLFlFQkiJLKpBEvxh2JiGQxFX9JoPG/RCQFlFQkiEYqvnrq1Vw99eq4oxGRLKXiLwny82HtWmYvnx13JCKSxTI2qZjZh8BmYCeww90L440ox+Xnw65dsH17aA0mIlILGZtUIie6+9q4g2gQSjpAKqmISB2oTkWCxKQiIlJLmZxUHHjezOaZ2aiyK81slJnNNbO5a9Rqqe6ipNLJW9GpdaeYgxGRbJXJxV/93H2FmbUHXjCz9919RslKdx8LjAUoLCz0uILMGdGgko80Px/OvCzmYEQkW2XslYq7r4juVwNPAMfGG1GOy8sL97rqE5E6yMikYmYtzaxVyWPgJGBBvFHluD32gDZtGP3FJEY/OzruaEQkS2Vq8de+wBNmBiHGx9z92XhDagDy8yna9Ql8WhR3JCKSpTIyqbj7EuCIuONocPLzYbtacItI7WVk8ZfEJD9fTYpFpE4y8kpFYpKfD9u/ijsKaQjWr4f586FNGzjyyLijkRRSUpFS7dvT450d0K573JFIrti+HYqLQwJJvK1YEda3bg3r1kETfRXlCv0lpVR+PmOfchj3P3FHItnms89C8li4MPn+/ffhq+jqt2lTOOQQOPFEOOII2LABbr4ZioqgUEP75QolFSlVMlTLmjVhemGRstxh0SJ46SWYMyckjuLi5P5NjRvDgQdC9+4wZAj06RNuPXuGpuslli8PSeWVV5RUcoiSipTKz2fUMOClKxnb46m4o5FM4B6uNl56qfS2cmVY164d9O4Np50WEkaPHuG+W7fk5FGRTp2ga9eQVEaPTue7kHqkpCKl8vNZ2A7YuCTuSCQO7iFhvP12uL36KsyYAatXh/X77Qff/GbprVcvCH3Jaq9/f3j++fDadT2WZAQlFSmlkYobjs2bYcGC0gRSclu/vnSbLl3g5JNLk8hBB6X+i79fP3j44VCk1l0NRHKBkoqUKkkqX6lZcc7Yvj1UmpdNHh9+WLpNy5Zw2GFw1llw+OHhdthhpePBpVO/fuH+lVeUVHKEkoqUat48VLLqSiV7rV0LU6bAtGmh6W5i66vGjUO9x7HHwiWXlCaQrl2hUUz9oHv1gn32CUnl4ovjiUFSSklFkhRsbgl77RV3GFITixfDv/4Vbq+8EqaF3nff0Knw5JNLk8chh0CzZnFHm6xRo3C18sorcUciKaKkIknu+OgQ2NA67jCkMu4wbx48+WRIJAuiAbwPPxyuvTa0xjrqqOyp+O7XD556KjQIiOb1keylpCLJ8vNh2bK4o5BEO3eGepAZM+Dll0tbZDVqFFpP/d//hURy4IFxR1o7ifUqZ54ZbyxSZ0oqkuR7vd6FNit5JO5AGrKvvgpXIjNmhNvMmbBxY1h3wAFw0kkweDB85zv1U5mebkcfHerzlFRygpKKJFm+53bY40v1G6hvH38MTz8dbtOmwZYtYXmvXnDOOeGKpH//kFRyzR57wHHHqV4lR1QrqZjZPuUs3uzuaiaUa5o2DQll06Ywgqykx44dMGtWSCL//ndpvUi3bqEV1Le+FYqFGkodQ79+cOut8MUXoYmzZK3qXqm8AXQGNgAGtAU+NbNVwA/dfV56wpN6VzK8xpo1SiqptmlTSCBPPgnPPReKtJo0gQEDYMyYUJzVs2fDvELs1y/UHc2ZA4MGxR2N1EF1k8oLwER3fw7AzE4CzgIeAO4CjktPeFLvmjYN92vWwMEHxxtLLvjsM5g8GSZODIlk2zbo0CF0NDz1VPj2t8Pw7w3dCSeEZPrKK0oqWa66SeV4d/9hyRN3f97Mxrj7pWaWlobvZjYE+CPQGLjf3W9Nx+tIshPaHwXL3k4edVZqZv360ER24sQwrtX27WHwxMsvh+HDwxdoXJ0NM1WbNmE4fNWrpMeXX8KSJaEzbOK8NmlQ3aSy0sx+AzwePT8HWGVmjYFdqQ4qOu5fgG8Dy4HXzewpd3831a8lyW7pfwNc+CCcuzruULLLtm2hz8i4cfDCC6HO5IAD4Gc/C4nk2GOVSKrSrx888EA4d5q0q+a+/DJ0hP3ggzCWWsn9okWhm4B72K5RozAkzlFHhe1TrLp/ufOB64Ano+czo2WNgREpjwqOBRa5+xIAM3scOA1QUkm3xDlVpHLu8MYb4YvwscfCpFOdOsEvfwlnnx2ayjbE+pHa6tcP7rxTk3ZVh3sYv2327FAPNWdOOG+JQyy1axeSx4AB4f7gg8MwPYceCi1ahG3S8PmsVlJx97XATytYvSh14XytI5DYA285ZeptzGwUMAqgS5cuaQihYTpryoVwXmMmKalUbM0aePRR+NvfQqfEZs3gjDNCq61Bg8IYW1Jz3/hGuNekXbvbsCEkjVdfLU0kJVMStGgBxxwDV1wRJkPr3j2MKB3TRHvVbVKcD/wa6A00L1nu7t9KU1xVcvexwFiAwsJCjyuOXLNuyzpo00RXKmXt2hXqR8aODRXvO3aEf+S77oJzz9VMmamgSbvC5+qDD+Ctt5LrPhJHuejRI8yoefzxoX7usMMyqriwupE8CowHhgKXARcB6fzWWUFowlyiU7RM6kPTPZRUSqxaFYq3xo6FpUtD8eDPfw4jR4Z/ZkmthjRp15o1yYlj/nx4551QPwchUfTqFc5Jnz6hIcMxx4RirQxW3aTSzt3/amY/d/eXgJfM7PU0xvU60N3MuhGSybmEOhypD02bNuyk4h6mzb3nHvjnP0M59cCBcMstoZirOlPlSu3k4qRdX321e6ur+fNLp2WG0My8Tx/46U/DfZ8+IaFk2qjS1VDdpFJS+7PSzL4DfAKU18s+Jdx9h5n9BHiO0Bjgb+7+TrpeT8poqEll/Xp46KGQTIqLoW1b+PGP4dJLwz+4pF82T9rlDp9+Wpo0Soqw3nsvFGtB+EHSu3cYv60kefTpk1MjJ1Q3qdxoZm2AK4A/A62B0ekKCsDdnwaeTudryO4GdRsEC3fA6rkNowjCPVR+3nMPjB8fmmUef3xoGjxiBOy5Z9wRNizZNGmXO7z7bqhjmzo1JJG1a0vXd+oUEsZ3vlNafNW9e2kH4xxV3dZfU6KHG4ETAcxsdJpikhj97pu/g1ebwbaZ8Pnn0KpV3CGlx+bNoQXXPfeEL4O99gr1JJdeCgUFcUfXcGX6pF1ffRVGjp48OdyWLg3LCwrg9NNLrzwOPzwkxwaoLk0GfgnckaI4JJMk9lXJtaRSVBQSyaOPhqR5xBHh+fnn5957zVYlk3atWhVmsIzbypXhSmTy5DDUzqZNYaj+QYPgqqvClUjHjnFHmTHqklRyvFykYTrl0VPgszU8AyGpZOvET4m2b4dJk+CPfwzt+5s3D82AL7ss9HTP9SK+bFNSrzJzZv3Or+IerjzefDN0ai25X7UqrO/QIRSJDhsW5rMp6UAoSeqSVNQ3JAdt3b4VmkR/2myvrN+wITQFvvNOWL48lGfffjtcdJH6lWSy+pq0a+vWUJQ1dSrMnRuSSMlkaI0bhwr1IUPgyCOhb98Ql4baqVKlScXMNlN+8jBANZi5KnGk4mxUXAx/+lOobN+yJcxNcvfdYVRgfSlkvnRN2uUePhvPPhuKsaZPDw0zmjULdSLnnRcSyFFHhT5IzZtXdUQpR6VJxd1VyNwQZWNScQ8zJt5xR5izZI894IILQs/sPn3ijk5qqmTSrs8/D40oamvz5nAlUpJIPvooLO/ZMzTKGDIkjI2loqyUyZy+/ZI5GjcOv9KyJals2gSXXBKGmm/fHq6/PtSXZEIlr9ROyaRdr75a8/lVPvqotHXWiy+GOrVWrcJxrr4aTj45DAcjaaGkIkmG9hgaHuQvKR2wLpO9/XaY8GrJktDjffRoFVvkgppM2rVrF7z+emkiKZknpEePMKTO0KGhTiTH+4dkCiUVSXJl3yvDg/aPZ/6VyoMPhomv2rYNv0j79487IkmVyibt2rUr9FKfOTPcnnsutNBq3Dhc4YwZE1po9ehR/3GLkopUID8/c5PK1q1h8qv774cTTwxzmXToEHdUkmolk3Zt3Ajz5sGsWeE2e3aYphnC5/TEE+G734VTTmmwHQ4ziZKKJBk4biAA0/O7hF+DmWbx4jCTYlERXHMN3HBDRg37LSlUMmnX3nuXzlrYu3eYAO0b3whFWgcfrH5GGUb/jVK+TLxSefLJMJRKo0YwZUroySy565RT4MILoUuXkESOP179i7KAkoqULz8/9PHYsiX+5paffAI33hj6mhQWwj/+odY7DUHr1mHUaMkq6gkm5cuEuepXrAh1JwceGHrG/+xnoeJWCUUkYympSPlKkkoczYpLkslBB4Xpei+4ABYuDGN3ZeGkRSINiYq/JMmI3iPCg53RpEH1eaWyYkXoRX3ffaHj20UXhcr4XBjUUqSBUFKRJD865kfhweLF4b4+ksqWLaGn8z33hD4II0eGZNKtW/pfW0RSSklFkmzZvgWAFvVVp7JiRehj8OabYaiVa69VnYlIFsu4pGJm1wM/BEq+za6JphaWenDqo6cCMP2iF8OgjOlMKvPmhYSyaVOYlGno0PS9lojUi4xLKpHb3X1M3EE0aGbp7asycSJ8//vhNWbO1EjCIjlCrb+kYulIKu5w002hV3RBAbz2mhKKSA7J1KTyEzObb2Z/M7Nyu9Ca2Sgzm2tmc9dkWs/vXJGfn9omxV9+Ga5Ofvvb0Ez4P//R8PQiOSaWpGJmU81sQTm304C7gYOAAmAlcFt5x3D3se5e6O6F+SWVypJa7dun7kpl9eowhPkjj4Te8Q8/rCHqRXJQLHUq7j64OtuZ2X3AlDSHIwlGFowsfZKq4q933w1T+a5eHYZYGT687scUkYyUcRX1Zrafu6+Mnp4BLIgznoZmt6Ty+eeh2Kq2VxXvvx+GJm/UCGbMCGN3iUjOyrikAvyPmRUADnwIXBprNA3M2i1rAchrkZc8/lfnzjU/2AcfwLe+FVqSvfgi9OqVwkhFJBNlXFJx9wvjjqEhGz4hFE1NHzm9bkllyZKQULZvh+nTlVBEGoiMSyqSQWrbq/6jj0JC2bIltPDq3Tv1sYlIRlJSkYq1jwaVrEmz4uXLQ0LZuBGmTQvzjItIg6GkIhWr6ZXKypUhoaxZA1OnwlFHpS82EclISipSsTZtoGnT6iWVVatCQvnkE3j+eTj22PTHJyIZR0lFklxeeHnpEzPIy6s6qaxdC4MHh7qUZ5+Fvn3TG6SIZCwlFUlyzmHnJC+oqgPk4sVw+umwaBFMmQIDBqQ1PhHJbJk69pfEZNnGZSzbuKx0QWVJ5dlnQ2fGFStCQhk0qH6CFJGMpaQiSS584kIufCKhq1B5SWXXrjDS8KmnQpcuMHeuEoqIACr+kqq0b5/cpHjTpjB3/JNPwvnnw9ix0LJlbOGJSGZRUpHK5eeHRLJtGyxdCmecEYZfuf12+PnPQ2W+iEhExV9SuZK+KvffH5oJr1sX+qCMHq2EIiK7UVKRypUklZ/8BA45BN54AwYOjDUkEclcKv6SJFeccEXygp49wxXJJZfAn/+sibVEpFJKKpJkWM9hyQt694b166Ft21jiEZHsouIvSVK8tpjitcXJC5VQRKSadKUiSS6dEuZEmz5yeryBiEhW0pWKiIikjJKKiIikTCxJxczONrN3zGyXmRWWWXe1mS0ys2IzOzmO+EREpHbiqlNZAJwJ3Ju40MwOBc4FegP7A1PNrIe776z/EEVEpKZiSSru/h6A7d4j+zTgcXffBiw1s0XAscDs+o2w4frtgN/GHYKIZLFMa/3VEZiT8Hx5tGw3ZjYKGAXQpUuX9EfWQAw+cHDcIYhIFktbUjGzqUCHclZd6+7/quvx3X0sMBagsLDQ63o8CYo+LQKgoENBrHGISHZKW1Jx99r85F0BdE543ilaJvVk9LOjAfVTEZHaybQmxU8B55pZMzPrBnQHXos5JhERqaa4mhSfYWbLgROAf5vZcwDu/g4wAXgXeBb4sVp+iYhkj7hafz0BPFHBupuAm+o3IhERSYVMK/4SEZEslmlNiiVmNw+6Oe4QRCSLKalIkr6d+8YdgohkMRV/SZJZy2Yxa9msuMMQkSylKxVJcs20awD1UxGR2tGVioiIpIySioiIpIySioiIpIySioiIpIwq6iXJHUPuiDsEEcliSiqSREPei0hdqPhLkkxdMpWpS6bGHYaIZCldqUiSG2fcCGgGSBGpHV2piIhIyiipiIhIyiipiIhIyiipiIhIyqiiXpLcO/TeuEMQkSwW1xz1Z5vZO2a2y8wKE5Z3NbOtZlYU3e6JI76GrGdeT3rm9Yw7DBHJUnFdqSwAzgTK+1m82N0L6jccKTG5eDIAw3oOizkSEclGsSQVd38PwMzieHmpxG2zbwOUVESkdjKxor6bmb1pZi+ZWf+4gxERkepL25WKmU0FOpSz6lp3/1cFu60Eurj7OjM7GnjSzHq7+6Zyjj8KGAXQpUuXVIUtIiJ1kLak4u41HufD3bcB26LH88xsMdADmFvOtmOBsQCFhYVet2hFRCQVMqr4y8zyzaxx9PhAoDuwJN6oRESkumKpqDezM4A/A/nAv82syN1PBgYA/2Vm24FdwGXuvj6OGBuqh894OO4QRCSLxdX66wngiXKWTwIm1X9EUqJzm85xhyAiWSyjir8kfuMXjGf8gvFxhyEiWUrDtEiSu+feDcA5h50TcyQiko10pSIiIimjpCIiIimjpCIiIimjpCIiIimjinpJMnHExLhDEJEspqQiSfJa5MUdgohkMRV/SZJxReMYVzQu7jBEJEspqUgSJRURqQslFRERSRklFRERSRklFRERSRklFRERSRk1KZYkT1/wdNwhiEgWU1KRJC2atog7BBHJYir+kiR3vX4Xd71+V9xhiEiWUlKRJBPemcCEdybEHYaIZCklFRERSZlYkoqZ/a+ZvW9m883sCTNrm7DuajNbZGbFZnZyHPGJiEjtxHWl8gJwmLv3ARYCVwOY2aHAuUBvYAhwl5k1jilGERGpoViSirs/7+47oqdzgE7R49OAx919m7svBRYBx8YRo4iI1FwmNCn+ATA+etyRkGRKLI+W7cbMRgGjoqfbzGxB2iJMnTxgbdxBVEOeXWxZESdZcj5RnKmSDTFC9sTZM9UHTFtSMbOpQIdyVl3r7v+KtrkW2AE8WtPju/tYYGx0nLnuXliHcOuF4kwtxZla2RBnNsQI2RVnqo+ZtqTi7oMrW29mI4GhwCB392jxCqBzwmadomUiIpIF4mr9NQT4NfBdd9+SsOop4Fwza2Zm3YDuwGtxxCgiIjUXV53KnUAz4AUzA5jj7pe5+ztmNgF4l1As9mN331mN441NX6gppThTS3GmVjbEmQ0xQgOO00pLnkREROpGPepFRCRllFRERCRlMjKpmNmQaJiWRWZ2VTnrm5nZ+Gj9q2bWNWFducO8VHXM+ozTzL5tZvPM7O3o/lsJ+0yPjlkU3drHFGNXM9uaEMc9CfscHcW+yMz+ZFHFWExxXpAQY5GZ7TKzgmhdSs9lNeMcYGZvmNkOMxteZt1FZvZBdLsoYXkc57PcOM2swMxmm9k7FoZROidh3TgzW5pwPgviijNatzMhlqcSlneLPiOLos/MHnHFaWYnlvl8fmlmp0fr4jifvzSzd6O/7TQzOyBhXWo+n+6eUTegMbAYOBDYA3gLOLTMNj8C7okenwuMjx4fGm3fDOgWHadxdY5Zz3EeCewfPT4MWJGwz3SgMAPOZVdgQQXHfQ04HjDgGeCUuOIss83hwOJ0nMsaxNkV6AM8BAxPWL4PsCS63zt6vHeM57OiOHsA3aPH+wMrgbbR83GJ28Z5PqN1n1dw3AnAudHje4DL44yzzGdgPdAixvN5YsLrX07p/3vKPp+ZeKVyLLDI3Ze4+1fA44ThWxKdBjwYPZ4IDIqyZ0XDvFTnmPUWp7u/6e6fRMvfAfY0s2Z1jCelMVZ0QDPbD2jt7nM8fOIeAk7PkDjPi/ZNlyrjdPcP3X0+sKvMvicDL7j7enffQBj/bkhc57OiON19obt/ED3+BFgN5NcxnpTHWZHoM/EtwmcEwmfm9AyJczjwjCd3o0il6sT5YsLrJw6RlbLPZyYmlY7AsoTn5Q3V8vU2HsYQ2wi0q2Tf6hyzPuNMdBbwhrtvS1j2QHQ5/Ls6FoXUNcZuZvammb1kZv0Ttl9exTHrO84S5wB/L7MsVeeyunHWdN+4zmeVzOxYwi/exQmLb4qKTm5PwQ+husbZ3MzmmtmckiIlwmfiMy8dWzBjzifhCrvs5zPO83kJ4cqjsn1r/PnMxKTSYJhZb+C/gUsTFl/g7ocD/aPbhXHERij26OLuRwK/BB4zs9YxxVIlMzsO2OLuiWPAZcq5zDrRL9SHgYvdveTX99VAL+AYQjHJb2IKr8QBHoZCOR+4w8wOijmeCkXn83DguYTFsZ1PM/seUAj8b6qPnYlJpTpDtXy9jZk1AdoA6yrZNx3Dv9QlTsysE/AE8H13//qXoLuviO43A49Rt1Gaax1jVIS4LoplHuHXao9o+04J+8d+LiO7/QpM8bmsbpw13Teu81mh6MfDvwnj9H09wKu7r/RgG/AA8Z7PxL/vEkL92ZGEz0Tb6DNS42OmI87ICOAJd99esiCu82lmg4FrCSOabKti35p/PlNVSZSqG6GX/xJCRXtJZVPvMtv8mORK2wnR494kV9QvIVReVXnMeo6zbbT9meUcMy963JRQLnxZTDHmA42jxwdGH6R9vPyKu1PjOpfR80ZRfAem61xWN86Ebcexe0X9UkIl6N7R49jOZyVx7gFMA0aXs+1+0b0BdwC3xhjn3kCz6HEe8AFRpTTwD5Ir6n8UV5wJy+cAJ8Z9PgmJdzFRY4x0fD5r/QbSeQNOJUzetZjwawngvwiZFaB59MFZFL3hxC+Ta6P9iklopVDeMeOKE/gt8AVQlHBrD7QE5gHzCRX4fyT6Yo8hxrOiGIqAN4BhCccsBBZEx7yTaGSGGP/mAwlD/SQeL+XnsppxHkMod/6C8Kv5nYR9fxDFv4hQrBTn+Sw3TuB7wPYyn82CaN1/gLejWB8B9ooxzr5RLG9F95ckHPPA6DOyKPrMNIv5796V8KOnUZljxnE+pwKrEv62T6X686lhWkREJGUysU5FRESylJKKiIikjJKKiIikjJKKiIikjJKKiIikjJKK5JQyI9cWWcII1tnOzI40s79Gj0ea2Z1l1k83s8JK9n/czLqnO05p2OKaTlgkXba6e0F5K6Kxv8xLhx3JNtcAN9Zh/7uBXwM/TE04IrvTlYrkNAvzwhSb2UOEDlydzexXZvZ6NJDfDQnbXmtmC83sFTP7u5ldGS3/+grAzPLM7MPocWMz+9+EY10aLR8Y7TPRzN43s0dLBrM0s2PMbJaZvWVmr5lZKzObkTiXRvT6R5R5H62APu7+VjXe83cTrtSKzWxptOplYHDCECYiKacPl+SaPc2sKHq8FPgF0B24yN3nmNlJ0fNjCcNOPGVmAwg9oc8FCgj/F28QeuRX5hJgo7sfE40wO9PMno/WHUkYNugTYCbwDTN7DRgPnOPur0djbG0F/gqMBEabWQ+geTnJo6RXc6JzzKxfwvODAdz9KeApADObALwULd9lZouAI6rx3kRqRUlFck1S8VdUp/KRlw6MeFJ0ezN6vhchybQiDPi3Jdrv65kEK3ES0MdKZ/prEx3rK+A1d18eHauIMFTHRmClu78O4O6bovX/AH5nZr8iDJUxrpzX2g9YU2bZeHf/ScJ7nZ640sx+TTgff0lYvJow+ZaSiqSFkoo0BF8kPDbgFne/N3EDMxtdyf47KC0qbl7mWD9198ThzDGzgUDi/Dg7qeR/zd23mNkLhAmVRgBHl7PZ1jKvXaloJNqzgQFlVjWPjiWSFqpTkYbmOeAHZrYXgJl1tDB3/QzgdDPbM6q/GJawz4eUftEPL3Osy82saXSsHmbWspLXLgb2M7Njou1bJdRv3A/8CXjdw8x7Zb1HVLxVFQvzjv8FONvdyyaQHuxejCaSMrpSkQbF3Z83s0OA2VHd+efA99z9DTMbTxj1djXwesJuY4AJZjaKMM9IifsJxVpvRBXxa6hkqlV3/8rMzgH+bGZ7Eq4YBhPmWp9nZpsI82qUt+/7ZtbGzFp5mB+mMiMJMyA+Gb3HT9z9VDPbl1Ac9mkV+4vUmkYpFimHmV1P+LIfU0+vtz9hoqleFTV5NrNfAJvd/f5avsYvgE3u/tdaBypSBRV/icTMzL4PvEqY/6KyPjR3k1xXU1OfAQ/WYX+RKulKRUREUkZXKiIikjJKKiIikjJKKiIikjJKKiIikjJKKiIikjL/H3DbH9bCFmNTAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "\n", "# Plot lag-frequency spectrum.\n", "plt.plot(cross.freq, lag, 'r')\n", "\n", "# Find cutoff points\n", "v_cutoff = 1.0/(2*delay)\n", "h_cutoff = lag[int((v_cutoff-0.0050)*1/0.0050)]\n", "\n", "plt.axvline(v_cutoff, color='g',linestyle='--')\n", "plt.axhline(h_cutoff, color='g', linestyle='-.')\n", "\n", "# Define axis\n", "plt.axis([0,0.2,-20,20])\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('Lag')\n", "plt.title('Lag-frequency Spectrum')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to Uttley et al (2014), the lag-frequency spectrum shows a constant delay until the frequency (1/2*time_delay) which is represented by the green vertical line in the above figure. After this point, the phase wraps and the lag becomes negative. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Energy Dependent Impulse Responses" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In practical situations, different channels may have different impulse responses and hence, would react differently to incoming light curves. To account for this, stingray an option to simulate light curves and add them to corresponding energy channels.\n", "\n", "Below, we analyse the lag-frequency spectrum in such cases. \n", "\n", "We define two delta impulse responses with same intensity but varying positions, each applicable on different energy channels (say '3.5-4.5 keV' and '4.5-5.5 keV' energy ranges). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "delays = [10,20]\n", "h1 = sim.simple_ir(start=delays[0], width=1)\n", "h2 = sim.simple_ir(start=delays[1], width=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we create two energy channels to simulate light curves for these two impulse responses." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sim.simulate_channel('3.5-4.5', var, h1)\n", "sim.simulate_channel('4.5-5.5', var, h2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute cross-spectrum for each channel." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "cross = [Crossspectrum(var, lc).rebin(0.005) for lc in sim.get_channels(['3.5-4.5', '4.5-5.5'])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate lags." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "lags = [c.time_lag() for c in cross]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get cut-off points." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "v_cuts = [1.0/(2*d) for d in delays]\n", "h_cuts = [lag[int((v_cutoff-0.005)*1/0.005)] for lag, v_cut in zip(lags, v_cuts)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot lag-frequency spectrums." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plots = []\n", "colors = ['r','g']\n", "energies = ['3.5-4.5 keV', '4.5-5.5 keV']\n", "\n", "# Plot lag-frequency spectrum\n", "for i in range(0,len(lags)):\n", " plots += plt.plot(cross[i].freq, lags[i], colors[i], label=energies[i])\n", " plt.axvline(v_cuts[i],color=colors[i],linestyle='--')\n", " plt.axhline(h_cuts[i], color=colors[i], linestyle='-.')\n", "\n", "# Define axes and add labels\n", "plt.axis([0,0.2,-20,20])\n", "plt.legend()\n", "plt.xlabel('Frequencies (Hz)')\n", "plt.ylabel('Lags')\n", "plt.title('Energy Dependent Frequency-lag Spectrum')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
moizumi99/CVBookExercise
Chapter-5/CV Book Ch 5 Ex 6.ipynb
1
534564
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from PIL import Image\n", "from numpy import *\n", "from pylab import *\n", "import scipy.misc\n", "from scipy import ndimage\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import stereo\n", "stereo = reload(stereo)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "im_l = array(Image.open('scene1.row3.col3.ppm').convert('L'), 'f')\n", "im_r = array(Image.open('scene1.row3.col4.ppm').convert('L'), 'f')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plane_sweep_ncc_2dir(im_l, im_r, start, steps, wid):\n", " \"\"\" Find disparity image using SAD (sum of absolute difference). \"\"\"\n", "\n", " m, n = im_l.shape\n", "\n", " # arrays to hold the different sums\n", " mean_l = np.zeros((m, n))\n", " mean_r = np.zeros((m, n))\n", " s1 = np.zeros((m, n))\n", " s1_l = np.zeros((m, n))\n", " s1_r = np.zeros((m, n))\n", " s2 = np.zeros((m, n))\n", " s2_l = np.zeros((m, n))\n", " s2_r = np.zeros((m, n))\n", "\n", " # array to hold depth planes\n", " dmaps_lr = np.zeros((m, n, steps))\n", " dmaps_rl = np.zeros((m, n, steps))\n", "\n", " # compute mean of patch\n", " ndimage.filters.uniform_filter(im_l, wid, mean_l)\n", " ndimage.filters.uniform_filter(im_r, wid, mean_r)\n", "\n", " # normalized images\n", " norm_l = im_l - mean_l\n", " norm_r = im_r - mean_r\n", "\n", " # try different disparities\n", " for displ in range(steps):\n", " # move left image to the right, compute sums\n", " # sum of nominator\n", " ndimage.filters.uniform_filter(np.roll(norm_l, -displ-start)*norm_r, wid, s1)\n", " ndimage.filters.uniform_filter(norm_l*np.roll(norm_r, +displ+start), wid, s2)\n", " # sum of denominator\n", " ndimage.filters.uniform_filter(\n", " np.roll(norm_l, -displ-start)*np.roll(norm_l, -displ-start), wid, s1_l)\n", " ndimage.filters.uniform_filter(\n", " np.roll(norm_r, +displ+start)*np.roll(norm_r, +displ+start), wid, s2_r)\n", " # sum of denominator\n", " ndimage.filters.uniform_filter(norm_r*norm_r, wid, s1_r)\n", " ndimage.filters.uniform_filter(norm_l*norm_l, wid, s2_l)\n", "\n", " # store ncc scores\n", " dmaps_lr[:, :, displ] = s1/np.sqrt(s1_l*s1_r)\n", " dmaps_rl[:, :, displ] = s2/np.sqrt(s2_l*s2_r)\n", "\n", " # pick best depth for each pixel\n", " dl = np.argmax(dmaps_lr, axis=2)\n", " dr = np.argmax(dmaps_rl, axis=2)\n", " dd = np.absolute(dl - dr)\n", " \n", " dl[dd>2] = 0 # if the difference is bigger than 2, ignore\n", " \n", " return dl" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "def plane_sweep_ncc_gauss_2dir(im_l, im_r, start, steps, wid):\n", " \"\"\" Find disparity image using SAD (sum of absolute difference). \"\"\"\n", "\n", " m, n = im_l.shape\n", "\n", " # arrays to hold the different sums\n", " mean_l = np.zeros((m, n))\n", " mean_r = np.zeros((m, n))\n", " s1 = np.zeros((m, n))\n", " s1_l = np.zeros((m, n))\n", " s1_r = np.zeros((m, n))\n", " s2 = np.zeros((m, n))\n", " s2_l = np.zeros((m, n))\n", " s2_r = np.zeros((m, n))\n", "\n", " # array to hold depth planes\n", " dmaps_lr = np.zeros((m, n, steps))\n", " dmaps_rl = np.zeros((m, n, steps))\n", "\n", " # compute mean of patch\n", " ndimage.filters.gaussian_filter(im_l, wid, 0, mean_l)\n", " ndimage.filters.gaussian_filter(im_r, wid, 0, mean_r)\n", "\n", " # normalized images\n", " norm_l = im_l - mean_l\n", " norm_r = im_r - mean_r\n", "\n", " # try different disparities\n", " for displ in range(steps):\n", " # move left image to the right, compute sums\n", " # sum of nominator\n", " ndimage.filters.gaussian_filter(np.roll(norm_l, -displ-start)*norm_r, wid, 0, s1)\n", " ndimage.filters.gaussian_filter(norm_l*np.roll(norm_r, +displ+start), wid, 0, s2)\n", " # sum of denominator\n", " ndimage.filters.gaussian_filter(\n", " np.roll(norm_l, -displ-start)*np.roll(norm_l, -displ-start), wid, 0, s1_l)\n", " ndimage.filters.gaussian_filter(\n", " np.roll(norm_r, +displ+start)*np.roll(norm_r, +displ+start), wid, 0, s2_r)\n", " # sum of denominator\n", " ndimage.filters.gaussian_filter(norm_r*norm_r, wid, 0, s1_r)\n", " ndimage.filters.gaussian_filter(norm_l*norm_l, wid, 0, s2_l)\n", "\n", " # store ncc scores\n", " dmaps_lr[:, :, displ] = s1/np.sqrt(s1_l*s1_r)\n", " dmaps_rl[:, :, displ] = s2/np.sqrt(s2_l*s2_r)\n", "\n", " # pick best depth for each pixel\n", " dl = np.argmax(dmaps_lr, axis=2)\n", " dr = np.argmax(dmaps_rl, axis=2)\n", " dd = np.absolute(dl - dr)\n", " \n", " dl[dd>2] = 0 # if the difference is bigger than 2, ignore\n", " # you could put -1 here, as invalid value, and add post processing to fill this area\n", " \n", " return dl" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "start = 4\n", "steps = 12\n", "wid = 9\n", "res = stereo.plane_sweep_ncc(im_l, im_r, start, steps, wid)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start = 1\n", "steps = 12\n", "wid = 3\n", "res2 = stereo.plane_sweep_gauss(im_l, im_r, start, steps, wid)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "start = 4\n", "steps = 12\n", "wid = 9\n", "res3 = plane_sweep_ncc_2dir(im_l, im_r, start, steps, wid)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "start = 1\n", "steps = 12\n", "wid = 3\n", "res4 = plane_sweep_ncc_gauss_2dir(im_l, im_r, start, steps, wid)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "image/png": 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DgFg8z9snd9BpZLlY0Wl4D301JHPa3YM6hfPh3Xd4fvmCohUvZN0O0XQooDGK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SrBGLM4DVI8dI0Q0+CpVR3bpirLFGA6M7S9MfcZxXKrfO+XaAgcgt1bEpQp8MxQhtMoj15JI0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\n73OLK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UeEXPjuk0+4mEbMy2fkeeYQJkyjElbjGjZ9ZpDEw1fucdR1JCaeXF5xOdzQOM8H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\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oioFiO0lOEcXVNbib9mTMaC4XJSNM6uqcZdfkWqRP7GZsVpnJCy5aVIrFsZixhncGPa+n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\nMyIHkWGTUqlFZUiqTAmtx5CSpxk0vP78i/zRez9ma7TLIvR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LzHjGic0tlsuOowuvsAyd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\nxtDwzoFveRIH4Zh4xCfucE41m3snNs8xAJQ2nRCKJmqjKFUb9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aXn/uMzTs9WfPTxp/z4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B7He2SklJlbpWPsDgoKMktqhCyLfR3u0b01pe8w4g664gFMr5TwqnDvAS5rE0xIdpSS2EDj\nGgHloimZlDpBRyYmnCmD9dEvhEPqN7i+Fc2ZgyZmySA5XqM7AyJ6XEYRaHriXoM2lHAyvZcjym8D\nnE8xgu0dm52ffnbB6+tb/ta/8pe529/wD3/8Oe9uZsokMX2vladPTvir3/8ez69fA8aTh+e8vnqt\nxo98zKzSgxlUNhGpWduGB9OWeZmF/KeCtwW3qIJTIZfMBBzqzOEwy8RgkhfkZIMq1tQMuhz8Ks52\nteXJ+QO2acVnL58L8e8t9FERitsrf+Mv/pD/+G//B/wXf/e/4el7z5j6ivceP+GXX/yEf/aLX3J7\nuKMU2Yh60jKuLVCu3qPYy1AytutspzXzXLm6vQr0KCtjwxLe4Pqwp5S1Fl5QS672jcWdd1d3lNWG\n2j3UfkJ3JnO6J3bXN+zOTlmP/BTX55jzirxeY9mxshozeH1NTFtipWNtoV9e8fmrV9SctBm4aLD9\ncKC1hcUaK894Npb5AGa0pXFzec2LN2/oSc1AbZ31ZmK1mmhtYd7vePHigt//8U+1OWYJii3G+C0m\nS0bXOD0Ru2Ejm9NcBh8tMIeSDauoofZGSxYTpWBNG0JtjKNNOjhTAuX6qXmyJGcn73Z0ccRE8U2p\nhRmkwItjOCn360EGMoCLr6/+Tcjf3DpLEyAhDacfNyIj4a2xuAXnPRqykWyAmhzvVfiK98iLCR3I\neI/u4BnLalxF07QQT0ejGXbQ5HBkNCKTKJqxmBx2h5IUhG19lA/SgXgTqBPGjnr/ix+nSz4KoHEY\n45rSaU4poCKQerNhHJDCCTGcVZMfqYYJY5sbv3a+lQ2zdRVeg6brPYTpldYzNO1vooiM5ktdpI+m\n3PRADIc7Qhs3+DTuHfOYzllW03b882+Gzn13fXf9aVeSy0ec0RlSjUI1kOduMrLqRZqPIbqM/EIr\nMZWINfCjn73iy3dX/Nu/85t0LyyL8X/87DlfvXnHJhgL6xX88NOP2KbCfpHD3JOTEy6v9hysaVpn\nPYqwoQsd2UjOtqywnDgcDiQUlNt7O4I1ZjCVIkaUV5be6csqHHE1yW8uZ1ptw0LU51pZr9dirQTY\nJ92StKdxLPFv/M5f5Xe//xv8/o//iLsupslqWrMuBeuNv/O//T22mymAL1TsB0UtWRcohCjL3VXc\nrm3F5Mbt/pbWx5TRub69IqGaiF74J//nj/i3/sbf4OH2Y3pdWE1ZDYsZy27h8uVL+oMzTqZQyyaH\nnCllheUJK7rfqY1ORZoj/WsNjl3l1fO33Ljem3uitUo9LCy7a7Yr0bzacsCDukZ3lt3MxdtL5mVm\ns9qKfoaRizEl0UN3dwcu3r7l4vVL9ss+zq+ukGZXs+pNE6BmPYBA1U3J5Dxocf5agPOyeb83kFNN\nIZCuWApzFo5adJ1ThRyZkZbUKHUbBN2IbulBqXAZyhRcbsnx/AxzjhSOa04+UvySZ0iiB7cwFqku\ndkopmUIAoeHgjGlIMNfGXBNLraJGggA7BPxLYq8VOnliifCYfKxXkwCKnmIdAVhEAbkm3x66QeEF\nyr81MTuyj0NTZ7GlRPZKa67aPRosc02n+gBPA64VUNrpPmqM0MxF3WN4+EroczWDIqoPnaxJvFcs\nTSxLTJtbohicbYxNeUDqjZxzuD93rJq0gE0uoo5YbeRE7TIW0zBQcp82hEs+ZlwttOgCQL2O0O0A\niWsTUJ0TtBhgRCPng3nQBW4dB1Hf4PqWNGcDk4kMBkMTGQtUICMLpxiqqNip9BphlbmG2HVQxDzQ\nZE2KaE67cepGVDWuO+2y0286fZoUzpgTfc783589B+B0s9JEKzLPxqsc92MkIAQIw4FKabMyWyzJ\n0eUw41aobU9rzipn9Rc1UeeFJU8S0YbNrqYVKnINODs75XsffEKmc3Z2yu3dLTeHGzbrLftdC/TP\naYEefe+Hv8GyrXz6g0+5vj7w9371D/hh/5TNasvNssPIbEphjXGaCzNIYzRcBlM4G/oQeIuru8yV\n9WpFSTI9kQU70uXV+eir0rrx/O07TRYOcLu/ZbMplHDUG5Mxi/tzfXHJsl2xWq0plimrFazXQI8M\nKQDRGD2aEIOgs4ItB95+dcG1yPo4UJcFK5nDfk/b37AsB6xNVDrz/iAecoermx37NoezoRCaktfM\ndzt2uwMvvvqKz168iomGLJzl9pciYLqIn43TMlqMvR3jH5zEUnuYJMlF0oo2HQ+NpAw/YgzeFcpq\nMYkb6oqOpihpWDD5kA7rOSQQLSecbr5m1WSpC+gwNXTWXRPp6Op6a7LONeMwO7vR9FbDqExWgERZ\ndYUpA31pzNk4rAp5ymyLxf4UE0CMYikaTAVIM+hQBikHZcCzpn2BvXg1oa1ekeW2aULogTclbYIy\nQJQTlpw3TQ1KJgAaHQaGwIA+JmDoGRK1IA4XnDxcH4+6CGK6qt8psbSJduQS6vcmJFz0itFc5Tj4\ndDj13uhZn33ucnPrXultEbdd40Qh5dGVK2JChW0LjdrS29GhqltoNHrHKAoc9fsIBr1rOUUN18rv\nru+uP+slujGSG6SEshlbPOsualw+SLPVh7ZUFLIBNHkgyfW2s193mhmbIiOD5XphedfIB7kjT6eF\ni6s9l1ZZ5zUzcqvDEq3XAKRUsHnsM0MZ1zscAjgCh2zsFVIUhhXOKk20XJm94S1RF1mQpySjhs46\n9OggBY9RVhMfPHzEdrPmZ198Rs6yzHcn3GkF9nScjz/9gMOm8eDpA05wbm/2/Pj5LyiT81vPfp11\n5BGuSmHKWfbksYJ7h1LUPKSU8dyYJkkXbue9WoMYZmWMs/WGw9yCNma8+Oot//SP/ogfPHsmN8Dg\nZA/2wrKbeXd4Q376iJxW5FIomy1pSuCzCuuox/SL4nA3FHGTOtTGFzc3WDSP3hqtN+ZlZt7dUDcb\nWl3YzzvwxFwVB1APC1c3t9IcgjQ9NKaS2N3u2N3tuXj1ktu7Gw77A0utAeq6wKyx/wdVUJTBSu6B\n4TGFAAAgAElEQVRIA9xdTI2oG40AIxFI7ziWJo52+4hqrxB1dVJ5sChckzU1all7bpL2W7puIInG\nFq1b0Hd1VpeuO9o8Rb4aWF/wPFFw3CopDD5a7zL6WEyAwOLQOqt1YTURcRYdugzj5qVyWCoFBVWn\n4erpEQhvOs96zA6yD9OaoRvV9McjcgcvocGXMUYvDasuoL7GWmqaPjmhXw+H9EynJdnlJ6TWzFnn\nZqeFh4CcEVsVK4UxOXITTddHTlyhNmJKp4bNHZZF2WPVO9ZTSACCI9LkMMmIuTAU89wbOa0DpA4N\ntsHSG3ObWbFi7oUpso97d0kCasVSkkutGTSPYUmNfcwVcxSNnmCVHs6bKiCSj9enNWTukgQ1QoJg\n32jf/VY0Z1F3H4WjHsXleHNjNHovLgZ6w5rS4/FJCHcgyMdAStAG7qZw6TAfGFNIofROCS3S7c2B\nOi+UbeF0sz46JBFIoetfoxj22LjV4ffauLze0fvC3GY2qzV1qezmmWzG7bKjLyPpfVadvRirsgFU\n+GuY3VmVwoOTNf/aD/8Scz/w5OQhqWT+8CdvWJYDp+sTuovGJxQCTs5O+PPPvsfnz7/k++9/wn/2\nv/+XbPMD/tkf/wk5FXb1wMON9Gj7ZWHfKje7O3oLe+DBUY6FZ9nZH/acn50LtWiNQ6usN1sVowYP\nNxueX95idhKbt+gQlkWP+OLVa7brLaePT5nrzNo09ZD1aWE5LPSlsmwb27NT0mqS1mwUlseNchSa\n0RljR/ThUA94aJ2cGLH3xDIfOFxdMB8aSwQhtmXQCZocKFEhXbsW1+5u5tXFO+a7C27vdtR5IWWn\nNgmFU1LLVIIHIbKBCIMtphqWxYE2R81GA0MLftACRhHtxECOHiJ5G+xW0fvMjl9pZLlemWE9Jrld\nKF516H2BXOIejmc8kUxUXHOP7HVNaQarJVnibqnUpVHjsxhrsk/OlGSmoWnWQk2F23mhLEa2QsrG\nSmOlo5BZ9Dzh2PexCxmoR3T5uJItdFR+0AbXuw59OrUrWNKTYa1FuGS4i3oiDwOPoDBaCuczy1Rf\ntEG2qgByjPGA56BrpjGttgH4JLBG6nLJ6q0JNkxVB20Xst2oJB2xetfueJdBgPeI7O2jkIVWPFD6\naNyOIZzj0EZgiAsIoOuw8u6RhRMUrq59kB4i6+CPjsDe5GHOMxxpvru+u/4lXLKW73gApOqLBODR\nu0CqoCVhRRSexhEwczt6iAZNWEZWrfsw3g26eGKaoLfOy4tbTlaZ8wdrcGOaVgqiJZDo0PFYasfX\naK7ieb8/0N3ZLzPc3Yk65bDbH1hNmavdLXU+cGhoUTsKGZaLBOuhB5oyeQWrYvy1P/+bnK0nVmXD\n//XTP+bJw3Pu6i60veVoyLRaFdae2O93nJ8+wFvm7//B3+V6J5rgm8srIMlobKnMzDTv7Hd30p6H\ncYDysDJeYSpr9vPCNMkEiK6pxTQZHzx6wi+efxU0Qljmzo9+8s/5d//m32RbJhXHjO1gUPqcm6tb\nNqeJ7ckmos0OjCYXBs4n0FI9XgCjof2uLaQMrdKqzvRWK4e7a673lbvbPft50W/uoo3td3v2h4Nc\nofHQFhu3V3uuLl8z7y6xXjFv6KyQQ7CZits+8saEYh+bjRpaM+L1yfx+0M3VoRhh9uJqGBLKWE2J\nECcMGnzY06eROapCW66dXXWnhZlF0OGI51lZdJWeCoQfgZvYXCmo68k9mjXFIdXqNEssi7TWrfXj\nOeKL2ErJmuiHiPK+WxrrubO2zJSNbI5ZVUyBmyY34b5oZjSv0rYluTs6jWyTPBpsqPagVU04rQ3d\neRjxxKTSo8lvvR2pnrXKmRMvigBgAIMpjPwKKXcB+gFAYv1ociJqsu6Rdxnz0KLQDht/t5jyxf97\n6NNk9pHxmFyptGnRtEGlRS6q7mGPJhkQZcg9mvNwa+xRB4zV0js1HCR7H69JXg8x9qNHDBPeIjBe\nBIOxXsbP8yS6KvRhffAvvL4VzZkzELB7C1mGyHB06kOP5Cl0OMofwLI2keT0uqiFi2743r40OKxJ\nCN7SHaYEJYUo38hmpJ7Z3S082qzZrNdY0YPe0NTL6+CXRhOp54fD4cAvv/oSy5m73S3racvJ6RqW\nLlFkXh35w0ubw4TAOVCZDzekqbDf37HebMgOm82aDx49IJUF643b/o5l73xx8ZaSN1SX5i2Zsbic\n8B6crfjizS94+/YFc8+0usZxlrvOoR+43S1sTla8ubmh765o3uhLizyKcuTv2tdQp3u57b1eC1OY\nMwan6xPa8pKcyjE/4+MnT7m6m7m4u+bnn/+KJ2dnPHv/EbZv2GpNb/0YnoypKZoPjZT2TKcPWSW0\nuI9aHD0hFmjpUJtp35Uteb27o9UqxMUd98r+pvL5zc+VB9dqoGydKWfME/Nh0Xv3HAho426/Y96/\nBZdNctQkJBTK7MG17tEkeRN9rFOEjogXc1z4QwM0Pk2PhiRFkWFNA3Q3TXeNmOxoYCut4hElHAW7\nJqY96UBI5vG7Q0eItGHeuiaMreM5GiavkAq9hslNUASTDaQrBLJxWM0BHq3No3AABJRzd2hsLbNO\nlb5a6TOTAo/eYuQ/dFXeg54SG3OrYVcrLUiahih30Ih0j0usdVw+rUfNIZCojDBOGepISwoVG+6L\niaCRjtm8KsvujW6G14VcihCzRkysRDVVTIB0dbK5d73HJrTOS/Ta2fXBYdF4iYoihnLVgdRWtKXR\nUmNmEYUjPs5qPaatyhaUo50x7w8s1WmtU6uiOHqHThTKqh/obqJgHfWQDU+Zb5hz+d313fWnXt3v\nnVbpwykuVlMzFZEJqIbbIuOinumthoZH52vELMX0QqBMW+SCJrlVit8nGnybGz0bh6Xy8KRoXzIU\nWB9ZpYEzxaRc0SC1N37+/DMOVSDNer1mGzrfu92ezXpNtsyhJXxp5GlS2HPtzHcHKJnb3Y5MoqTC\n+dkDPjh/yKPzNUs9sJiz2WxZalCOTTrgGg7M54/P+NEXf8jJZsvl7S1//MVXXF0eOFmdcLe/42qv\nc/Yw75lvGi/qzGSJw/4ALfrDaA69t6hljNoq65UyncYZWHvnvccP+dUXX+JTOhqLff7VC6p3cp6o\nVOnTk+DE5EgvfljwvKOcbildE/jsoWfF6EHlHs7U2kPD3TDAtaU26phmtU5fZt589YrP72Z2h73Y\nIOieb1YbWrvi0A9yNHYZQ/TemXe37K7eMWUB827SEOlJCHdgwCIH6V5fbTRzMgLllEXmlKLfqcGp\nWAdjfy0p0apMOAbkW1KKwO/Qh6XBJuqkrhzRiklDFrprslq5FrWY2DWixEFQ7H3wX/TfZqaA55oF\nmJqcPYdEQTK/MHOyRB2f+SiHA5A9zJ2bqbJKsF3rjPOu92EWEQ9RE/RecTT9OZpK4pGNKgBxtVpR\nD51OnFfWj+YokvF0etfryX3RZxAMlxwRMIPy11xOmq0rv67XWeeWm6aLA3Afrh5kTTVdPdkAKgWj\nqn4vkY+WTFNUbyPDLMDgmOD2ZKIcN8J8TLrQ2hsZOXL25tS5UsvCoTfKtFZf4F0FQ3MIp2bMaPMi\nh/F5YZ4b89LDVDrR+hysVoGqw1DEm/bKZAJ5BQJYUJ5MRoTf4Pp2NGc+Rp2aLgxRou6I33N7m+N5\nUORQkUKDWJhue9qSFOo7UKLhNmGZycRzje3n2PRpoJGoDWp00+tpYp0L2cRLRuAJ7z844931jpu2\nP1q3WwWSs0oZZ02rlVU6pVvl4u5AzjOlFB6fPmSdC8UTzy8vqPtKyRphV28sS6XSefroKWntpGLY\nQS/47nrPm4u3nJw+YD7swBruk0p/Mz5+8oR6dWC5nrm5Xri+u+HRex+xzBdYdj55+gE//+xnnD08\n5Xe/99v8/h/+Ift2YD2t9Bk1fe5CSuYYxXYhSLkzbSeNZTsMSpdFeLg2YaFnDx+dcbe7wL3zJ7/8\njB9+7zd5dfGGjx88Y+4z1hO20hSzWxZppFfqIr51nQolNrYxHbOB/WmUeo/i5QTWubu9pbUqTKh1\nUsrUGhRDd0qOQy1Dq5X97Y7b/S2kcCcyOTbN+zvRIs3Y18quLZqw4FgqcpXMKWhyJYIZ0Vg8nifv\nTimip/SkkPneooDpjmUJYdW8QS+RGVK6tEgmoABHeqSsBjpFw2eWxtCXmLnFACpoRqmR0koIXmxo\nmQiCDFDAbaBBjZzLscixaCYVDRE89kAUB/1TGjHpQg+tsWUS0cP9mOlBiNHbWLcDj3U7NtUtnEZb\ni82+NXWkyaM5zEHBTOO2R6MlsKUHyklFDqpeNWULsEHOh7FXQDTcQXuBI03Fx6TR0IQuKMX34a8E\nchtNbvydRVFmnkWPctdhgGjBLWae1Z06N/7pl1+ScmZabcCMUgqtViyHG61lahvUiYTXpjDrecGX\nhWU+MB8O+CgkcoF41hXBMKazalTvlnub5e+u767/v1ePDUa+QwGyaGFyHMk0AFdhVGYwpy1daH7z\n4143ANYpJbInKosCbVPSVKx3khV6haXBdgNlSpxuNqyKzmw1hHA2FXJa8eXdjtUU9KUu6//S1hyW\nPaTGrt9RtqekZBzmhWWpPD1/j8fbBCfS7lzeLewPe6bVCp9n2tJYrPHo9AEPtmes1rCZMr07765v\nmKaJNh8EW6Zj1wlmfPzkfXy/MNfC/nLm1eevaSSevfeUu90du8M1bTZ6avzms0/5/ItfMfeZVqsa\nRQgzj6/FaSxVRfKo0gmKHiaNVReI3IPCdXlxxVcvXvH+bz5hvppZn6xjEgZyFlaGam8t9Hp2nIiN\nKQqMgB6xVNzHnydIjcN+z25ZaAGodRrmiWWpLE0AoaUsLbHDvN9ze3dgWRpTyqLBZ+iHxry/A6sx\neXV6M+bFqd7oZLJ5+HsolgYPQJ6gogV4K3NvD81zmK8R529Q1WsbZ1hIN7zj1nC6SIJJDeDR6Mua\natGR4GIRYmCOdTWPolUItM3pvq3wEaJMOppQyJGj359rbpoYyQEmpoFaWFl9b2TC6hQV1d5pizOv\nxPaw0HPV3vTfFoZS/rVhQvoa6B5vp6OGvC01AP+sejuQj3EGSj8Vms5kMT0MxogLOEggJkqXFf0Y\niJilAIo5Tp9EcJPEY9w7xfpI893Dadqyzm7vHuZqki55zqEH1+e0b001fxpSBU1DpZ7p/PztO2zK\npFzY3tySS2Jab7CUyVPBrNCbsoZ7rwLLuyaYPUCYw25m3u1VZ9YFb2LhJRM/KHui2xLvTVtdznKL\nzDQ5bgbD5e4bIqffiubsblYXrs0bBn/U4DjK9NDSDNNoFWwxMu8aN4/NKXs7TgVUdenhLNNE8kxJ\nxsnJiptFm8h6k3l6fiYtUNqw2Uy8f36KL3IkzD1xaDO1wZfzJX0ebix6OJt3dvOBniQ0frA9YU3m\n3X4velpN9Gws3shmXBwuqXXBLNOrk7LjtdEsM00rTlcbiK/FGt0zz99ccGiVbWzaOYWguDvr08IP\nPv41bm9uWZUNre053W45nC/83t/6V7Ff3PGP/tkXFDOmzYY6KU9stZ2wNjLWiBG+NjRcEuXDPLOa\n4D/8d36P//kf/z4/vfhcXOyspSQUDUbTNBU9mdng9cUV3uHnP/8Vn/71T1kub9mst/dTI5OwUttX\nTMXyGliwATnFzx4AHlE6Gw2bO1989YY3F+/YrNZ4VnGaMxGmaOSSqMusMXqgtHf7Ow7zTMmJQ6ts\nthuW61uW3TXJNaFoTeLnFNOyGlolDQ2zBKDNFN4NQVfUZ9jdqLSgVujZS7mwPTvnZNrw8vXnlNWK\nXlXIGEigewxVHJt8wbqTnXtXvgS5JywVmg/79/G7EXroPR55TQv7oIXo6Mc9qLw2JkLjYLDgS2ec\nxmbKlOwk0zQHRnZRuC+RNCS0LB1VrM7ko3hIgcUFwjYGY54iYmCJaaOEvfRCt6qBn0sT4DEJtDZA\nFh14lqTXy0moZNdOjC/G7bwjFTg/SSytklKRnXZOMSV0NTXhgmUWlr7hLDesk3s0p6J12L1Ri4uW\nIU2b8gpb/PP42mT7SDKnN+fluwUKlLzQU6ekFXKiqpHxlKPv7HqfTfQJr4uKqKWRWg5a8KBYRD0V\nx+hAWVMZHPjvru+uP9u1H0BSrGdCqK89uYklEA54ouGaNEg2Q1fkioVjoQ1316QMw9YXihXWk0VE\nhvH40ZoSa2+zTjzcbnh0dsbp6kQFdE7sa+NuX3EWpjzxtVkerXUObc/Z9oR1Srz34DH7Zcf1bk9f\nOimtmPuBlBKH+cBhf2A+VO1lVfvoUg/k9Zb1asMqZ5Y2M7If3767plhiiT0gp0EBc9abzKcfvE8J\ni/aSRHpfTSvurPIX/81f4wfrx/zX/90/oEyF9UmSk3Dq5CJr/toWUcDSimOeVTKsOrSsDDQTU6Ua\n3O13ctHsC5kJt8RcG29fX3Lx8SWnZRNFuWjcrbd7g66mQGjPE0K50rERy/eHL/A1zTPaZ798+YrN\nyQmtzqQyhdRZ+2MpMeGoM0uD3hutdna7O92p0PZtNytudnv6vAu6Ww6WiwzOGIZro+DtiV4TOaJ9\npCMjcNvI76x+bGT6AK8QW2cqJ6zXp1xfPRfISmTcDv1aGIy4FTw10dh9PLMp5AT2tbicoRFPYe0f\nO7ENl2E9840wiRhsuKFBi9dJUq6qNRmfdDfVN1MixYTOGec5YnN5ozU1040UgwKd0vIuExvIHVEC\nc9Dfw1cAFCidumsK5jKZsRhWOPdxAW1M87ySeoKmiV/1OLtRA1WbWCA3hx3FnIenJ2pkIKRAnWwT\nnvrxWUlR9ycXC0VgeQ72jZrTYUiTbNT+IUGoNbLcgjKNi/KfQjrQMwmYd522JCxVdnUBF1vHUiIn\nx3PXZ5QIJ+ixtrWuu4MvC33R3lerPhtvFg1myF2S9N655aBfa5JXszJQ6RUzaVW/yfWtaM7Es01f\n0wnGi7dAqdEkTRM1FVRRTXLsv7yLWtE6Pfu9dseMvC58+OFDnpxu+OOff87ZtvAXvv8B36uV3W7h\nZq5cH3bklDlfr3j88ISpFF5evOPt6yugaNH0aA+DwubjoUlCo/rcuaq33O73KtyaphCdBWZnP+14\ndvYez5+/4KOn7wu9aJXb3czcHbPCapp4dXXBw7P3ANVqV5d3/OTzLzg7fahEcjTFCFCGv/xbv8H5\nds2bm0smT5BhTnvSRwc++L1H/MP/5OfccqBl2Yg+nE41MbRO8kzXDoqVSdNIN1qtPDw5ZbfsOT99\nxP/0B/+Ii8MtrYui0mojF3GYhR7Gw52Nqcgs49HTB/zuD/8S/9V/+3f48OlHvPf+A/KysC2bMPjQ\nw1+QHqzt9/TVmj7dW6rfN2VxxVQJN+YXb/npm5ecnp0xH3Yk16RkntUSdKC1pgXvjWV/4O2bd7x6\nd8G0miheSNvMg5MT3ry7ZD7ccbLeCOGrxr51PMvJM1sM6jz45U3Pn7JfhBqLAhtERhcoYENoG45h\nX11c8OZmx4ePS0zagCSrYExUlWLSMFhWk2dmUFHRb9JGJ9pxCp8SZJMzotv9hkfko7CAT2mAQaSh\nWYpFtipCYzcpoq6TUXoWh9wsuNoBRvROivyxHlxIHQJqgmiuzZXK8TQyuThqQpVkXYs+y2RFiChA\n6aEBGRtBu6dE5kSvx20hkD3xubuLstEcfvH8hj95d8Xv/LkPOe2TEN0uA0XROmLv0ABOiH0jRNVC\nVXugvdQkN88kWqcQZx2IXR7a5GwsPTb3AIIcgU3dg07RlWU49SJ0NVpl64bloj2LsPJGgEBtYhOY\nF8yicGGWGL6Hk1tvmujF5M1IeJYou/s3OwC+u767/rRrPEcqYjvhmA14oPRwb3vqeLXjVN8IxoCN\n6Bn44KMHfPz4IX/yq69Yr1ZMxfitP/ch3/t4Zm7O88srltp4cLrh0XbN4wcnTFPndn/D2zc33M6u\n6Zp70JY79BwaUtUQbW5czlfgxqvLSwFt2WJvqlzd7Hh69oiLyyvSVPjwyTN6m9m1hXk+MPfEqmRu\n7264ofPxk3Mc483lLT/+1Wes8vrIIAHVJB3nN3/j13ny8Iyb6+to8jSRsKlz+8kFv/sf/evc/Pef\naZDhxtNHT/gsfyE6XKlYV76ZmeqL5LDMC+vNivc2D3lzc8nDs3NWwwnuAD/9+S/xKYdzbeTA9okP\nTh7xv/6jf8Bf/yt/lccPz0VJS6ICemuiLTZnubujns2UdYbhTvj/OXiHrpq2qCC5uxWTBoFcg/Lt\nnvBlwYMJ0VqjdWd/d8uLF6+5W3ZsNxsO84yZc7bZ8m7/grvDDafrDTLLgnkJFlFonTo6T6Wb7qGx\nRWBs6wFYiq5J/hqqaxZTtEIqK85Oz7i6vePN7cz5g0Sx2O9JanE8QEF1E0cabtxkwGRf75IxHF2W\n25giJ4aHfTdRrSQ9sGj+1HimaOi6Q0+N4kZaGRsfpl6h2Q6zDNwjxkJZqHjFmCSb8E4eLodDU9dG\n5q9kAHJLldyDnkOSHG6JpqmXJfEYW12ixg2dVfIjS6qJIEWi0XxQTgXwpu70bvz4V1d8fnfL73z/\nGQ+s0G2GNKHTLLTrMfEVDqmixEroy7DQymmK6FHjdFetYUkAbW9hDIT8DYbb89BUmkez7onqUY81\nwxfDSgYv0XjfD4G8Z3ptclUM4p4TNXI8fwQ9cxgOkqXvG71GiqGQI7M1+QdIfxd+cpoQf4PrW9Gc\nWdAGNRIcrzyKcM1Gox0LO1CCG4xS0LH43tBESacSxXHwTF++vuXtuxtKnpg2sl23ZszLQg1O7HZK\nLO60beXdzR23hwNLczkphjmBnIZisxpjo6BPLmEZK13IsfLGPbGe1jx7+D6dxrOnH2Alczat6W2h\n98TtQWYi19c7zDLzB+eQ4N3dDV+9uaB2Z4XBEnS1rjnIZlP45Nkzrm7vmKaJvlSSJ9q18+hHH/Cf\n/nv/A+vNhvPVKX0P+3nPz1/+SojDHbTYROAexRFw3/nowWO+un3F+emG9apwMk28jko3ATncJj2J\nwtf2nWyF1XpNq51Hj8748PvfZ+Xw5u0FlheePnyCTVBSFkc6RvZqCJcoNO/1WfEp35sQ+nAY6rx5\n/Vp0lM1Kk4sYzRyNQYbhQ1tYWuftxRVvb66ZVgVc8eZlrQZqtdogbZ042q3d0yJzCiRwNOKunJzc\nE4tpSqjcufFkDDGyR+EC9bDn5nbmUCtPTtbiq9uYWLVoFpTtpYDkDq3e0yFLNMBokmau1yq0pg9m\nr/QhrkNH9Mv0NStgTZenADNaNNSeEjlps64uGq+VEeQZh1RgJ7IJljlPCiay3nE+Nk0CPHWAGAiR\nVJui5sIMaBHGXujeqRYGtp5j6iOhvcdEFtfPGc1JNdMkKZ4P5aI1npxO9HzKw8npfqAUceIVjCvE\nLueY+DUbAyhGqK6ZGi71j2oAO8r+yymK1TG59nZ8Qs0Edjg9Mk04ulAp28yjee7jVot6E9oxYuKq\n1Ab9PG+DLgQEKmnjoB2FAQp59eRADfQQBgnpu+u7689yDfoY1sNwIDStPhQh6Hx2JwWI6b4cMTRG\nYRTb15uLG673BzJOKZnJVODX7vTF2R8WVqtMy51eTHpkd/a7hd1uIZXIDIzfL/MhRJc3i30uBtDB\ngnDAAkyjO5+89wy88vDRQ16/u8By42S1ZmoTh1R4dXXN/naP90a1zpPzLSWteP7yrYJwTYVimuwI\nwm43K37t2fvsd7uYdq3ifTXYzTz9+Sf85//+/0j1hf1NZTp1fvblZ+yWHdWkK22lqQkOw7GhfT60\nxvuPHtO9s9muuemRqZScj58+4/mbCxoLovslLDeeffIxP/rFj3j15g3bzYreG9P27JipmsLMxZtD\nX6DlY8l1vK9Rb9mgsAIY7C9v0JTynnnQggHUxpmL09vCfj/z4sUr9r0xZTUU4OQpQ8qspykicHpQ\nDLU3ttZD82QC8RxNwsoAwKCNGBOXRm4Yn6uZk95MUgCnLzNfvnzJXaucrCdKGG0QR6/Flp/iQ+jN\n9ENK1sRsStIQm+aOFvRHy4IWU9eZ3cyxDL16gI7K3FLjoN4NPJxOpYs0C42aFZKJUZUSYW0f1lkB\nBo6A7WR6f5ZM9LkmOl7vTil6rQo+DllD7ZTBr9AfHk3HdM8bziQgeejQLb63dDWVg247zC7w+KfW\nCcAH723IG3iynVjqzDDNs1yAFlKZcq89C/8C4rMYN0TaVNEMpX28B2BaMFmGazNICxunoRotohaK\nA15rKlFQA4uH4VhK5NqpvVIS906X7vcyCnO6q+H13sK9OdaJDylEZuhxzRKpmxLAWhiP2ZiURtf3\nDa5vSXMWL9cs3HhabOZ+7GpH6J94XOrGPEWXHA2cJhRRxacI2EP/r4uzHBo5d9JOyLfFxAsSHaE8\nu2Xh3Tzz1x5t+OjRI1mJTtKDmCUV3S0axhFoiFGXTs6i9MUfHYtl0Ma1Xa95sDlhn2/4o5e/4Pzs\nnP1BU7Zsib7IUhUqicSHD57yz3/xBVfXBxkoyJ2CXqOR6o2n5485yRNv9tdsNhtOVifs1nIw+uri\nBcu8cFhmrtMtORcOu4VfffacXiAHpTGgzuCVD9GtcdGu+a1PvscHjx+zKpmfHb6ivXkVdrOZ5+8u\nmFYrZVw0GV24Q+pquh49fkQ+P+PTjz5id33LG6/sr/aUVeHh+RlPnzwJjvcECZb5lnqYSGmrzy2m\nFMcywBGdwTVd+NVXL0kl32/kSBuUY/xf0QZ4mBfeXlxyeXlJnoS0WHIqzjpNOkyLHJ6wgveF1r5m\nrxrPptzALATGsshVwKJoIAmlxaeYygCiE7ROypmtNU6nCTfx83vX5E0Tp4pnbeKWE31xchbvX0Yn\nPUbuQmoocmrU5yIL3UHBIKii5pDd8RwTTlMx0xC/3MZhEU2LZSM3PatHVKgaOXW8Z5pXynC+Cp1F\nGkOoMOrA5GrpTMNBXxlsE+EuqV+as7aemhQyPSS+PXnw1DOdguku0nPSQUcObZgfab3JOlxAkyEA\nACAASURBVO4qLh49XHF+vtIBBtAb5agjCDer+Kx6kkNqMqi9knNBgup83JzVUA6pNPHZ3COqY3re\nhz4yjfuRlVtDTNUc0ThNgbGW/Th9a8F3J26FB3LpAUL17mF2oG0uZ82FO5Cs4SnQWTL0Kq3DNzwA\nvru+u/60yxkAEkfgS/+ScSpuA93XlDkeYJ2T3Y8Fs46YTpuNQ1VxZNlxFnLKCr0N4GWplbntuDks\nPJjv+PTpA87PzrXbpVEExeRa9aIKKML9bdFC8ZjUDyG+ABCnrCbeP3uPR7c7rHVeXV/z6OSU7o39\ncoDemOeqc35KrPLEk7NzXr56y+nJlnmuYUMO3mWi8fh8y7OH57x+9YK8mjiZMo+3D6gHTSLe3Vyw\n3NwxW6OUQt1VPv/iS1pQnfAwcQKZTeRJn7HJbv3N/pZP3n+fDx8/4w9+8hMWlBf68vqN2BM2xRRr\nZjttefjkCWfbE96+vQzJvXF+dsf5w3NW68za1qQCte6o8x29bKO5RZ+TDbAtqHGxb5sbX37xkuSZ\nSlfjFM6EevVhYFIXbm7vePXmgqUt0ZiNwtQoRdlsZS3aofYvo3ZpjhYVT4y8xm4C8Tsp3HIdC7dH\n1YFNMUR6zID7sGPJJJyTydhOK6CKEhlfTtfXKVNTxbglsSRSl1e48qoSWW1WUOKB5uQMS7BoDBSd\nEvym3gXQpa8V+mqitZaceszV6haJuiWc/XrokelQJlqVS7P1aGDy/ZhguEomu9ffeRKIvnQZcLUm\n90cijsLTAtGyDap8Si76KBbAuajKCVH03Y0WunkPbZqWnxqWR2cTj88mzBrZiqifwT7B1fjpPMtY\n1OBLWBmXYkG2Uc0ngEV1bw5f/TF9sjA7ERAuerAnAdspJ7onTfrQTXGDnAUAUAFrx+e9JRO9E8Vt\nuFVS6kHbDJYcM2aSLFQtkiAF6Wzu1skmoDSjCSxd2sjYAbUfpoj/+gbXt6I5Gw3OaMQCaGNYWg4T\nAP19FDEI5eiM80CFufRSmpxZ10OhBT4EL5FTFAWTwLQek5HO3BpzbXgycs9BTbRjDpIFZcviFcha\ns8XwqcZkRXzD7CYtCSrEvnj3XK49QF86726v5Zw3lvwxcDBxcXvN//KTP+DVzQ2XuxvW0wnLso+H\nUw+qlYKXiZ9+8Tl3+50WYF+4vV443M3ceaDs3bHU9NB2pzYJOhk/x4iFE6fwVNiWpzw8/4DzJx9Q\nNlsu51t63hwRHMfZ1UZelePEAcv88vVblsMBmxIPzs6wlPjB9z7lZ5/JEGG325N3mbvrPXfXB0pO\nrNdrppKwFxm3X3D24IyzBye898FjVqsTSklkm8SXBkCc4F+9fcN6uwF3igmJa0vToZ/ULF3vd7x5\nfcHt7k7iz69lgnSrEeLtas6KrHBrc2avMaHQR1KyMlVwTa56jU0nkGQhy4bVrs03kBLaeG7UyXRr\npOpQKlNWeyUL2YHCONZkzOJoqtLpeJa4+vhzujZSvYawUTdopoNAzluR1zEE4I7iISJDTtQg6cu0\nHlTp9A4pKRtFttmKlGgNoWZHJEjPdzeHqsnRUTvqkfnlFcud1hPSFPR7nYa7bOsNkq2imIrPLSaU\nrRPTJYsfq6lpyZmDV3qaAnVTEdl7P9oPu99PwpRpU0mTnDHlAN6DXjFyzpJcDsOcIKcerodGScqY\n8T60ChIE53AFsmgOMVEx+qLvMxdooXTQic4haCATtBmvi1ZTL5rkdafmjLWqz7ktKgBb5WhM0oGc\nj2ivJoIpDu18nGZ8d313/Vmv4RQrrWc0ZlEwjqmaAyQXrTwmBHr8AlSNAuZYeA2zr4g/GdEj3ZRD\nBM7inf3hwNwnPn3vlLOy4v+V05RteGPE5P6eL2Rxph3pQ+ObUJH59uaKl9evNWVww5fOm6t3Mkda\nGvLcK2EikbnaH/j7P/pD7nrjhA0652Xe1FE+Wlmv+Oeff8Ht/iYq9MzuaoaUqfvKZb2h1hqNz6gB\nVOwKcUkMfpD4+Auejc3pE6ay4uzsIU/f/4APzx/zT/2PMCtiMbCm+S2JTPMUb9N4//E5n7z/Ia+v\n31Hnxlxn7t7teXN6zWZVePDglNV6TerGiy9ecHK25fT8jNPtipPTU9Ynp+RBr2M0Xh3bH/jq1Sux\nBtL9BCXF3puiWb2+uuHV63e4Oblk0dFGPmROrFdysC62IgwisQDaWuu0ZdDI5USYrcs8Zpy3QMsC\n15MLuJqKwrBHjqQ3w1OTQUQ05hrkaMKqK5pjuwfzbVBFelcuVUQ9uPejrpLIsBQwhkzjEEjXAsCU\ndqzrc0v6OdIuB2igDoqpZJmoEHiAK+MrJZd/CFkasqgRO87KPTx5eujLHGgCjYl81WS0GpPu7niS\nU3kxgRvN81HG0EPCQJeTpXkFspwFTbIQeseLHQcWbWlHQ5p+zOqUE3Rqqo0JIDib0uVSKjEQ6FF3\nh/sl6Od35Gbd2nEgYX5fpwqwbORuWqfDZj+aMIIG6fGM9N4olphdDLrEoj2nzhjO3PJxr7AybPkl\nO0hClAU8906rilGQsRtAw7roqQIvJE0Zvz9mGHL1jmfDRqbON7i+Fc1ZD1qgIB4Lpzjj2HOq2kCb\naxgCoE1Bt2B8nT4gs8zRqREhJykOAY8mzxlTLxVzefCzsgqqz1++48T2pEkhf9lyLGhxVr2N/BA5\n/3hw0Hs42Qzahaa2Lqve3aKwZTPwIkvV1lh80evv8WYNvnp5yVcv3uq/0xT8WdW92TLNF3LO3O1m\nfvb8ZTRgiflw4Ha3xwMVSFZC6KoGN2Vtdinl44g+r+KJLgZUUlmTslCPy3ZDQYui9gWyECZZhHcd\njq7mNJHY3e31MFriZLvFHJ5+9B7v3t1w23as81q0Mozr3R3FlD8j4WuiLpWXL99hGKtSlBuznjh5\ncMp2u+LkdMtqJZvjVHI0CMOQQQ2KVyF6DXj14g27ece0WkFXQPgIIi6BDCWSrOhzodZ9NOo5wqZN\njqxdhYSbY5NojD3oJ8kSrQf32vQc9h4hxjE5OeowLIXFvO4Hwz44RGAaACtcme5yJup+PCAtnNIs\n+O3WdQANoCE1I01pgIdxqFhww7WRp0DwhtOQGq0SaJyEzgnoVVOmQcXL4zxDh4EjLrjbhNuiL+gR\nDB00xR6cfm3w2rBq67G5GbkUBT96lbFAG+LehqUJZRNGRlrQW7zDobdw1tfrqL1ieXXcxEf0jSFK\naso9Gj5p3KrduzbSOj3lKBY7+BST2IRC5XWPLBrS7i67FyMEzFqzm4cPefbwAy4vL/ny5RdM2w11\nrjqY6kwOt08dlDUOlyan1KzsQ2hYztIYZkJrq0IB73ieZLjiQo5t0jRfxa0EyTa4Qd9d311/5mtQ\nGFX8WfzPiaPKopgNmhGGijwXndpbGIl0u1+Q8T2WMxZW39qDnCkYMgKqoB/gxatb6uGtztUU5gxN\nZ7v0ncZR6xMmEZpU1NgAwIqm4LkY19fXTNnYlBPRpnAmhLQr+DiAymC9vHz5ltfpivV6dZxmy+BI\nf2858+5uz+7LFyr6TOZxd9e3uCeaNVJ3yEaJEG8JYY08NLAeVD8v5JzDrQ7K6oTtesXZdsNVveH5\nHspqwxwuckaSA7AL0LOUSZ6Y1oVf+/Rj3vzBhc7WsS/Wxt08syydUu4EUvbIbrJESYk8ZR49OmOz\nXXF6esLJ6Yr1esUqZw7vbnl9d4dNo4mROUOXcJdOp7bOm4sLSAoWls290Vno3ZlKJmVpj/KkTDDv\nVdqc3mk9tHqAOCkLkJSBHZMfM6OM5zCpHlGklT5fD/pYAmofmuJ+nHwRe2azEZVSZd5RBcSSR51I\nWOQHMBrnN2YU98g37ZgI6Pq5BpblltA8aRpF0CaT6luxV/Qb5DAtun+Ohqu6vi5lJ4UbeHN0ViSo\n4vqpdkgxcEiF3mYKWewM1/d7nMkOan5d9WgizpgWYMnxbNL5MQBduuE51vbIAF3CcCWAaU+iGHb0\nGXnWRDAkp6pF445Gm6Y71MJ0C9USKZgiyVQFHLPJLONpTBzFPsGL6KOW8NQ5f/yYx9unvLm44Pnr\nL9mcbNnPC1FNRS0k3bz7HuUiT1oXVrGWITwVtMYVZQUW0+mYbJtDtdC+WvyceNo83j81tGr3ZmgW\nzdsgVf2Lrm9FcwaBtLluXbd7dCRGYdp/xyh5TNTgSCMCD7tTlbGWQ8gYfFePn6FnOtq+ZHhPlKLu\n20ohY6SS2aQthYmz8wdsVms2Jte1XIJ+YOU4wXJkR9qbR7FqrFdynSpF/OHzkwecbU64PtyxO9xx\ndnqqzdoyKWXm+UApRQVXFM1LbXz17ioeBg+tUyK56I/vPzrnL/3ar3Oz27POE4t3aq3cHHasco4O\nXzS4HgeXjExEwXRMi70oZLnE4mxFn/+0Mj7cTJwl51Cct0lW9MllPLHdFN7dLmQzDoGGnORE7Ymb\nFtNLM9YnGz795BP++POfUlYTWFeeS1eTVKnkDkxTONc5vVfmuXGoM5c3Tnp7FeYbymQrRaGK83yI\nGIQZs0QN96C6VPaHmUNbKGWKviHufDgi5ZTwnsmrFU+fPeCrX/yMXb8J4ae4znU8L44mNNHcOqLf\nek6Bbon2IMfDhFlFdvZZmy5GbQGW4RQTlcCiCB/dRGUUN9IFtNpjcKzuq2VNhErQdzuQkXtTMpOr\nI+CEW5E5y7CgTaLituYU82P2ipxNg7brjiOthJ71oDwUU4HlCL3zctTkSXsAyXXIklQgefOgAHfp\nx+LzsthkQ8JLbTq8E0joWwreEm7aGB01ZAIHw+QEwCPGoYlGkt2D+ufShHxteGSDhtxED83HbUSU\nSYEqCpRMsc8oZy/d00cZCJ4hp0VZPfcoSi2teH53R8rw4OQhN8stORXtbWEP3HunV6dkQyGaOoyt\njQJXCG5rmqBiUKyKiRFggrKgdDvoAbZkBWna8XD4rjn77vqXc1msU7MSRXwAo6FHEMiBptc9+jA4\nGhB50Le7h6YqwEuIvc307JKMadC1I/dstZ4Q1bywOl1TpnUg71nBy70IdOldxVtX9paK03AVtnBq\nNsdS4Xvvf8j1/o67eU+rhfe25zRvTGlFMWNequhyQdGelwNvr28xk8Z6ODWPKeKjB2f8+Q8+oYb7\nZEIuwLcnd5hlcpDceuqkPMmtzTVNIabeo8bJlkgmy6TeKxQ1tKtV5fFmzbPNA37cKy0Z7pUHm8zl\nXqBX701aMqThev/Dp5z/yRneOyXH2VqUBdbaAlkuebkoUgbEImpL58XLt+AyXzGT66ElfW0tTq2i\nxg0aWG8yRVkOM7c3d2qgg2YXm6P2qizddrLENK159PgpybMYGUFn03mqzLavNzbWhzuv6I9BBozz\nWJmlEXuuGgKPPVzsJplihNmTKXNLViBdTAk04enD9AsB3tW7Jqx96IqV5bVYaPdcE+FhjDZYJukI\nZmbVTNGTY5Hg5mquUjbqUslpBSYQWJOz8MfCjwwVC7DfPPA6jN7bvXGP2RFIxJ3a5ByZUowzhqnP\n0E9X/dNTx1oXBROj90ROkd3WxSxLaH3LUCacpcPMynuwYWqTSsUcC3dlRmSBi2bpwX6LpU+vHBu0\n5vefTXawnvXMkAW2OHIYN7AAfCFkFmniy5sr1itjPRUaEZ7dZaTSu0ui4cJyHCelJcxTwJLL3McT\nqVV6UtPqJhBE+nuxf1oGi3oaFwBlYRrTMJKX49leg/LpR8D0m7Vn35rmDLfjZAiIwilGg7EXuin7\nSLou0RaDmcQo4hgNmLhRoX9RIrtFC6/BQT7yZM2U1r5KmdPVhscPz1ivTrDe+e3v/QD9JHGkUylx\nZ0O/E1XjfnfHoTYOi5C8p48fsSklNmAnTQWjszl5iLUHQDQYDliO3KcxqUgkh7fvbvnly5ds1xvm\numgsPo3JArz39DEnpxvm3DWRywmvnTM7iclWw3OmddEwzPXg6XdA8k5rRp40/sbuA49T73hJXO9v\nuZpvMDcq4s+LQgXrshJ/2KQD6IAXSDXCveNmpFT48MMP+er1C1qSa5TettGrEjfi8Q55gGElhbzQ\npYOKAHFNibRh3NwcuL67oqQSmsCMN4nRa1OjOhW5Gw0KaifhSY55YLz/7BkfffAeeen88T/ZsD8s\nmK10IPSYIMUhpcmrRKo5uez0ITZ/J2fxlt2KgIKc1SA08My9rsgtNlaPRqIIFY5NW9MbOSHl7Mcm\nNsXUZjSL7poIeQutQByGakCSpnI9UDn9dSBZTkUuTqmb+PVj3bk+qpGnEv+pKamryalNGjJti4GO\n2dDjWaCkmhJ2ryFCH68/aMTR7tAbxYzuC+5yHcMVtG2sZEkc77kT1AATj3+gXN0jiDrLrrj1Tok1\nm62RUlb2WG9H2rHEvI3Bm+9h5pLiZkl83gJpqwJjPF5/k4X2mOJrz0rsri+xvGW37GCZRelMheZJ\n4Ig7Ejb7YGpDCQMjyrHK7d3umyyHmgrWG2PYqj0sTude5X7pOnFTMulWh+D6u+u7689wiTJrR41p\n1Hz38KgTk6sxyRoA6gjfEHsklUzuotJbChvy0Bm5ReZTQuAbcHq65fz0jLPNltWkE/i3f/3PBfUc\nofVWKC5b8x6NXl0q726umaucXk9OTznbrFmt1iR3UtZk6OHpRO+P1FSWcOtLkwCkDpbjLCTz+s0b\nvnp7xdl6zaHXo9bc3aAUPv7wA95/8oib/Q6Lgv3Et5z3EzUpoRUfER2OCsZmFrEjdpyi9GD+yKW2\nh7ZZ33dYZr64+jLMjDqpZFbrQirG3CqTTZrAlYnmzmq75QeffMqXFy/I0yQnPtO96NWP0SzDHS96\n7QjCjvPFRMvrEOAz7G4Xbm4ug1JN0NrFGJrng6Z6OQ3mnxo42pEiaSmzmjb8xb/wfXZvrvnHSxMT\nBQHbo+nw7lSiAO9ixUCcgSFX6dH7EbTD7OhcdkjFtNdbSBmKwG0YUUtDBiPZiqV7PVSOjba7zvwa\ntUweJ1fovHtHhlp5uEga3kSlbNHQuBk9MrNSxAwY92uJrhq093sDD1znmCULMFllZy5iaZU0plEC\nm807xVQ/9fjB+taYipFwW8JwRQfJsKZX7lun2aJm0MIj3Ydi1CR/aJ1g3oOF47P3MOAJzVXU3XQo\nxdTYu75fn3ZAPcaoAPCkkOwBgUaPQwugvYc+UPGefvQ2sGBA9WjAr99dghVulh0Nx2uPkOwALi3i\nfHqLKB0bo0M9P11TtVQMkgY6uLT/YlOpKsFkje+9RweZjvtRD3aBrPxV+5fs9D7A/MzXh0t/2vUt\nac70YpONPHqLB1Z/K8eeFlOjMJ1gdOWDEype+6CfWXTsCrFWA6ech7Dmdw9zCTV5RDHZitNWiWWQ\n2acgcaQaSe3iEh8D6LyzVOdgjX2aFTw8rZUDYWNS1ZRV1TXF6+ICUtGKEzoRHO4huMN5fX1x5M6C\nJhbYBK2xWa14dHLKm+srzDI3XVEAtMgAGVOCOpq5MEHRBx0Ip2u6MSe8Vy3co1FKo3Uj5aSwXEvc\nXN+yWuXY1Izbw45UJo424B12+x1WRREsRYWvmXP25JS//Fd+m88/e848H9i3GYgROnZcFBYujtpM\ndH88h0lGDQqeW1BYG+upkPNECevxZnpf2RchGAlNr1AIYJ5MzVAuPH3ymE8//oiUErUf2J6c0F53\nUSAJPdUwUjQFRaaghrYYt3t1UklUdD8NI3eXOL7rQPI4YAyGZ4MOg6DSuvegMoIa1aDVBN/ZcLkD\n9jlcojT1oosXbamJKhhAgIZnPRrxhHWnp9gWfUydJFw2M5rpwBJ+CykO6+D26c+SDjPQszwqNGsd\nLzmQYB3k435KnymqUuvRFJm0XiUVHVxI4I2n6KEdI6yyceXImaxotWalA9UESZq84cikJqfrfhch\ntIxDwIIo3YOS6boZRhSONUKwXVq7XP4f9t70x5Ykye77mblH3JvLW+pVVXd198x0DwlwSIrQFwL8\n8/VNkAQQIEBIgETNRrKX6aWWt2TmvRHubvpwzCNfjQSwoZ4PReBFo1FV+fLdJcLd3OzYsXNmAjYo\ntUpFc0Si/1104Uk5jEFY0FuD7TsqhWvTgLIDJw2HQPUD2WVCLKEiz/LzTTrmSM8dj0gltdBhnwCO\ndIm0pjqigFlSNua6+nR9uv7Ua45zKEYF0Q/eSc4EKT7FpFfDUbBFziyFpOeYscEzRsu70bBqWBdY\npj2OVPKqY6txHY1aKqzAog4KptdpHYonactgRKNbEBWsVMqpYicjJghS0ncqVIBRBi0Bv2KNwUgz\n6OxuAV8/vs+UpMknrLgMogesa+XF3R3vnh4IBr0l5d/37PBZqhHmKESOG4ykj9khHy4hjcjCxAn2\n3ii1pDqdrDNiwLV1FVgXeHradC6VObah3KcnE+jP//JnnO5v+P3bb9ivlXCnx6YOkYmyV6qkwkup\nWahl/Egz6dFngdCZEacsS3ZSOj079T3noKXKbvRmx0jKBAjPpzt+9MVrPv/8NedloZ+uLMtKiysL\nJ31zkzVMjOtB6S6LRLOI9GlLoQXFfgM0nzQVdOe9z2qHgoyFh8fR2S2WXRUQ4Jyvp5lIY47ZKKNf\nlMAzz2UZYk/pj95HitVIIEU+bUlrDHWXSmSnKVRUCdefklean1KmEjmHXTTXHGmPUxIsSVyuxxBj\nZlRKjvKomdHpu8YRJIShQqq3HLVoA191QPSmYlQn8RTfADFDaoLvqcwZXeMeRjY89Hyj6Uw9VBxb\nYy0q1MLIZz/pzUPm4/0Z16lWcVOHjOxEjp5WTSHLIK+FEbtEPjxHCfpI0DNHWvZGbxcBu33Qzaj5\njATXSk08EiCK0RMILmK8MdkA6kYKoBF7SF6o05s3v3eCpWZBNOWIWkEJaM3cq8MoMwe07Pb9t68f\nRHGmbfbMY5/u7eqECRWYPNgxq+3kpFuiy5bVNFay6s5WbNGmjExMxe0FMvgKjuhEh71deP/hgd9/\n8y4RjXmTOdBBHUvy9PBSiCazRLXMBRWZO7/8ze91GFhuxMn5ngliSodOdIzIRD6hIHO51Z/XG/a2\nJYpVkvLU6I/B//zv/6MQkpr8d03lMs1CR1dnQGpIMt31RHDMFyz583OOxj2533Phuni1vetn7sb5\n5iapdME3373lvJ5oocTZgW+/eaAshdPplvvb+1QhrJgXXn/xmtcv7ggvYIPf/fo7/vDtN1z2jaCx\nLon8eU0Pr86yFKb/xXwebddG2u5uuOcOcw1llmXlfF44l5Vf/vq/EtW4WU9CJV2t7nVdWGrBvEIK\nThhOrYOXt6/oY/DyfOL2prKNxqWlMeL0BmvZ3s8DmqNA0JxD76Lu5eJMxaAU6AgF5gkWBKjbYVNG\nWFRUiX7mnjAl4Zr7k2mkAF2H0qVyODKIJLpGCXrXITnGUKfLhGmHtPAVzLJO8yGJenH/O72ouIr8\nXEaRMmUGouqil3h2eZUaLXqdKPnaBt6xsrD1OARPpA5Z1cGMfgiYdHOsGnUkaGKNPqCWymk983S5\nqiuqaktUBB/07Eh1nDIk8W9hCLx1qaCaOmqzGznGOLp4AlJ1GE9OupeF1jWn6kW9zBZ7dt79oKOM\nbhrkN92PCO3Bjkw6vRbWsgg1ywNFyUdPBDdgqDOKZ3Kbc4o211MeThLsmaJImnWbVE0lvJrLk0by\nLLM/XZ+uP+2aaYQSDaWv6rBkW2RK3lm2dPPnim3pBzgG3TVbZgN6F+psNURj7lVJdc7Xxhg8bTu/\n/f0fcp4qk2VSlRat/xxc1eewcXgPiSmYxaB4leRkvs5Yi2RFpDiDO6NJlW0keDUBHS8LxQqn9cy2\nZfwZLrNkN7annf/l3/8HIhM/0aOFyEcqUI5Ueh6muTgFan3uiJ5zxAlLJ+g1VYlLyZncMlsWTqnG\n3c0t9ME//MMfqLcrZ68pAW6stqQYmnO+u+UXr1/wF+1nTObIh7fv+dv//F+UpBKsNWfWajk6paUU\nWssuYU87kNjZt0bxwmevXufMFiz1xGld2S8Xfv/Nb1nOZ05LFQ2yFJZaOd2cOZ8WsSNGg2GEw+nm\nls9ev+FX3/6S84tKrcHdeWHrnbdXLano0LoASI+PgKzMq3CHXZ1EpS2aFZZ/rvK/bjmOwZhHMz3m\n3DUC3dWakw9lAqvkmYAl8BWyAyhW0z81z908S3U2SBSiCF1jTzGPCegzND+WvTmODHjORAWamS+D\n0f1ZyKSnEnKeu2ZOp4IVRtfZYaNq9c6Z6pBPa1gcXr3hlRjKRXsCH0HICD48aX4qfc/nGy6XJyiV\n0XKNmqlwM9nldGtiDuEHxb8NvVbxyhZkV0158972jBOinY6IfK2StE35J465b5H6+GTV9aF9onk/\nWF2stmGRQj0qBc+lsCyerBt15iYraQKvEelbFspVog+d63gKHiVdOnrm/5qHH+g8Dhcl1YunpUHW\nHKhQ7qZiXcJueSrbH3c2/yCKs3kEzNLnKNYmz9HgecB9HEWbkKasdpuCu4q7pLFB8rt5NpZLfGT0\nSR8wGLqZR3Voktac1EWy0wWTw6wAKwPMmQQHhzR/ooyWwWGS9kbSqvJX8iyb9MxEzWJgpqHgWrJd\nmoiIzwJRcMRR5Qs0fEbeOdq+nhSGeZdNVDBToWVFbuVmBc/Cx7M1HUmYFm88+dRD31dU4nIki5GI\nfkuUyUO0jHlLLQJqGpaWirl47D/9iy/50Y8/4+s/vOXr776lridqEVpSzGhTKrd3UTmymnBvjDF4\ncf+C6s7p5paXbz7n9ZdvePnmFeuH92yXt5Sbk9aNyZMtImdziKPFPa0OylIYyw3/56/e8s/L5/zq\n7YW9OQ/7lp4Vc7ZK84nDI6OEScodce9hEJs6OPLc0TOzGdxNSkSWqkvFlehHzM7NyKFxrWcvzlev\nv+DzF1/w3fuv+frdt1y3K7UGe7PMT56pCz3plaOBbZpNjBwi1sGUBZerSKEENYvxHk0Hj6uQn0gg\noW7hnCdxC/wS8LZRl5bUHR2Ge2TRkAPwFhmVqgwe3R2aVLB8dKLU3HtJNRjjSMi8JJ4iuQAAIABJ\nREFULBjGuj6wXa+4w8vb8yG2M4a8Sq6tcbsuWBnQB8tShTYXSXaXpbK4a36viBffQutLhqD5bLP7\na9QM9o51Bd6WFBWzwt4l/y10r+AJxcY0Y7fCaLsOd7+wrjdSdZx7fvLXEzpRvSlaYqTtQgw18SxD\nIIkQj+yIL9WgdyIRwcHIvTVyfX9qnX26/gmuRAOUEOoMHgkcWSZYk/5zFG4BH/G1BBqSoJKF1vpU\nPWOeVerYx1CSOrooiGwjswEyHTd1xiK7+T07GCOyENInM8u5mbDs6sExOmGGd3SGCrFi2vfMmf84\n2DgccyWHymQWWKJxJyF/ZLdpMntK0rEKH41S6LPobog9MZhndM993495OwkZiH0jG5+WgKnO/4nO\ne9LImV1My5hik05esBoQEtJ68+UrlvLP+M3vv+badtbzDZ4UbCNpnR4sRV2G0cUa6QNsBPf3Nyzn\nW27uXvDZF5/x5kdfcP/qht/9h/8DP3XOpzMDKShPphJM0F2gkzy0glEqD0/Gf/r1d/z0xyv7aEQL\nvn7feHvN8xSwXWdjJAUvUiVTCXMjohNbKi8XiDQGVveqCRSL+eyGuiIDMMnTj67COfrA6uBQYc7i\n3prW1IvTDX/55V/w+PTE3/zm75nG59GQSnSeeyMLc7oKNXNpFtAGYx8MSzGNMcGMmfgbXmTgPbqo\ngdpXUyhD3bDvLldKGfz9d1dwnWmW1ENKYezi8YwI3YtVfnJYkUJw0Rk/5j6arLIEao3Mj8u38khb\nCr1pFOf+tGK+iaUWV8ILvTfqGlh0qlX6uCrnQhTJWleBq8XY951AoyjVjX0M+uhUX7BQI6DlbOto\n09dNXeaGZa5QGW2nlEVsm5jNCRWuvatpUcywpeIpEpPpsq7xHJvMBAzN7ikRSXlVLFPe3zPXFigl\nP2PUTDAVbeYCiqZ9sJkRBY2e+ITY/zvqnJGbyEOJvG5eFmqKylm4JXpG8o8z2BNBZCcIVJTgpDrK\nRxQft+OvKIDnz7MqVscoDwEjk2rLLsTR6tB7ZPtflTgZDPP1U559AvMW8wBT1yIOdI1M5jyHj02K\nMZ4Gv/n5ppoPlv4JPrnTnl9PSbBULvV+7hKD8FJTMU8Ds+LaOne3N9yUM2Ns7D3Y951hQlIont0d\n0bb2HIK+XNvzIzOIHThZ0gzQ4Gk1hucSNE+KVm6aYqm2RSoJVU73C3/26p6fXb+gn+453b/A943o\nnev2yLhc2B6euDZ50EQY1+1K78Hrz3/EqzevefXlF5xub0U/s463C4tX8Jr+OYn8zoM0Ol6FYAUh\nO7UwzqdbGM7tzT2351uiGddNNJYQP1IAlhklCg97Z1ksaa46yEmPtxgwSlXrfaoTCjAUlaCMpLJM\nCmNQF63uFlKN8hjc3NzAaeXbyze83R957I0RRms9h4tVXIwuPrZAjZxVClmixYije3w0jXuibx2h\n2kfCld2tYbBk0hVFzaXkau9jJgKDrennDKlj9j3RaQ1uiI4R2mMxqcJjJE0gMG9HMqd7MjLBG1g1\n3Beqd82LFVjKoAZKFsegeKUOpD7Ws4M4es4opIrW1ngqRs2Dpw14t8PTteNNh4ytxpevb/hnX76R\nLHImPj3FdCI7XHsfjGhUywOpyqOP0ViWNbupxt53ARa983Sds2wxg5meQaRXm7mMqJcs1A0VkV10\nXkM4gLx9WtItlCBqNhBR53mOWZ9mzj5d/xTXhEuJLBCSmqw/NNVfk9P+ERg3gT+S4TLnYjSz8fw6\n0gFJa4vsjMVkk7Wkag/REWVrk/Yj0eW1FDDFksKywDqgymefNWZy65HzTQI0x1SIzu60myhNAs2K\nFBAn/Z9IRTsl7koY1SUTSwC8JeXeIimfKShk5aPOe5epshs3Vnl1cycquhmP+5UW0KIxek3atApP\nt4V9qNgoJpr5TG+qTjYIZzktB2vI2PT9qJo/MoDCm6/e8ObLz9n7lfrycyrBaJ3ojTZ2rt+85+l6\n4doHYzR6D/a2sa+D2xj8xV/9c+7fvOF0vhGJI66c6oq7Z8E459gmG0EWJo4zfHavFGfdz2x7obVK\nZ3CqZ2ppLFGSmfEssmET5HXnsolGq7ngSFp9ys8PqLUmc6VorqppHUUH6xKPkhJpz/NK546nOXEt\nRuxia1nAuhRON7c8bB94aBf2qSqY2gWa+dKa8FGgZKEQEhzpLbDWU1jO2ds4imcBpalE2coxgtN6\nNiCKS9itWDKgjO2Kxm1Qfmw2VJy557x4Avs9sGtLUHMkKybX9MxD6cngHJrvz+c3kYS6DHprLGuw\nWGVxyexbMc1dBYwtiBbY0qWiWBvRNe5Ry8j5O+WWEYN3T4OH7SLg0gc/+9ENv/jsC+pM9V2U3tY6\nvgi4HT3ovSUQOliWJRsamrWM1rOHIFZUhPHr7/6g4jBMKsiZm8/G+5h1x6wpstlu2Um1oU4ZJusB\nPZughh+NEwGoWWz7SHZZyXzTVWkNjaz8sdcPojhTwjgyqFkOJE6kQ0/KEdJvOU9nQwsjslNks1vW\nJyKdRnrZcTlAbciEOIuqYQcfXslNR8P5EnkQ0qcrDkgsB5GyGMSkeqQE15nqVJIDHZnIWnpB6TCb\nB1y4ChsZJxtzNsayg9Vbcm4PIYNk75aSqnY9v0ZhKYVTWbi2Kf+d1X8xjJXRJHe+lIV6rqy3KxYr\nt8kFLscQtN5361t2FuHD5cLjdlHhWHUWW80WeyERiAXvez47OJ1OOShsxwErxBGsaO7LXf9e7xbW\n9QY/nfG7O8yN1ZT3uo18Xg2ujXG5sEdQzrcZrNOjI7Sge1JRWlIGMVe7O3Rg9iYpfP085X77zquX\nJ/7qZ1/wkzcnXp2/4t27C33sklv2yr7rkInsWLz/8ECzhVJVuCsZibRK4KBhZtQXBTEL1jxhFDAt\nO7SzgEyqRXHnXBcePrzl63dvWUrh9c0d1Ui6CSyLDB8PPnyXmfrogw142nYeHp4OxNHKpPVarmMT\nxWSkzUJyB8IkKiIxkqbvOJTsKDkZjKRQRnLNlcDNha0ARnaIMFFnBG4pyRFPdM50TkhrJlPkftAM\nqYzNR9JHktpk6rLWWg61ME+a85yREZOoUXuhDxWAoIKGLtPJPgZ/9uoVX312x48+v+VcTzApRWMc\nIgSj7cKyc9as9Y1hxskLe2vUpRy0pDFEB+2984fvOv/31x+UjBAC6t2TJmoq4ItmFR0yiGcHsWuu\nxzUgkonpgPT9EZQ3kAR3jkdkTPp0fbr+1EuS96g7kKIzTJGBuV9DcxTzjD1gVPtIZIEsTo49niwP\nkoli6g7MLoIlbQg4Ykt8BERBHg4j29FJGTYkHmCpWmgmBT/Pws9NydxAdG7jwHcTMBocYwxmUCpY\nqBiEfIeuTlzmDV5GhvrKKDtuhaVUenQJEyi067uFgNVhnbou+FLoN4g+WQr3cZLwlz1v4dZ3IpyW\n8+TvPzzpnkXS4mL2XPSdl6XSrLOUAi07dVlEaOwj8HUVOGq32M1LfFkVL4tyk/KLgOhY73B5ZFw6\nl+1CL4V6c2I532SOItNdD8sCMRNSF0vFc9RiAGEmVkbPDkOouPnxmzv+5Z+95mdffcF1fyJG8PMv\nXvC//92veNgbve+UtbBdt+xuOhGF69YlBJb5Rga+Q9GwbR2vExPzI9Wz8PS8InPOVFnW02SOUHhd\nRLs3zWGt60rswd++/RWXvfHTVy9pvVHqgjNYamV00T5by9nELbiOxtYHFy7sLdhph9gWoS014XYx\nsJKuOXRWRAy8NwHNkb65ydyyBEcORVSQ0FWe9fmDBAMz30TUT0o/KPV0O0YPyP1mSdWfIh+eZ/sg\nkg4sRlFBYxTyihu0roKvYjQb1DFHJ+yg62KF1nbYwe+dH7+650evXvD56zOrL5QqcMSygeFetP5H\nrnc3tutGWYvuSd8oa82OZxHlNPPY//XvfktNcKYUda8HqiE8u9RzbGh4zt6HYojYAhptiGj0pFkC\ndGRabiMkBpL1godPPEs2DyZS1Bjg5bme+G9dP4jiLJhJu7pBRGd6KxnJA0+6IShp0+LVISF0hll5\n5Y3JIJuvMsasarUpNOoVSZmP/BRS4BujkwoKx6Y/zDgzGA4Xp9gy8oodZ5hne9wUjGT8nOhc0QaK\nkbNH6TNGesSUlHiPibCNnioxZFGm91iWE7enM9Ure59y48HdjcRC+uWJ4jecFqculafLRnHn8fED\nAby6e8mL21tevxBiV4rjuehGBK0PLtdH3l8eMIzH6xNRgtvTCYg08ZYUvc2OYMlB08XlkebG6Xwm\nhvzEpGRUpbKEJSe+HCIS8m06jnWmnqJufxZWtsIN+M1LTtp66kjMHmVkIV6MpbhUm7KVbDlnENlt\nmu/jJkpM5AYiKYruwc1pQSw802eciYMbT/uugyxIoQjLddjZuyBhAXyW8rZxzAdZdsuk85CG1cUo\nVZSPIFiqJbW10trOuZwxNz5/9YIlZykmXRHm7AJsW+MPDw+8vz5xd3PP/bpyvVxSYv55oFkdLBNa\nZ5NgNzteH/Hoc9lblEww/Djo5eGmmnsMkio7C82iw6mhgIXM2yMQevnccNbhcdB8eQYoICOA0z0H\nozM3VI0WWFkYcnxmznkMV0xQb08JpRcj9XP1vfqkFwV3L1fe3N9mx8+ZDXcs6VBmhEupar6qBtVd\nyWdI+7IH2kfhmYA5A9FVJ61qGo1rPi+ye5YqnVHo0XRw5u8G6trKBLRBXUXTyufXLGdGLcRrH0Yt\n5bh7n65P159ySc5bWZ9VATG5mA+qnjrduZstEqHPE3UENvQ7I6HpWdtZZiw2nuc/Is9vxSd9hhGR\nNPnsxM2OQJ5ZqmHGUbAwxBKJbCsluy/Pz+xWIBZHCBk9SpsJjhEyr5+dvDEmSKzv4e7c3d1TwxgU\nPGfHt/1Krc6L04lv3r8n6oqPoFZYy8oeaRGzPXB/vuP+5obbm1tWr5zWSklGSVhgY6iT1nYubdD6\nTt8bD/Z0UKaoMg0eMf0kkzkgAF90dXTkecZYn/lK75pzngl4KmUeaVNJYaXTDfUV3Gfxp9s9VTmT\ngdLlR2rmKk7yPmOyZRFrRUyVMdkiBHUpnG9P9BknQzTVUgenk3NNOmV0cBxclPMW6H0nNe05UYPw\nnPPVXBKRRUgCkdIZmDG2ZP6m5+e9H+rfZs8Ky14K61IZ1lntRLjx1euXbNuFUqrOruLElmIVQ+v5\nrx9+xxLO7XqTzeWLZqXIjmIqS8wuox2Uqed8l0j2kc/9lV1Sm36ouT7nxnIBKhLZGHnOJ2UTY7i6\nuX02A2JSKpUfay56drm1ES1BW/nIga2FniMfI/P2hkYyFA9yVi7zrZ77XtAGGE0pWzV+/sUXnKua\nGSX/vmpmjX54UjA5mhMdY02RnOx8muZbvUgJPBIgBde8fjzf0vAhmmKevwf7bqio1Rz5IEjVbdc7\nEgIe4ui0DmBN8a4m0RlyFqEPzBb5ghfdy9no+GOvH0RxNnkM6vbkHJMADQXMyRmfJNBZqFmRQTD2\n3BXzvNF5E3OkiMn00XpMVZXIbpTPxF6HRWTR8bGAyCwA5+cqs3LLoGNukm4dKkym70qY4yULr67E\nXE7hmcSO58OtR3oYFdehuGsBSKHNkqJnjGi8v77H0nNBiy64dk9H+EorjwwqZ7/BfLCuC72fuMaF\nfTyy2cITGx6d2AstZMY50GG69Y0t9Dw+bI/84idf8Wb9jP/pP/5v/OzLH/F+e+LnP/op/9cv/44X\n51c8XjfGgM9fv+RUTvz6m6+5uTnDdjlUqaYCRZggLCs6TMo8ibPjIZLuQcI77vMzJDt/BjH+MYO3\nQC2UPNA9C7Gp7jQBX9FIVfCe6qI2uc4LJldkqUWzSi4lKkzJSc/ZgYnYEkLjVAT6cQhYFn/hA4+K\n0Z47Vya0ioJUNh3evPqcH794w9/88m+4uXtJYFSCwolegkt74v3lyuLPg8i2ADnvZVSu1yvvrzvX\nPejjkaUYG6LhxMhAPAZYIsIJNpjF0TXT7+bXSCRyBq6YlAdy6BXZMcRhjI0ekAmACBsHzfiY6597\nFUR1spL7Pp8pc9OOo9tkw4icExiJahsy+G7DjtmGibqL0qGPU53s0sGgHkppsXQ+e3XPX7x+zYhN\nhuNHsmlMk9mI0IzKUGAPT8qI7ZhLaapFUMMyELuorPqwFKt5SKk7LwQ716ChZxJJI55JJDCiq2bN\n2OSWfIsYEIsKYCNjxvM2GTMx+nR9uv7E60hebAbfzPAzlguMmb8r8GJMxYXczyPBMWIkmFkgrTMi\nYxMJVqrjnPFtpL8WHGf/sapnrJ4xOFUf51mt0YA5S6NYNRHt6oVuMp30SMoy5JlfGDHwop8ooUof\nRlNnnpyZu24XrinyJFZNMNqgNOdxf6S3LoW+IQ81S8Gf29OZfVxpdK7ROFtns54gdKSxLrTWoevv\nb6PRW+OyX2mjsQLmxs+++JL37y+82x+5sZPiSVKhBXhPkqdnPK0f/XnerxRbmHOvgIDs8fz3n5Gz\nWTyM7z+IsrKc5WXpln5tzGRU8Tqwo5NADE5rZcRCpeRSyeI8gHDuTiuP184l87VBV6ExTMBkFaPA\nTXOIOpMkBMeQiJdm/0wm5DNOmqhmZn6klDYpWUullEqphS/vX/Ld+wfO1annipfCyVdubxd+9d1v\nebc9cd13TnT2revexjjAhD2M7dLZRpcRdvQUAokj/neyQGN6gcUBWkTeh6Nw6jq3RLUS2EwEYUV1\nQkkQOA8+ebLl3kuhg8h9+bwnc51bsmYyveYjeE+50xAAGDrDes6Pj26pqjgoXqRmGppBH6E8evSR\niqD6rjUkcuYleHU+8fJc2fsOlraozH0/bau0LscsvBGTJkIUZeXGnZJWGFgRcGpGG1fcxVryEGNm\nBJSiPTyOohi+z/zJaHMA2vrhMRpD5MfaE9i3BK0HJdLb1EPn+Sw+fBydyz/m+kEUZzOfmw9hDidK\nxjNRndlVsRx6TWd2PFKgUX/mXol2zcHehMkjM/qIRC+ciJaL0zJIjSPhdFwxKh/k/JTTW0QNO8my\nL2Xh9u6emype80FZtKB1FYCe/632Z3DdGr//9luWumQtoP6Pe1VAnOqPQ4WnJ2LiqQ4351+CLnWo\nFFLoB1e4QYNmF67XJ22ue0ms60wyHp8eeHl7g5uzt0a0dkihXnu+biZ6NuDFy1u+uH3F2DstJHLy\n6v6G/fqUc2UKIK9f33PqZ/7Lb3/LuqzQB6Us4uDm8KwVzRKVZVXnK40uZ7INPM8LzrXxjzsBlovc\nxaPWI8rKuVaV+C6ZVbeSG0QH2uKF6jWlTzXHZ2MSGubMVcErnJfK3lTA930e6LMbllIvRgYR8Xqi\n2HFmzeeLGx4Ft0VSzp4obB6mY3S2fuHaHnnsG94Kw+DpcXBeTmxjgzH4w4fvMmymt5nLE0+hZKft\nO9vWYASXtnNxFQ1ButzHSPR0LuwsFHKvQVpEmIqyeSRj9qxuNVGKEJoZ2fkqiDowJkAyXZTnwWMf\nBfxETeRxlJ8lpptZdvDymUYBSkj2OQUx8JwzKIm698CXkvMchvcgvB888m5DmzkG07p7XZy/+PIN\n1eCyqdjuQ1Rg11ajzZm9pDZJ2AcWC1pYIthB0OhRiO4UF7opIYAh/7sxWMqq/XnQkIR4W4aq3rsO\nnSXjFhmEAGzQmcpiAnbUiawQAn5qQUCICYz4dH26/tRL/oVazzNGRHQJ+qT6mkJ2qqFlEacumWfS\nnTNWQ0qlzE53zvkIiEMdLyPn0DJHz9gQCjZMAbDJJhFa42Dqjp9PN9zfnihFyrfDHZKW3vuepvBp\nuhxwfRLgNSKoS6WPHXApRybdsqfMNpQEhlS47NctgSpLdow+3zDF9xHQ25WIQe87F3sAL7y+f4kF\nVFyshvOZE1JbbH1nn4q8MWhN1GvNJUV2P5zpQfb55y95+PCUyTBEOOt5Jch51EwccReov6zUpShG\nmbxXLWOSjTnDrjsOeY/R880f6M8+UvSbRfI8ZzWqMeafCqR0sYSKVyibALkwxdtkLx0q1mUBC061\ncFoLl82ymCrJLEp14XBEe5+0/gTSPBN453ud3bDQ6wTKo4CSXSxqJDggc+2owdP+gWs80tvCzbgn\n+oW3Tx84l8oA/uHte1rbqUUqnV4WdUyqrF8IKZzubWePKZU/2WG53r3kypo03wST0ed0EyvHY56M\nutfzfBRVNH//wEOMYbM7NtRkmGd6psJZ1wECx0eyQSZIquWXaz3XuGNYcarJ2L3PcaG5U9PGYBao\nGtO33AupzwDyrjPHl8KfffWZbCmi47bA6Lp/IVaKhWb0PbvDR7yIHc3JFeUgI33lbFBKSYsBFeEx\nijrpuSdsJFhvIuIy+lGsWk+/XmVM+XaNyeQaE0glgZ8QIDCfQ8vm5YiBd2WU3i1/xwXa+x93Nv8g\nijMTjJB811DykdQIMsmZg6WiIUhdQcO7AWMm4elPELO9r46WugNzyR3tkVxS40h2ZXqXMx2R75kr\nfsrkT8XAcMNKYTkt3J5PnE9LDsO24/1ba1gOggxzxt647DuXdiUC9q5uRPWSCKOqeTu2Rz+q+h6z\nABKna3Y25j+PDmFSPidPuDcJEbx//15zQ1543K6MFtw/3nC7nA6+82LOZW9Uq7TolGLyn/FEIrPF\nX3Kj+7ISAbV4ojXGu8cPnCwFTHKJj5ibVwHBS8VrxZdFiJU5JXYpgc8E3p4LkGze89zm14HhB7rj\nz4sphSjuloWtFlFqkqMnFl/Jz+ZE7LjVpNRAWVdRWr0xrFOscrOuXPedS88uB1LG6uMZ/RoxC5ss\n1zIgxgysGcykFdHBFFTmmiKLq3ePD7x//IDhPO57IjYdq7I2jL3DqFrrWZDLd1kDsdUq/dol2NHz\nfjeIRWu9TFriiHwmWstTfnbevigJbo/kabsKEC9CkIp9RFOyXHPY4UM3h6RjzrKlrwiJjh387jFR\nwtxzqTZl5SNqx4wBAaM8FzOWyUtgLHWhpy8fE311jiHqCNESpJ6pI24kGWNZKn3bkIdLw6wT3Yki\nKrSFHVxxTMnEWuTDpu+in6nb67nOdNioXNU8qvx3etKVDR8j508tfVT0vbp1GM+SwmpsTsBKwFEf\nAUU0TyGl2mttTF3YWdx9uj5df+KVa3ygGRJ1BUwg2qQNhkDEmJlf2j+EoW57kB1nnV2mqfqPzuME\nX8tM9I9/MHFZgWYhAYJJl3IVHXNaqNbKzd2J13e3or9lmtX7KV/tpCSvB5frx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Ea5OjgxbVxUI76vQFpxymeA5pPgC12Cjbp4pK6m5MKcxwfKWFIL4NpqKt0yX8fVJRqimZ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iiehzBqdieUjoeZVKSAQtRWSlXhOTIKx5ffUUvdIpeznXIvXzbJSceyjSr//UOD7/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\n9GAcxg6+9/u9qpFvDDpkaTabTfV7NpSYFPolBODYRkYvobexK2bcu4lVT9A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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc36ed33ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure(figsize=(16, 16))\n", "gray()\n", "subplot(3, 2, 1)\n", "imshow(Image.open('scene1.row3.col3.ppm'))\n", "axis('off')\n", "subplot(3, 2, 2)\n", "imshow(Image.open('scene1.row3.col4.ppm'))\n", "axis('off')\n", "subplot(3, 2, 3)\n", "imshow(res)\n", "axis('off')\n", "subplot(3, 2, 4)\n", "imshow(res2)\n", "axis('off')\n", "subplot(3, 2, 5)\n", "imshow(res3)\n", "axis('off')\n", "subplot(3, 2, 6)\n", "imshow(res4)\n", "axis('off')\n", "show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 }
unlicense
theandygross/TCGA_differential_expression
Notebooks/OneOffs/James_DX_methy.ipynb
1
113902
{ "cells": [ { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'/cellar/users/agross/TCGA_Code/DX/Notebooks'" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cd .." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "importing IPython notebook from <a href='./Imports.ipynb' target='_blank'>Imports</a>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import NotebookImport\n", "from Imports import *" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "store = '/data_ssd/TCGA_methylation_2014_04_16.h5'\n", "matched_tn = pd.read_hdf(store, 'matched_tn')\n", "matched_tn = matched_tn.groupby(axis=1, level=[0,1]).first()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_hdf(store, 'BRCA')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/cellar/users/agross/TCGA_Code/Methlation\n" ] } ], "source": [ "cd /cellar/users/agross/TCGA_Code/Methlation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "importing IPython notebook from <a href='./Setup/DX_Imports.ipynb' target='_blank'>Setup/DX_Imports</a>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "importing IPython notebook from <a href='./Setup/Imports.ipynb' target='_blank'>Setup/Imports</a>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from Setup.DX_Imports import *" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/cellar/users/agross/TCGA_Code/DX/Notebooks\n" ] } ], "source": [ "cd /cellar/users/agross/TCGA_Code/DX/Notebooks" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False 387422\n", "True 98090\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "promoter.value_counts()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "df_p = df.ix[true_index(promoter)].dropna()\n", "genes = probe_annotations.ix[true_index(promoter)]\n", "genes = genes.Gene_Symbol.ix[df_p.index].dropna()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f86c0e58950>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CPgL8DHi5fu3kg6vWllAt234UeIDqeaDlA42ZuRVYTBU8m6ieB7o0Mzc2znE7VVitppo5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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f86c0e41850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "genes.value_counts().hist()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(10703,)" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genes.value_counts().shape" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "BRD2 54\n", "KIAA1949 54\n", "SGCE;PEG10 53\n", "PTEN;KILLIN 52\n", "PPT2 51\n", "TRIM27 50\n", "CUTA 50\n", "BAT3 45\n", "B3GALT4 45\n", "PSMB9;TAP1 40\n", "PRR3;GNL1 39\n", "FLOT1 39\n", "RASSF1 39\n", "PSMB8 39\n", "TAPBP 38\n", "HLA-L 38\n", "TAP2 37\n", "KIFC1 36\n", "GPSM3;NOTCH4 36\n", "MICA 35\n", "DDAH2 35\n", "RAB1B 35\n", "HDAC4 35\n", "MSH5 35\n", "RASA3 34\n", "BLCAP 34\n", "BAT5 34\n", "DAXX 34\n", "BAT4;CSNK2B 33\n", "STK19;DOM3Z 33\n", "HLA-F 33\n", "ZNRD1;NCRNA00171 33\n", "ZBTB12 33\n", "HLA-E 32\n", "DHX16 32\n", "MAD1L1 32\n", "NAP1L4 32\n", "RPP21 32\n", "RGL2 31\n", "EHMT2 31\n", "ZBTB9 30\n", "FAM50B 29\n", "LTB 28\n", "HNRNPF 28\n", "BAT2 28\n", "APC 27\n", "HSD17B8 27\n", "PHTF2;TMEM60 27\n", "SKI 26\n", "VPS52;RPS18 26\n", "dtype: int64" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "genes.value_counts().head(50)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "31" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brca2 = true_index(genes == 'RGL2')\n", "len(brca2)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = extract_pc(df_p.ix[brca2])\n", "#r = extract_pc(df_p.ix[brca2, ti(r['pat_vec'] < .5)])" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f86c0554810>" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Bbd39XxZJYVcQoXyTibj8JYiB/EZiTvhD7j4jWwy7kBjsLxDzlQd6LXN5dSKC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\nrHT3rx1V6u4XOwZVd7/YMai6+8UOIYQQQgghhBB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f86c07f51d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "r['gene_vec'].order().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [], "source": [ "v = r['pat_vec']" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3+8fFPLVEYhvp9KaqDimEiWUZWFacwcGN2dVPwkECWg2k00ksa3x8iVWbaRpk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\nD7SzalWU667r54wzEg23v59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"text/plain": [ "<matplotlib.figure.Figure at 0x7f86c0a1e650>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "violin_plot_series(v.clip_upper(.1))" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T1XOauK4PCMDE95soFNLV5wrX7WF0dIje3j4cZ56enubVeVM1Gs1b5qpiYtt2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\n1TE1Nd1njjl6dIA77wxjGF7SafsavJouLi5XCldMbkACAR+PPNLLsWP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f86decd6c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "series_scatter(v.ix[:, '01'], rna_df.ix['RGL2'][:,'01'])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
dereneaton/ipyrad
newdocs/API-analysis/cookbook-vcf2hdf5.ipynb
1
243371
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <span style=\"color:gray\">ipyrad-analysis toolkit:</span> vcf_to_hdf5\n", "\n", "[View as notebook](https://nbviewer.jupyter.org/github/dereneaton/ipyrad/blob/master/newdocs/API-analysis/cookbook-vcf2hdf5.ipynb)\n", "\n", "Many genome assembly tools will write variant SNP calls to the VCF format (variant call format). This is a plain text file that stores variant calls relative to a reference genome in tabular format. It includes a lot of additional information about the quality of SNP calls, etc., but is not very easy to read or efficient to parse. To make analyses run a bit faster ipyrad uses a simplified format to store this information in the form of an HDF5 database. You can easily convert any VCF file to this HDF5 format using the `ipa.vcf_to_hdf5()` tool. \n", "\n", "This tool includes an added benefit of allowing you to enter an (optional) `ld_block_size` argument when creating the file which will store information that can be used downstream by many other tools to subsample SNPs and perform bootstrap resampling in a way that reduces the effects of linkage among SNPs. If your data are assembled RAD data then the ld_block_size is not required, since we can simply use RAD loci as the linkage blocks. But if you want to combine reference-mapped RAD loci located nearby in the genome as being on the same linkage block then you can enter a value such as 50,000 to create 50Kb linkage block that will join many RAD loci together and sample only 1 SNP per block in each bootstrap replicate. If your data are not RAD data, e.g., whole genome data, then the ld_block_size argument will be required in order to encode linkage information as discrete blocks into your database. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Required software\n", "If you are converting a VCF file assembled from some other tool (e.g., GATK, freebayes, etc.) then you will need to install the `htslib` and `bcftools` software and use them as described below. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# conda install ipyrad -c bioconda \n", "# conda install htslib -c bioconda\n", "# conda install bcftools -c bioconda" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import ipyrad.analysis as ipa\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pre-filter data from other programs (e.g., FreeBayes, GATK)\n", "\n", "You can use the program `bcftools` to pre-filter your data to exclude indels and low quality SNPs. If you ran the `conda install` commands above then you will have all of the required tools installed. To achieve the format that ipyrad expects you will need to exclude indel containing SNPs (this may change in the future). Further quality filtering is optional. \n", "\n", "The example below reduced the size of a VCF data file from 29Gb to 80Mb! VCF contains a lot of information that you do not need to retain through all of your analyses. We will keep only the final genotype calls. \n", "\n", "Note that the code below is bash script. You can run this from a terminal, or in a jupyter notebook by appending the (%%bash) header like below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "# compress the VCF file if not already done (creates .vcf.gz)\n", "bgzip data.vcf\n", "\n", "# tabix index the compressed VCF (creates .vcf.gz.tbi)\n", "tabix data.vcf.gz\n", "\n", "# remove multi-allelic SNPs and INDELs and PIPE to next command\n", "bcftools view -m2 -M2 -i'CIGAR=\"1X\" & QUAL>30' data.vcf.gz -Ou | \n", "\n", " # remove extra annotations/formatting info and save to new .vcf\n", " bcftools annotate -x FORMAT,INFO > data.cleaned.vcf\n", " \n", "# recompress the final file (create .vcf.gz)\n", "bgzip data.cleaned.vcf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A peek at the cleaned VCF file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>NC_018152.2</th>\n", " <th>51273</th>\n", " <th>.</th>\n", " <th>G</th>\n", " <th>A</th>\n", " <th>280.482</th>\n", " <th>..1</th>\n", " <th>..2</th>\n", " <th>GT</th>\n", " <th>0/0</th>\n", " <th>...</th>\n", " <th>0/0.9</th>\n", " <th>0/0.10</th>\n", " <th>0/0.11</th>\n", " <th>0/0.12</th>\n", " <th>0/0.13</th>\n", " <th>0/0.14</th>\n", " <th>0/0.15</th>\n", " <th>0/0.16</th>\n", " <th>0/0.17</th>\n", " <th>0/1.1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>NC_018152.2</td>\n", " <td>51292</td>\n", " <td>.</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>16750.300</td>\n", " <td>.</td>\n", " <td>.</td>\n", " <td>GT</td>\n", " <td>1/1</td>\n", " <td>...</td>\n", " <td>1/1</td>\n", " <td>.</td>\n", " <td>1/1</td>\n", " <td>1/1</td>\n", " <td>1/1</td>\n", " <td>1/1</td>\n", " <td>0/0</td>\n", " <td>1/1</td>\n", " <td>1/1</td>\n", " <td>1/1</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>NC_018152.2</td>\n", " <td>51349</td>\n", " <td>.</td>\n", " <td>A</td>\n", " <td>G</td>\n", " <td>628.563</td>\n", " <td>.</td>\n", " <td>.</td>\n", " <td>GT</td>\n", " <td>0/0</td>\n", " <td>...</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>NC_018152.2</td>\n", " <td>51351</td>\n", " <td>.</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>943.353</td>\n", " <td>.</td>\n", " <td>.</td>\n", " <td>GT</td>\n", " <td>0/0</td>\n", " <td>...</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>NC_018152.2</td>\n", " <td>51352</td>\n", " <td>.</td>\n", " <td>G</td>\n", " <td>A</td>\n", " <td>607.681</td>\n", " <td>.</td>\n", " <td>.</td>\n", " <td>GT</td>\n", " <td>0/0</td>\n", " <td>...</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>NC_018152.2</td>\n", " <td>51398</td>\n", " <td>.</td>\n", " <td>C</td>\n", " <td>T</td>\n", " <td>510.120</td>\n", " <td>.</td>\n", " <td>.</td>\n", " <td>GT</td>\n", " <td>0/0</td>\n", " <td>...</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " <td>0/0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 29 columns</p>\n", "</div>" ], "text/plain": [ " NC_018152.2 51273 . G A 280.482 ..1 ..2 GT 0/0 ... 0/0.9 0/0.10 \\\n", "0 NC_018152.2 51292 . A G 16750.300 . . GT 1/1 ... 1/1 . \n", "1 NC_018152.2 51349 . A G 628.563 . . GT 0/0 ... 0/0 0/0 \n", "2 NC_018152.2 51351 . C T 943.353 . . GT 0/0 ... 0/0 0/0 \n", "3 NC_018152.2 51352 . G A 607.681 . . GT 0/0 ... 0/0 0/0 \n", "4 NC_018152.2 51398 . C T 510.120 . . GT 0/0 ... 0/0 0/0 \n", "\n", " 0/0.11 0/0.12 0/0.13 0/0.14 0/0.15 0/0.16 0/0.17 0/1.1 \n", "0 1/1 1/1 1/1 1/1 0/0 1/1 1/1 1/1 \n", "1 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 \n", "2 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 \n", "3 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 \n", "4 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the VCF as an datafram\n", "dfchunks = pd.read_csv(\n", " \"/home/deren/Documents/ipyrad/sandbox/Macaque-Chr1.clean.vcf.gz\",\n", " sep=\"\\t\", \n", " skiprows=1000, \n", " chunksize=1000,\n", ")\n", "\n", "# show first few rows of first dataframe chunk\n", "next(dfchunks).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting clean VCF to HDF5 \n", "Here I using a VCF file from whole geome data for 20 monkey's from an unpublished study (in progress). It contains >6M SNPs all from chromosome 1. Because many SNPs are close together and thus tightly linked we will likely wish to take linkage into account in our downstream analyses.\n", "\n", "The ipyrad analysis tools can do this by encoding linkage block information into the HDF5 file. Here we encode `ld_block_size` of 20K bp. This breaks the 1 scaffold (chromosome) into about 10K linkage blocks. See the example below of this information being used in an ipyrad PCA analysis. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Indexing VCF to HDF5 database file\n", "VCF: 6094152 SNPs; 1 scaffolds\n", "[####################] 100% 0:02:22 | converting VCF to HDF5 \n", "HDF5: 6094152 SNPs; 10845 linkage group\n", "SNP database written to ./analysis-vcf2hdf5/Macaque_LD20K.snps.hdf5\n" ] } ], "source": [ "# init a conversion tool\n", "converter = ipa.vcf_to_hdf5(\n", " name=\"Macaque_LD20K\",\n", " data=\"/home/deren/Documents/ipyrad/sandbox/Macaque-Chr1.clean.vcf.gz\",\n", " ld_block_size=20000,\n", ")\n", "\n", "# run the converter\n", "converter.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Downstream analyses\n", "The data file now contains 6M SNPs across 20 samples and N linkage blocks. By default the PCA tool subsamples a single SNP per linkage block. To explore variation over multiple random subsamplings we can use the `nreplicates` argument. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Samples: 20\n", "Sites before filtering: 6094152\n", "Filtered (indels): 0\n", "Filtered (bi-allel): 0\n", "Filtered (mincov): 794597\n", "Filtered (minmap): 0\n", "Filtered (combined): 794597\n", "Sites after filtering: 5299555\n", "Sites containing missing values: 0 (0.00%)\n", "Missing values in SNP matrix: 0 (0.00%)\n" ] } ], "source": [ "# init a PCA tool and filter to allow no missing data\n", "pca = ipa.pca(\n", " data=\"./analysis-vcf2hdf5/Macaque_LD20K.snps.hdf5\",\n", " mincov=1.0, \n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run a single PCA analysis from subsampled unlinked SNPs" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subsampling SNPs: 10841/5299555\n" ] }, { "data": { "text/html": [ "<div class=\"toyplot\" 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If the signal in the data is robust then we should expect to see the points clustering at a similar place across replicates. Internally ipyrad will rotate axes to ensure the replicate plots align despite axes swapping (which is arbitrary in PCA space). You can see this provides a better view of uncertainty in our estimates than the plot above (and it looks cool!)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Subsampling SNPs: 10841/5299555\n" ] }, { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t6242fdb68d0d4a57ab68b1b44d28a1eb\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t1ab6529cdf2c4192aa20c8b1d221f741\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g 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style=\"fill:rgb(0%,0%,0%);fill-opacity:1.0;opacity:1.0;stroke:rgb(100%,100%,100%);stroke-opacity:1.0;stroke-width:1\" transform=\"translate(88.52245392847702, 173.12586121605082)\"><title>SRR1024051</title><circle r=\"3.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t8dd95e51514b486a8b04d69e16cdc266\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"\" x1=\"19.670251214851294\" x2=\"217.21869964449115\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-7.2250000000000005\" y=\"8.555\">-25</text></g><g transform=\"translate(55.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"8.555\">0</text></g><g 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style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-2.78\" y=\"-4.440892098500626e-16\">0</text></g><g transform=\"translate(189.75002530675886,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.56\" y=\"-4.440892098500626e-16\">25</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-63.69\" y=\"0.0\">PC1 (14.4%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text 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20.723633495393127, -14.218050541664708]],\"toyplot\");\n", "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n", " {\n", " tables.set(owner_id, key, names, columns);\n", "\n", " var owner = document.querySelector(\"#\" + owner_id);\n", " function show_item(e)\n", " {\n", " return owner.contains(e.target);\n", " }\n", "\n", " function choose_item()\n", " {\n", " io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n", " }\n", "\n", " context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n", " })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t1534199859674508a8c1eabec945c39a\",\"data\",\"point\",[\"x\", \"y0\"],[[-8.7892978023227, -7.376873090327244, -7.262240711664125, -12.525288422560077, -7.243407656524104, -6.91791713106154, 72.3583070943878, -7.003092870466415, -8.630940609751272, 10.232736321819802, -7.33307527321553, -12.656506338452056, -12.634773719886844, -7.539668409709932, -12.934929526893198, -1.2703615983765564, 71.49261728758297, -13.86510276747676, -12.610391106228107, -7.489793668874081], [-8.343120617511094, -7.28563908367855, -22.878450556199855, 20.32764829419997, -22.854058797501352, -23.139307277994572, 5.9664929619820075, -23.88941831051271, -8.267501920115475, -12.943013832718831, 10.093460314636296, 20.625758646525156, 20.81575280425669, -24.880415784088726, 26.434073539996966, 6.196531485793858, 5.826675865844557, 26.07934226342926, 20.145388867862945, -8.030198864206518]],\"toyplot\");\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"t8dd95e51514b486a8b04d69e16cdc266\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 75.0, \"min\": -25.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"t971863a9e172485fb333d2dd07edf9ea\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 27.999389089204836, \"min\": -30.52542056096229}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pca.run_and_plot_2D(0, 1, seed=123, nreplicates=25);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More details on running PCAs, toggling options, and styling plots can be found in our [ipyrad.analysis PCA tutorial](https://ipyrad.readthedocs.io/en/latest/API-analysis/cookbook-pca.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
PytLab/catplot
examples/energy_profile_examples/simple_use.ipynb
1
17701
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# catplot 的简单使用" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from catplot.ep_components.ep_canvas import EPCanvas\n", "from catplot.ep_components.ep_lines import ElementaryLine" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 创建一个Energy Profile 画布" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10c97ffd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "canvas = EPCanvas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 创建针对一个基元反应的 Energy Profile Line" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "line = ElementaryLine([0.0, 1.2, 0.8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 像画布中添加 Energy Profile Line" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "canvas.add_line(line)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 进行绘制" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "canvas.draw()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 显示效果" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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PP73xcXwRaYo+oSp5q6GhgVWrVkXr2nMXSZ/KXfLWxo0b2b17NwDHHHMMffr0CZxIpHCo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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10c97ffd0>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "canvas.figure" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
therealAJ/python-sandbox
data-science/learning/ud1/DataScience/Python101.ipynb
1
13272
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Whitespace Is Important" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 is odd\n", "2 is even\n", "3 is odd\n", "4 is even\n", "5 is odd\n", "6 is even\n", "All done!\n" ] } ], "source": [ "listOfNumbers = [1, 2, 3, 4, 5, 6]\n", "\n", "for number in listOfNumbers:\n", " print number,\n", " if (number % 2 == 0):\n", " print \"is even\"\n", " else:\n", " print \"is odd\"\n", " \n", "print \"All done!\"\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Modules" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 27.71196545 13.97740511 27.80259305 24.50141469 22.49409004\n", " 29.5092022 28.42493931 36.27837029 26.17803185 30.37013729]\n" ] } ], "source": [ "import numpy as np\n", "\n", "A = np.random.normal(25.0, 5.0, 10)\n", "print A" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lists" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "x = [1, 2, 3, 4, 5, 6]\n", "print len(x)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[:3]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[4, 5, 6]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[3:]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[5, 6]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[-2:]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.extend([7,8])\n", "x" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.append(9)\n", "x" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12]]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = [10, 11, 12]\n", "listOfLists = [x, y]\n", "listOfLists" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[1]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = [3, 2, 1]\n", "z.sort()\n", "z" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3, 2, 1]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z.sort(reverse=True)\n", "z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuples" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Tuples are just immutable lists. Use () instead of []\n", "x = (1, 2, 3)\n", "len(x)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = (4, 5, 6)\n", "y[2]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(1, 2, 3), (4, 5, 6)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "listOfTuples = [x, y]\n", "listOfTuples" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "32\n", "120000\n" ] } ], "source": [ "(age, income) = \"32,120000\".split(',')\n", "print age\n", "print income" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dictionaries" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Janeway\n" ] } ], "source": [ "# Like a map or hash table in other languages\n", "captains = {}\n", "captains[\"Enterprise\"] = \"Kirk\"\n", "captains[\"Enterprise D\"] = \"Picard\"\n", "captains[\"Deep Space Nine\"] = \"Sisko\"\n", "captains[\"Voyager\"] = \"Janeway\"\n", "\n", "print captains[\"Voyager\"]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kirk\n" ] } ], "source": [ "print captains.get(\"Enterprise\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print captains.get(\"NX-01\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Voyager: Janeway\n", "Deep Space Nine: Sisko\n", "Enterprise D: Picard\n", "Enterprise: Kirk\n" ] } ], "source": [ "for ship in captains:\n", " print ship + \": \" + captains[ship]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "def SquareIt(x):\n", " return x * x\n", "\n", "print SquareIt(2)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "#You can pass functions around as parameters\n", "def DoSomething(f, x):\n", " return f(x)\n", "\n", "print DoSomething(SquareIt, 3)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "27\n" ] } ], "source": [ "#Lambda functions let you inline simple functions\n", "print DoSomething(lambda x: x * x * x, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Boolean Expressions" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "print 1 == 3" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print (True or False)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "print 1 is 3" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All is well with the world\n" ] } ], "source": [ "if 1 is 3:\n", " print \"How did that happen?\"\n", "elif 1 > 3:\n", " print \"Yikes\"\n", "else:\n", " print \"All is well with the world\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Looping" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3 4 5 6 7 8 9\n" ] } ], "source": [ "for x in range(10):\n", " print x," ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2 3 4 5\n" ] } ], "source": [ "for x in range(10):\n", " if (x is 1):\n", " continue\n", " if (x > 5):\n", " break\n", " print x," ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3 4 5 6 7 8 9\n" ] } ], "source": [ "x = 0\n", "while (x < 10):\n", " print x,\n", " x += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Activity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Write some code that creates a list of integers, loops through each element of the list, and only prints out even numbers!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "2\n", "4\n", "6\n", "8\n" ] } ], "source": [ "l = range(10)\n", "\n", "for num in l:\n", " if(num % 2 == 0):\n", " print num" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
Unidata/MetPy
talks/MetPy Exercise.ipynb
5
10734
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime, timedelta\n", "from siphon.catalog import TDSCatalog\n", "from siphon.ncss import NCSS\n", "import numpy as np\n", "import metpy.calc as mpcalc\n", "from metpy.units import units, concatenate\n", "from metpy.plots import SkewT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just some helper code to make things easier. `metpy_units_handler` plugins into siphon to automatically add units to variables. `post_process_data` is used to clean up some oddities from the NCSS point feature collection." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "units.define('degrees_north = 1 degree')\n", "units.define('degrees_east = 1 degree')\n", "unit_remap = dict(inches='inHg', Celsius='celsius')\n", "def metpy_units_handler(vals, unit):\n", " arr = np.array(vals)\n", " if unit:\n", " unit = unit_remap.get(unit, unit)\n", " arr = arr * units(unit)\n", " return arr\n", "\n", "# Fix dates and sorting\n", "def sort_list(list1, list2):\n", " return [l1 for (l1, l2) in sorted(zip(list1, list2), key=lambda i: i[1])]\n", "\n", "def post_process_data(data):\n", " data['time'] = [datetime.strptime(d.decode('ascii'), '%Y-%m-%d %H:%M:%SZ') for d in data['time']]\n", " ret = dict()\n", " for key,val in data.items():\n", " try:\n", " val = units.Quantity(sort_list(val.magnitude.tolist(), data['time']), val.units)\n", " except AttributeError:\n", " val = sort_list(val, data['time'])\n", " ret[key] = val\n", " return ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# METAR Meteogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to grab the catalog for the METAR feature collection data from http://thredds.ucar.edu/thredds/catalog.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/nws/metar/ncdecoded/catalog.xml?dataset=nws/metar/ncdecoded/Metar_Station_Data_fc.cdmr')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up NCSS access to the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = list(cat.datasets.values())[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ncss = NCSS(ds.access_urls['NetcdfSubset'])\n", "ncss.unit_handler = metpy_units_handler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What variables do we have available?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ncss.variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a query for the last 7 days of data for a specific lon/lat point. We should ask for: air temperature, dewpoint temperature, wind speed, and wind direction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "now = datetime.utcnow()\n", "query = ncss.query().accept('csv')\n", "query.lonlat_point(-97, 35.25).time_range(now - timedelta(days=7), now)\n", "query.variables('air_temperature', 'dew_point_temperature', 'wind_speed', 'wind_from_direction')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "data = ncss.get_data(query)\n", "data = post_process_data(data) # Fixes for NCSS point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Heat Index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need relative humidity:\n", " $$RH = e / e_s$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "e = mpcalc.saturation_vapor_pressure(data['dew_point_temperature'])\n", "e_s = mpcalc.saturation_vapor_pressure(data['air_temperature'])\n", "rh = e / e_s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate heat index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# RH should be [0, 100]\n", "hi = mpcalc.heat_index(data['air_temperature'], rh * 100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the temperature, dewpoint, and heat index. Bonus points to also plot wind speed and direction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "times = data['time']\n", "fig, axes = plt.subplots(2, 1, figsize=(9, 9))\n", "axes[0].plot(times, data['air_temperature'].to('degF'), 'r', linewidth=2)\n", "axes[0].plot(times, data['dew_point_temperature'].to('degF'), 'g', linewidth=2)\n", "axes[0].plot(times, hi, color='darkred', linestyle='--', linewidth=2)\n", "axes[0].grid(True)\n", "axes[1].plot(times, data['wind_speed'].to('mph'), 'b')\n", "twin = plt.twinx(axes[1])\n", "twin.plot(times, data['wind_from_direction'], 'kx')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Sounding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First grab the catalog for the Best dataset from the GSD HRRR from http://thredds.ucar.edu/thredds/catalog.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cat = TDSCatalog('http://thredds-jumbo.unidata.ucar.edu/thredds/catalog/grib/HRRR/CONUS_3km/wrfprs/catalog.xml?dataset=grib/HRRR/CONUS_3km/wrfprs/Best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up NCSS access to the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "best_ds = list(cat.datasets.values())[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ncss = NCSS(best_ds.access_urls['NetcdfSubset'])\n", "ncss.unit_handler = metpy_units_handler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What variables do we have?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ncss.variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up a query for the most recent set of data from a point. We should request temperature, dewpoint, and U and V." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "query = ncss.query().accept('csv')\n", "query.lonlat_point(-105, 40).time(datetime.utcnow())\n", "query.variables('Temperature_isobaric', 'Dewpoint_temperature_isobaric',\n", " 'u-component_of_wind_isobaric', 'v-component_of_wind_isobaric')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = ncss.get_data(query)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "T = data['Temperature_isobaric'].to('degC')\n", "Td = data['Dewpoint_temperature_isobaric'].to('degC')\n", "p = data['vertCoord'].to('mbar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot a sounding of the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(9, 9))\n", "skew = SkewT(fig=fig)\n", "skew.plot(p, T, 'r')\n", "skew.plot(p, Td, 'g')\n", "skew.ax.set_ylim(1050, 100)\n", "skew.plot_mixing_lines()\n", "skew.plot_dry_adiabats()\n", "skew.plot_moist_adiabats()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also calculate the parcel profile and add that to the plot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prof = mpcalc.parcel_profile(p[::-1], T[-1], Td[-1])\n", "skew.plot(p[::-1], prof.to('degC'), 'k', linewidth=2)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's also plot the location of the LCL and the 0 isotherm as well:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lcl = mpcalc.lcl(p[-1], T[-1], Td[-1])\n", "lcl_temp = mpcalc.dry_lapse(concatenate((p[-1], lcl)), T[-1])[-1].to('degC')\n", "skew.plot(lcl, lcl_temp, 'bo')\n", "skew.ax.axvline(0, color='blue', linestyle='--', linewidth=2)\n", "fig" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
james-prior/cohpy
20160826-dojo-regex-travis.ipynb
1
12106
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with the original regular expression and\n", "string to search from Travis'\n", "[regex problem](https://github.com/deeppunster/regex_problem/blob/master/regex%20problem)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pattern = re.compile(r\"\"\"\n", " (?P<any>any4?) # \"any\"\n", " # association\n", " | # or\n", " (?P<object_eq>object ([\\w-]+) eq (\\d+)) # object\n", " alone\n", " # association\n", " | # or\n", " (?P<object_range>object ([a-z0-9A-Z-]+) range (\\d+) (\\d+)) # object range\n", " # association\n", " | # or\n", " (?P<object_group>object-group ([a-z0-9A-Z-]+)) # object group\n", " # association\n", " | # or\n", " (?P<object_alone>object ([[a-z0-9A-Z-]+)) # object alone\n", " # association\n", "\"\"\", re.VERBOSE)\n", "\n", "s = ''' object-group jfi-ip-ranges object DA-TD-WEB01 eq 8850\n", "'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The regex had two bugs.\n", "- Two [[ near the end of the pattern string.\n", "- The significant spaces in the pattern (such as after object-group) were being ignored because of re.VERBOSE.\n", "\n", "So those bugs are fixed in the pattern below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pattern = re.compile(r\"\"\"\n", " (?P<any>any4?) # \"any\"\n", " # association\n", " | # or\n", " (?P<object_eq>object\\ ([\\w-]+)\\ eq\\ (\\d+)) # object\n", " alone\n", " # association\n", " | # or\n", " (?P<object_range>object\\ ([a-z0-9A-Z-]+)\\ range\\ (\\d+)\\ (\\d+)) # object range\n", " # association\n", " | # or\n", " (?P<object_group>object-group\\ ([a-z0-9A-Z-]+)) # object group\n", " # association\n", " | # or\n", " (?P<object_alone>object\\ ([a-z0-9A-Z-]+)) # object alone\n", " # association\n", "\"\"\", re.VERBOSE)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('',\n", " '',\n", " '',\n", " '',\n", " '',\n", " '',\n", " '',\n", " '',\n", " 'object-group jfi-ip-ranges',\n", " 'jfi-ip-ranges',\n", " '',\n", " ''),\n", " ('', '', '', '', '', '', '', '', '', '', 'object DA-TD-WEB01', 'DA-TD-WEB01')]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "re.findall(pattern, s)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(4, 30), match='object-group jfi-ip-ranges'>\n", "groups (None, None, None, None, None, None, None, None, 'object-group jfi-ip-ranges', 'jfi-ip-ranges', None, None)\n", "groupdict {'object_group': 'object-group jfi-ip-ranges', 'object_eq': None, 'any': None, 'object_alone': None, 'object_range': None}\n", "<_sre.SRE_Match object; span=(31, 49), match='object DA-TD-WEB01'>\n", "groups (None, None, None, None, None, None, None, None, None, None, 'object DA-TD-WEB01', 'DA-TD-WEB01')\n", "groupdict {'object_group': None, 'object_eq': None, 'any': None, 'object_alone': 'object DA-TD-WEB01', 'object_range': None}\n" ] } ], "source": [ "for m in re.finditer(pattern, s):\n", " print(repr(m))\n", " print('groups', m.groups())\n", " print('groupdict', m.groupdict())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above works, but keeping track of the indexes of the unnamed groups drives me crazy. So I add names for all groups." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pattern = re.compile(r\"\"\"\n", " (?P<any>any4?) # \"any\"\n", " # association\n", " | # or\n", " (?P<object_eq>object\\ (?P<oe_name>[\\w-]+)\\ eq\\ (?P<oe_i>\\d+)) # object\n", " alone\n", " # association\n", " | # or\n", " (?P<object_range>object\\ (?P<or_name>[a-z0-9A-Z-]+)\n", " \\ range\\ (?P<oe_r_start>\\d+)\\ (?P<oe_r_end>\\d+)) # object range\n", " # association\n", " | # or\n", " (?P<object_group>object-group\\ (?P<og_name>[a-z0-9A-Z-]+)) # object group\n", " # association\n", " | # or\n", " (?P<object_alone>object\\ (?P<oa_name>[a-z0-9A-Z-]+)) # object alone\n", " # association\n", "\"\"\", re.VERBOSE)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_sre.SRE_Match object; span=(4, 30), match='object-group jfi-ip-ranges'>\n", "groups (None, None, None, None, None, None, None, None, 'object-group jfi-ip-ranges', 'jfi-ip-ranges', None, None)\n", "groupdict {'og_name': 'jfi-ip-ranges', 'object_range': None, 'object_group': 'object-group jfi-ip-ranges', 'oe_r_start': None, 'oe_i': None, 'oa_name': None, 'or_name': None, 'oe_name': None, 'oe_r_end': None, 'any': None, 'object_alone': None, 'object_eq': None}\n", "<_sre.SRE_Match object; span=(31, 49), match='object DA-TD-WEB01'>\n", "groups (None, None, None, None, None, None, None, None, None, None, 'object DA-TD-WEB01', 'DA-TD-WEB01')\n", "groupdict {'og_name': None, 'object_range': None, 'object_group': None, 'oe_r_start': None, 'oe_i': None, 'oa_name': 'DA-TD-WEB01', 'or_name': None, 'oe_name': None, 'oe_r_end': None, 'any': None, 'object_alone': 'object DA-TD-WEB01', 'object_eq': None}\n" ] } ], "source": [ "for m in re.finditer(pattern, s):\n", " print(repr(m))\n", " print('groups', m.groups())\n", " print('groupdict', m.groupdict())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following shows me just the groups that matched." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "og_name 'jfi-ip-ranges'\n", "object_group 'object-group jfi-ip-ranges'\n", "\n", "oa_name 'DA-TD-WEB01'\n", "object_alone 'object DA-TD-WEB01'\n", "\n" ] } ], "source": [ "for m in re.finditer(pattern, s):\n", " for key, value in m.groupdict().items():\n", " if value is not None:\n", " print(key, repr(value))\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at the above,\n", "I see that I probably don't care about the big groups,\n", "just the parameters,\n", "so I remove the big groups (except for \"any\")\n", "from the regular expression." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pattern = re.compile(r\"\"\"\n", " (?P<any>any4?) # \"any\"\n", " # association\n", " | # or\n", " (object\\ (?P<oe_name>[\\w-]+)\\ eq\\ (?P<oe_i>\\d+)) # object\n", " alone\n", " # association\n", " | # or\n", " (object\\ (?P<or_name>[a-z0-9A-Z-]+)\n", " \\ range\\ (?P<oe_r_start>\\d+)\\ (?P<oe_r_end>\\d+)) # object range\n", " # association\n", " | # or\n", " (object-group\\ (?P<og_name>[a-z0-9A-Z-]+)) # object group\n", " # association\n", " | # or\n", " (object\\ (?P<oa_name>[a-z0-9A-Z-]+)) # object alone\n", " # association\n", "\"\"\", re.VERBOSE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it tells me just the meat of what I want to know." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "og_name 'jfi-ip-ranges'\n", "\n", "oa_name 'DA-TD-WEB01'\n", "\n" ] } ], "source": [ "for m in re.finditer(pattern, s):\n", " for key, value in m.groupdict().items():\n", " if value is not None:\n", " print(key, repr(value))\n", " print()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
GuidoBR/python-for-finance
Markowitz+-+Bovespa.ipynb
1
228330
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'numpy'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-e82a68559aeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_datareader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy'" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pandas_datareader import data as wb\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "assets = [\"VALE\", \"PETR4\", \"ITUB4\", \"BBDC4\", \"ABEV3\", \"BBAS3\", \"ITSA4\", \"BVMF3\", \"KROT3\", \"CIEL3\", \"UGPA3\", \"BBSE3\"]\n", "bvmf_data = pd.DataFrame()\n", "for t in assets:\n", " bvmf_data[t] = wb.DataReader(t, data_source='google', start='2015-1-1')['Close']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>VALE</th>\n", " <th>PETR4</th>\n", " <th>ITUB4</th>\n", " <th>BBDC4</th>\n", " <th>ABEV3</th>\n", " <th>BBAS3</th>\n", " <th>ITSA4</th>\n", " <th>BVMF3</th>\n", " <th>KROT3</th>\n", " <th>CIEL3</th>\n", " <th>UGPA3</th>\n", " <th>BBSE3</th>\n", " </tr>\n", " <tr>\n", " <th>Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-01-02</th>\n", " <td>7.94</td>\n", " <td>9.36</td>\n", " <td>27.96</td>\n", " <td>23.62</td>\n", " <td>16.01</td>\n", " <td>22.65</td>\n", " <td>7.58</td>\n", " <td>9.50</td>\n", " <td>14.80</td>\n", " <td>22.99</td>\n", " <td>50.66</td>\n", " <td>30.47</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-05</th>\n", " <td>7.73</td>\n", " <td>8.61</td>\n", " <td>28.10</td>\n", " <td>23.66</td>\n", " <td>15.66</td>\n", " <td>22.18</td>\n", " <td>7.60</td>\n", " <td>9.22</td>\n", " <td>13.86</td>\n", " <td>22.20</td>\n", " <td>49.41</td>\n", " <td>29.51</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-06</th>\n", " <td>8.14</td>\n", " <td>8.33</td>\n", " <td>28.55</td>\n", " <td>24.44</td>\n", " <td>16.11</td>\n", " <td>22.49</td>\n", " <td>7.66</td>\n", " <td>9.31</td>\n", " <td>12.65</td>\n", " <td>21.76</td>\n", " <td>49.10</td>\n", " <td>30.66</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-07</th>\n", " <td>8.34</td>\n", " <td>8.67</td>\n", " <td>29.59</td>\n", " <td>25.41</td>\n", " <td>16.51</td>\n", " <td>23.48</td>\n", " <td>7.95</td>\n", " <td>9.71</td>\n", " <td>12.66</td>\n", " <td>21.96</td>\n", " <td>50.40</td>\n", " <td>30.30</td>\n", " </tr>\n", " <tr>\n", " <th>2015-01-08</th>\n", " <td>8.57</td>\n", " <td>9.18</td>\n", " <td>30.05</td>\n", " <td>25.54</td>\n", " <td>16.48</td>\n", " <td>23.56</td>\n", " <td>8.03</td>\n", " <td>9.61</td>\n", " <td>13.56</td>\n", " <td>22.57</td>\n", " <td>50.24</td>\n", " <td>30.16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " VALE PETR4 ITUB4 BBDC4 ABEV3 BBAS3 ITSA4 BVMF3 KROT3 \\\n", "Date \n", "2015-01-02 7.94 9.36 27.96 23.62 16.01 22.65 7.58 9.50 14.80 \n", "2015-01-05 7.73 8.61 28.10 23.66 15.66 22.18 7.60 9.22 13.86 \n", "2015-01-06 8.14 8.33 28.55 24.44 16.11 22.49 7.66 9.31 12.65 \n", "2015-01-07 8.34 8.67 29.59 25.41 16.51 23.48 7.95 9.71 12.66 \n", "2015-01-08 8.57 9.18 30.05 25.54 16.48 23.56 8.03 9.61 13.56 \n", "\n", " CIEL3 UGPA3 BBSE3 \n", "Date \n", "2015-01-02 22.99 50.66 30.47 \n", "2015-01-05 22.20 49.41 29.51 \n", "2015-01-06 21.76 49.10 30.66 \n", "2015-01-07 21.96 50.40 30.30 \n", "2015-01-08 22.57 50.24 30.16 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bvmf_data.head()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "log_returns = np.log(bvmf_data / bvmf_data.shift(1)) # Normalizing returns by using log" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.11246052 0.1497936 0.04287148 0.03265698 0.12607904 0.01852747\n", " 0.01410171 0.17025479 0.00054792 0.12489561 0.05034853 0.15746236]\n" ] } ], "source": [ "def rand_weights(n):\n", " ''' Produces n random weights that sum to 1 '''\n", " weights = np.random.rand(n)\n", " weights /= np.sum(weights)\n", " return weights\n", "\n", "# Create a random portfolio distribution weigths, that sums 100%\n", "print(rand_weights(len(assets)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pfolio_returns = []\n", "pfolio_volatilities = []\n", "max_sharpe = 0\n", "max_weights = np.random.rand(len(assets))\n", "for x in range (50000):\n", " weights = rand_weights(len(assets))\n", " retorno = np.sum(weights * log_returns.mean()) * 250\n", " volatilidade = np.sqrt(np.dot(weights.T,np.dot(log_returns.cov() * 250, weights)))\n", " if (retorno/volatilidade) > max_sharpe:\n", " max_sharpe = (retorno/volatilidade)\n", " max_weights = weights\n", " pfolio_returns.append(np.sum(weights * log_returns.mean()) * 250)\n", " pfolio_volatilities.append(np.sqrt(np.dot(weights.T,np.dot(log_returns.cov() * 250, weights))))\n", " \n", "pfolio_returns = np.array(pfolio_returns)\n", "pfolio_volatilities = np.array(pfolio_volatilities)\n", "\n", "pfolio_returns, pfolio_volatilities\n", "sharpe_arr = pfolio_returns / pfolio_volatilities\n", "sharpe_ind_max = sharpe_arr.argmax()\n", "max_sr_ret = pfolio_returns[sharpe_ind_max]\n", "max_sr_vol = pfolio_volatilities[sharpe_ind_max]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "portfolios = pd.DataFrame({'Return': pfolio_returns, 'Volatility': pfolio_volatilities})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x2b583f901d0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3d927n0bmIa1IdGcEU0dJHWe3cFOLH4s2VBrGqWX+Ujxd34DEGhGTRFe2yXRd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/hdcj1EJJpc98HkZ78B5hpjJpJQVr/JZ/v5xph/NMZMAGqAc40xi4E1wMXJce4PcC6v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\nHKyZmZmZ1ZiDNTMzM7Ma+wMNvdTgxWTNpwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2b583ef6780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "portfolios.plot(x='Volatility', y='Return', kind='scatter', figsize=(10, 6));\n", "plt.xlabel('Expected Volatility')\n", "plt.ylabel('Expected Return')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x2b58417c320>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SP/QUcOOkZz+Bv/AVqKwhcfwFxCd1vV40Nu4oYuO6P/iSRCXuiAMINqwsekBI\nTD6u3CE9LmxrJb1iCU5VNfFxExFn55nSxPiJ1Lz/fFqefRwAEQdVZdCVX8ap7IY3rMaYHtNjQauq\n+iLyFWAq0f3X21R1gYhcnXn8JhEZCcwG6oBQRL4GHAYcBXwGeFNE5mZO+Z+q+nhPjXdf98lPf5pb\nb7mFn3RSInBLPM4nP/OZXhxV12jQDG1vgjsQKg/tG7OW1/wdCfPTq5l3yFqFjPpY6f4tK8sHgmEy\nWhhgdww7ASqGRMFjrl2VRE37D7gw+mryFWiyHt75Z5RRDT044IPIYV8oOJVzyo8Jn70aUo1l6zw1\nm1ku4h7xL3DM1ejWhVFHgCGH7db3SQaMxj2y81ve/vO/I1j8TK4cQFu3R6+xFPeqdYkdel7pc2nd\nQfKhH2Vuh+dPgNAokbxuEfGiY4JNK2i787pML1NFm+pJ3vcjqK4rv4ypn6b1zu9Eq0O5cZJP3YJU\nD0KbtufaXrWtXkBw4kVUvv+zXXptelLNR75E459+kJm85EE8gSQqqTpvN95UvUstLz5F40N3RxPR\nVJGqavb7128RH7XzqRN1F1xG9Ymnk1owD0kkqDzqPTYBy/QP+/hErB7t05oJMh8v2nZT3ucbicoG\nik2jK7MqTLf5yrXX8t6//IWLOpiMNZ0oaJ3x9a/39tA6FW75M7rpVyDxKCBLjMYZdyuS2LXbzt1u\n3d9AC28/CmT6nPpEK4G109oJ0QSXbGG8aBRbuVVQ17Vb0AAaelG/1PiAXFAo4sBpt6Fv/Bdsfg1Q\nGHgoctwPkERdbh855jr0sKuhdT1Uj8o9VvAcakfjXPQAbHyNcOVUWP1CFOCiUf1kGORN1Mk865qR\nyIDov7mMPa3Lz2V3aNMWgkVPFQaKYRAt+1o1IAq6RaBqIIlzv4PUDm0/VhVv2h34s/6Rt5BC8a8j\nQaoHlVw3/cKfS2f9+yloLlPfTNTNAD+Mzp+5ljZszHRiy1zTS5Ge/hCJ4y/EGbBf2fP0ltj+Exj0\n9d+RfG0qwea1xMZOouL4s3CqB/TqONLvLKPxoXvA89DMG21NJdl2488Z/sPfdinjGhs6nNgZu1Z+\nYYzZs2xFLAPAhAkTuOO++7g406f1Ks/jAKKSgFvicW6Jx7njvvv6VLsrbZ6BbvoNaCr6AEi9Q7jy\nKpyDH+vVjKuGHtS/An4z7HcCBK0d7xymo8b+2WO9Jnjzp5ngJdNKCEFdQdwaGH1h59f2mtG5v4Q1\nj2eytRK/gS7MAAAgAElEQVRlVo/7DjI6ChKlcghy0u/RTLZVYtVlzyWJAZCYXPax3D5ODEafgjv6\nFHTSAsKlD0BqO7L/6bDmFXTj7GgcTgzcBO6ZP+v0fD0h3LI0ClCLs5tBGme/8cTP/iYapJG6USU/\nJ8Gy6Xhz7ge/qFdYticpgBMjdnTpilvBhg4mtKEQS5QuGpDtwFC0rT1jnckKxx2CtYuRye/FXz6P\nYP0KnMHDiR96IhLv2mpW3cUZOITqc7tU6bVb/PotND14N6nFbyIVFVSfeja151yIuC4t056NesUW\n0WQb3oqlJCZ2/jNsTL/VD2f8dycLWk3O+eefz4z587nxN7/hlL/+tX1FrM98hhlf/3qfClgBwq13\ngBavwhRGiwaklkJl7zTy18aF8PoXMhlUjTKPVaOi5v7FixdUjobYwMLj374Rmlfm7Zv5peQOhpPu\n6jDABAhDH174PDSuIHfLWxWSW9EZ34YzbkKGHJnbX9z2meW67W3CBX+GhhUweDLOEVciA8fv0nOX\noYfjDs1bGergD6H1i9HN86F6CDLmtFwv2HK0cT3atBHZbzxSNTjaFqTxZ9xKuPAx8FLIqCOInf5V\nnCFdH5sMGN7BcrMuMmg0UjsUrV+N98Kf0LYG3INOxJ10CuK4+K8/mDdJJ68kQDNvJ0RwjzwPd9Qh\npaev2Q9Nlpms6LjET/o43vR7wM1kpFXQZOkEpihgddDcbUCBpEdq0Uzanr+fcPOaXMmCujFiBx1F\nuGMz7ogDqTrj0oKlUvuroKmRrdd/H22NlubVZBvNTz2Mv2Etg6/8MtrcVH5GighhspM3jMb0Z0re\n74V9kwWtpsCECRP49Q038OsbbtjTQ9m5oL78dnHBL5wJr6rQNAcaZ0JsMAw5H4kPKt3HbwC3GnG6\nlr3S0Ic3ro6Oy/8b2rIanMwtc/WjlakkDof+qDQDvP7JoglYGV4jxEtvQWeFq6fCGz+Nsrv5siWb\nYQpdfBtySmlvXd00h/CFa9sXE2hcRbjqaeSE/8CZWGai2C6QIYcgQ0oDuoLre634U3+AbpgXTc4K\nPJxDPoh76lfxn/xvwjVzcqte6fp5eA9cQ+ITtyO1nbe103QrwdsvEtSvhqqB0OIVBq9uDPfoD+Mt\nfA7vqd9FwZ8GBEtfwXn9QSo+/nPC5mwj/vzvU+ZFVSCeIHH658teP3HKx0k9/r+FbbViFcSPPpeK\nUy4n8Z4LCTYuRaoHE2x6h+TjN2bqXzPjVzKN+6WgK5Mq+HNfKpjlFv28evhL5gAQbFpDev60aFnb\noWOheQcEAfFDp1B93mdw6gZ3+tr1Ja3Tnot6qeYHpl6a5Pw5+Fs3U3nMFNLLFpf0W9XAJ3FQL646\nZ4zpVRa0mn5L6s5C2xa2lwZkqQ9V7dk/VR9d/JUoYA3bwKmAVdfDoTcjdVOifeqfR5f9MFpuVRx0\n+MXIhP9EnAo61fBGpsVTwciif7KJ07ojYeDRMOZTSPUBZU7SWQ+T8o/p5tkw50fts/1LSy6jQxuW\no42rYMABBcFyOOv6wk4BmWvpzJ+iAw9Chh3RyZiKjlJF180iXDoVAGfiuciYEzotz/Bf/BW6fm6U\nlc4Ep+HbT0CiriBgzQk8/Pn/JH7yF9uvm2zEm/0PwmWvQEUN7qQz8F+7OyoJ8JJRmy1xEIlW7aJ6\nEPGzr0Nqh+E9fU1hYOklCTcvJ1j4LM6IiQT15brzZZ7PmCmEW1bhjJ5c8hzjh52JNm8nPe1vmf40\nIbEj3kfi/VcRbHyHtqm3EKx5G6msIX7CB3HHH0PwTmb1q1iivZa1qBeqCGjY2c9Je+mCaohuXp0b\nb3r+NLzlbzLoG/+LVPSP2fHeO0ugzKRQicXw16+hasoptEx7jmDjumgpVkASCWov+ChOddd75xrT\nv/TPBQG6kwWtpt+S/T6JbrsXvE2gSaKiw0pk1LeiWtCsLQ9B42tRwArRjHxA3/4qTHkZmt5CF1+b\n244Cmx9GgxbkkF92Poggc91cPFGUnQsVEqORSf/R8TlGnQtrHizKtjow+BjELR9kaHF7qpIdMkNq\n2Yw++VmoGoJz6s+RwQdH2eHGVR0cp4Rz/4B7zo0dn7tI8Mqv0WVTc90EglUvIxPOIXbadeUv4afQ\nFS9lJnDl8VOEix+Jbp+XCVp1y5L2c6RaSP7tS9Da3kbK37Qkyj5mf6l70fdGxYkCwpQQbl6Nv2ha\n+Sfip/AXvUD89KsIFr5YOm4FcNAls2h7Zx6xQ0+j4oJ/KwlcEyd8mPhxF6JNW5HqgeDG8d6aRtuj\nN+ZqWrVlB+lp9xM78gyqP/Ff+O/MxamuQwaOovXe60sb+O+yvFWrwhBNtpKa+zKVJ5b2hO2L3FFj\nYMnCqEtBHg0C3KHDkXicoV/7Hm2zXyX5xkykppaa086yLKsxezkLWk2/JW4tzsR/otvuQRufhdhQ\nnKGfRWqmFOynmx9oD1jzhSloWYiu+WNptjRMwdZn0PQ2JNHJjO1Bx5FbDKAsheTGzp/HIdeg9bMh\nuSmawOVWg1uJHP3fHR/UUpQJzC5gkPlcs9v8EEhC8zrC576Ec8mj4FZEHx0FvTuWt5823YJunAex\nSmTkUdEkrDzhxjfRJY9B/rKzfhJd9hR66MXI0DITYvwkaJibbJSt0RJRSDaCFjeSApwYMvTg9lMs\neBLaGoomWuUtQYvTfmtZFdIpNJ0i/cIfiTrwBQVvL3K7xhK4IyfijDuOcNVcsm3Icnepg8z5vRT+\nomnEDjuN2EHvKRmuxOLI4FH4K+bR8o9fgNdWWmPrpfDmPoc7ZCxINelliwi2Phu1vyqi2e8pinSp\nsUrRPl4Kf+1S2M2gNTl3Bi3PPULY1Ehi8hHUfuBS3MFDd37gLqo57Wzapj2HBoWlHfGx44iPjtas\nkXic6pPOoPqkM7r9+sb0WVbTakz/JW4tMuzzMKx8jWFmr85P0raKsrfhnQSkN0EHQauGHmybA8M/\nCBsfLV+XKgkY2nmbJ4nXwen3wuaXoHEJVI+FUWchbmXHBw05Clo3UNDbNZdcEwgUgqg2MicM0LUv\n4oz7AEz8MLx9V9ETynzUjAIgePtRwum/jepyUXATxD7wy1wgqi1b8Z+8DkK/9BUO0oSrp+OWC1or\n6lAEDbO9T6NaBlUBVZyxR6Pr5xVmW90EsaPalzENV80pvL1fQMr+KyiKRgG2SK48VAMnt1+wfD6p\n1x5AqUADzXv5BIKivrtekvSr95UNWgHClkZa7vpJrmdr/osU1a4K+Epy6l8LA1pHCt6A5B0VTdDK\n36KU1L6WFU/gDu98xbCdaX7qQVqefQgydaTJWS+TenM2Q677Oe6g7m3FFRs6nP2u+TYNf78Vf9N6\nEIfKo6cw8PLPdet1jOlvbCKWMXs5GX4p2rKgNNvqVkLN4TDg6EzgWpThUg8qy9WggjYvh5mfz0xk\nCqMAJDEE/O3t55E4JAbC2E90ODbNZCjFicHI90cfXXlOh34BXf9i1JM1G8a4lXDI59H5f4WwpfSg\nIA1tWwFwjv0y4cZZ0LCsMDByKnCOugrdtiwKWINU+3KsXiv+k98g9okHETeOP/OmTNaUMl2blGDB\nIzhHfgyJR90P1I+Wd5VYApVEe0lH9IyyB+IedSnh4DGEC58Av717gAwY3v7860ZEdaodLSuq7aFq\n3qtGbrDZzGpxYB94eM/djqoiudWzyjzBzOZg9QL8NQuJjS3tpZt+6+UoU5g9PD/AVNozJsUZ2DDK\nFDtDRqChR9iwLQqgO/hjlcuq522ILil5oxcqjntf4XFeGn/9KqS6htiwzvsah8k2Wp55ELy8NxJh\niKaStLzwGHUf2r1FR9RL0zpzOsm35uLUDaT2tPeTGDeRYf/5U8JkG+LGkHiZDLwxZp9iQavZ+w27\nGLY9Cw2vRD1SnQTgIJN/HzXeP+D/ofVPZ3qrZv76O1Ww/78gsdJJHaoKc66B9HYKogW/DQ74F2h6\nC9L1MPR0GHclkijtAKAt69HX/we2zAYEHXEyctx3kKqu3WqVAQfA++9A37oR6udC5VDkkCshNggN\ng8JYLq+/qAw7OvrXieGc/xfCN/4PltwfZR8rBiHvuQYZcwr+9N+W1pwChD66fjYy9iR01avRrexy\nsZQCyUaCuXfjTDoP75lfoBsXRtcecwxUDIJ0e9lEri+pCv6Cx0mcfR1y2jVR8FhmQlfsmEsIFj1d\nkG0tzjKWCzWFbIZVMhObSnukoiGo5K2REM3kLzevTMOQ1Mv3EPtkYSlH6KVJPnd3piZT8qPozIGF\nWdtywuYmqi64ipaH/1S6+lh+DXWiCqduEOHWdUT5ZNpLRLLn8hVicVSV9MLXaZ56L8H61VH9MIo7\nbBQDP3cdsf3Kd2cINq1DnMIsb/RAgLd0Qenrokp6+RLa3piFxGNUH38y8f3LvwEMUym2XP9Dgq1b\nom4AjkPrjFcY/KnPUX3CSba0qjFZSkluZV9jQavZ64m4MPn30Dy3sOVVLLOKT+s60EEQtES9/WN1\nMO4byKiPlj9h89IoKC3+Ex60QcMC5ITbOh2P+kn0+SsgtYPcb6BNr6IvfA4+8EBJ3ahunEm4+G/Q\nVo/sfyoy+RNIxUCkbhxy8vWFQ1j+BBr4JVm56FY8BFO/gUz8IIw+Hl30ANpWjxx8Oc6ki5EBeY32\nU43ls5hhSPDazfjP/hxSyeg6omjxAkShgHqES57Gn/9oplY1U5W59o1oqViVXDwX3XoHEMLlr5Fc\n+QlkxCHgezijDsM9/BzcoePyzq9ozWioXxnFfpIJCsPOg8GCZWbdBKhTsgCBZpv752V/VRWKn2Pm\nXOG20k4DLXf/HJLNeT8iUY2tOplxilO2IiWfU11LbNzhBS2x8i8t2eeT9iA+EBlSEQ1p4yoKl6oV\nJB7HW/YWqYVzSc55qX1mfmZiWLBhNdt+fA0VJ51D3SWfKV2wIBZHU+VroN2iQFdVabjrNtpmz4iC\nUHFoefEZ6i74CLVnf7D0tZr2PP6Wze1Z3DCEMM2Ou/5M1bHv6fXFE4wxfZcFrWafICIw4NjoI482\nvAnzvooESXJRSaoNtr2BjP5Y+ZOFaUojmIygzISvYmufztzWz69HDSC9Aza+AqPbJ5aEi+9G5/8h\nN2lKG1ehKx7DOf9OpKJokYKti9D5t2eyn0WBWzbC8VrQxQ+gi+5vDyJ3rCJY/iTuRbcQrHiBcOGD\nkGqIgn0tmr3tJ2HrCgpu62umhragDjPzhZ+Ogq78NKiG4OdN3ArzzgVR1jf00TVvRJnX9Qvw59yL\n7HcgFZf8F+Gm5aSf/BXqpxCcKBubDdIl71IqmcUAynwP4pU4Qw4g3LImM7kqb3jFt+Ezz0cDip5j\ndHJ35MTC3b0UwfI32muEc6J2Ne6EI4kdcATJ5+4uCa+jLllRWjd+6MkkX3saVafk+5CrcggdCAV/\n9dLceLLlD5p3dgHCHfUk57xctpUURN+Wtleeo23GyzjVNVRNOZWaD3wYp6KS5LxZ0flUC2togaqT\nzy44T3rFkvaANXtiL03jo/dTNeW9JfWvba/PLCw7yA1aSK9eScUE6whgTI7VtBqzD3vnpqJZ9AIE\nsOFRdMzHkUHHlB4zYDKIW7rdqYRRpZmkYtq0snxwG6SheXX7fl4rOv//2mtKIQqYUzvQt+9Gjvp/\n7fvuWEHw1JejYHhnv9M0KLy9HHqQbCB44mto45bMrehs9lQyN5wzwVC5W+rZ8xRnDmMVMOhAaJpX\nunv23nt21doOBx3NllcFrV9F8u5vRkGwn8oLm6MpVpndM+JEizoUxcsaxz30dOKHnYF70BS8N54g\n/fytaCqF+tn6Vs093YKRqBT2SJQQiblUnF5Ysxw21hcFrIUzpSpOvJiWe37bXnuaGWMYSMEfpOSL\nj0C8AoLMJDVpr8cVUUInlrlGWHD+bHCpeWMN2tL4O7aX7UyQOSwKjlHwPcLGHbS8NJXUkgUM+cYP\nSS95s/1NTt7rKbE4xAprTZNzZ6NlglBxhOSCedScUlhb61R1sOJbGOJUdDIZ0Zh9kO7jfVo7SBcZ\ns49oybZ3yo9SMv++/fOyh4gTh6N+EgWpknnf51ZD7QQYe9lOLymDDoZy/VfdBNTlZe12LM3M3C8S\nptEN0ws3zf9zaW/TXRF6aMOqvNpJiWIhicPgCciEs1GqMqs1dSx7a12dBDL6GJxDL4R4RzWJTpcb\nZeeye+mWssuzSibjqwEQq44C5mwWOBNAagj4IcGCabTe+wuafvJhvAWv4Bx6FhrEyP/+F5QRZANf\nLfr5UIeK91+JO/zAwmc1YD9KfpbyRppeMD3qY5B9rcLM2HJ1rnk/i14qt6wroRu9/uqgoUP8oKOi\noLZYNlubfy6F5GvPgbOzX/l54/U9gi0bSL/9Fu7gYVFJQ9HzUXFwBxautCXxeNn0topEQW6RmjPO\nRhJFJQAiOIMGEdt/7E7Gmzm3KhqGhJ5H86vT2XrHX2l4cipBU1OXjjfGdExEzhORt0VkmYh8u5P9\njhcRX0R2/ofwXbJMq+mTtOl1dNM/wG9ChpwHQ86LgsXuVjM5qmktO4ZFsGkaNC+HmgNh+Km5elMZ\nfjp66gOw9p+Q2gxDT4ERZ3VtjKPfD2/9H7R55Hq8ShyqR8OIE9v3qxhc2P80X9GELd32dnsNakfJ\n0J2RwoBGVSAVoFvWQ/2WKLscUhjEZTOAolFAlQ2WNA7Dj8U56FR47c/QmNeeSxyoqoN0suNAuyhL\n2X7LXzvMFkbBqYt78KnEj7uQ5D++gyZb2l+MTKcADYNM4CsEK9+Cd94qfSmKJ09pccAWfe4tnknF\newuXvZVEJe6YSQSr3y4/zKbt7RO0aC8J6PDbVi5jKw7u6Al4yxeXtr0q9/Koop5XmCYteFzyd829\nmdDWFE2P3suASz5O8q05mdv4mX1dl/j+BxIbPqrgVFXHn0zz81NLv0+hUnlkYXkOQNWRx5B+/3k0\nP/MEEouBKk51NUP/9RudrqoGELa2Un/XXbTMnAW+nwuYNZ1G4nF2PPIII791HRUHHtjpeYzpF7K/\nc3uRiLjAjcA5wFpglog8rKoLy+z3c+CpnhyPBa2mzwnX3Qprf59p+K9ow6uw6R9w+O2IdPOP7EFf\ngi3PlX9MFZ3zrfaOAxWD4dQ7kIohAEj1GJh0TZcvpY2r0NVT0dBHjvsurH4M1j8HODD2A8iRX426\nGWRI3QEwcAJsf7uwptGtxJlceEtaBo5HG9dC0W3yXIgSCpKf/cufpwNRljdwgLx6x+zt6sDLTFaS\nzC3yzDmy58pNpMrLEPopgpl3QmUdWjEUUhvJ3dhxFBzFGTEJ3bwsipIyXQAKbj2Xi68EpG442rCh\nYOf229sSBdfqELqDUK9w8lD2VnxUNxodWK5Zf3S+bMmDdvgeIFi9CG/Fm8TGH1EQYFWe/Smab/8v\nCNq7K4iAxitwx04m/c7CwmYCQHZiWskTLvg3M7ZAaX32ofZb9lEZbF7wWm7EStVp55Oc8UzUrsrz\nCpv3Uxiw5gLztatpuPdO6i7/Ak33/xl8Hw1DEgdNYtC/lP78x0eNoe6Sj9P44D2I40RBZBiy3+e+\n3OESqwMvvpTaM84ivWIZTm0tiQmTomM7oaps/NWvSK9Zm1s5S/PqddXzwPPYesut7P+jH3Z6LmNM\nh04AlqnqCgARuRu4BFhYtN81wP3A8T05GAtaTZ+i3jZY87+g+XWcbdCyAOqfgqE7rxnt0nWSm2Dz\nyyAxdNj7kC0vkB/FKQ74kmmDRbSsZlsKnf8/yPG/2eXrhUvuQeffEGVONUSX3A0TPox7yctR5m/t\ny4Szfg2JATgTLkIGR2UCcurP0Oe+DC1Rg3XERY79N2REYUN756grCNa/llf/qu23uAMnmrwumdKG\nA89EGtahO1ZE5QcaIFO+hM6/H5qijGjB7fG8V6Vdfmqvg1vhfpJg2h+hta1wNn4g0NoCo44kcfrV\nhFuW4y2Zhr4zqz2qpEyo6CZwxp1A4owvkLr324Q78lYayxuvt/Bl0m9Oy8y6LzxHGGb2ywvMNFPD\nWhDQFtXoKiWJ6Gh7ENB850+ITziamk9chzguYRCQnPksGkiUoc0+H9dF09D2wsPghYREHQTECctm\nT7KlA3mFv9H2MG+HzHOIalmzm6NzlSQpVamachq1530Mb8UigpYmGu78AxL6eZls2s+Z96KF2+tx\nKqoZ/qM/4K1dRfMLT5Oc9wYbv3MtlUcfx8BLP4Fb1z4xsPbMc6k67gRSC+aBG6PyyGM7rl3NcAcO\nourYKZ3uky+1fAXeho0lS70W8zZvJmhsxK2r6/K5jemremBxgaEiMjvv65tV9ea8r/cH1uR9vRbI\nuyUIIrI/8GHgfVjQavYpjTOjQCooavMTtqL1TyGZoFWDVtg+O9p30BTE6VpbHN3yKrrwemh5J8rI\nOXHQAB10ONKyNPo6TEfLn/qZe+G5gwPY+CKqYUFGdKfXbN2Mzvt9putARpCE5f8kHHsWOvdPsHVB\npqOAQ7j0IWTKN5BxZxM+dx00bQdfo3pEx8WpO6jkGjLkEJz3/YJw5q/RxlVREBSSqUHNfO4mcC69\nG6cmalGkTevQZAMyeALBshfR5qb2CVIlPawy12lPPnbtuSdbMhlQoCi3GLz1JBWnfg5nxGSkbjSp\ntQvAyy44kM0gOojEMr1nXcIls6FyPxJX3kzbrz4c7Vc0kUtT6Q7HJwWxaF7gGma3Z7aVqbXVUAsC\n11xQGaTwls8jPX8awfqVJKc/geZ3R8jVpyqQtz37Uqub2ScsWHm28HsQvSZaLqjMfw7Z10K0oGxA\ngYqDjyQ2MqoRTUw6EvXSpE58H8nZL0M687PZQf9Y9dL4G9ZScdhRbL/zNryN68GPgsXWmTNILVnE\nyB9eX9Ceyq0b1KNLrHobO18eOUcV3DITJ40xAFtVtevvFsv7LfDvqhrurKRnd1nQavoWt6aDgMOB\nWJTJ0U1PwqLvRkFnlKpCj7oRGVS6nKb6zbB1erRP41JYfmt78Kh+lEFVoH4JOu5jyKBDYb8p8Pyl\nQLn2VSH6zn3IQeXbYYXvPIIuuCVaeWrAOJxjvopms6TFghS64C95AWt0foIUOvvXaNMGaFidGa/k\nWkEFL/8A99IHS+r9nNHH43zoLvzHrkE3vkFJ8OEmkKr2STMyYH9kwP6Eq2cRvPTrzC36zDGOm4mT\nytw+7mLAGh2Qu1rRA4I276DlN1Hg6Yx7D86EUwiXTYtKERwXRJC6cYRbVmeC6Sjg8996Fmf4QThj\njiBcs6BgQF0OqIUOasOkgzEruQUJsoFgGGVlRYB0itYn/oq2tUYBaxfGkEsqh9noMq9moyh7XTgR\nrDNObmwiednXUEivWYeGIf7mDez42614K1eACLFRo8BrQ9NJYkNG4q1ZXdKCSuIJYsNHklqyKOqp\n6md/LqIxBzsaaHjoPgZd9smyowpammmZMYNg2zYqJk6k6qijkd0MJBOjRxXWk5TjOFRMmIBbU74s\nwZh+p/e7B6wD8mdEjslsyzcFuDvzN2ko8EER8VX1we4ejAWtpm+pe29UP1q8DKmTQEZ8FG1bC4v+\nM1Pvmmfel9BTni9YwUo3TIU3vxcFQKrRrf6Cekwy2UiAAJbfHf2Rrz0IhrwHNk8rvEb27+OiP6Dj\nL436mOYJl/4DnXdDewuthqWE076JTOxgkQJxoHF1XsCa/3xj6IrHC7OzWekmaFyN1o0lnPd3wjfv\niRYDqBuDc9iHcY69kuDpRQWrRRGrxDnu8yULFwD4c/4KfqogiyfZGfp5S6XmHg+lsO9I7o5+e31o\nfq/UjgigXhsghMumQ2UdlR/5EcGaN5FEJc64KbTd/m/RYgn5c3rCFN7sh6i69Hu03vGNaEJX5hZ3\n9uPdvdnPS0uWH22mcUF2YYD856rQ3BAFtUWZ305O2i4WQ32vsO44f6JWbvGF9lKGgrMXn16dwpIM\nINy+lW03XU965Uq0tSV3On/jBmJDRzD8h78C32fzf19L6LdP2tLMBeITD6FtxjQ0na0bLQysm194\nDnfE/sSHDadi0uRcTWp61Uo2/fJ6CELUS9P84gvERoxgxLe+jVNRpvtBFyXGj8cdOhR//frSB+Nx\nxHVxamoY9oWr3vU1dld64xa2PjCVlgVLidXVMvTS8xgw5cg9Nh6zF+j9Pq2zgINFZDxRsHo5UPDu\nVFXHZz8XkT8Dj/ZEwAoWtJpOqL8D3fZ3tHkGJMbiDP0XpLJnG32LE4dDb0EXXQWauc2rHhzwTaT2\nCHTF/9HB9GjY+jyMvDD6qm0jvPndKLjNTlon8/9dM/O0c1nDol8CzSugbX1258IEWCAQtKCzf4zU\nHoiMuwCpGhrdvn7r5vaANVs3GaTR5Y9FraRKAo0Amjbmzl0wA1xDtK05b6UowMk0dlcFN0Ew84/o\non+2t6lqXEMw43+joP+Qi5ENr6M71kBlHc7xX8SddH7Z1zxsWF/47j0bd8Vihe2llPaWV0peFo/M\nJK5ojLnXa2erU2VaS0n2hF6SYONyEid/OhpX49aorjX37ZbchcP6DUjdMKqv/D3NN34ByUyc0sCJ\nbo2zk8B1p5nQ8jWm0S37cktjtdfidnjJzoJp1aiRRF6TguwStlrw/AU0zF/boOQNQsclBJB++03U\nKep9GgQEO7aRXrqYismHMfjqa9l6/X+1Z9kVwlRA/f/9hrrzLurw3Br4NNz9N3BiuIMGMvyb38ap\nq2PrzX9Ek+0T4jSVwtuwgcapTzLo4ks6eEF2Lr1yNd6G+lxHi2hYDhWTD6H2hOOIDRlC1eGHl0zo\n8jZvRT2P+KgRO53s1ZHGGW+w9a6H8LZsIz50MEMvv4i6kwvvsKbWbmTlf15PmPQAxd+yjbW/+BM1\nxx3O2H//4k47IxjTF6iqLyJfAaYCLnCbqi4Qkaszj9/Um+OxoNWUpd4WwmUfgqARNAUtswh3PIJz\nwO+QuveVPyZMwbbH0MbXoGJ/ZNjHkMTIXb621B6OHv0IzP8StLwNGkeW3ICufRySG6MgtuTiAfjN\n7Q3gIjoAACAASURBVF9vfJKdRiZh/i5Ff0CCFMQGgNeUC3I1BLxMScI7j6BOAl10K87pv4cB49oz\npsUzsFMNgAMSFt59DqSgpVU2qFEFTXoU1hcqhII6IAPGQMUgdOEDpbW/SrQK1Zv/RJ14FGQlU4Qz\nbkFHHoPUFbYnAiC7clFumnvmPF4QlWAUvUaajVizGcC82svQl6hTgaNFFRFa9Bpnmt+rk8vkiZ8i\n3LAYDQOC5XPwV7+VqSvOG1tuMldA2/2/IPR9UBcNtCh4046/+x0G0/ljLB4vZb7O397J9fKOLagz\nzR3gROUrKnkZ1byKkpI60+zkruIa1y4GQUW3/lVBU2kaHvwHtWedz/9n78zjLSnKu/+t7nPuOnf2\nfRhmgBmHAVkFjIiCIErcUDTRRI2ixmhMNDHLazSvSUw0bxKT1xi3aHBF8XWJIoIKKiCKIPsywAwM\nw+z7neXu53TX8/5RVd1V3X3OvYODAec8fC5zbnctT1X3PfWrXz1Lc9d2RNW8kFUGKDc3PEJz905j\nH5pUhGITrPd+QrK7wd7PfobZr38j6b59FTo0Gb31Fqa/8CJUFJlQVYco+666BmkmmHU0l4l161nw\nh28lnjYtuH7wp7ey6wv/DxmfgDgi6u9n0TveRN8Jh7YRP3jLnez4xJcyxrm5ay87PvVlJNXMeM5Z\nWbndV1yVAVb/2YzcuYYDN9zKzOf9xiH125GO5Cmmf9X9yjXANYVrlWBVRN74ROrSAa0dqRS96xOQ\n7CN3HElBUvTW9xEN/LTkiCTJEPLgb0FjJ+hRUF3Ijs/C0z6DGjh0Z0J17zthaJ1le1JgHA7eY77/\nW5AjMv1k5LErYOgRmNiF0tXpKqcmYoLUp0nOnqYFYKAboEHf8j7Ub37bmjU0y2lJbXMGoNlvnCrg\npGITFF+LAQxS4cxT7yc+70MwsruStnOORpLqzLRA6SYk4yQ//Hvql3wiKJ/uehgaY/igKtMXrB4q\nPJK2oaAkixno1XXxPbUCpbMwTNkROpYR1Sp3ErOLuqgaatZSRr/4l+hdG61TVmEePUkevsUyq8US\nVq+yS3x4ZC4hmPC97qvMHYwNq/fyeXOSefS2WFAyswW/iJ1DFUdoXaNkP6ylTfoXlc+/jQ2WP5/W\nOiBAvSsDrn4kgubGDey//DKo122qV1Wqf/BbX4OuPkiGSvcC0ZqJdWvRjUZLMJ8M7mPjO94JStF3\n0tOZ+8Y3EE8faDXgkjS3ba++UauR7B0MQOve/76awf++2qucovcfZOu/fIxj/vUD1GbPnHK/e674\nDtJoBmOWiSY7PvNV+lavpD7X2I2PPvgI1Zsf2HvVjzugtSMdeRzSyYjVkWoZup7A09lJOgzNLaXL\nsuPTMLHVAFYAaYAeQx79C+QQt4Yysh6G1xZAm5Vg1bcS9SLzXgS3vg3WfgQ2fwP23FLNfSllMlnF\nPTY7UKs/AQUzV8Mp74XuOaBqIC0cRyYOoEa3oVa/AaLuvH5RbzDH6Gk5sxAAffOJzvlbZNoKA5ar\ntDr+t2DaYpq3/CfSGC/dN1jXgTYMMNIgWiN71iFj+4PysmMNiOR2k+5H22N59zk1LGDwKEtAxQfr\n5qg+92gHSSIkiZEkNtmdsrm3wDdNkRT0zg0eYJ2CtGQf3HF6ZH9iSGv5M9CRCeqvLcNpmXff7MGB\nVZ1E6DQ2gNsBdSmUSe38Z5OTZ71yrLTYbFbG5tS8B2lDDPuXsaUOAFt2X5Vf+mxOre6iTd9p0+ip\npZ7pA1Yvt9FImkEbAXBvTCDjYy3tGASQ0bF83P7cV2zE4oEB6gsWlNoTQDeaVumU0fvuZ/s/f/iQ\nviu6li2r1jNJqc+bl/+67wCDV37fG4BXtpGy+YMfQY9P/X1r7h4sXLHv+0SDx97zTyT7DwIQT2sd\n4kuPHcL73ZGO+KLV4f15ikkHtHakWuJWMQ01RNPKlwe/Z4BqUZJ9MFEGuW1lYhdUJRHwmSQN1GbD\n/N+Ekz8KQ9ugeTBnRXXDLPrZ0aGCuBe1/HWoZ1+BOvOTqItuhuPfbgCNA2ipBXtxN2rlm4mOfgnq\nomtRL/oxTD+uhcICUR21+g2ok95GJSC1ardl4g7uIHn057DrwZZl9LrraH7xEnjsZ4hW6EQFwBDt\ns58+ALJzULAHlr7Z5pZWeZli3Qz8UbAvrRIVfBYdG/CaRgiRF2OwXF+A5h1XG/OGScSPYuCHK81b\nClsOj9W88UlhvkTlcyiKtBmRJnEGMENGOp9bY+oQY0wVagbgagdsc4AqDhh7G4rWjl+Aiug62R05\nh+PQidtEGCAsqdsIqIwhF23GkD0DrXJTilb4ME2zjUww3wAaJE1tf6Y9ndr3sOggoiJ2feKT9P3G\ns4mmTUP19Bg76diE+ArKpynJ3r2Mr1vXQqmyzLr4RaX0r6qri+kXnEfUl6cOHnvoYYiK71v+LJOd\ne9j5mS9Pud/6nJkt7IYV6dgEg9dcD8DsS15I5d+JgmnPePqU++tIR3JR2cb5cP081aQDWjtSKWru\nG0EVc8bXoO9MVG12RYWe8jUwq/IUY6hmMu34agCcLbQKiGDWs1BP/zDMPBMG76K0CosYnRe/FJZc\nDKd/DFb9OWrgONTs01FxL+q4N8K0E0MGrlmDVe9EzTILi1IKVZ+GOu4Sw9AGC76CaUtR/YtQShEd\n/1qYXw695fy5gpPpYpE0QtZdBZYjLoEGAfbvhPEDtmcDrCSNkGZkfKYq7TUt0Jq+CNWXPzs9tJv0\nzm97tr0V9VT4uzvaD+KJTlUmqyMgY+Vc8SIYYGSZXp9lNB8scI/qdJ19if0yNp7+uhmhkzhjff02\n2+muNejUxU8timcyIAqtLYOaLQSmjEhMHoM1/8nnsGqDUJSIGb/zNmorT7J1DWOrE8ceOyCsCu8l\n5GG0nM5eH9K+Xx9cZwDb7brEH4fVg8jOga0PpBMJE+vWse/b34aZ86gdtZyeE0+ia8Xx1r+v2LeQ\n7NrTYh7K0rV4EYv/6s/oWbUSVa8Tz5rJrFdezOxXXxLOYG9PweGq0K8Iw7ffm7Gt6egYutnatGjO\nq1+Kqrc4dUkSRtc8DMDMc5/J9HMK3wVKEU8fYO4rXzilMXakIx0JpWPT2pFKUTNfgYw9CINXgOoC\nSaFnBdHR/1pdYf7vwJZ/Ae2Hb4qgbxWqa8Gh9d01Cznq9bDlK3l7JZOAblhqU5kq48hSjiqgIOpG\nnfzB4Kok4zC8GXrnIhuvhoOPFhCMwLor0AufB5uuRZojRIvPRpZeBGu+AmM7subp7Sc6+5/MsWZz\nFGo9qFWvRnbdTdlG0Y7D2iEGQ0oignBHKRB5ZcSUoWAjF3iPJ5GxwqzEIYras03KTb1zHRPf/TsY\n2lVVsL1kzKRkYClL+lR4RkYPsbaXNn6oiGXAKURLIJsbd88ANJvKy5XTgErJASAWqJsMZvVTLyTZ\n/AjJo2uMDalSYfv2d3/exDKlykZn0BrLWkJrMOnpnrHbhfGo8ul1MYKAA4JFXV1Wrp6TzkDVu5j5\nuj9kz4f/Gtl7sGV72ZsRviIFUbSmWPN2RArzDqhY0XPamYzeelsB8Pvl4tym2jHLY02aj240CYKV\nglpk7WoLwFCga+lRbXUrSvcxy1j83j9vW6bv6cej6nWTdKKFKAUj961l91euorFzD0rBwDNPZcHv\nv4a4N9yQzzjnTJJ9B9h9+ZWVDXUtmGs/Kpa88w3M/s1z2fOt60gPDtF/6gnMeuFzqA104sZ25HHK\nU5AdPZzSAa0dqRSlFPHi9yHz3grjD0J9YdtwV2r+a5Dh22Hfj+0qGoHqR83+XWR8G6pn8aEpcNyf\nwrSnwaYvQGMvJCPGaDCqgSSw8t2omadZXWNkwXmw8wYCO9ioC5a8JGhWP/QFePAzBuTqxGTAKnrg\nA4zvRq55lVmtdYJe+1WoD8C4BxoEaGj0ll8ga/4YxgYh7oKnvQK6ZsHYnnIueFEIzgwhPFoszKiJ\nFuAyWzkmTUnLsElZ2tMK0CICxH3I6D4mvvHn0CxsLqYgoXe+Raqi7JRXHPebCPfGHtYCONE5NC8d\n4ieO+bNMczZHftsCEmcn4FmoLRQkmqGPvhO6+hAJwbsBqR5DrF0WrPw5mD1PQFVOAgBdcTUpFhTJ\nbxcZXsmYS/u7N6bxu+5g14Y/Ye5ffICBF/8O+7/06ayBIgB2Kky2pGn7PhkiVgIAb9qrlZQ0YDim\n/+znMnrHXdBM/DvheFLJxmOSL/ibDkE1U6hHxqbcGd3W63Qfdyzdy5dNov3UZGLjVgav/hHNnbvp\nXb2ChX/4JnZ+6vOkB4crn1PU28u2j34hiwggwNCt95DsP8jR739nqfyclz6fkTsfYHTdhiCSgqrX\nmP2SC4KyvSuXs/Qvf/+wjKsjHTnSRR2qk8yTWc444wy5/fbbJy/YkSdMZGw9HLwVNn8NGdtkAvDr\nJsx7IRz/oVJA/im3KwJDDyATJi6jivtg9ikmrisgjX3Iz99kQ2JZsDRtBeqZn0bVjEOEbL4Ouf3v\ncrvXtv1hmc3ColyIICC4Y1qPVVUxzDwO9q8N23Ke58rRfmWwKOL6sGurD8q81baYTjT35veAjK0i\nYOZs0alEy3+D5GefQ3SaNVeVrMu3YVRKFQCr1S/NAVfVUS/aHeerfNwBmJRcR6EQTQDrqV8FhsWG\nhmpzvF0AdE50ltrWTFCZMRTvc/nX4KKQ26pWMM1BM6XIA+6YPe/XMNct2lMK+gdgeKjt+HyGN3xm\nHoNbZGoinQPenj5IBGlUhMVCUT96OfU5sxi9515j+9rCNjl3PCsz/073/rPPZOye+6BeY+Ccc5h5\n8UuJHkfoq6KM3L2Gbf/3MyYclggSx0Q9XRz9gb9g4rFN7PqvrxhwaidIddXpWb2K4bsfgjQ8rVFd\ndZb/0/+ie3H5tCgdHWP7x7/EyN0PQqSIentY+PuvYeDMk3/pMXTkySVKqTsOQ6rTX1pOP2qG3PTO\nsw9rm9P+1/efFGObqnSY1o4cVlG9xyEbPgmj61E+kNt1NfQuh+Vvf3ztKoWM7YU7/wpwBFcMZ34E\nNed0VNcseO43Ye9tMLIRBlbCrFNDluehz00JsNoeW1yWcNHXFZ40ksK+dcY7XQSKR81ZfQnNDLMj\ncmsGUHJ6yuu5hb+85zT2nIHDuRjAJDvXIbVpSOJHY3d42yFYciCZ2sxPkWP2zM3s6N45LVViR3PM\nXwJJUVjGt3OVIKqCBRQVgKedM9ekog3TnbVQmkM3cQXOUvx/nN6TM9QuOkFJXykAU/89DQCryi8O\nl8NMVbLuUvicvSxQZq5B0thkko2EnpXHI40mEw8+EOpj22pu2sTA8y9E9fUz8tOflbpWXV0mCkSS\nlkxAAoki5r3lzVMO7i8iNDZvQ0+YKAt7v3E1449uojZ7JvNedwn9Jx2fldvx6S9nIanMO6pJmxNs\n+PMPMeuic9Hamj5oMWxv1E3z4GgJsAKoOKa5a28laI37ejnqL95KOjKKHh2nNmfm405W0JGOTFk6\n5gEd6cjhE9EN2HMtRXtO0LDp048btMr4LuSOvyyBTrn1j+DCa42jlIpg7jPNT5WM761uO/ufdy2p\nG3ME5S28lYcSbb5AlHf8WSyX2eAWGtVRoXxLW4BA7wzIpSrPMpqNRZl20yb60V9UthsynQWVUq81\nUQiC6DjTo33mqcI4NEgUmjiIYMJgVaDfLCuUkDscTVGq7UdVaYRhHNlihSJqdmNpr0emb9U82z4d\njswiN6gAGRfqqqBu1Rgrvf09kC+VTnpuI2A2DZqYOa9/Lds/8LfI2KhXxtQVLez9wpfoWX08vaed\nxviaNTkrG8fEM2Yw81WvYu8Xvog+MNYauA7MYPTBdYzd9xDRtH6mn31my1ipje072fbhT5LsMw6I\neqKRbegawyNs/dB/MPd1lzD7xReQDO5HD48WHO3c34Zm3zU3eDds1JDRMaKRURPZwB71iwX46XiT\n5uAQkmpUXA1I4/4+4v7W4a060pGOHD7pgNaOHF7RExWANb8n4ztQPYeeJUu2fM8DgMEd2PEjWDp5\nOkip9cHE3vKy7Y7vHXhJFHi5IY0Jp6AiE9LIxVB1XvQKylhAgFkrYY8LMF68r01Wq8a+/LZn56o9\ngFZmG1XOwmZZk5wSFqBaNtaUK9oSVEnrY3bDSkXhNQz7nQGtinplkBQCVyc6M8MoljWgMQSsk4NW\nVz4Dhe76JGll84L5cbpP/AasvQXTWTnxCE0fLNlIBpntrGsbzLG8e3mcg1IbW1F/fK1+L8+7fZ9S\nA9cnY67H7rybrWsepO8ZpzN2++02w1XYjzQajD+0ljlveD3dT3sawzfcgDSb9D3jGcx48YuJ+vvR\n4wm7P/NZkPxUwG9D7z/A9g9/DGlqqNUY/O+rWfj2N9DYvpuDN92KihTTzzubGRecw5YPfZR034Gs\nsoIsw7KTPV/+FjMvOo+otwet09ZsfGHyjDmO0Nw5iOqu23BekpumJLDzs//N4NU3svzv30nc1yJK\nSkc68iuSp2KYqsMpHdDakcMqqjaAqDqlkFUCqBoyugnZcT3suA7q01HLXoOaO4XMMM0D5TYBJIHG\nwfL1YrHxvTC8M1fFX0RtKlJJqWa5BCBGFj0X2b8NDmw0wFUbcCs1CZdGwZgu9C9Cdj0KkpbBAsD4\niGezmvepEw90qIpFFgIQGaor+YUKpq81q0il5OUdQDP1Agcz5xjk66nN3qVVJANn75qbQ7Qohxwy\nYDUflLU3lex6q1zvRdtPc015z0FQcT46SZVv2ZBvDtxUeIApy1pVxdBqkyFNKVDdPdSPW0Xjgbtd\nq1MabzgG661fMFvQ1lbWPaUy++x0s9cmJhhb8wDxwoWkO3YgzTz7U8bKjjbY/anPUZszm5kvfRED\n5z47m9/B73yP/Vd9LwvmUYzUkEmSGF2TBBHY/tHLUHEtA8p7v/YdDv70F+jRserNlg9cRRi990Gm\nnfZ0ajNn0dxdkTq2OCf+mJuauLdO70nLGLkn3Gjq8Qka23ax++vfZ+EbXt623Y50pCNPrHRAa0cO\nv8w+F/ZcZz57C4voCO7/JxjdAtoc88ueW5Dj3ky0or13rZr3LGTDFZCOFe5EMO9ZyNhumNgPA8tR\ncR2ZOIBs+B4yuh01+wRkZCeouskG5IeSsqxUbkfYAihEfcjG20ykgcwV3AILLUhUwI2qjn74JvN7\nrKqPcdOk1J8UwY1U5PVKvPihWlqwnYcGegIAUNTHA5WBU5Zf2QFX7WJ35iypAy3VjkMG0FUdIzuA\nPJmUmMdU4WfbakUuB0DMd7rL5tP1bTY0QTPi9HM6ls0OwLGeeZlQPKZ2fIzx++4x4FK0ieLWZoyZ\n3pL3nDGsRVCaJUbIAXx+v3ozoA8coP9ZzyI+/Rnsv+oqJEnz8jp/N5M9e9nzpStI9u1j1sUvZmzd\nIwx+8yqLCvN2q+2vi50K4qVelkaTxpZt5oSjIMX2BNjzresYW7+ZdKS93XrwTntDTw8O09i+l6o/\nBmkmHLjpjg5o7cj/rPh/f0eodEBrRw6/rHgP7P056GHzuwCqF6Y/E3bfkQFWwIDQRz6NLH0lqrsi\naYGTOWcYW9U9t+bANe6Fheej7/y/sOduEw4LBSteA2u/ZljYZMIczWYLkWXfsr97G+O10vQgF2kM\nV6RfdcDRHsdnhUEmPM/qqpirFUfVggNcvkT56lyol7FrfvgsJQbweAhaJLzv6pbDJUnWpotXilYl\nxqwV+MwAq9diK/HtM7UbR6ltZdhv21RLb/ngcziv5rg8MqGWLFsauWhdRbCabRTI/JeKwCvoq8AQ\nO9YUXKxXj2GdZJ3xTQxMhitpUcc+KY/R9aMs+NFgqpzM8nmXtiBSBIau+xGL/vd7OfCD65DE2rdW\nOJVJM2Hw29eYVKmpb8Odb1ycXs4cIrQ5bSNam7mo0C9w4hMYe2gD4+s3Z2lqK8cVm0QcJixG+X6y\nv5zcwkn1tqQjHenIr1I6ro4dOeyiehbBWd+E+S+BeC70rYRVfwM6DgGrk6gL9t/Tvk2lUGf+K+qU\nv4H5z4YF56JO+yCyfzfsudOkbU1GoTkCaz5n4romfvxVjz1zeebBxFXtXZzfqxJvgWyhnQGuWiGJ\nQho1j7VV+f0USGntEGPNFMp9WTbYHTdDnks+VWHqVh0hiTIZh5SxF5XES6WaxAao6SgHbu4Hk10L\nDWlTmbIOEAkGQGplQ12FTKWJUJCDaRfiKQMsIkFfpqDTWWVZuQJ9UshTlJYZ07x/7xi+AOp1lgkr\nQhIzB2lToe34srSnxc2IA/nZ8X6eLjcHjIUfW0ZrTNviMeKtWOyMJfVBnGGKQ2Cn0GlE2oxJmzV0\nUkOnUW5m4eZdoizbVrXjmn2XxAfrBVbRtac1o3fdzazXvLpQSoXPydlYl7zvvXSROjY/oozDkyrO\nebWori76TjkB1V2dVS/XwSavaCYtwXA0rY95r34ZUU/rDH0qiug+an5ph6TqNWY8tyLTXUc68iuW\n0vf2L/nzVJMO09qRJ0RU71I48Z+Da7L7TgxIKCxuoqE+Y/I2VQxLLkItuchUG9kGgw+YJAFhg7Rf\nEA24kd5ZRMsuRt97ORkbVqzmmCmbqQk8lrBYNLNdbN2vgM3qFK6LWmOP/ancSmZ2k1plzKRj3FSJ\nMlW2nIAU2WGrhyZPCiACsbZAtsiWEvjVma4iE0BeGTZQO+93x+QFYa5ieyvNT989wJIT3LYNydle\ncUA4i4ygwdmWWkBqbDXF69/Mi3KOXoHtosd8TkkqWHXJ/lcSEdUifqzKqhVNRMSLgxvGcTX/c2yp\ndhuaFuYkIvY5AC58hGAY1VzxAgM9iUOHaGHknjU0Nu8wGx0VMrxTkaJNNF19zH39JYytXc/wrXda\nBjw2LLWoUtYqFccs/KM3MbZmLQeuvRE9Nk7/WacS9XQzdMtdjKzZgFSckggQdXchaUpUqxFN62PZ\n37+bkXvX5cxt1deEgqPefSmP/c1/oCeayEQD1d1F96J5zPuti6Y87o505IkQ8xV5ZDP+HdDakV+Z\nqGW/jWz7XoFtVVCfDrNOPfQGxweNSYCuyGg1mUR11OyzSO//GmgbVklJACwyhlUcy4mJMiACsQMF\ntqjDBi3CRmXNpQpS4xQjNZ33lXhAsbD9FTAMbZqznqZYC7bKnswKdZByBi3HhAbOTWkcggsH0nUV\nSLLAU/LoCeI0DdJ/WtCLGNZRCyoOh2chZq5XWrBf9JMZ6LiQqVfZY+coq58xiWkOZINJoVjfjYey\nlACua6PNoqGwWaCq2ipoYH9Jk8Jz9By3RImdv+rNhxNdcWwP5h1RnklIfj1vQyS8qSK3IVBMPPxY\ntjGS4gagOJCpiAg9xyxnxnnnMPGiCxld8xBxXx/9Z5xCc+dutn/0MtL9JrRVbc5sFr3rLcQ93Ux7\nxslMe0YYtL9r2dEMv+/fKjpRoCKW/OmbaGzfTX3+HKaddgIqjul/+sogIkL+twQqjlj8jtfSfdQC\nVn7i/Qz94j4auwbpPfYo+k9Z1YnB2pGOPAmkA1o78isTNeME5MT3wAP/hwy9dM1GnflxE2O1ILJv\nLXrdFTCyDbXgTNSK30J1e7Ecpx9LlrbVAUzLUE0qWqM3/DAjfUWUOb5X4pl/emAiqsOEW5wVpFKy\nUxU/65UTLyZpdt910IwMUIYQQIoFxgpExMRZlRDMiRZTp806qqTCiSssUfiXHIkrssxcUwUovp2h\nX0ehrMWvY2j9Ej5gLcao9RuuYD3JWWafcQ7ZwCrQmTfr7Gh9e9r2R2YueUOLd6wiA5TRs6hXsa8i\nW563104Mw1puoxQH1zH12WsWlnUMuwj27yDsO9jQtAHPVfoFr0wUmWgAQPfSxXQvzdM7x8cczfJ/\n+1uau/aglKI+f27LdgF2ffm7LTcd/SetYtppJ8Jp4fXarOm5KQlhClv6pjH9LAOMo+4uZjynYw7Q\nkSehdByxOtKRX51ES1+BLHoh7L8P6gMwfXUlANBbbkBu/d/GVlU0MrgGWf9NogsvNwBy9z3QNQAn\nvBXu/RQ0PQeQKILufkgbkIwTsGMOI4qEQDGjXFQGgDUY5yuJodYDqkF+Vq7Ml0etG9Jxa1taBF1i\nzBAiFzPVsmUBBgxtNcNQUo4RLWTUsvVcHz7gct0asKBpiWpbgaEScKsCKCHwm7RNHHC19d3JfSVQ\nrAB1hefUEi9aMKlyTFKpSXjDOYCFjkntY7r6QNnXs0VxQoDcqr2WfbVtt1rPsK9WYDMvm7+H4d/J\n42FUy6Dfa0Nruo9bVqqz8yvfYd/VNyDNBNVVZ+4rL2LuKy5s28/4xq2VfSoF83+v2svf2LyaTWGQ\n2Q1gvLUDV6Z+o8n+m+9jYvMuepYtZMaznk5U7yyjHenIr0o6f20d+ZWLqvW1zloFiE6ROz4YZr/S\nDZg4gP7Ze2DPOojrxpZNA6mfmBODhrrnE53yFvTwFrjvc8ZBCyw2tSxvsLh6YEYw9oWZfaJAsxhq\ny0rSQFQv4HQtM4JibVGRELBm7BcWJLnuFNZ7X8opYANRNhVlRsxm7RqPeUImyd23x7yhmQAoyxy3\nzXDl6x58VtUn8FkZs0kQO9YyKJQAz2fsoPgbAcn0bnWc7+tflffeifPuVzG2TV95fywhy+v00qkp\npyJj3JAlmiikY51SqKcikVyQPMxYlS5l0BrYy0YVIN/fw7kNRHb03+YhtrhXOb4MACs3CFDGQfDR\nP/l7+k58Go0de0kG9yO1mGT77rxqo8nuK65CJynz29iR1ufNZmLYZe3ylKh30b1wXmWdqKebroVz\naGzbXbIN7Ft9TMu+ABp7D/Dwn32UdGQcPd4g6ulm+xeuYeWH/5j6rIG2dTvSkcMi/ibrCJWOkU5H\nnnwysrXg+W9FEth7jwGwzRFojEEyVrGOChzYANOOIl79OqKXfh2WvQCiXqj1wZyTEOml5TGnn3Fq\nEhEEkkZLW1aBgmOSvS5kGbAy4OHCRqWKtGE9/YNj2RwwGVBqvbITwHqtp40I3ajlEQJS63SlYcpx\nIAAAIABJREFUTT3dNNcMaHPmCtahyfdGrzjKzgYkhnDWzShzhjIMdR5ryzdMkAxsOibbB1oeUvcy\nUYnTy38uHggqgWb7Za4TRdqMjONSoZzTRqcKSWsYc4XIs5N13u5mLrUFwSJ55AMzduXNWYxO47aM\nZzYmKXrdU7ATrZZSHQecW9T1bbIlzeciQ/XZfLkyUL0UVOilInOSURxfquxcuufmx71VENVIE4Ue\nT2ju2c+BG29jbO2jNHcPBoDVl73furbtvMz/rYtQXXVPV4Xq7mb2i85FtWE/F//Bq1DddYisflFE\n1NvNoje8rG1/Wz7+TZr7htDjxllMj0/Q2HuArZ+5sm29jnTksIr/PXo4fp5i0mFaO/Lkk/o0MlvV\nonggwEg1myaikX0Po2atQPXOJj7nA9k9vfM+uPbdFXXEEn5FYFXo3z9BzY6FVXjTL2+BaXDHsxdV\ntnrOkIVfKFJgy5wNYjYRElsGk0zvAKzp4nikoHfWsu1X519mHvmYz72LMKDyxAo+g4wBVP4ItVa5\nl3zl0bPpIAOspfueSDhflNp0gFVKTKevu18/i1+bhOBNVN6G9uax2K5hWCV4hnmwfzE/jgF3ID67\nr1304MKolQdOPWezrOBkC47bDJhHmpPHKmvDhE6LvUZ9Dcq2u6oWM+/Nr2fnJz9v0p16TmO+qURm\nJxwpek54GqMPPgY6nZx19iVJAvvh0bWPMfj9m2keHGLg9BOYdcFZLPr932bn5VeiR8YgjpnzonOZ\n+cLnsPvKG0kOjjBwykr6T1oRjGPaSSs57h/fxe5v/ZiJLTvpXXk0815+Pl0L57RURUQ4eMdaF1Q4\nl1Rz4NY1hzCojrQS3UzZe9Nahtdtp+eo2cw//0TivtbhyTpyZEoHtHbkSSeqZzbMPRV23xWC1yL7\n55iiqHr9llpv+LtoZOsd6H0bkagHZKxgU6mMc1QUoJmKhq0aYmOvevCsaOdoTADMBe30d8fxdmGX\nIhMbOBGJYT+9pAHhwm/79Gx0i/ctErcVcyATHN96g8sC7ivykEn+ZqHIlHoOX37oruya30TVZsAf\neoVTUVEEcmchHBB1vyiyXQB2I2Kvh0H4QwWCRAB+T6JI01Dn7JkVdXJ9ixAwjZZlzY/2Cqy8RKR2\nkkw0sPy9037GNq/PbMNkVW5t52v/1VFmt5s5XmXPwupmN1C5c5N1OnPJo+p1+k47iYGzz2T7x79k\nnOqCviQjYd2d/jNOQScgjcntRUuiFHuv/DF9q49jdP1mdn7pu8iEaWfkrrXs+Oy3mXvxeaz85N8h\n4xNEvT0M37+etW//R7OZaibs/e5N9J94LMvf9yZUnDsz9ixfzNI/fV3b7kWE3Vf9jJ1fv55k/xAK\nqS44FXuajrSV5sEx7v6DzzKxZxg91iDqqbPhkz/itE9dSu/S1puJI0/yWN1HqnRAa0eelBI960Po\nm94Fex/ILxZBqw6BRAgWI6KZx+X3R/fSvOqPYWwQms45K0IiS0FpZcNOqdapUT02T1JAovJCJnkx\nF97K6ZSBxQCkFVnQYoce8EkLYMUvWelRaq+VUjlNAgjdfKgWZSVnGkV8djJkTqX4vIp6UQQ9qnC/\nousM5Dt86MeV9Zr1gCvYEE6tzEEqdMnH4AF9Ckxn8CD9f4vP0wHWVmNzwFThZ46qNtNQ2f/Fa1OU\nEMWeM5lNMKB1xVG+Y9KLAf7FdFwKkQbEvT1MP/8culesYM//u7qlWUPGbmtTb3TDDlRXFzKFrHOl\ntojY+ZVrII6RRlrcaYAIe777EyTVLLr0YiRJ2fQvX8yALYAebzB8/6Psu/FOZp9/5iH1v/OrP2LH\nN67P2rPTE0otZubZJx1Sux0py2OfuZ7x7QeydMF6vImeaLL2Q9/h1E9e+j+sXUeeTNKxae3Ik1JU\n90yiCz4P0XSbTSoyGbXcop2BEusw5QCUS5+ZxKRXvhXZsxaA5MZ/hKEd1qEqZ99IYmjULDtoj69d\n5iipIbU+W9b+I6AbMTSN3ahOYmPb2YzQiULEZJySJPLSvqrMq91f53UxVmo2+PKlLHB9ZodamrFM\n//z3QhuYIPU6jZAWvFFgGiE5MPJtMHUaWd3tXInJsJRlPyqaFmQDqgBzQTlaZr4KdXCADI89Dds3\n5Yz9qk6czW1xvtr97vpu9RXZCliX9QecH1JQL7OdzbJh2R2PneeyWYdq3aYoa+tss6CJKsRuVVk5\nE40inBNpOX7FwHOezbKPfJCDtz3E1n/7LHu/9YPqd0Mcg2vHlsY0tu1h4rFtNova1Bki7ZJfpNqA\nxlZ2BUnK4A9uRk80GH14E1LKygUy0WDfj26bct8Aupmw85s3BAAYrBqRgtjYwXYvmM2St5RtYSVN\n0ROPg10+QmX3jx/IAGsmAkMPbCMdbVRXOlJFq8P78xSTDtPakSdc9Pg+5O5/h603AgJHPY/o1HeF\nMVcrRCmFOvGNyH2XhZEEVA2mL4M9m8jSS/l/fIK53hgmue49xK+8HNl2F0FqJ8iJz+wIlRAk6Bii\nfqR3OozsMItus5ZVVrg1v5AJSgVd/JLis51ON8kuZwRudSr1CskBaRWbnHmkCygbliDDU37iAHFg\ny593NyNlhrLd6LL2UoWz/PW95XVi0VnAVHqUdsUYsEA6AM4q6LFCWgPRSSV/GSpDuPnAzkSGIJ9L\nb0ju3yIr2zIurKeAZJs68epXbIiKUyAV1zwZuvkOxjZsZWLrdtA529jKVAV8wFy4j4nYEE8fID04\nbOIzxxHzX3cxM889i5EH17P5Xz4PJcDX5oRAKZIDw22D/6v40PiZZN9QBQA2kxf1dLPgt86n5+gF\nTH/G8UHb6XiDTZ+4isEf34OkKb1Hz2fZO1/OtBOXHVL/R5q0fL8n/wo5skSKf3NHnnSY1o48YSI6\nIb3zI8iVF8HGayAZgWQUNv4Afd2lSDo5E6FWvw61+rVQ64W4G2p9qFPeSvSiy1HHXWRitvp9Zguw\n/aZrjCB71k36l1621cSA3NF9MGMZWnoNqwhBGeXq+qyYGBDtnHF0Ehn2S7dnQtvq5n3WqWlPN2wk\ngIQsIoBXsmJ8kP/JW/1EsiPp/Dja60srwyanMcVj6pydU969Kka13eCsHkmEslmfsjGJsp7vRcZx\nMvF0yenInMXMAPEkqtlyU1okKthhJ1pDmkZoG7zfsdSBqoXPqnBMX6VD5TWtssgSLVX1WNGpSDo2\nwfj6zRlgzd4LnUdb8AfQ7h2PB/o59iP/G2p9CD2kukbajDhwyxqIY+qzZkwBoIeiIkVt1nR6Vyz1\nogl497u7mPGc0ypZ2FYS9fcgzSpnUEV93mwWvOp5zDjrhBIYXv8PX2Hw+ntMXS2MPbaTde/9HONb\n9xzSmI40mf/Ck1H1Qja8WDHjlKOJezvOWB3JpQNaO/KEib77Y/DwN8qrqyQwsQ+23ThpG0opopN/\nn+iV1xK99BtEr7qW6ITfM9fPfCfMWGYArX8sK8Hqj4rrMPvYvHsN0lRII4KmS0/pqefMDByo23Q7\nNFITM7UKwJRSaDrm1YEH8yP2OCazRcyO1MtNijspdmNxACFwGLIMs1iGTeLsuF5bEJsdPQtIUlgU\ncO2pfLye449OoixL1dRxRHEuWiO+ENzknvLOWcmE7WrXcfXcmbYrGEb7QeE9A08XB/p9MwRjWlAF\nECWoWzWu/Pi/2oRhMgxeffxe7MMDigG76oB+qKdO8s2U2PeDOEL1dIM3L745SNGkoWx7rEp6tRqc\nqtXZ9p/fpLFzED3RQBoJ0kgYfWADu7/5Q7qPXkTU0xPWUaC66lbHgsQxs1/0HKJ6DRVHLH/vpUS9\n3UQ9XVCLIY7QzZTNH/sW973279h11U/bzrmTkYc2Q1yrfLa16dMq60xsH2TonkeRRgh2dTNhxzen\n1u+RKsvffC79x84n7u1C1SLivi66Zk/jae9tH4bsSBO3JhzOn6eadEBrR54QkWQcHvlvE1O1SpJR\n9L6Hp9yeiuuovnmoKLdoUd0DxC/7PNEF/wTLnw+q24Ic7w8xbcLs46id91fmDz7FOFxJuLiLdl8G\n5HajWtnPOTAQyRf7bNE32hQULi7s7rMFro0a0owDe1z8H6urTkE3LFNbmVY17FdEIc0IkpoxZWjW\nkEZs+qoEEo6Z8+fEG6tjQqd0JFVs34EgU1m7WLG6ABC9z0Vw1/ZLVbkv8QKoa6OrCKQpARh1dtCS\nRqTN2DLjsT1ujwKQlzlGebab4Xy5PkzUAV10EsvmKX/uRQBt5kpZZtb/8eepEFkACO1vc3Y5Y9KD\n9yd/97uOPYbFf/pWFv7JW1C1ejgvhYUtiDhQbMv93t1Ffd6sPA6qJ8nQKCP3Phw4ZYmAbjQZ/MHN\nqChi6V+8kainK2NNo54u+lcfw+rPf4CFb34F1OvZc9cp7Pzvmzh4p7Fd7z9+Oas/+34W/8ElTDv1\neDQ1dBMk1aTDY2z//PfYe93k9q2SpFCrVWwaFFF/b2Wd8W17q7NjpZrxx3ZO2ueRLHFfF6d95s2c\n8I+/zTF/cD6r3ncxZ339j+lZMON/WrWOPMmkY9PakSdGGgfa36/1oqYve1xNy9h+0ge+g+xcg5q1\nnPjpryB6zntJt92LjO4Obe2SCH3T/6V2/ntBdxsQWwB9mWmAZT/d9ZJ4maTM8XhBXL+uvSrdBetQ\n5vpQtl2NKMmxhqg89BQOLLTJeZ/pOMlRuni3pOqiBeRBJi4xoE2JB279uu30sgxqqkpzkoUWE6dK\nqLcBThEiOgu7ZPrKmVBc3FMbJzUbe1Gd4GGZedWpf709o4sFLGGINLHhsKJAr5CBrJ4bN7bgmtJE\nkc9mFjckEVqL53yFZ29c9cxD5rR6nIqxBzew9aOXM+93XsLsV7+c3Zd/C0mt/bcDy6p1+lx/TABd\nC+Zz9Hvfxvp3/x/SkdFg7mUizX73NywAzcERNv7zF1n6p7/Lyk+9nwM/vYvkwBD9JxxH/0krUTaj\nVprGSNM/HknY+OGvcOIX38/Y+m0M3bee2kAfQ2s2mqgDnuiJJtuv+CFzLmwfSWDgpGNtrLYoe9ag\niHq6mH3eqZV1epfNRzfKJgWqFtO/emllneGHtrD3+vsQEeY87yQGWpQ7EkRFillnHMOsM475n1bl\nyS1PQXb0cMoTClqVUhcB/w7EwH+JyP8p3D8e+BxwOvA+EfnwVOt25EkuPXOMvalzoPJwkQAq7oEl\nzzvkZmVoO81vvhWScUgbyNbb0Q9eiVp8BjK0D4gQ5TtnJej1NyBnvwMWPh3Zcld52XYn7i2D2nu/\nV4RlymOB+m20aMoCCVUCJI75sgHbs9igHqi2oKhlTE7LjBXbzvsUh0HzcUt+P2snLWeikmBsIbB0\n8TxRYTrdzCzBG0t2D8kygpkmwy1AAOokRlJBRea5avt48Y/d/Y2EWCYVULGJkxDM2aSssSoUyn/3\n5ynNMmMBRGGzKneGEsrPrDKBgkToVFMVsSB7BxyIco1mgKosWf/eEX+rdyc9MMTOz36DaPYMdNNN\nsKeHF45rMrOGiU07qM0YQPUPIAfHzbOtAuFBJjLz6cCta6h96RqWvOllzPnNc0pt77v+zpJHP5gj\n+PXvv4yRhzahkxRVi5GJRuWYk8GD7QcAxH3dHP2uV7LxI98w8WjTlKini4HTVjLzWSdU1umaO4PZ\n553Evp/cn0cOUIqou86CVzy7VH7zZ3/I9m/cnAHdXd+9nQUvfybL3vrCSfXrSEeOVHnCQKtSKgY+\nDlwIbAFuU0p9R0S8wJsMAu8EXv446nbkVyiSNpC1X0E2XAWSopZdhFr9BlSt+qhMRTXUSX+A3PNx\nSMdCFlJHSGMc+dbvwrPfQ7TkmVPWI/n5J6ExnK94OgGdIBt/mgEXY+OZAwwV1ZCRPTB3NWy+u82i\nO9kOtgxYwYE2IDAVaI2MyqDStBEynJ54IaiqwJ35V2XxS1uSrJL55hcYQcOuKudxPxnrWNDb6aEQ\npKBADopDxyJlwZzlryxYt+C9Ii6pAV9ubsQEj5dyTNF85q1eKQiKKDYPR4rmI1MQB8yzvrF6Bvae\nrud8PloB8pYmJUAOSh1gdBuN8N0T/CBVrcczWZrYYLPSaJJs34PvrFcqb+nd0EmsoEtkme5U23lS\n2S2lgDiCuAZjzbIOzYQ9V/6EaSet4MCtD6DHJpj57JOZ8cwTUXFEVOFoBSCJZvjBTVkSA+N0ZZ5J\ncaPXfdT8lvPhy5zzTmXaqqXs/fGdpMPjzHjmagZOOa7tacfyP72E7iVz2f2dW0hHJxg4+RiWvvVF\ndM0Nj7nHNu9m29d/Fti/6okmO799C/Oefwp9xy6cko4dOdLE+R0cufJEMq1nAY+IyKMASqmvAhcD\nGfAUkV3ALqXUiw+1bkd+dSIi6J/8Cey9D9IJc+2hy5FtPyW68POBnakv0arfRvfMRO6/DBnZBWOJ\nR68kMLqL9Ed/hV7xUmTzrabOihfA/BPQ674PjWGiY59HtOIFxpkKkC23hWeKTsdsEfeWT2WK6kYD\nNbCISDdJhRLzZQDnJAt7AeRVSw6I3HF+qa00qgStoQ5e3ZC+s0fCBeBqnbQm87DXSWTYUC+zlrHn\nNBd8sHfoYo5us1i0hePodhFtDLDwv4ynokMIjEUrC4qKc26BiwYtzj45BKCV2dSy/zvg4xIqmMI6\nyJJV1tuBclNYeVnWAMeIl/pVBcCqfGXC9lXIjitl7Dt9+9NwLsoAzreNDXWpnn/JGOXixikPcaUU\n1GZOY/iutd4zzYG+cgazmcOda8t7nho2/P3ncDvdg794gK7F8yCqMbFtD0JsNs7+3zCqRdat8O9Q\nddVZ8iaz3Azdt4G9192JpJrZ553C9DNWlgBp96I5LH7thZXzUSUqjln8O89j8e88r225fTevhYpk\nCzrR7Pv5Qx3Q2pFqkfBv/EiUJxK0LgE2e79vAaZKqf0ydTtyuGXPPbB3TQZYAeNgNbwFtv8Mlpzb\nsmq07AXIUeeRfuOVIIPBPRGg2UQevDIDovruL2Ey9hjAkO64D/3Q1dRe+lEDjms90BwtdySUGJVs\nUbcgQC06Ebnzm6iosOxmoDSs69vGZs5C9n+VQMeVDRxexNiq6sgyc/YYOZIsGlPZeSgHS2UAoXI8\n6zOAhS+z4jzkoaOMfmKP2o3OsUdLl8eVD3wycWYIuWgblcCPPFXZfBGYlbFMINraIAfgUbBbFwmn\nThRa+45oYaMui5OviwnnlLOd/nPy08FOTVTbTZHfb5EBb8faZ5mnxJpfBpsm92OuKZvdTKkwhFex\nP/9dLD0vZ44gMXmYNHf0n9dv7hliwwcvo2RK455PCqSJN7RC/7jr5pdktEHyyPaCMhGqKyKqxah6\njdrc2Yw9srVynqK+bpSCnqPms/gNv8nAKSvY8l/fZ9d3fo5uNEFg30/XMOucE1ny5ovY9uUb2H/r\nQ8T9PSy85NnMfcFphxyGazJR9RiT8zYEripSqHo1m9yRjnTk18ARSyn1VuCtAEcfffT/sDa/niKD\na0AqWIxkFL3nPuI2oBVANv4EGiOt7obMqaThGp2MI4OPoh+9nnjFhaglZ6AfuTZY1kOv8yILGUG9\nG731HiQRSGObM92iq0jyxTio5xZ9L+xSJMYOMyqDWizQKUUvQJl1yVvAM3tOJfayKi3eoVQA12Ds\nBEdGJU/0NCoDCOfAJFE4Z0WcLKUZLenjO5+JZSXRyrMTFrAbheKciZioCOaeCtuzj7MMwN14XKHC\n/PjAp4LN9D+LCFpHXsB8yZ97Vl7l4OyXZDlyEwgCxt9/jtUMrIRl3fvWYmxFL/8iKxvo4j5XmE4U\n36VSuKvgOXiSHc+H/QbttX3n/f4ryihFz9GLWPjq8xk4/Wns/PqNjG/aWQo3JQJ6TBg4fQVH/+HF\nRL1djG/Zw84rbw6P5scbDN50H/tuWUcyOg6J+U7a+B/fYeSRbSx/x0tKKqTjDQZveoDGrgP0r1rC\njNOPbZvgwJc5zz2RTZ+5tnJcc849cUptdOQIlQ7T+oTJVsB3hTzKXjusdUXk08CnAc4444wp8UFH\nmogIjO2CqI7qmX3oDfTOh6jL2I/6Eveg+qdwjHVwU8jSTiJZCksw65UeQz/2U+IVF8LMYzJwFIgG\nuqYhzaG8HS/fvIzsI/nxJ8kbtStoao7MJcXY4lkmshgNTgSUsw/UmDqQgzVRBUclHwiGC71C2e5t\nfy2YWx9wtCR6xHn7R6gopzkzW9K0DETMfTdGZ4Xq6Vz8Kyoxc+X7uYmCNwcZ8LTHxErI7TvzwPQZ\ncCr2Z+vndrsUnMSmBvKF9nPsH/uHR29VnwXHKLs2Jw3oYL3QTfshM+nPuUg0aRQGk02ruMlxrGf+\nTrXS33TvRWDwmNUQ5IplsX1xmz1VAKC/xCLqwmLp1l/dVe+cQmjuG6Y+azr3X/phUg9oBuW1Qqcp\n+295mAN3fgRE6F44q/yOA+lYAmPlaAO7r76Nxa95Ll1zppMMj6MnmiQjYzzwJ5ehGwl6oknUXadv\n2XxW/+ulxD2TB8Pvmjud4/785az/8LcN0FUgqeaYP3kZ3QvaZwrsSEeOZHkiQettwEql1DEYwPka\n4Hd/BXU74okMrkFu+2sY3QkIMnMV6qx/RPUvmnIbaslzkTs/DMkYwbd9VEMdPQVP15nHQK3PZMMq\ntx78phNCD3y3pscmsHg0exk67stMBDIbwKhOtOL56Aeuto2E7SZbHkSaRW9iBcp4bCORObZsw/5k\ndpPYPr2p0KUYqiGgqAaO4OxQ84XWAM8MVKSgonAhzr3yLVtqHZREEwDXFlRjQXxgHDJ6wX3tzXWp\n7WrGz09Eha0r7hd3rwSMwjZcZRcft7q/anFdOU/9tpuDAOzRFhPrwLmpdcasNHEbg6qG8g1LSe8K\ndllsNiqdgcwyY1zldFUckutXF1hVM1deSK9K+2K7sRG/3dbvWKv5zq4rxcBpqxjfvJNk/1CJJXVl\nw/csB+j12dNZ997Pokfdhtjq4v3p+XFmXfvjW/ccEsyO6jEH79nAnuvu5cCd6w3Q1mJiuVrRYw1G\nHt3Btq/exNI3XjCldudecAozzljJ/lvWIiLMetYq6jP6D0GzjhyJcqTbtD5hyQVEJAH+CPgB8CDw\nNRFZo5R6m1LqbQBKqYVKqS3Au4G/VkptUUpNb1X3idL111VkfBC56e0wvNnYoOomDD6A3PgWpMia\nthEVdxNd8GmYudIwrlEXDCwjet6nUF0Dk9c/+jnQOxNU6Bmv6n1QGyA4wtRFJs3e2/UoANGy34De\nWUBsFtakZsBbM0XfezXSlCwYvlgWUg830Gt+QMhImV50isl0pe0x9WSB9KUcn9UHn4ckwRFu/uP0\nNvpE6KZNBduM0c0YSWIbZcBnNsEEwrdZsVKVAUIpDCi3dwx/XDaq8IcMDLlsW0ZPP8tTcTxTGLo2\n2ZnC+rTYNPig5RDmOHMEUkEyg+y22xD5aVV9sOwxvDpVNhWrwj96lyyrmcpSm6ZpRJpEmGh9Vc94\nCqqXno/tP3sGoSiP/fT1c/8hRWa78FPJprcA28H8qIzJLv4EcxxcV0gUobq6UD29TP+Nk+lZudx7\nD1Uw7lAXq69S9K1aVmBo3ViUtxmsGINusVug+rJozdbLb+TAneuRJDVZvJK0XK6RsPvauyvbbSX1\nGX3Me+FpzL/o9A5g7ciURPTh/XmqyRNq0yoi1wDXFK59yvu8A3P0P6W6HTk0kY1XWVTmi4bmMOy8\nBRaV4yC2EjWwjPiFlyNje0BSVN+CyfsfHUTG9qJmLCV+8afRv/gosvFGQMGck5CxMeTAVlA9gLOZ\nrVpMFOw1oFVFMfUX/B0TX/2DjGFUgZEe+ZE/5DaVwQJswatWXjxUxzRimaSCDSGQHae7P/QSK9Vi\nHoQMPJkg7T77WgHQRCEO0AWEbYFFy4533SLttWKPXbM89z55bZ2jHOD12Sz/s1K2slKeqUU5hJAD\ndQ44TSYuzWzA9PlzqLR5Fjo/Ei+GjSo3WuzD/RtlY9IaoihkRk2CgeJzMOPMUsp6GwPx5ilg7LN+\nbfguBxbbqZwxnhB5iSXAgnrvvclNGKqD/BuHNz8ZhdVM5f+Gz7nwvqhJNmsVurt+q99jKfSb9696\nuoinTyfZN8y+n9xrQmCl2oQ0y/7mvCQKLcDzgVvXosdbZNwrlC3+rrprIIKqxejRidZjjxRd82Yy\nvnN/JVAtyaFMYkc60pFDlqe8I1ZH2sjIFtAVtqSSwNjjTCvYPRNGdiGNYVRXdQ5uaY6RXP9BZMst\nJsGApESnvYHac/8GAP3YzSQ//DtIPN3iLqKTf5v0ji9XtylC86HrqB9/Ieljt2WMbClOp8MYmd0f\nVDN3Eti8BvUFw1RaIGGSLVkQmCjQcY4hC8C2GL9S0oiinaeIwhrIthajIrnNa3nhDo5wXXn7u2gT\n2kpshqxwR+3mxgcqDqh6IKPkJW/rqRC4OscoEbKoCK0kt6GMwmveHItNX5rfNwWUyi2ZC2FTA3GM\nZygqu5dtWrx5UV6bBkzGpbreKKrHld1181TWzbXnoipkOtuxq8iG5yrMfT5vqgTgzabIz2CW61h1\nRF/ax5LveVKbVngqtrphFi5fV7NLKkYqAFBxzMznPIM9196WsaQq1d7mKysZhgkr9q3FHPNXbJRU\nd52+FYsZ37gLJQo92rBxW736KTz90+9kbOMu1n/wazYZQL4pc9KzeA6LXnMuj/3H1e0nA1BdNea9\noDpbVkc6clgkO9U7cqUDWn+NRc09Hdn0fRPcP7wDsw/dQzV9+Br0Lz5mTQ00atlzic95D6rWE5RL\nfvLPyJZbTcrU1DCo+q4voqcvJjr2eSQ3fzwErABpA73xZohqSJpUhq5Kb/x3aivPRe9cOwVty4tp\nIJMQIsbhxeanV4KK7HG7z/75+qUq85DPrzsEHQK/DIRNpr+iJXFTeYRbAK5aSwWLOLmETjouy5Q/\nn4JywfqzL1HbuZSn1thj5gBZF7KG5UBZWsxZPmbDlhb68BhEw44WHeKKDLXKwHzQhP0d6unQAAAg\nAElEQVS/bpkZLdPYbEiCcVhUqcjskFtJYHIRbHacaYubT7//fByiFUQ5CGtrf+r1mf9bficym+XM\n7EF7epX1D/urZm3T1LHEBgjWemLqixey5/u3lXUrRS4Qcy3GPHAPdJZOPry/R9VdZ+Dpy1nxD5ei\nlCIZGuP+3/93mgdGgjZUV509N6xhaM1mezl8v52c+LG3k45MoFuxrJYljnq76F06l8WveW51uY50\npCOHRTqg9ddZllwAD/4XjG4z9qwAcQ/MOxM18/hDakpvux3983/L07ICsuknpDel1J739/m1xjDy\n2E15f06ScZK7v0xt7mo42CKIxL6NqBMvQe79RrYQZQuUs1Hb9TB6eG9LPTNAoF2EAcnBicceySRA\nTsTASgfYjIOYW4RNC/nk2AVXO298CzBaAE6zqBs21BGOIcM5BZDZCnQ77Che5IBJm2tRIDOXKIIT\na99qFclBg4sgIBngltSO16by9Nm5jD3Mq7RgERwDprI+REsB8LUCZC0Y6hKYd+2H9aqYylbAT2sH\nVqvr+ja0xeN5fyxSudEobHx0galuIwELXLHpCvtwN6IMuFa1V50cIRdtYxKb8qbd2uKFjD26w+vL\nNdhKF6H/+GVE3TWG7n3UhqqzVewpiW+vraKIuS84g6Vve3F2MlEb6OXET7yDjZ/4LoM/uR93cpEc\nGGfzZT/KFAiZZfNh0WueS62/h1p/D3MvOJm9P74vT88aR9QGelnyuvNIh8boX7WEmWeumHLIq450\n5PGIuO/YI1g6oPXXWFTcBed/AXnwMthyHcR1WP4K1MpDD8Sg7/1SAFgBSBvIpp8h4/tRPTZMy8QQ\nRHEJtEqqYMcGmldcanSr6qRvNrJ7gwGBysC/ADDphOajv0C2rQUvqxN4nwUksUxdxeIvsZ8lAGs7\nV0UnqbDdViYG1pYwK1dgEWl1xCmGEVQWuGa6V6UZtexl0XS3VbvZR1HVmLVUufos2+LQFuMPSpWu\n5c5NDsj6elFiOgEkM5lQQdlsLK5uoE8IXKYkhfdCxGdn3cWSKhVt+DqaNnUiWdD/YtQHgDSF0HnO\nteOeQZuxKG/urLTDSLl9tq2uwni4k0tkmWvx4tf6SRqwds9V4LzMlo+v35GNoyQVQ1dxzNwXnMmc\nFzyDte+5jKG715NvBnNw7TYcqlZn7ovOQsWhw2d99gDJSNOYsVSKY7wFFUf0HjOfpW+6kFnPXJWV\nOPbdF9N33EJ2futW0rEJZj7zaSx94wV0zZ3eos3DIy2/nzpyxEoHtHbk11pUfRrq5HfBye/6pdqR\n4R3VN+IajA2CA6398yHugsRjZDXW4UmMWYAy67bCW9TjLuKTX41+7E7KHsNW+maR3vJV8BbjAMhl\nHvP++XGhjdSxoe6ImFIMDUlyJjZYLwoLq0mBasNNQSENa65XsZ3cPtEyVlkig7DPDHQUv6S84+WS\njm4uHNMlOrADzUImldg27/jZ0y0Dn5N8T1baTupqgBSaFLjeJXOs88dUaQZB8dokYM/TzzlXVQXY\nLwNI7HtWaNsDTc4JLe9HZUBDUmvfa9XLbU/LE2rmSTxb3HaTnl/PQaWpEyRoQOGH58oOLWjFtFaL\nSNziObjEDGLH4M3nFN6ZYDhVjy+OGDj5WJRSLHr1eQzdtaF1E7WY3mMX0ndMddzokYeLGbXKSogo\nJIGTPvGHqLjwpaBhwUvOYtErnjVJO7+86CTlsf/6CVu/eQfp6ATTVi5k5Z+9kBknVfosd6QjR5R0\nQGtHpiRqwSnI8HaHhnIRgYElSNqE5hjSNQ11wiuRe76UJyNI40Idg540CpKaoyhpXv+fMH2xSVpQ\nYHVFIqTRRebEowWUIJHY0FeW2ZkUXFnPdd/RR9tFP2Ox8ixREnh154Ag82wWH2TkZTLR5EwqflEP\n9GiMKYJWQYpZE/IqnztxDjgZUwmqVkjL6bzIM70scM3YRB9gmTk0Q/dRtQOsRVarjRTYX+OcVd5Y\nIFSmNM1MMfwmK4BSKasSfpkMbQeozIF4UX4YMP8ovjjGwuesPzfXObPux+cNwH7GNho2UikfkPqA\nXYKx+oCv0iyhMCfOtlaQwI42Z2SLc53359suh45nYdn2c+7mUGd9Goa/bLPdirwWoGveTNIDo9nx\ne9RTZ84LzqB7kUmGMuP0FXQvmcPE1rJpkKCozZlJc0K48/c+woxTjmHJa59Lz8JZWZnuhTNp7huu\nGkQg8bSeALAmQ2M88uGr2XvTg4gWBlYvYcVfvpT+Y+ZP2tbjlXX/dA27fvgAesJ8fw6v28E97/oK\nz/ivS+k/dt4T1m9HniLSYVo70pHJJT71DSQbbzQJBhxwrfWgTn0jyc8+iX7oe8Yt2cViFQVxDEog\n7i44XllAlBY9noGD20AitIoNjIkM04PuggM7yMGBBYx+9qwpiGEOy0eXZYBWQf/44FAKoKpiUc+A\nYgKC9o5yPUBlowsIFkSiyLMe5aAjy6SkfaAA0nTgsxpgZuApYxcLrKBLMasMwlSRB5amAFgdkHIg\nNR+fZB8zMOOZTkzumd76XisQJdb2OKoRPiuNBf/VQHFyUB4CW7EbEX+8vqNTyHa6zYLO37ugO/89\nc3NkBxiVQWPJjMG3QdXZyGxZldUv9mccpaaSXayqr7JoHXt/h9KyaDZ3hOOKBgZIRlOi7m56l85h\nwSXPYebZJwR1V37g9dz/jk+SjjVBhLgrBq3ROmZ8635gPwC7tu1j7w33c/J/vp2exQb0HvV757Pu\nb6/IbVIrJOqps+hVOZMqItz3ri8yunEXYrNtDT2whXv/8LOcccUfU595eOOqigi7b1jLju/dn0U7\nyCJ0NBI2XX4zq99/8WHtsyMdeapJB7R2BBnajn7sJyCaaNlzUDPKx1BqYAm1l11GetdlyI67oXcO\n8SmvJ334ZvT6GyBt2LUyJVux3NFkTWHjL+UNZqGHyiyaiLZe+v4C3SiVo3TM3P6IGHxmrXAtsLEU\ny3R5IaEKAM707/dXBMEUbOhi483vhYTKWd28DcMICsXj80wX5erZ8hmAL+jnwGgwTgIGL2dAbX1r\nLCtQzlRVOO4tmxC0AD6OUQ9COJVjvYaSA6sqqQaszvkn9rIN+9xekUmd/F2p6iPz+tfVwMw3hyiD\nzck2AOH7IFqhlM4eTauoCsVrpWxXAkrl713qpzd2e4tK1ao40Yq/n9Qva/v2zFKC6hLZ6AohMzu6\nbpspEymSsR3M2HGA/T9fS/eS2Ry8ewPp6Dg7r/wFOpHseyVtuDa8qAAK0EI61mDzF65n5V+9EoCZ\nZ63kmHdfzKZPfY9keBylFL3HLGRsw05jGKw1C156Jke99tysraH7NzO2dRBpet9bArqZsuO7d7H0\ndVOPcz2ZpONN7nnXFQyt3RE6nEk+puGHdx22/jryFJXsBOXIlQ5oPcIlfeDb6Fs/lq1e+o7LiE6/\nlPiU15bKqulHUTv3b7LfZfwg+vsfzMJa2VLlTkSX7FzbsXghk9amTNaO31fxownznoPPCnBV5U0u\nhh1FEYS50jadpgOcxqwgXMzFMY+VII4s/mR29B9gqMgAIiriTwaErq9rkb1z4yyUy87ry2q5OKvi\nbI+VuaYqqoMBKiIuZ3oRYPuDKgJWrzkpPmtTz9lFikfcVx2VlwLXl2KVOvAbMo4+G5oNrEo/76rr\nJ3v+hUgIrkblM8s2V63FgC8/IoBLKBCbAww7nFa20cG1AjOctZ9NdK73VGxbgzkmnMfy6YSr5P5m\nXGWC+RIdF567EZ0IJAkbP/V9e6HN5iLrP39SmY5aOFiwgZ33/FOYe/5JJEPjxP3dRLWYdLxBY/dB\nuuZOJ+7tCsqPbRmkNMGANBJGNxxeALnpK7cytHZHZhJQ2ljFimlPmzyhS0d+/eVId8TqxOc4gkWG\ndxrAmtoUr7pp4qXe+Tlk/6ZJ6+s965HUxpb0Ui+WREXULnwfEvVkZcQHkiXFpqC7t2CLmDzvaRIF\nekiWCrU1kKxyCso+++kgtSJtxEgzRjdqJpVqYr2rAXH/SWuG0ACyyNatsiEkC9fVctyZ09YkE+SN\nI5sPqZ5zw1L6KUmj1rt5Ad2M0GmtENvTtaWyNJy2eHu9tP+vdz2NgMiaT/hjIE+Y4PpIVeYQV36+\nReBG0F5+L39ulnBGpy4JgPlJmhZEZskPFD4AbAXMJxPDVode+VX6+teKQLIs1c8vT0M7NcnAekrp\nWZRPOsL+lTN3ED/972R92XJa2gPWQl/Bc7DvU21mH5LoIMaqiiLqM/qIauZ9iXtMfNUiYAXoP3ZB\n5Qsc9dSZtnrJFHWbmuy4+l7S8aTwXuZ/D1G9xtGvP/uw9tmRjjwVpcO0HsGiH7upxY0U/diNxKe+\nvmVdmRimee2H7JnsJItgrQfmrICeeciBbWE77viL8pd1VblsEQeUKNJE4TtV6VRhMgYJKosTWQZX\neYVWulvwZVPChqlSPSCkIy9QvJDvA9tlhgqBlO9Yld0XKXl5p4ljLb26VUyQD8ZKoTZN0gHfRKE6\nOL3PzHk6BBm+IsMce8yl70FenPeQJTROUcUeJJISc5t73OdjcCAzB49tQgM5hjgDWyr36idvOt8H\nKe+Z+husqQGv0vucAUz7jiiPh1Z5ClwnxWxZ+bNyNGJhOiqYxrJUMcPtxTcBcWN3epWZ7/LJQAi0\nq8Fy8Z345cW8U6IVQ2t387Pn/wMAAyccxaq/fgU9i2e1r+7JtFWLGFi9hINrtiANy4BGiqi7zr67\nt/Dof95E1FVj0ctOZfml5xB1Pf7lVE8kwTMXQEXmnZ62aj6r/vI36V8+93G335FfI5nqXu7XVDpM\na0eqZRKKKHng+9AYCYqLNkBEJ87+0hwB6rEmjc+/2fytxV1Zed2I7DGzB97s7zqN0EmMtmwmQNpU\n6EaMbhrGM00cgFLhTwAwW4+pHVsVHH22SQmr7JmtsQuNyVNRtmDdsgD5dtxJ5DljqRzs2VBTrpxO\nlXfM7towTJZOwzStxSPt4vxIGqETRdqMPLvbor4KE2BeZT9phQ5QPGr3x+vY21CfbHwtdGvF3Pl9\nah0humaevzen1XoYltCxfhmA85h0B8hEx/aYvpjtajLxnmnACjvdbUvaPG+dmn/TZlSao9zrvzgu\n7/32rrt3xoXzqrbb9t/pFnNU+HFt5322GLkK23CfweikPRa9CPynfmpQoXOr6+J9sIzt0P2buf21\nH2N0455D6uOEf/5dFr/iTGrTe4l66sw++2kkacyeG9aSjkzQ3DfClitu5b7/9fXHNwggGR5nYt8E\nxXdctKJv+VzO/PxbmH7C4WV2O9KRp6p0mNYjVGR0EDVnVXjNLWq1mOiYc9vX335fFhFABA/YOfBh\n4lSaY3AbOWD/LlTvNIgiZMwwF5JGJp5llKcFFeeJ7BY6HaNFKC54rbFEkTH0GEXPEQrPgchnyLJ6\npXSe1Qt3MQFBlhWqTe50M0ehvspScBkzlSobLolKMOfb9Yo2GYxchAIXbaBqXJmOU2LdvDKVrHS7\nNvJ5c7FEc0BxaKyfL7rkZGYbrmL6xD3HYl+hM5h7fmV2UZdtZ/2efcBmj/gLyXxtYH6Fs+ssPQ+P\n6dVFBzhvLEV7Vq0NQ5tFm8j0lpJugGGwIxNcrDgOt6Fy3v3FSBNYNjdji+29qPCOF4F0KOEzc/eV\ncjMmTPZOFNtMkyiMeNGyP5BUc887Psfc85/O4E/XEfV2sfiVZ7L45c9ARf+fvfeOm+so78W/z5zd\nt6nLKi6yZdmWe8FNuBdiMM3EBkIxgeDkhhtuSLhwCeRSLiEkv5AAIYRQQzOh2phimgu4EzfZslxk\nW81qVu962+6emef3x/Rz5uy7kmWMpH0+XuvdU2aemTNn5zvfeUq63qy3jjl/+TLM+cuXAQDWXPcA\nNt+3KjJdUM0cOx5ZhcGlGzD+mN23O11/y5OgjMBRYAOtz8yXn7Lb5XVlf5ZuRqwuaD3AhAc3Ir/1\nY+BNi7XXbK0XIAVusgeBEsgf+ylqF/xV9ZbrpMP8FBOykjAHrI1gCE6kAo8Moveqj2P0Rx8HlI0I\n4OORum1yDidhO50lQJthZ/WeKwdZiPR3lQvNDjrWFGCyWYvYMI0MUYsnRGf/WsGwRn1aADy+TUaN\naDIOGTkqtcmXadmWzLW1WixzKCAdxWVjhFbcEYDj1HUeUMTfUwuCTsJXRZEY9kBC85CYibQSJ1LQ\nN1WDwGLZ+t/ChMAAkPmEDABK7YcdSwUQHYh13tIgM67bxXuNzDiKOmsgGo1PZdlZf7DMsrJrHysC\nQwAS2jVRKFeNG7/SOxZyqJssMq2279m0iaOxVJ5U20eLYBZQ0r8jRL7uIlCX0tjKhrbKCm4x6hYq\nicUBAch3DmP9Tx9yHvrPfOHX2LXoWRz/4epQUhtvexIrvvFbNDbugqgLyJFWuWwhMLh04x6B1sbG\nXVCjeflERt20sF3pSkG6oPUAEmaF1o3/G9i1Xv/SKwB5A9ou0MSSBACWUE/+CnLqHNROuqKiMOGJ\nkXasGRUmPMnAwBTQuKngHRVZtgLGMgJLlfjD1M9woYhYCtROuQzqkTt0u6K5XIcsUoHzi8oDRGBn\n7A7EbgGn89YnZl5zj8pFbFdZLLeY5jSIbVmu3wNiD/b9cSF0nySbpDS4D0GKL49dPFIXND7QIWQf\ni8ccUDMR0MIQXDaBghiDWUuygW1FgJUK7C53B7ACfkDHixXPXaKgr2Gzw/sqRObUVh+f6MDWVhwz\nVAplZY+7DFxuvIhg292uPhBlbLPMfFFvTVgHLKIDrGEvBOMNjHLs2FhSYL0sxmxDsQ6GEUQdsES6\nNjOoYM3NLkqVaY5tGwATIsu896MtbL5tEUauuQj9h5XtXdf8cD6Wff52qNE4QorPQuY0SN7fiUw6\n+VBk/XUdfzaQrKeGiScdskdldmX/FEZqUXhgSXcZdwAJr10IjGxDaACpmQmOjgEA8lGohdV2WnLZ\nfwOSvEnBbliHq7VPonbmH2qWN6koEDOXHoi1FzPBKs3GtBbcVm6XraL04lP8GaMub7OoGU6vny/X\nbrE6O12ltzNVbjzjK7z5dWGxPrYZ4fWxPWBwbXSvgFICShlP+IBVtIwUKx2UP28JyFam7S2VAKsM\nShLyVgYps2AL2tdvyww/NnWotjnODNNtF0bGo1wF9rulT7BNr4znemBbW/lMDICyHxvFINXHGgQG\nz0RmCUCUGiPl7yztwiVdDyvREYCO7kk+5yAcGMfP28emrdAxyRQn7MFhxwZFgDWut1x+qGvV96q/\ntSphzGFhbIszKJX556NqSNvWWvvuUD9O1pd6RlQTGHw6TvOaD45iZMMOLP/SnQXAmr6//7ApmHjy\nntmdHnTuURh31DSIXs8hiV4NWCeffvgeldmV/VQsgbAXP/uadJnWA0h4qMoJIc14cWMXWo/8FLx1\nJcTMY5Edewmo3qdPypYe8HkGQAFZeSuPc2HYHYptSYd3oH7um8HbnkW+0MRjZAXU+4GWDr3lAWB5\nUoxsDIvsU2l71Do2kWsm2dSsVQDIkkrRpFf2Ttde7h6wVgITssA1BYrK9oUyz5BaT9p0s+y+k+/f\nuLpCmbbtdrvYsExJxx0PcnQ5MbBRSiXsQMvgO97GD8eGr0vZeLR6k939gDIbNpYDxywZjB8iw8QV\nxi0Xx4N//qEODrxzGGbKsGeximOKcj/85Pq9HMfUf0+x1KH5hFKa2nZMI0LAHitX2oVoQ1y7tL+U\nuDeScGyEi7uwXkZx11oaE51kH8DaVhdNHGAYXx2ho/r16dC0JBOAjBep4X0kdPrnIvBmZvTOmAgA\naG4fxqKP/Qzb5q8EwLZhZZ2yzP2OHHT+XBz3t6/crYVJXJbAGV+8Gqu++yDW/eIxkCAccsWpOOLN\nZ+9xmV3pyv4qXdB6AImYcTykSv0IJ34YSYBHhpHf859A3oCs96F177Xoe/PnQeOmApNmAVs3mnsz\nQEkXCYkZQKsW2KGyZtaM00bz4ZuBqUeCe6ZCNgja0I6ARgNi2iE6/evG1Qkd2ZfPdsIjv+1JAMLQ\nR5b1CydetpMWR5O8LjMEO+avqGx/2jr3OJs/e1lqjmkz4WpQ6NsV61Fsv9123xP7UMNwWqcXBtjF\nz6zSzdTLrghohtjG5jUHzeRdBGspu8S4fBNgXulygzPakY9DFBaDz7LtJpUXMG7Bog8rsAGqhfbZ\nq0t6FlFg/L1omlHKkpVgSz1jGaM+ZhUtEIoMcRpkhoPAPqdih1snsNSmWrtnX1zIxNcqpYGr19O+\nD1wCpx6U+3fYL45C2/H0oA6ZZrvIS9kGH/3eV2Pzrx/DjsdXoVavQymJrLcXoreObHwvpl16IlZ9\n67dAI7AfzQT6Dp6MCScdBmbGI3/9fQwt32TStrIruygTjj8YL/rc1aBMPKdQV06Nvjrm/Ol5mPOn\n3VisXRlDDnDzgC5oPYCEJh8OmnMheMVvXXYqyurgnglAc1hntmIFZHVAKXBTwXn+t0aBvIXW3V9G\n/fK/Ba9fjmgCVxmQQ9uVWSegAHAAMOCEwTu3oHHjp6GaTXMuiLO6eR36XvUuiKmHYvg7Hy54qARg\nzQR8R+jhb0COyzjlAGCZ3YwZPwAyADPuj8JEbUGTzQmP0OaPnQplNivQJfl7ExxMWzMEBWk9WFpG\nDhFgbHsjEIAl3o0fvxi4egbUFmXKyXwECJV7FtOybMmYnBURCdJMu1ZC5vZ4zJhXs1JpNtnr4q8h\nUsHySNcZOst5+9HQyStcFMHFwbWgXSbDeMVlWnORsRzuyt8tZRscY4CDBxY+q3aLCFtQObJEWncb\nx7fYtxachosCX6//Hi8aPBubltQzM2cI6D1kMg5+5YtQmzAAGhiAauaY+fJTMOOyk6JxMemUI/D0\n/3cjWtuGwIox8ZTDccJHrwIRYeeT6zC8eqsBrLbOsj6ir4Yj/+zCZEKCrnSlK8+vdEHrASa1l3wQ\nctGNUE/8FMhHIeZchOyMPwYPbYFceD14xxrQwacgf+B6lH6wWUIuuxf10UGgMRSfs+BDpRgdczvb\n8wTkzcQ2tpbGfT/BhHd+EbXTX43WQzdGrB0ATcwqAnNmN5/NCTPJGOciDSiRAHUBk5WXt/cJoV7l\nGV61AFEz4NGyewywYogsBL1l5rQIGphhWEWraxVwYb99bsESa9aTMs8+ObtSKRxItkBAl6tBiQ5o\nH7Y9VaepxjKqDEBwxC5G+klRcBYif84AmZgELDN47YWcvaz7BJ1d3Y50PXGosnAx4wFXCFvcdjmH\nb0YV6Gaz3Z/O1BXrEYba8rp1KjrNqxk3jrGkYJfBP4f4nSsyyUBo6mHXixpMx6xj6BhWBJp2zFsb\naBLFeoMxmuiHnukT0XfwJLS2D2Hk2e1QkoM+8osLq0fWIzDrbZfggbd8GY0NO3UqVEHY/vAq5EMt\nHHbl6a6OyWcciXnX/xUaG3Yi66+jPmnAnRtdtyMZ+ooZoIwgahmygTqOftcf4KBzjy5d93zJpv9e\ngeXffACjm4Yw7ZzZOPqaeeibMf53Vn9Xfr/kQHfE6oLWA0xIZKidfBVw8lXx8b6JEC95PwCAZY78\nwRsQBBL1IjKgdwDIagi8P2wpY9U+5jXMAG9+Fjs+/25gxwYNKonBxCYwu2U2TXml4gxgkMIDaAsy\nRAE0ycyDk4Ra7HcIXXV2K1TlgPe+1scANt7ygd2n1IhZSThWNLJnzENQwx7oGoAZTtYahAt/uW2v\nzQJmtmWVrPkynTmEcnXr76HzWFo0Y1pgCUvsYqBHgTkrnlN54VyayIruq9KrHEXBh16KbZ6rkhWk\n6/P3GftSO9Yo2Pbm9pEAimXtTv1Wwnb4qlIgEyBYMw8LDs3iprSA9GxmeF3UX+yvC00YQgcnIAac\nDMNQG3MB5SJfmAWAIjCxi+mq+7aYttaGzwLGHXMwTvrnqwEAyz5/C579/n2m3FAfnSWNGRD1fiz+\nl5vAreD3SDFUI8eST/4KOx59FrPfeg7GzZlm2kvoO3hSqfcnHH8wuFXc6iDNrF5zPg694lTUJ49L\nA1vFWPnd+Xjm2vvR3D6CnqkDmPPWeZh99VmVMWA7kRXfX4Cn/+0uSBMSa3j1dqy76Slc8IO3dYHr\nASndOK3d6AFdKQllNYg5LwZE5pg7ZgBZHdkJl2nge/oVJe//sQBQKhxNyuuaJcDrV4JHGrDe1ywz\n7dgVecan61F5FoCV4NrA0SfOyJQoJ5p4LQJF/B1FFs2iUqEnauORHmWzYtImACrcto3v120GbHIA\nVoBqhdm/imLrzGLAGv0rHDsWp1j1/ea/WAY4bG/7fo/6jo3jmf1YU45COd6+0390pAPv+Z9sa2W9\n8b9KdqZvsQwpASWz+PkzmagKGWx2MJUau9H3GITqCA7Upm3le8C2/+Jn5t7LgNkOn6uM4tPGz87q\nEr3bCB3zQpBafGZUyKJmy9SZ63R2tnCcBv2n7DM2SRii+snoQNj+2DoMrdiM7QtWYs0P5kNJCgBr\neTzmu0ahmok4p9BgcsNNj+HBa76BLfcvr+p0AED/oZMx/dLjIPo8l0OZQG18Hw577RnomTq+EoAu\n/tydWPL5u9DcOgwoRnPzEJ7+zO24963XQrXSzlxjiWzkePqzdzvACgCcK7R2NbDsmw/sUZld6cq+\nLl3Qup+J2rgY+YIfQj79G3BrdI/Lqb/k3eBsIrhZ159WHdw/A/Xz/lSfv+DtqJ32KoB0tM1U6Ixo\nUiKguE3uWNMwbJIkY19qJkmXGlZYt4hSHdH35Na1/+7Y00RYoejvEsAOgWt4rgL0Sh3ayjO+ogyU\n226PB2F8IgBuK0jUWcnseUAAZUJQmX51ZXHheRnzi2pJbAnDgJ1iilZGot8AKvVvub/Lz6jMNob3\nOACsCMkIDAFAy1vChFOynvtwwLSoawy0rdORiPosKjvXoZqqQKANuWV14tJ4s2DVhn/Sz8suADSj\nrsdGETzHNqmpseDPWQCe5zq8lE1aUJUxLT3G/LvFLKpNG9gD4zByhV9M6bLyHdnVpj8AACAASURB\nVCN49N3fxsL3fs/Yl6ZTDceh3aoXzawYajTHU//4S3DVRUZO/H+vxtF/cTH6Z01Gz9RxOPjVp+Ds\na69BfUJf5T35cBOrfvAwVLMMTnct3oTVNzzSts4qGXxmS5rVzRU237tyj8rsyj4uhd+bvfHZ16Rr\nHrCfCCuJ1q8+DrXyQT0zZDXgjs+h57Wfgph+TEdltJ64Da3ffhu8azPQOx4YbQQVALxjB+TKhajN\nPQckMvRc+g7U5v0RRn/yD+BVT+twMkFaR5bQ4JBqADKAmz7sUE4BOAjeHhlvG+qASP7vZNvDCbuQ\n6z26DtBRn1zCgzIArrKztRp0IjHQDE8IE7aqs18KvfWLBIA25wt/jbVtxEoU+pAN+6xDTlGynnbA\npbi1TElwOhbAj2orlhe2MooZGgA9u80tlN/25jKQkblOAqeiOKembBUmX03rGUctsOVnYI7DgDEH\nod5YgZPttwsJC8LDBZ1mqolE3E6zACgv3tiUBVggOpZDl9vGT+jWPllA+Z0p9aV5j+LrgoWTKye8\nhxEuMhqbBpP1PRdp7RhBY9Mu9M2YiF1LN2H7w6vRM3UA0y48BpmJkUqZwOFvmofD3zSv43JH1+2s\n7m/FWPuLJzD7TWfutr69UwYqWdq+mV3TgK4cmNIFrfuJyCdvgVo5H8gN0FQ6IHbz5x9F79u/PaYN\nXmvhr9D8zZf1/Qwg34bShJE30Lz7v1Cbe447JMZNwcBbPo3h730U+dKHAYkAKQSe6sjBNlNPKg1n\nAFCKoKitkxAbcEwmjFBiwgzrUMb2USkD30qTq1MjWVfqWCHCUPuAzaW4qDFz6ACbIo1R2IOIUqxT\npxAhBtxFNpJLW9Iu8YEBLyRgED3DM+K+/Fh0MHsibavoanRBZNtQbbsFQrwpg1vsyHK4KEPja0YS\nPuSZvo9NEgENZKWsAnTVulsW1qUHQ7EMETF4oSmEDTOWrtSPhXJs1xCwhnqVB2E6hitH5RZFJd7B\naIeB7OIq3R/e6a8IetstyKjwCeotMe3Fcsp6aBtj+7thxkDhthBQs2KI3joe+/DPsPH2xfq3oiYg\nagJnfvnNmDB3Rhvdq6Vv5gSThrZYt9Ez27MNzb6ZEzDljFnY+tDqyNY266vhqD85e4/K7Mq+LYyx\nyYn9XbrmAfuJyCd+6cJYRTK6A7x1Rdt7mRWad33TA952125dg8YDN0INbnPHWosfRGv5Yz7LRiIO\npb4ZJuGAYaKco5NlkcxWbZCtw27daj2Lepsy2diwBuxacevDTcjKZKRi0raLObk6lYztK9ttnbhz\nxe0WwAHTdjaMfiu5bFvI1vNfUZBjPnW/BwDMZLJthTarDOfFnmubWL8d68siImMPaz6KIKUwdcfg\nwm5LA9ZeVxj7z1DPzpnkTq+z4LqY7CBmCj0Ta+/Lc69fGWzujsQmGqnn6djLyPbYM6TVDQzGj2Hp\n4/KLDG2Z7Sz3SRpEegBeLoei28pjSOun700DVv93OKbtokFKm60s1QnlhVH1uE/d4wE21TOgVosA\nK9UEpp59JDbfswwb71wC1cihmjnkcBOtnaN45L03YCzTgSqpje/FrCtPTZ5TSuCgc+bsUbkAcMa/\nvBoHnXk4RE+GbFwPsoE6jn/PxZh+7pF7XGZX9nGxSWX21mcfky7Tur9IMmkAAFCbc0aaI0BjuKNq\nuNVC47Zr0bjtWvS/7gOoHX0mRn78r0BunQVikBOFd1I1xGwbIyTz0uwPaycky/4IvxULNg4z4UQp\nScdRJcu92HI008YhsGHhIgS56xyYK4fpceUEdVtm0zPCnJjQ45BPnk0FoIwOFDKcaSeVEIBo8Bh4\ntzshsDQI3cUTjU0uQnAZeZsH2+rhtWyjIbB2UqJorWv7I9FZpfoqFh6gRF/bZ2T/ZpSTB1SLzEOW\nrRMW0GmEdF+Fz6YYc9aer9AtTVjG4wCx/XAYvmxsoJ2qOwVs4cZmexMC87A5XADY8euTYaTqsdda\nExgNWgsRMlwfjtWw+LwyDmkQAqIn0w85DLdBQG3SOLz4B3+BZ758J9b+bCFEvQaWEuOPmYETP3oF\nHnnvDVAj5bSsre0jGFy2GROOmT6GTmk54W/+ABvuXIbGhp2Bvvp3a8Ndz2DuX5y/R+XWJ/Zh3pde\nj9GNg2huHca4OVOdKUNXunIgSnf07yeSnfAy5FtXltnSej9o2lHtb673AfVenynGob3yBK7DADUB\nACM//iT63/KP4NFhxBNMCBf9vSjZm9rJ0VxdACWa2QmTB0B75JNEnILTA1bA3sNmLk9NqgjSusZs\njbdHBUAKIAVBcF7j9ppiOzTLyiARbv/H/yplJ34zoZsyNSD05VknlVBsOCCnaSnyQPisLDDngFUr\nlkkmu5b09yWYM53aVEAp3e72QCMF9uLzsQ2kHScWGdm/7HMkxNu/YwNWzwSX22LPJ8ObcaxzCKJV\nIXSUDUMWglcdzsuPOyo+D+YSZLYR5aqclzzAtPXGJYQmBen7PcBmBmSuHQKrUqa2Y3d1HfG/7cSO\n69iG1ZZlAasFsHF7UjpFvwUKUKMSoreO8cdMw+CSDWAGJp16GE76+9egZ2Ifjvuby3HkNedjcMkG\n9M6ciPFHaTCq8vQCXjUlFn/uLsy++kwcNG82dj65AU984jfY8cR61PrrOPz1p+HYv7wAop52TqRM\noLGtUYhyoGXnUxuhWrLy3k6kb8b4boirrgAozw0HmnRB634i2UmvhFx6F3jjEqA1AmQ9gBDoecVH\nXBD5KiGRoX7OG9H67Xc86LWTtqMgC3Er9Y2Qqx6vKlX/IwRAApAM1OpAXmQ5ygDXShU4hI3xSMHE\nXQphRQXI7YGJjZ1aniXJ3GDuZG3KoNz9KeauAC4le1MFmzmrADY5YF+LsThjUBc8A2OPmwJuYaah\nuDXk8WABsPo+0fEuPRiNyyEiz3BF9cVSbAOgWVmdo75wA5NjgZ1Zge33pOd7OD4qaEvAbGNb8JgS\n2++pe0OAXGQkqycJbY6hwYh3AuNobLryFcObzYTjPrbbDev314bHTHxSEWedSuonM4DsQsv0NZNe\nkInyM/PjJQaxbBNZBMC5aI8bleP+Lo9ZIuNcyayjNQRjU2QK4YLBlqN/exA9C9XIsWvJJrAUoHqG\nHYs24b6rv4kzv/hmTDhmOnqnjUfvtBjoHfKKkzG4bDPUaBwiS+UKm+5ehq3zV2P6xcdg4x3LIA0j\nmw81sfL7CzC6fhde9IlXlzvZSNZXd+VG6aMVYcuDqzH9vCMr77Wy/vblWPqNh9DYPIzp5x+BY//8\n7C5Y7UpXAumC1v1EqNaDntd+CmrlfKg1C4FxU1E77g9AA5M7ur8+7/UACbTuuhYs9Y81t4yHfxXJ\nxQCNm4xK4Nk3DipncC7BOYPQSm0makym/DdCmslhB57sTGvmBVEEO8E9DqjGgLvttqstKwSwSSmz\neBzGSbVtipIaxKBM61EOiu/ExZZNgcr2dFca0FQBvooyCoDVMowiZbJcAiyZdlAhH7PAmyKgwKIb\nwGl3eqOHUxxfMUPnD1OQWtXcWWpuGZh6ZjrQTxIo821JATMN5GKW1ALXon2kLrvItHnbYvvsRRig\nv+LZWeBrQ10Vs1XZ+uz2tDexCM9nugARHoyvU6UIGIRyv6ecn3w5bc0Q+gaAkVwvaE1ZSoogCoQF\n+0VHxKCuptR9aByV5BCw4N3X48KfvdOFixrdNIinP3snNt21FMgE6hP6IKkBOdJyG0p2QShHWlh3\ny1MomsmoRo4Nty3ByIad6J85MdmcI/7oVDzzrYeQj+Tx7xEDD777Z5j3+Ssxbd7hFZ0BLP3GQ3j6\n8/dBjphEAs/uxNpfLcElP34L+qaPq7yvKweQRIvZA1O6jlj7kRAJZEfOQ/2CP0f99Nd1DFj1vYSe\nea+Dqk2DatSgGjVjg6onvQouFD0nXgAx7bBEgQIs+qGGW+CG1Ha1pXICwGqCk1s7unLInBCwWlBJ\nenKQOgaqdgoShr2BrkwhkZTAtCk12YZArwD64sxEid6QqWQDMDFP4/IsmNYOKnHg+EifqLyiUHRd\n2na0asURn7exPyMv+Ag8xWBbqbANgMwzSJkZB67QEU+YZyrAgYNSOZ4u+X9YZxBTstwHehwgiFVq\n+jAnE1A/BlrFPgkZVK1PGZgxC0DosFOxLWg1uI96lHQ5UgqThEAg5ZgYOkRZnaTsrI7wHuu45Z5h\nIZuXj4sagnQNfrXznQgcKPV1eZ6ZZzaWU2JxQROWj+jf8DowIAdbxv46bJN+j2XuY8a2k9T55pYh\nbL5XJxLIR5q4963/hXU3P4l8sInW9lEMbxhBc4RB4wegUjsYEoAqFyxbEne97lt45KM3Y+3Ni8GF\na+a+41xMO382UklA1GiOp/7tnsp25ENNPP0fHrAC0IkEBhtY9s2H23dCB6JaEmt+tRSPfPwuLP76\nAoxu6cyHoStd+X2TLmjtihNuDIMHbairwsSvMadn72o9GHjdB0C1Hgy86YOggUnaLpYEIGpA30So\nbduDmYtcYH/DCwHjpkAceTq41ge77a8TDtjbiixQhd4Ri2VBmI5EoNgfiyU9sXIhH7oFARp8VE/e\n1QAzYIWVD2MUr5aFBjh5VtouTZbVpv4QWMVMWVUHmnBY5uMZMpRYy1gHE4VAZsHixkQoMEHqVZAm\nt1xG+ZkQhUyeBbdh20yUB6XBMbukDbashINYoU9S4DM+Zm2MoctLPnOzWOoIwLZjtosLAg2YZdsx\noMWZbLB/DtIu2pTN5lXQubBgCsesXVToRVQN4XP216R18aYVxfKpst81YC8XaMeAHU+U1SB667u1\nqFO5wqMf/gVaO0ex9pdPIt/VAGTACDPAktHa0TDvZKGvihlm4e9r7WhgzY+ewMPv+wV+eea/Y9O9\nK91pUc9w6kdfDqqlp9XB5VuSxwFg17Ktyfu4pbDp3lWV93Ui+VATv3nt9Xjog7dh2bcfw6LP3o+b\nX/ptbF24/jmV25UXQooRbp77Z1+TLmjdj6T15N0Y+so7MPipqzD89b9C/syC3SsgMStp0KaBCbcy\nAwYF0DMV2ZzTAQDZQYdh/Lv/E2LGXChZg2xmkNsHEU/G5CZFpQi57EdryxCaS56G4j6Iw46FBR6s\nMs1amrirbsIbswHlrcyqkB7W+YmlYdQUaUa2AHaUCf0EtsDOsz/RhFxMWR71YQAqXAgsP9G7KALK\npAq1Yb46Yty8DpbplBa4lPoDUaEWWKjcpk/VDGecWrfcf7o9NpNU+AlYTBU++87bEYvvH828VocA\nq74/bKt+lrKVQbYswxgCdOGu9aBP2zVHwCwAP+76QCz7HP4rpWfUU+lfi3pXgTS34CnuHFT0dfy9\nCERT4D2R/jj4HjLs4Udaxlv6BVgywYarsx2Y96JyRmskD8ZXeRFSLgSQIzlW3bAQO55Yp80A2ozn\noohMlDz0w0WNA+e5wgPv/DFauxpQLQlWjPr4HmR9aau7/sMmVdbZO606kUD/IRPG1LmdPP3VBRhc\nuR35sDb7kqMS+VAL97/3FuxpmK+uvHCyP4BWInoNEX3KfK7YnXu7Nq37iTQfvRXNW77oHKnUxuUY\nveHj6HvdR1Az4HIsob5xEIccDfXsEtgZOXaG8iBQjQxCrlqE2pEnAwBaj9+L1uoVQMuirSKLZn74\nnYe/cciSOTAika9c4UBAMYg+K9YMaIIw1exvecK2bJnigDkLGU4yeYoq40Za5xWvhzujBJjYAx3n\nkMIF/T2gVYlJ0zM/sf7MBM4BEpaaDgFneTvWgYNCvVYPmVuQw/oyYdthwmZFZWp7TJvcoDq5Q8yu\nef2LOvrwR1G7zSKkZCtaAWhkoKduW7mvy8K+XBusPyxHUbB7YBYeYUi1aNxDG7M4FtaeZ8TPLpxU\nfB9YsREkbNntwk65GLuhA+QY2dRSk1DMuBf/5ZLu1WXb9zi0/7ULLEqMcYv0gutZ9yNRkNwjAUCL\nC1X7ngsEFxlwKQ0Yi9rRlNj64CpMO/dIiN4aZMHxKhZCNq6u+ypXmHrWHGy+b1VZn4SdMUvGHVf9\nF0Y2DENkhENfeSzmvPUMLP/m/Girn3ozZOP78fOzvggi4NDL5+LE956H3qkDAICBQydi6mmHYMuC\ntaVEAke//Yw2uo8tq3+xBKpRBsSNzSMYXrML4w5P2+h2pSvPhxDRPwGYB+A75tBfE9G5zPzBTu7v\nMq37gTAzWndeWw53lTfQvOPrnZczOozaiZeAa/3geq8+KDJThwdgmp1iqF0+wcDoA7cCrbGTEwDk\nbU5DFMpcPWEmth7t3/ZfJUl75tsPQ3vpKwG2LJ213VP6WhWwsGUoYG0eK5jG3OSoLzjyRH1lvtts\nTCkgn65dXyvzDDKvx3oyR+WX2awiU+z7zyYD0EyjCbqPMrNmt91TqVBjYFkErIjKckxuIYEC2wxV\nib5KLSDiqA1kQi6FADbdj55JMAyqSvdTBByVqGDNCQThmHdtfyqitmk2VTgmPcWEJldeSbHPKx5j\n7cwS7G6GYzsNM22fRZjsQqm4zy04bs+8UPA8jWmGY30RbO1b0XVLichuVpuQaB3GHT3dLRyYAWnG\nhnKAtTA2hQDVMmQDveg7eBKmXXoCcpXp+8wilc3zrk/qw2FXnKxjurZp1sSTZuLUj70Cp/zdKzD1\n7KOw6d5VkKPSJ9twpijpQkbX7wIUQ7UU1v5qCTb+92rMfec5qE/sBQShZ/oAlMyw5cG1kMMt5EMt\nrPrRItz5+u9HYPqsf3sVpp09C6InQ21cHbXxPTjlQ5dg2tmz2jyTsUVUmCswM0RPFwLsc2Lfub31\n+d3LqwC8lJm/zsxfB/ByANVhOQrSZVr3B2k1wMM7kqfUljUdFdF88gEMX/8ZQ8MJIJfIjj8XtQlT\n0FhwO7jRiifLkRY46/HfZZrJsAwF2yD6aSI2uh5AzMBYL3O2cTDZgSelKPbYt+VIAZAy2/m6wpJ9\noZ0YKfg7ONdO0luccTpPd50BWZ39POir4viqGaQjSkJFwwknOE6e9VRSBHFFiyxb2JayHswElmwY\nX1tF8Yeu2KoYDNrnL1XwQB2I9BUzNJglojL7mtSRDPtqResYRjWQkpw3vm5LGXiUHf70WBUidc4D\n6BCkq8pQVmnRQFtfl97OJ//ecNH7vv0oisdlaGZQfvZujKgsOOb1T4eyKgDvArsah9Bi52SZ0p0Z\nGFq7q/AcAR/Rtjy2Jp58KCafdCi2ProOOx7fgMFnnzKMuQ075uPSbnl4LeqT+nDON67G4x+7Cdse\nW196NKKvhrnvOA8zLjoaw2t3YtMHbkqwktXPtMhOq6bEzqc24dQPX4qj334WVEti/t/chPW3Li/d\nO7ppCM/etARHXHkCAKBnUh/O/c+rMLppCM3tIxg/e4oG3M9R5rzhRDzxmftjtlkQJhw1Bf0zu+G0\nuvKCyGQAW83f1XYzCemC1v1B6j1AzwDQGCydoolj59NWQzsxfN2/AnkzOp4vXogJ7/wUGo/fByS2\n14Zv/Cp6jj8bJAR6XnQhRjauBjebUJIgMv0j77M2adDmJj8VE3bhlixTD2qzj0a++hmoxkgRZgQM\nICeBiBVvf5oO3u6vs5OwB8PauchsFYf3Gv2rA6ynK6qKZZm6p8RCd1B+OLE6NtMA1rjOGLyUt+TL\n17PKoik77ss2K5CgSs3O+2elwYUH6H7iL3dqboPipwp2ouPNKmX7gAAlDNhPgWzfV3GMXH2NXXyU\n4pgWRN+TYrvbAVebMlhACAUSYRY10yeyTZgnV3Tc9+lxw7DgNRVGLM5WZRca/v1KZf7aHSBt07ZW\nmZi0djYSzmrV/Te0fAu2P7E5AJbxKjc0yWhsHsLD7/s5tjy0Vm+zv+MCjKzZinW3PK13XnKgb9oU\n5KMKzIyhldsg6llyK1301krHq54wCYGhldtRn9SHx/7pbqy99ZmoZW5MSca2R9c70Gqlb/q4vRri\n6ui3nIJN9z2LjfetASuGqAnU+us457OX77U6uvK7Efvbvo/LPwFYQES3Q78SFwH4205v7oLW/UCI\nBHrOewOa93wn3qKv9aL3oreNeX9r0X0Vs6NC66kHICbOgNy1q3y6MQy5YRVqhxyJvnkvQ2Phb9Fa\n8QzADJUDIInStngRPBnDNUvGqFwjnOayZwzyqvk0ouWW6/KrzkX/JprnJmfjOGSUsIwwkZnyi7NT\ngV0pnUwAP8CAsxLILsY8DVm79lIGA5798jFQuQLMFCUEP1x5PEyGoK9rvyCwehaZSGs7q5lQz97p\na9nErmWzvZ1amJQhQ/E5VrUv1KOopz0XA8MY0KbvCdoa2j5SmOI31lWztMKE1tK2v0JwwRwk9Wp6\nZz5hWHDlspWl2lkErOX2RneQZxBVOEFycB5VQNSLUp0saSjZxjIw1tIabJnsY+my9L2m3Jyx7jfL\nXBzYZV97ENMvmoOBOTOxa+kWsJQYfGYbHvngzdh8/2rM/fOzoZplwEo1gSNefwqOfee5eOQjt2DL\nfatAGWHckVOx46nN4GaMIlSu0D9rIu54w3VobB0JdOPotwUAxs3uPCzhnoqoZzjvS6/Ctsc3YuvC\nDeifOQ4HXzz7OWXo6soLJ/uix38ozPw9IroDwNnm0AeYueNQFl3Qup9Ifd5rARCa9/4AaAyDBiah\nfsnbUTu+Oue12rkNjQW3o/X0/PT2vpLg5iiSkeStWEaq3oO+816D1uov+KxXXEMIDNKTpPkxV4DK\nRYheDUOn92mJJEgEUyD7tJQVPGuwvcoVk7/Zhi+mXmTP1pGZaGLGLQ0qkqYB4XdnFxffr5QHg6lU\nqsUyyejGAYgIzxfDRHlHqCoIUQQtaRY5zo6lASszkNWCi4MOcTpUxWQFDDNaBFvkwnC5RUkCuKUk\ntLG0Y2+sHPflrW/9tzMriBjH8qLEg/gCYDWXFxO4lpwbnYggFFSabfQ2wBb0hlv76fSsoY67J3YB\nUBzXwXglBH8UdHT9WX7/zFq1raTuUWkH+1hnqxIjircqR3NsuG0ZkMVsqhzNsfL6xzDnLS/CzEuP\nxoY7lkM1/O+h6Mlw1FvPQM+kPsz799e4441tI7jtVddC5U1Xj+jNMP2cWdj++EbkQ03E8V79M2UG\nRI0w+6oTx2rQXpMpJ8/AlJPH3nnrSleeDyGi45n5KSKynoXWdvFQIjqUmTsKSNwFrfuJEBF6Xvxa\n1OddpR2yar1tJ+vWikXYde0/aM8H2QSJBAyo9aB+/NmgidMwvH5VydGKevuRHTzbfc+fXV6RptVI\n5RYrQeV+m5Ktx7wFiAww16CkgtsmtudSBLHbxkwd9xKaLkQaO9CmQVo6vWY8SYdtteDTgl0NTBOA\nJqiLSLOKLhtYCsuF9dltIlE+F6/E0179qf4YS4oZySwbFgIJKmAcDgLod1wPoxQvt9P74qxnbbIx\npe6tFHJmBtZmEoAL4k/WbrYiixUr3yXFsWPLSjHmoX1pscxwG19fy8n33TPE5eP235TtbrG+4Cx0\nWDqGi8CRuCf8HmHb6BoPaov3emevMuvf0TOlcLEUl82pSAKK8cS/3IV5n7sCT//HvVh5/aOQw01M\nOe0QnPzBl2CgEK5KNnJsuGcVDnn58dj2yFrsWrYFWU+GI646ESe+7wI8+g93RdEDIh1MS87811do\nZ62udGU3ZB9mWt8L4B0APp04xwBe0kkhXdC6nwkRAfW+ttewUhj8wWeApgWhpCchB1wJqPeg57SL\nUZs1F9khR6G56H7kK58Emk1tQ0uE8Vf/DShgYdXwUJLLcxNjxRysZMx0an4zapUBJKH9namJ2Xkf\nR+DS5v6mELx4lgOcqqesNxCHL4rPlY+xEo6JJMFWRZMtKwR8XhciQt4knYUpnGg5/CMAOsJnqmIb\niinoq7KYa1M6qzITmWLELZBIM4TBdcq3zYKrKikCOH/CLxr0dwCibF8Zfk+ZXoTseFkP3xk6EgCZ\nbfyYSbWxaPUCKTDdCPRTkp0Nd1qK4cF8/e3Ao783/u71DkEvIWT6HRMKeJ1Lo52i6+J7EpdH9ZNb\nmOnr2ZgDhC+6ff4ExRbghu9ceeFnTUni6Asx81x6121JBEAQRCYw/riZ2L6w7HhVJczAxntWYeeS\nLTjhPRfghPdcUHnt8LpduOONP0RrsOnCbYEISggs+8GTmHjiTEw89iBkfeVQWxZwiP46Jh03vTPl\nutKV/UCY+R3mz1cw82h4jojag5ZAuqD1ABS1ZS14dKhwVANX6h+P+snnoue0C5EdcYJmcLIME/7k\nw8hXLEL+zBOg8ZPRc8r5EP3jILdvxeDPvoPmogXglh6HlmEEENnE6e3exORcEQ4qmqwdYxSDJu1w\nY0CZu5ldLNIA2/p7KyfkglpO1xQUD/QzjJiSNQdCNVANKwuAY5ERtEApARZL98KWG7JrZdDpGWKv\nuw5xxWb+99veIbhzWb9Yg+MqR6SISXOA1oO+khc5l6MChIx4qIeKnKcMWFEENs/VMc3BY6lyTgi3\n7uN/PTC1jlRxClv9PCMd7CJIxeUDAjLniIUdW0IQZhdG3r65Cqh6neP22evCrXv3XEz5NgpEOxOC\ncMFSXKxFDmtGKyI4sMqIFxthf8cmJ8X2kBk/Vex6caEUR11gBupTBnDa378MK3/4BEY2DGHrwk2A\nLFg2CcK4WZOwa8X2+A2zfakY97ztBlz6ozdj3BHVtqYLPnoHRjcPx1v/DCjDrC746B249Lo/KoHW\n4jvUP3PvOVt15UCRFy4hwF6U/wZQDD6cOpaULmg9ECWrJXNrAwSaPAO951+JwR99Da1l/wiIDL2n\nnYvxV74d9TknoT7nJHe1Gh3Gts98CGpwZzyTu+1SzzCyyMBKAUpB1Hbf/TEFWC2YCRlMeziyLy2A\n3/KkGW+tFgGVlRBI6+3+DKyoNDnba3VZhR8Y4ykfs8IBUEx4UmsWq9jGAJgHesvA095AIpBQAYMr\nXH9Ydk7mNkau718LujUQ4wjA2DYRMaSM9Qq3tKW0zDgjlWBAh0Fjnb6XhvmVOQAAIABJREFUWDsV\nkdGxxORq0MiKHQOqr2vP6hYBawjMVGHs+LiuSsfgdXX7f9nZ4JbHpG17FThPaAdvzmEWFoF9s2Z/\n/SSVZf69qXLWUyYJh7ex9de5v4UebHZMMOufAyFsT1sGv6AzAyJajFqgH2QSi/pDO5aVFzx+YWMX\nWCFgTbK80eKqQLOCoFrA/PfepDNTST8mbH9qpRn1GZOglu+EoPg3yNkIN3Ms/eYCnPb/LkVKWDE2\n/nZVxe+nb8CGe1bh4h+8AQ++72Zsf2xD1AdZfx3H/o8zSlm3fteSj+YQdQGRdWO17kuyr4JWIjoY\nwGEA+onodPgfi4kABjotpwtaD0DJph4MMXUG1KZn4+V/vRc9L7oY2//jI+CRIYuC0Hj4bjQevR/j\nrrwG/Wdf7EwCRh+8C2p0JAKszAQyW8QcphJVEpaCla2QwvMvYDm8TigBs2qYSj9JFssgHbZTMGRL\nx+q0lwRXFliluG6WepbUEz/psETmvIxCMHmGLsVKAaYs02ROgKtKgFtoexHIKaWAKNySBxC+TRSk\nsq0ATsHqvWhj6YPJl8tI9b8VKcPrfVSAuF6452gD9+tyudSXLq5p0WnOxMZtxx7adtjvRcDngKVr\nYxGwhuLDR5VZaMJYznjleov959O9Wkc3CyX1IkBBA74y68iWrTRb9elnw66tth+YGWSAqntdE0Cb\niKHYHi0nCCntjkBAKYUwdqoet7Bkv7su6q8SI8v2PwTN9vXWBWSLE1mvwsWF7p9N968BmCBN3OSQ\npQYAzhk7ntyE5yJKMmRDYvwRk3DpdW/A5vlr8ei/3IOdT29B79R+HPeOMzHnTSc/pzqei6z/77V4\n4EP3YNeKnRA9Ase88Tic8aFzkPV2owl05XmVywG8HcAsAP8aHN8FoKNsWEAXtB6wMuHq92Pnf34E\nnDd14nAC6sedAZUD3GqWZltuNTH4468jX7kEE9+gTVNaK5dFzllu2xZpJiicuGKTgDLoi+oOty0d\nOxQzg2EZdiKSOcEG5ifSk2cR3YRb2/5gEeBQwWu5CODCbX9bhGdCmQFik+aSUcYRvuISC+ulDLSK\n4CoGL+G1BB23K+2s45nXCq1Kfe2Px9vBFpyUHYWK94ZllFnu+LnG28qpbeayPacty/4bMq3JNiZs\ne8u62vKLYC4sV5i4s7a/BVLRK0LdvM4FW95CG235YZvc+1ZK1VqFlv1YLfZj2iQhBMdkgGt1P6WA\nK7uXwzwDF/fX6EmpsVfsg+D5CoCzGtSoAmUCVM8gB4sOoKlyYNB4eqEFAFQjTDqh2taUBOHgi4/E\n+rtWgPN0H2f1DIdceqT7Pu2sQ/GS695QWebvUrYt2oLb//Rm5yQmRyWW/uBpjG5r4MLPdeQH05UX\nUqLF9b4lzHwtgGuJ6HXMfMOeltMFrQeoZNMPw+T3fxmtxQ9D7dqG2uzjUTv4SOz8/heAVjN9U6uF\n0YfuxrjLrkQ2dQZqB89Co14HWi1Nyra0zSQRDGDr9OVKg06AAJGBBiYD27YhHQ4qAVwBl4ULsOUZ\nMCHZ6eYAgwjK4+ifCNAVbf6iFhAcc2jvY7fVm5l629Bu0AA5BDi2LplbxjqY4F1zvR42JFUSGRfi\n2bYDcGUpT/JlcGmPmTbbGJ3FOl1KW3b9FV6XBm0euJYWGE4EmFXJmcilJrVtJcvcptoYSxFoVh1L\ni2YyY+Ae270qMz7LrGg7YBW/KzYtavq6tNjFRnlRoQ0CFFPinA0dN/ZCJFWftq0tmqGY+5ndTkS1\n6OvqU/owMHsGtsxfC0CAc0Dt0gCsc3tiXR6zb499LqKnhmPefnrbO0//2CW4400/RHP7KPLhOFNg\n1l/DnD86CZPbAN8XUh7/wiOQjZiRlqMSq29egZFNw+if3vEubVe6skfCzDcQ0asAnASgLzj+953c\n3wWtB7BQrY6eE18cHavNmoPGo/dVA9eshtaqZcimzkD/iy/B8G0/hWq2DGD1TJgFh51MJOEEyspn\n0wID2YxDoaIUstUOHQ7gqCCcFfnoAjYea2jLB2JAFbaXI8ZyT1e1Gih5RzQTJshyTEkA6dumJOAz\nLRUuJiDlvBbqnhJW+jYli/eWWUpbVWVZyWdg+5XNVr8ISg4BTzUoTQtVALuiCMecR+A31NWaHpho\nBEr58Zcy2yiajdhnVNVP9ln6rXfPzMbPWB+LQfjujTVnuhIAfqUowOfFxBXBWCs9P/188kKIt/SC\nsjh22gFkc4kLX1VmcAFjqxwUk1pTzP2f56Dv0El45EO/KZ9Ems2O2pIRkNt+B0JTGjBj/JET8OLP\nvAKyoXD/e27Btic2YuIxU3HC/zorim/aN30cXnbTH2PdHSswuGI7qCYwvGYnAODwVx2Lg844pLI/\nXmjZsWS7zXgcSdYjMLRmsAtaf89Fz2X7JtNqhYi+BG3DeimArwJ4PYAHOr2/C1q7EknfmRdj+Nc/\nBrdaCCcrKwSGmDgFACAmTMKUd/0dNn7qYwCaKLNxMUDzk6WZOBjgPCvhj9DHQa3fgN7jToTcsrm9\n84OrE1HMV7dlnwBMsRd50e4u3raO77HX+/MxEPBAKLQVtQBFFBhXZgMkyU7gFvCk2C44ZyRbrqs/\nuC7VL0AIWGPdbaSBIniUQSrYuJ5qQM/GSSoF2NI6tgOi4b3twC47pjkVn7PIyLPS5hqhGYtSMcgL\nFxMhyBFCldjw4vWuVor/Dp/bWIuvKkCoM38hGltSWp40aCPKcYYVB0BaU91mnIboNqClC/oV32nb\n7rR9r9c3/Df9HIXTx6pWLHPNTcvBrXJ2AVePed8pcIaz5+oTeyGZoAZbxlGrPJ6G141gdPMI7n3X\nTZANCSjG4Iod2HD3apz3pVdi5vmHu6tFPcNhLz26pMvvuxx02nTsWLItcFbTopoKE+ZMfIG06soB\nJucx86lE9Cgzf4yIPg3gV53e3HUb7Eokon8AU979j6gfc5JnKAE9iwgBMWEy6nOOc9c3Vq0Cmi2k\nWT8CiQzo7TceySbnuiKoXIBbFrAWgFQIBKTE6OKl4EARP2EmwF8eOyKxtSWFrScWC1yZhcvCpB2t\nys5ZhTsDVi1Wx29Le6bUg5kMSvq6lBImtFIWmTQUEwSEemiGzX+XudZZ5iJgGcsiZahT2Ac2vJFv\nk1ICMoh7avuWpf7ENpFhP+ksTSFgtXWMJaEO8fGqOwhggpIZlMy0DXPkqJUCSfq7B2oUfbwZgl5k\nSJkFz1Lfa/s4NEPweod9lh5vYXvb9wVQBeSlIrONHwLWYpv12JdK65xLYZ6NcDsAKTOP8t/+Wt0X\n/r6wL1LvS9VuhWWF7ce/HyIah248SoHB5VsxtGZHuRwmKLb36b+Vssf156B5R+DS696I6S+eFfRV\nQYiw4GMmKYD90WCdMWvBx+5KPod9TU7+X6eVHK6y/hqOefPx6J3ccajMrryA4n8n986nEyGilxPR\n00S0lIj+NnH+D4noUSJ6hIjmE1F1oGPA5jYeJqJDAbQAdLw90RHTSkTnA/g7ALPNPQSAmfmoTivq\nyu+XcN7C8N03Y+T+OwEAvSedCVYMtX0Leo49GZOueT+azzyJXd/7AlRjFFAKtUNnY9KfvMcBkNba\nNdjx3a9XEEKGVTz4cGRTD8HwgvmAsTkEUHAcqdDRzhuNJgChQ/WUAE1QuZn4yi+j3TLV16ogDJAN\n6aSktz8kImOPqlx1bvLM7SsTxoKNO0CVPNw9Q6VFpEMKuTZXM1Iw4F+bO4RsbnhtvFXqgcnY/a2v\niyMS+FS3IeIqFxcx8rtnYOhEKR16qb25QKCvrq1Qf6dgOQVm9bOSMjR3YVe+tY+2EQ5sKK9w69/H\nrK3QuQB4Kx3gTEiwYmgzv5jg5Bgqtqm8a2D7i50uMZveLimEtdWN22NZUvv+AaiwHQ5teENdwogF\nZGuK9WaAMjJmNu5QVI4/rgcREQGCcNRbTsX42ZNxwdevxKLPPYAnvzC/vHPDjOG1g8lWD67cDtmQ\nEeBrDTYxtHoXBg4dj55J+0ZmqwlHTsLlP3oNHvr4fdj08Eb0TOzBCX9+Ck74s1NeaNW60okEhMXv\nSogoA/B5AC+FTr36IBHdyMyLgst+A+BGZmYiOhXAdQCOryjy50Q0GcAnATwM/Rp/tVN9OjUP+BqA\n9wB4CMCY2Z+78vstzIxtX/oEWquWAa0mmIF83Tp3fnThgxi69ac46H3/gIP+3xchN68H9fYhmzQ1\nKmfozlsBlU5VCADo6UW+cxSNNQuNHZUBW9QOkXjgoLfCPfOocnKzvHVYAoK/lWVIU6CvCFj9ZKmZ\ny0LYHdKsqLaPTW3fIpgxyX2p3vIttzN9rD3IKsa4TDm2WDDlWUPd7rFwpJIZrJOUd4yhpA2cZjm5\nuIYwpgHtwVhKfL9ZQBQCRSpdqwJW2h9PbRzZGLExW91OlAoZWAT/hosBfUynd7ULniKSj53q2Nqb\nBuxmEZCG+klrklDq/1AvLhwrSxVw1+Mni+psHzosYKMNw4locRSbGKTGQFhO2J4y+GS/3R/iVsWg\nmgDnypRXzeTaxYUQhG2LNmP6i2dh66MbsGPx1qjn7PV5k0GZDdMV15v11iDqPkTYo5+8H4u/8Rio\nLqBaCkf+4Vyc9fELIeq//2GjppxwEC777qteaDW6su/IPABLmXk5ABDR9wH8IQAHWpk5XO2NQ4le\n8sLMHzd/3kBEPwfQx8w7qq4vSqegdQczd2xzYIWIXg7gs9Cxeb7KzJ8onCdz/pUAhgG8nZkfNufe\nA+B/QDf+MQDXFFN/dWXPpLV0EfLVyx1gLYXQkBJyyybs/PG3Mfkt/xO1GYcmy5FbNvuhmWDeaofO\nRmP5Cjuzw02ybbdFrXOM/vF3E74FjoIMzR8zQ8wAtbGjKzuX+L9tNiILBm17GCFg7QyEeuaqcJb8\nlifZCsYsc0+FzPZ2zI6GYDDpSBQCAAWwS51aAaaZIpY5jOeaBiuJIoLjyi1QqoGWUjazl9XLtyk+\nZstPD9C0aYPXoxOmOBynIStrn7WSgMhsMoQymGFPFZek1QoYx45MK4wWhedaxfrqsViM8WtZ1lRC\nAJh3gkttKfel1Z3j4P6ABqLOnrZ4f5EN5vIyQAJ5S8eVLdqGl3XQ/6ocWPrNhdj80AasuWU5oDRM\nFqYZTASZm8VZS8GBXWOKlPXVcNTVJ7soI0v+63EsvvZxbffa0L9tK29cgvqEHpz+ofPa6NSVrjxX\n6XxLfzdkGhHND75/hZm/Enw/DMDq4PsaALEHNwAiugrAPwGYAaCjVREzN4joIiJ6PzO/tJN7OrVp\nvZ2IPklE5xLRGfbT7oaAUn4FgBMBvJmITixc9goAc83nHQC+aO49DMBfAziLmU+GBr1v6lDXrowh\nzRVLdCzWMWT0oXvbnu894WRQvccfcNuEwMClL0fr2fUBYLUS2y0m7eACW0OlyADWaltNe6/3BLdA\n1k/8xZii7cqx4mOzVjNVxe/tbPjAnhsrMZRG7zgeLJeu6TxGHyU+mlFWsmCHyNZ5rQDQ7fmEg5bX\nKXhO7J9T/CzK7ZRSb3Mr5T9lwAnjcBT0TzJRQvmHPBwHVjcpCXkujG1vzI5LKSBlBilrGGuM+PLL\nujjHM8OSKlkrgGzbRwSGQJ4T8pxMf2iw2mplsCYancSOtXahcburWUgP4MOf//IiUCmdJCJvZZB5\nTdv3RmYTRVMcKpWjlEAu7cfabbdtUlKnsG0GckKqrIMJXLdl15phrP7VcrDUiwXFBJkDEOQXply4\nTwiI3gyHv3ouTvk/57gzT315oYtzakWOSiz97iIo+Tveu+1KV567bGbms4LPV8a+pSzM/GNmPh7A\nlQA+XjxPRC8hosVENEhE3yaiUwxY/gQM9utEOmVaLao+K9QRQLtoxGNSyub7t1jTIfcR0WQisga5\nNeh0Xy3o8AhrO9S1K2NINnEyUO8Bmo2217HMoYaHIAbSObIHzrsEQ3fcArlzB5CbH/F6DwbOuxiT\nX3c1hu69r7rsIFZkmDGnCF6IoKPl2K1SViXPYtkKM1QBfvbplCW11xh2J9jSrQYN/lqrBysB5gwE\nmfR+jsEgRWppwKrBVN7SEQZIhPA2PYGrIORR6JGeBoG2/sxYddgJtrx29SYTFNltxnayvswQsNpt\nYilVKeRSCG6tzqm6QxtjuNizth/GBnIhkCoDTPuMlfG8L5oDpMInlT3Sq3QpM4fla8hh5pTzmQfU\nVW0LmVVrI6ojPaigjLg9/m8yIcnMM4gYdRMyTiUWMcFuQlmvdpneAtMbW0tiNyLV1rAndDivQn8x\nIGpIdpfVSZaiSVgWV6d/jeziw/sl8JIbXo/Jx02Ljje2pTf8VFNCNSVEf9e/uSvPn7wAIa+eBXB4\n8H2WOZYUZr6LiI4iomnMvDk49WlocvJeaMLyXgB/y8z/sTvKjAlaiUgA+CIzX7c7BaMzSjl1zWHM\nPJ+IPgVgFbSn2S3MfEuFfu+A7ggcccQRu6nigSm1I+aCJXR2KQp+/Yvbp0RAVm2jJfr7Mf0Df49d\nv/4lRhc+BNHfj3GXXI7+s88FAAycez4Gb/u1B7QIJ7zQg5kCwNWe1ZS5gKgpd8w7RgVsCQXfozL0\nZJUOVVUGhRbwFMGK/+7v8QyUnaQDwOnKDxlmYWKxwgGGEFwpJYAgBaat34NEyzr6MExOFxNQPxl3\n1OkDc28VfR2z1MUt45DJi4B7xO5lUCY2rX+2sc1qChgqZWLs7pb5BCPuw3jhY/Wz7dE6+agAVeDZ\ni3fIsmXYBUMUUkpZEKxFULrssNy4nVXPI1zU2fvYjIHw/hRQ9tBPSgI4i86zSXlsI2lowGp1aac8\nBdd1JpphZggq9m8ayComHa0iuCb+myBb0O9JFBfa/BHEZi3r3l5vBvDopx7ARf/5Sndsx9LtoLoA\nErh13GETUOuvty2zK115LsJ4QUDrgwDmEtEcaLD6JgBXhxcQ0TEAlhlHrDMA9ALYUiiHmfkO8/dP\niOjZ3QWsQAeglZkVEb0f2hvsdyJENAWahZ0DYDuA64noj5n52wn9vgLgKwBw1llndf7reYDKjp9e\nh6HbbwYoA1RmmAq9BxtNIkToOe5UiN72YVDE+AmYdOUbMenKN5bOTbriSjSXLUNrzSqwUkBLp1os\nOtAAltkLbCIFlycwu+Waa3s2nWUH8UxXAVh9IQQWwWRs6o0LCDP3eDCkpM5YBWNLxwb0l+wFlQ67\nQ1DObi5mQW08UW/DGU+gZWZLBrFPbXn2Gl12lmTAIr1C4jaoS3NOBSBfuM8ypEWQFp5Ls6chqCL3\nnO0xqThi+rwJRMw+kqcm0+1yfdUuFa69rhMwxlG5SgKgDIKkjnWqTPa3YE0nJRVAJEOx2egvjhF2\nsH1MHey9ZdMIisaVX2QVGXo291u9i8+JHHANQ6m1A/4pUFxmp6tZWRU8y6rFqh0LITsb1h3qpJic\ni7C1dVWKMGPeYdj4wDpApseO7Z6U7gCw7o7VsHbKo1tGcMtrf4LWrpZ/AhYb99Vw5sfaRfnpSlf2\nTWHmnIjeBeBmaFPNrzPzE0T0F+b8lwC8DsDbzM74CIA3Mpfe/slE9Nrgey38zsw/6kSfTs0Dfk1E\n7wPwAwBDQSVb29zTCaVcdc1lAJ5h5k0AQEQ/AnAegBJo7UrnMrroUQzfeSuQF/J0905ENnsO8iWL\n9I8wEcTkaeg/7w+Qb9mM2kHTwEphdNETGFm4UDOq552P+sEHt61P9PRixt/8XzSXLUVz9SoM3v8A\nRp9eHoDEAABGdn+s7ViFAgjBdiXcNS5sThvP6KSzEeDixWoAVrzfBlv34COc7DW7G18PUm4S0+yp\nAV3IIHPlnHFCYJ6Kl1oGCibupFJgzkqAsaqtqeN+OzVdZ/jrYhlw2zdxiKLYfELKLNC7zJ4W60rZ\n/MbObp5Fj9hAA8babSu3i5IQ90MHLFtg9uASMjCgqBaDRKVgoitVsJ4auGaEqN+0vqmaK8CbOzcW\ny2Kdwey9DDAZ0/KyA1V5YSgiuNvu2pQUmVCVWEC4cG+E8ngkNiHP/ILNOkWWtTJHzOO0IbNkMJ62\nPLJJ7yqV3gVdYm1iL1ojOXhUjbnoW/a9p6Aa0gNpOx4zwsyLZuPJbyzC2t+uw3FvOxHjZ01oX1hX\nurKH8gIwrWDmXwL4ZeHYl4K//xnAP49RzJ0Argi+3xV8ZwB7FbRaGu0vg2MMoF2c1jEpZQA3AniX\nsXd9MXSUgnVEtArAOUQ0AI3a/wDAfHTlOcnwb+8AJ+xYOc8x8fI/hLjyTWiuWYmR+Q9j5PHH0fzG\n18B5jr7jT4BSQHPJYnCjAWQZdv36Vkx569sw/txqb1m5cyeGHl4ISImBF70Iu+5fWGZZo2D4BYZR\naWNWD1iLbGCRaQpAQTTR2brMP461jEGWNwnw4KDosOPYyggRZ66usvOLiJyr9HZ52t60ypnIgg1X\nN5OPWVsBVm17Q8LYAsYU66ftIYs/iDZDVNgfcP3hgsIXHJJSmaUAoH2mKv13GMS/AJtgt/N1H4b2\nsrbe6h/z0LTCH/OdkwL4ml2P2cYyCy3Asl0aWl8+oMNY2eeaspdmtlv+tnH6ITJT27TIrBCs4XQf\npSIWlPs0WIC0BBjCXxHVxR6EIq2HCsaCit4hdrF3dT/YdMr2/QvfJQ/mWaX7qDPR9+XDufuuVLiD\nomVkm0TWU4NSLaOLVsK+C6yAJ774CES9hkf//WFw00dCccA6B9bcuhKyxVh397NY/K1FuOy7r8T0\nM2buoe67JypXePxLT+DJa59Ca6iFwy87HGf93zMw7pC0L8LzIf639HcPqLqybwgzX7M3yukItDLz\nnN0tuENK+ZfQ4a6WQoe8usacu5+IfggdeDYHsADGBKArey6qyvGKCGg1UT/6WIw8sQijixYBeQ42\ntqiji54AmH3qPynBUmLbt/8LA6efDtHXXypy8L4HsPlr1zoaZMv3rkt4DftQUyEo8sDWIokUw2SB\nLYxTSXAmU7AmpfGuMhmbIOEmdw72zD0L5xlGlaeSIGjWrcQGoriNGQMhz7DG59z9FdukrvAQHKrg\nRAJMwQDUcLvXM6bW+S2wIUUMmq0uqkQHmnIdoEs8G0Y51FFpYZKWMNVpsV77rHS61gxSwjmJhUBP\nAzEPatNb1Olj/mPBl65btFV77IlaKQqC54c6xKYRIQtPZN8F2+f6WYzVr8ycCAtWLZYlVoF9bzVb\nTgBxgrk0YaPC8RC8e9KtBOJFSvkd8HFgy4uETsdO4jpCwPIH55X2/PdjR7/Flq2WOeGRTz4EURdQ\nozLZNwxAtfSqQbUUVEvh3vffjdf8+vVt9d1bcue77sLKm1dBjmhAveyGZVhz2xq8/p7Xonfy85v0\nYGTLKO7+wP145herwYpx+CWH4KJPn4MJs8Y/r/UesJJYfB9o0mlGrLeljjPzt9rd1wGlzIjZ2/C6\njwL4aCf6daUzGTjzHDSXLS5HDVAK9aPmAgAGf3MruFkIhyVlGnQIgdGnnsbAi14UX75rFzZ/7Vpw\ny5shVE0mLl+8A2WFScUynSlGUYWTujvqUrkKUXYesg5TzGyYKXIpVdkpqu08UWJ4PbWZmkxTWa40\nEOEAiHB0Lg0OwvoM41QCh0ZHy8JBBWdjYBxvXQeLA+eQVAae9j7vVGbKCEBLWxBhuiq0S4xOjCmp\na0LdQ3vg1DUCUrIbA9rONW67/+6PywqnHx29c2xJm6VUL7rCetxzCrfGw3tN35fYXrtAYW1XS45B\nHauf9fOXKojU0MYxz2cGi1aCLuVtqYfCopg60smNOxC8mQrQP7Mfza0NcO638UNQqxejbcoVZGxe\ny/WHZdg+IQhtu8wESIY0ofvsojRqRuJd2Ll8B5o7m+iZ2IPnU3au2ImVN60ywNuoIxmtwRae/s5i\nnPqXz1+mKyUVfvKqm7BzxS6oXPfe6jvW4YaX/hJvmX8V6uO6Tml7X56XOK37lHQam+Ps4HMhdErX\n1zxPOnXleZL+s89Fz+w5oF6z+hYCqPdg0puvgejRx9TISJsSykK18rpn+OGFSFNT5VnFMatRHnhX\nuvuXld6+tB8lqc12s4nLmWfRR8ka/Na8vl9KASV1Pnatg/lb+XiUZX2K9fm/PRj0eviYm16/VD+k\n6vP2samaNcBhSVAyc/FHQ8ZQKSDPM5SBqdVPjPkjqMF+4NHfkWe/0d3E6mQTU7MY3qnIbMb9URbr\nne9ZybCfy+3TkRQskwxjH62zadnYqDYWbGgLGT5DN54CU5aYlQ0XBvExr0upVyM93Se1cAv7VAlv\nF60IkY0wA+C4ve1Ep6sVpbamrzWe/PYDAcUC0sXYbTeV+LJVqW/K9YDD/tf6ZQN96JneDykpMrFg\nwIPLdiLaj1nF8VgssuK2HYo9QGYGUBOlbLCABrVZz/Mf+mrL41shauV65KjEhvs37PX6Gjub2L5s\nJ2RDYvXt6zC0ftgBVsAA5uEcy366cq/X3ZWuAJ2bB/xV+N3kjf3+86JRV543oayGg971AYw+vgCj\njy2AGD8B4869GLWZh7hreuceh9FHH0ncjDLmFAKqJbHuk5+F3LkT/aedgskvvwysZCVhYz1x/Xdb\neNWEohmeMK6kvjFUrFgHDAANzxcZPsuO6rqjAARUPakWdQvrLG+vx2WW72V3bVV93kaX2sy5/oRS\niLbFLcDrJEh9atvZBoinqJ4SzVQoy7LnBCHiPi86lGq7WLstrWL2LNDJA0ObajbWoF3ztOc/GUDn\nwU4lMEyw6FYHWWAZi31it/xdDRQej6XUvhJbmXrpjI6q/LyKsWjbi34fyv3gn1EqVJjV2ene0XsS\nV6vcaPI3F1nTWAjD64YwbtYEsIk1pRy1qq8W9QyiRwCSMXDoeAyvH0I+kgNEyHoETn73mXjss4+U\nkgKEdURKVooxjTGXZMggegmq4c1oRF1g1mVHIOvr1GVkz2XC4eOdbXAooi4w6ZhJe60e2ZC44733\nYemPngHVdMa4WRcfDNksexPmQzm2PrV9r9XdlYK0sd3fF8T4Kv3OI1jzAAAgAElEQVQfAEcw858T\n0VwAxzHzzzu5f0/fqiEAc/bw3q68gEJZhv7TzkL/aWclz0+47KUYefQRFG3KFGWgmoAQAtojBOg/\n81xs/MJXwU2dDnZ01Vpsu/EWZBPHQzUlKJiU/XZzAXAEUQHab5eXWY9wm7CitRV/W/bQl2usAiq9\n8MO6/DUeCMfxM+05DSKJqkMwWT2Y0TYPvf27Gqj4uu22qpQ2FW57ZMGFZ12+YCzA7PvBs1V6kVF0\nyko51Xk9BJgVlNLR4oVJrmDL8yYkxfvTiltHGhtTNa17dEclYAXCMSxKx4vlpmLber3KCxzN9Cf6\nxPzPLu7i562QZTGojHW3aVTL70o1mwgwBFixCx1lWVbAOuuxe+q7i1k9c83a6YsZYfIKrljAypEc\nO5dtDxYd8XWsMkw+bjrO+cSFGDh0PG7/s5ux8f71ZtGQoWfqOEw4ZjK2Pbq59A6VMZ8fy1mPwMzz\nD8Pa21e7Y/ZfAkCCMPPFh2LDfesg6rrfJh83Bef+y0W73TN7IgedehAmz52ErYu2GbtaLaIucMLb\nj99r9dz1/vux9McrIBsKMAB91a+fTf5m1cbVcNBJU/Za3V3Z7+QbAB4CcK75/iyA6wHsPdBKRD+D\n/30S0GlZr98tNbuyT8jo8megUAexialqJljq7cHkK16NbPw4UF8/eo89Fqvf9yFws+UnYfOLLnfs\nAjILMiQo2BJWnMHGkXQxWS0A5TJr5sFltRQZq7FSnVaWGdZP9jogZL8sk+aD/jM4CaQsYyVgwVAK\ncHKYlEAp6LivBd0qGNwUQxuFCnL/egCV6tswmLwwYcZsG9r1vS8vNEkoh/OyTlntt8vt/VaXMPkE\njEOZv69oU5gaN+m6qpnPuN/bjaF4YVIlUpbj2uq6w74htLMPZmdyYEFaeJ2AlAo2NFqxDKUARubU\njMeOrjcVkUCHeCNv30vFxZp/X5M62wVg6pz7y7TLmZGkFk6FhaZk5yxWYsCbCpse3oD5H78PYGDT\n/A06qoNkqJbCAx/5LYTZyg9xVhVwB4C5Vx+H0z8wD/WJPbhh3ncxtH40enYEhmwpnP/vl6K1o4lt\nT27B+CMmYupJB6U75nkQIsLLv3857vrfd2PNbTqi5ITZE3DhZ87HhCP2Ttit1nCOxdc9A9mIU3LL\nhkLWKyB6BJRhXCkj9E7swdGvmb1X6u5KWfYDm9ajmfmNRPRmAGDmYdqNsBOdMq2fCv7OAaxk5jW7\noWRX9hFRIyPa8SoIZA8ALCWoVsf4CzWDMPLUYlBWAyOM+RqMO6nDh3OU096UZbaMWRIoAxhs7PTK\nbKNlytqF+iHyk2HazrV4U0Lf8Jw5bJMZpFlBG9qp2nYwtC1MsW4so+ocOI0uC2wWtUMXKr2koy10\neFBpj+uEDCYWpypuQ+t/NSNZdM7SGpVBd/idkqljbbk6lFNo25sSy1AWWMhSUH0utC0o0CwmtOe9\nATRBW9PAyDN9cT2xpBcI1X3DKguepXJjm03iibY2DQjblloU2S3yDDo1BEfAUrORiZTIzFF/skFx\n9j4LWL1YgMo60loxlFkBnFrTC1Zls/YyQIzHV1vACgCZMFEzKhZxElh7R3pakiMSkiQAStqgRkLA\n9DNm4JxPeLZ01uVH4alvPlXQiTDu8Inom9KHvil9mHDkxDEKfn6kd0ovXnrtZWgNtSAbEn1T2yeE\n2V1pbG8mGVUAqI+vY84rZmHpT1aCpcLsyw/HBf94Nmr9z79pRFf2WWkSUT/scp3oaADtc8oH0unI\neiUzfyA8QET/XDzWledfmBmjjy3Erjt+Ax4eRv+ZZ2P8RZdC9O6d0CYDp56KXb+5TcdjDYSI0H/K\nye57NnEi2HjUplgefcIcLsT+hIJmTJjAOUNkdoIWOhB4uE1rvcSpHPtTX5NFrI+3KUzHIw3/rRJf\ndxWoMHZtqmaArWprAuAmfl+DAaOigFpJ94mJJ6mcva3dqg1Zt5Dp9P0hrcNUQhelKGCO27Ut9vT2\nfRq0gK09ahwLtMp212b08nqXQ2JpU2iKyikDVl+mj8YVFuTBFGAB61jA1J+3hXinsRD06bBGMZCG\nS5ARtkWWFmsx4FIsClnfooHgFyCcDrcVmynYUG4w2+3VY5yI9LAL9GcTGsG3K66QAMeGKvOEFBeT\nKSgLnd0xWdj6ryZT4nanGHOlCKqQjrVzbiZopwXaFffWxtX/f/a+PN7Oorz/O+97zt2Sm+RmX0hC\nQgIhgCEhQABRFtmkFVTcrQLVWurWzVb8ubaW2oVabVW0WqpSsSwiKKBsiigBkhDCFiBkIft2s93c\n7Zx35vn9Mfu88557bhZCzHk+XHLOu8w8M++8Z77znWdB2pLizK+e4x1f/1AcDPds7kVop3+opDyk\nfFA89tvGtSBtKSHr9ZlWMGD86WNx7tfPwrlfP+uA19uQvOQW6YenfAHALwBMZoz9L4CzAFxZ7831\ngtYLAIQA9ZLIsYYcZNl9509UWCoJKivr16Hn0d9i7LWfR9K0/+FVmmfMQNucOehZtswAV9bcjPaz\nz0Z5/HiQENh1/2+w8+f3g/fKiQrGZjLOsoXfLQbSXv56wnZAWehUQgyCu2wVcqDSACmRAIyQpv7M\nzbNaE6dTjigG4p79oAnHJWd8gg8iC/shZrvoXM+N05kFjXKrWeT0t+yq3JaP2S/q+t04rQOJD4Y1\n665ZxcFM0m6w/JAhs43mGSDDORO0aYa7HV2/KJAswi1za8IQj0PqA9cwK5o8FobZkudJAEK5F0l7\nUHvOlbBNJBKQsgyNLbBEJN2xFpEzSZG1k2G12QALKbcNMKx0Xk91FbPjTIL6xNsBIUrU+A9ZWmvX\n7LLjUr/a7L0+xmPhtDDw4tOTBBJUZy6IVlo2JRh/5kSMnTcOQye3Y8qbp+WYwt6t8agqWU8G3sd/\nr5nFJE1w1j+cgt/81eMGuLIEKLWWsOD/zT3E2h15Mqhx/xoUIrqfMfYkgAWQP0CfJKLt9d5f801j\njF0D4M8ATGeMPe2cagfwu33QtyH7IXz3bnTd/wsgczxgq1Vk27eh54mFGPr6N+53HYwxjP6TD6N3\n2TJ0/ea3QMLQfu45aJ09G0SEjdd/Gz1PPWuuJ0XRsJAFFRLMhl7OgJ5eAxbKZbpybBgz1+UTFFiR\nDk0KLBADz+zFNtyU1dEet985tx7vIWMlw2y59q0KtGjmMBFmbtVOQDlWR7HCmkm1QCGclPNMoA8S\nlE7G+74egDI4iW9PaxYyZssbyywVt7fUINpfIKj+VEksWFLPr3NsAVBUZ23RAMvVJVeX2V4PIk7A\nRiaotTDQoA9wx6QeB+SBNJL4HRnIK9PaJ0cWhCQjbbACFWLAMDxfS3TfhoBYL5YkGLUgnKvxKTgh\nUQktDIMcsvfQbXfbtv+DmhhDWk7BEoaMZyAhQT1BtuPY98zEGf94lnQyLZCOWR3Y/lR+Xm2b0Ia0\nJZZ57PdLZr17BtrGtmHJ9U+ja+1ejDt1DE792zkYedyIQ61aQw5PeSOA10O+9mUAd9R740DLwx8B\nuBfAPwL4tHO8i4h2DFLJhuyn9K9cAVYqmUxVWqhSQd/Ty/YZtFKWoWvhIux94kkkba0YcvJJ2Hn3\ng6is3wgwht7VWzH+o1cDQqDnmeXB3Yqp0RNm5rKkpgYfyAQzan4CrgU4AkZMWfTZAOfuZGpTtYaT\nMeeJdDoyOjDDbMkUmCpuqwajpAGr1ctniRiIl8BCUE0CLh5lBGSZjqUZtquWyJic1cze5ztNaT1q\nFOEwjYWXOMArdOoC9HOqjWw8QK23kAPwLkxiCFmXL5b9swCv1njJixv6K182qagOuk0uQA8WUGHb\nAgZc3z+w+HbImmh27+VcZ6Vy2mp0zi+4iuqR1zAVAcDqaM1e8neZZ6bMEWLb9DWHFuk/uZhJU3Li\np8o/G6ZK16UWvF47/cVZPZLTFUBSSjDi+JHY8fxuiIzAWArRL1Ae2oy+XTp5inwmL/5gBXa9uAcX\n33Ix0qY4AD3186fhvvf+0gvin7amOPULp70mTANeDZly3kRMOW/ioVbjiJfD3TyAMfZNADMA3KwO\nfYQx9iYiiiaaCqUmaCWi3QB2A3gPY+z1AGYS0Y2MsdGMsWlEtHp/lG/I4CQZWuANyhiS4fu24qWM\nY8N1X0X/2vWgfhm6qut3i71rqlu2Yf11X0PHJefLaOx5BeQWaZjGUdaAXPiftASkDFTJJDhkLvNU\nQ1cdbcAFMCp4vn+duw0ZTkIEaVzIVGilsA4oj37ZLhAgsjTH1LrXa93jYELaMlonKs2qDpYFhAd6\nALV9rGKbutcJzpCkfp8KZUfMEAckfj06ooBm+NzzNvWrDlcmWWZrMsCzEIyS/s8CxIjtZF4sAAv7\ngnMJSqPb6gTo6AOa9fMYc+Ez5tJ5S9HiYN4zraXXQOA90My5T6NCh+F34tXG6wqPFUWkcO5RjLjr\njCerzfenBKUWXCaB46A9L8s3JikFzzAz2adiCxJXdxutVY8LIoBT/Pnm9BbQ1jm+JCmIUvU+E7K9\ncqHfn+nn7MuWx7fgwasfxIU3XRita/yC8bjo/y7Gk/+0BDtf2In2qcMw71PzMOmcScUKNqQhDYnJ\neQCOVxlRwRj7PoDn6r253pBXXwAwH8BxkDG2mgDcBGlA25BXSZpnzETS1gbe3+/9SrNSGUPPOXef\nytz7xBIDWK1EwEqlgh33PFRcUOG2qGW29PekVEb7/JOw6zdL1b0EMGEnskiaUCKrlmf/mcXifpIK\nbh/aURIk+5oU2DWGest/9aS/r4SKBYIWFO1LWVF2lizol4BV9ofgAEuEibUpzRKYAo4+IJH2sswB\n+iGojoAc5xoJDBiSVDjlhvdT9PkN3GapM/dScEo2lAuAMeE5KgkvioE2YSBbFrE8YCaAKHVMXOLA\n1YJtv12WpZTPSHry++9Qnh1xvNhp8AsZd2HmHfec06QpQ+ZFT7D95d4vDMvsfs8/KO2kJ3IhuPK/\nGcW623ETsu9ml4Z0uDkATDprRi2I1ILZaTIAIOvh6Hx6e96cqIZ50YaHN2DLoq0Yd+rY6Plxp47D\nJbe9uUbLGtKQgy1hrObDUl4GMAXAK+r7ZHWsLqk3z9xbIdO2dgMAEW2EtGttyKsoLEkw5i/+BqWx\n48CamsFaWsFaWtDxR1eiadLkfSpz7+KnAsBaIEKAenujgGNgEKInRDsxj3n/5QDToZkYBJepWaXd\nqOJDieSf0CowZFXrUGMDxscY0PyxerJCuXnctcmBa3taeB/Zz7FznOuUonGGUbNGRf0rCphJAlNp\nWmHiampwJniKrFqSKWyFdqaSjJ71jtfOW7pfY4uOsFIf4HjglXSqVfknTJv8awYjNvSYmy5Ul5fI\nPuAJMl5SDDELrrF2v8WpfwPG2U0VSv7YiEnGEwiVqtamN/XvF8RkGlLujifmxB7NS3F/yUgQRk+h\nAasDlGFtRC2LbMeCGZtBu/R4s2lb4YwN18yheGEzEButwaQ7ZvwUtvaPVNQL/1kAWSZT63LBkKl+\nFcoBL20JLWYHFsoId7/1Hjz0pw9jx/Kd2LJ4K7Keogxar45Uu6voWt8NkdVA2w05osSNbHIg/g6B\ntANYzhj7NWPsVwCeBzCMMXYXY+yugW6u1+WxQkTEVPwSxtiQfde3Ifsj5bHjMP5L16G6cT2orw9N\nU44GK+9bmBMde1XHeax5rZ60JFlpjgmTu1xt9NViMEspWJJgwsc/AL6nG6xcgqhyz9vabhWGL5Q+\nw5BV1eXGXCCsqBjM6uNy4iVzv2Zy/WDuugzJIoXZnfJb9j7zBkABFCc0EeU9xbUYm1xla2uZTwCK\nHQ6FlG1oFnia58FzeL8LIn1A6W7d5qUIwDMzFtx4nLpfk4B5HAyrWCx6cVHM8sWc12rVbRcPiUpn\nprOa5fvfMOiRgPcwnvyAdryz9rCKASYqsLvV5UumNBe7mPznFrbGBckhuGQMSi+bdEDrCxC4sO+z\nfr8Sk17XeTcG8fhcRtUsYohBZIiab4T3AkyFTHOze0UWGorMBwHIZJD9rtVdkfKK6xJVwso7VmPl\nnatRaiuDOGHBl+Zj9gcPXHapeoT3czz8qSfw0i2rwBImw3D9/SmY/b4Zr6oeDWnIQZDP78/N9YLW\nWxhj3wYwgjH2YQBXA/ju/lTckH0Xxtg+M6tadj34W2y/+acyo1XGZGjPAm9t80OvJgvBdWpNNfE5\n8TzdzEt267GE8ugRGP7GeRj5prNQHj0Soq8fxEWwnRnUl5+O7XHyJ0NXdAD92iYAvsc2HJvC0Cte\ns00WuLoTrY1pSkRe2fIvceoCJOIXuclTOPWLzDLQnsaRWLV6K1zWXQzcXNvGoj7RjHdRIHFdZzHA\nKGLhtJ2pXB4V158/VitjkWY/BXSWtUjbCOA5p6x4XxFp+2gN2vRNwueVnbEdjlX3O1PRMyTIc/vE\n1i2d/pyyI45TOo6qFs4t6ynXiTZDW9w2Vi+87O5EGCfXjtnYs0sAQb79Klk9Y6KBqanXWxDClEVU\n/3afjqgQF18RXgFGHDcSvVt6wSvchLoC9CLE1z33HDlQ7ZKJUx77/CJ0HDsCE84YX7em+yu//qvH\nseJ2lTYVQNbL8cjfPIEhY1sx9YKGHe2RKvHdsMNHGGMpgC8S0bn7WkZdvxdE9K8AbgNwO6Rd6+eJ\n6Ov7WmlDDq3sXbQM235wO0R3L6iqtr+IgZISktZmpMPbMeKi81CeMBasqUkCRO5PujLUVfDLz5ja\nTofZphM8BQRQ3boL2+94BH3rtwEAkpZmjPrDNyIGcOITYX4L0weH8rjgDCIrqeD9eSbGlqX/lTau\nMkh+Hlj429vyT4hEgUUbT7VaZciyErIsRZYl4DwF56npD10/kXRU4pkM3yUEA88Sw1JqD/YwDBSg\nY1465XCtu9umsL/0ca2zz6i620SCW6e1IjMF2SfM6Rf/XJFjjgbwlKvf/xOkOT943uchWObcMrs6\nsoN7jd12llEgNIMqBFPPJjHb9HqLPMtKuT6XwDCBMCYVtt+kI5I9rse86U+hTEIiY1zrKMKYvELr\nSXC3y/UWuYw8kcIdewRtkmAXSfnn79Sh+kVuqcsxXs2kiUX82THD2tr2wVmkyScfRuqw4wveDgZ5\nOg28je/VoseINx7iY27d/etxye1vxqwPHo/RJ48GF9JBTBBTfeX8wY1960vWy/HMt58fQMsDJ9uf\n3YHlP1qFai83bQWkne7i65951fRoSEMOtBARByAYY8P3tYy6IyIT0f0A7gcAxljCGHsfEf3vvlbc\nkEMnnbffA6pE7FirAqM/9F4Me/3p0n4WV6DnhZVYf903AOd6VkqQlJohuvNlGPvI2HZ1tYp1/3wj\njvvel5C2NmPMOy7C1lsfxICzVk5sjFDGEsUiqviP3IJP4gQwAhLFeAnFKDN3mxEG4OZqiU6KenvX\nHpERyFwTACAM+2UD+7vHYq9fMVspdWLKKamYTYsDWPmvyLVVgXMHPJGwobR0O2XEANeJicyEX2ye\nkT8mQYvP8vFMLYIALwyZBoGmBOaAlQgjKOPikiYgjbOYDMXmOuy5TKdsiwal0QWTCfNlAbSp090y\nD9vujJ9YP0XvUTcKISM9hGHV4qKfR33xQk2ZCkD65hVBnzr97S9k9LhRiQNgr1GkutErlHCRRSTN\nk3QCgiKxixh3pMcTMwAAKzGITOC0z5+GH51ya8iXgxMwbt4obF26Pfe+htKzuafm+QMlO1fsxm0X\n3eeYTJFZ6CUM6FrX/aro0ZDXrtSVqvy1LXsBPMMYux/KTwoAiOgT9dw8UHKBYQA+CmASgLsgQetH\nAfw1gGUAGqD1MJTqjp3R40TA1h/fjaYpU9A8fjSSlma0zToGY69+B7Z9/zYADJRxNE+fjHTUGOx5\nZHG0jBgINdudjKFr8XNoPWay3eqgfErPuH6uM5M+RkgSy/4EtSqGygcSBAKp7VHBtWOSPGnD7LgT\neb5cDdok45gWOHnVe6yec3rCJtROwzqQWNYx3EY2tp9giu1LjFZxYGqZNXfLd+C+C9g42C15mwXK\nHUi6v9W/hZmiipwLpJ2tNE1gwfWmhZ5e5goHvIfBkvSlhYyf7dBBPi5me5f851JU0ECsY5HI+xID\nqt1/4+9yvnxhrouP9/huR3CNekZEZMYbOedj9fsAOS+Vbo5n/ms5hk1tR3VvNRc5IG1O0T5tOLY+\n2em1O5S0OcHkC46KV3KAZeHfPYWs13X+ct4pBow/bcyrokdDXrtyOJsHKPmJ+tsnGYhp/SGAnQAW\nAvgQgM9AvkWXE9FT+1ppQw6ttEyfgp5lYZIAKdWtXVjz/74GgDD6svMx+h0XYcQ5Z2DYWfNRWb8Z\n6dAhqHTuxoZv3yZ/6AHvdxWIs29ma7m/gnX/cStIJBCVCkAMSRKxgxV5BxpuTBRCxrKYpSlis+TW\nsmXP9DVCBbSXoaJqow295Z27psYEvr+iPf+B2kDSv8dvB8/F0/XtXUnI7VMJnqiG2k6Z6noTYsv0\nXT5zltlO507fsaL22O1lluTT2ObbGh978nj+Xg+g5drlxxM12Z5AEBQC4NoSS3Ygs8UNXIZhDhXr\nVgscuqG24ix4pGz3vU2cY7lxXMCamnr8ceaaXujjtcaTvjZNfN3tAscvW7fb18///vJPViMpJSCe\n977nfRwr73oFGWfKUdDpa2eRLZCia30vti3rxJg5o+LKHyDZ8OjWwqx/pZYUp316zkGtvyENOdhC\nRN/fn/sHAq3TiegkAGCMfRfAJgBTiKhvfyptyKGV0e++DGuffgEk/ExVQoFCHQJr+50PoTS6Ax3n\nnY6kXEbLtMnoeXkdVn/hBlB/Vd4bxE+Ew9yEIgQDryQALJPAmL9lbZkyCXg0CNKMUH7G0wwdQWQ2\nnanvLBNO4sz8G27v6v7g3P+eZ4IdM4hBYlF3YnSOeu0pvnfg8xosWJDEwHnixGcNgb9y5GISSHGH\nyRQEpAO0L+Oabdae/O7Dt5nGzLMUbixVo7jr04fYcxY8jTgQxfrA/8xFOEj98/6ZkNWzZhDmmUMv\nBPz6ogs1sokddLID09+I963VycJUqGPFCxYJ+CRLSSYVcSGzadYW7oKCAsAUf9e8I0wu9LgAUjVs\ndLis/L3KmatwYcFUO8gAV7dtA79okToJEFUR7a+0JTURO4SOjCK1kT1PCcASZHsFnvvBy3jx/1bj\ntM/MwdyPnTCAHvsuraNb0NfZnzvOEoa33nMRRh63z6aADfm9kEMWpuqACWNsJmSW1dkAWvRxIppe\nz/0DOWJVnQI5gPUNwHr4iqhm2Hb7fVj3rzdClNugQ/Jo1ouoZLa8hWAQfRVsv+N+r4wtP7oX1C+H\nBVEKoRyKQFDOMG6cTKjrJLvHM9fZyU6qRDLGpRAlhE4mxqHLA7Yw2/6k4qjyasljIT0nCw90+Mya\nO1n63zXLFynPOJoV97XOgGXa7zj+COEDCg8QkV+Gr5f+lv/REkI7eKUQPAWJVH2W/U4iMccLWWBi\nBrC6TmHCq9vVD6gaxyBbjn2eqYqbKp+njg+aT16g64/X4QJtIRInHqf/XETE5IGbjGjWCS0U+VyK\nQZGt39FW2W5rJzrhOHW59XAFWO14Sky/AhGHIANO7VjUjlBCJCo6QP4erhwDtVOWdpQT5DsuuWOM\n57JWDX4ylPrK9nCRgNI0CLUVll8Mpslck6j4q2pc1wVY4+IuyuV7yJApp7OkOQVFV2QMbROGImku\nQ1SVokI6ZD3+5afQfRDtW0/589kotfm2yWlLiuPeNR1jTz64LG9DGvIqyY0AvgXJXp0L4AeQyarq\nkoGY1jmMsT3qMwPQqr4zAEREwwavb0MOhRAR1n75m+h9eS2ootciqXqSAsbD2mFeiID+TTvBe/qQ\ntskFUd+ajUHJTAKhppJkN3U0AsOAakARA0uSfZH6FWxlkgKnxGx8WOPlzKBznevvNp6kLjeckP06\n8xO2oaAUuEwUu0cS3ASAK0k4dKYpXSZ3wyYFW5YasOqtZr11b0GiG07LBa1xYOWyydFwRbk0tv59\nWuw2eVi+jOkpQCil/r5lxmUwfR1uyr1HlerWACj2ilAbgoRMKYnQQSqxGdZU3ZoxSxObuz4E+ULr\n6jJ4nHnPL27W4verAafOmJWJA2xIMndxUtxQdR9JEMyYHHOSJbXOhWEYK3IZYN320ARD22ZrUMlU\nha7+kacQjsFCJyc9XuA/L16psZIr6gaKfVeLmJy5yb6VLQF8Auf1RrWHQCKvb6mthGHThqFr3Zbc\nOV4R+J8TfoL2qe1Y8Nk5OPZtRw9ap1oy693TsXvNXiz9+vNISgl4hePoCyfhnOtPO6D1NOQwlchv\n9GEorUT0IGOMEdErAL7IGFuCOuO31gStVK87akNe89KzfCV6V65zAKsUlpbQNGk0+jd0gvo5ckCD\ngG13PIjx77sUANA8cQyyHXvMFYbd6iWUhreh1J4g29WF0vAhKE2eiN2LVprJvXZ8zoIJlGCZPCHg\nBuq3//pARQNXl1XKB9an4Ht4zgJUG1MyAC8gcJ7KbW9VEeelgjK9lqF4m8eCV9nkPCspARMD6a1m\nYdtaj5klUSS8j4c+bJ+6elWzNID2ljWrL4A/U6w8i54msgHn5ffYRhCzAASqLOdZSocr5XGdsw3U\n2/sK8IkE9lmTTtDm6JN/5lnm65R45gqx6AzxBQIAw4qaaxx2Vu7u69Birg4EQYBMFmvHgq7fXCOk\nfbEQsHaqzkISCijHRJB8Tq5tqW6DUN5oTI0ht1zNujLUHod6oRPb/vf7So5tawdbG7iG498yykzp\nJRe5+pqslyMpJ0hbZbxp3s9Rai1h0hvGo31KOzb+bguIB3UI+d7vXtWFBz+6ECITmPXOunY16xLG\nGBZcOwfzPjYbu1buwdCJbWgb23rAym9IQ14D0s8YSwCsYIx9DMAGAEPrvbn+uM4NOaylb+VaUMZz\nxynLMHTuiRh/5VtRgCSw57fW527cey4GayrrU3AdkbLdPVH9d3MAACAASURBVOjf1Y/R77gEvLkD\nuxetRiw0kV88Uw45ReeTyOc8G+iKTJWZeHEzRQ7A1Fqx6tBO7rZu7Cq1Ray2qzmP6VcETENd/Otz\nYID864RgELwkt/xJ2/LWtwIXHsixz4dgEyH4zK39k9Z+fkremJ4xCbe+PZ2ETIXKhUzHyp3QZUIw\nVDP7PIUXixQI+1uQjJMrKI0+Yz/CgF2Q5FKFEkzcVCIXsNr+sMAtLtxEp8iPxTDFqtsmE0s0YHm9\n8Uj5caM/cyEXGVyouMGZigjBYSJwFOmtzRe48J8X59r0QJkCkPwTynzB1bW4bJj2yTayCGB1xhsB\nnBJznRDw3mX3WYXfhUr1SiRjSodxZAG59T7qxFHgVeUs11LG5iU78fJd6wZ8n7JejoVfOjj+yE3t\nZYw9eVQDsDbEE/k+sgP6dwjkkwDaAHwCwCkA/gjAB+u9ue44rQ05vKU0qgNJuQTBfeDKmptQHtOB\n9vkngn3vp1Fgy5rsMBlywjGY8qkPYON370Dfxl3IbUf3VbHhB/c75JeexBQ3FzAhpFKUkiBnK9Ce\ndz2bw4gC9oWz25kxdsyW515nt1xjW6BhPngvrqt3rZzgi9i0WlI8sUtGyZoSqNZoRxfOEFtv1mJa\nNUdVzIQ6DDLFvruMm8t4SabTxqFVvHSBHnaBYhsfy+Kk44fa9iQQ7oCoISEzmdffL0YocCiZXssh\nM8a061CNfpMALGE2nqbPOsfHoo2NGl/UmOvU5kfuvUHeLMNtT1i2UO9aWFuor//srRlG0W6INUGw\n592xlmfg7cTr1u/2rwacXl+oNLraMY1yL48/XgeOZSyl0lXFxoXbzHvcvcU6QaVlWUS5rYTq3kza\n1zqTPAPQtb5HsvTG8Y2Q9XCU2mwYPF4VWHXvBnQu342Rxw7DMZdOQtrU2MRsyL7J4W4eQESL1Me9\nAK4a7P0N0HqESPupJ2Hzf98G9Fe8GYSlCYafNQ/pkDY0T52IvtXrde5Meb65jI4Lz/TKGnbqCRh2\n6glY9tZPR0EuKL6Nl2MXBQM0a0YMPCMTXN5kO3KYMPlvCJhgjuuIAEXi20om3vHc5DowNgoSBliQ\nFJZZWw/9yWfKNIDQIagEJWBCqDSfevKN93EYb9KwUAM3KQdO3Bi38hSp8FO2Hi90lboySe0iRddf\nrVpbX0bWZpMK4s4KklvDLiuqwUze3ISphY+/2AGz4M4skmDbqAFgzg7XibRAsCxdXhTIcltvFlr7\nN7mY+51HLbR5BfTyIBJcPwKG9aKLnGvcIOWyjwju2CNoM4XBSh7sW2c+Zvrc6Cv0hUULKTLA2ABB\nIZ+JjljgjrMss9fFdHPfBLOAVdW4t/EqkLamOPmjs7HsOy+ie3vFKUMC6FJrCpZIXZ76zkt47Lqn\n0b+7ipYRTTjz83Mw4w8n4+bz70PP1j5UuzOUh5TQOrIZ737oQgwdf/iyqIIL7H6lBy0jymgd2Xyo\n1WnIYSSMsWMBfArAVDgYlIjOq+f+Bmg9QiQpl3D0338SG/79B+hftwkAUB43Gkd98gNIh7QBAKb8\n9Qew6rP/CdHTJwEAEdrnHo/hZ52Mnpc3oGlcB0rtbabMpnEj0b9hm1dPsRe2tB8kIjnp6uvSBKyp\nCXxvPwC55e1v9fmsGcAi4NQCkNrClDOVy+wJA7AACdI4TxUoohyYjbNHoS7+pBiKxyoJC4iSVJ9X\nQIgzJKldQxAlgFojJEHop9B8wD1umDloxx4/BFUsVqkEfhFArgGTYGApORmy/GcuOMASiTS4CXHl\nOhN53VQowt06dgC1Hkau45NkSnMITtmO+lthaSLDIFnPev8+Itk/nOs64iDIsvsJGBO5c/F7FIvO\nNFgsRobSRhcqpisQzQSm6ikKjm8ltlvgvjtM1eksElTHM9S3e+CWK4hUqttiNlkW77LO5J3Pf7ZC\ngqHqhRHzF7FxwE1gKVPh2QCe2TVBrH3EgR0vdwHlEpxgOkYvXiH0bOvDijvX4refX4qsR76gvZ39\nePjaJXjuplXoWtdtohBU92bI+jge+otFeMvNb4i267UuL/x0He796GJUuzMITph2/nhc9j8L0DKi\n6VCrdkTI4c60ArgVwA0A/gtmRqtfGqD1CJLmCWMx/Z/+GtmuPSBBKI/0Y/41jRuF4274LPYuewnV\nzt1oPWYytv3iCTx75VfAyimoyjHqgvmY/KeXgaUJxr3nAqz72i1OxACZZYZnSYSBdSYjZ+LlFQHq\n749s0UfYrxriOi8Vn9dpXm2ZRKmcuMykbRldIiBN7fZy3i62yPlJ6uIBSXLb5NYD4zTEhQRKjBGy\njCFhCXhG3vWqRLklHdRqCPIAfEo7xHxf6hSmGpzbPmSw9pIuG+czvHknIbf9ElSAFYe4qof5NWCE\nImCGlKORAd3xBVN+21sC94QJWDORCNMrAGn6ITxAWLRoCR3X9LFwcWFDbAFQrLS7ENPb48buTF4m\nOd+CsebrUyyxqAr6swnT5i0Ka797RWYTLks94Psr9/3rqk+XrU065PfwTbCMcay/zv6X07HlyU48\nf9NKeK9M5HpRFVhx5zpkffFYr6W2Ena8uBuP/eMzBrBqyXo4Ni3qzP0sUUZYde8GyN2hwwuAbFyy\nA3d+8DFkvbatqx/YjFuveAR/9MD5h1CzhhxGkhHRt/b15gZoPQKlNKI4UhlLU7TPOx4AsOlH96Pz\ngSWgamaAaeeDS1DuGIq0vR3r/+tegKVg4GAgJK3NaJo8Eby3gv5NnSaeq/nVJjjMnGWo3GuKmCD7\nmQomIzjgIz97CwEVnN4vs9YkDgCcE3QCBJvu1d9OzesBGbMWKlOXSvXqXOHo5YA3ZSKhJ2MegKSw\nH/z4qcxcKwyN6RrbF4OQkGkkEws0yV3n6hPz7peAghmby6TA7lJeK4FSmCkqd53OzhWb33V9VHBe\nCYuAZGm3XEs/7ZiWQAgBQqLYO3esugyh38chew2EzlxKD84AZrO6cc8x0YJb7Zmf19O52lzgj81a\n75Vc2DjHyI4lfW2ReYQmSl1zFpOgwzOficvA7LBfGcFdLBWVr34ngrNEQGl4M379N0uQlBJl+kJA\nwW+GPpz1ScPi2O8Orwi0HzUEPVsLwpfX27bDRB7/txdUf1jhFYGNi3Zg58oudBzTfog0O1LkkDlP\n7bcwxkaqjz9jjP0ZgDsAGCNyItpRTzkN0HqEieirYNfCZ1HdsQdDZk3FkNlHF672t975Owd4SqH+\nKrb85BGICoMw50oACKgS+p/ZgKSpBCQM6dA28N5+iApXE6EFCcaWzEwEIVNTB0Njrg2P+J7CQqRO\neKP8xBNzFtHfGZMspRAu2FAxXImQpPAmM9+ekUHwPLAl0mlS4ZTpKJNj8Aq2Zkl7gifewUSlO7V2\nwC6wCsVdRBBIpEE/+Ayv2wY9kWu1OYeyTwX0M+AKQBcDysRzuAr7sXasU+20Ewesugzu2W667QrD\nevlly3s14E3NgoBFALIFbTb0mgWgGpBG2GL9XEja5HKRB7XmO4VZw6zoDG6W2dTZ5OLt02MqB1gL\nRDvG6S13w1Cq6BmhsBp1F+lj7mXBO+VcJ7htX+3y7QI3bVbmMK1lVLo5eL8A7xfOdfJf0qhY6cBK\nDFl/cT1pc4Ip54zH8KOHYtiUIdiztjt3TVN7CVkvNw5+gCx32sWTDjuWFQB2rdkb/RlJywm6NvY2\nQOvBFkP8HJayBP6L+ynnHAGoK3ZcA7QeQdK7ZjNWXHsDqJpBVDMk5RKGzJqK6V+4CknZHwq9r2wB\n39sbLUf09kNkofcrM4G6RUWysrxPgASQJH7GJFdCts33UM9fb++Lf3eZR55poDzAhBxhxEybPBbS\nAYF6+9O49jtlCZfxDdtrGVBTSw31YsH79XHO4+0TIkWSiJpb5nFxWdnYZO2AW7KsbsL0dm2oS8hk\n+eyey1SSE1TV6/MaTJXeJtZB4xMmDNPnMr7hPUyxpdK0IM+2at2MLbE5XgT8Ldur75A2k25mt1qN\nCQF0wZjXugnNhEqQxXPhtC3gTljt8RWJrW/rc4CtLlcECygLWP3FDQkAOuKGy9AX6OMzvRSUVnsc\nx5hmcp7dmZ+Zg7GnjMYdb/2VA1ZjIoFr0pxizodnYtW9G7Hr5a5cXYwBaVOCWe88Guf8y6kAgNM/\nfSIe+stF4H22/FJrivO+Oh8Lv/wMerb3G0eslhFNOP+r82vo8dqVqeeMxZand+X6kVc4xpw44hBp\n1ZDDQYho2oEopwFajxAhIqz+xx+Cd9kUhIJXsHf5Gmy/ZyHGXna2Od6/eSeWf/KGQoaPNZWBrNaP\nv6nU/KMhix+o3DpEyWxCtkzpWR5W7tqJMuMJzrQzTi7do407qq40ZcSY0SLHJtcekkg5GTEoNlMC\nprizidTZgl9ZTsYtqB2IbHH1zIf78kGiBcxQgF33AQAmkCRxhlALzxKH/SlWzGVZiRLDpha0wFQi\nSDKK8lAYV1bqqQPf59ne+HMTjkmIoASJ8uMn80yKxpB0zmIElYFKlxeyu2GHMQP0rAOQvU6CXbuN\nH0Y4iPdTDeSor1DP3Iux63VSbIHhLwa06O31xISYyz/rELC6VcXAbFi3sY0l91g+05ZeeIThqQhu\nGLUiJp08Fh1QgFKHhlPP4bj3HgNRqWE/qsJa8YpAWk4wdk4HzvrcyRgyvg0Lv/y0Z7/JUoaxczvw\nrvsuQlpOsP35Xbj7Qwux/bldIAGUmktIINAxYxjO+uLJmH7xJBz39qOx8u71JuTVjD886rANeXXa\nJ2Zh2Y2r0csrIMUel4ekOPVjx6K1o+GIdbDFLuAOP2GMnQpgHRFtVt8/AODtAF4B8MWGecBhIL0r\n16P7+ZUoDRuCYaefhKTl4IUOqWzZgcq2Xbnj1F9F532LPNC6+bZHICpVgCeASt2pd62TpjJGnH0y\ndjz0tOeAVSSc+5OODnNkYo2a4342IQmgyPPsd7fyAAVKiHnxhuxESgHwkff5LK7WMTX3uOUIo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d/0N0P7MCrJSChMDYd1yIMW87f/CFUbG3eGybPya7l65yftHVFqQCGUlzGaVhbTjm2neiaVQ+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ykqdOMo9uAo9jB39p+8+OT82i6aEEKIBqImg9btQG7I+5aBZVXaRillxh+wfqC1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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x2b583ef6588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "plt.scatter(pfolio_volatilities,pfolio_returns,c=sharpe_arr,cmap='plasma')\n", "plt.colorbar(label='Sharpe Ratio')\n", "plt.xlabel('Volatility')\n", "plt.ylabel('Return')\n", "plt.scatter(max_sr_vol,max_sr_ret,c='red',s=100,edgecolors='black')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Retorno máximo (max Sharpe): 0.119392370945\n", "Volatilidade (max Sharpe): 0.231315109814\n", "Composição do Portfolio\n", "VALE 0.0138784429757\n", "PETR4 0.0244419248252\n", "ITUB4 0.0348819859944\n", "BBDC4 0.00439187818111\n", "ABEV3 0.190887668615\n", "BBAS3 0.0877010163648\n", "ITSA4 0.0543235224135\n", "BVMF3 0.241022790499\n", "KROT3 0.0713081632879\n", "CIEL3 0.037322146906\n", "UGPA3 0.231034142768\n", "BBSE3 0.00880631716984\n" ] } ], "source": [ "print('Retorno máximo (max Sharpe):', max_sr_ret)\n", "print('Volatilidade (max Sharpe):', max_sr_vol)\n", "print('Composição do Portfolio')\n", "for x,y in zip(assets, max_weights):\n", " print(x,y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "python-for-finance", "language": "python", "name": "python-for-finance" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
walchko/pygecko
dev/images/image-message.ipynb
1
1075642
null
mit
ShantanuKamath/PythonWorkshop
2. Python Advanced.ipynb
1
51409
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Advanced\n", "\n", "***Disclaimer*** *-* *This document is only meant to serve as a reference for the attendees of the workshop. It does not cover all the concepts or implementation details discussed during the actual workshop.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Variable Scope\n", "\n", "When you program, you'll often find that similar ideas come up again and again. You'll use variables for things like counting, iterating and accumulating values to return. In order to write readable code, you'll find yourself wanting to use similar names for similar ideas. As soon as you put multiple piece of code together (for instance, multiple functions or function calls in a single script) you might find that you want to use the same name for two separate concepts. \n", "\n", "Fortunately, you don't need to come up with new names endlessly. Reusing names for objects is OK as long as you keep them in separate scope. \"Scope\" refers to which parts of a program a variable can be referenced from. \n", "\n", "If a variable is created inside a function, it can only be used within that function. \n", "\n", "Consider these two functions, word_count and nearest_square. Both functions include a answer variable, but they are distinct variables that only exist within their respective functions:\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def word_count(document, search_term):\n", " \"\"\" Count how many times search_term appears in document. \"\"\"\n", " words = document.split() \n", " answer = 0\n", " for word in words:\n", " if word == search_term:\n", " answer += 1\n", " return answer\n", "\n", "def nearest_square(limit):\n", " \"\"\" Find the largest square number smaller than limit. \"\"\"\n", " answer = 0\n", " while (answer+1)**2 < limit:\n", " answer += 1\n", " return answer**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the variable answer here is defined within each function seperately, you can reuse the same name of the variable, as the scope of the variables itself is different. \n", "Note : Functions, however, can access variables that are defined outside of its scope or in the larger scope, but can only read the value of the variable, not modify it. This is shown by the `UnboundLocalError` in the example below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "egg_count = 0\n", "\n", "def buy_eggs():\n", " egg_count += 12 # purchase a dozen eggs\n", "\n", "# buy_eggs()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In such situations its better to redefine the functions as below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n", "24\n" ] } ], "source": [ "egg_count = 0\n", "\n", "def buy_eggs():\n", " return egg_count + 12\n", "\n", "egg_count = buy_eggs()\n", "print(egg_count)\n", "egg_count = buy_eggs()\n", "print(egg_count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## List Basics\n", "\n", "In Python, it is possible to create a list of values. Each item in the list is called an *element* and can be accessed individually using a zero-based index. Hence avoiding the need to create multiple variables to store individual values. \n", "Note: negative indexes help access elements from the end of the array. `-1` refers to the last element and `-2` refers to the second last element and so on.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List : [1, 2, 3, 4, 5]\n", "Second element : 2\n", "Length of list : 5\n", "\n", "List : ['red', 'blue', 'green']\n", "First color : red\n", "Third color : green\n", "Last color : green\n", "Second last color : blue\n", "Length of list : 3\n", "\n", "List : ['Shantanu Kamath', 'Computer Science', 20, 1000000]\n", "Fourth element : 1000000\n", "Length of list : 4\n" ] } ], "source": [ "# list of numbers of type Integer\n", "numbers = [1, 2, 3, 4, 5]\n", "print(\"List :\", numbers)\n", "print(\"Second element :\", numbers[1]) ## 2\n", "print(\"Length of list :\",len(numbers)) ## 5\n", "print() # Empty line\n", "\n", "# list of strings\n", "colors = ['red', 'blue', 'green']\n", "print(\"List :\", colors)\n", "print (\"First color :\", colors[0]) ## red\n", "print (\"Third color :\", colors[2]) ## green\n", "print (\"Last color :\", colors[-1]) ## green\n", "print (\"Second last color :\", colors[-2]) ## blue\n", "print (\"Length of list :\",len(colors)) ## 3\n", "print() # Empty line\n", "\n", "# list with multiple variable types\n", "me = ['Shantanu Kamath', 'Computer Science', 20, 1000000]\n", "print(\"List :\", me)\n", "print(\"Fourth element :\", me[3]) ## 1000000\n", "print(\"Length of list :\", len(me)) ## 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since lists are considered to be sequentially ordered, they support a number of operations that can be applied to any Python sequence. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "|Operation Name|Operator|Explanation|\n", "|:-------------|:-------|:----------|\n", "|Indexing|`[ ]`|Access an element of a sequence|\n", "|Concatenation|`+`|Combine sequences together|\n", "|Repetition|`*`|Concatenate a repeated number of times|\n", "|Membership|`in`|Ask whether an item is in a sequence|\n", "|Length|`len`|Ask the number of items in the sequence|\n", "|Slicing|`[ : ]`|Extract a part of a sequence|" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "[3, 3, 3]\n", "[1, 2, 3, 4, 3, 3, 3]\n", "True\n", "4\n", "[2, 3]\n", "[2, 3, 4]\n" ] } ], "source": [ "myList = [1,2,3,4]\n", "\n", "# Indexing\n", "A = myList[2]\n", "print(A)\n", "\n", "# Repititoin\n", "A = [A]*3\n", "print(A)\n", "\n", "# Concatenation\n", "print(myList + A)\n", "\n", "# Membership\n", "print(1 in myList)\n", "\n", "# Length\n", "print(len(myList))\n", "\n", "# Slicing [inclusive : exclusive]\n", "print(myList[1:3])\n", "\n", "# Leaving the exclusive parameter empty\n", "print(myList[-3:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mutability\n", "Strings are immutable and list are mutable. \n", "For example : " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n", "Python\n", "['Welcome', 'to', 'coding', 'with', 'Python', 'v2.7']\n" ] } ], "source": [ "# Creating sentence and list form of sentence\n", "name = \"Welcome to coding with Python v3.6\"\n", "words = [\"Welcome\", \"to\", \"coding\", \"with\", \"Python\", \"v3.6\"]\n", "\n", "print(name[4])\n", "print(words[4])\n", "\n", "# This is okay\n", "words[5] = \"v2.7\"\n", "print(words)\n", "\n", "# This is not\n", "# name[5] = \"d\"\n", "# print(name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Passed by reference\n", "The list is stored at a memory locations and only a reference of this memory location is what the variable holds. So changes applied to one variable reflect in other variables as well." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Python', 'Java', 'C++', 'C', 'C#']\n", "['Python', 'Java', 'C++', 'C', 'C#']\n" ] } ], "source": [ "langs = [\"Python\", \"Java\", \"C++\", \"C\"]\n", "languages = langs\n", "langs.append(\"C#\")\n", "print(langs)\n", "print(languages)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## List Methods\n", "\n", "Besides simple accessing of values, lists have a large variety of methods that are used to performed different useful manipulations on them. \n", "Some of them are: \n", "\n", "- **list.append(element):** adds a single element to the end of the list. Common error: does not return the new list, just modifies the original.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New list : ['Hermione Granger', 'Ronald Weasley', 'Harry Potter']\n" ] } ], "source": [ "# list.append example\n", "names = ['Hermione Granger', 'Ronald Weasley']\n", "names.append('Harry Potter')\n", "print(\"New list :\", names) ## ['Hermione Granger', 'Ronald Weasley', 'Harry Potter']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.insert(index, element):** inserts the element at the given index, shifting elements to the right.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New list : ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']\n" ] } ], "source": [ "# list.insert example\n", "names = ['Ronald Weasley', 'Hermione Granger']\n", "names.insert(1, 'Harry Potter')\n", "print(\"New list :\", names) ## ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.extend(list2):** adds the elements in list2 to the end of the list. Using + or += on a list is similar to using extend().\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Full list : ['Ronald Weasley', 'Harry Potter', 'Hermione Granger', 'Neville Longbottom', 'Luna Lovegood']\n" ] } ], "source": [ "# list.extend example\n", "MainChar = ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']\n", "SupChar = ['Neville Longbottom', 'Luna Lovegood']\n", "MainChar.extend(SupChar)\n", "print(\"Full list :\", MainChar) ## ['Ronald Weasley', 'Harry Potter', 'Hermione Granger', 'Neville Longbottom', 'Luna Lovegood']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.index(element):** searches for the given element from the start of the list and returns its index. Throws a ValueError if the element does not appear (use 'in' to check without a ValueError).\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index of Harry Potter in list : 1\n" ] } ], "source": [ "# list.index example\n", "names = ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']\n", "index = names.index('Harry Potter') \n", "print(\"Index of Harry Potter in list :\",index) ## 1\n", "\n", "# Throws a ValueError (Uncomment to see error.)\n", "# index = names.index('Albus Dumbledore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.remove(element):** searches for the first instance of the given element and removes it (throws ValueError if not present)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modified list : ['Ronald Weasley', 'Hermione Granger']\n" ] } ], "source": [ "names = ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']\n", "index = names.remove('Harry Potter') ## ['Ronald Weasley', 'Hermione Granger']\n", "print(\"Modified list :\", names)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.pop(index):** removes and returns the element at the given index. Returns the rightmost element if index is omitted (roughly the opposite of append()).\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Modified list : ['Ronald Weasley', 'Hermione Granger']\n" ] } ], "source": [ "names = ['Ronald Weasley', 'Harry Potter', 'Hermione Granger']\n", "index = names.pop(1)\n", "print(\"Modified list :\", names) ## ['Ronald Weasley', 'Hermione Granger']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.sort():** sorts the list in place (does not return it). (The sorted() function shown below is preferred.)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sorted list : ['a', 'b', 'c', 'd', 'e', 'f']\n" ] } ], "source": [ "alphabets = ['a', 'f','c', 'e','b', 'd']\n", "alphabets.sort();\n", "print (\"Sorted list :\", alphabets) ## ['a', 'b', 'c', 'd', 'e', 'f']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- **list.reverse():** reverses the list in place (does not return it).\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reversed list : ['f', 'e', 'd', 'c', 'b', 'a']\n" ] } ], "source": [ "alphabets = ['a', 'b', 'c', 'd', 'e', 'f']\n", "alphabets.reverse()\n", "print(\"Reversed list :\", alphabets) ## ['f', 'e', 'd', 'c', 'b', 'a']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Others methods include :\n", "- Count : **list.count()** \n", "- Delete : **del list[index]**\n", "- Join : **\"[Seperator string]\".join(list)**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## List Comprehensions\n", "In Python, List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence, or to create a subsequence of those elements that satisfy a certain condition.\n", "\n", "It can be used to construct lists in a very natural, easy way, like a mathematician is used to do.\n", "This is how we can explain sets in maths:\n", "- Squares = {x² : x in {0 ... 9}}\n", "- Exponents = (1, 2, 4, 8, ..., 2¹²)\n", "- EvenSquares = {x | x in S and x even}\n", "\n", "Lets try to do this in Python using normal loops and list methods:\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Squares : [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n", "Exponents : [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]\n", "Even Squares : [0, 4, 16, 36, 64]\n" ] } ], "source": [ "# Using loops and list methods\n", "\n", "squares = []\n", "for x in range(10):\n", " squares.append(x**2)\n", "print(\"Squares :\", squares) ## [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n", " \n", "exponents = []\n", "for i in range(13):\n", " exponents.append(2**i)\n", "print(\"Exponents :\", exponents) ## [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]\n", "\n", "evenSquares = []\n", "for x in squares:\n", " if x % 2 == 0:\n", " evenSquares.append(x)\n", "print(\"Even Squares :\", evenSquares) ## [0, 4, 16, 36, 64]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These extend to more than one line. But by using list comprehensions you can bring it down to just one line.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Squares : [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n", "Exponents : [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]\n", "Even Squares : [0, 4, 16, 36, 64]\n" ] } ], "source": [ "# Using list comprehensions\n", "\n", "squares = [x**2 for x in range(10)]\n", "exponents = [2**i for i in range(13)]\n", "evenSquares = [x for x in squares if x % 2 == 0]\n", "print(\"Squares :\", squares) ## [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n", "print(\"Exponents :\", exponents) ## [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]\n", "print(\"Even Squares :\", evenSquares) ## [0, 4, 16, 36, 64]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Searching\n", "Searching is the process of finding a particular item in a collections of items. It is one of the most common problems that arise in computer programming. A search typically answers either True or False as to whether the item is present.\n", "\n", "In Python, there is a very easy way to ask whether an item is in a list of items. We use the ***in*** operator.\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n" ] } ], "source": [ "# Using in to check if number is present in the list.\n", "\n", "print(15 in [3,5,2,4,1])\n", "print('Work' in 'Python Advanced Workshop')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sometimes it can be important to get the position of the searched value. In that case, we can use ***index*** method for lists and the ***find*** method for strings.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Value present at 1\n", "Found 'is' in the sentence at position 2\n", "There is no 'is' here!\n" ] } ], "source": [ "# Using index to get position of the number if present in list.\n", "# In case of lists, its important to remember that the index function will throw an error if the value isn't present in the list.\n", "\n", "values = [3,5,2,4,1]\n", "if 5 in values:\n", " print(\"Value present at\",values.index(5)) ## 1\n", "else:\n", " print(\"Value not present in list\")\n", "\n", "# Using find to get the index of the first occurrence of the word in a sentence.\n", "\n", "sentence = \"This be a string\"\n", "index = sentence.find(\"is\")\n", "if index == -1:\n", " print(\"There is no 'is' here!\")\n", "else:\n", " print(\"Found 'is' in the sentence at position \"+str(index))\n", "\n", "# Using index to find words in a list of words\n", "sentence = \"This be a string\"\n", "words = sentence.split(' ')\n", "if 'is' in words:\n", " print(\"Found 'is' in the list at position \"+str(words.index('is')))\n", "else:\n", " print(\"There is no 'is' here!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*For more efficient Search Algorithms, look through the Algorithm Implementation section of this repository*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Sorting\n", "Sorting is the process of placing elements from a collection in some kind of order. \n", "For example, a list of words could be sorted alphabetically or by length. \n", "A list of cities could be sorted by population, by area, or by zip code.\n", "\n", "Python lists have a ***built-in sort()*** method that modifies the *list in-place* and a ***sorted() built-in*** function that builds a *new* sorted list from an iterable.\n", "\n", "- ***list.sort():*** Modifies existing list and can be used only with lists.\n", "- ***sorted(list):*** Creates a new list when called and can be used with other iterables.\n", "\n", "##### Basic sorting functions\n", "The most basic use of the sorted function can be seen below :" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unsorted list : [7, 4, 3, 6, 1, 2, 5]\n", "New list : None\n", "Old list : [1, 2, 3, 4, 5, 6, 7]\n", "\n", "Unsorted list : [7, 4, 3, 6, 1, 2, 5]\n", "New list : [1, 2, 3, 4, 5, 6, 7]\n", "Old list : [7, 4, 3, 6, 1, 2, 5]\n" ] } ], "source": [ "# Using sort() with a list.\n", "\n", "values = [7, 4, 3, 6, 1, 2, 5]\n", "print(\"Unsorted list :\", values) ## [7, 4, 3, 6, 1, 2, 5]\n", "newValues = values.sort()\n", "print(\"New list :\", newValues) ## None\n", "print(\"Old list :\", values) ## [1, 2, 3, 4, 5, 6, 7]\n", "print()\n", "# Using sorted() with a list.\n", "\n", "values = [7, 4, 3, 6, 1, 2, 5]\n", "print(\"Unsorted list :\", values) ## [7, 4, 3, 6, 1, 2, 5]\n", "newValues = sorted(values)\n", "print(\"New list :\", newValues) ## [1, 2, 3, 4, 5, 6, 7]\n", "print(\"Old list :\", values) ## [7, 4, 3, 6, 1, 2, 5]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Sorting using additional key\n", "For more complex custom sorting, sorted() takes an optional \"key=\" specifying a \"key\" function that transforms each element before comparison. \n", "The key function takes in 1 value and returns 1 value, and the returned \"proxy\" value is used for the comparisons within the sort." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['d', 'bb', 'ccc', 'aaaa']\n", "['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']\n", "['BB', 'CC', 'aa', 'zz']\n", "['zz', 'aa', 'CC', 'BB']\n" ] } ], "source": [ "# Using key in sorted\n", "\n", "values = ['ccc', 'aaaa', 'd', 'bb']\n", "print (sorted(values, key=len)) ## ['d', 'bb', 'ccc', 'aaaa']\n", "\n", "# Remember case sensitivity : All upper case characters come before lower case character in an ascending sequence.\n", "sentence = \"This is a test string from Andrew\"\n", "print(sorted(sentence.split(), key=str.lower)) ## ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']\n", "\n", "# Using reverse for ascending and descending\n", "strs = ['aa', 'BB', 'zz', 'CC']\n", "print (sorted(strs)) ## ['BB', 'CC', 'aa', 'zz'] (case sensitive)\n", "print (sorted(strs, reverse=True)) ## ['zz', 'aa', 'CC', 'BB']\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Basics on Class and OOP\n", "\n", "This section is built around the fundamental of *Object Oriented Programming (OOP)*.\n", "It aims strengthening basics but doesn't justify the broad topic itself. As OOP is a very important programming concept you should read further to better get a grip on python as well as go deep in understanding how it is useful and essential to programming.\n", "Below are some essential resources :\n", "- [Improve Your Python: Python Classes and Object Oriented Programming](https://jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/)\n", "- [Learn Python The Hard Way](https://learnpythonthehardway.org/book/ex40.html)\n", "- [Python For Beginners](http://www.pythonforbeginners.com)\n", "- [A Byte Of Python](https://python.swaroopch.com/oop.html)\n", "\n", "### OOP\n", "In all the code we wrote till now, we have designed our program around functions i.e. blocks of statements which manipulate data. This is called the procedure-oriented way of programming.\n", "\n", "There is another way of organizing your program which is to combine data and functionality and wrap it inside something called an object. This is called the object oriented programming paradigm. \n", "\n", "Most of the time you can use procedural programming, but when writing large programs or have a problem that is better suited to this method, you can use object oriented programming techniques.\n", "\n", "### Classes and Objects\n", "Classes and objects are the two main aspects of object oriented programming. A ***class*** creates a new type where ***objects*** are ***instances*** of the class. \n", "\n", "Objects can store data using ordinary variables that belong to the object. Variables that belong to an object or class are referred to as ***fields*** or ***attributes***.\n", "\n", "Objects can also have functionality by using functions that belong to a class. Such functions are called ***methods*** of the class.\n", "\n", "The simplest class possible is shown in the following example :\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Person object at 0x1049b64a8>\n" ] } ], "source": [ "class Person:\n", " pass # An empty block\n", "\n", "p = Person()\n", "print(p)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Methods\n", "\n", "Class methods have only one specific difference from ordinary functions - they must have an extra first name that has to be added to the beginning of the parameter list, but you do not give a value for this parameter when you call the method, Python will provide it. This particular variable refers to the object itself, and by convention, it is given the name self.\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, how are you?\n" ] } ], "source": [ "class Person:\n", " def say_hi(self):\n", " print('Hello, how are you?')\n", "\n", "p = Person()\n", "p.say_hi()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The __init__\n", "There are many method names which have special significance in Python classes. We will see the significance of the __init__ method now. \n", "\n", "The __init__ method is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object. Notice the double underscores both at the beginning and at the end of the name.\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, my name is Shantanu\n" ] } ], "source": [ "class Person:\n", " def __init__(self, name):\n", " self.name = name\n", "\n", " def say_hi(self):\n", " print('Hello, my name is', self.name)\n", "\n", "p = Person('Shantanu')\n", "p.say_hi()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Object variables\n", "Now let us learn about the data part. The data part, i.e. fields, are nothing but ordinary variables that are bound to the namespaces of the classes and objects. This means that these names are valid within the context of these classes and objects only. That's why they are called name spaces. \n", "\n", "There are two types of fields - class variables and object variables which are classified depending on whether the class or the object owns the variables respectively. \n", "\n", "***Class variables*** are shared - they can be accessed by all instances of that class. There is only one copy of the class variable and when any one object makes a change to a class variable, that change will be seen by all the other instances. \n", "\n", "***Object variables*** are owned by each individual object/instance of the class. In this case, each object has its own copy of the field i.e. they are not shared and are not related in any way to the field by the same name in a different instance.\n", "\n", " An example will make this easy to understand.\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Initializing R2-D2)\n", "Greetings, my masters call me R2-D2.\n", "We have 1 robots.\n", "(Initializing C-3PO)\n", "Greetings, my masters call me C-3PO.\n", "We have 2 robots.\n", "\n", "Robots can do some work here.\n", "\n", "Robots have finished their work. So let's destroy them.\n", "R2-D2 is being destroyed!\n", "There are still 1 robots working.\n", "C-3PO is being destroyed!\n", "C-3PO was the last one.\n", "We have 0 robots.\n" ] } ], "source": [ "class Robot:\n", " ## Represents a robot, with a name.\n", "\n", " # A class variable, counting the number of robots\n", " population = 0\n", "\n", " def __init__(self, name):\n", " ## Initializes the data.\n", " self.name = name\n", " print(\"(Initializing {})\".format(self.name))\n", "\n", " # When this person is created, the robot\n", " # adds to the population\n", " Robot.population += 1\n", "\n", " def die(self):\n", " ## I am dying.\n", " print(\"{} is being destroyed!\".format(self.name))\n", "\n", " Robot.population -= 1\n", "\n", " if Robot.population == 0:\n", " print(\"{} was the last one.\".format(self.name))\n", " else:\n", " print(\"There are still {:d} robots working.\".format(\n", " Robot.population))\n", "\n", " def say_hi(self):\n", " ## Greeting by the robot. Yeah, they can do that.\n", " print(\"Greetings, my masters call me {}.\".format(self.name))\n", "\n", " @classmethod\n", " def how_many(cls):\n", " ## Prints the current population.\n", " print(\"We have {:d} robots.\".format(cls.population))\n", "\n", "\n", "droid1 = Robot(\"R2-D2\")\n", "droid1.say_hi()\n", "Robot.how_many()\n", "\n", "droid2 = Robot(\"C-3PO\")\n", "droid2.say_hi()\n", "Robot.how_many()\n", "\n", "print(\"\\nRobots can do some work here.\\n\")\n", "\n", "print(\"Robots have finished their work. So let's destroy them.\")\n", "droid1.die()\n", "droid2.die()\n", "\n", "Robot.how_many()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### How It Works\n", "This is a long example but helps demonstrate the nature of class and object variables. Here, population belongs to the Robot class and hence is a class variable. The name variable belongs to the object (it is assigned using self) and hence is an object variable. \n", "\n", "Thus, we refer to the population class variable as Robot.population and not as self.population. We refer to the object variable name using self.name notation in the methods of that object. Remember this simple difference between class and object variables. Also note that an object variable with the same name as a class variable will hide the class variable! \n", "\n", "Instead of Robot.population, we could have also used self.__class__.population because every object refers to its class via the self.__class__ attribute. \n", "\n", "The how_many is actually a method that belongs to the class and not to the object. This means we can define it as either a classmethod or a staticmethod depending on whether we need to know which class we are part of. Since we refer to a class variable, let's use classmethod.\n", "\n", "\n", "We have marked the how_many method as a class method using a decorator.\n", "Decorators can be imagined to be a shortcut to calling a wrapper function, so applying the @classmethod decorator is same as calling:\n", "how_many = classmethod(how_many)\n", "\n", "Observe that the __init__ method is used to initialize the Robot instance with a name. In this method, we increase the population count by 1 since we have one more robot being added. Also observe that the values of self.name is specific to each object which indicates the nature of object variables.\n", "Remember, that you must refer to the variables and methods of the same object using the self only. This is called an attribute reference.\n", "\n", "All class members are public. One exception: If you use data members with names using the double underscore prefix such as ***__privatevar*** , Python uses name-mangling to effectively make it a private variable.\n", "\n", "Thus, the convention followed is that any variable that is to be used only within the class or object should begin with an underscore and all other names are public and can be used by other classes/objects. Remember that this is only a convention and is not enforced by Python (except for the double underscore prefix).\n", "\n", "\n", "*There are more concepts in OOP such as Inheritance, Abstraction and Polymorphism, which would require a lot more time to cover. You may refer to reference material for explanation on these topics.*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## File I/O\n", "File handling is super simplified in Python compared to other programming languages.\n", "The first thing you’ll need to know is Python’s built-in ***open*** function to get a ***file object***. \n", "The ***open*** function opens a ***file***. When you use the ***open*** function, it returns something called a ***file object***. File objects contain ***methods*** and ***attributes*** that can be used to collect information about the file you opened. They can also be used to manipulate said file.\n", "\n", "For example, the ***mode*** attribute of a file object tells you which mode a ***file*** was opened in. And the ***name*** attribute tells you the name of the file that the ***file object*** has opened.\n", "\n", "#### File Types\n", "In Python, a file is categorized as either ***text*** or ***binary***, and the difference between the two file types is important.\n", "\n", "***Text files*** are structured as a sequence of lines, where each line includes a sequence of characters. This is what you know as code or syntax.\n", "\n", "Each line is terminated with a special character, called the ***EOL*** or ***End of Line*** character. There are several types, but the most common is the ***comma*** {,} or ***newline*** character. It ends the current line and tells the interpreter a new one has begun.\n", "\n", "A ***backslash*** character can also be used, and it tells the interpreter that the next character – following the slash – should be treated as a new line. This character is useful when you don’t want to start a new line in the text itself but in the code.\n", "\n", "A ***binary file*** is any type of file that is not a text file. Because of their nature, binary files can only be processed by an application that know or understand the file’s structure. In other words, they must be applications that can ***read and interpret*** binary.\n", "\n", "#### Open ( ) Function\n", "The syntax to open a file object in Python is:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "file_object = open(\"filename\", \"mode\") ## where file_object is the variable to add the file object.\n", "```\n", "The second argument you see – mode – tells the interpreter and developer which way the file will be used.\n", "\n", "#### Mode\n", "Including a mode argument is optional because a default value of ***r*** will be assumed if it is omitted.\n", "\n", "The modes are:\n", "\n", "- ***r*** – Read mode which is used when the file is only being read\n", "- ***w*** – Write mode which is used to edit and write new information to the file (any existing files with the same name will be erased when this mode is activated)\n", "- ***a*** – Appending mode, which is used to add new data to the end of the file; that is new information is automatically amended to the end\n", "- ***r+*** – Special read and write mode, which is used to handle both actions when working with a file\n", "\n", "#### Create a text file\n", "Using a simple text editor, let’s create a file. You can name it anything you like, and it’s better to use something you’ll identify with.\n", "For the purpose of this workshop, however, we are going to call it \"testfile.txt\".\n", "Just create the file and leave it blank.\n", "\n", "To manipulate the file :\n", "```python\n", "file = open(\"testfile.txt\",\"w\")\n", "\n", "file.write(\"Hello World\")\n", "file.write(\"This is our new text file\")\n", "file.write(\"and this is another line.\")\n", "file.write(\"Why? Because we can.\")\n", "\n", "file.close()\n", "```\n", "\n", "#### Reading a text file\n", "Following methods allow reading a file :\n", "- ***file.read():*** extract a string that contains all characters in the file.\n", "```python\n", "file = open(\"testfile.text\", \"r\")\n", "print(file.read())\n", "```\n", "- ***file.read(numberOfCharacters):*** extract only a certain number of characters. \n", "```python\n", "file = open(\"testfile.txt\", \"r\")\n", "print(file.read(5))\n", "```\n", "- ***file.readline():*** read a file line by line – as opposed to pulling the content of the entire file at once.\n", "```python\n", "file = open(\"testfile.txt\", \"r\")\n", "print(file.readline())\n", "```\n", "- ***file.readline(lineNumber):*** return a specific line\n", "```python\n", "file = open(\"testfile.txt\", \"r\")\n", "print(file.readline(3))\n", "```\n", "- ***file.readlines():*** return every line in the file, properly separated in a list\n", "```python\n", "file = open(\"testfile.txt\", \"r\")\n", "print (file.readlines())\n", "```\n", "\n", "#### Looping over file\n", "When you want to read – or return – all the lines from a file in a more memory efficient, and fast manner, you can use the loop over method. The advantage to using this method is that the related code is both simple and easy to read.\n", "\n", "```python\n", "file = open(\"testfile.txt\", \"r\")\n", "for line in file:\n", "print line\n", "```\n", "\n", "#### Using the File Write Method\n", "This method is used to add information or content to an existing file. To start a new line after you write data to the file, you can add an ***EOL*** (\"\\n\")) character.\n", "```python\n", "file = open(\"testfile.txt\", \"w\")\n", "\n", "file.write(\"This is a test\")\n", "file.write(\"To add more lines.\")\n", "\n", "file.close()\n", "```\n", "#### Closing a file\n", "When you’re done working, you can use the fh.close() command to end things. What this does is close the file completely, terminating resources in use, in turn freeing them up for the system to deploy elsewhere.\n", "\n", "It’s important to understand that when you use the fh.close() method, any further attempts to use the file object will fail.\n", "```python\n", "file.close()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Exception Handling\n", "There are two types of errors that typically occur when writing programs. The first, known as a ***syntax error***, simply means that the programmer has made a mistake in the structure of a statement or expression. For example, it is incorrect to write a for statement and forget the colon." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ( Uncomment to see Syntax error. )\n", "# for i in range(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other type of error, known as a ***logic error***, denotes a situation where the program executes but gives the wrong result. This can be due to an error in the underlying algorithm or an error in your translation of that algorithm. In some cases, logic errors lead to very bad situations such as trying to *dividing by zero* or trying to access an item in a list where the index of the item is outside the bounds of the list. In this case, the logic error leads to a ***runtime error*** that causes the program to terminate. These types of runtime errors are typically called ***exceptions***. \n", " \n", "When an exception occurs, we say that it has been ***raised***. You can ***handle*** the exception that has been raised by using a **`try`** statement. For example, consider the following session that asks the user for an integer and then calls the square root function from the math library. If the user enters a value that is greater than or equal to `0`, the print will show the square root. However, if the user enters a negative value, the square root function will report a **`ValueError`** exception." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please enter an integer -10\n" ] }, { "ename": "ValueError", "evalue": "math domain error", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-27-ef0cdd33095c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0manumber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Please enter an integer \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Give input as negative number and also see output from next code snippet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: math domain error" ] } ], "source": [ "import math\n", "anumber = int(input(\"Please enter an integer \"))\n", "# Give input as negative number and also see output from next code snippet\n", "print(math.sqrt(anumber))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can ***handle*** this exception by calling the print function from within a **`try`** block. A corresponding **`except`** block ***catches*** the exception and prints a message back to the user in the event that an exception occurs. For example:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bad Value for square root\n", "Using absolute value instead\n", "3.1622776601683795\n" ] } ], "source": [ "try:\n", " print(math.sqrt(anumber))\n", "except:\n", " print(\"Bad Value for square root\")\n", " print(\"Using absolute value instead\")\n", " print(math.sqrt(abs(anumber)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible for a programmer to cause a runtime exception by using the **`raise`** statement. For example, instead of calling the square root function with a negative number, we could have checked the value first and then raised our own exception. The code fragment below shows the result of creating a new **`RuntimeError`** exception. Note that the program would still terminate but now the exception that caused the termination is something explicitly created by the programmer." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "ename": "RuntimeError", "evalue": "You can't use a negative number", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-29-c2dee6db69a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0manumber\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You can't use a negative number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mRuntimeError\u001b[0m: You can't use a negative number" ] } ], "source": [ "if anumber < 0:\n", " raise RuntimeError(\"You can't use a negative number\")\n", "else:\n", " print(math.sqrt(anumber))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many kinds of exceptions that can be raised in addition to the **`RuntimeError`** shown above. See the Python reference manual for a list of all the available exception types and for how to create your own." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Importing Modules\n", "So far we haven't explained what a Python module is. To put it in a nutshell: every file, which has the file extension .py and consists of proper Python code, can be seen or is a module. \n", "There is no special syntax required to make such a file a module. A module can contain arbitrary objects, for example files, classes or attributes. \n", "All those objects can be accessed after an import. There are different ways to import a modules. \n", "\n", "#### Import one module:\n", "```python\n", ">>> import math\n", ">>> math.pi\n", "3.141592653589793\n", ">>> math.sin(math.pi/2)\n", "1.0\n", ">>> math.cos(math.pi/2)\n", "6.123031769111886e-17\n", ">>> math.cos(math.pi)\n", "-1.0\n", "```\n", "#### Import more than one module in one import statement:\n", "```python\n", "import math, random\n", "```\n", "If only certain objects of a module are needed, we can import only those:\n", "```python\n", "from math import sin, pi\n", "```\n", "Instead of explicitly importing certain objects from a module, it's also possible to import everything in the namespace of the importing module:\n", "```python\n", ">>> from math import *\n", ">>> sin(3.01) + tan(cos(2.1)) + e\n", "2.2968833711382604\n", ">>> e\n", "2.718281828459045\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## Scripting\n", "This topic is a little more complex than you would have expected it to be.\n", "Probably practice more python before you venture into this topic.\n", "But just to get you to understand what this topic consists of and also appreciate the powerful nature of python,\n", "Here are a few video clips to showing the result of various python scripts. \n", "[Articles showcasing python scripts](https://www.quora.com/What-are-the-best-Python-scripts-youve-ever-written) \n", "[Movie subtitle downloader](https://youtu.be/Q5YWEqgw9X8) \n", "[Python Script to download course content from Blackboard learn](https://github.com/NasaGeek/blackboard-export) \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
fcollonval/coursera_data_visualization
Making_Data_Management.ipynb
1
141471
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment: Making Data Management Decisions - Python\n", "\n", "Following is the Python program I wrote to fulfill the third assignment of the [Data Management and Visualization online course]( https://www.coursera.org/learn/data-visualization/).\n", "\n", "I decided to use [Jupyter Notebook](http://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Index.ipynb) as it is a pretty way to write code and present results.\n", "\n", "## Research question\n", "\n", "Using the [Gapminder database](http://www.gapminder.org/), I would like to see if an increasing Internet usage results in an increasing suicide rate. A study shows that other factors like unemployment could have a great impact.\n", "\n", "So for this third assignment, the three following variables will be analyzed:\n", "\n", "- Internet Usage Rate (per 100 people)\n", "- Suicide Rate (per 100 000 people)\n", "- Unemployment Rate (% of the population of age 15+)\n", "\n", "\n", "## Data management\n", "\n", "For the question, I'm interested in the countries for which data are missing will be discarded. As missing data in Gapminder database are replace directly by `NaN` no special data treatment is needed." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# Load a useful Python libraries for handling data\n", "import pandas as pd\n", "import numpy as np\n", "from IPython.display import Markdown, display" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read the data\n", "data_filename = r'gapminder.csv'\n", "data = pd.read_csv(data_filename, low_memory=False)\n", "data = data.set_index('country')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "General information on the Gapminder data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "variables": { "len(data)": "<p><strong>NameError</strong>: name &#39;data&#39; is not defined</p>\n", "len(data.columns)": "<p><strong>NameError</strong>: name &#39;data&#39; is not defined</p>\n" } }, "outputs": [ { "data": { "text/markdown": [ "Number of countries: 213" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/markdown": [ "Number of variables: 15" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(\"Number of countries: {}\".format(len(data))))\n", "display(Markdown(\"Number of variables: {}\".format(len(data.columns))))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Convert interesting variables in numeric format\n", "for variable in ('internetuserate', 'suicideper100th', 'employrate'):\n", " data[variable] = pd.to_numeric(data[variable], errors='coerce')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "But the unemployment rate is not provided directly. In the database, the employment rate (% of the popluation) is available. So the unemployement rate will be computed as `100 - employment rate`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['unemployrate'] = 100. - data['employrate']" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "scrolled": true }, "source": [ "The first records of the data restricted to the three analyzed variables are:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>internetuserate</th>\n", " <th>suicideper100th</th>\n", " <th>unemployrate</th>\n", " </tr>\n", " <tr>\n", " <th>country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>3.654122</td>\n", " <td>6.684385</td>\n", " <td>44.299999</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>44.989947</td>\n", " <td>7.699330</td>\n", " <td>48.599998</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>12.500073</td>\n", " <td>4.848770</td>\n", " <td>49.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>81.000000</td>\n", " <td>5.362179</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Angola</th>\n", " <td>9.999954</td>\n", " <td>14.554677</td>\n", " <td>24.300003</td>\n", " </tr>\n", " <tr>\n", " <th>Antigua and Barbuda</th>\n", " <td>80.645455</td>\n", " <td>2.161843</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>36.000335</td>\n", " <td>7.765584</td>\n", " <td>41.599998</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>44.001025</td>\n", " <td>3.741588</td>\n", " <td>59.900002</td>\n", " </tr>\n", " <tr>\n", " <th>Aruba</th>\n", " <td>41.800889</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>75.895654</td>\n", " <td>8.470030</td>\n", " <td>38.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " internetuserate suicideper100th unemployrate\n", "country \n", "Afghanistan 3.654122 6.684385 44.299999\n", "Albania 44.989947 7.699330 48.599998\n", "Algeria 12.500073 4.848770 49.500000\n", "Andorra 81.000000 5.362179 NaN\n", "Angola 9.999954 14.554677 24.300003\n", "Antigua and Barbuda 80.645455 2.161843 NaN\n", "Argentina 36.000335 7.765584 41.599998\n", "Armenia 44.001025 3.741588 59.900002\n", "Aruba 41.800889 NaN NaN\n", "Australia 75.895654 8.470030 38.500000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subdata = data[['internetuserate', 'suicideper100th', 'unemployrate']]\n", "subdata.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data analysis\n", "\n", "We will now have a look at the frequencies of the variables after grouping them as all three are continuous variables. I will group the data in intervals using the `cut` function.\n", "\n", "### Internet use rate frequencies" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "variables": { "\"{0:.2f}, {1:.2f}\".format(subdata['internetuserate'].min(), subdata['internetuserate'].max())": "<p><strong>NameError</strong>: name &#39;subdata&#39; is not defined</p>\n" } }, "outputs": [ { "data": { "text/markdown": [ "Internet Use Rate (min, max) = (0.21, 95.64)" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(\"Internet Use Rate (min, max) = ({0:.2f}, {1:.2f})\".format(subdata['internetuserate'].min(), subdata['internetuserate'].max())))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <style type=\"text/css\" >\n", " \n", " \n", " #T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow0_col0 {\n", " \n", " 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}\n", " \n", " #T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " </style>\n", "\n", " <table id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" None>\n", " \n", "\n", " <thead>\n", " \n", " <tr>\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"col_heading level0 col0\">Counts\n", " \n", " <th class=\"col_heading level0 col1\">Cumulative counts\n", " \n", " <th class=\"col_heading level0 col2\">Percentages\n", " \n", " <th class=\"col_heading level0 col3\">Cumulative percentages\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th class=\"col_heading level2 col0\">Internet use rate (per 100 people)\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " </tr>\n", " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level0 row0\">\n", " (0, 5]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow0_col0\" class=\"data row0 col0\">\n", " 26\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow0_col1\" class=\"data row0 col1\">\n", " 26\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow0_col2\" class=\"data row0 col2\">\n", " 0.122\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow0_col3\" class=\"data row0 col3\">\n", " 0.122\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row1\">\n", " (5, 10]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow1_col0\" class=\"data row1 col0\">\n", " 23\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow1_col1\" class=\"data row1 col1\">\n", " 49\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow1_col2\" class=\"data row1 col2\">\n", " 0.108\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow1_col3\" class=\"data row1 col3\">\n", " 0.23\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row2\">\n", " (10, 15]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow2_col0\" class=\"data row2 col0\">\n", " 19\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow2_col1\" class=\"data row2 col1\">\n", " 68\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow2_col2\" class=\"data row2 col2\">\n", " 0.0892\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow2_col3\" class=\"data row2 col3\">\n", " 0.319\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row3\">\n", " (15, 20]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow3_col0\" class=\"data row3 col0\">\n", " 8\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow3_col1\" class=\"data row3 col1\">\n", " 76\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow3_col2\" class=\"data row3 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow3_col3\" class=\"data row3 col3\">\n", " 0.357\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row4\">\n", " (20, 25]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow4_col0\" class=\"data row4 col0\">\n", " 6\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow4_col1\" class=\"data row4 col1\">\n", " 82\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow4_col2\" class=\"data row4 col2\">\n", " 0.0282\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow4_col3\" class=\"data row4 col3\">\n", " 0.385\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row5\">\n", " (25, 30]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow5_col0\" class=\"data row5 col0\">\n", " 11\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow5_col1\" class=\"data row5 col1\">\n", " 93\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow5_col2\" class=\"data row5 col2\">\n", " 0.0516\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow5_col3\" class=\"data row5 col3\">\n", " 0.437\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row6\">\n", " (30, 35]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow6_col0\" class=\"data row6 col0\">\n", " 8\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow6_col1\" class=\"data row6 col1\">\n", " 101\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow6_col2\" class=\"data row6 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow6_col3\" class=\"data row6 col3\">\n", " 0.474\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row7\">\n", " (35, 40]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow7_col0\" class=\"data row7 col0\">\n", " 10\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow7_col1\" class=\"data row7 col1\">\n", " 111\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow7_col2\" class=\"data row7 col2\">\n", " 0.0469\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow7_col3\" class=\"data row7 col3\">\n", " 0.521\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row8\">\n", " (40, 45]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow8_col0\" class=\"data row8 col0\">\n", " 17\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow8_col1\" class=\"data row8 col1\">\n", " 128\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow8_col2\" class=\"data row8 col2\">\n", " 0.0798\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow8_col3\" class=\"data row8 col3\">\n", " 0.601\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row9\">\n", " (45, 50]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow9_col0\" class=\"data row9 col0\">\n", " 8\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow9_col1\" class=\"data row9 col1\">\n", " 136\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow9_col2\" class=\"data row9 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow9_col3\" class=\"data row9 col3\">\n", " 0.638\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row10\">\n", " (50, 55]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow10_col0\" class=\"data row10 col0\">\n", " 7\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow10_col1\" class=\"data row10 col1\">\n", " 143\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow10_col2\" class=\"data row10 col2\">\n", " 0.0329\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow10_col3\" class=\"data row10 col3\">\n", " 0.671\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row11\">\n", " (55, 60]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow11_col0\" class=\"data row11 col0\">\n", " 2\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow11_col1\" class=\"data row11 col1\">\n", " 145\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow11_col2\" class=\"data row11 col2\">\n", " 0.00939\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow11_col3\" class=\"data row11 col3\">\n", " 0.681\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row12\">\n", " (60, 65]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow12_col0\" class=\"data row12 col0\">\n", " 7\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow12_col1\" class=\"data row12 col1\">\n", " 152\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow12_col2\" class=\"data row12 col2\">\n", " 0.0329\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow12_col3\" class=\"data row12 col3\">\n", " 0.714\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row13\">\n", " (65, 70]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow13_col0\" class=\"data row13 col0\">\n", " 7\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow13_col1\" class=\"data row13 col1\">\n", " 159\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow13_col2\" class=\"data row13 col2\">\n", " 0.0329\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow13_col3\" class=\"data row13 col3\">\n", " 0.746\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row14\">\n", " (70, 75]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow14_col0\" class=\"data row14 col0\">\n", " 8\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow14_col1\" class=\"data row14 col1\">\n", " 167\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow14_col2\" class=\"data row14 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow14_col3\" class=\"data row14 col3\">\n", " 0.784\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row15\">\n", " (75, 80]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow15_col0\" class=\"data row15 col0\">\n", " 8\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow15_col1\" class=\"data row15 col1\">\n", " 175\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow15_col2\" class=\"data row15 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow15_col3\" class=\"data row15 col3\">\n", " 0.822\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row16\">\n", " (80, 85]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow16_col0\" class=\"data row16 col0\">\n", " 10\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow16_col1\" class=\"data row16 col1\">\n", " 185\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow16_col2\" class=\"data row16 col2\">\n", " 0.0469\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow16_col3\" class=\"data row16 col3\">\n", " 0.869\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row17\">\n", " (85, 90]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow17_col0\" class=\"data row17 col0\">\n", " 2\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow17_col1\" class=\"data row17 col1\">\n", " 187\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow17_col2\" class=\"data row17 col2\">\n", " 0.00939\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow17_col3\" class=\"data row17 col3\">\n", " 0.878\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row18\">\n", " (90, 95]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow18_col0\" class=\"data row18 col0\">\n", " 4\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow18_col1\" class=\"data row18 col1\">\n", " 191\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow18_col2\" class=\"data row18 col2\">\n", " 0.0188\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow18_col3\" class=\"data row18 col3\">\n", " 0.897\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row19\">\n", " (95, 100]\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow19_col0\" class=\"data row19 col0\">\n", " 1\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow19_col1\" class=\"data row19 col1\">\n", " 192\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow19_col2\" class=\"data row19 col2\">\n", " 0.00469\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow19_col3\" class=\"data row19 col3\">\n", " 0.901\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94ed\" class=\"row_heading level3 row20\">\n", " nan\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col0\" class=\"data row20 col0\">\n", " 21\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col1\" class=\"data row20 col1\">\n", " 213\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col2\" class=\"data row20 col2\">\n", " 0.0986\n", " \n", " <td id=\"T_dbaf8c7a_1825_11e6_900c_001bdc0f94edrow20_col3\" class=\"data row20 col3\">\n", " 1\n", " \n", " </tr>\n", " \n", " </tbody>\n", " </table>\n", " " ], "text/plain": [ "<pandas.core.style.Styler at 0x809a080>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "internetuserate_bins = pd.cut(subdata['internetuserate'], \n", " bins=np.linspace(0, 100., num=21))\n", "\n", "counts1 = internetuserate_bins.value_counts(sort=False, dropna=False)\n", "percentage1 = internetuserate_bins.value_counts(sort=False, normalize=True, dropna=False)\n", "data_struct = {\n", " 'Counts' : counts1,\n", " 'Cumulative counts' : counts1.cumsum(),\n", " 'Percentages' : percentage1,\n", " 'Cumulative percentages' : percentage1.cumsum()\n", "}\n", "\n", "internetrate_summary = pd.DataFrame(data_struct)\n", "internetrate_summary.index.name = 'Internet use rate (per 100 people)'\n", "(internetrate_summary[['Counts', 'Cumulative counts', 'Percentages', 'Cumulative percentages']]\n", " .style.set_precision(3)\n", " .set_properties(**{'text-align':'right'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Suicide per 100,000 people frequencies" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "variables": { "\"{0:.2f}, {1:.2f}\".format(subdata['suicideper100th'].min(), subdata['suicideper100th'].max())": "<p><strong>NameError</strong>: name &#39;subdata&#39; is not defined</p>\n" } }, "outputs": [ { "data": { "text/markdown": [ "Suicide per 100,000 people (min, max) = (0.20, 35.75)" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(\"Suicide per 100,000 people (min, max) = ({:.2f}, {:.2f})\".format(subdata['suicideper100th'].min(), subdata['suicideper100th'].max())))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <style type=\"text/css\" >\n", " \n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col3 {\n", " \n", " text-align: right;\n", " \n", " 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\n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " </style>\n", "\n", " <table id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" None>\n", " \n", "\n", " <thead>\n", " \n", " <tr>\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"col_heading level0 col0\">Counts\n", " \n", " <th class=\"col_heading level0 col1\">Cumulative counts\n", " \n", " <th class=\"col_heading level0 col2\">Percentages\n", " \n", " <th class=\"col_heading level0 col3\">Cumulative percentages\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th class=\"col_heading level2 col0\">Suicide (per 100 000 people)\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " </tr>\n", " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level0 row0\">\n", " (0, 2]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col0\" class=\"data row0 col0\">\n", " 11\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col1\" class=\"data row0 col1\">\n", " 11\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col2\" class=\"data row0 col2\">\n", " 0.0516\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow0_col3\" class=\"data row0 col3\">\n", " 0.0516\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row1\">\n", " (2, 4]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow1_col0\" class=\"data row1 col0\">\n", " 16\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow1_col1\" class=\"data row1 col1\">\n", " 27\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow1_col2\" class=\"data row1 col2\">\n", " 0.0751\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow1_col3\" class=\"data row1 col3\">\n", " 0.127\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row2\">\n", " (4, 6]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow2_col0\" class=\"data row2 col0\">\n", " 32\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow2_col1\" class=\"data row2 col1\">\n", " 59\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow2_col2\" class=\"data row2 col2\">\n", " 0.15\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow2_col3\" class=\"data row2 col3\">\n", " 0.277\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row3\">\n", " (6, 8]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow3_col0\" class=\"data row3 col0\">\n", " 29\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow3_col1\" class=\"data row3 col1\">\n", " 88\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow3_col2\" class=\"data row3 col2\">\n", " 0.136\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow3_col3\" class=\"data row3 col3\">\n", " 0.413\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row4\">\n", " (8, 10]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow4_col0\" class=\"data row4 col0\">\n", " 26\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow4_col1\" class=\"data row4 col1\">\n", " 114\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow4_col2\" class=\"data row4 col2\">\n", " 0.122\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow4_col3\" class=\"data row4 col3\">\n", " 0.535\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row5\">\n", " (10, 12]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow5_col0\" class=\"data row5 col0\">\n", " 24\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow5_col1\" class=\"data row5 col1\">\n", " 138\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow5_col2\" class=\"data row5 col2\">\n", " 0.113\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow5_col3\" class=\"data row5 col3\">\n", " 0.648\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row6\">\n", " (12, 14]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow6_col0\" class=\"data row6 col0\">\n", " 18\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow6_col1\" class=\"data row6 col1\">\n", " 156\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow6_col2\" class=\"data row6 col2\">\n", " 0.0845\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow6_col3\" class=\"data row6 col3\">\n", " 0.732\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row7\">\n", " (14, 16]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow7_col0\" class=\"data row7 col0\">\n", " 13\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow7_col1\" class=\"data row7 col1\">\n", " 169\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow7_col2\" class=\"data row7 col2\">\n", " 0.061\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow7_col3\" class=\"data row7 col3\">\n", " 0.793\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row8\">\n", " (16, 18]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow8_col0\" class=\"data row8 col0\">\n", " 4\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow8_col1\" class=\"data row8 col1\">\n", " 173\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow8_col2\" class=\"data row8 col2\">\n", " 0.0188\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow8_col3\" class=\"data row8 col3\">\n", " 0.812\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row9\">\n", " (18, 20]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow9_col0\" class=\"data row9 col0\">\n", " 4\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow9_col1\" class=\"data row9 col1\">\n", " 177\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow9_col2\" class=\"data row9 col2\">\n", " 0.0188\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow9_col3\" class=\"data row9 col3\">\n", " 0.831\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row10\">\n", " (20, 22]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow10_col0\" class=\"data row10 col0\">\n", " 4\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow10_col1\" class=\"data row10 col1\">\n", " 181\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow10_col2\" class=\"data row10 col2\">\n", " 0.0188\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow10_col3\" class=\"data row10 col3\">\n", " 0.85\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row11\">\n", " (22, 24]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow11_col0\" class=\"data row11 col0\">\n", " 2\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow11_col1\" class=\"data row11 col1\">\n", " 183\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow11_col2\" class=\"data row11 col2\">\n", " 0.00939\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow11_col3\" class=\"data row11 col3\">\n", " 0.859\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row12\">\n", " (24, 26]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow12_col0\" class=\"data row12 col0\">\n", " 1\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow12_col1\" class=\"data row12 col1\">\n", " 184\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow12_col2\" class=\"data row12 col2\">\n", " 0.00469\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow12_col3\" class=\"data row12 col3\">\n", " 0.864\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row13\">\n", " (26, 28]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow13_col0\" class=\"data row13 col0\">\n", " 3\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow13_col1\" class=\"data row13 col1\">\n", " 187\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow13_col2\" class=\"data row13 col2\">\n", " 0.0141\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow13_col3\" class=\"data row13 col3\">\n", " 0.878\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row14\">\n", " (28, 30]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow14_col0\" class=\"data row14 col0\">\n", " 2\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow14_col1\" class=\"data row14 col1\">\n", " 189\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow14_col2\" class=\"data row14 col2\">\n", " 0.00939\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow14_col3\" class=\"data row14 col3\">\n", " 0.887\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row15\">\n", " (30, 32]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow15_col0\" class=\"data row15 col0\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow15_col1\" class=\"data row15 col1\">\n", " 189\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow15_col2\" class=\"data row15 col2\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow15_col3\" class=\"data row15 col3\">\n", " 0.887\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row16\">\n", " (32, 34]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow16_col0\" class=\"data row16 col0\">\n", " 1\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow16_col1\" class=\"data row16 col1\">\n", " 190\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow16_col2\" class=\"data row16 col2\">\n", " 0.00469\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow16_col3\" class=\"data row16 col3\">\n", " 0.892\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row17\">\n", " (34, 36]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow17_col0\" class=\"data row17 col0\">\n", " 1\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow17_col1\" class=\"data row17 col1\">\n", " 191\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow17_col2\" class=\"data row17 col2\">\n", " 0.00469\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow17_col3\" class=\"data row17 col3\">\n", " 0.897\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row18\">\n", " (36, 38]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow18_col0\" class=\"data row18 col0\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow18_col1\" class=\"data row18 col1\">\n", " 191\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow18_col2\" class=\"data row18 col2\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow18_col3\" class=\"data row18 col3\">\n", " 0.897\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row19\">\n", " (38, 40]\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col0\" class=\"data row19 col0\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col1\" class=\"data row19 col1\">\n", " 191\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col2\" class=\"data row19 col2\">\n", " 0\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow19_col3\" class=\"data row19 col3\">\n", " 0.897\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94ed\" class=\"row_heading level3 row20\">\n", " nan\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col0\" class=\"data row20 col0\">\n", " 22\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col1\" class=\"data row20 col1\">\n", " 213\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col2\" class=\"data row20 col2\">\n", " 0.103\n", " \n", " <td id=\"T_dbe080de_1825_11e6_ab8e_001bdc0f94edrow20_col3\" class=\"data row20 col3\">\n", " 1\n", " \n", " </tr>\n", " \n", " </tbody>\n", " </table>\n", " " ], "text/plain": [ "<pandas.core.style.Styler at 0x8960320>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "suiciderate_bins = pd.cut(subdata['suicideper100th'], \n", " bins=np.linspace(0, 40., num=21))\n", "\n", "counts2 = suiciderate_bins.value_counts(sort=False, dropna=False)\n", "percentage2 = suiciderate_bins.value_counts(sort=False, normalize=True, dropna=False)\n", "data_struct = {\n", " 'Counts' : counts2,\n", " 'Cumulative counts' : counts2.cumsum(),\n", " 'Percentages' : percentage2,\n", " 'Cumulative percentages' : percentage2.cumsum()\n", "}\n", "\n", "suiciderate_summary = pd.DataFrame(data_struct)\n", "suiciderate_summary.index.name = 'Suicide (per 100 000 people)'\n", "(suiciderate_summary[['Counts', 'Cumulative counts', 'Percentages', 'Cumulative percentages']]\n", " .style.set_precision(3)\n", " .set_properties(**{'text-align':'right'}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unemployment rate frequencies" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "variables": { "\"{0:.2f}, {1:.2f}\".format(subdata['unemployrate'].min(), subdata['unemployrate'].max())": "<p><strong>NameError</strong>: name &#39;subdata&#39; is not defined</p>\n" } }, "outputs": [ { "data": { "text/markdown": [ "Unemployment rate (min, max) = (16.80, 68.00)" ], "text/plain": [ "<IPython.core.display.Markdown object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(Markdown(\"Unemployment rate (min, max) = ({0:.2f}, {1:.2f})\".format(subdata['unemployrate'].min(), subdata['unemployrate'].max())))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <style type=\"text/css\" >\n", " \n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " 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#T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col0 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col1 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col2 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " #T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col3 {\n", " \n", " text-align: right;\n", " \n", " }\n", " \n", " </style>\n", "\n", " <table id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" None>\n", " \n", "\n", " <thead>\n", " \n", " <tr>\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"col_heading level0 col0\">Counts\n", " \n", " <th class=\"col_heading level0 col1\">Cumulative counts\n", " \n", " <th class=\"col_heading level0 col2\">Percentages\n", " \n", " <th class=\"col_heading level0 col3\">Cumulative percentages\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th class=\"col_heading level2 col0\">Unemployement rate (% population age 15+)\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " <th class=\"blank\">\n", " \n", " </tr>\n", " \n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level0 row0\">\n", " (0, 5]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col0\" class=\"data row0 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col1\" class=\"data row0 col1\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col2\" class=\"data row0 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow0_col3\" class=\"data row0 col3\">\n", " 0\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row1\">\n", " (5, 10]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col0\" class=\"data row1 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col1\" class=\"data row1 col1\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col2\" class=\"data row1 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow1_col3\" class=\"data row1 col3\">\n", " 0\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row2\">\n", " (10, 15]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow2_col0\" class=\"data row2 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow2_col1\" class=\"data row2 col1\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow2_col2\" class=\"data row2 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow2_col3\" class=\"data row2 col3\">\n", " 0\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row3\">\n", " (15, 20]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow3_col0\" class=\"data row3 col0\">\n", " 6\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow3_col1\" class=\"data row3 col1\">\n", " 6\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow3_col2\" class=\"data row3 col2\">\n", " 0.0282\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow3_col3\" class=\"data row3 col3\">\n", " 0.0282\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row4\">\n", " (20, 25]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow4_col0\" class=\"data row4 col0\">\n", " 8\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow4_col1\" class=\"data row4 col1\">\n", " 14\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow4_col2\" class=\"data row4 col2\">\n", " 0.0376\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow4_col3\" class=\"data row4 col3\">\n", " 0.0657\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row5\">\n", " (25, 30]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow5_col0\" class=\"data row5 col0\">\n", " 13\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow5_col1\" class=\"data row5 col1\">\n", " 27\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow5_col2\" class=\"data row5 col2\">\n", " 0.061\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow5_col3\" class=\"data row5 col3\">\n", " 0.127\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row6\">\n", " (30, 35]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow6_col0\" class=\"data row6 col0\">\n", " 18\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow6_col1\" class=\"data row6 col1\">\n", " 45\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow6_col2\" class=\"data row6 col2\">\n", " 0.0845\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow6_col3\" class=\"data row6 col3\">\n", " 0.211\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row7\">\n", " (35, 40]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow7_col0\" class=\"data row7 col0\">\n", " 29\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow7_col1\" class=\"data row7 col1\">\n", " 74\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow7_col2\" class=\"data row7 col2\">\n", " 0.136\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow7_col3\" class=\"data row7 col3\">\n", " 0.347\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row8\">\n", " (40, 45]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col0\" class=\"data row8 col0\">\n", " 44\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col1\" class=\"data row8 col1\">\n", " 118\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col2\" class=\"data row8 col2\">\n", " 0.207\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow8_col3\" class=\"data row8 col3\">\n", " 0.554\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row9\">\n", " (45, 50]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col0\" class=\"data row9 col0\">\n", " 23\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col1\" class=\"data row9 col1\">\n", " 141\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col2\" class=\"data row9 col2\">\n", " 0.108\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow9_col3\" class=\"data row9 col3\">\n", " 0.662\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row10\">\n", " (50, 55]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col0\" class=\"data row10 col0\">\n", " 18\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col1\" class=\"data row10 col1\">\n", " 159\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col2\" class=\"data row10 col2\">\n", " 0.0845\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow10_col3\" class=\"data row10 col3\">\n", " 0.746\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row11\">\n", " (55, 60]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col0\" class=\"data row11 col0\">\n", " 14\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col1\" class=\"data row11 col1\">\n", " 173\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col2\" class=\"data row11 col2\">\n", " 0.0657\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow11_col3\" class=\"data row11 col3\">\n", " 0.812\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row12\">\n", " (60, 65]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col0\" class=\"data row12 col0\">\n", " 3\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col1\" class=\"data row12 col1\">\n", " 176\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col2\" class=\"data row12 col2\">\n", " 0.0141\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow12_col3\" class=\"data row12 col3\">\n", " 0.826\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row13\">\n", " (65, 70]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col0\" class=\"data row13 col0\">\n", " 2\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col1\" class=\"data row13 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col2\" class=\"data row13 col2\">\n", " 0.00939\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow13_col3\" class=\"data row13 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row14\">\n", " (70, 75]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col0\" class=\"data row14 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col1\" class=\"data row14 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col2\" class=\"data row14 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow14_col3\" class=\"data row14 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row15\">\n", " (75, 80]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col0\" class=\"data row15 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col1\" class=\"data row15 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col2\" class=\"data row15 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow15_col3\" class=\"data row15 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row16\">\n", " (80, 85]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col0\" class=\"data row16 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col1\" class=\"data row16 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col2\" class=\"data row16 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow16_col3\" class=\"data row16 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row17\">\n", " (85, 90]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col0\" class=\"data row17 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col1\" class=\"data row17 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col2\" class=\"data row17 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow17_col3\" class=\"data row17 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row18\">\n", " (90, 95]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col0\" class=\"data row18 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col1\" class=\"data row18 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col2\" class=\"data row18 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow18_col3\" class=\"data row18 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row19\">\n", " (95, 100]\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col0\" class=\"data row19 col0\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col1\" class=\"data row19 col1\">\n", " 178\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col2\" class=\"data row19 col2\">\n", " 0\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow19_col3\" class=\"data row19 col3\">\n", " 0.836\n", " \n", " </tr>\n", " \n", " <tr>\n", " \n", " <th id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94ed\" class=\"row_heading level3 row20\">\n", " nan\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col0\" class=\"data row20 col0\">\n", " 35\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col1\" class=\"data row20 col1\">\n", " 213\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col2\" class=\"data row20 col2\">\n", " 0.164\n", " \n", " <td id=\"T_dc12d4c8_1825_11e6_a6a5_001bdc0f94edrow20_col3\" class=\"data row20 col3\">\n", " 1\n", " \n", " </tr>\n", " \n", " </tbody>\n", " </table>\n", " " ], "text/plain": [ "<pandas.core.style.Styler at 0x8966f28>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unemployment_bins = pd.cut(subdata['unemployrate'], \n", " bins=np.linspace(0, 100., num=21))\n", "\n", "\n", "counts3 = unemployment_bins.value_counts(sort=False, dropna=False)\n", "percentage3 = unemployment_bins.value_counts(sort=False, normalize=True, dropna=False)\n", "data_struct = {\n", " 'Counts' : counts3,\n", " 'Cumulative counts' : counts3.cumsum(),\n", " 'Percentages' : percentage3,\n", " 'Cumulative percentages' : percentage3.cumsum()\n", "}\n", "\n", "unemployment_summary = pd.DataFrame(data_struct)\n", "unemployment_summary.index.name = 'Unemployement rate (% population age 15+)'\n", "(unemployment_summary[['Counts', 'Cumulative counts', 'Percentages', 'Cumulative percentages']]\n", " .style.set_precision(3)\n", " .set_properties(**{'text-align':'right'}))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Summary\n", "\n", "The Gapminder data based provides information for 213 countries. \n", "\n", "As the unemployment rate is not provided directly in the database, it was computed as `100 - employment rate`.\n", "\n", "The distributions of the variables are as follow:\n", "\n", "- Internet Use Rate per 100 people\n", " * Data missing for 21 countries\n", " * Rate ranges from 0.21 to 95.64\n", " * The majority of the countries (64%) have a rate below 50\n", "- Suicide Rate per 100 000\n", " * Data missing for 22 countries\n", " * Rate ranges from 0.2 to 35.75\n", " * The rate is more often between 4 and 12\n", "- Unemployment Rate for age 15+\n", " * Data missing for 35 countries\n", " * Rate ranges from 16.8 to 68\n", " * For the majority of the countries the rate lies below 45\n", "\n", "From those data, I was surprised that so few people have access to the internet especially now that smartphones are cheap.\n", "\n", "Another astonishing facts is the high unemployment rate, I was expected much less; especially in so called developped countries. But I presume that long school time and retirement can explain those high values as people of age 15+ are considered here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> If you are interested by the subject, follow me on [Tumblr](http://fcollonval.tumblr.com/)." ] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" }, "toc": { "toc_cell": false, "toc_number_sections": true, "toc_threshold": "3", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 0 }
mit
amueller/advanced_training
05.1 Trees and Forests.ipynb
1
374567
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Trees and Forests" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib notebook\n", "from preamble import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Decision Tree Classification\n", "==================\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plots import plot_tree_interactive\n", "plot_tree_interactive()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Forests" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from plots import plot_forest_interactive\n", "plot_forest_interactive()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x7fae5c55d278>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.datasets import make_moons\n", "from sklearn.model_selection import train_test_split\n", "\n", "X, y = make_moons(n_samples=100, noise=0.25, random_state=3)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n", "\n", "forest = RandomForestClassifier(n_estimators=5, random_state=2)\n", "forest.fit(X_train, y_train)\n", "\n", "fig, axes = plt.subplots(2, 3, figsize=(20, 10))\n", "for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n", " ax.set_title(\"tree %d\" % i)\n", " mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n", "mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)\n", "axes[-1, -1].set_title(\"random forest\")\n", "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=60, cmap=mglearn.cm2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting the Optimal Estimator via Cross-Validation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=5, error_score='raise',\n", " estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=200, n_jobs=-1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'max_depth': [5, 7, 9], 'max_features': ['sqrt', 'log2']},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.datasets import load_boston\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestRegressor\n", "\n", "boston = load_boston()\n", "X, y = boston.data, boston.target\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n", "\n", "rf = RandomForestRegressor(n_estimators=200, n_jobs=-1)\n", "parameters = {'max_features':['sqrt', 'log2'],\n", " 'max_depth':[5, 7, 9]}\n", "\n", "grid = GridSearchCV(rf, parameters, cv=5)\n", "grid.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.97946287860156334" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.score(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.71472766354731021" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.078, 0.014, 0.081, 0.007, 0.068, 0.287, 0.036, 0.056,\n", " 0.011, 0.048, 0.084, 0.026, 0.203])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.best_estimator_.feature_importances_" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exercises\n", "Compare training and test performance of the decision tree (``sklearn.tree.DecisionTreeRegressor``) and random forest on the bike dataset.\n", "What is the effect of changing ``max_depth`` to training and test set score?\n", "How do the feature importances of trees and forest differ?\n", "Use ``mglearn.tools.get_tree`` to visualize a decision tree." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
jrbourbeau/composition
analysis/parameter-tuning/KS-test.ipynb
1
60789
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "from __future__ import division\n", "import argparse\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn.apionly as sns\n", "import scipy.stats as stats\n", "\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import validation_curve, GridSearchCV, cross_val_score\n", "\n", "from composition.analysis.load_sim import load_sim\n", "from composition.analysis.preprocessing import get_train_test_sets, LabelEncoder\n", "from composition.analysis.features import get_training_features\n", "from composition.analysis.pipelines import get_pipeline\n", "import composition.analysis.data_functions as data_functions\n", "from composition.support_functions.checkdir import checkdir\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_palette('muted')\n", "sns.set_color_codes()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/composition/analysis/load_sim.py:65: RuntimeWarning: divide by zero encountered in log10\n", " df['reco_log_energy'] = np.nan_to_num(np.log10(df['reco_energy']))\n", "/home/jbourbeau/composition/analysis/load_sim.py:66: RuntimeWarning: invalid value encountered in log10\n", " df['InIce_log_charge'] = np.nan_to_num(np.log10(df['InIce_charge']))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "events = 72644\n" ] } ], "source": [ "df, cut_dict = load_sim(return_cut_dict=True)\n", "selection_mask = np.array([True] * len(df))\n", "standard_cut_keys = ['reco_exists', 'reco_zenith', 'num_hits', 'IT_signal',\n", " 'StationDensity', 'max_charge_frac', 'reco_containment',\n", " 'min_energy', 'energy_range']\n", "for key in standard_cut_keys:\n", " selection_mask *= cut_dict[key]\n", "\n", "df = df[selection_mask]\n", "\n", "feature_list = get_training_features()\n", "X_train, X_test, y_train, y_test, le = get_train_test_sets(df, feature_list)\n", "\n", "print('events = ' + str(y_train.shape[0]))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x817fdd0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LtwAAAAAAgK4RRAAAAAAAAF0jiAAAAAAAALpGEAEAAAAAAHSNIAIAAAAAAOga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\njlbVH6x+lQAAAAAAwOWMdBBRVd9Ncm+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"text/plain": [ "<matplotlib.figure.Figure at 0x83c93d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max_depth_list = np.arange(2, 15)\n", "ks_proton = []\n", "pval_proton = []\n", "ks_iron = []\n", "pval_iron = []\n", "\n", "for depth in max_depth_list:\n", " pipeline = get_pipeline('RF')\n", " pipeline.named_steps['classifier'].set_params(max_depth=depth)\n", " pipeline.fit(X_train, y_train)\n", " test_probs = pipeline.predict_proba(X_test)\n", " train_probs = pipeline.predict_proba(X_train)\n", " k_proton, p_proton = stats.ks_2samp(test_probs[:,0], train_probs[:,0])\n", " ks_proton.append(k_proton)\n", " pval_proton.append(p_proton)\n", " k_iron, p_iron = stats.ks_2samp(test_probs[:,1], train_probs[:,1])\n", " ks_iron.append(k_iron)\n", " pval_iron.append(p_iron)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(max_depth_list, pval_proton, markersize=10, alpha=0.5)\n", "ax.plot(max_depth_list, pval_iron, marker='^', markersize=10, alpha=0.5)\n", "plt.xlim([0,len(max_depth_list)+2])\n", "plt.ylim([0,1.1])\n", "# plt.show()\n", " \n", "# plot_decision_regions(X_test_std, y_test, clf, scatter_fraction=None, ax=ax)\n", "ax.set_xlabel('Max depth')\n", "ax.set_ylabel('KS test p-value')\n", "# ax.set_title('Max depth = {}'.format(depth))\n", "# ax.legend()\n", "# plt.tight_layout()\n", "# plt.savefig('/home/jbourbeau/public_html/figures/composition/parameter-tuning/RF-decision-regions.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
Jwuthri/Mozinor
mozinor/example/Mozinor example Class.ipynb
1
114565
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use mozinor for classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import the main module" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jwuthri/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from mozinor.baboulinet import Baboulinet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Prepare the pipeline\n", "\n", " (str) filepath: Give the csv file\n", " (str) y_col: The column to predict\n", " (bool) regression: Regression or Classification ?\n", " (bool) process: (WARNING) apply some preprocessing on your data (tune this preprocess with params below)\n", " (char) sep: delimiter\n", " (list) col_to_drop: which columns you don't want to use in your prediction\n", " (bool) derivate: for all features combination apply, n1 * n2, n1 / n2 ...\n", " (bool) transform: for all features apply, log(n), sqrt(n), square(n)\n", " (bool) scaled: scale the data ?\n", " (bool) infer_datetime: for all columns check the type and build new columns from them (day, month, year, time) if they are date type\n", " (str) encoding: data encoding\n", " (bool) dummify: apply dummies on your categoric variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The data files have been generated by sklearn.dataset.make_classification" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cls = Baboulinet(filepath=\"toto.csv\", y_col=\"predict\", regression=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now run the pipeline\n", "\n", " May take some times" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Reading the file toto.csv\n", "Read csv file: toto.csv\n", "args: {'encoding': 'utf-8-sig', 'sep': ',', 'decimal': ',', 'engine': 'python', 'filepath_or_buffer': 'toto.csv', 'thousands': '.', 'parse_dates': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'predict'], 'infer_datetime_format': True}\n", "Inital dtypes is a float64\n", "b float64\n", "c float64\n", "d float64\n", "e float64\n", "f float64\n", "g float64\n", "h float64\n", "predict int64\n", "dtype: object\n", "Work on PolynomialFeatures: degree 1\n", "Optimal number of clusters\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(10000, 9)\n", "\n", " Polynomial Features: generate a new feature matrix\n", " consisting of all polynomial combinations of the features.\n", " For 2 features [a, b]:\n", " the degree 1 polynomial give [a, b]\n", " the degree 2 polynomial give [1, a, b, a^2, ab, b^2]\n", " ...\n", "\n", "\n", " ELBOW: explain the variance as a function of clusters.\n", "\n" ] }, { "data": { "image/png": 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1jSm7xcyqzGy5mV0UU15uZovCvrvMzEJ5tplNC+Vzzawsps5UM1sRHlPj9R7jIT3NuGHS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\n7l7c1kEKm07EzCqPZYhhsnW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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb81410fc50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Optimal number of trees\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " OOB: this is the average error for each training observations,\n", " calculted using the trees that doesn't contains this observation\n", " during the creation of the tree.\n", "\n" ] }, { "data": { "image/png": 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qLiAgQCxVIdFoFALf/I273Z1YNLboP8bqLCoqio4dO1o6DE2rVlJTU6lTpw5JSUkEBgay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\nB8/0VzHmjPL7lT+XHeOBzVWUH20jOyWeu4vm87H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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb813e411d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Estimator ExtraTreesClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " ExtraTreesClassifier: as in random forests, a random subset of candidate\n", " features is used, but instead of looking for the most discriminative\n", " thresholds, thresholds are drawn at random for each candidate feature and\n", " the best of these randomly-generated thresholds is picked as\n", " the splitting rule.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 9.1s finished\n", " Best params => {'n_estimators': 100, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 0.6, 'criterion': 'entropy', 'bootstrap': False}\n", " Best Score => 0.865\n", "Estimator XGBClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Gradient boosting is an approach where new models are created that predict\n", " the residuals or errors of prior models and then added together to make\n", " the final prediction. It is called gradient boosting because it uses a\n", " gradient descent algorithm to minimize the loss when adding new models.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 38.4s finished\n", " Best params => {'subsample': 0.9, 'n_estimators': 50, 'min_child_weight': 6, 'max_depth': 8, 'learning_rate': 0.5}\n", " Best Score => 0.855\n", "Estimator KNeighborsClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " KNeighborsClassifier: Majority vote of its k nearest neighbors.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 4.1s finished\n", " Best params => {'n_neighbors': 17, 'p': 2, 'weights': 'distance'}\n", " Best Score => 0.853\n", "Estimator DecisionTreeClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Decision Tree Classifier: poses a series of carefully crafted questions\n", " about the attributes of the test record. Each time time it receive an answer,\n", " a follow-up question is asked until a conclusion about the calss label\n", " of the record is reached.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 2.0s finished\n", " Best params => {'min_samples_split': 5, 'min_samples_leaf': 2, 'max_depth': 10, 'criterion': 'entropy'}\n", " Best Score => 0.750\n", "Check the decision tree: 2017-08-1813:13:19.847449.png\n", "Work on PolynomialFeatures: degree 2\n", "Optimal number of clusters\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dot: graph is too large for cairo-renderer bitmaps. Scaling by 0.880171 to fit\n", "\n", "\n", " Polynomial Features: generate a new feature matrix\n", " consisting of all polynomial combinations of the features.\n", " For 2 features [a, b]:\n", " the degree 1 polynomial give [a, b]\n", " the degree 2 polynomial give [1, a, b, a^2, ab, b^2]\n", " ...\n", "\n", "\n", " ELBOW: explain the variance as a function of clusters.\n", "\n" ] }, { "data": { "image/png": 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7RJE2oSBqRQoiebte3bKLv83fxNQFG9m8cz89umQwaVRfrhgzgDOG5pOepvNJ\n0nEpiFqRgkjeqfp656U12/nb/I1MW7yF3QdqKerVhYtH9ePiUX0pK+6tUJIOR0HUihRE0pr2H6rj\n6eVbmbpgI7NWVHGgtp7Cnl2YNLIvF5/Ul3HFvclI13Rwaf8URK1IQSTJsvdALTNf28oTizfzzKtb\n2X+onvzuWUwc2Zf3nNSPM4YqlKT9UhC1IgWRtIV9B2uZ9VoVTyzZwtPLK9l3sI687Ewmjoj2lCaU\nFpCpUJJ2REHUihRE0tb2H6pj1ooqpi3ezFPLt7LnQC053TK5cEQRl4RQ6pKh2XeS2hRErUhBJHHa\nf6iO51du44klm5mxrJLd+2vp2SWDd48o4uJRfTlrWAHZWRlxlynyb1oaRPrtFUlxXTPTefeIIt49\nooiDtfXMXrWNJxZtZvqySh6dv5EuGWmML8nnghOLuODEPvTL6RZ3ySLHRHtELaA9IklFh+rqmbOm\nmqeWV/L08q2sq94HwMj+vbjgxCLefWIfRvXPIU3TwiUmOjTXihREkurcnYqte3hq+VaeXl7JK+tq\nqHfo07MLF5zYhwuGFzGhtEBXdZA2pSBqRQoiaW+q9x7k2de28vTyrcxaUcWeA7V0yUjjrNKCw4fw\ninp1jbtM6eAURK1IQSTt2cHahEN4r1ayvvoNAE4akMP5w/twwYl9GNk/R1d2kFanIGpFCiLpKNyd\nlVv3HD6v9Mq6GtwhNzuT8SX5TCgt4OzSQgbnZ8ddqnQACqJWpCCSjmr7ngM8X7GN51du4/mKbWze\nuR+AQb27cVZpARNKCxhfUkDv7lkxVyrtkYKoFSmIpDNwd1Zv28vsEEwvrtrO7gO1mEUz8SaUFnBW\naQGnFfema6YmPUjzFEStSEEknVFtXT2LNu5kdthbemVdDYfqnKyMNE4rzjscTDq/JEejIGpFCiKR\n6Fp4c9ZUHz6M9+qW3UB0fums0gIuHFHEecP70KtrZsyVSqqI/coKZjYI+B1QBDhwj7vfYWbfAK4F\nqkLXr7j7E2HMzcA1QB3wGXd/MrSfCtwPdAOeAG50dzezLuE1TgW2Ax9097VhzBTg/8JrfMfdHwjt\nQ4CHgHxgHvARdz+YpI9BpMPIzsrg3BP6cO4JfQCo2n2AF1ZFh/FmvraVxxdtJjPdOGNoPhPDlSB0\nlQdpiaTtEZlZP6Cfu79iZj2J/tG/HPgAsMfdf9yo/wjgj8A4oD/wFHC8u9eZ2RzgM8DLREF0p7tP\nM7NPAyexF8GaAAAPUklEQVS7+yfNbDJwhbt/0Mx6A+VAGVEIzgNOdfcaM3sE+Ku7P2RmvwQWuvvd\nTb0X7RGJNK2u3lmwvobpyyqZsbSS1dv2AnDKwBwuHFHExJF9GdanB2Y6hNeZpNyhOTObCvwcmMCR\ng+hmAHf/fnj+JPANYC0w092Hh/YPAee6+yca+rj7i2aWAWwBCoHJDX3CmF8BzxLtCVUBfd291szO\nDOMvaqp2BZHIsanYuofpy7YwfWklC9bvAKA4P/twKI0dnKfzSp1A7IfmGhVTDIwh2qOZANxgZlcT\n7bV83t1rgAHASwnDNoS2Q2G5cTvh53qAECw7iQ65HW5vNCYf2OHutUfYVuOarwOuAxg8ePCxvmWR\nTq20Tw9K+5Ty6XNLqdy1n6eWVzJ9aSX3v7CWe59bQ373LC44sQ8TR0RXD9csvM4t6UFkZj2AvwCf\ndfddZnY38G2iQ2bfBm4DPp7sOo6Vu98D3APRHlHM5Yi0W0W9unLV6cdx1enHsXv/IWatqGL60kqm\nLd7CI+Ub6JaZzjnHF3DeCX04fWg+xfnZOoTXySQ1iMwskyiE/uDufwVw98qE9fcCj4enG4FBCcMH\nhraNYblxe+KYDeHQXA7RpIWNwLmNxjwb1uWaWUbYK0rclogkWc+umVx6cn8uPbk/B2vreXnNdqYv\nrWTGskqeXBr909CnZxfGDenN6UN6c/rQfEoLe+gK4h1cMmfNGfAbYLm7/yShvZ+7bw5PrwCWhOXH\ngP9nZj8hmqwwDJgTJivsMrMziA7tXQ38LGHMFOBF4P3AM2E23ZPA98wsL/SbCNwc1s0MfR8KY6cm\n4/2LSNOyMtI4e1ghZw8r5FuXjWRV1V7mrKnm5TXbeXl1NY8viv6Z6N09i9OK8zh9SD7jhvTmxH69\ndH6pg0nmrLmzgOeAxUB9aP4K8CFgNNGhubXAJxqCycy+SnSYrpboUN600F7Gm9O3pwE3hFDpCvye\n6PxTNTDZ3VeHMR8PrwfwXXe/L7QPJQqh3sB84MPufqCp96LJCiJty91ZX/0GL63ZfjicGi7W2rNr\nBqcVR3tM44b0ZtSAHDLT02KuWI4k5WbNtWcKIpH4bdrxxpt7TGuqWV0VTRHPzkrn1OPyOH1Ib8qK\ne3PKwFzddylFKIhakYJIJPVs3b2fuWtqeDnsNTVc6SE9zRjRrxdjB+cy9rg8xg7OY2BeN02AiIGC\nqBUpiERSX83eg8xfX8Mrr+9g3us1LNywg30H6wAo7NmFsYNzOTUE06gBOZoy3gZS6ntEIiLJltc9\ni/OHF3H+8CIgumjra5W7eeX1Gl5Zt4NX1tUcnpmXmW6M7J/D2MF5jD0ul7GD8+ifq8sRxUV7RC2g\nPSKRjqFq9wHmrwvBFPaaDtRGc6n65XRl7OA8ThqYw8j+vRjZP0f3YXqHtEckItJIYc8uTBzZl4kj\n+wJwqK6e5Zt3Me/1N8PpH4s3H+7fL6crI/v3YkT/KJxG9Oul801JoCASkU4rMz2NkwfmcvLAXD42\nIWqr2XuQZZt3sXTTTpZu2sXSTbt45tWt1IeDRzndMhnRr1e01zQg2nMaWtCdDE0hf9sURCIiCfK6\nZzEh3Ca9wRsH63h1y67DwbRs005+/9Lrhw/rdclIY3jfnof3nE7o25Pj+/QkJ1v3ZmoJBZGISDO6\nZaUzZnAeYwbnHW6rratn9ba90Z7Txiig/rFoE3+cs+5wn8KeXTi+qAfD+vSktE8Pji/qybA+PcjT\nuae3UBCJiLwNGelpHF/Uk+OLenLFmKjN3dm44w1WVu5h5dbdrKjcw8qte/hT+Xr2hqnkAAU9shjW\npyfDinowrE8PhoWAyu/RJaZ3Ey8FkYhIKzEzBuZlMzAvm/OG9znc7u5s2rmflZW7qdi6hxWVu1m5\ndQ+PvrKR3QdqD/fr3T0rBFMPTuwXnX86oahnh79ShKZvt4Cmb4tIMrg7W3btD3tQe1gZAmpF5W52\n748CKs2gpLAHI8KsvZH9cxjRv1e7mFqu6dsiIinOzOiX041+Od045/jCw+3uzoaaN6KJEZujyRFz\n11QzdcGmw3365XQ9PHtvRPjeU3udWq4gEhFJMWbGoN7ZDOqdzaRRfQ+3N0wtX7Ypml6+bPMunl1R\nRV2YW96zawYj+vU6vPdU0qcHQwu6k5ud2ntPCiIRkXbiSFPL9x+q47Utuw9/92nZpl08NGc9bxx6\nc3JEXnYmQwt7MKSgO0MKujO0oDtDC3twXH52SlxzT0EkItKOdc1M55RBuZwyKPdwW1298/r2vazZ\nFj1Wb9vL6qo9PLeyij/P23C4nxn0z+nG0MI3A2pIYbQX1T+3W5vdgFBBJCLSwaSnGUMLezC0sMe/\nrdtzoJa1IZzWVO1lzbY9rN62999m8GVlpFGcn83dHz6VkiNspzUpiEREOpEeXTIYNSCHUQNy3tLu\n7mzbczDag6rac3hPqncbnF9SEImICGZGYc8uFPbswrghvdv0tXWVPhERiZWCSEREYqUgEhGRWCmI\nREQkVgoiERGJlYJIRERipSASEZFYKYhERCRWuh9RC5hZFfB63HU0oQDYFncRLdRealWdrau91Ant\np9b2UOdx7l7YXCcFUQdgZuUtuflUKmgvtarO1tVe6oT2U2t7qbMldGhORERipSASEZFYKYg6hnvi\nLuAYtJdaVWfrai91Qvuptb3U2SydIxIRkVhpj0hERGKlIBIRkVgpiNoJMxtkZjPNbJmZLTWzG4/Q\n51wz22lmC8Lj6zHVutbMFocayo+w3szsTjOrMLNFZjY2pjpPSPisFpjZLjP7bKM+sXymZvZbM9tq\nZksS2nqb2QwzWxl+5h1l7CQzey18vl+Ooc4fmdmr4c/2UTPLPcrYJn9P2qjWb5jZxoQ/30uOMjbu\nz/ThhBrXmtmCo4xt08+01bi7Hu3gAfQDxoblnsAKYESjPucCj6dArWuBgibWXwJMAww4A3g5BWpO\nB7YQfQEv9s8UOAcYCyxJaPsh8OWw/GXg1qO8j1XAUCALWNj496QN6pwIZITlW49UZ0t+T9qo1m8A\nX2jB70asn2mj9bcBX0+Fz7S1HtojaifcfbO7vxKWdwPLgQHxVvW2XQb8ziMvAblm1i/mmi4AVrl7\nSlxBw93/BVQ3ar4MeCAsPwBcfoSh44AKd1/t7geBh8K4NqvT3ae7e214+hIwMFmvfyyO8pm2ROyf\naQMzM+ADwB+T9fpxUBC1Q2ZWDIwBXj7C6vHhkMg0MxvZpoW9yYGnzGyemV13hPUDgPUJzzcQf6hO\n5uh/uVPhMwUocvfNYXkLUHSEPqn22X6caO/3SJr7PWkrN4Q/398e5XBnKn2mZwOV7r7yKOtT5TM9\nJgqidsbMegB/AT7r7rsarX4FGOzuJwM/A/7W1vUFZ7n7aOBi4HozOyemOlrEzLKA9wJ/OsLqVPlM\n38Kj4zAp/d0LM/sqUAv84ShdUuH35G6iQ26jgc1Eh71S2Ydoem8oFT7TY6YgakfMLJMohP7g7n9t\nvN7dd7n7nrD8BJBpZgVtXCbuvjH83Ao8SnRoI9FGYFDC84GhLS4XA6+4e2XjFanymQaVDYcww8+t\nR+iTEp+tmX0UuBS4KoTmv2n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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb802b53a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Optimal number of trees\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " OOB: this is the average error for each training observations,\n", " calculted using the trees that doesn't contains this observation\n", " during the creation of the tree.\n", "\n" ] }, { "data": { "image/png": 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6L6saVT3tQ2vGuOtEPRJTeTzZjeHRKxIRGSIiW0Rku4hMKmf5WBFZKyLrRGS5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\nN1LkcA730qZum5LEEh0aTfPaza05zFQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb802b57908>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Estimator ExtraTreesClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " ExtraTreesClassifier: as in random forests, a random subset of candidate\n", " features is used, but instead of looking for the most discriminative\n", " thresholds, thresholds are drawn at random for each candidate feature and\n", " the best of these randomly-generated thresholds is picked as\n", " the splitting rule.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 17.0s finished\n", " Best params => {'n_estimators': 50, 'min_samples_split': 3, 'min_samples_leaf': 1, 'max_features': 0.1, 'criterion': 'gini', 'bootstrap': False}\n", " Best Score => 0.857\n", "Estimator XGBClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Gradient boosting is an approach where new models are created that predict\n", " the residuals or errors of prior models and then added together to make\n", " the final prediction. It is called gradient boosting because it uses a\n", " gradient descent algorithm to minimize the loss when adding new models.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 1.3min finished\n", " Best params => {'subsample': 0.9, 'n_estimators': 100, 'min_child_weight': 7, 'max_depth': 4, 'learning_rate': 0.5}\n", " Best Score => 0.857\n", "Estimator KNeighborsClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " KNeighborsClassifier: Majority vote of its k nearest neighbors.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n", "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 36.3s finished\n", " Best params => {'n_neighbors': 11, 'p': 2, 'weights': 'distance'}\n", " Best Score => 0.853\n", "Estimator DecisionTreeClassifier\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Decision Tree Classifier: poses a series of carefully crafted questions\n", " about the attributes of the test record. Each time time it receive an answer,\n", " a follow-up question is asked until a conclusion about the calss label\n", " of the record is reached.\n", "\n", "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=1)]: Done 30 out of 30 | elapsed: 7.5s finished\n", " Best params => {'min_samples_split': 7, 'min_samples_leaf': 8, 'max_depth': 6, 'criterion': 'gini'}\n", " Best Score => 0.738\n", "Check the decision tree: 2017-08-1813:18:56.364832.png\n", " Estimator Score Degree\n", "0 (ExtraTreeClassifier(class_weight=None, criter... 0.864667 1\n", "1 XGBClassifier(base_score=0.5, colsample_byleve... 0.856800 2\n", "2 (ExtraTreeClassifier(class_weight=None, criter... 0.856667 2\n", "3 XGBClassifier(base_score=0.5, colsample_byleve... 0.855333 1\n", "4 KNeighborsClassifier(algorithm='auto', leaf_si... 0.853333 1\n", "5 KNeighborsClassifier(algorithm='auto', leaf_si... 0.852933 2\n", "6 DecisionTreeClassifier(class_weight=None, crit... 0.750400 1\n", "7 DecisionTreeClassifier(class_weight=None, crit... 0.737867 2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Stacking: is a model ensembling technique used to combine information\n", " from multiple predictive models to generate a new model.\n", "\n", "task: [classification]\n", "metric: [accuracy_score]\n", "\n", "model 0: [ExtraTreesClassifier]\n", " ----\n", " MEAN: [0.86173333]\n", "\n", "model 1: [XGBClassifier]\n", " ----\n", " MEAN: [0.84853333]\n", "\n", "model 2: [KNeighborsClassifier]\n", " ----\n", " MEAN: [0.86053333]\n", "\n", "model 3: [DecisionTreeClassifier]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/15 [00:00<?, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ----\n", " MEAN: [0.75666667]\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Stacking 4 models: 100%|██████████| 15/15 [00:21<00:00, 1.63s/it]\n" ] } ], "source": [ "res = cls.babouline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The class instance, now contains 2 objects, the model for this data, and the best stacking for this data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### To make auto generate the code of the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate the code for the best model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Check script file toto_solo_model_script.py\n" ] }, { "data": { "text/plain": [ "'toto_solo_model_script.py'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cls.bestModelScript()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate the code for the best stacking" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Check script file toto_stack_model_script.py\n" ] }, { "data": { "text/plain": [ "'toto_stack_model_script.py'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cls.bestStackModelScript()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### To check which model is the best" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Best model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Estimator (ExtraTreeClassifier(class_weight=None, criter...\n", "Score 0.864667\n", "Degree 1\n", "Name: 0, dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.best_model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Model: ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',\n", " max_depth=None, max_features=0.6, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=4, min_weight_fraction_leaf=0.0,\n", " n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False),\n", " Score: 0.8646666666666667\n", "\n" ] } ], "source": [ "show = \"\"\"\n", " Model: {},\n", " Score: {}\n", "\"\"\"\n", "print(show.format(res.best_model[\"Estimator\"], res.best_model[\"Score\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Best stacking" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Fit1stLevelEstimator [(ExtraTreeClassifier(class_weight=None, crite...\n", "Fit2ndLevelEstimator DecisionTreeClassifier(class_weight=None, crit...\n", "Score 0.8736\n", "Degree 1\n", "Name: 0, dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res.best_stack_models" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " FirstModel: [ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='entropy',\n", " max_depth=None, max_features=0.6, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=4, min_weight_fraction_leaf=0.0,\n", " n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False), XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n", " gamma=0, learning_rate=0.5, max_delta_step=0, max_depth=8,\n", " min_child_weight=6, missing=None, n_estimators=50, nthread=-1,\n", " objective='multi:softprob', reg_alpha=0, reg_lambda=1,\n", " scale_pos_weight=1, seed=0, silent=True, subsample=0.9), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=17, p=2,\n", " weights='distance')],\n", " SecondModel: DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=10,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=2,\n", " min_samples_split=5, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best'),\n", " Score: 0.8736\n", "\n" ] } ], "source": [ "show = \"\"\"\n", " FirstModel: {},\n", " SecondModel: {},\n", " Score: {}\n", "\"\"\"\n", "print(show.format(res.best_stack_models[\"Fit1stLevelEstimator\"], res.best_stack_models[\"Fit2ndLevelEstimator\"], res.best_stack_models[\"Score\"]))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", 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mit
aidiss/disciplines
ipynb/1 Theories.ipynb
1
10545
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces major package in disciplines - *theory*.\n", "*Theory* is a package dedicated to implement theories.\n", "It enables easy reading, and supports developer with tools to implement theories into Python code.\n", "\n", "For purpose of our work, *theory* should be understood as \n", "> articulated knowledge that describes structure and dynamics of some phenomena. \n", "\n", "Alternatively, we could use definition by Merian-Webster, where theory is defined as\n", "> an idea or set of ideas that is intended to explain facts or events\n", "\n", "In *disciplines* we deal with theories that articulate emergence, existence and death of disciplines, their types and some other aspects.\n", "Most theories come from publications." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets start import the module and listing all theories that are under implementation." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from disciplines import theory\n", "from disciplines.theory import list_all_theories\n", "all_theories = theory.list_all_theories.do() # list all theories. prints a list of theories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Currently this list is was created manually. We foresee two stages of development. During first stage all availables theories will be found automatically. Secondly, all theories will be moved to database. The second stage will be implemented only after/if standard form of theories will be found." ] }, { "cell_type": "heading", "metadata": {}, "level": 1, "source": [ "Some theories" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, we have a distinct set of theories. How did we get them? They were types there." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Emergence of discipline \n", "\n", "This module is an implementation of theoretical work by Ziman.\n", "It checks a stage of development and contains multiple functions \n", "designed for looking for evidence of stage of emergence.\n", "\n", "Vars:\n", " author\n", " concepts\n", " data\n", " claim\n", " theory\n", " approach\n", "\n" ] } ], "source": [ "# Lets import a particular theory that describes emergence of a discipline.\n", "from disciplines.theory import emergence\n", "print emergence.__doc__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Power strugles\n", "\n", "Described as such by Vinck in Sociology of Scientific Work\n", "\n", "Note:\n", " Find struggles in data.\n", " Get data for struggles. Maybe special function for. Connecting database and theories?\n", "\n", "Disciplines mentioned:\n", " molecular biology,\n", " cellular biology,\n", " theology, \n", " physics, \n", " molecular biology, \n", " sociology, \n", " economics, \n", " sociobiology, \n", " neurosciences,\n", " cognitive psychology,\n", " botany, \n", " zoology,\n", " physiology,\n", "\n", "Concepts mentioned:\n", " \"soft sciences\",\n", " \"inhuman sciences\"\n", "\n", "Paricular relations mentioned:\n", " \n", "\n" ] } ], "source": [ "# Lets import a particular theory that describes emergence of a discipline.\n", "from disciplines.theory import power_strugles\n", "print power_strugles.__doc__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Disciplines mentioned:\\n molecular biology,\\n cellular biology,\\n theology, \\n physics, \\n molecular biology, \\n sociology, \\n economics, \\n sociobiology, \\n neurosciences,\\n cognitive psychology,\\n botany, \\n zoology,\\n physiology,\\n\\n']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "disciplines_mentioned = re.findall(r'(?:Disciplines mentioned).*?\\n\\n', power_strugles.__doc__,re.DOTALL)\n", "disciplines_mentioned" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The list above can be used to match theory to any other theory or dataset. For example, we can see if this theory refers to same disciplines as Biglans classification of disciplines and consensus map of science article by... We will show this later" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Structuring of research fields.\n", "\n", "This module investigates concept used by Vinck in Sociology of Scientific work.\n", "It contains four functions that investigate whether research field can be\n", "considered as having one type of structuring. It looks for four types of \n", "structuring: polycentric oligarchy, partitioned beurocracy, fragmented adhocracy,\n", "professional adhocracy, polycentric profession, technologically integrated bureocracy \n", "and conceptually integrated bureaucracy.\n", "\n", "Example:\n", " is_polycentric_oligarhy('philosophy')\n", " > 0.1\n", " is_polycentric_oligarhy('sociology')\n", " > 0.35\n", "\n", "Args:\n", " authors\n", " data\n", " concepts\n", " urls\n", " reading\n", "\n", "Data:\n", " researcher positions : are they in power\n", " schools : hard to identify, have to be based on some research\n", " methods : result assessment methods\n", " training proggrames : check whether they are standardised\n", " focus : are they focused on analytical work\n", "\n", " coalition_list : is discipline in coalitions\n", " research resource coordination : who coordinates.\n", "\n", " working procedures : are they common?\n", " controversies : what is a framework of their solutions?\n", "\n", " instruments : what instrumentas are used?\n", " methods : what methods are used?\n", " empiricity of Knowledge : is knowledge empiric\n", " specificity of knowledge : is knowledge specific?\n", "\n", " theoretical framework : is it unified? \n", "\n", "\n" ] } ], "source": [ "from disciplines.theory import structuring_of_research_fields\n", "print structuring_of_research_fields.__doc__" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Ziman']\n", "['nodal point', 'organize little research conferences', 'hierarchy of authority', 'association develops into a learned society', 'newsletter becomes reputable primary journal', 'primary journal']\n", "['citation network', 'journal', 'newsletter', 'conference participants']\n", "\n", " 1. Emerging specialty is only observable as a nodal point in the network of \n", " citations. \n", " 2. Scientists whose research is associated with this co-citation cluster \n", " organize little research conferences to discuss their common interests, or are \n", " commissioned to write articles for a special issues of a primary journal \n", " drawing attention to progress in this particular problem area. \n", " 3. An ‘invisible college’ begins to condense out, in the form, say, of a semi-\n", " official association held together by further conferences, the regular \n", " exchange of pre-prints and re-prints and the publication of an informal \n", " ‘newsletter’. \n", " 4. The association develops into a regular learned society, whose newsletter \n", " has become a reputable primary journal. \n", " 5. A hierarch of authority is soon set up to preside over conferences, edit \n", " journals, allocate resources, and confer recognition on the members of the new\n", " discipline.\n", " \n" ] } ], "source": [ "print emergence.author # shows author of theory\n", "print emergence.concepts # list concepts used in a theory, mainly its variables. Possibly shows to what part of theory they are neded\n", "print emergence.data # List what kind of data is required for theory to be falsified/proved.\n", "print emergence.theory" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import inspect\n", "inspect.ismodule(theory)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "all_disciplines = [\n", " #characterisation_of_organisational_structures,\n", " emergence,\n", " #interdisciplinary_models,\n", " #justifications_for_interdisciplinary_work,\n", " #learning_styles_and_disciplinary_differences,\n", " power_strugles,\n", " #regimes_of_knowledge_production,\n", " structuring_of_research_fields,\n", " #taxonomy_of_interdisciplinarity\n", "]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "name": "", "signature": "sha256:33a73323267b8d0c1c8f32d584bfaee217ad5673f9135a1ebf6995b3297e08aa" }, "nbformat": 4, "nbformat_minor": 0 }
mit
QuLogic/folium
examples/GeodedeticImageOverlay.ipynb
1
31335
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5.0+105.g065f6f3.dirty\n" ] } ], "source": [ "import os\n", "import folium\n", "\n", "print(folium.__version__)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "\n", "def sample_data(shape=(73, 145)):\n", " nlats, nlons = shape\n", " lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n", " lons = np.linspace(0, 2 * np.pi, nlons)\n", " lons, lats = np.meshgrid(lons, lats)\n", " wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n", " mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n", "\n", " lats = np.rad2deg(lats)\n", " lons = np.rad2deg(lons)\n", " data = wave + mean\n", "\n", " return lons, lats, data\n", "\n", "\n", "lon, lat, data = sample_data(shape=(73, 145))\n", "lon -= 180" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib\n", "\n", "cm = matplotlib.cm.get_cmap('cubehelix')\n", "\n", "normed_data = (data - data.min()) / (data.max() - data.min())\n", "colored_data = cm(normed_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Bad" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7fe615d5e4a8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", "\n", "folium.raster_layers.ImageOverlay(\n", " image=colored_data,\n", " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", " opacity=0.25\n", ").add_to(m)\n", "\n", "m.save(os.path.join('results', 'GeodedeticImageOverlay_0.html'))\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Good" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
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                left: 0.0%;
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    <style>
        .leaflet-image-layer {
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        image-rendering: crisp-edges; /* safari */
        image-rendering: pixelated; /* chrome */
        image-rendering: -moz-crisp-edges; /* firefox */
        image-rendering: -o-crisp-edges; /* opera */
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  "detectRetina": false,
  "maxNativeZoom": 18,
  "maxZoom": 18,
  "minZoom": 0,
  "noWrap": false,
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                    [[-90.0, -180.0], [90.0, 180.0]],
                    {
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  "className": "",
  "crossOrigin": false,
  "errorOverlayUrl": "",
  "interactive": false,
  "opacity": 0.25,
  "zIndex": 1
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                    ).addTo(map_2b310c838ec2470197bcc67b73d21ec8);
            
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7fe615ce5cc0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", "\n", "folium.raster_layers.ImageOverlay(\n", " image=colored_data,\n", " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", " mercator_project=True,\n", " opacity=0.25\n", ").add_to(m)\n", "\n", "m.save(os.path.join('results', 'GeodedeticImageOverlay_1.html'))\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Same as above but with cartopy" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>" ], "text/plain": [ "<folium.folium.Map at 0x7fe615cf6ac8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import cartopy.crs as ccrs\n", "from cartopy.img_transform import warp_array\n", "\n", "source_extent = [lon.min(), lon.max(), lat.min(), lat.max()]\n", "\n", "new_data = warp_array(colored_data,\n", " target_proj=ccrs.GOOGLE_MERCATOR,\n", " source_proj=ccrs.PlateCarree(),\n", " target_res=data.shape,\n", " source_extent=source_extent,\n", " target_extent=None,\n", " mask_extrapolated=False)\n", "\n", "\n", "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", "\n", "folium.raster_layers.ImageOverlay(\n", " image=new_data[0],\n", " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", " opacity=0.25\n", ").add_to(m)\n", "\n", "m.save(os.path.join('results', 'GeodedeticImageOverlay_2.html'))\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TODO: Try [rasterio](https://github.com/mapbox/rasterio/blob/ca75cf0a842943c1b3da4522e6ea3500215130fd/docs/reproject.rst). Rasterio can warp images and arrays." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compare to original" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"900\"\n", " height=\"750\"\n", " src=\"http://scitools.org.uk/cartopy/docs/latest/gallery/waves.html\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.IFrame at 0x7fe615d5e048>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", "\n", "url = 'http://scitools.org.uk/cartopy/docs/latest/gallery/waves.html'\n", "IFrame(url, width=900, height=750)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
danresende/deep-learning
autoencoder/Convolutional_Autoencoder.ipynb
1
408854
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x11f13a048>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11549df60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = mnist.train.images[2]\n", "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Network Architecture\n", "\n", "The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\n", "\n", "![Convolutional Autoencoder](assets/convolutional_autoencoder.png)\n", "\n", "Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughly 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\n", "\n", "### What's going on with the decoder\n", "\n", "Okay, so the decoder has these \"Upsample\" layers that you might not have seen before. First off, I'll discuss a bit what these layers *aren't*. Usually, you'll see **deconvolutional** layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but it reverse. A stride in the input layer results in a larger stride in the deconvolutional layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a deconvolutional layer. Deconvolution is often called \"transpose convolution\" which is what you'll find with the TensorFlow API, with [`tf.nn.conv2d_transpose`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). \n", "\n", "However, deconvolutional layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In [this Distill article](http://distill.pub/2016/deconv-checkerboard/) from Augustus Odena, *et al*, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with [`tf.image.resize_images`](https://www.tensorflow.org/versions/r1.1/api_docs/python/tf/image/resize_images), followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling.\n", "\n", "> **Exercise:** Build the network shown above. Remember that a convolutional layer with strides of 1 and 'same' padding won't reduce the height and width. That is, if the input is 28x28 and the convolution layer has stride = 1 and 'same' padding, the convolutional layer will also be 28x28. The max-pool layers are used the reduce the width and height. A stride of 2 will reduce the size by 2. Odena *et al* claim that nearest neighbor interpolation works best for the upsampling, so make sure to include that as a parameter in `tf.image.resize_images` or use [`tf.image.resize_nearest_neighbor`]( `https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.001\n", "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='input')\n", "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='input')\n", "\n", "### Encoder\n", "conv1 = tf.layers.conv2d(inputs_, 16, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 28x28x16\n", "maxpool1 = tf.layers.max_pooling2d(conv1, (2, 2), (2, 2), padding='SAME')\n", "# Now 14x14x16\n", "conv2 = tf.layers.conv2d(maxpool1, 8, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 14x14x8\n", "maxpool2 = tf.layers.max_pooling2d(conv2, (2, 2), (2, 2), padding='SAME')\n", "# Now 7x7x8\n", "conv3 = tf.layers.conv2d(maxpool2, 8, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 7x7x8\n", "encoded = tf.layers.max_pooling2d(conv3, (2, 2), (2, 2), padding='SAME')\n", "# Now 4x4x8\n", "\n", "### Decoder\n", "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7, 7))\n", "# Now 7x7x8\n", "conv4 = tf.layers.conv2d(upsample1, 8, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 7x7x8\n", "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14, 14))\n", "# Now 14x14x8\n", "conv5 = tf.layers.conv2d(upsample2, 8, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 14x14x8\n", "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28, 28))\n", "# Now 28x28x8\n", "conv6 = tf.layers.conv2d(upsample3, 16, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 28x28x16\n", "\n", "logits = tf.layers.conv2d(conv6, 1, (3, 3), padding='SAME', activation=None)\n", "#Now 28x28x1\n", "\n", "# Pass logits through sigmoid to get reconstructed image\n", "decoded = tf.nn.sigmoid(logits)\n", "\n", "# Pass logits through sigmoid and calculate the cross-entropy loss\n", "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n", "\n", "# Get cost and define the optimizer\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(learning_rate).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "As before, here wi'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.Session()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 1/20... Training loss: 0.7062\n", "Epoch: 1/20... Training loss: 0.6997\n", "Epoch: 1/20... Training loss: 0.6952\n", "Epoch: 1/20... Training loss: 0.6922\n", "Epoch: 1/20... Training loss: 0.6902\n", "Epoch: 1/20... Training loss: 0.6886\n", "Epoch: 1/20... Training loss: 0.6873\n", "Epoch: 1/20... Training loss: 0.6860\n", "Epoch: 1/20... Training loss: 0.6847\n", "Epoch: 1/20... Training loss: 0.6831\n", "Epoch: 1/20... Training loss: 0.6814\n", "Epoch: 1/20... Training loss: 0.6794\n", "Epoch: 1/20... Training loss: 0.6771\n", "Epoch: 1/20... Training loss: 0.6742\n", "Epoch: 1/20... 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Training loss: 0.0962\n", "Epoch: 20/20... Training loss: 0.0982\n", "Epoch: 20/20... Training loss: 0.0996\n", "Epoch: 20/20... Training loss: 0.1014\n", "Epoch: 20/20... Training loss: 0.0973\n", "Epoch: 20/20... Training loss: 0.0989\n", "Epoch: 20/20... Training loss: 0.0956\n", "Epoch: 20/20... Training loss: 0.1016\n", "Epoch: 20/20... Training loss: 0.0992\n", "Epoch: 20/20... Training loss: 0.0994\n", "Epoch: 20/20... Training loss: 0.0961\n", "Epoch: 20/20... Training loss: 0.0974\n", "Epoch: 20/20... Training loss: 0.0981\n", "Epoch: 20/20... Training loss: 0.0973\n", "Epoch: 20/20... Training loss: 0.0971\n", "Epoch: 20/20... Training loss: 0.1022\n", "Epoch: 20/20... Training loss: 0.0980\n", "Epoch: 20/20... Training loss: 0.0994\n", "Epoch: 20/20... Training loss: 0.0967\n", "Epoch: 20/20... Training loss: 0.0938\n", "Epoch: 20/20... Training loss: 0.0962\n", "Epoch: 20/20... Training loss: 0.0972\n", "Epoch: 20/20... Training loss: 0.0989\n", "Epoch: 20/20... Training loss: 0.0957\n", "Epoch: 20/20... Training loss: 0.0955\n", "Epoch: 20/20... Training loss: 0.0976\n", "Epoch: 20/20... Training loss: 0.1023\n", "Epoch: 20/20... Training loss: 0.0982\n", "Epoch: 20/20... Training loss: 0.0997\n", "Epoch: 20/20... Training loss: 0.0998\n", "Epoch: 20/20... Training loss: 0.1000\n", "Epoch: 20/20... Training loss: 0.1009\n", "Epoch: 20/20... Training loss: 0.0996\n", "Epoch: 20/20... Training loss: 0.0968\n", "Epoch: 20/20... Training loss: 0.0993\n", "Epoch: 20/20... Training loss: 0.0981\n", "Epoch: 20/20... Training loss: 0.0959\n", "Epoch: 20/20... Training loss: 0.0986\n", "Epoch: 20/20... Training loss: 0.0980\n", "Epoch: 20/20... Training loss: 0.0985\n", "Epoch: 20/20... Training loss: 0.0992\n", "Epoch: 20/20... Training loss: 0.0970\n", "Epoch: 20/20... Training loss: 0.0997\n", "Epoch: 20/20... Training loss: 0.0978\n", "Epoch: 20/20... Training loss: 0.1004\n", "Epoch: 20/20... Training loss: 0.0969\n", "Epoch: 20/20... Training loss: 0.1001\n", "Epoch: 20/20... Training loss: 0.1014\n", "Epoch: 20/20... Training loss: 0.0986\n", "Epoch: 20/20... Training loss: 0.0929\n", "Epoch: 20/20... Training loss: 0.1011\n", "Epoch: 20/20... Training loss: 0.1010\n" ] } ], "source": [ "epochs = 20\n", "batch_size = 200\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " imgs = batch[0].reshape((-1, 28, 28, 1))\n", " batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n", " targets_: imgs})\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11ffec5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\n", "\n", "for images, row in zip([in_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "\n", "fig.tight_layout(pad=0.1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Denoising\n", "\n", "As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\n", "\n", "![Denoising autoencoder](assets/denoising.png)\n", "\n", "\n", "Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before.\n", "\n", "> **Exercise:** Build the network for the denoising autoencoder. It's the same as before, but with deeper layers. I suggest 32-32-16 for the depths, but you can play with these numbers, or add more layers." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "learning_rate = 0.001\n", "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n", "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n", "\n", "### Encoder\n", "conv1 = tf.layers.conv2d(inputs_, 32, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 28x28x32\n", "maxpool1 = tf.layers.max_pooling2d(conv1, (2, 2), (2, 2), padding='SAME')\n", "# Now 14x14x32\n", "conv2 = tf.layers.conv2d(maxpool1, 32, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 14x14x32\n", "maxpool2 = tf.layers.max_pooling2d(conv2, (2, 2), (2, 2), padding='SAME')\n", "# Now 7x7x32\n", "conv3 = tf.layers.conv2d(maxpool2, 16, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 7x7x16\n", "encoded = tf.layers.max_pooling2d(conv3, (2, 2), (2, 2), padding='SAME')\n", "# Now 4x4x16\n", "\n", "### Decoder\n", "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7, 7))\n", "# Now 7x7x16\n", "conv4 = tf.layers.conv2d(upsample1, 16, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 7x7x16\n", "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14, 14))\n", "# Now 14x14x16\n", "conv5 = tf.layers.conv2d(upsample2, 32, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 14x14x32\n", "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28, 28))\n", "# Now 28x28x32\n", "conv6 = tf.layers.conv2d(upsample3, 32, (3, 3), padding='SAME', activation=tf.nn.relu)\n", "# Now 28x28x32\n", "\n", "logits = tf.layers.conv2d(conv6, 1, (3, 3), padding='SAME', activation=None)\n", "#Now 28x28x1\n", "\n", "# Pass logits through sigmoid to get reconstructed image\n", "decoded = tf.nn.sigmoid(logits)\n", "\n", "# Pass logits through sigmoid and calculate the cross-entropy loss\n", "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n", "\n", "# Get cost and define the optimizer\n", "cost = tf.reduce_mean(loss)\n", "opt = tf.train.AdamOptimizer(learning_rate).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.Session()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 1/100... 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Training loss: 0.1679\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-20-3f51beac5ee6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# Noisy images as inputs, original images as targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n\u001b[0;32m---> 19\u001b[0;31m targets_: imgs})\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", "\u001b[0;32m/Users/danielcmresende/miniconda3/envs/dl-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 776\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 779\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/danielcmresende/miniconda3/envs/dl-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 983\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/danielcmresende/miniconda3/envs/dl-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1033\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n", "\u001b[0;32m/Users/danielcmresende/miniconda3/envs/dl-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m 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fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1019\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1020\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "epochs = 100\n", "batch_size = 200\n", "# Set's how much noise we're adding to the MNIST images\n", "noise_factor = 0.5\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " # Get images from the batch\n", " imgs = batch[0].reshape((-1, 28, 28, 1))\n", " \n", " # Add random noise to the input images\n", " noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n", " # Clip the images to be between 0 and 1\n", " noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n", " \n", " # Noisy images as inputs, original images as targets\n", " batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n", " targets_: imgs})\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking out the performance\n", "\n", "Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprisingly great job of removing the noise, even though it's sometimes difficult to tell what the original number is." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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Z2dq1a4MY1/S06F9YCyGEEEIIIYQQQgghhMgI9MJaCCGEEEIIIYQQQgghREag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\n70sL/sbs+bNSpUouh/fRHXfc4WpwbKxbt87VLF2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4624778320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n", "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n", "\n", "reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n", "\n", "for images, row in zip([noisy_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "fig.tight_layout(pad=0.1)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
avtlearns/automatic_text_summarization
TextRank_Automatic_Summarization_for_Medical_Articles.ipynb
1
19243
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic Summarization of Medical Articles\n", "### Author: Abhijit V Thatte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Solution:__ We will use TextRank for automatic summarization of medical articles. NIH's (National Institues for Health) PubMed repository consists of links to hundreds of thousands of medical articles. We will use articles relevant to various types of cancer. We will use the abstract of each article as the \"ground truth\". We will apply the TextRank algorithm to only the body of the PubMed article without the abstract to generate an extractive summary. We will use a Java based implementation of ROUGE software to evaluate the precision, recall and F1 score of extractive summary with respect to the ground truth. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 1: Import required modules__" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nltk.tokenize.punkt import PunktSentenceTokenizer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.feature_extraction.text import TfidfTransformer\n", "import networkx as nx\n", "import re\n", "import urllib2\n", "from bs4 import BeautifulSoup\n", "import pandas as pd\n", "# -*- coding: utf-8 -*-" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 2: Generate a list of documents__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "urls = []\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1994795/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=1994795')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC314300/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=314300')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383356/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4383356')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596899/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4596899')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4303126/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4303126')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4637461/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4637461')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4690355/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4690355')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505152/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=3505152')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976810/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=3976810')\n", "\n", "#https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061037/\n", "urls.append('https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pmc&id=4061037')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 3: Preprocess the documents__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preprocessing documents. This may take few minutes ...\n", "Preprocessing document 1 ...\n", "Preprocessing document 2 ...\n", "Preprocessing document 3 ...\n", "Preprocessing document 4 ...\n", "Preprocessing document 5 ...\n", "Preprocessing document 6 ...\n", "Preprocessing document 7 ...\n", "Preprocessing document 8 ...\n", "Preprocessing document 9 ...\n", "Preprocessing document 10 ...\n", "All documents preprocessed successfully.\n", "We have 10 documents with 10 abstracts and 10 texts.\n" ] } ], "source": [ "documents = []\n", "abstracts = []\n", "texts = []\n", "\n", "print 'Preprocessing documents. This may take few minutes ...'\n", "for i, url in enumerate(urls):\n", " print 'Preprocessing document %d ...' % (i+1)\n", " # Download the document\n", " my_url = urllib2.urlopen(url)\n", " raw_doc = BeautifulSoup(my_url.read(), 'xml')\n", " documents.append(raw_doc)\n", "\n", " # Extract the cleaned abstract\n", " raw_abstract = raw_doc.abstract\n", " my_abstract = re.sub(r'<\\/?\\w+>', r' ', str(raw_abstract)) # remove xml tags\n", " abstracts.append(my_abstract)\n", "\n", " # Extract the cleaned text\n", " text = raw_doc.body\n", " text = re.sub(r'\\\\n', r' ', str(text)) # remove newline characters\n", " text = re.sub(r'<[^>]+>', r' ', str(text)) # remove xml tags\n", " text = re.sub(r'\\[[^\\[^\\]]+\\]', r' ', str(text)) # remove references\n", " text = re.sub(r'\\[', r' ', str(text)) # remove any remaining [\n", " text = re.sub(r'\\]', r' ', str(text)) # remove any remaining ]\n", " text = re.sub(r'[\\s]{2,}', r' ', str(text)) # remove more than a single blank space\n", " text = re.sub(r'\\.\\s+,\\s+\\S', r' ', str(text)) # remove , after a period\n", "\n", " text = text.decode('utf-8')\n", " texts.append(text)\n", "\n", "print 'All documents preprocessed successfully.'\n", "print 'We have %d documents with %d abstracts and %d texts.' % (len(documents), len(abstracts), len(texts))\n", "assert len(documents) == len(abstracts)\n", "assert len(documents) == len(texts)\n", " " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "__Step 4: Split the documents into sentences__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "punkttokenizer = PunktSentenceTokenizer()\n", "text_sentences = []\n", "\n", "for text in texts:\n", " sentences = []\n", " seen = set()\n", " for sentence in punkttokenizer.tokenize(text):\n", " sentences.append(sentence)\n", " text_sentences.append(sentences)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 5: Count the term frequency for sentences__" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating sentence simiarities. This may take few minutes ...\n", "Calculating sentence simiarities of document 1 ...\n", "Calculating sentence simiarities of document 2 ...\n", "Calculating sentence simiarities of document 3 ...\n", "Calculating sentence simiarities of document 4 ...\n", "Calculating sentence simiarities of document 5 ...\n", "Calculating sentence simiarities of document 6 ...\n", "Calculating sentence simiarities of document 7 ...\n", "Calculating sentence simiarities of document 8 ...\n", "Calculating sentence simiarities of document 9 ...\n", "Calculating sentence simiarities of document 10 ...\n", "All documents processed successfully.\n", "We have 10 documents with 10 tf_matrices 10 tfidf_matrices and 10 cosine_similarity_matrices.\n" ] } ], "source": [ "tf_matrices = []\n", "tfidf_matrices = []\n", "cosine_similarity_matrices = []\n", "\n", "print 'Calculating sentence simiarities. This may take few minutes ...'\n", "for i, sentences in enumerate(text_sentences):\n", " print 'Calculating sentence simiarities of document %d ...' % (i+1)\n", " tf_matrix = CountVectorizer().fit_transform(sentences)\n", " tf_matrices.append(tf_matrix)\n", " \n", " tfidf_matrix = TfidfTransformer().fit_transform(tf_matrix)\n", " tfidf_matrices.append(tfidf_matrix)\n", " \n", " cosine_similarity_matrix = tfidf_matrix * tfidf_matrix.T\n", " cosine_similarity_matrices.append(cosine_similarity_matrix)\n", "\n", "print 'All documents processed successfully.'\n", "print 'We have %d documents with %d tf_matrices %d tfidf_matrices and %d cosine_similarity_matrices.' \\\n", " % (len(documents), len(tf_matrices), len(tfidf_matrices), len(cosine_similarity_matrices))\n", "assert len(documents) == len(tf_matrices)\n", "assert len(documents) == len(tfidf_matrices)\n", "assert len(documents) == len(cosine_similarity_matrices)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 6: Calculate TextRank__" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating TextRanks. This may take few minutes ...\n", "Calculating TextRanks of document 1 ...\n", "Calculating TextRanks of document 2 ...\n", "Calculating TextRanks of document 3 ...\n", "Calculating TextRanks of document 4 ...\n", "Calculating TextRanks of document 5 ...\n", "Calculating TextRanks of document 6 ...\n", "Calculating TextRanks of document 7 ...\n", "Calculating TextRanks of document 8 ...\n", "Calculating TextRanks of document 9 ...\n", "Calculating TextRanks of document 10 ...\n", "All documents processed successfully.\n", "We have 10 documents with 10 similarity_graphs 10 graph_ranks and 10 highest_ranks.\n" ] } ], "source": [ "similarity_graphs = []\n", "graph_ranks = []\n", "highest_ranks = []\n", "lowest_ranks = []\n", "\n", "print 'Calculating TextRanks. This may take few minutes ...'\n", "for i, cosine_similarity_matrix in enumerate(cosine_similarity_matrices):\n", " print 'Calculating TextRanks of document %d ...' % (i+1)\n", " similarity_graph = nx.from_scipy_sparse_matrix(cosine_similarity_matrix)\n", " similarity_graphs.append(similarity_graph)\n", " \n", " ranks = nx.pagerank(similarity_graph)\n", " graph_ranks.append(ranks)\n", " \n", " highest = sorted(((ranks[j],s) for j,s in enumerate(text_sentences[i])), reverse=True)\n", " highest_ranks.append(highest)\n", " \n", " lowest = sorted(((ranks[j],s) for j,s in enumerate(text_sentences[i])), reverse=False)\n", " lowest_ranks.append(lowest)\n", " \n", "print 'All documents processed successfully.'\n", "print 'We have %d documents with %d similarity_graphs %d graph_ranks and %d highest_ranks.' \\\n", " % (len(documents), len(similarity_graphs), len(graph_ranks), len(highest_ranks))\n", "assert len(documents) == len(similarity_graphs)\n", "assert len(documents) == len(graph_ranks)\n", "assert len(documents) == len(highest_ranks)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 7: Save extractive summaries__" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving extractive summaries. This may take a few minutes ...\n", "Writing extractive summary for document 1 ...\n", "Writing extractive summary for document 2 ...\n", "Writing extractive summary for document 3 ...\n", "Writing extractive summary for document 4 ...\n", "Writing extractive summary for document 5 ...\n", "Writing extractive summary for document 6 ...\n", "Writing extractive summary for document 7 ...\n", "Writing extractive summary for document 8 ...\n", "Writing extractive summary for document 9 ...\n", "Writing extractive summary for document 10 ...\n", "All documents processed successfully.\n" ] } ], "source": [ "print 'Saving extractive summaries. This may take a few minutes ...'\n", "for i, highest in enumerate(highest_ranks):\n", " print 'Writing extractive summary for document %d ...' % (i+1)\n", " out_file = '\\\\TextRank\\\\system\\\\article%d_system1.txt' % (i+1)\n", " with open(out_file, 'w') as f:\n", " for i in range(5):\n", " f.write((highest[i][1] + '\\n').encode('utf-8'))\n", "print 'All documents processed successfully.'" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "__Step 8: Save ground truths.__" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving ground truths. This may take a few minutes ...\n", "Writing ground truth for document 1 ...\n", "Writing ground truth for document 2 ...\n", "Writing ground truth for document 3 ...\n", "Writing ground truth for document 4 ...\n", "Writing ground truth for document 5 ...\n", "Writing ground truth for document 6 ...\n", "Writing ground truth for document 7 ...\n", "Writing ground truth for document 8 ...\n", "Writing ground truth for document 9 ...\n", "Writing ground truth for document 10 ...\n", "All documents processed successfully.\n" ] } ], "source": [ "print 'Saving ground truths. This may take a few minutes ...'\n", "for i, abstract in enumerate(abstracts):\n", " print 'Writing ground truth for document %d ...' % (i+1)\n", " out_file = '\\\\TextRank\\\\reference\\\\article%d_reference1.txt' % (i+1)\n", " with open(out_file, 'w') as f:\n", " f.write(abstract.strip() + '\\n')\n", "print 'All documents processed successfully.'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Step 9: Calculate ROUGE score__" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\ROUGE\n", "0 [main] INFO com.rxnlp.tools.rouge.SettingsUtil - Using rouge.properties file specified as 'rouge.properties'\n", "\n", "========Results=======\n", "\n", "ROUGE-1+Stemming\tARTICLE6\tSYSTEM1.TXT\tAverage_R:0.45902\tAverage_P:0.55263\tAverage_F:0.50149\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE7\tSYSTEM1.TXT\tAverage_R:0.45977\tAverage_P:0.66946\tAverage_F:0.54514\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE8\tSYSTEM1.TXT\tAverage_R:0.29877\tAverage_P:0.54751\tAverage_F:0.38658\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE9\tSYSTEM1.TXT\tAverage_R:0.33146\tAverage_P:0.35542\tAverage_F:0.34302\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE1\tSYSTEM1.TXT\tAverage_R:0.51389\tAverage_P:0.39362\tAverage_F:0.44578\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE10\tSYSTEM1.TXT\tAverage_R:0.36593\tAverage_P:0.58000\tAverage_F:0.44874\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE2\tSYSTEM1.TXT\tAverage_R:0.47895\tAverage_P:0.48404\tAverage_F:0.48148\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE3\tSYSTEM1.TXT\tAverage_R:0.44697\tAverage_P:0.44697\tAverage_F:0.44697\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE4\tSYSTEM1.TXT\tAverage_R:0.60982\tAverage_P:0.54128\tAverage_F:0.57351\tNum Reference Summaries:1\n", "ROUGE-1+Stemming\tARTICLE5\tSYSTEM1.TXT\tAverage_R:0.45559\tAverage_P:0.57194\tAverage_F:0.50718\tNum Reference Summaries:1\n", "\n", "======Results End======\n", "\n", "110 [main] INFO com.rxnlp.tools.rouge.ROUGECalculator - Results written to results.csv\n", "\n", "\n", "Please find results file in: results.csv\n" ] } ], "source": [ "%cd C:\\ROUGE\n", "!java -jar rouge2.0_0.2.jar" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ROUGE-Type Task Name System Name Avg_Recall Avg_Precision \\\n", "8 ROUGE-1+Stemming ARTICLE4 SYSTEM1.TXT 0.60982 0.54128 \n", "1 ROUGE-1+Stemming ARTICLE7 SYSTEM1.TXT 0.45977 0.66946 \n", "9 ROUGE-1+Stemming ARTICLE5 SYSTEM1.TXT 0.45559 0.57194 \n", "0 ROUGE-1+Stemming ARTICLE6 SYSTEM1.TXT 0.45902 0.55263 \n", "6 ROUGE-1+Stemming ARTICLE2 SYSTEM1.TXT 0.47895 0.48404 \n", "5 ROUGE-1+Stemming ARTICLE10 SYSTEM1.TXT 0.36593 0.58000 \n", "7 ROUGE-1+Stemming ARTICLE3 SYSTEM1.TXT 0.44697 0.44697 \n", "4 ROUGE-1+Stemming ARTICLE1 SYSTEM1.TXT 0.51389 0.39362 \n", "2 ROUGE-1+Stemming ARTICLE8 SYSTEM1.TXT 0.29877 0.54751 \n", "3 ROUGE-1+Stemming ARTICLE9 SYSTEM1.TXT 0.33146 0.35542 \n", "\n", " Avg_F-Score Num Reference Summaries \n", "8 0.57351 1 \n", "1 0.54514 1 \n", "9 0.50718 1 \n", "0 0.50149 1 \n", "6 0.48148 1 \n", "5 0.44874 1 \n", "7 0.44697 1 \n", "4 0.44578 1 \n", "2 0.38658 1 \n", "3 0.34302 1 \n" ] } ], "source": [ "df = pd.read_csv('results.csv')\n", "print df.sort_values('Avg_F-Score', ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
mne-tools/mne-tools.github.io
0.24/_downloads/b7659d33d6ffe8531d004e9d6051f16f/forward_sensitivity_maps.ipynb
1
6052
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Display sensitivity maps for EEG and MEG sensors\n\nSensitivity maps can be produced from forward operators that\nindicate how well different sensor types will be able to detect\nneural currents from different regions of the brain.\n\nTo get started with forward modeling see `tut-forward`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Eric Larson <[email protected]>\n#\n# License: BSD-3-Clause" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\nimport mne\nfrom mne.datasets import sample\nfrom mne.source_space import compute_distance_to_sensors\nfrom mne.source_estimate import SourceEstimate\nimport matplotlib.pyplot as plt\n\nprint(__doc__)\n\ndata_path = sample.data_path()\n\nfwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\n\nsubjects_dir = data_path + '/subjects'\n\n# Read the forward solutions with surface orientation\nfwd = mne.read_forward_solution(fwd_fname)\nmne.convert_forward_solution(fwd, surf_ori=True, copy=False)\nleadfield = fwd['sol']['data']\nprint(\"Leadfield size : %d x %d\" % leadfield.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute sensitivity maps\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grad_map = mne.sensitivity_map(fwd, ch_type='grad', mode='fixed')\nmag_map = mne.sensitivity_map(fwd, ch_type='mag', mode='fixed')\neeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show gain matrix a.k.a. leadfield matrix with sensitivity map\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "picks_meg = mne.pick_types(fwd['info'], meg=True, eeg=False)\npicks_eeg = mne.pick_types(fwd['info'], meg=False, eeg=True)\n\nfig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True)\nfig.suptitle('Lead field matrix (500 dipoles only)', fontsize=14)\nfor ax, picks, ch_type in zip(axes, [picks_meg, picks_eeg], ['meg', 'eeg']):\n im = ax.imshow(leadfield[picks, :500], origin='lower', aspect='auto',\n cmap='RdBu_r')\n ax.set_title(ch_type.upper())\n ax.set_xlabel('sources')\n ax.set_ylabel('sensors')\n fig.colorbar(im, ax=ax)\n\nfig_2, ax = plt.subplots()\nax.hist([grad_map.data.ravel(), mag_map.data.ravel(), eeg_map.data.ravel()],\n bins=20, label=['Gradiometers', 'Magnetometers', 'EEG'],\n color=['c', 'b', 'k'])\nfig_2.legend()\nax.set(title='Normal orientation sensitivity',\n xlabel='sensitivity', ylabel='count')\n\nbrain_sens = grad_map.plot(\n subjects_dir=subjects_dir, clim=dict(lims=[0, 50, 100]), figure=1)\nbrain_sens.add_text(0.1, 0.9, 'Gradiometer sensitivity', 'title', font_size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare sensitivity map with distribution of source depths\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# source space with vertices\nsrc = fwd['src']\n\n# Compute minimum Euclidean distances between vertices and MEG sensors\ndepths = compute_distance_to_sensors(src=src, info=fwd['info'],\n picks=picks_meg).min(axis=1)\nmaxdep = depths.max() # for scaling\n\nvertices = [src[0]['vertno'], src[1]['vertno']]\n\ndepths_map = SourceEstimate(data=depths, vertices=vertices, tmin=0.,\n tstep=1.)\n\nbrain_dep = depths_map.plot(\n subject='sample', subjects_dir=subjects_dir,\n clim=dict(kind='value', lims=[0, maxdep / 2., maxdep]), figure=2)\nbrain_dep.add_text(0.1, 0.9, 'Source depth (m)', 'title', font_size=16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sensitivity is likely to co-vary with the distance between sources to\nsensors. To determine the strength of this relationship, we can compute the\ncorrelation between source depth and sensitivity values.\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "corr = np.corrcoef(depths, grad_map.data[:, 0])[0, 1]\nprint('Correlation between source depth and gradiomter sensitivity values: %f.'\n % corr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gradiometer sensitiviy is highest close to the sensors, and decreases rapidly\nwith inreasing source depth. This is confirmed by the high negative\ncorrelation between the two.\n\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
rohinkumar/galsurveystudy
APTest/.ipynb_checkpoints/DR72_VAGC_correl_AP_MI_V06-fast-cdist-rr200k-opt-checkpoint.ipynb
1
80132
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR72 SDSS VAGC Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import healpix_util as hu\n", "import astropy as ap\n", "import numpy as np\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "import astropy.io.ascii as ascii\n", "from astropy.io import fits\n", "from astropy.constants import c\n", "import matplotlib.pyplot as plt\n", "import math as m\n", "from math import pi\n", "#from scipy.constants import c\n", "import scipy.special as sp\n", "from astroML.decorators import pickle_results\n", "from scipy import integrate\n", "import warnings\n", "from sklearn.neighbors import BallTree\n", "import pickle\n", "import multiprocessing as mp\n", "import time\n", "from aptestmetricdt import *\n", "from aptestmetricdz import *\n", "from scipy.spatial import distance as d\n", "from apcat import *\n", "from progressbar import *\n", "from tqdm import *\n", "from functools import partial\n", "import pymangle\n", "from apdz import *\n", "from apdt import *\n", "from scipy.optimize import curve_fit\n", "#from astroML.datasets import fetch_sdss_specgals\n", "#from astroML.correlation import bootstrap_two_point_angular\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.160002, 5.964543, 0.235823],\n", " [ 0.160018, 3.089162, 0.28616 ],\n", " [ 0.16002 , 4.085181, 0.284492],\n", " ..., \n", " [ 0.469989, 3.059253, 0.349895],\n", " [ 0.469994, 3.506603, 1.073682],\n", " [ 0.469996, 2.217024, 0.108364]])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting back the objects:\n", "with open('../output/datzAP.pkl') as f: # Python 3: open(..., 'rb')\n", " dat = pickle.load(f)\n", "dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01 reading from pkl files for faster read" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.160002, 5.9527 , 0.007988],\n", " [ 0.160004, 3.420454, 0.123662],\n", " [ 0.160007, 3.876158, 0.295957],\n", " ..., \n", " [ 0.469993, 2.683356, 0.525876],\n", " [ 0.469994, 5.75026 , 0.212152],\n", " [ 0.469996, 2.732208, 0.523037]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting back the objects:\n", "with open('../output/rdatzAP.pkl') as f: # Python 3: open(..., 'rb')\n", " datR = pickle.load(f)\n", "datR" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rr2d=np.zeros((20,20))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = np.array([[0, 0.02], [0, 0.02]])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rrbt=BallTree(datR,metric='pyfunc',func=APdz) " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0.02],\n", " [ 0. , 0.02]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rng" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.0,0.02001,0.001)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007,\n", " 0.008, 0.009, 0.01 , 0.011, 0.012, 0.013, 0.014, 0.015,\n", " 0.016, 0.017, 0.018, 0.019, 0.02 ])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 26/211661 [00:05<12:22:25, 4.75it/s]/Users/rohin/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py:973: RuntimeWarning: invalid value encountered in greater_equal\n", " not_smaller_than_edge = (sample[:, i] >= edges[i][-1])\n", " 35%|███▌ | 74595/211661 [8:50:21<14:55:11, 2.55it/s]" ] } ], "source": [ "%%time\n", "for i in tqdm(xrange(len(datR))):\n", " ind=rrbt.query_radius(datR[i].reshape(1,-1),0.021)\n", " for j in ind:\n", " dist0=d.cdist([datR[i],],datR[j],APdz)[0]\n", " dist1=d.cdist([datR[i],],datR[j],APzdth)[0]\n", " rr2d+=np.histogram2d(dist0, dist1,range=rng,bins=(bins,bins))[0]\n", "print rr2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('rr2ddr72v06cdist200kopt.pkl','w') as f:\n", " pickle.dump(rr2d,f) \n", "rr2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot reshape array of size 1 into shape (1,0)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-37-2abeeb5bf42c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'ind=ddbt.query_radius(dat[0].reshape(1,-1),bins[0])\\nind1=ind.reshape(1,0)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/rohin/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2113\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2114\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2115\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2116\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n", "\u001b[0;32m/Users/rohin/anaconda/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rohin/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m \u001b[0;32mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1181\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1182\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 1 into shape (1,0)" ] } ], "source": [ "%%time\n", "ind=ddbt.query_radius(dat[0].reshape(1,-1),bins[0])\n", "ind1=ind.reshape(1,0)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ array([ 78, 65, 54, 102, 97, 9, 27, 156, 116, 190, 161, 55, 69,\n", " 170, 77, 30, 125, 144, 47, 71, 46, 98, 14, 182, 70, 40,\n", " 191, 101, 50, 86, 74, 31, 175, 189, 122, 63, 20, 53, 37,\n", " 32, 93, 177, 173, 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1.60442000e-01 1.18390000e-01 1.28770000e-02]\n", " [ 1.60358000e-01 3.70501000e-01 -1.78040000e-01]\n", " [ 1.60296000e-01 3.90689000e-01 -1.63910000e-02]\n", " [ 1.60565000e-01 4.01300000e-01 -1.31940000e-02]\n", " [ 1.60521000e-01 4.49435000e-01 -1.70570000e-01]\n", " [ 1.60058000e-01 4.86250000e-01 -1.65317000e-01]\n", " [ 1.60162000e-01 4.76180000e-01 2.32323000e-01]\n", " [ 1.60774000e-01 6.15974000e-01 -1.64328000e-01]\n", " [ 1.60644000e-01 2.04400200e+00 5.12437000e-01]\n", " [ 1.60976000e-01 2.06564200e+00 5.12409000e-01]\n", " [ 1.60792000e-01 2.06544700e+00 5.26077000e-01]\n", " [ 1.60299000e-01 2.04005000e+00 5.33478000e-01]\n", " [ 1.60389000e-01 2.05441200e+00 7.03576000e-01]\n", " [ 1.60867000e-01 2.05407700e+00 7.04991000e-01]\n", " [ 1.60441000e-01 2.18745700e+00 4.62760000e-01]\n", " [ 1.60178000e-01 2.11351300e+00 5.15975000e-01]\n", " [ 1.60668000e-01 2.12222000e+00 5.47658000e-01]\n", " [ 1.60743000e-01 2.11776500e+00 5.36898000e-01]\n", " [ 1.60268000e-01 2.21548200e+00 6.07208000e-01]\n", " [ 1.60406000e-01 2.26746800e+00 6.22283000e-01]\n", " [ 1.60266000e-01 2.17119300e+00 8.72532000e-01]\n", " [ 1.60532000e-01 2.11250800e+00 8.51319000e-01]\n", " [ 1.60083000e-01 2.20344600e+00 8.66432000e-01]\n", " [ 1.60920000e-01 2.21828800e+00 8.51988000e-01]\n", " [ 1.60404000e-01 2.17430400e+00 1.00295300e+00]\n", " [ 1.60241000e-01 2.34819800e+00 4.06600000e-02]\n", " [ 1.60978000e-01 2.30891000e+00 1.27447000e-01]\n", " [ 1.60562000e-01 2.39786600e+00 1.61690000e-01]\n", " [ 1.60276000e-01 2.50837300e+00 2.20870000e-02]\n", " [ 1.60474000e-01 2.51767900e+00 7.70600000e-03]\n", " [ 1.60415000e-01 2.54390200e+00 3.05970000e-02]\n", " [ 1.60183000e-01 2.57648500e+00 8.19840000e-02]\n", " [ 1.60884000e-01 2.53475600e+00 1.90217000e-01]\n", " [ 1.60976000e-01 2.30349600e+00 2.62213000e-01]\n", " [ 1.60666000e-01 2.30642400e+00 3.61519000e-01]\n", " [ 1.60351000e-01 2.56403700e+00 2.69466000e-01]\n", " [ 1.60116000e-01 2.54575400e+00 2.94491000e-01]\n", " [ 1.60292000e-01 2.49658900e+00 5.51961000e-01]\n", " [ 1.60227000e-01 2.46100000e+00 7.08299000e-01]\n", " [ 1.60185000e-01 2.57263700e+00 5.84215000e-01]\n", " [ 1.60504000e-01 2.34436600e+00 7.34716000e-01]\n", " [ 1.60893000e-01 2.34257600e+00 7.51282000e-01]\n", " [ 1.60880000e-01 2.55744200e+00 7.68823000e-01]\n", " [ 1.60729000e-01 2.65985000e+00 8.10720000e-02]\n", " [ 1.60765000e-01 2.72383400e+00 7.58010000e-02]\n", " [ 1.60924000e-01 2.75199200e+00 7.51540000e-02]\n", " [ 1.60256000e-01 2.71593000e+00 7.67780000e-02]\n", " [ 1.60410000e-01 2.72024800e+00 8.30040000e-02]\n", " [ 1.60600000e-01 2.62315400e+00 1.75893000e-01]\n", " [ 1.60374000e-01 2.63993200e+00 1.90957000e-01]\n", " [ 1.60743000e-01 2.76445600e+00 1.76570000e-02]\n", " [ 1.60473000e-01 2.83277900e+00 4.30400000e-03]\n", " [ 1.60214000e-01 2.83356300e+00 -1.87700000e-03]\n", " [ 1.60805000e-01 2.60258100e+00 3.23189000e-01]\n", " [ 1.60751000e-01 2.61194100e+00 3.30729000e-01]\n", " [ 1.60265000e-01 2.67113700e+00 3.69434000e-01]\n", " [ 1.60565000e-01 2.76838800e+00 2.69632000e-01]\n", " [ 1.60854000e-01 2.78800800e+00 2.32986000e-01]\n", " [ 1.60692000e-01 2.84203600e+00 4.59446000e-01]\n", " [ 1.60755000e-01 2.95022600e+00 4.28480000e-02]\n", " [ 1.60432000e-01 3.07422800e+00 -5.84920000e-02]\n", " [ 1.60568000e-01 3.17889200e+00 4.18940000e-02]\n", " [ 1.60668000e-01 2.97794400e+00 2.70958000e-01]\n", " [ 1.60315000e-01 3.06936100e+00 4.10091000e-01]\n", " [ 1.60544000e-01 3.01109700e+00 4.51782000e-01]\n", " [ 1.60018000e-01 3.08916200e+00 2.86160000e-01]\n", " [ 1.60078000e-01 3.14752500e+00 2.58412000e-01]\n", " [ 1.60774000e-01 2.69835100e+00 6.94235000e-01]\n", " [ 1.60086000e-01 2.70046900e+00 6.87017000e-01]\n", " [ 1.60048000e-01 2.71326800e+00 7.13375000e-01]\n", " [ 1.60945000e-01 2.75059000e+00 7.00823000e-01]\n", " [ 1.60309000e-01 2.89654900e+00 5.23679000e-01]\n", " [ 1.60237000e-01 2.89256100e+00 5.95641000e-01]\n", " [ 1.60886000e-01 2.88454000e+00 7.17592000e-01]\n", " [ 1.60771000e-01 2.95015700e+00 5.98659000e-01]\n", " [ 1.60068000e-01 3.04979600e+00 6.54096000e-01]\n", " [ 1.60039000e-01 3.10823700e+00 5.78983000e-01]\n", " [ 1.60146000e-01 3.10801500e+00 5.79045000e-01]\n", " [ 1.60918000e-01 2.67988800e+00 7.98889000e-01]\n", " [ 1.60939000e-01 2.76307800e+00 8.81138000e-01]\n", " [ 1.60163000e-01 2.91464200e+00 7.57777000e-01]\n", " [ 1.60278000e-01 2.80580200e+00 8.23979000e-01]\n", " [ 1.60575000e-01 2.97542300e+00 8.79430000e-01]\n", " [ 1.60712000e-01 3.16402500e+00 7.44529000e-01]\n", " [ 1.60896000e-01 3.14207900e+00 8.58702000e-01]\n", " [ 1.60271000e-01 3.23847400e+00 1.43086000e-01]\n", " [ 1.60516000e-01 3.26771500e+00 3.39149000e-01]\n", " [ 1.60140000e-01 3.29096800e+00 3.39444000e-01]\n", " [ 1.60590000e-01 3.28230800e+00 3.84141000e-01]\n", " [ 1.60789000e-01 3.38646400e+00 2.33326000e-01]\n", " [ 1.60305000e-01 3.49018100e+00 2.13899000e-01]\n", " [ 1.60043000e-01 3.49099200e+00 2.86227000e-01]\n", " [ 1.60790000e-01 3.48795000e+00 2.96686000e-01]\n", " [ 1.60694000e-01 3.44997100e+00 3.33809000e-01]\n", " [ 1.60461000e-01 3.45093700e+00 3.29212000e-01]\n", " [ 1.60588000e-01 3.51720700e+00 3.67491000e-01]\n", " [ 1.60115000e-01 3.47340200e+00 4.26715000e-01]\n", " [ 1.60126000e-01 3.56515600e+00 1.82094000e-01]\n", " [ 1.60815000e-01 3.71982500e+00 -3.94730000e-02]\n", " [ 1.60037000e-01 3.69857500e+00 -1.59620000e-02]\n", " [ 1.60952000e-01 3.74350500e+00 4.43260000e-02]\n", " [ 1.60733000e-01 3.75659700e+00 1.67635000e-01]\n", " [ 1.60311000e-01 3.76096800e+00 1.59528000e-01]\n", " [ 1.60770000e-01 3.66409400e+00 3.95963000e-01]\n", " [ 1.60593000e-01 3.71848000e+00 2.87606000e-01]\n", " [ 1.60770000e-01 3.69310200e+00 3.03870000e-01]\n", " [ 1.60033000e-01 3.79545500e+00 2.05735000e-01]\n", " [ 1.60847000e-01 3.80445300e+00 2.07804000e-01]\n", " [ 1.60705000e-01 3.82666500e+00 2.07953000e-01]\n", " [ 1.60647000e-01 3.81008200e+00 2.20867000e-01]\n", " [ 1.60715000e-01 3.83610500e+00 2.26191000e-01]\n", " [ 1.60082000e-01 3.81192400e+00 2.48460000e-01]\n", " [ 1.60879000e-01 3.83959600e+00 4.41995000e-01]\n", " [ 1.60477000e-01 3.83567400e+00 4.17240000e-01]\n", " [ 1.60335000e-01 3.34248000e+00 5.95731000e-01]\n", " [ 1.60652000e-01 3.35949500e+00 6.33086000e-01]\n", " [ 1.60765000e-01 3.36006500e+00 6.39900000e-01]\n", " [ 1.60145000e-01 3.49299300e+00 4.99928000e-01]\n", " [ 1.60833000e-01 3.60721300e+00 4.86124000e-01]\n", " [ 1.60256000e-01 3.75797800e+00 7.03292000e-01]\n", " [ 1.60375000e-01 3.77437900e+00 6.90626000e-01]\n", " [ 1.60221000e-01 3.78045400e+00 6.84915000e-01]\n", " [ 1.60747000e-01 3.25272000e+00 7.61084000e-01]\n", " [ 1.60656000e-01 3.39779800e+00 1.06290900e+00]\n", " [ 1.60904000e-01 3.49659700e+00 1.16833100e+00]\n", " [ 1.60978000e-01 3.61966000e+00 8.23500000e-01]\n", " [ 1.60565000e-01 3.65736800e+00 8.29584000e-01]\n", " [ 1.60487000e-01 3.79400700e+00 7.60107000e-01]\n", " [ 1.60597000e-01 3.62662600e+00 1.02487400e+00]\n", " [ 1.60204000e-01 3.71821000e+00 9.99537000e-01]\n", " [ 1.60681000e-01 3.79858400e+00 1.02125900e+00]\n", " [ 1.60860000e-01 3.76638600e+00 1.03603200e+00]\n", " [ 1.60451000e-01 3.97202500e+00 -4.40290000e-02]\n", " [ 1.60041000e-01 3.87986100e+00 1.01975000e-01]\n", " [ 1.60491000e-01 3.88558400e+00 9.25790000e-02]\n", " [ 1.60504000e-01 3.88919000e+00 1.12721000e-01]\n", " [ 1.60648000e-01 4.08663400e+00 1.64610000e-02]\n", " [ 1.60414000e-01 4.04839500e+00 1.74674000e-01]\n", " [ 1.60081000e-01 4.05508900e+00 1.77667000e-01]\n", " [ 1.60688000e-01 4.04623900e+00 1.76442000e-01]\n", " [ 1.60699000e-01 4.06680800e+00 1.95643000e-01]\n", " [ 1.60799000e-01 4.04204100e+00 1.74528000e-01]\n", " [ 1.60881000e-01 4.04211600e+00 1.74027000e-01]\n", " [ 1.60672000e-01 4.13669000e+00 1.36578000e-01]\n", " [ 1.60091000e-01 3.86404500e+00 2.40823000e-01]\n", " [ 1.60695000e-01 3.90191600e+00 2.57042000e-01]\n", " [ 1.60132000e-01 3.88958500e+00 2.92928000e-01]\n", " [ 1.60786000e-01 3.96964000e+00 2.26226000e-01]\n", " [ 1.60271000e-01 4.07838400e+00 2.84384000e-01]\n", " [ 1.60020000e-01 4.08518100e+00 2.84492000e-01]\n", " [ 1.60644000e-01 4.06079700e+00 3.33115000e-01]\n", " [ 1.60351000e-01 3.85863300e+00 4.15064000e-01]\n", " [ 1.60785000e-01 3.94719000e+00 4.18301000e-01]\n", " [ 1.60724000e-01 3.98340100e+00 6.27588000e-01]\n", " [ 1.60461000e-01 3.98586100e+00 6.29676000e-01]\n", " [ 1.60632000e-01 4.05872500e+00 6.48528000e-01]\n", " [ 1.60593000e-01 3.84156000e+00 6.75805000e-01]\n", " [ 1.60386000e-01 3.86370100e+00 6.73753000e-01]\n", " [ 1.60223000e-01 4.02552100e+00 7.39549000e-01]\n", " [ 1.60231000e-01 4.08254400e+00 9.12077000e-01]\n", " [ 1.60279000e-01 4.05575200e+00 9.81174000e-01]\n", " [ 1.60545000e-01 4.19623900e+00 8.75690000e-02]\n", " [ 1.60659000e-01 4.22575500e+00 3.68920000e-01]\n", " [ 1.60163000e-01 4.27150700e+00 4.30567000e-01]\n", " [ 1.60113000e-01 4.28761700e+00 4.27315000e-01]\n", " [ 1.60969000e-01 4.26482700e+00 4.78107000e-01]\n", " [ 1.60113000e-01 4.17642900e+00 6.23417000e-01]\n", " [ 1.60609000e-01 4.36403400e+00 5.67614000e-01]\n", " [ 1.60742000e-01 4.39095600e+00 5.80688000e-01]\n", " [ 1.60908000e-01 4.25156700e+00 7.59569000e-01]\n", " [ 1.60936000e-01 4.37557800e+00 6.61733000e-01]\n", " [ 1.60499000e-01 4.37547700e+00 6.92598000e-01]\n", " [ 1.60480000e-01 4.41103800e+00 4.02009000e-01]\n", " [ 1.60145000e-01 4.53858600e+00 4.64704000e-01]\n", " [ 1.60674000e-01 4.54088300e+00 4.66905000e-01]\n", " [ 1.60464000e-01 5.53281900e+00 -1.34216000e-01]\n", " [ 1.60250000e-01 5.63569300e+00 -1.19451000e-01]\n", " [ 1.60868000e-01 5.48322800e+00 1.56710000e-02]\n", " [ 1.60512000e-01 4.52818200e+00 5.27379000e-01]\n", " [ 1.60416000e-01 4.54742600e+00 4.88898000e-01]\n", " [ 1.60349000e-01 4.40960800e+00 5.76832000e-01]\n", " [ 1.60744000e-01 4.48355200e+00 6.22055000e-01]\n", " [ 1.60751000e-01 5.67213200e+00 -1.35287000e-01]\n", " [ 1.60519000e-01 5.67937100e+00 -1.28753000e-01]\n", " [ 1.60667000e-01 5.69617900e+00 -1.36130000e-02]\n", " [ 1.60357000e-01 5.64316800e+00 -1.15791000e-01]\n", " [ 1.60456000e-01 5.80115000e+00 1.51800000e-02]\n", " [ 1.60825000e-01 5.96638800e+00 2.44394000e-01]\n", " [ 1.60002000e-01 5.96454300e+00 2.35823000e-01]\n", " [ 1.60251000e-01 6.08264500e+00 -1.51931000e-01]\n", " [ 1.60465000e-01 5.98505300e+00 2.23334000e-01]\n", " [ 1.60691000e-01 5.98427300e+00 2.28815000e-01]\n", " [ 1.60705000e-01 5.98280300e+00 2.34411000e-01]]\n" ] } ], "source": [ "for i in ind1:\n", " print dat[i]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(array([[ 2., 3., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]), array([ 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014,\n", " 0.016, 0.018, 0.02 ]), array([ 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014,\n", " 0.016, 0.018, 0.02 ]))\n", "[[ 2. 2. 2. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0.]]\n", "CPU times: user 18.1 ms, sys: 4.82 ms, total: 22.9 ms\n", "Wall time: 19.6 ms\n" ] } ], "source": [ "%%time\n", "for i in ind:\n", " dist0=d.cdist([dat[0],],dat[i],APdz)[0]\n", " dist1=d.cdist([dat[0],],dat[i],APzdth)[0]\n", " print np.histogram2d(dist0, dist1,range=rng)\n", " dd2d+=np.histogram2d(dist0, dist1,range=rng,bins=(bins,bins))[0]\n", "print dd2d" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function histogram2d in module numpy.lib.twodim_base:\n", "\n", "histogram2d(x, y, bins=10, range=None, normed=False, weights=None)\n", " Compute the bi-dimensional histogram of two data samples.\n", " \n", " Parameters\n", " ----------\n", " x : array_like, shape (N,)\n", " An array containing the x coordinates of the points to be\n", " histogrammed.\n", " y : array_like, shape (N,)\n", " An array containing the y coordinates of the points to be\n", " histogrammed.\n", " bins : int or array_like or [int, int] or [array, array], optional\n", " The bin specification:\n", " \n", " * If int, the number of bins for the two dimensions (nx=ny=bins).\n", " * If array_like, the bin edges for the two dimensions\n", " (x_edges=y_edges=bins).\n", " * If [int, int], the number of bins in each dimension\n", " (nx, ny = bins).\n", " * If [array, array], the bin edges in each dimension\n", " (x_edges, y_edges = bins).\n", " * A combination [int, array] or [array, int], where int\n", " is the number of bins and array is the bin edges.\n", " \n", " range : array_like, shape(2,2), optional\n", " The leftmost and rightmost edges of the bins along each dimension\n", " (if not specified explicitly in the `bins` parameters):\n", " ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range\n", " will be considered outliers and not tallied in the histogram.\n", " normed : bool, optional\n", " If False, returns the number of samples in each bin. If True,\n", " returns the bin density ``bin_count / sample_count / bin_area``.\n", " weights : array_like, shape(N,), optional\n", " An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.\n", " Weights are normalized to 1 if `normed` is True. If `normed` is\n", " False, the values of the returned histogram are equal to the sum of\n", " the weights belonging to the samples falling into each bin.\n", " \n", " Returns\n", " -------\n", " H : ndarray, shape(nx, ny)\n", " The bi-dimensional histogram of samples `x` and `y`. Values in `x`\n", " are histogrammed along the first dimension and values in `y` are\n", " histogrammed along the second dimension.\n", " xedges : ndarray, shape(nx+1,)\n", " The bin edges along the first dimension.\n", " yedges : ndarray, shape(ny+1,)\n", " The bin edges along the second dimension.\n", " \n", " See Also\n", " --------\n", " histogram : 1D histogram\n", " histogramdd : Multidimensional histogram\n", " \n", " Notes\n", " -----\n", " When `normed` is True, then the returned histogram is the sample\n", " density, defined such that the sum over bins of the product\n", " ``bin_value * bin_area`` is 1.\n", " \n", " Please note that the histogram does not follow the Cartesian convention\n", " where `x` values are on the abscissa and `y` values on the ordinate\n", " axis. Rather, `x` is histogrammed along the first dimension of the\n", " array (vertical), and `y` along the second dimension of the array\n", " (horizontal). This ensures compatibility with `histogramdd`.\n", " \n", " Examples\n", " --------\n", " >>> import matplotlib as mpl\n", " >>> import matplotlib.pyplot as plt\n", " \n", " Construct a 2-D histogram with variable bin width. First define the bin\n", " edges:\n", " \n", " >>> xedges = [0, 1, 3, 5]\n", " >>> yedges = [0, 2, 3, 4, 6]\n", " \n", " Next we create a histogram H with random bin content:\n", " \n", " >>> x = np.random.normal(2, 1, 100)\n", " >>> y = np.random.normal(1, 1, 100)\n", " >>> H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))\n", " >>> H = H.T # Let each row list bins with common y range.\n", " \n", " :func:`imshow <matplotlib.pyplot.imshow>` can only display square bins:\n", " \n", " >>> fig = plt.figure(figsize=(7, 3))\n", " >>> ax = fig.add_subplot(131, title='imshow: square bins')\n", " >>> plt.imshow(H, interpolation='nearest', origin='low',\n", " ... extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n", " \n", " :func:`pcolormesh <matplotlib.pyplot.pcolormesh>` can display actual edges:\n", " \n", " >>> ax = fig.add_subplot(132, title='pcolormesh: actual edges',\n", " ... aspect='equal')\n", " >>> X, Y = np.meshgrid(xedges, yedges)\n", " >>> ax.pcolormesh(X, Y, H)\n", " \n", " :class:`NonUniformImage <matplotlib.image.NonUniformImage>` can be used to\n", " display actual bin edges with interpolation:\n", " \n", " >>> ax = fig.add_subplot(133, title='NonUniformImage: interpolated',\n", " ... aspect='equal', xlim=xedges[[0, -1]], ylim=yedges[[0, -1]])\n", " >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')\n", " >>> xcenters = (xedges[:-1] + xedges[1:]) / 2\n", " >>> ycenters = (yedges[:-1] + yedges[1:]) / 2\n", " >>> im.set_data(xcenters, ycenters, H)\n", " >>> ax.images.append(im)\n", " >>> plt.show()\n", "\n" ] } ], "source": [ "help(np.histogram2d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "d." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ array([ 78, 65, 54, 102, 97, 9, 27, 156, 116, 190, 161, 55, 69,\n", " 170, 77, 30, 125, 144, 47, 71, 46, 98, 14, 182, 70, 40,\n", " 191, 101, 50, 86, 74, 31, 175, 189, 122, 63, 20, 53, 37,\n", " 32, 93, 177, 173, 140, 151, 183, 44, 72, 112, 66, 143, 85,\n", " 34, 163, 147, 45, 103, 168, 131, 149, 76, 105, 124, 59, 99,\n", " 1, 11, 155, 15, 8, 186, 57, 39, 176, 154, 10, 5, 26,\n", " 181, 185, 29, 51, 106, 137, 178, 49, 95, 23, 108, 159, 56,\n", " 7, 160, 132, 82, 107, 19, 21, 164, 4, 187, 141, 58, 153,\n", " 110, 152, 3, 167, 136, 117, 138, 13, 172, 87, 60, 119, 150,\n", " 24, 166, 43, 67, 35, 146, 120, 179, 192, 104, 89, 111, 33,\n", " 128, 169, 79, 6, 90, 92, 118, 73, 12, 129, 134, 162, 174,\n", " 126, 16, 133, 22, 158, 48, 2, 115, 62, 157, 139, 81, 114,\n", " 109, 68, 36, 38, 52, 100, 121, 28, 17, 188, 18, 113, 142,\n", " 180, 184, 91, 88, 25, 127, 83, 41, 171, 94, 75, 61, 145,\n", " 148, 96, 123, 64, 80, 165, 0, 42, 84, 130, 135])], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ind" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([3, 0, 1])]\n" ] } ], "source": [ "np.random.seed(0)\n", "X = np.random.random((10, 3)) # 10 points in 3 dimensions\n", "tree = BallTree(X, leaf_size=2) # doctest: +SKIP\n", "#print(tree.query_radius(X[0], r=0.3, count_only=True))\n", "ind = tree.query_radius(X[0].reshape(1,-1), r=0.3) # doctest: +SKIP\n", "print(ind) # indices of neighbors within distance 0.3" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function query_radius:\n", "\n", "query_radius(...)\n", " query_radius(self, X, r, count_only = False):\n", " \n", " query the tree for neighbors within a radius r\n", " \n", " Parameters\n", " ----------\n", " X : array-like, last dimension self.dim\n", " An array of points to query\n", " r : distance within which neighbors are returned\n", " r can be a single value, or an array of values of shape\n", " x.shape[:-1] if different radii are desired for each point.\n", " return_distance : boolean (default = False)\n", " if True, return distances to neighbors of each point\n", " if False, return only neighbors\n", " Note that unlike the query() method, setting return_distance=True\n", " here adds to the computation time. Not all distances need to be\n", " calculated explicitly for return_distance=False. Results are\n", " not sorted by default: see ``sort_results`` keyword.\n", " count_only : boolean (default = False)\n", " if True, return only the count of points within distance r\n", " if False, return the indices of all points within distance r\n", " If return_distance==True, setting count_only=True will\n", " result in an error.\n", " sort_results : boolean (default = False)\n", " if True, the distances and indices will be sorted before being\n", " returned. If False, the results will not be sorted. If\n", " return_distance == False, setting sort_results = True will\n", " result in an error.\n", " \n", " Returns\n", " -------\n", " count : if count_only == True\n", " ind : if count_only == False and return_distance == False\n", " (ind, dist) : if count_only == False and return_distance == True\n", " \n", " count : array of integers, shape = X.shape[:-1]\n", " each entry gives the number of neighbors within\n", " a distance r of the corresponding point.\n", " \n", " ind : array of objects, shape = X.shape[:-1]\n", " each element is a numpy integer array listing the indices of\n", " neighbors of the corresponding point. Note that unlike\n", " the results of a k-neighbors query, the returned neighbors\n", " are not sorted by distance by default.\n", " \n", " dist : array of objects, shape = X.shape[:-1]\n", " each element is a numpy double array\n", " listing the distances corresponding to indices in i.\n", " \n", " Examples\n", " --------\n", " Query for neighbors in a given radius\n", " \n", " >>> import numpy as np\n", " >>> np.random.seed(0)\n", " >>> X = np.random.random((10, 3)) # 10 points in 3 dimensions\n", " >>> tree = BinaryTree(X, leaf_size=2) # doctest: +SKIP\n", " >>> print(tree.query_radius(X[0], r=0.3, count_only=True))\n", " 3\n", " >>> ind = tree.query_radius(X[0], r=0.3) # doctest: +SKIP\n", " >>> print(ind) # indices of neighbors within distance 0.3\n", " [3 0 1]\n", "\n" ] } ], "source": [ "help(ddbt.query_radius)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr2d=np.zeros((20,20))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = np.array([[0, 0.02], [0, 0.02]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "dist0=d.cdist([dat[0],],datR,APdz)[0]\n", "dist1=d.cdist([dat[0],],datR,APzdth)[0]\n", "print np.histogram2d(dist0, dist1,range=rng)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "for i in tqdm(xrange(len(dat))):\n", " dist0=d.cdist([dat[i],],datR,APdz)[0]\n", " dist1=d.cdist([dat[i],],datR,APzdth)[0]\n", " dr2d+=np.histogram2d(dist0, dist1,range=rng)[0]\n", "print dr2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('dr2ddr72v06cdist200k.pkl','w') as f:\n", " pickle.dump(dr2d,f) \n", "dr2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "raw", "metadata": {}, "source": [ "%%time\n", "dist0=d.cdist(dat[0:10],dat,APdz)\n", "dist1=d.cdist(dat[0:10],dat,APzdtheta)\n", "#print np.histogram2d(dist0, dist1,range=rng)\n", "#print dd2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dist0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0.size" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0.flatten\n", "print dist0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dist0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "dist0=d.cdist([dat[0],],dat,APdz)[0]\n", "dist1=d.cdist([dat[0],],dat,APzdtheta)[0]\n", "print np.histogram2d(dist0, dist1,range=rng)\n", "#print dd2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0=dist0[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0[1:len(dist0)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dist0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0.size" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(dist0.flatten)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dist0[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.array.flatten(dist0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dist0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "while len(dat)>0:\n", " i=len(dat)-1\n", " while i>0:\n", " dist[i]=APcat(dat[0],dat[i])\n", " i-=1\n", " dd2d+=np.histogram2d(dist[:,0], dist[:,1],range=rng)[0]\n", " dat=np.delete(dat,0,axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dd2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "while len(dat)>0:\n", " i=len(dat)-1\n", " while i>0:\n", " dist=500*APcat(dat[0],dat[i])\n", " dd2d+=np.histogram2d(dist[0], dist[1], bins = 10, range = rng)\n", " i-=1\n", " dat=np.delete(dat,0,axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def binDists2d(dat, dz = lambda u, v: abs(u[0]-v[0]), zdth = lambda u, v: 0.5*(u[0]+v[0])*np.arccos(np.sin(u[2])*np.sin(v[2])+np.cos(u[2])*np.cos(v[2])*np.cos(u[1]-v[1]))):\n", " i, j = np.triu_indices(dat.shape[0], 1)\n", " dist0 = dz(dat[i], dat[j])\n", " dist1 = zdth(dat[i], dat[j])\n", " rng = np.array([[0, 0.02], [0, 0.02]])\n", " return np.histogram2d(dist0, dist1, bins = 10, range = rng) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "td=np.random.rand(1000,3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "binDists2d(td)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "binDists2d(dat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def binDists2d(dat, f1 = 'euclidean', f2 = 'cosine'):\n", " dist0 = APcat(dat, f1)\n", " dist1 = d.pdist(dat, f2)\n", " return np.histogram2d(dist0, dist1, bins = 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dd2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.contour(dd2d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('DR72DD2DMI.pkl', 'w') as f:\n", " pickle.dump(dd2d,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('DR72DD2DMI.pkl') as f:\n", " DD2D = pickle.load(f)\n", " \n", "DD2D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dzbin=zdthbin=np.arange(0.002,0.022,0.002)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.contour(dzbin,zdthbin,dd2d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dzbin" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.contour(dzbin,zdthbin,dd2d,levels=[ 5041., 13955., 23161., 31557., 38796., 46402., 53552.,\n", " 60708., 67437., 74549.])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(len(dat)-1):\n", " for j in range(len(dat)-1):\n", " dist=APcat(dat[0],dat[i])\n", " ind0=int(dist[0]/0.002)\n", " ind1=int(dist[1]/0.002)\n", " if ind0>9 or ind1>9:\n", " pass\n", " else:\n", " dd2d[ind0,ind1]+=1\n", " dat=np.delete(dat,0,0)\n", " print len(dat)\n", " print dd2d" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(len(dat)):\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0,0.022,0.002)\n", "print bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from apmetric6 import *\n", "BTdat6 = BallTree(dat,metric='pyfunc',func=APmetric6,leaf_size=5) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "BTdat6" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "per6=BTdat6.two_point_correlation(dat,bins)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print per6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One has to change if condition in the metric definition of dz<=0.002 to 0.002<dz<=0.004 and so on" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from apmetric4 import *\n", "BTdat4 = BallTree(dat,metric='pyfunc',func=APmetric4,leaf_size=5) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "BTdat4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "per4=BTdat4.two_point_correlation(dat,bins)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print per4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "from apmetric3 import *\n", "BTdat3 = BallTree(dat,metric='pyfunc',func=APmetric3,leaf_size=5) \n", "\n", "BTdat3\n", "\n", "%%time\n", "per3=BTdat3.two_point_correlation(dat,bins)\n", "\n", "print per3print bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Nbins=len(bins)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Nbins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LCfmetric=LCDMmetric" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "LCfmetric(dat[0],dat[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "start_time=time.time()\n", "counts_DD=BTDLC.two_point_correlation(dat,bins)\n", "print counts_DD\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTDcDDLCf.pkl', 'w') as f:\n", " pickle.dump(counts_DD,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('BTDcDDLCf.pkl') as f:\n", " counts_DD = pickle.load(f)\n", " \n", "counts_DD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DD=np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DD,'ro-')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "BallTree.two_point_correlation works almost 10 times faster! with leaf_size=5 Going with it to the random catalog" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=ascii.read(\"./output/rand200kdr72.dat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=ascii.read(\"./output/rDR7200kLCsrarf.dat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR.remove_column('z')\n", "dataR.remove_column('ra')\n", "dataR.remove_column('dec')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rs=np.array(dataR['s'])\n", "rrar=np.array(dataR['rar'])\n", "rdecr=np.array(dataR['decr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=np.array([rs,rrar,rdecr])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR.reshape(3,len(dataR))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=datR.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Saving the objects:\n", "with open('rDR7200kLCsrarf.pkl', 'w') as f: # Python 3: open(..., 'wb')\n", " pickle.dump(datR, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Getting back the objects:\n", "with open('rDR7200kLCsrarf.pkl') as f: # Python 3: open(..., 'rb')\n", " datR = pickle.load(f)\n", "datR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "BT_RLC = BallTree(datR,metric='pyfunc',func=LCfmetric,leaf_size=5) \n", "\n", "with open('BTR200kdatsLCf.pkl', 'w') as f:\n", " pickle.dump(BT_RLC,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('BTR200kdatsLCf.pkl') as f:\n", " BTRLC = pickle.load(f)\n", " \n", "BTRLC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "start_time=time.time()\n", "counts_RR=BTRLC.two_point_correlation(datR,bins)\n", "print counts_RR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTR200kcRRLCf.pkl', 'w') as f:\n", " pickle.dump(counts_RR,f)\n", "\n", "with open('BTR200kcRRLCf.pkl') as f:\n", " counts_RR = pickle.load(f)\n", " \n", "counts_RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counts_RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR=np.diff(counts_RR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],RR,'bo-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR_zero = (RR == 0)\n", "RR[RR_zero] = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%time\n", "start_time=time.time()\n", "counts_DR=BTRLC.two_point_correlation(dat,bins)\n", "print counts_DR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTR200kcDRLCf.pkl', 'w') as f:\n", " pickle.dump(counts_DR,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('BTR200kcDRLCf.pkl') as f:\n", " counts_DR = pickle.load(f)\n", " \n", "counts_DR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DR=np.diff(counts_DR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corrells=(4.0 * DD - 4.0 * DR + RR) / RR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corrells" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],corrells,'go-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*corrells*(c*1e-5)**2,'go-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*corrells[1:len(bins)]*(c*1e-5)**2,'go-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')\n", "plt.savefig(\"correl2xlsLCf.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'bo-')\n", "plt.savefig(\"correl2x1lsLCf.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.yscale('log')\n", "plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n", "plt.savefig(\"correllsfiglogLCf.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.yscale('log')\n", "plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'ro-')\n", "plt.savefig(\"correllslog2xLCf.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n", "plt.savefig(\"correllsloglogLCf.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from functools import partial\n", "\n", "def harvester(text, case):\n", " X = case[0]\n", " return text + str(X)\n", "\n", "\n", "partial_harvester = partial(harvester, case=RAW_DATASET)\n", "\n", "partial_qr=partial(BTD.query_radius,count_only=True)\n", "\n", "if __name__ == '__main__':\n", " pool = multiprocessing.Pool(processes=6)\n", " case_data = RAW_DATASET\n", " pool.map(partial_harvester, case_data, 1)\n", " pool.close()\n", " pool.join()\n", "\n", "mapfunc = partial(BTD.query_radius, count_only=True)\n", "map(mapfunc, volume_ids)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ascii.write(\"DR72DDbinned.dat\",(bins[1:len(bins)],DDresult))\n", "start_time=time.time()\n", "@pickle_results(\"DR72DDmp1.pkl\")\n", "def ddcal(BTD,dat,bins,Nbins):\n", " counts_DD=np.zeros(Nbins)\n", " for i in tqdm(range(Nbins)):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", " DD = np.diff(counts_DD)\n", " print counts_DD\n", " print DD\n", " return DD\n", "\n", "def mf_wrap(args):\n", " return ddcal(*args)\n", "\n", "pool=mp.Pool(8)\n", "\n", "arg=[(BTD,dat,bins,Nbins)]\n", "%timeit DDresult=pool.map(mf_wrap,arg) \n", "#DDresult = ddcal(BTD,dat,bins,Nbins)\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%timeit dat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DDresult[0],'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def myfun(a,b):\n", " print a + b\n", " return a+b\n", "\n", "def mf_wrap(args):\n", " return myfun(*args)\n", "\n", "p = mp.Pool(4)\n", "\n", "fl = [(a,b) for a in range(3) for b in range(2)]\n", "\n", "p.map(mf_wrap, fl)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counts_DD=np.zeros(Nbins)\n", "\n", "for i in range(Nbins):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", "DD = np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print counts_DD\n", "print DD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DD,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=dataR[1].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.array(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "type(tdata[4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.atleast_d(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.reshape(len(tdata),3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.asarray(data)\n", "tdata=tdata.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BTD.two_point_correlationpoint_correlationpoint_correlationpoint_correlationtime\n", "stime=time.time()\n", "tpcf=BTD.two_point_correlation(dat,bins)\n", "print time.time()-stime\n", "print tpcf\n", "plt.plot(bins,tpcf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stime=time.time()\n", "tpcfd=BTD.two_point_correlation(dat,bins,dualtree=True)\n", "print time.time()-stime\n", "print tpcfd\n", "plt.plot(bins,tpcfd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0)\n", "X = np.random.random((30,3))\n", "r = np.linspace(0, 1, 10)\n", "tree = BallTree(X,metric='pyfunc',func=LCDMmetric) \n", "s = pickle.dumps(tree) \n", "treedump = pickle.loads(s) \n", "treedump.two_point_correlation(X,r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BT_D = BallTree(data)\n", " BT_R = BallTree(data_R)\n", "\n", " counts_DD = np.zeros(Nbins + 1)\n", " counts_RR = np.zeros(Nbins + 1)\n", "\n", " for i in range(Nbins + 1):\n", " counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n", " count_only=True))\n", " counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n", " count_only=True))\n", "\n", " DD = np.diff(counts_DD)\n", " RR = np.diff(counts_RR)\n", "\n", " # check for zero in the denominator\n", " RR_zero = (RR == 0)\n", " RR[RR_zero] = 1\n", "\n", " if method == 'standard':\n", " corr = factor ** 2 * DD / RR - 1\n", " elif method == 'landy-szalay':\n", " if sklearn_has_two_point:\n", " counts_DR = KDT_R.two_point_correlation(data, bins)\n", " else:\n", " counts_DR = np.zeros(Nbins + 1)\n", " for i in range(Nbins + 1):\n", " counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n", " count_only=True))\n", " DR = np.diff(counts_DR)\n", "\n", " corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n", "\n", " corr[RR_zero] = np.nan\n", "\n", " return corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7fdat=np.array([data['s'][0:300] data['rar'][0:300] data['decr'][0:300]])\n", "dr7fdat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7fdat[2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def LCDMmetric(p1,p2):\n", " costheta=m.sin(dec1)*m.sin(dec2)+m.cos(dec1)*m.cos(dec2)*m.cos(ra1-ra2)\n", " s1=DC_LCDM(z1)\n", " s2=DC_LCDM(z2)\n", " return np.sqrt(s1**2+s2**2-2.0*s1*s2*costheta)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
phungkh/phys202-2015-work
assignments/assignment05/InteractEx03.ipynb
1
39699
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.html.widgets import interact, interactive, fixed\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Using interact for animation with data" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "A [*soliton*](http://en.wikipedia.org/wiki/Soliton) is a constant velocity wave that maintains its shape as it propagates. They arise from non-linear wave equations, such has the [Korteweg–de Vries](http://en.wikipedia.org/wiki/Korteweg%E2%80%93de_Vries_equation) equation, which has the following analytical solution:\n", "\n", "$$\n", "\\phi(x,t) = \\frac{1}{2} c \\mathrm{sech}^2 \\left[ \\frac{\\sqrt{c}}{2} \\left(x - ct - a \\right) \\right]\n", "$$\n", "\n", "The constant `c` is the velocity and the constant `a` is the initial location of the soliton.\n", "\n", "Define `soliton(x, t, c, a)` function that computes the value of the soliton wave for the given arguments. Your function should work when the postion `x` *or* `t` are NumPy arrays, in which case it should return a NumPy array itself." ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false, "nbgrader": { "checksum": "b95685e8808cf7e99f918ab07c87c11a", "solution": true } }, "outputs": [], "source": [ "def soliton(x, t, c, a):\n", " i=(((c**(1/2))/2)*(x-c*t-a))\n", " return ((1/2)*c*(np.cos(i)**(-2)))\n", " " ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "bcd15232a87c4354cbc68dcca28654ee", "grade": true, "grade_id": "interactex03a", "points": 2 } }, "outputs": [], "source": [ "assert np.allclose(soliton(np.array([0]),0.0,1.0,0.0), np.array([0.5]))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "To create an animation of a soliton propagating in time, we are going to precompute the soliton data and store it in a 2d array. To set this up, we create the following variables and arrays:" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "tmin = 0.0\n", "tmax = 10.0\n", "tpoints = 100\n", "t = np.linspace(tmin, tmax, tpoints)\n", "\n", "xmin = 0.0\n", "xmax = 10.0\n", "xpoints = 200\n", "x = np.linspace(xmin, xmax, xpoints)\n", "\n", "c = 1.0\n", "a = 0.0" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Compute a 2d NumPy array called `phi`:\n", "\n", "* It should have a dtype of `float`.\n", "* It should have a shape of `(xpoints, tpoints)`.\n", "* `phi[i,j]` should contain the value $\\phi(x[i],t[j])$." ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "phi=np.ndarray((xpoints,tpoints), dtype=float)\n", "\n", "for i in range(200):\n", " for j in range(100):\n", " phi[i,j]=soliton(x[i],t[j],c,a)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "90baf1a97272cee6f5554e0104b50f47", "grade": true, "grade_id": "interactex03b", "points": 4 } }, "outputs": [], "source": [ "assert phi.shape==(xpoints, tpoints)\n", "assert phi.ndim==2\n", "assert phi.dtype==np.dtype(float)\n", "assert phi[0,0]==soliton(x[0],t[0],c,a)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write a `plot_soliton_data(i)` function that plots the soliton wave $\\phi(x, t[i])$. Customize your plot to make it effective and beautiful." ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false, "nbgrader": { "checksum": "d857aa7adb31b1de9c4d53a7febb18d3", "solution": true } }, "outputs": [], "source": [ "def plot_soliton_data(i=0):\n", " plt.figure(figsize=(9,6))\n", " plt.plot(x,soliton(x,t[i],c,a))\n", " plt.box(False)\n", " plt.ylim(0,6000)\n", " plt.grid(True)\n", " plt.ylabel('soliton wave')\n", " plt.xlabel('x')\n", " " ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false, "nbgrader": {} }, "outputs": [ { "data": { "image/png": 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W89jO7ozuqSnyeNdUUSOtkmEThYv9kEvSCu0G/HjIfoefVoPjoWVpOI9hXbLF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\nBL5Cd3btXZl51XRbJWC/zNz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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb81259e630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_soliton_data(0)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "a76632040b08c7c76c889e67ee93deb0", "grade": true, "grade_id": "interactex03c", "points": 2 } }, "outputs": [], "source": [ "assert True # leave this for grading the plot_soliton_data function" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Use `interact` to animate the `plot_soliton_data` function versus time." ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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W89jO7ozuqSnyeNdUUSOtkmEThYv9kEvSCu0G/HjIfoefVoPjoWVpOI9hXbLF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\nBL5Cd3btXZl51XRbJWC/zNz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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb81292b128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "interact(plot_soliton_data, i=(0,100,5))" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "ef5ed9fcab6418650cdf556757a4486a", "grade": true, "grade_id": "interactex03d", "points": 2 } }, "outputs": [], "source": [ "assert True # leave this for grading the interact with plot_soliton_data cell" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mysticPrince/hcpf
algos/.ipynb_checkpoints/plot_graphs-checkpoint.ipynb
1
62704
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_5 = np.loadtxt('../results/movielens/hcpf_ndcg_5.txt')\n", "x_10 = np.loadtxt('../results/movielens/hcpf_ndcg_10.txt')\n", "x_20 = np.loadtxt('../results/movielens/hcpf_ndcg_20.txt')\n", "x_50 = np.loadtxt('../results/movielens/hcpf_ndcg_50.txt')\n", "x_100 = np.loadtxt('../results/movielens/hcpf_ndcg_100.txt')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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qVaqEz1O+FrwEbSGEeAHouk70lWj2xu69ZTuceJhsQzYA1ubWlPIoRYPABoR4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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe71d3fe1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,6))\n", "plt.xlabel(\"Iterations/1000\")\n", "plt.ylabel(\"NCDG\")\n", "plt.plot(x_5,'black',label=\"K=5\")\n", "plt.plot(x_10,'green',label=\"K=10\")\n", "plt.plot(x_20,'red',label=\"K=20\")\n", "plt.plot(x_50,'blue',label=\"K=50\")\n", "plt.plot(x_100,'orange',label=\"K=100\")\n", "plt.legend(loc=\"lower right\")\n", "plt.grid()\n", "plt.savefig('x_matrix.png')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_5 = np.loadtxt('../results/movielens/hcpf_mae_5.txt')\n", "x_10 = np.loadtxt('../results/movielens/hcpf_mae_10.txt')\n", "x_20 = np.loadtxt('../results/movielens/hcpf_mae_20.txt')\n", "x_50 = np.loadtxt('../results/movielens/hcpf_mae_50.txt')\n", "x_100 = np.loadtxt('../results/movielens/hcpf_mae_100.txt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.figure(figsize=(12,6))\n", "plt.xlabel(\"Iterations/1000\")\n", "plt.ylabel(\"MAE\")\n", "plt.plot(x_5,'black',label=\"K=5\")\n", "plt.plot(x_10,'green',label=\"K=10\")\n", "plt.plot(x_20,'red',label=\"K=20\")\n", "plt.plot(x_50,'blue',label=\"K=50\")\n", "plt.plot(x_100,'orange',label=\"K=100\")\n", "plt.legend(loc=\"lower right\")\n", "plt.grid()\n", "plt.savefig('x_matrix.png')\n", "plt.show()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:bml]", "language": "python", "name": "conda-env-bml-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
daniorerio/trackpy
benchmarks/maxima_benchmarks.ipynb
2
1054
# must be run in ipython import numpy as np import trackpy as tp from trackpy.preprocessing import scale_to_gamut def b(command): get_ipython().magic(command) dummy_noise_image = scale_to_gamut(np.random.randint(0, 100, (100, 100)), np.uint8) real_image_raw = tp.ImageSequence('../trackpy/tests/video/image_sequence')[0] real_image = scale_to_gamut(tp.bandpass(real_image_raw, 1, 10, threshold=1), np.uint8) big_image = scale_to_gamut(tp.bandpass(np.tile(real_image_raw, (2, 5)), 1, 10, threshold=1), np.uint8) very_small_image = scale_to_gamut(tp.bandpass(real_image_raw[:200, :200], 1, 10, threshold=1), np.uint8) #print 'Locate using Python Engine with Default Settings (Accurate)' #b(u"timeit tp.locate(real_image, 9, engine='python', preprocess=False)") print '1x: Find local_maxima only' b(u"timeit tp.feature.local_maxima(real_image, 9, 10)") print '10x: Find local_maxima only' b(u"timeit tp.feature.local_maxima(big_image, 9, 10)") print '~0.1x: Find local_maxima only' b(u"timeit tp.feature.local_maxima(very_small_image, 9, 10)")
bsd-3-clause
CalPolyPat/Python-Workshop
Python Workshop/Containers.ipynb
2
15684
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Containers\n", "Containers are set of objects including **lists**, **sets**, **tuples**, and **dictionaries**. They hold other variables in various different ways following different rules. These are arguably the most used, and most useful, objects you will encounter in today's course." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Lists\n", "Lists are objects that hold variables in a particular order that you choose. They can hold any variable you wish, in fact, one list can hold more than one type of variable. Recall that our \"basic\" types are int, float, str, and bool. Let's see some examples of lists.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4]\n", "['a', 'b', 'c', 'd']\n", "[True, False, True]\n", "[1, 2, 'c', 'd']\n", "[False, 2, 4, True]\n" ] } ], "source": [ "list1=[1,2,3,4]\n", "print(list1)\n", "list2=[\"a\",\"b\",\"c\",\"d\"]\n", "print(list2)\n", "list3=[True, False, True]\n", "print(list3)\n", "list3=[1,2,\"c\",\"d\"]\n", "print(list3)\n", "list4=[2>3, 2, 4, 3>2]\n", "print(list4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's notice a few key things:\n", "1. To make a list we use []\n", "2. We separate individual elements with commas\n", "3. The **elements** of a list can be any type\n", "4. The **elements** need not be explicitly written" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Other Properties of Lists\n", "1. You can add two lists to create a new list.\n", "2. You can **append** an element to the end of a list by using the append function.\n", "3. You can **append** a list to the end of another list by using the += operator.\n", "4. You can access a single element with something called **slicing**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, '1', '2', '3']\n", "[1, 2, 3, 4]\n", "['1', '2', '3', 1, 2, 3, 4]\n" ] } ], "source": [ "la=[1,2,3]\n", "lb=[\"1\",\"2\",\"3\"]\n", "print(la+lb)\n", "la.append(4)\n", "print(la)\n", "lb+=la\n", "print(lb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Slicing\n", "At one point, you will want to access certain elements of a list, this is done by slicing. There are a couple of ways to do this.\n", "1. la[0] will give the first element of the list la. Note that lists are **indexed** starting at zero, not one.\n", "2. la[n] will give the (n+1)th element of the list la.\n", "3. la[-1] will give the last element of the list la.\n", "4. la[-n] will give the nth to last element of the list la.\n", "5. la[p:q:k] will give you every kth element starting at p and ending at q of the list la. Ex/la[1:7:2] will give you every second element starting at 1 and ending at 7.\n", "5b. If p is omitted, it is assumed to be 0. If k is omitted, it is assumed to be 1, if q is omitted, it is assumed to be the last **index** or -1, which is really the same thing as we see above." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "4\n", "10\n", "8\n", "[1, 3, 5, 7, 9]\n", "[4, 6, 8, 10]\n", "[3, 4]\n", "[3, 4]\n" ] } ], "source": [ "la=[1,2,3,4,5,6,7,8,9,10]\n", "print(la[0])\n", "print(la[3])\n", "print(la[-1])\n", "print(la[-3])\n", "print(la[0:10:2])\n", "print(la[3::2])\n", "print(la[2:4:])\n", "print(la[2:4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercises\n", "\n", "1. Make 2 lists and add them together.\n", "\n", "2. Take your list from 1., append the list [\"I\", \"Love\", \"Python\"] to it without using the append command, then print every second element.\n", "\n", "3. Can you make a list of lists? Try it.\n", " \n", " Consider the list x=[3,5,7,8,\"Pi\"].\n", " \n", "4a. Type out what you would expect python to print along with the type of object printed would be for the following slices:\n", " \n", " x[2]\n", " \n", " x[0]\n", " \n", " x[-2]\n", " \n", " x[1::2]\n", " \n", " x[1::]\n", " \n", " x[::-4]\n", "\n", "4b. Check your answer by creating that list and printing out the corresponding slices." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Sets\n", "Sets are a special type of list that adhere to certain rules. If you have taken any higher level or proof-based math classes, you will recognize that sets in Python are exactly the same as those in mathematics. Instead of using [], {} are used to create a set. Sets have the following properties:\n", "\n", "1. Sets will not contain duplicates. If you make a set with duplicates, it will only retain one of them.\n", "2. Sets are not ordered. This means no slicing.\n", "3. Sets have the familiar set operations from math. These are outlined below.\n", "4. You can convert a list to a set in the following manner: Let t be a list, then set(t) is now a set containing the elements of t." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Set Operations\n", "\n", "Consider sets s={1,2,3} and t={1,2,3,4,5};\n", "\n", "| Operation | Meaning | Example |\n", "|:----------:|:-------:|:-------:|\n", "| s&#124;t | Union | {1,2,3,4,5} |\n", "| s&t | Intersection | {1,2,3} |\n", "| s-t | Difference | {} |\n", "| s^t | Symmetric Difference | {4,5} |\n", "| s<t | Strict Subset | True |\n", "| s<=t | Subset | True |\n", "| s>t | Strict Superset | False |\n", "| s>= | Superset | False |\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5}\n", "{2, 3}\n", "{8, 7}\n", "{2, 3, 7, 8}\n", "{8, 2, 3, 7}\n", "True\n" ] } ], "source": [ "t = {1,2,3,3,3,3,3,3,3,3,3,3,3,3,3,4,5}\n", "print(t)\n", "s = {1,4,5,7,8}\n", "print(t-s)\n", "print(s-t)\n", "print(t^s)\n", "print(t-s|s-t)\n", "print(t^s==t-s|s-t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercises\n", "1. Write a set containing the letters of the word \"dog\".\n", "2. Find the difference between the set in 1. and the set {\"d\", 5, \"g\"}\n", "3. Remove all the duplicates in the list [1,2,4,3,3,3,5,2,3,4,5,6,3,5,7] and print the resulting object in two lines." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "##Tuples\n", "Tuples are just like lists except that you cannot append elements to a tuple. You may, however, combine two tuples. To create a tuple, one uses ()." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 2, 3, 4, 5)\n", "('a', 'b', 'c')\n", "(1, 2, 3, 4, 5, 'a', 'b', 'c')\n" ] } ], "source": [ "a = (1,2,3,4,5)\n", "b = ('a', 'b', 'c')\n", "print(a)\n", "print(b)\n", "print(a+b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Dictionaries\n", "Dictionaries are quite different from any container we have seen so far. A dictionary is a bunch of unordered key/value pairs. That is, each element of a dictionary has a key and a value and the elements are in no particular order. It is good to keep this unorderedness in mind later on, for now, let's look at some examples. To create a dictionary we use the following syntax, { key:value}." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'Sally': 89, 'Jeff': 45, 'Lucy': 75, 'Jose': 96}\n", "{'Jake': 75, 'John': 23, 'Devin': 64}\n" ] }, { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'dict' and 'dict'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-57-07c4638eecae>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mscores2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'Devin'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'John'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m23\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Jake'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m75\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mscores2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'dict' and 'dict'" ] } ], "source": [ "#Let's say we have some students take a test and we want to store their scores\n", "scores = {'Sally':89, 'Lucy':75, 'Jeff':45, 'Jose':96}\n", "print(scores)\n", "#We can, however, not combine two different dictionaries\n", "scores2 = {'Devin':64, 'John':23, 'Jake':75}\n", "print(scores2)\n", "print(scores+scores2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike with lists, we cannot access elements of the dictionary with slicing. We must instead use the keys. Let's see how to do this." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "89\n", "23\n" ] } ], "source": [ "print(scores['Sally'])\n", "print(scores2['John'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, the key returns us the value. This can be useful if you have a bunch of items that need to be paired together." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Accessing Just the Keys or Values\n", "Want to get a list of the keys in a dictionary? How about the values? Fret not, there is a way!" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['Sally', 'Jeff', 'Lucy', 'Jose'])\n", "dict_values([89, 45, 75, 96])\n" ] } ], "source": [ "print(scores.keys())\n", "print(scores.values())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercises\n", "1. Build a dictionary of some constants in physics and math. Print out at least two of these values.\n", "2. Give an example of something that would be best represented by a dictionary, think pairs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##In and Not In\n", "No, this section isn't about fashion. It is about the **in** and **not in** operators. They return a boolean value based on whether or not a value is **in** or **not in** a container. Note that for dictionaries, this refers to the keys, no the values." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True\n", "True\n" ] } ], "source": [ "print('Devin' in scores2)\n", "print(2 in a)\n", "print('Hello World' not in scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Converting Between Containers\n", "But what if I want my set to be a list or my tuple to be a set? To convert between types of containers, you can use any of the following functions:\n", "\n", "list() : Converts any container type into a list, for dictionaries it will be the list of keys.\n", "\n", "tuple() : Converts any container type into a tuple, for dictionaries it will be the tuple of keys.\n", "\n", "set() : Converts any container type into a set, for dictionaries it will be the set of keys. Note that as above, this will remove all duplicates." ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3]\n", "[1, 2, 3]\n", "[1, 3]\n", "(1, 2, 3)\n", "(1, 2, 3)\n", "(1, 3)\n", "{1, 2, 3}\n", "{1, 2, 3}\n", "{1, 3}\n" ] } ], "source": [ "a = [1,2,3]\n", "b = (1,2,3)\n", "c = {1,2,3}\n", "d = {1:2,3:4}\n", "\n", "print(list(b))\n", "print(list(c))\n", "print(list(d))\n", "\n", "print(tuple(a))\n", "print(tuple(c))\n", "print(tuple(d))\n", "\n", "print(set(a))\n", "print(set(b))\n", "print(set(d))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Exercises\n", "1. Take the tuple (1,1,1,1,3,4,3,5,57,6,4,4,4,6,5,6) and remove its duplicates. Then turn it into a list.\n", "2. Earlier we saw that you could retrieve the keys and values of a dictionary individually, but they weren't lists yet. Using the dictionary d = {'tall':624, 'short':234, 'Feynman':'diagrams', 'dead':'cat', 'alive':'cat'}, try converting the keys and values into lists and print them out.\n", "3. Consider a = [1,2,3,4,5] and b = ['a','b','c','p'] is 'p' in a+b? Is 34 in a+b? Write a boolean statement the is true involving 34 and a+b." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
drphilmarshall/LocalGroupHaloProps
Notebooks/Untitled1.ipynb
1
36373
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = np.load('/lustre/ki/pfs/mwillia1/LG_project/Consuelo_Boxes/4001/4001hlist.npy')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1230.37, 1180.23, 1068.14, ..., 27.67, 28.33, 24.95])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h['vmax']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x3b03450>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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300cohZ50PZiuDyJMTDsoksDUsKVRkRNYiDGSum3dFHb8U4BTga8A/5hmdBBC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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x3b016d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scatter(np.log10(h['mvir']), h['vmax'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hal = h[np.log10(h['mvir'])>11.3]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1178747,)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hal.shape" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 1.10440400e+06, 6.22570000e+04, 9.11300000e+03,\n", " 2.13200000e+03, 5.89000000e+02, 1.71000000e+02,\n", " 5.10000000e+01, 2.10000000e+01, 6.00000000e+00,\n", " 3.00000000e+00]),\n", " array([ 83.97 , 261.746, 439.522, 617.298, 795.074, 972.85 ,\n", " 1150.626, 1328.402, 1506.178, 1683.954, 1861.73 ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEACAYAAAB/BTv2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3f1/59LTRnZu314AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x518b610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(hal['vmax'])" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "154.57024336\n", "70.4488375839\n" ] }, { "data": { "text/plain": [ "83.969999999999999" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print np.mean(hal['vmax'])\n", "print np.std(hal['vmax'])\n", "hal['vmax'].min()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v = h[np.abs(h['vmax'])>80]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 3.10920000e+04, 9.78800000e+05, 7.43493000e+05,\n", " 2.74583000e+05, 9.70070000e+04, 3.21220000e+04,\n", " 9.66500000e+03, 2.36800000e+03, 3.56000000e+02,\n", " 2.70000000e+01]),\n", " array([ 10.31407799, 10.81022436, 11.30637073, 11.8025171 ,\n", " 12.29866347, 12.79480984, 13.29095621, 13.78710258,\n", " 14.28324895, 14.77939532, 15.27554169]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x43a29d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist(np.log10(v['mvir']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
Jim00000/Numerical-Analysis
2_Systems_Of_Equations.ipynb
1
55943
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CHAPTER 2 - Systems Of Equations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import modules\n", "import sys\n", "import numpy as np\n", "import numpy.linalg\n", "import scipy\n", "import sympy\n", "import sympy.abc\n", "from scipy import linalg\n", "from scipy.sparse import linalg as slinalg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.1 Gaussian Elimination" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def naive_gaussian_elimination(matrix):\n", " \"\"\"\n", " A simple gaussian elimination to solve equations\n", " \n", " Args:\n", " matrix : numpy 2d array\n", " \n", " Returns:\n", " mat : The matrix processed by gaussian elimination\n", " x : The roots of the equation\n", " \n", " Raises:\n", " ValueError:\n", " - matrix is null\n", " RuntimeError :\n", " - Zero pivot encountered\n", " \"\"\"\n", " if matrix is None :\n", " raise ValueError('args matrix is null')\n", " \n", " #Clone the matrix\n", " mat = matrix.copy().astype(np.float64)\n", " \n", " # Row Size\n", " m = mat.shape[0]\n", " \n", " # Column Size\n", " n = mat.shape[1]\n", " \n", " # Gaussian Elimaination\n", " for i in range(0, m):\n", " if np.abs(mat[i , i]) == 0 :\n", " raise RuntimeError('zero pivot encountered')\n", " for j in range(i + 1, m):\n", " mult = mat[j, i] / mat[i, i]\n", " for k in range(i, m):\n", " mat[j, k] -= mult * mat[i, k]\n", " mat[j, n - 1] -= mult * mat[i, n - 1]\n", " \n", " # Back Substitution\n", " x = np.zeros(m, dtype=np.float64)\n", " for i in range(m - 1,-1,-1):\n", " for j in range(i + 1, m):\n", " mat[i, n-1] = mat[i ,n-1] - mat[i,j] * x[j]\n", " mat[i, j] = 0.0\n", " x[i] = mat[i, n-1] / mat[i, i]\n", " \n", " return mat, x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Apply Gaussian elimination in tableau form for the system of three equations in three\n", "unknowns:\n", "\n", "$$\n", "\\large\n", "\\begin{matrix}\n", "x + 2y - z = 3 & \\\\ \n", "2x + y - 2z = 3 & \\\\ \n", "-3x + y + z = -6 \n", "\\end{matrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 0. 0. 3.]\n", " [ 0. -3. 0. -3.]\n", " [ 0. 0. -2. -4.]]\n", "[x, y, z] = [3. 1. 2.]\n" ] } ], "source": [ "\"\"\"\n", "Input:\n", "[[ 1 2 -1 3]\n", " [ 2 1 -2 3]\n", " [-3 1 1 -6]]\n", "\"\"\"\n", "input_mat = np.array([1, 2, -1, 3, 2, 1, -2, 3, -3, 1, 1, -6])\n", "input_mat = input_mat.reshape(3, 4)\n", "output_mat, x = naive_gaussian_elimination(input_mat)\n", "\n", "print(output_mat)\n", "print('[x, y, z] = {}'.format(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional Examples\n", "\n", "1. Put the system $x + 2y - z = 3,-3x + y + z = -6,2x + z = 8$ into tableau form and solve by Gaussian elimination." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1. 0. 0. 3. ]\n", " [0. 7. 0. 7. ]\n", " [0. 0. 1.85714286 3.71428571]]\n", "[x, y, z] = [3. 1. 2.]\n" ] } ], "source": [ "input_mat = np.array([\n", " [ 1, 2, -1, 3],\n", " [-3, 1, 1, -6],\n", " [ 2, 0, 1, 8]\n", "])\n", "\n", "output_mat, x = naive_gaussian_elimination(input_mat)\n", "\n", "print(output_mat)\n", "print('[x, y, z] = {}'.format(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 Computer Problems\n", "\n", "1. Put together the code fragments in this section to create a MATLAB program for “naive” Gaussian elimination (meaning no row exchanges allowed). Use it to solve the systems of Exercise 2.\n", "\n", "See my implementation **naive_gaussian_elimination** in python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Let $H$ denote the $n \\times n$ Hilbert matrix, whose $(i, j)$ entry is $1 / (i + j - 1)$. Use the MATLAB program from Computer Problem 1 to solve $Hx = b$, where $b$ is the vector of all ones, for (a) n = 2 (b) n = 5 (c) n = 10." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(a) n = 2 → x = [-2. 6.]\n", "(b) n = 5 → x = [ 5. -120. 630. -1120. 630.]\n", "(c) n = 10 → x = [ -9.997365 989.771861 -23755.13378 240195.71429\n", " -1261048.597184 3783198.501116 -6725765.489567 7000357.237863\n", " -3937735.417591 923673.408496]\n" ] } ], "source": [ "def computer_problems2__2_1(n):\n", " # generate Hilbert matrix H\n", " H = scipy.linalg.hilbert(n)\n", " \n", " # generate b\n", " b = np.ones(n).reshape(n, 1)\n", " \n", " # combine H:b in tableau form\n", " mat = np.hstack((H, b))\n", " \n", " # gaussian elimination\n", " _, x = naive_gaussian_elimination(mat)\n", " \n", " return x\n", " \n", "with np.printoptions(precision = 6, suppress = True):\n", " print('(a) n = 2 → x = {}'.format(computer_problems2__2_1( 2)))\n", " print('(b) n = 5 → x = {}'.format(computer_problems2__2_1( 5)))\n", " print('(c) n = 10 → x = {}'.format(computer_problems2__2_1(10)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 2.2 The LU Factorization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def LU_factorization(matrix):\n", " \"\"\"\n", " LU decomposition\n", " \n", " Arguments:\n", " matrix : numpy 2d array\n", " \n", " Return:\n", " L : lower triangular matrix\n", " U : upper triangular matrix\n", " \n", " Raises:\n", " ValueError:\n", " - matrix is null\n", " - matrix is not a 2d array\n", " RuntimeError :\n", " - zero pivot encountered\n", " \"\"\"\n", " if matrix is None :\n", " raise ValueError('args matrix is null')\n", " \n", " if matrix.ndim != 2 :\n", " raise ValueError('matrix is not a 2d-array')\n", " \n", " # dimension\n", " dim = matrix.shape[0]\n", " \n", " # Prepare LU matrixs\n", " L = np.identity(dim).astype(np.float64)\n", " U = matrix.copy().astype(np.float64)\n", " \n", " # Gaussian Elimaination\n", " for i in range(0, dim - 1):\n", " # Check pivot is not zero\n", " if np.abs(U[i , i]) == 0 :\n", " raise RuntimeError('zero pivot encountered')\n", " for j in range(i + 1, dim):\n", " mult = U[j, i] / U[i, i]\n", " for k in range(i, dim):\n", " U[j, k] -= mult * U[i, k]\n", " L[j, i] = mult\n", " \n", " return L, U" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DEFINITION 2.2\n", "\n", "An $m \\times n$ matrix $L$ is **lower triangular** if its entries satisfy $l_{ij} = 0$ for $i < j$. An $m \\times n$ matrix $U$ is **upper triangular** if its entries satisfy $u_{ij} = 0$ for $i > j$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Find the LU factorization for the matrix $A$ in\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "1 & 1 \\\\ \n", "3 & -4 \\\\ \n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = \n", "[[1. 0.]\n", " [3. 1.]]\n", "\n", "U = \n", "[[ 1. 1.]\n", " [ 0. -7.]]\n" ] } ], "source": [ "A = np.array([\n", " [1, 1],\n", " [3, -4]\n", "])\n", "\n", "L, U = LU_factorization(A)\n", "\n", "print('L = ')\n", "print(L)\n", "\n", "print()\n", "\n", "print('U = ')\n", "print(U)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Find the LU factorization of A =\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "1 & 2 & -1 \\\\ \n", "2 & 1 & -2 \\\\ \n", "-3 & 1 & 1 \\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = \n", "[[ 1. 0. 0. ]\n", " [ 2. 1. 0. ]\n", " [-3. -2.33333333 1. ]]\n", "\n", "U = \n", "[[ 1. 2. -1.]\n", " [ 0. -3. 0.]\n", " [ 0. 0. -2.]]\n" ] } ], "source": [ "A = np.array([\n", " [ 1, 2, -1],\n", " [ 2, 1, -2],\n", " [-3, 1, 1]\n", "])\n", "\n", "L, U = LU_factorization(A)\n", "\n", "print('L = ')\n", "print(L)\n", "\n", "print()\n", "\n", "print('U = ')\n", "print(U)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Solve system\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "1 & 1 \\\\ \n", "3 & -4 \\\\ \n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "x_1 \\\\ \n", "x_2 \\\\ \n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "3 \\\\ \n", "2 \\\\ \n", "\\end{bmatrix}\n", "$$\n", "\n", ", using the LU factorization" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x1 = 2.0, x2 = 1.0\n" ] } ], "source": [ "A = np.array([\n", " [ 1, 1],\n", " [ 3, -4]\n", "])\n", "\n", "b = np.array([3, 2]).reshape(2, 1)\n", "\n", "L, U = LU_factorization(A)\n", "\n", "# calculate Lc = b where Ux = c\n", "mat = np.hstack((L, b))\n", "c = naive_gaussian_elimination(mat)[1].reshape(2, 1)\n", "\n", "# calculate Ux = c\n", "mat = np.hstack((U, c))\n", "x = naive_gaussian_elimination(mat)[1].reshape(2, 1)\n", "\n", "# output the result\n", "print('x1 = {}, x2 = {}'.format(x[0][0], x[1][0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Example\n", "Solve system\n", "\\begin{matrix}\n", "x + 2y - z = 3 & \\\\ \n", "2x + y - 2z = 3 & \\\\ \n", "-3x + y + z = -6 \n", "\\end{matrix}\n", " \n", " using the LU factorization" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x1 = 3.0, x2 = 1.0, x3 = 2.0\n" ] } ], "source": [ "A = np.array([\n", " [ 1, 2, -1],\n", " [ 2, 1, -2],\n", " [-3, 1, 1]\n", "])\n", "\n", "b = np.array([3, 3, -6]).reshape(3, 1)\n", "\n", "L, U = LU_factorization(A)\n", "\n", "# calculate Lc = b where Ux = c\n", "mat = np.hstack((L, b))\n", "c = naive_gaussian_elimination(mat)[1].reshape(3, 1)\n", "\n", "# calculate Ux = c\n", "mat = np.hstack((U, c))\n", "x = naive_gaussian_elimination(mat)[1].reshape(3, 1)\n", "\n", "# output the result\n", "print('x1 = {}, x2 = {}, x3 = {}'.format(x[0][0], x[1][0], x[2][0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional Examples\n", "\n", "1. Solve\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "2 & 4 & -2 \\\\ \n", "1 & -2 & 1 \\\\ \n", "4 & -4 & 8 \\\\\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "x_1 \\\\ \n", "x_2 \\\\ \n", "x_3 \\\\\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "6 \\\\ \n", "3 \\\\ \n", "0 \\\\\n", "\\end{bmatrix}\n", "$$\n", "\n", "using the A = LU factorization" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x1 = 3.0, x2 = -1.0, x3 = -2.0\n" ] } ], "source": [ "A = np.array([\n", " [ 2, 4, -2],\n", " [ 1, -2, 1],\n", " [ 4, -4, 8]\n", "])\n", "\n", "b = np.array([6, 3, 0]).reshape(3, 1)\n", "\n", "L, U = LU_factorization(A)\n", "\n", "# calculate Lc = b where Ux = c\n", "mat = np.hstack((L, b))\n", "c = naive_gaussian_elimination(mat)[1].reshape(3, 1)\n", "\n", "# calculate Ux = c\n", "mat = np.hstack((U, c))\n", "x = naive_gaussian_elimination(mat)[1].reshape(3, 1)\n", "\n", "# output the result\n", "print('x1 = {}, x2 = {}, x3 = {}'.format(x[0][0], x[1][0], x[2][0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Computer Problems\n", "\n", "1. Use the code fragments for Gaussian elimination in the previous section to write a MATLAB script to take a matrix A as input and output L and U. No row exchanges are allowed - the program should be designed to shut down if it encounters a zero pivot. Check your program by factoring the matrices in Exercise 2.\n", "\n", "See my implementation **LU_factorization** in python." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = \n", "[[1. 0. 0.]\n", " [2. 1. 0.]\n", " [1. 0. 1.]]\n", "\n", "U = \n", "[[3. 1. 2.]\n", " [0. 1. 0.]\n", " [0. 0. 3.]]\n" ] } ], "source": [ "# Exercise 2 - (a)\n", "A = np.array([\n", " [ 3, 1, 2],\n", " [ 6, 3, 4],\n", " [ 3, 1, 5]\n", "])\n", "\n", "L, U = LU_factorization(A)\n", "\n", "print('L = ')\n", "print(L)\n", "\n", "print()\n", "\n", "print('U = ')\n", "print(U)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = \n", "[[1. 0. 0. ]\n", " [1. 1. 0. ]\n", " [0.5 0.5 1. ]]\n", "\n", "U = \n", "[[4. 2. 0.]\n", " [0. 2. 2.]\n", " [0. 0. 2.]]\n" ] } ], "source": [ "# Exercise 2 - (b)\n", "A = np.array([\n", " [ 4, 2, 0],\n", " [ 4, 4, 2],\n", " [ 2, 2, 3]\n", "])\n", "\n", "L, U = LU_factorization(A)\n", "\n", "print('L = ')\n", "print(L)\n", "\n", "print()\n", "\n", "print('U = ')\n", "print(U)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "L = \n", "[[1. 0. 0. 0.]\n", " [0. 1. 0. 0.]\n", " [1. 2. 1. 0.]\n", " [0. 1. 0. 1.]]\n", "\n", "U = \n", "[[ 1. -1. 1. 2.]\n", " [ 0. 2. 1. 0.]\n", " [ 0. 0. 1. 2.]\n", " [ 0. 0. 0. -1.]]\n" ] } ], "source": [ "# Exercise 2 - (c)\n", "A = np.array([\n", " [ 1, -1, 1, 2],\n", " [ 0, 2, 1, 0],\n", " [ 1, 3, 4, 4],\n", " [ 0, 2, 1, -1]\n", "])\n", "\n", "L, U = LU_factorization(A)\n", "\n", "print('L = ')\n", "print(L)\n", "\n", "print()\n", "\n", "print('U = ')\n", "print(U)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Add two-step back substitution to your script from Computer Problem 1, and use it to solve the systems in Exercise 4." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def LU_factorization_with_back_substitution(A, b):\n", " \"\"\"\n", " LU decomposition with two-step back substitution\n", " where Ax = b\n", " \n", " Arguments:\n", " A : coefficient matrix\n", " b : constant vector\n", " \n", " Return:\n", " x : solution vector\n", " \"\"\"\n", " L, U = LU_factorization(A)\n", "\n", " # row size\n", " rowsz = b.size\n", " \n", " # calculate Lc = b where Ux = c\n", " matrix = np.hstack((L, b))\n", " c = naive_gaussian_elimination(matrix)[1].reshape(rowsz, 1)\n", "\n", " # calculate Ux = c\n", " matrix = np.hstack((U, c))\n", " x = naive_gaussian_elimination(matrix)[1].reshape(rowsz)\n", " \n", " return x" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1. 1. 1.]\n" ] } ], "source": [ "# Exercise 4 - (a)\n", "A = np.array([\n", " [ 3, 1, 2],\n", " [ 6, 3, 4],\n", " [ 3, 1, 5]\n", "])\n", "\n", "b = np.array([0, 1, 3]).reshape(3, 1)\n", "\n", "x = LU_factorization_with_back_substitution(A, b)\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. -1. 2.]\n" ] } ], "source": [ "# Exercise 4 - (b)\n", "A = np.array([\n", " [ 4, 2, 0],\n", " [ 4, 4, 2],\n", " [ 2, 2, 3]\n", "])\n", "\n", "b = np.array([2, 4, 6]).reshape(3, 1)\n", "\n", "x = LU_factorization_with_back_substitution(A, b)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 2.3 Sources Of Error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DEFINITION 2.3\n", "\n", "The **infinity norm**, or **maximum norm**, of the vector $x = (x_1, \\cdots, x_n)$ is $||x||_{\\infty} = \\text{max}|x_i|, i = 1,\\cdots,n$, that is, the maximum of the absolute values of the components of x." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DEFINITION 2.4\n", "\n", "Let $x_a$ be an approximate solution of the linear system $Ax = b$. The **residual** is the vector $r = b - Ax_a$. The **backward error** is the norm of the residual $||b - Ax_a||_{\\infty}$,\n", "and the **forward error** is $||x - x_a||_{\\infty}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example \n", "\n", "Find the backward and forward errors for the approximate solution $x_a = [1, 1]$ of the system\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "1 & 1 \\\\ \n", "3 & -4 \\\\ \n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "x_1 \\\\ \n", "x_2 \\\\ \n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "3 \\\\ \n", "2 \\\\ \n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "A = np.array([\n", " [ 1, 1],\n", " [ 3, -4]\n", "])\n", "\n", "b = np.array([3, 2])\n", "\n", "xa = np.array([1, 1])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2.00000000000000 1.00000000000000]\n" ] } ], "source": [ "# Get correct solution\n", "system = sympy.Matrix(((1, 1, 3), (3, -4, 2)))\n", "solver = sympy.solve_linear_system(system, sympy.abc.x, sympy.abc.y)\n", "\n", "# Packed as list\n", "x = np.array([solver[sympy.abc.x].evalf(), solver[sympy.abc.y].evalf()])\n", "\n", "# Output\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "backward error is 3.000000\n", "forward error is 1.00000000000000\n" ] } ], "source": [ "# Get backward error (differences in the input)\n", "residual = b - np.matmul(A, xa)\n", "backward_error = np.max(np.abs(residual))\n", "print('backward error is {:f}'.format(backward_error))\n", "\n", "# Get fowrawd error (differences in the output)\n", "forward_error = np.max(np.abs(x - xa))\n", "print('forward error is {:f}'.format(forward_error))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Find the forward and backward errors for the approximate solution [-1, 3.0001] of the system\n", "\n", "$$\n", "\\large\n", "\\begin{align*}\n", "x_1 + x_2 &= 2 \\\\ \n", "1.0001 x_1 + x_2 &= 2.0001 \\\\\n", "\\end{align*}\n", "$$" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "A = np.array([\n", " [ 1, 1],\n", " [ 1.0001, 1],\n", "])\n", "\n", "b = np.array([2, 2.0001])\n", "\n", "# approximated solution\n", "xa = np.array([-1, 3.0001])\n", "\n", "# correct solution\n", "x = LU_factorization_with_back_substitution(A, b.reshape(2, 1))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "backward error is 0.000100\n", "forward error is 2.000100\n" ] } ], "source": [ "# Get backward error \n", "residual = b - np.matmul(A, xa)\n", "backward_error = np.max(np.abs(residual))\n", "print('backward error is {:f}'.format(backward_error))\n", "\n", "# Get fowrawd error \n", "forward_error = np.max(np.abs(x - xa))\n", "print('forward error is {:f}'.format(forward_error))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **relative backward error** of system $Ax = b$ is defined to be $\\large \\frac{||r||_{\\infty}}{||b||_{\\infty}}$.\n", "\n", "The **relative forward error** is $\\large \\frac{||x - x_a||_{\\infty}}{||x||_{\\infty}}$.\n", "\n", "The **error magnification factor** for $Ax = b$ is the ratio of the two, or $\\large \\text{error magnification factor} = \\frac{\\text{relative forward error}}{\\text{relative backward error}} = \\frac{\\frac{||x - x_a||_{\\infty}}{||x||_{\\infty}}}{\\frac{||r||_{\\infty}}{||b||_{\\infty}}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DEFINITION 2.5\n", "\n", "The **condition number** of a square matrix A, **cond(A)**, is the maximum possible error magnification factor for solving Ax = b, over all right-hand sides b.\n", "\n", "The matrix norm of an n x n matrix A as \n", "\n", "$$\n", "\\large ||A||_{\\infty} = \\text{maximum absolute row sum}\n", "$$" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def matrix_norm(A):\n", " rowsum = np.sum(np.abs(A), axis = 1)\n", " return np.max(rowsum)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## THEOREM 2.6\n", "\n", "The condition number of the n x n matrix A is\n", "\n", "$$\n", "\\large cond(A) = ||A|| \\cdot ||A^{-1}||\n", "$$" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def condition_number(A):\n", " inv_A = np.linalg.inv(A)\n", " cond = matrix_norm(A) * matrix_norm(inv_A)\n", " return cond" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional Examples\n", "\n", "1. Find the determinant and the condition number (in the infinity norm) of the matrix\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "811802 & 810901 \\\\ \n", "810901 & 810001 \\\\ \n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "determinant of A is 1.0000645141117275\n", "condition number : 2.6330e+12\n" ] } ], "source": [ "A = np.array([\n", " [ 811802, 810901],\n", " [ 810901, 810001],\n", "])\n", "\n", "print('determinant of A is {}'.format(scipy.linalg.det(A)))\n", "print('condition number : {:.4e}'.format(condition_number(A)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. The solution of the system\n", "\n", "$$\n", "\\large\n", "\\begin{bmatrix}\n", "2 & 4.01 \\\\ \n", "3 & 6 \\\\ \n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "x_1 \\\\ \n", "x_2 \\\\ \n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "6.01 \\\\ \n", "9 \\\\ \n", "\\end{bmatrix}\n", "$$\n", "\n", "is $[1, 1]$\n", "\n", "(a) Find the relative forward and backward errors and error magnification (in the infinity norm) for the approximate solution [21,-9]." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "relative forward error : 19.999999999998934\n", "relative backward error : 0.011111111111110676\n", "error magnification factor : 1799.9999999999745\n" ] } ], "source": [ "A = np.array([\n", " [ 2, 4.01],\n", " [ 3, 6.00],\n", "])\n", "\n", "b = np.array([6.01, 9])\n", "\n", "# approximated solution\n", "xa = np.array([21, -9])\n", "\n", "# correct solution\n", "x = LU_factorization_with_back_substitution(A, b.reshape(2, 1))\n", "\n", "# forward error\n", "forward_error = np.max(np.abs(x - xa))\n", "\n", "# relative forward error\n", "relative_forward_error = forward_error / np.max(np.abs(x))\n", "\n", "# backward error\n", "backward_error = np.max(np.abs(b - np.matmul(A, xa)))\n", "\n", "# relative backward error\n", "relative_backward_error = backward_error / np.max(np.abs(b))\n", "\n", "# error magnification factor\n", "error_magnification_factor = relative_forward_error / relative_backward_error\n", "\n", "print('relative forward error : {}'.format(relative_forward_error))\n", "print('relative backward error : {}'.format(relative_backward_error))\n", "print('error magnification factor : {}'.format(error_magnification_factor))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(b) Find the condition number of the coefficient matrix." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "condition number : 3003.000000000063\n" ] } ], "source": [ "A = np.array([\n", " [ 2, 4.01],\n", " [ 3, 6.00],\n", "])\n", "\n", "print('condition number : {}'.format(condition_number(A)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3 Computer Problems\n", "\n", "1. For the n x n matrix with entries $A_{ij} = 5 / (i + 2j - 1)$, set $x = [1,\\cdots,1]^T$ and $b = Ax$. Use the MATLAB program from Computer Problem 2.1.1 or MATLAB’s backslash command to compute $x_c$, the double precision computed solution. Find the infinity norm of the forward error and the error magnification factor of the problem $Ax = b$, and compare it with the condition number of A: (a) n = 6 (b) n = 10." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def system_provider(n, data_generator):\n", " A = np.zeros([n, n])\n", " x = np.ones(n)\n", " \n", " for i in range(n):\n", " for j in range(n):\n", " A[i, j] = data_generator(i + 1, j + 1)\n", " \n", " b = np.matmul(A, x)\n", " \n", " return A, x, b\n", "\n", "def problem_2_3_1_generic_solver(n, data_generator):\n", " A, x, b = system_provider(n, data_generator)\n", " xc = np.linalg.solve(A, b)\n", " \n", " # forward error\n", " forward_error = np.max(np.abs(x - xc))\n", " \n", " # relative forward error\n", " relative_forward_error = forward_error / np.max(np.abs(x))\n", " \n", " # backward error\n", " backward_error = np.max(np.abs(b - np.matmul(A, xc)))\n", " \n", " # relative backward error\n", " relative_backward_error = backward_error / np.max(np.abs(b))\n", " \n", " # error magnification factor\n", " error_magnification_factor = relative_forward_error / relative_backward_error\n", " \n", " # condition number\n", " condA = condition_number(A)\n", " \n", " return forward_error, error_magnification_factor, condA" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(a) n = 6, forward error = 8.13e-10, error magnification factor = 5.61e+06, condition number = 7.03e+07\n", "(b) n = 10, forward error = 0.0017, error magnification factor = 1.4e+13, condition number = 1.31e+14\n" ] } ], "source": [ "def problem_2_3_1_solver(n):\n", " return problem_2_3_1_generic_solver(n, lambda i, j : 5 / (i + 2 * j - 1))\n", "\n", "# (a) n = 6\n", "print('(a) n = 6, forward error = {:.3g}, error magnification factor = {:.3g}, condition number = {:.3g}'.format(*problem_2_3_1_solver(6)))\n", "\n", "# (b) n = 10\n", "print('(b) n = 10, forward error = {:.3g}, error magnification factor = {:.3g}, condition number = {:.3g}'.format(*problem_2_3_1_solver(10)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Carry out Computer Problem 1 for the matrix with entries $A_{ij} = 1/(|i - j| + 1)$." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(a) n = 6, forward error = 4.44e-16, error magnification factor = 2.92, condition number = 8.61\n", "(b) n = 10, forward error = 1.22e-15, error magnification factor = 10.3, condition number = 11.3\n" ] } ], "source": [ "def problem_2_3_2_solver(n):\n", " return problem_2_3_1_generic_solver(n, lambda i, j : 1 / (np.abs(i - j) + 1))\n", "\n", "# (a) n = 6\n", "print('(a) n = 6, forward error = {:.3g}, error magnification factor = {:.3g}, condition number = {:.3g}'.format(*problem_2_3_2_solver(6)))\n", "\n", "# (b) n = 10\n", "print('(b) n = 10, forward error = {:.3g}, error magnification factor = {:.3g}, condition number = {:.3g}'.format(*problem_2_3_2_solver(10)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Let A be the n x n matrix with entries $A_{ij} = |i - j| + 1$. Define $x = [1,\\cdots,1]^T$ and $b = Ax$. For n = 100,200,300,400, and 500, use the MATLAB program from Computer Problem 2.1.1 or MATLAB’s backslash command to compute $x_c$, the double precision computed solution. Calculate the infinity norm of the forward error for each solution. Find the five error magnification factors of the problems $Ax = b$, and compare with the corresponding condition numbers." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n = 100, forward error = 6.1e-12, error magnification factor = 6.7e+03, condition number = 1e+04\n", "n = 200, forward error = 3.3e-11, error magnification factor = 1.4e+04, condition number = 4e+04\n", "n = 300, forward error = 9.9e-11, error magnification factor = 4.4e+04, condition number = 9e+04\n", "n = 400, forward error = 2e-10, error magnification factor = 8.5e+04, condition number = 1.6e+05\n", "n = 500, forward error = 2.7e-10, error magnification factor = 1.2e+05, condition number = 2.5e+05\n" ] } ], "source": [ "def problem_2_3_3_solver(n):\n", " return problem_2_3_1_generic_solver(n, lambda i, j : np.abs(i - j) + 1)\n", "\n", "# n = 100\n", "print('n = 100, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_3_solver(100)))\n", "# n = 200\n", "print('n = 200, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_3_solver(200)))\n", "# n = 300\n", "print('n = 300, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_3_solver(300)))\n", "# n = 400\n", "print('n = 400, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_3_solver(400)))\n", "# n = 500\n", "print('n = 500, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_3_solver(500)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Carry out the steps of Computer Problem 3 for the matrix with entries $A_{ij} = \\sqrt{(i - j)^2 + n / 10}$." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n = 100, forward error = 3.7e-09, error magnification factor = 5.1e+06, condition number = 6.2e+07\n", "n = 200, forward error = 1.3e-06, error magnification factor = 1.4e+09, condition number = 1.3e+10\n", "n = 300, forward error = 4.6e-05, error magnification factor = 3.5e+10, condition number = 6.2e+11\n", "n = 400, forward error = 0.00092, error magnification factor = 5.6e+11, condition number = 1.5e+13\n", "n = 500, forward error = 0.022, error magnification factor = 2.1e+13, condition number = 2.3e+14\n" ] } ], "source": [ "def problem_2_3_4_solver(n):\n", " return problem_2_3_1_generic_solver(n, lambda i, j : np.sqrt(np.power(i - j, 2) + n / 10))\n", "\n", "# n = 100\n", "print('n = 100, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_4_solver(100)))\n", "# n = 200\n", "print('n = 200, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_4_solver(200)))\n", "# n = 300\n", "print('n = 300, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_4_solver(300)))\n", "# n = 400\n", "print('n = 400, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_4_solver(400)))\n", "# n = 500\n", "print('n = 500, forward error = {:.2g}, error magnification factor = {:.2g}, condition number = {:.2g}'.format(*problem_2_3_4_solver(500)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5. For what values of n does the solution in Computer Problem 1 have no correct significant digits?" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "n = 11, forward error = 0.0513, error magnification factor = inf, condition number = 4.82e+15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-28-4e0128b0a498>:30: RuntimeWarning: divide by zero encountered in double_scalars\n", " error_magnification_factor = relative_forward_error / relative_backward_error\n" ] } ], "source": [ "print('n = 11, forward error = {:.3g}, error magnification factor = {:.3g}, condition number = {:.3g}'.format(*problem_2_3_1_solver(11)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# 2.4 The PA=LU Factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Apply Gaussian elimination with partial pivoting to solve the system\n", "\n", "\\begin{matrix}\n", " x_1 - x_2 + 3x_3 = -3 & \\\\ \n", "-1x_1 - 2x_3 = 1 & \\\\ \n", " 2x_1 + 2x_2 + 4x_3 = 0 \n", "\\end{matrix}" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. -1.]\n" ] } ], "source": [ "A = np.array([1, -1, 3, -1, 0, -2, 2, 2, 4]).reshape(3, 3)\n", "b = np.array([-3, 1, 0])\n", "lu, piv = linalg.lu_factor(A)\n", "x = linalg.lu_solve([lu, piv], b)\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Solve the system $2x_1 + 3x_2 = 4$,$3x_1 + 2x_2 = 1$ using the PA = LU factorization with partial pivoting" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-1. 2.]\n" ] } ], "source": [ "\"\"\"\n", "[[2, 3]\n", " [3, 2]]\n", "\"\"\"\n", "A = np.array([2, 3, 3, 2]).reshape(2, 2)\n", "b = np.array([4, 1])\n", "lu, piv = linalg.lu_factor(A)\n", "x = linalg.lu_solve([lu, piv], b)\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.5 Iterative Methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jacobi Method" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def jacobi_method(A, b, x0, k):\n", " \"\"\"\n", " Use jacobi method to solve equations\n", " \n", " Args:\n", " A (numpy 2d array): the matrix\n", " b (numpy 1d array): the right hand side vector\n", " x0 (numpy 1d array): initial guess\n", " k (real number): iterations\n", " \n", " Return:\n", " The approximate solution\n", " \n", " Exceptions:\n", " ValueError\n", " The size of matrix's column is not equal to the size of vector's size\n", " \"\"\"\n", " if A.shape[1] is not x0.shape[0] :\n", " raise ValueError('The size of the columns of matrix A must be equal to the size of the x0')\n", " \n", " D = np.diag(A.diagonal())\n", " inv_D = linalg.inv(D) \n", " LU = A - D\n", " xk = x0\n", " \n", " for _ in range(k):\n", " xk = np.matmul(b - np.matmul(LU, xk), inv_D)\n", " \n", " return xk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Apply the Jacobi Method to the system $3u + v = 5$, $u + 2v = 5$" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = [0.99999998 1.99999997]\n" ] } ], "source": [ "A = np.array([3, 1, 1, 2]).reshape(2, 2)\n", "b = np.array([5, 5])\n", "x = jacobi_method(A, b, np.array([0, 0]), 20)\n", "print('x = %s' %x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gauss-Seidel Method" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def gauss_seidel_method(A, b, x0, k):\n", " \"\"\"\n", " Use gauss seidel method to solve equations\n", " \n", " Args:\n", " A (numpy 2d array): the matrix\n", " b (numpy 1d array): the right hand side vector\n", " x0 (numpy 1d array): initial guess\n", " k (real number): iterations\n", " \n", " Return:\n", " The approximate solution\n", " \n", " Exceptions:\n", " ValueError\n", " The size of matrix's column is not equal to the size of vector's size\n", " \"\"\"\n", " if A.shape[1] is not x0.shape[0] :\n", " raise ValueError('The size of the columns of matrix A must be equal to the size of the x0')\n", " \n", " D = np.diag(A.diagonal())\n", " L = np.tril(A) - D\n", " U = np.triu(A) - D\n", " inv_LD = linalg.inv(L + D)\n", " xk = x0\n", " \n", " for _ in range(k):\n", " xk = np.matmul(inv_LD, -np.matmul(U, xk) + b)\n", " \n", " return xk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Apply the Gauss-Seidel Method to the system\n", "\n", "$$\n", "\\begin{bmatrix}\n", "3 & 1 & -1 \\\\ \n", "2 & 4 & 1 \\\\ \n", "-1 & 2 & 5\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "u \\\\ \n", "v \\\\ \n", "w\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "4 \\\\ \n", "1 \\\\ \n", "1\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2., -1., 1.])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([3, 1, -1, 2, 4, 1, -1, 2, 5]).reshape(3, 3)\n", "b = np.array([4, 1, 1])\n", "x0 = np.array([0, 0, 0])\n", "gauss_seidel_method(A, b, x0, 24)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Successive Over-Relaxation" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def gauss_seidel_sor_method(A, b, w, x0, k):\n", " \"\"\"\n", " Use gauss seidel method with sor to solve equations\n", " \n", " Args:\n", " A (numpy 2d array): the matrix\n", " b (numpy 1d array): the right hand side vector\n", " w (real number): weight\n", " x0 (numpy 1d array): initial guess\n", " k (real number): iterations\n", " \n", " Return:\n", " The approximate solution\n", " \n", " Exceptions:\n", " ValueError\n", " The size of matrix's column is not equal to the size of vector's size\n", " \"\"\"\n", " if A.shape[1] is not x0.shape[0] :\n", " raise ValueError('The size of the columns of matrix A must be equal to the size of the x0')\n", " \n", " D = np.diag(A.diagonal())\n", " L = np.tril(A) - D\n", " U = np.triu(A) - D\n", " inv_LD = linalg.inv(w * L + D)\n", " xk = x0\n", " \n", " for _ in range(k):\n", " xk = np.matmul(w * inv_LD, b) + np.matmul(inv_LD, (1 - w) * np.matmul(D, xk) - w * np.matmul(U, xk))\n", " \n", " return xk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Apply the Gauss-Seidel Method with sor to the system\n", "\n", "$$\n", "\\begin{bmatrix}\n", "3 & 1 & -1 \\\\ \n", "2 & 4 & 1 \\\\ \n", "-1 & 2 & 5\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "u \\\\ \n", "v \\\\ \n", "w\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "4 \\\\ \n", "1 \\\\ \n", "1\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2., -1., 1.])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([3, 1, -1, 2, 4, 1, -1, 2, 5]).reshape(3, 3)\n", "b = np.array([4, 1, 1])\n", "x0 = np.array([0, 0, 0])\n", "w = 1.25\n", "gauss_seidel_sor_method(A, b, w, x0, 14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.6 Methods for symmetric positive-definite matrices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cholesky factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "\n", "Find the Cholesky factorization of \n", "$\\begin{bmatrix}\n", "4 & -2 & 2 \\\\ \n", "-2 & 2 & -4 \\\\ \n", "2 & -4 & 11\n", "\\end{bmatrix}$" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 2. -1. 1.]\n", " [ 0. 1. -3.]\n", " [ 0. 0. 1.]]\n" ] } ], "source": [ "A = np.array([4, -2, 2, -2, 2, -4, 2, -4, 11]).reshape(3, 3)\n", "R = linalg.cholesky(A)\n", "print(R)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conjugate Gradient Method" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def conjugate_gradient_method(A, b, x0, k):\n", " \"\"\"\n", " Use conjugate gradient to solve linear equations\n", " \n", " Args:\n", " A : input matrix\n", " b : input right hand side vector\n", " x0 : initial guess\n", " k : iteration\n", " \n", " Returns:\n", " approximate solution\n", " \n", " \n", " \"\"\"\n", " xk = x0\n", " dk = rk = b - np.matmul(A, x0)\n", " for _ in range(k):\n", " if not np.any(rk) or all( abs(i) <= 1e-16 for i in rk) is True:\n", " break\n", " ak = float(np.matmul(rk.T, rk)) / float(np.matmul(dk.T, np.matmul(A, dk)))\n", " xk = xk + ak * dk\n", " rk1 = rk - ak * np.matmul(A, dk)\n", " bk = np.matmul(rk1.T, rk1) / np.matmul(rk.T, rk)\n", " dk = rk1 + bk * dk\n", " rk = rk1\n", " return xk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example \n", "Solve \n", "\n", "$$\n", "\\begin{bmatrix}\n", "2 & 2 \\\\ \n", "2 & 5 \\\\\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "u \\\\ \n", "v \n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "6 \\\\ \n", "3 \n", "\\end{bmatrix}\n", "$$\n", "\n", "using the Conjugate Gradient Method" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 4., -1.])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([2, 2, 2, 5]).reshape(2, 2)\n", "b = np.array([6, 3])\n", "x0 = np.array([0, 0])\n", "conjugate_gradient_method(A, b, x0, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example \n", "Solve \n", "\n", "$$\n", "\\begin{bmatrix}\n", "1 & -1 & 0 \\\\\n", "-1 & 2 & 1 \\\\\n", "0 & 1 & 2 \\\\\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "u \\\\\n", "v \\\\\n", "w \\\\\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "0 \\\\\n", "2 \\\\\n", "3 \\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1.])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = np.array([1, -1, 0, -1, 2, 1, 0, 1, 2]).reshape(3, 3)\n", "b = np.array([0, 2, 3])\n", "x0 = np.array([0, 0, 0])\n", "conjugate_gradient_method(A, b, x0, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example \n", "Solve \n", "\n", "$$\n", "\\begin{bmatrix}\n", "1 & -1 & 0 \\\\\n", "-1 & 2 & 1 \\\\\n", "0 & 1 & 5 \\\\\n", "\\end{bmatrix}\n", "\\begin{bmatrix}\n", "u \\\\\n", "v \\\\\n", "w \\\\\n", "\\end{bmatrix}\n", "=\n", "\\begin{bmatrix}\n", "3 \\\\\n", "-3 \\\\\n", "4 \\\\\n", "\\end{bmatrix}\n", "$$" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = [ 2. -1. 1.]\n" ] } ], "source": [ "A = np.array([1, -1, 0, -1, 2, 1, 0, 1, 5]).reshape(3, 3)\n", "b = np.array([3, -3, 4])\n", "x0 = np.array([0, 0, 0])\n", "x = slinalg.cg(A, b, x0)[0]\n", "print('x = %s' %x )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preconditioning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2.7 Nonlinear Systems Of Equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multivariate Newton's Method" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "def multivariate_newton_method(fA, fDA, x0, k):\n", " \"\"\"\n", " Args:\n", " fA (function handle) : coefficient matrix with arguments\n", " fDA (function handle) : right-hand side vector with arguments\n", " x0 (numpy 2d array) : initial guess\n", " k (real number) : iteration\n", " \n", " Return:\n", " Approximate solution xk after k iterations\n", " \"\"\"\n", " xk = x0\n", " for _ in range(k):\n", " lu, piv = linalg.lu_factor(fDA(*xk))\n", " s = linalg.lu_solve([lu, piv], -fA(*xk))\n", " xk = xk + s\n", " return xk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Use Newton's method with starting guess $(1,2)$ to find a solution of the system\n", "\n", "$$\n", "v - u^3 = 0 \\\\\n", "u^2 + v^2 - 1 = 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.82603136, 0.56362416])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fA = lambda u,v : np.array([v - pow(u, 3), pow(u, 2) + pow(v, 2) - 1], dtype=np.float64)\n", "fDA = lambda u,v : np.array([-3 * pow(u, 2), 1, 2 * u, 2 * v], dtype=np.float64).reshape(2, 2)\n", "x0 = np.array([1, 2])\n", "multivariate_newton_method(fA, fDA, x0, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example\n", "Use Newton's method to find the solutions of the system\n", "\n", "$$\n", "f_1(u,v) = 6u^3 + uv - 3^3 - 4 = 0 \\\\\n", "f_2(u,v) = u^2 - 18uv^2 + 16v^3 + 1 = 0\n", "$$" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1.])" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fA = lambda u,v : np.array([6 * pow(u, 3) + u * v - 3 * pow(v, 3) - 4,\n", " pow(u, 2) - 18 * u * pow(v, 2) + 16 * pow(v, 3) + 1], \n", " dtype=np.float64)\n", "\n", "fDA = lambda u,v : np.array([18 * pow(u, 2) + v, \n", " u - 9 * pow(v, 2), \n", " 2 * u - 18 * pow(v, 2), \n", " -36 * u * v + 48 * pow(v, 2)], \n", " dtype=np.float64).reshape(2, 2)\n", "\n", "x0 = np.array([2, 2], dtype=np.float64)\n", "\n", "multivariate_newton_method(fA, fDA, x0, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## MIT License\n", "\n", "Copyright (c) Jim00000\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy\n", "of this software and associated documentation files (the \"Software\"), to deal\n", "in the Software without restriction, including without limitation the rights\n", "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n", "copies of the Software, and to permit persons to whom the Software is\n", "furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all\n", "copies or substantial portions of the Software." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 4 }
unlicense
jeicher/cobrapy
documentation_builder/solvers.ipynb
3
15731
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solver Interface\n", "\n", "Each cobrapy solver must expose the following API. The solvers all will have their own distinct LP object types, but each can be manipulated by these functions. This API can be used directly when implementing algorithms efficiently on linear programs because it has 2 primary benefits:\n", "\n", "1. Avoid the overhead of creating and destroying LP's for each operation\n", "\n", "2. Many solver objects preserve the basis between subsequent LP's, making each subsequent LP solve faster\n", "\n", "We will walk though the API with the cglpk solver, which links the cobrapy solver API with [GLPK](http://www.gnu.org/software/glpk/)'s C API." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cobra.test\n", "\n", "model = cobra.test.create_test_model(\"textbook\")\n", "solver = cobra.solvers.cglpk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Attributes and functions\n", "\n", "Each solver has some attributes:\n", "\n", "### solver_name\n", "\n", "The name of the solver. This is the name which will be used to select the solver in cobrapy functions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'cglpk'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.solver_name" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Solution 0.87 at 0x7fd42ad90c18>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.optimize(solver=\"cglpk\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### \\_SUPPORTS_MILP\n", "\n", "The presence of this attribute tells cobrapy that the solver supports mixed-integer linear programming" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver._SUPPORTS_MILP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### solve\n", "\n", "Model.optimize is a wrapper for each solver's solve function. It takes in a cobra model and returns a solution" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Solution 0.87 at 0x7fd42ad90908>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.solve(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### create_problem\n", "\n", "This creates the LP object for the solver." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<cobra.solvers.cglpk.GLP at 0x3e846e8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lp = solver.create_problem(model, objective_sense=\"maximize\")\n", "lp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### solve_problem\n", "\n", "Solve the LP object and return the solution status" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'optimal'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.solve_problem(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### format_solution\n", "\n", "Extract a cobra.Solution object from a solved LP object" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Solution 0.87 at 0x7fd42ad90668>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.format_solution(lp, model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_objective_value\n", "\n", "Extract the objective value from a solved LP object" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8739215069684909" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.get_objective_value(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get_status\n", "\n", "Get the solution status of a solved LP object" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'optimal'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.get_status(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### change_variable_objective\n", "\n", "change the objective coefficient a reaction at a particular index. This does not change any of the other objectives which have already been set. This example will double and then revert the biomass coefficient." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.index(\"Biomass_Ecoli_core\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.7478430139369818" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_variable_objective(lp, 12, 2)\n", "solver.solve_problem(lp)\n", "solver.get_objective_value(lp)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8739215069684909" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_variable_objective(lp, 12, 1)\n", "solver.solve_problem(lp)\n", "solver.get_objective_value(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### change variable_bounds\n", "\n", "change the lower and upper bounds of a reaction at a particular index. This example will set the lower bound of the biomass to an infeasible value, then revert it." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'infeasible'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_variable_bounds(lp, 12, 1000, 1000)\n", "solver.solve_problem(lp)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'optimal'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_variable_bounds(lp, 12, 0, 1000)\n", "solver.solve_problem(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### change_coefficient\n", "\n", "Change a coefficient in the stoichiometric matrix. In this example, we will set the entry for ADP in the ATMP reaction to in infeasible value, then reset it." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.metabolites.index(\"atp_c\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.reactions.index(\"ATPM\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'infeasible'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_coefficient(lp, 16, 10, -10)\n", "solver.solve_problem(lp)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'optimal'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.change_coefficient(lp, 16, 10, -1)\n", "solver.solve_problem(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### set_parameter\n", "\n", "Set a solver parameter. Each solver will have its own particular set of unique paramters. However, some have unified names. For example, all solvers should accept \"tolerance_feasibility.\"" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "solver.set_parameter(lp, \"tolerance_feasibility\", 1e-9)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.set_parameter(lp, \"objective_sense\", \"minimize\")\n", "solver.solve_problem(lp)\n", "solver.get_objective_value(lp)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.8739215069684912" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "solver.set_parameter(lp, \"objective_sense\", \"maximize\")\n", "solver.solve_problem(lp)\n", "solver.get_objective_value(lp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example with FVA\n", "\n", "Consider flux variability analysis (FVA), which requires maximizing and minimizing every reaction with the original biomass value fixed at its optimal value. If we used the cobra Model API in a naive implementation, we would do the following:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 171 ms, sys: 0 ns, total: 171 ms\n", "Wall time: 171 ms\n" ] } ], "source": [ "%%time\n", "# work on a copy of the model so the original is not changed\n", "m = model.copy()\n", "\n", "# set the lower bound on the objective to be the optimal value\n", "f = m.optimize().f\n", "for objective_reaction, coefficient in m.objective.items():\n", " objective_reaction.lower_bound = coefficient * f\n", "\n", "# now maximize and minimze every reaction to find its bounds\n", "fva_result = {}\n", "for r in m.reactions:\n", " m.change_objective(r)\n", " fva_result[r.id] = {\n", " \"maximum\": m.optimize(objective_sense=\"maximize\").f,\n", " \"minimum\": m.optimize(objective_sense=\"minimize\").f\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead, we could use the solver API to do this more efficiently. This is roughly how cobrapy implementes FVA. It keeps uses the same LP object and repeatedly maximizes and minimizes it. This allows the solver to preserve the basis, and is much faster. The speed increase is even more noticeable the larger the model gets." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8.28 ms, sys: 25 µs, total: 8.31 ms\n", "Wall time: 8.14 ms\n" ] } ], "source": [ "%%time\n", "# create the LP object\n", "lp = solver.create_problem(model)\n", "\n", "# set the lower bound on the objective to be the optimal value\n", "solver.solve_problem(lp)\n", "f = solver.get_objective_value(lp)\n", "for objective_reaction, coefficient in model.objective.items():\n", " objective_index = model.reactions.index(objective_reaction)\n", " # old objective is no longer the objective\n", " solver.change_variable_objective(lp, objective_index, 0.)\n", " solver.change_variable_bounds(\n", " lp, objective_index, f * coefficient,\n", " objective_reaction.upper_bound)\n", "\n", "# now maximize and minimze every reaction to find its bounds\n", "fva_result = {}\n", "for index, r in enumerate(model.reactions):\n", " solver.change_variable_objective(lp, index, 1.)\n", " result = {}\n", " solver.solve_problem(lp, objective_sense=\"maximize\")\n", " result[\"maximum\"] = solver.get_objective_value(lp)\n", " solver.solve_problem(lp, objective_sense=\"minimize\")\n", " result[\"minimum\"] = solver.get_objective_value(lp)\n", " solver.change_variable_objective(lp, index, 0.)\n", " fva_result[r.id] = result" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-2.1
ucsc-astro/coffee
15_01_27_sqlalchemy/Learning SQLAlchemy.ipynb
1
39280
{ "metadata": { "name": "", "signature": "sha256:1834ef0c2b3c1bae82186376fd5217f01a31d10dde6b09c086f67e4f5ca1eda6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Learning SQLAlchemy</h1>\n", "<table>\n", "<li><a href=\"Learning SQLAlchemy.ipynb\">Basic SQLAlchemy</a>\n", "<li><a href=\"One to Many.ipynb\">One to Many Relationships</a>\n", "<li><a href=\"Many to Many.ipynb\">Many to Many Relationships</a>\n", "<li><a href=\"Examples.ipynb\">Practical Examples</a>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Basic SQLAlchemy</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>What is SQL?</h3>\n", "\n", "SQL (Structured Query Language, pronounced both as S-Q-L and Sequel) is a programming language designed to interface with data held in a relational database. These are databases within which you can hold different kinds of entries and define relationships between them. For example, you can define many Star entries and many Observation entries and then define a <b>relationship</b> in that each Observation is of a specific Star or of multiple Stars.\n", "\n", "This can be exceptionally useful in working with large sets of data. Each entry in your database has a set of <b>columns</b>, which can be strings, floats, ints, or booleans (though SQLAlchemy can perform some tricks to expand this list). The power of using databases comes from the ability to query your data according to these columns. For example, the Star entry can have an RA column and a DEC column, and so, you could query for a list of Stars in a particular part of the sky. \n", "\n", "But this is thinking too small. With a relational database, you could get a list of all the stars in your database with at least three observations with good seeing above a particular magnitude in only a few lines of code. And now, with SQLAlchemy, you can do it all in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>What is SQLAlchemy?</h3>\n", "\n", "SQLAlchemy is a Python module that acts as an interface between Python code and a SQL database. Effectively, you can write object classes in Python whose instances are rows in a particular table in your database. This lets you manage your database straight from Python, giving you access to a full programming language of possibilities. I'll offer some practical examples in the Practical Examples notebook, but here's an example I've done in my research: you can generate plots directly from the database using an arbitrary query, and can use the relationships between database entries to make interactive plots.\n", "\n", "For more details on SQLAlchemy, check out their website <a href=\"http://www.sqlalchemy.org\">http://www.sqlalchemy.org</a>. In particular, you should walk through their <a href=\"http://www.sqlalchemy.org/docs/orm/tutorial.html\">tutorial</a> (of which I'll be giving a taste here).\n", "\n", "A final note: there are other tools to do this in Python, and another that I've used is Django. I often find that Django has a much better user interface than SQLAlchemy; however, it isn't really designed to be used just for its databasing. Django is a web framework, and <a href=\"http://stackoverflow.com/questions/579511/using-only-the-db-part-of-django\">extracting the database part of the framework looks like it would be a bit tricky</a>. If your goal is to write a database for a website, though, I'd definitely recommend Django over SQLAlchemy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Getting Started</h3>\n", "\n", "__Instructions: Sentences in bold are instructions. These notebooks will not work unless the instructions are completed. Another note: if you find that SQLAlchemy is complaining that you are modifying an existing table, you should restart the notebook (the circular arrow in the toolbar above) and execute all the cells down from the top. This is a quirk of SQLAlchemy, and one that hopefully will make sense with some experience.__\n", "\n", "To get started using SQLAlchemy, we have to create an <b>Engine</b>. This is the interface between SQLAlchemy and the actual database, and this step points SQLAlchemy to the database of interest. \n", "\n", "For the moment, to avoid polluting your computer, we'll create a temporary database in memory as shown below. To create a real database, the first argument to create_engine should be 'dbtype://path/file_name'. The echo argument is for debugging purposes. We'll turn it on now for you to see a bit of what's going on behind the scenes, but feel free not to set it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import create_engine\n", "\n", "engine = create_engine ('sqlite:///:memory:', echo = True)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the interface to our database, we need to define a table to put into the database. SQLAlchemy allows you two ways to do this: the declarative way and the classical way. For the most part, the declarative method is more \"Pythonic,\" so I'm going to focus on that one. Let's make a Star entry. To do this, we need to load the declarative method." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy.ext.declarative import declarative_base\n", "\n", "Base = declarative_base()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This generates a base class from which all of our entries will inherit. Effectively, this base class sets up all the methods that our entry object will need to work as part of a database. __Try adding ra and dec float columns.__" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import Column, Integer, String, Float\n", "\n", "class Star (Base): \n", " __tablename__ = 'stars' # This is the name of the table in the database. Must be unique\n", " \n", "# After the table's name, we add the desired columns. These contain the queryable information about\n", "# the current entry.\n", "\n", " id = Column (Integer, primary_key = True) # This is the primary key of the entry. \n", "# The primary key is the unique identifier of the entry. It's usually best to leave it as an int.\n", "\n", " name = Column (String, unique = True) # The unique argument enforces uniqueness for the column\n", "\n", "\n", " def __repr__ (self):\n", " # A string representation of the object\n", " # We can call the various columns with self.column_name, as with any normal class variable\n", " return \"<Star Object %s at (RA=%f, DEC=%f)>\" % (self.name, self.ra, self.dec)\n", " \n", "# CheckConstraint('col2 > col3 + 5', name='check1')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the class has been declared, we need to create it in the database, i.e. we need to connect the Engine class to our new Star class. We can do this through our Base class, which will add any subclasses it can find as tables in our database. If you set echo = True in the creation of your engine, this will be a bit verbose." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Base.metadata.create_all (engine) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,736 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test plain returns' AS VARCHAR(60)) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,737 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,739 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT CAST('test unicode returns' AS VARCHAR(60)) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,739 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,742 INFO sqlalchemy.engine.base.Engine PRAGMA table_info(\"stars\")\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:PRAGMA table_info(\"stars\")\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,743 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,745 INFO sqlalchemy.engine.base.Engine \n", "CREATE TABLE stars (\n", "\tid INTEGER NOT NULL, \n", "\tname VARCHAR, \n", "\tPRIMARY KEY (id), \n", "\tUNIQUE (name)\n", ")\n", "\n", "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:\n", "CREATE TABLE stars (\n", "\tid INTEGER NOT NULL, \n", "\tname VARCHAR, \n", "\tPRIMARY KEY (id), \n", "\tUNIQUE (name)\n", ")\n", "\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,746 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-26 11:24:48,748 INFO sqlalchemy.engine.base.Engine COMMIT\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:COMMIT\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congratulations! You've made a database! Of course, making it and using it are two different things. To interface with the database, you need a <b>Session</b> object. This object serves as your interface to SQLAlchemy. With it, you can create, delete, modify, and query entries. The syntax follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy.orm import sessionmaker\n", "\n", "Session = sessionmaker (bind = engine)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that Session is a class, not a class instance. When you want an instance of a session, you will need to do so explicitly (as I'll show in a moment).\n", "\n", "To give an idea of how to use this, all of the above code would exist as a Python module that defines your database and gets it ready for use. When you need to query the database, you import this module and run, for example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "session = Session () # Start the SQLAlchemy session\n", "\n", "newStar = Star (name = \"Polaris\", ra = 2.5303, dec = 89.2641) # Make a Star to put into the database\n", "\n", "session.add (newStar) # Add newStar to the database" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've <i>almost</i> added a new object to the database. SQLAlchemy wants to be very careful to prevent you from making mistakes. To commit the new objects to the database, use" ] }, { "cell_type": "code", "collapsed": false, "input": [ "session.commit () # Commit the changes to the database" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:14,706 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:BEGIN (implicit)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:14,708 INFO sqlalchemy.engine.base.Engine INSERT INTO stars (name, ra, dec) VALUES (?, ?, ?)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:INSERT INTO stars (name, ra, dec) VALUES (?, ?, ?)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:14,708 INFO sqlalchemy.engine.base.Engine ('Polaris', 2.5303, 89.2641)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:('Polaris', 2.5303, 89.2641)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:14,710 INFO sqlalchemy.engine.base.Engine COMMIT\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:COMMIT\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And that's it! You've successfully added Polaris to the database. __Try adding a few more; these will be useful later.__ You can also try modifying the properties of newStar, but don't commit your changes just yet." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can always check whether SQLAlchemy has anything to commit with session.new and session.dirty. Note that we don't have to add newStar into the database again, the session's already tracking it." ] }, { "cell_type": "code", "collapsed": false, "input": [ "session.new" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "IdentitySet([])" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "session.dirty" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "IdentitySet([])" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "session.commit ()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To delete an object from the database, just use session.delete." ] }, { "cell_type": "code", "collapsed": false, "input": [ "session.delete (newStar)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, say we didn't want to commit that last command. Just use the rollback method of session." ] }, { "cell_type": "code", "collapsed": false, "input": [ "session.rollback ()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's all there is to adding and deleting entries with SQLAlchemy! But of course, we haven't actually <i>checked</i> that the things we've added are in the database. This brings us to our next point: querying the database. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Querying the Database</h3>\n", "\n", "The power of databases comes from your ability to query them. Effectively, you need to tell SQLAlchemy what to look for in the database. An interesting note here: when you call a query, this returns a Query object, not the results of that query. You'll see what I mean in a minute.\n", "\n", "To query the database, use the query method of the session object. This takes as arguments the types of objects you'd like to query. In this case, we're querying the Star object." ] }, { "cell_type": "code", "collapsed": false, "input": [ "session.query (Star)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "<sqlalchemy.orm.query.Query at 0x1053eb358>" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "As I said, this returns a Query object. The power of this is that <i>SQLAlchemy won't actually look through the database until it actually needs to</i>. This can be very useful when you start to use filters, but for the moment, let's figure out how to retrieve objects from the database:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "query = session.query (Star)\n", "\n", "query.all () # Outputs a list of everything that matches the query" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:21,577 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:BEGIN (implicit)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:21,578 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.ra AS stars_ra, stars.dec AS stars_dec \n", "FROM stars\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.ra AS stars_ra, stars.dec AS stars_dec \n", "FROM stars\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:21,579 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "[<Star Object Polaris at (RA=2.530300, DEC=89.264100)>]" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "query.order_by (Star.ra).first () # Outputs the first entry found, should be used with order_by" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:22,057 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.ra AS stars_ra, stars.dec AS stars_dec \n", "FROM stars ORDER BY stars.ra\n", " LIMIT ? OFFSET ?\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.ra AS stars_ra, stars.dec AS stars_dec \n", "FROM stars ORDER BY stars.ra\n", " LIMIT ? OFFSET ?\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-23 20:16:22,058 INFO sqlalchemy.engine.base.Engine (1, 0)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:(1, 0)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<Star Object Polaris at (RA=2.530300, DEC=89.264100)>" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "query.count () # Outputs the number of matches" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:06:15,157 INFO sqlalchemy.engine.base.Engine SELECT count(*) AS count_1 \n", "FROM (SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT count(*) AS count_1 \n", "FROM (SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars) AS anon_1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:06:15,157 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "2" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "query.one () # If there's only one match, output it; otherwise, raise an error" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:07:20,575 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:07:20,576 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "ename": "MultipleResultsFound", "evalue": "Multiple rows were found for one()", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mMultipleResultsFound\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-23-91f992e02acb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mquery\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# If there's only one match, output it; otherwise, raise an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/sqlalchemy/orm/query.py\u001b[0m in \u001b[0;36mone\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2376\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2377\u001b[0m raise orm_exc.MultipleResultsFound(\n\u001b[0;32m-> 2378\u001b[0;31m \"Multiple rows were found for one()\")\n\u001b[0m\u001b[1;32m 2379\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2380\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMultipleResultsFound\u001b[0m: Multiple rows were found for one()" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These queries can be filtered and ordered as well." ] }, { "cell_type": "code", "collapsed": false, "input": [ "query.filter (Star.ra < 5).all ()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:09:16,110 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra < ?\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra < ?\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:09:16,110 INFO sqlalchemy.engine.base.Engine (5,)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:(5,)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "[<Star Object Alpha Ursae Minoris at (RA=2.530300, DEC=89.264100)>]" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "query.order_by (Star.dec).all ()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:10:07,986 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars ORDER BY stars.dec\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars ORDER BY stars.dec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:10:07,987 INFO sqlalchemy.engine.base.Engine ()\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:()\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "[<Star Object Alpha Orionis at (RA=5.920000, DEC=7.407100)>,\n", " <Star Object Alpha Ursae Minoris at (RA=2.530300, DEC=89.264100)>]" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But remember, this is Python. Star.ra and Star.dec are Column objects, and the SQLAlchemy team have added algebraic operations to these objects, so you can filter and order by combinations of columns." ] }, { "cell_type": "code", "collapsed": false, "input": [ "query.filter (Star.ra - Star.dec / 2 < 5).all ()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:13:44,505 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra - stars.dec / ? < ?\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra - stars.dec / ? < ?\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:13:44,506 INFO sqlalchemy.engine.base.Engine (2, 5)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:(2, 5)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "[<Star Object Alpha Ursae Minoris at (RA=2.530300, DEC=89.264100)>,\n", " <Star Object Alpha Orionis at (RA=5.920000, DEC=7.407100)>]" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The filter method returns another Query object, so you can continue to add filters and orderings." ] }, { "cell_type": "code", "collapsed": false, "input": [ "query.filter (Star.ra < 5).filter (Star.dec > 0).all ()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:25:31,117 INFO sqlalchemy.engine.base.Engine SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra < ? AND stars.dec > ?\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:SELECT stars.id AS stars_id, stars.name AS stars_name, stars.dec AS stars_dec, stars.ra AS stars_ra \n", "FROM stars \n", "WHERE stars.ra < ? AND stars.dec > ?\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "2015-01-14 19:25:31,118 INFO sqlalchemy.engine.base.Engine (5, 0)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:sqlalchemy.engine.base.Engine:(5, 0)\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "[<Star Object Alpha Ursae Minoris at (RA=2.530300, DEC=89.264100)>]" ] } ], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Feel free to play around. You might want to add some more entries to the database so that you can truly see what you can do. In what order do things pop out when you order by one column and then another?__" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 37 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You now have a good amount of experience with adding entries to a table that already exists and with querying that database using filters. For more information on this, check out the documentation (or just search in StackOverflow). It will generally help if you can figure out the terminology for what you're trying to do in SQL (e.g. union, subquery), as that will help narrow your search.\n", "\n", "If you're bored of normal, non-relational databases, follow the <a href=\"One to Many.ipynb\">link</a> to the next notebook! If you want to learn how to add constraints to your objects, keep reading." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3>Adding Constraints</h3>\n", "\n", "Another convenient aspect of databases is the ability to add constraints. These will effectively set certain conditions on anything added to the database and raise an error if anything that violates these constraints is added to the database. You've already seen one of these constraints, the <b>unique constraint</b>, which forces a particular column to be unique in the database. However, we can take this a step further. What if we want two columns to be <b>unique together</b>? For example, what if we want to make sure that no two stars in our database have the exact same RA and the exact same Dec? We do this by explicitly adding a constraint." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import UniqueConstraint\n", "from sqlalchemy import Column, Integer, String, Float\n", "\n", "class Star (Base): \n", " __tablename__ = 'stars' # This is the name of the table in the database. Must be unique\n", " \n", "# After the table's name, we add the desired columns. These contain the queryable information about\n", "# the current entry.\n", "\n", " id = Column (Integer, primary_key = True) # This is the primary key of the entry. \n", "# The primary key is the unique identifier of the entry. It's usually best to leave it as an int.\n", "\n", " name = Column (String, unique = True) # The unique argument enforces uniqueness for the column\n", " \n", " ra = Column (Float)\n", " dec = Column (Float)\n", " \n", " __table_args__ = (UniqueConstraint (\"ra\", \"dec\"),)\n", " \n", " def __repr__ (self):\n", " # A string representation of the object\n", " # We can call the various columns with self.column_name, as with any normal class variable\n", " return \"<Star Object %s at (RA=%f, DEC=%f)>\" % (self.name, self.ra, self.dec)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Paste this new definition of Star up to the original definition and restart the notebook (each time you change the table in the database, you'll most likely want to nuke the database and bring it back from scratch; there are ways to avoid this, called __migrations__, but these are quite complicated and usually not worth it except in production level databases). __Try adding two stars with different names but the same RA and Dec and see if you get an error.__" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The new piece we've added is the \\_\\_table\\_args\\_\\_ member of the object. This is a special object that SQLAlchemy will read to add special commands to your database. Among other things, constraints are placed here. This member should be a tuple of all the constraints you want to add to your object. Let's look at another type of constraint, the <b>check constraint</b>. This makes sure that certain conditions are met and has the following notation:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import CheckConstraint\n", "\n", "CheckConstraint (\"ra < 24\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "CheckConstraint(<sqlalchemy.sql.elements.TextClause object at 0x10cb86278>)" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Try adding this constraint in addition to a minimum for RA and likewise for Dec to your Star.__" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
kfollette/ASTR200-Spring2017
Labs/Lab9/Lab 9.ipynb
2
27075
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### *** Names: [Insert Your Names Here]***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 9 - Data Investigation 1 (Week 1) - Educational Research Data\n", "\n", "## Lab 9 Contents\n", "1. Background Information\n", " * Intro to the Second Half of the Class\n", " * Intro to Dataset 1: The Quantitative Reasoning for College Science Assessment\n", "2. Investigating Tabular Data with Pandas\n", " * Reading in and Cleaning Data\n", " * The `describe()` Method\n", " * Computing Descriptive Statistics\n", " * Creating Statistical Graphics\n", " * Selecting a Subset of Data\n", "3. Testing Differences Between Datasets\n", " * Computing Confidence Intervals\n", " * Visualizing Differences with Overlapping Plots\n", "4. Data Investigation 1 - Week 2 Instructions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#various things that we will need\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import scipy.stats as st" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Background Information\n", "\n", "### 1.1 Introduction to the Second Half of the Class\n", "\n", "The remainder of this course will be divided into three two week modules, each dealing with a different dataset. During the first week of each module, you will complete a (two class) lab in which you are introduced to the dataset and various techniques that you need to use to explore it.\n", "\n", "At the end of Week 1, you and your lab partner will write a brief (1 paragraph) proposal to Professor Follette detailing an investigation that you would like to complete using that dataset in Week 2. You and your partener will complete this investigation and write it up as your lab the following week. Detailed instructions for submitting your proposal are at the end of this lab. Detailed instructions for the lab writeups will be provided next week. \n", "\n", "\n", "### 1.2. Introduction to the QuaRCS Dataset\n", "\n", "The Quantitative Reasoning for College Science (QuaRCS) assessment is an assessment instrument that Profssor Follette has been administering in general education science classes across the country since 2012. It consists of 25 quantitative questions involving \"real world\" mathematical skills plus 24 attitudinal and demographic questions. It has been administered to more than 5000 students at eleven institutions. You will be reading the published results of this study for class on Thursday, and exploring the data in class this week and next. \n", "\n", "A description of all of the variables (pandas dataframe columns) in the QuaRCS dataset and what each numerical answer choice \"stands for\" is in the file QuaRCS_descriptions.pdf. \n", "\n", "## 2. Investigating Tabular Data with Pandas\n", "\n", "### 2.1 Reading In and Cleaning Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# these set the pandas defaults so that it will print ALL values, even for very long lists and large dataframes\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.max_rows', None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in the QuaRCS data as a pandas dataframe called \"data\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data=pd.read_csv('AST200_data_anonymized.csv', encoding=\"ISO-8859-1\")\n", "mask = np.where(data == 999)\n", "data = data.replace(999,np.nan)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once a dataset has been read in as a pandas dataframe, several useful built-in pandas methods are made available to us. Recall that you call methods with data.method. Check out each of the following" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# the * is a trick to print without the ...s for an ordinary python object\n", "print(*data.columns)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 The `describe()` method\n", "\n", "There are also a whole bunch of built in functions that can operate on a pandas dataframe that become available once you've defined it. To see a full list type data. in an empty frame and then hit tab. \n", "\n", "An especially useful one is dataframe.describe() method, which creates a summary table with some common statistics for all of the columns in the dataframe. \n", "\n", "In our case here there are a number of NaNs in our table (cases where an answer was left blank), and the describe method ignores them for mean, standard deviation (std), min and max. However, there is a known bug in the pandas module that cause NaNs to break the quartiles in the describe method, so these will always be NaN for any column that has a NaN anywhere in it, rendering them mostly useless here. Still, this is a nice quick way to get descriptive statistics for a table. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3. Computing Descriptive Statistics\n", "\n", "You can also of course compute descriptive statistics for columns in a pandas dataframe individually. Examples of each one applied to a single column - student scores on the assessment (PRE_SCORE) are shown below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.mean(data[\"PRE_SCORE\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#or\n", "data[\"PRE_SCORE\"].mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.nanmedian(data[\"PRE_SCORE\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#or\n", "data[\"PRE_SCORE\"].median()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].mode() \n", "#where first number is the index (should be zero unless column has multiple dimensions\n", "# and second number is the mode\n", "#not super useful for continuous variables for example, if you put in a continuous variable (like ZPR_1) it won't\n", "#return anything because there are no repeat values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#perhaps equally useful is the value_counts method, which will tell you how many times each value appears int he column\n", "data[\"PRE_SCORE\"].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#and to count all of the non-zero values\n", "data[\"PRE_SCORE\"].count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#different generally from len(dataframe[\"column name]) because len will count NaNs\n", "# but the Score column has no NaNs, so swap this cell and the one before our with \n", "#a column that does have NaNs to verify\n", "len(data[\"PRE_SCORE\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#standard deviation\n", "data[\"PRE_SCORE\"].std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#variance\n", "data[\"PRE_SCORE\"].var()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#verify relationship between variance and standard deviation\n", "np.sqrt(data[\"PRE_SCORE\"].var())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#quantiles\n", "data[\"PRE_SCORE\"].quantile(0.5) # should return the median!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].quantile(0.25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].quantile(0.75)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#interquartile range\n", "data[\"PRE_SCORE\"].quantile(0.75)-data[\"PRE_SCORE\"].quantile(0.25)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].skew()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data[\"PRE_SCORE\"].kurtosis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### Exercise 1\n", "------------------\n", "\n", "Choose one categorical (answer to any demographic or attitudinal question) and one continuous variable (e.g. PRE_TIME, ZPR_1) and compute all of the statistics from the list above ***in one code cell*** (use print statements) for each variable. Write a paragraph describing all of the statistics that are informative for that variable in words. An example is given below for PRE_SCORE. Because score is numerical ***and*** discrete, all of the statistics above are informative. In your two cases, fewer statistics will be informative, so your explanations may be shorter, though you should challenge yourselves to go beyond merely reporting the statistcs, and should interpret them as well, as below. \n", "\n", "*QuaRCS score can take discrete integer values between 0 and 25. The minimum score for this dataset is 1 and the maximum is 25. There are 2,777 valid entries for score in this QuaRCS dataset, for which the mean is 13.9 and the median is 14 (both 56\\% of the maximum score). These are very close together, suggesting a reasonably centrally-concentrated score distrubution, and the low skewness value of 0.1 supports this. The kurtosis of the distribution is negative (platykurtic), which tells us that the distribution of scores is flat rather than peaky. The most common score (\"mode\") is 10, with 197 (~7%) of participants getting this score, however all score values from 7-21 have counts of greater than 100, supporting the flat nature of the distribution suggested by the negative kurtosis. The interquartile range (25-75 percentiles) is 8 points, and the standard deviation is 5.3. These represent a large fraction (20 and 32\\%) of the entire available score range, respectively, making the distribution quite wide.\n", "\n", "*Your description of categorical distribution here*\n", "\n", "*Your description of continuous distribution here*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your code computing all descriptive statistics for your categorical variable here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your code computing all descriptive statistics for your categorical variable here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4. Creating Statistical Graphics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### Exercise 2 - Summary plots for distributions\n", "\n", "*Warning: Although you will be using QuaRCS data to investigate and experiment with each type of plot below, when you write up your descriptions, they should refer to the **general properties** of the plots, and not to the QuaRCS data specifically. In other words, your descriptions should be general descriptions of the plot types that could be applied to any dataset.*\n", "\n", "### 2a - Histogram\n", "The syntax for creating a histogram for a pandas dataframe column is: \n", "\n", "dataframe[\"Column Name\"].hist(bins=nbins)\n", "\n", "Play around with the column name and bins and refer to the docstring as needed until you understand thoroughly what is being shown. Describe what this ***type of plot*** (not any individual plot that you've made) shows in words and describe when you think it might be useful. \n", "\n", "Play around with inputs (e.g. column name) until you find a case (dataframe column) where you think the histogram tells you something important and use it as an example to inform your answer. Inputs that do not produce informative histograms should also help to inform your answer. Save a couple of representative histograms (good and bad, use plt.savefig(\"figure name\")) and integrate them into your written (markdown) explanation to support your argument. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#this cell is for playing around with histograms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your explanation here, with figures*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### 2b - Box plot\n", "\n", "The syntax for creating a box plot for a pair of pandas dataframe columns is: \n", "\n", "dataframe.boxplot(column=\"column name 1\", by=\"column name 2\")\n", "\n", "Play around with the column and by variables and refer to the docstring as needed until you understand thoroughly what is being shown. Describe what this ***type of plot*** (not any individual plot that you've made) shows in words and describe when you think it might be useful. \n", "\n", "Play around with inputs (e.g. column names) until you find a case that you think is well-described by a box and whisker plot and use it as an example to inform your answer. Inputs that do not produce informative box plots should also help to inform your answer. Save a couple of representative box plots (good and bad) and integrate them into your written explanation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your sample boxplot code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your explanation here*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### 2c - Pie Chart\n", "\n", "The format for making the kind of pie chart that might be useful in this context is as follows: \n", "newdataframe = dataframe[\"column name\"].value()counts \n", "newdataframe.plot.pie(figsize=(6,6))\n", "\n", "Play around with the column and refer to the docstring as needed until you understand thoroughly what is being shown. Describe what this ***type of plot*** (not any individual plot that you've made) shows in words and describe when you think it might be useful. In your explanation here, focus on how a bar chart compares to a histogram, and when you think one or the other might be useful.\n", "\n", "Play around with inputs (e.g. column names) until you find a case that you think is well-described by a pie chart and use it as an example to inform your answer. Inputs that do not produce informative pie charts should also help to inform your answer. Save a couple of representative pie charts (good and bad) and integrate them into your written explanation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your sample pie chart code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your explanation here*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### 2d - Scatter Plot\n", "The syntax for creating a scatter plot is: \n", "\n", "dataframe.plot.scatter(x='column name',y='column name')\n", "\n", "Play around with the column and refer to the docstring as needed until you understand thoroughly what is being shown. Describe what this ***type of plot*** (not any individual plot that you've made) shows in words and describe when you think it might be useful.\n", "\n", "Play around with inputs (e.g. column names) until you find a case that you think is well-described by a scatter plot and use it as an example to inform your answer. Inputs that do not produce informative scatter plots should also help to inform your answer. Save a couple of representative pie charts (good and bad) and integrate them into your written explanation. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your sample scatter plot code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Your explanation here*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5. Selecting a Subset of Data\n", "\n", "<div class=hw>\n", "### Exercise 3\n", "--------------\n", "\n", "Write a function called \"filter\" that takes a dataframe, column name, and value for that column as input and returns a new dataframe containing only those rows where column name = value. For example filter(data, \"PRE_GENDER\", 1) should return a dataframe about half the size of the original dataframe where all values in the PRE_GENDER column are 1. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your function here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#your tests here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*** If you get to this point during lab time on Tuesday, stop here***\n", "\n", "## 3. Testing Differences Between Datasets \n", "\n", "### 3.1 Computing Confidence Intervals\n", "\n", "Now that we have a mechanism for filtering the dataset, we can test differences between groups with confidence intervals. The syntax for computing the confidence interval on a mean for a given variable is as follows. \n", "\n", "variable1 = st.t.interval(conf_level,n,loc=np.nanmean(variable2), scale=st.sem(variable2))\n", "\n", "where conf_level is the confidence level you with to calculate (e.g. 0.95 is 95% confidence, 0.98 is 98%, etc.)\n", "n is the number of samples and should generally be set to the number of valid entries in variable2 -1. \n", "\n", "An example can be found below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## apply filter to select only men from data, and pull the scores from this group into a variable\n", "df2=filter(data,'PRE_GENDER',1)\n", "men_scores=df2['PRE_SCORE']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#compute 95% confidence intervals on the mean (low and high)\n", "men_conf=st.t.interval(0.95, len(men_scores)-1, loc=np.mean(men_scores), scale=st.sem(men_scores))\n", "men_conf " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### Exercise 4\n", "------------------\n", "\n", "Choose a categorical variable (any demographic or attitudinal variable) that you find interesting and that has at least four possible values and calculate the condifence intervals on the mean score for each group. Then write a paragraph describing the results. Are the differences between the groups significant according to your data? Would they still be significant if you were to compute the 98% (3-sigma) confidence intervals?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#code to filter data and compute confidence intervals for each answer choice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***explanatory text***" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Visualizing Differences with Overlapping Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=hw>\n", "### Exercise 5 \n", "---------------\n", "\n", "Make another dataframe consisting only of students who \"devoted effort\" to the assessment, meaning their answer for PRE_EFFORT was EITHER a 4 or a 5 (you may have to modify your filter function to accept more than one value for \"value\").\n", "\n", "Make overlapping histograms showing (a) scores for the entire student population and (b) scores for this \"high effort\" subset. The \"alpha\" keyword inside the plot commands will set the transparency of your histogram so that you can see both. Play around with it until it looks good. Make sure your chart includes a legend, and describe what conclusions you can draw from the result in a paragraph below the final chart. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#modified filter function here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#define your new high effort dataframe using the filter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#plot two overlapping histograms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*explanatory text here*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Data Investigation - Week 2 Instructions\n", "\n", "Now that you are familar with the QuaRCS dataset, you and your partner must come up with an investigation that you would like to complete using this data. For the next two modules, this will be more open, but for this first investigation, I will suggest the following three options, of which each group will need to pick one (we will divide in class):\n", "\n", "* Design visualizations that compare student **attitudes** pre and post-semester\n", "* Design visualizations that compare student **skills** (by topical area) pre and post semester\n", "* Design visualizations that compare students' **awareness of their own skills** pre and post semester\n", "\n", "Before 5pm next Monday evening (3/27), you must send Professor Follette a brief e-mail (that you write together, one e-mail per group) describing a plan for how you will approach the problem you've been assigned. What do you need to know that you don't know already? What kind of plots will you make and what kinds of statistics will you compute? What is your first thought for what your final data representations will look like (histograms? box and whisker plots? overlapping plots or side by side?)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", "div.hw { \n", " background-color: #fcf2f2;\n", " border-color: #dFb5b4;\n", " border-left: 5px solid #dfb5b4;\n", " padding: 0.5em;\n", " }\n", " </style>\n", "\n", "<style>\n", "div.sidebar { \n", " background-color: #d3d3d3;\n", " border-color: #dFb5b4;\n", " border-left: 5px solid #dfb5b4;\n", " padding: 0.5em;\n", " }\n", " </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mikemcfarlane/TickleMeNAO
Idea_Development/Microphone_anomaly_analysis_from_Jonny.ipynb
1
263204
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pysoundfile import SoundFile\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import numpy as np\n", "import scipy, pylab\n", "import copy\n", "from mpltools import style\n", "style.use('ggplot')\n", "mpl.rcParams['figure.figsize'] = (18, 7)\n", "\n", "#short time windowed fourier\n", "def stft(x, framesamp, hop):\n", " w = scipy.hamming(framesamp)\n", " X = np.array([np.fft.rfft(w*x[i:i+framesamp]) for i in range(0, len(x)-framesamp, hop)],dtype=np.float64)\n", " #Note: might need to convert to phase and amplitude\n", " return X\n", "\n", "#read wav\n", "data = SoundFile(\"tickle_kitchen.wav\")\n", "#flatten and subsample\n", "data = np.ravel(data)[::1000]\n", "#short time fourier with 100 freqs (200/ nyquist freq)\n", "out = stft(data,200,1)\n", "\n", "#transpose for viewing\n", "out = out.T\n", "\n", "#freq band pass power summation - this need tuning as ther\n", "outD = np.sum(out[0:20,:],axis=0)\n", "#threshold 1.5 an arbritrary constant\n", "outRaw = copy.deepcopy(outD) #deepcopy is housekeeping\n", "outD[outD<2]=0\n", "outD[outD>=2]=1\n", "#plot - maybe a little phase lag in spectrogram (the middle plot) due to windowing\n", "f, (ax1, ax2,ax3,ax4) = plt.subplots(4, 1, sharex=True)\n", "ax1.plot(data) #first plot raw signal\n", "ax2.imshow(out,aspect='auto') #spectrogram: frequency spectrum at a specific time\n", "ax3.plot(outRaw) #band pass signal.... \n", "ax4.plot(outD) #just a simple threshold - clearly some analysis in the time domain would improve this\n", "#misses the first noise but gets the end two - you get the picture though :) " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "[<matplotlib.lines.Line2D at 0xd4fc6d0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+tmvMBM8V9PEIoBZ9tKV3QjhoCRn9tGFOI268PDBG2FcWYp4lPvivElxUVqpy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\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VZSwJbidDjJ0sosxNkQnoXPSF7CRG1stTzqJ1FGbQQbUZGNuIJh0WrN8RmOtr302AJ5Zo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f1GQv9FKNzFQK2vY5d1d92H8uf7g9zKCDlCmvLCX8H4+RlARwfUR+9kfafm30oRiB5vV92JWIcd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\nCZXz1/mV6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"text": [ "<matplotlib.figure.Figure at 0xc219e90>" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#markov laughing\n", "\n", "#markov p martix all rows sum to 1\n", "decision = np.array([[0.2,0.3,0.5],\n", " [0.3,0.4,0.3],\n", " [0,8,0.1,0.1]])\n", "#laugh states\n", "event = [\"hee\",\"haa\",\"hoo\"]\n", "\n", "#number of laughs\n", "for i in range(10):\n", " lastMove = 0\n", " #length of laugh\n", " for j in range(3):\n", " #make a weighed random choice\n", " lastMove = np.random.choice(3,1,decision[lastMove,])\n", " print event[lastMove],\n", " print \"\\n\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hoo hoo hee \n", "\n", "hoo hee hoo \n", "\n", "hee haa hee \n", "\n", "haa haa hee \n", "\n", "hee hee haa \n", "\n", "haa haa hoo \n", "\n", "haa hee hee \n", "\n", "haa hoo haa \n", "\n", "hoo hoo haa \n", "\n", "hoo hoo hoo \n", "\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
racu10/emapy
notebooks/test funcions.ipynb
1
2234134
null
bsd-3-clause
cathalmccabe/PYNQ
boards/Pynq-Z1/base/notebooks/video/opencv_face_detect_hdmi.ipynb
4
5510529
null
bsd-3-clause
weikang9009/pysal
notebooks/explore/spaghetti/Spaghetti_Pointpatterns_Empirical.ipynb
5
9410
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---------------------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $SPA$tial $G$rap$H$s: n$ET$works, $T$opology, & $I$nference\n", "\n", "## Tutorial for `pysal.spaghetti`: Working with point patterns: empirical observations\n", "#### James D. Gaboardi [<[email protected]>]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Instantiating a `pysal.spaghetti.Network`\n", "2. Allocating observations to a network\n", " * snapping\n", "3. Visualizing original and snapped locations\n", " * visualization with `geopandas` and `matplotlib`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:25.919391Z", "start_time": "2019-05-14T00:25:25.896788Z" } }, "outputs": [], "source": [ "import os\n", "last_modified = None\n", "if os.name == \"posix\":\n", " last_modified = !stat -f\\\n", " \"# This notebook was last updated: %Sm\"\\\n", " Spaghetti_Pointpatterns_Empirical.ipynb\n", "elif os.name == \"nt\":\n", " last_modified = !for %a in (Spaghetti_Pointpatterns_Empirical.ipynb)\\\n", " do echo # This notebook was last updated: %~ta\n", " \n", "if last_modified:\n", " get_ipython().set_next_input(last_modified[-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:25.933253Z", "start_time": "2019-05-14T00:25:25.926333Z" } }, "outputs": [], "source": [ "# This notebook was last updated: Dec 9 14:23:58 2018" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----------------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.016615Z", "start_time": "2019-05-14T00:25:25.942023Z" } }, "outputs": [], "source": [ "from pysal.explore import spaghetti as spgh\n", "from pysal.lib import examples\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.lines as mlines\n", "from shapely.geometry import Point, LineString\n", "\n", "%matplotlib inline\n", "\n", "__author__ = \"James Gaboardi <[email protected]>\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Instantiating a `pysal.spaghetti.Network`\n", "### Instantiate the network from `.shp` file" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.155155Z", "start_time": "2019-05-14T00:25:27.026125Z" } }, "outputs": [], "source": [ "ntw = spgh.Network(in_data=examples.get_path('streets.shp'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Allocating observations to a network\n", "### Snap point patterns to the network" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.473692Z", "start_time": "2019-05-14T00:25:27.167257Z" } }, "outputs": [], "source": [ "# Crimes with attributes\n", "ntw.snapobservations(examples.get_path('crimes.shp'),\n", " 'crimes',\n", " attribute=True)\n", "\n", "# Schools without attributes\n", "ntw.snapobservations(examples.get_path('schools.shp'),\n", " 'schools',\n", " attribute=False)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 3. Visualizing original and snapped locations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## True and snapped school locations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.494084Z", "start_time": "2019-05-14T00:25:27.475176Z" } }, "outputs": [], "source": [ "schools_df = spgh.element_as_gdf(ntw,\n", " pp_name='schools',\n", " snapped=False)\n", "\n", "snapped_schools_df = spgh.element_as_gdf(ntw,\n", " pp_name='schools',\n", " snapped=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## True and snapped crime locations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.522298Z", "start_time": "2019-05-14T00:25:27.498845Z" } }, "outputs": [], "source": [ "crimes_df = spgh.element_as_gdf(ntw,\n", " pp_name='crimes',\n", " snapped=False)\n", "\n", "snapped_crimes_df = spgh.element_as_gdf(ntw,\n", " pp_name='crimes',\n", " snapped=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create `geopandas.GeoDataFrame` objects of the vertices and arcs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.949426Z", "start_time": "2019-05-14T00:25:27.525105Z" } }, "outputs": [], "source": [ "# network nodes and edges\n", "vertices_df, arcs_df = spgh.element_as_gdf(ntw,\n", " vertices=True,\n", " arcs=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting `geopandas.GeoDataFrame` objects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:27.964697Z", "start_time": "2019-05-14T00:25:27.952322Z" } }, "outputs": [], "source": [ "# legend patches\n", "arcs = mlines.Line2D([], [], color='k', label='Network Arcs', alpha=.5)\n", "vtxs = mlines.Line2D([], [], color='k', linewidth=0, markersize=2.5,\n", " marker='o', label='Network Vertices', alpha=1)\n", "schl = mlines.Line2D([], [], color='k', linewidth=0, markersize=25,\n", " marker='X', label='School Locations', alpha=1)\n", "snp_schl = mlines.Line2D([], [], color='k', linewidth=0, markersize=12,\n", " marker='o', label='Snapped Schools', alpha=1)\n", "crme = mlines.Line2D([], [], color='r', linewidth=0, markersize=7,\n", " marker='x', label='Crime Locations', alpha=.75)\n", "snp_crme = mlines.Line2D([], [], color='r', linewidth=0, markersize=3,\n", " marker='o', label='Snapped Crimes', alpha=.75)\n", "\n", "patches = [arcs, vtxs, schl, snp_schl, crme, snp_crme]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-05-14T00:25:28.589923Z", "start_time": "2019-05-14T00:25:27.969251Z" } }, "outputs": [], "source": [ "# plot figure\n", "base = arcs_df.plot(color='k', alpha=.25, figsize=(12,12), zorder=0)\n", "vertices_df.plot(ax=base, color='k', markersize=5, alpha=1)\n", "\n", "crimes_df.plot(ax=base, color='r', marker='x',\n", " markersize=50, alpha=.5, zorder=1)\n", "snapped_crimes_df.plot(ax=base, color='r',\n", " markersize=20, alpha=.5, zorder=1)\n", "\n", "schools_df.plot(ax=base, cmap='tab20', column='id', marker='X',\n", " markersize=500, alpha=.5, zorder=2)\n", "snapped_schools_df.plot(ax=base,cmap='tab20', column='id',\n", " markersize=200, alpha=.5, zorder=2)\n", "\n", "# add legend\n", "plt.legend(handles=patches, fancybox=True, framealpha=0.8,\n", " scatterpoints=1, fontsize=\"xx-large\", bbox_to_anchor=(1.04, .6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----------" ] } ], "metadata": { "_draft": { "nbviewer_url": "https://gist.github.com/cbf4e5b556b77f3a03ff2e65c3481b63" }, "anaconda-cloud": {}, "gist": { "data": { "description": "notebooks/Spaghetti_Pointpatterns_Empirical.ipynb", "public": true }, "id": "cbf4e5b556b77f3a03ff2e65c3481b63" }, "kernelspec": { "display_name": "Python [conda env:py3_spgh_dev]", "language": "python", "name": "conda-env-py3_spgh_dev-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
JudoWill/ResearchNotebooks
Dream2Problem.ipynb
1
6048
{ "metadata": { "name": "Dream2Problem" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "import os, os.path\n", "from pandas import read_csv, DataFrame, Series, Index, MultiIndex, concat\n", "import csv\n", "from collections import defaultdict\n", "from itertools import islice\n", "from StringIO import StringIO" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import sys\n", "sys.path.append('/home/will/tdlData/will/IpythonNotebook/')\n", "import DreamMicroUtils\n", "\n", "os.chdir('/home/will/Dropbox/DREAMLargeData/WillStuff/')\n", "datafile = '/home/will/Dropbox/DREAMProject/DREAM7_DrugSensitivity2/ACalifano_DLBCL_Ly3_14Comp_treatment.txt'\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_headers(handle):\n", " reader = csv.reader(handle, delimiter = '\\t')\n", " ids = reader.next() #junk-line\n", " drugnames = reader.next()[2:]\n", " timepoints = reader.next()[2:]\n", " concs = reader.next()[2:]\n", " headers = [('probeid', '', '', ''),\n", " ('genename', '', '', '')]\n", " found = set()\n", " for tup in zip(drugnames, [int(x) for x in timepoints], concs):\n", " num = 0\n", " while tup + (num,) in found:\n", " num += 1\n", " found.add(tup + (num,))\n", " headers.append(tup + (num,))\n", " \n", " headerindex = MultiIndex.from_tuples(headers, \n", " names = ['drug', 'timepoint', \n", " 'concentration', 'replicate'])\n", " return ids, headerindex\n", "\n", "def iterate_lines(handle):\n", " reader = csv.reader(handle, delimiter = '\\t')\n", " for row in reader:\n", " if ' /// ' not in row[1]:\n", " yield '\\t'.join(row)\n", " \n", "\n", "with open(datafile) as handle:\n", " ids, headers = get_headers(handle)\n", " odf = read_csv(StringIO('\\n'.join(iterate_lines(handle))), sep = '\\t', names = ids)\n", "#odf.columns = headers" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "gene_level_data = odf.drop(['AffyID'], axis = 1).groupby('Genename').agg('median')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "gene_level_data.columns = headers[2:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "gene_level_data = gene_level_data.reorder_levels([0,2,1,3], axis=1)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "drugs = sorted(set(gene_level_data.columns.get_level_values(0))- set(['DMSO', 'Media']))\n", "timepoints = sorted(set(gene_level_data.columns.get_level_values(2)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "from itertools import product\n", "from pandas import Panel\n", "\n", "mi = MultiIndex.from_tuples(list(product(drugs, timepoints)), \n", " names = ['Drug', 'TimePoint'])\n", "fold_change_panel = Panel(items = ['IC20', '1/10 of IC20'], \n", " major_axis = gene_level_data.index, \n", " minor_axis = mi)\n", "pval_panel = Panel(items = ['IC20', '1/10 of IC20'], \n", " major_axis = gene_level_data.index, \n", " minor_axis = mi)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy.stats import ttest_ind\n", "\n", "untreated = concat([gene_level_data['DMSO'], \n", " gene_level_data['Media']],\n", " axis = 1)\n", "\n", "for drug, tp, c in product(drugs, timepoints, ['IC20', '1/10 of IC20']):\n", " \n", " m1 = gene_level_data[drug][c][tp]\n", " \n", " \n", " fold_change_panel[c][drug][tp] = np.log2(m1.mean(axis=1)/untreated.mean(axis=1))\n", " _, pvals = ttest_ind(m1, untreated, axis=1)\n", " #print pvals.shape, l.values.shape\n", " #raise KeyError\n", " pval_panel[c][drug][tp] = Series(pvals, index = untreated.index)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "pval_panel['IC20'].to_csv('/home/will/Downloads/Dream2pvals.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
ahuang11/ahh
examples/vis/plot_map_CENTRAL_LONGITUDE.ipynb
1
480370
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from ahh import vis, exp" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "da = exp.arr_ds()['air'].max('time')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x7f98ce31f518>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WTUBVVZIt6wf8bl1OKZLFTTrQiBz191+lzoghvwZVTpJsqx2wTn1uFaLRRsrvRYmH+i0n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uboDBiGiHtQbwhAqPMGAyEYjop+ODgCEp+McVFtD/B3dwYmnciq350s/66ODbvdnL8o\nmWuPSkcMESwMwd2W698sZ12BC5tbYk6eEbE5C9dp85M4cXYib6xroKTex/u/2Pm9fOC/7whG0BN8\nt8OJrASD5AcDBou8HEyo8gW4dkcFAQUOTY5jXoKBJX9OareRkZ+u46vfndz3YTUHTYzj1hMyGZUY\nXkFUVt4NySf1ed/7Mt1sx7aHIjlQVt5NRaOfRz6r4YNfbZh0IgatyJv2xojb2C/fyNHT41nXwTuj\nNxAUZXDncY8EgiAcAKz+5sZxHDA+WEWxoNrL8Q8VkmxSse8YI+MzgpXbSup9lNT7kWSFq49MY8m+\nQSXw9bUN/PODapbMSqDa5uf3Ci93J6ST3ybqfmeTl0u3lnFkShzXjElDIwqUevx8We/kszoH5d4A\n+yUYOTbVzP6JxpD5/1sUyu6En6IoXLW9gipfgKcmZ4dM/daxzRZEIlgDssyy8gberLQhA3PiDdw6\nNp1Jl4Q2Hbdt8ydbE9fvrOTq0aksSQ/6Urconoqi8KbTxi6/DwGBqVodS03mHvkpOoC+zGEQSwIQ\nCv25wA0Gxb/j+wzVp1DftDcxA4qi8G61ndf8NlxeCZUgYNCJqASod0mMTdNyycGpLJnV9T1eXlPP\nvR9UcdKcRDSqYJ2BPx+exuQsPb6AzCUvlPBjYRMCMCZVyxnzkzh3YXLr9YOx+FJ/LZQOpwdzXPt5\n0tV7iLZfvX2nQ1FhiCU6vr8rXiphZ5WXT67ND0uOASrsMqPiu97M6W2q7I5tjSCIv++s4Denlycn\nZ7eLEexo9bS7ZZxeiaxETURrbCTfdDhiMMiAtvPwhrfKWbnBxtJZCdxwXAbxBhVun0y9K4BA8DuW\nN/qx1niRZDDpRRQ5mHZWFOCI6WZMOhVrdjo56J5dAAsVRVnTm/712DJgsVh0wDIgwWq1Htd8zAhc\nA+wP+ID3rFbrS22umQjcAGiBf1ut1vXNx88D/gg8arVa32tz/nPA21ar9ZNo+1dQHcyU8NIlY8IG\nCrZg1VYHd71fhSjAL7ubeOycHP54926urCjn6jGpHJ4cLJg13qjj4uxknimrp8Dt48rcFMxqFeeM\nSuTMzETeqbbxfrWdWwqqyNapuWdcJqM7+OVHI/B+d3k5JNnUJRGA4K7m9gov5Y1BkiPNBrNBZPR3\nqnbuQm2hFkUuyUnh3FFJbNzXx63vVvDdBDeTCE0G2u7e7BtvYEGCkUeKa0nRqjgg0dRqIRAEgdPj\nErwmAF4AACAASURBVKiRJD5pcrDF5yFPo8GrKMzU6dEJkQuigSACg2Fhi0Sxb+n/YCQBbY937F9v\nv2nb7/PDLhc3vFVOtT3AIZPjuO7odLwBhdd+aEAU4LCpZg4Yb4pokXz/Fxtz8ozcdfIobn23gq+3\nOfl6m5O5FiNGrch6axP/PDWLw6eZ26UVbsFgIgEtiFQh760ZPu7LB4lmSylUvzreN5bvczi7GfQE\nGpWAX1K6/WZdKY1t5Vtvs+OMoDMqvAHMapFNTjdZOnW7RB0tEASBBKOKBGPkLsp7IxGA6CwK0cqe\nSGRKxzb/dGgqKzfYyEvVtlYTNmhFsrV79MWsJA1zLLErDNsdeuMmdD5QBbTVBP4KxAOnA4nAgxaL\npdJqtX7W/PvFwC2AC7gbWN/mWjtwrsVi+cRqtbp70S/8AYU1O4Pmk0gWKW8geJYoCMhysIjHirvG\n8ue7SrjXWsMPjU1cnptCmlbNmaMSGWfUcm9RDX/dHvQh3j/ByD3jMzkzM5HTMxJYb3dzX1EN5/9W\nyiOTspgWF72QFASBk9LjebPKxhEpZmaZO7vxWN0+lpc3sPEOD06v3O63gE0hSaPi2FQzx6WaydCF\nNh/mX2wkTzbw0pp6vt/p4uKDU8P2qYUQqAWBW/LT+fvOSu4oqOae8ZmUlu9JvSoIAulqNakqNcUB\nPx+4gnEFTllmsTEu4ndQA6RFfHZkCKW4DgYC0BbR7JQPNCnoqq+h+tTxm0b6rB2/0eodTv60vIT0\neA2nzUvkHyfsSZl7x0mjImqzLdLj1eys8vL8N3W8u76Rs/dPwpKm4+Xv6ymq9XHzkoxurQtDCeEW\nvJ4o4bVOmdS43ikZ/UWmBtp3ejBg6awEPtviYFOJu8uCVturAkzMiFxF6C0pGHEXCqLaF0AnChQ3\n+bm/qBYRgaNSY7M1Fu03Ha6I9WZDtORiVHP1YL1m8GR26tGosFgsE4B5wBPAbc3HdMChwJVWq9UJ\nOC0Wy3vAsUALGRA7/NcWmwluHJ4KvEQP8MUWB1/+5mRdoQtrjY/zmtOBdof8dC1alUBZo59zDgj6\n3SfHqfnn+Ezeq7bzXFk9a21NTDTpmGzScWF2Msun5rDZ6eGmXVX8YGvitoIqlqbFM9OsZ16Ckb+M\nTuG2gmoaQqT57AhHQKLOL5Gl06BtNtvKitJqHrxuR5B0LEgwEq8WMatV1PslVtU7SVCrOOmIROaO\nNTI2TUvt2z5UQJk3wAe1dl6vbOTVikYmmXTMiTcwLU6PPSBR6QugFQSke+ALrYviOh/XHJXeZT/b\nCmq9GKx3cN2OCm7aVclSrbldrQEAi0bLBq8bvSAyUaPlB08TOkHgAL0xot1ahyiQFsNcuh0Vz6Hi\nAhQJ2j5bfxCD7pT4cH1o+aaSouCU5ZDntEW4b/Tu+kbS4zV8eM1Y9N0EBEeCfccYeHe9jXUFZciy\ngs0tceb+SZw+P5FKW6BTQN4I9sDmUUiNnOMPOPY2S4Gw9KZ2z9ziV+7ydj3/yu0yEzOiv9+IpSBy\nyIrC8vIGXq5oDLtxOcUU9GyIxfvs6TcdQWyhVkFKnJr1Vjd/XNj592irVsekT9FeYLFYVMB1wMO0\nV+hHN7e3q82xXcBZbf5eBtzVfN6jIZp/BviXxWJ532q1Rh5N0YwXV9eTk6xlQqaOm5dksv+47lOn\nldT5OOERK7KsYNSKfP6bg0sOTiXBqCL3QgMnLxM4KMnE65WN7Pb4ebPSxlpbE0elmDktI4E3pufy\nYa2Dlysa+abBxbQ4PVePTsUtBaf2ZFNnFyVnQMIuyfzQ2MSKajulXn/rb/FqkbvyM/lPSS07m9pX\ngKwPSJR4/TgCMgoK52YlcVpGAuOPC+7ulC3zkNVsAcjQaZgVb6DBL/F5nYMf7W7eqLThV4KvVSRY\newBg0ew4bjgug0UToks1Z1KJ/Gt8Jpf/Xs57TjsHGkxM1epaTZoTNUEi5lFkbM2K39duF1O0OpL7\nuQhZJESg46IZCl2RgK6U8P6sUdDX1oLePItfUfi6ycUvXjeiRiCtVsPRYXa9wgnBTzbb+bGwiZQ4\ndUyIAAQVpESjyHEzzfgDCp9tcXDTEol4g2qECAxjtMz34U4K2sq2WkcwPbRJ17O5E+lGSE9Iwd5k\nHbD5JU7YuLvT8XkJBs7LSsIgiozWaxAFYYRYDXKEsw6ETZcqCBw13cyHG+2dfus4/mMZl9MVeqKR\nnQHsslqtmywWy8w2xw2Ax2q1tt0KdwKtdkir1boFOCdcw1ar9TeLxbIBOBt4LNqOnTYvkftOz47q\nmpaFfnKWnhuOz+C8Z4v5dLOd0+YHLQTZF+hhmYc/jw66z6xpdPF2lY2nSusp8/q5KDuZMW3iArY4\nPVy4tZRzRgUr962zNXFsWvuCRTfsqmKL04NJJeKSZHL1Gi7KTuZnexPv1zj4y/ZyOmJBopG7x2WG\nfIauhGeSRsVpmYmclpmIR5axun0kq1WkatVICrhlmSkX9jzftFmtYh9Fx5uyh5UuOyUBA8eYgsqd\nDKibg2EcsswkrY59tPpBRQQ6TuBwhKDjO45W0W57fiyJQVeLZ19YCyLpe6h7KYrCNr+PTyQ/TR43\n4zVabCqZB3fXcGiyCZ0o4pZkavwBcnUaci5sn5pWlhU+2exg1e8OPtpoZ9YYA9ce3bUlKxp8+ZuD\nnGQtT583mt11Po79dyH/+8XGWQuSu794BEMee5O1YFNJ0As3q4vMM7FEfykzQw0ra/YogkkaFQcn\nmZhs0nFIcly7WL+Rdzc00d3G4uZSD5Y2nitd6XH9YW2LSiuzWCzZwBLgohA/uwGdxWJRtSEEJqAp\nyj49BzxtsVjejvI6slPj+HK7P+RvOjUszNfglxS+3RVo91tephm1TsRaLyCLOuTmINctFQGq7Ap1\n7jb8RqfnlFwduloHHza6sAdq2ej0otbrmR1vYG1j8HHVegM58QHur3CwUxK4MD0OsyiwxStTI6hR\ntDqcACowGnSo9Hrm6fX84pYocTaRolVzXFYqGlFALQiM0qlZ427vcjRXL6IVBH72SHi68KbJUgtY\nNCI2WaBe1FAvwy7PHhPxlsddpBwWHJSLJwYXiK92+OnoodP2PeRpBLLVIsV+mUKViEqvJ1FUYdTp\nKVAF359XllHpdXg9HqbodOTEBUlCQZh+ZsgycQpUiwIOQaBSACWEO5FOUciRFXxAiarr3a2L84P+\nC1u8MjZZIeUwLdu2+xHmnxY84ZutAKSlmpkxNZe6Bie/Jp6Isu6/YZ+9crcTNVAsCvi7cHeKlxXS\nFAWHANVtUqz+u7k2w/ycPf66BzQHEa11S3TlWDZGLTD/UiPWOonCWplt2/1wwJ4AsrovgtYkNTDf\noEJWFH7wyKSO2ePHsa60/ZRMl2XMCtSIAvYwzzM/x4ip+Te3rLChCxeDApVIliRjAMpFAbcgsMXn\nYWvAS4JOz4EGE9WaAGUuL0syTDxTWs9PTg8lAQEFGJenY/rbBhZPiUPd/H1f+K6OnwsaGJWg5thZ\nqRw+LYF6jxByvmcnikzKUFFhl9la0bWb3qET1AiCQEDQUdPkZ9UOP4IgkpVqZsUmD5kpwfbzU0Xy\nUlQU1UkU1IZ/drUIB43XoCgKq3YEwp4HMDlTRVaCyLYqibLG8G0atbC/RYPHr7CmsOs2981VkWwU\n2VgWoNYZXigkmwT2zVHT6Jb5ubjrd7S/RY1RK7CuKIDT27lNR/NYiNcLpMWJOL0KVY6uXVDGpogI\ngkBRvYTUxanJRoEko0ijW6bOFf55VALkpQTnQUFt18+THidi1gvUuWQa3SHafP4OAHRHXUludjKB\ngMTukrou2xyVmYjRoKWy2obL5Q17nsGgJSszEa/XT2l5Q5dt5uYko9WoKStvwOMNva4BxJv1pKXG\n43R5qKruvNPYihkXk7fxGQ6dYub572xc9lIFD5zZefOs6h0vCaJAsUfi0y8CqA/dQxpa5AsErcs5\naoFstUBJQKE00MV4e7KJeefr8fihqF7GEWbB0qgg70QN0rs+Sv0KFVL4NtNUArlqAYcMpQGZUJ8S\nwCBAjlrELEJpQKG6izZHqQRyNAK1kkKpXyHcW48TgufpBCjxK9R34c6aow72s6z5HVndftK1KhpV\nQT3g0pwURhs0JIgCOWoBZ/PztF2v2kIQIC9ZJC9FxFonU1QXfgKlxQnkpYh4A2Ctk9lYGkCWO8t4\ntQosKSKZ8SJFdTIlDeHbzIwXyEtRYfcoFNVJdHBgaIVBE5yTiYbgPK+wdfGOEoPPU+2QKaqX8YUR\nc3G64PMYNcE2qx3h28xLEclLFilpkLHWyZ30mRYkGoJtikJwbNaHkTMt792SIlIY6r3/6zaE+aeh\nrPsvqXECeckiPgmK6mTsHcZ7pc3PxnKZE2YlsLtegvd8lAQUyruYQ2kqgabn3K3jw63AR1VdzPco\nEVVqUYvFchTBbEEtAb5qghYBB3Ar8ABwhdVq3dF8/unAAqvV+tdu2j0PGGe1Wm9u/vv/mtvOJ4Js\nQi2pRd+eMZq5CXsUrEhZ1IXPF/PDLhcqESQZXrl0DLPyOgdWdWRub1fZeLykjmlxerY4PUw16bg1\nP4PtLi+LkkwEFIVzNpcgA89OySZeHVys1jS6uHlXFaP1Gv41YRQZ2uZCE4rCyZuKafRLPDk5m4kh\nXIz6GuHeWTjWGlAUzlpfTL0kcWVi5wJmO3xe/uu0MUWr50hjHKYo6g4UigJjexEzEI1FAMCqWopF\nWtn6d1tmH+r5o91t761FIJpdgViY2lueL9p+t1z3u8/LGrcLj6LQKEvM1RkYFxdPrizzhKsegAS1\niFtS2C/RyCSTDoMo8Irfhq1J4qE/ZHPk9Hj8AYV9btkGwK93TkSrjnwMRbqj8tCn1Tz3dR0/3zER\nrUpg2k3bOGKamYfPyonq2fdGFNRK5Kf2vOjiYMZwtBIoK+/mhW/ruP/jan66bQImXftv1zJnfnBL\n7G/Y81tvXXj6W34NNqxucNEQkPj37j1VgKfH6Xlo4qiQachbEMvd4K92+jhk/EixuYHGyf+x0uAK\n8Oplecjvdh9XGg6nbtzNj3Y3DEBq0a+An9v8PRX4P4KWggZgFXCBxWK5E0gCTgKe70G/XiQYRBx+\nOyQChCvv3RHHzIhnXYGLvFQdU7P1TM6KrLrs4uQ4Hi+pY4szeGy0QUu6Vk16s3KvFgRuy8/gqu3l\n3LSrigcnjEIrCozRBydjscePvk2eZ0EQuH5MGjfsquSTWseAkIFohfBzZfVUCxKL9KGzUuSqNeyj\n1bPR5yFOEDnCNDCRhuGIgFW1NOLrO76bcG44XSnPkQj2aEyC4dyaIq1n0RV6Q16K/T7ecdrIVKmx\naLSkqVTM1RmwCqBNEYP5xIB9zAYuzk5ul0t7veRhXWETs5sJuSDAkn0TeP8XG2c/vZvnLhjdmo6t\nI8I9b1fv1O6WeGNtA8fsE49OLXD/R9UAnLdoxEVob8dwjSmYMTrohvfUqrp27naRuCqMoGe4paAK\ngHFGLbuat9PvGZfRJREYwfCEtcbLafOSekUEANI1sXO5jqolq9XqJZgdEACLxdIIKFartab570eA\na4G3AC/BOgOfhWqrm/vUWCyWFQTjE2KGcMLs5AsSmZ9vJM2sblfhrwXfbHOy4udGEk0qDBkiNY4A\nRb942er04JJkTCqR87KSOCWjs/I00aTj1rHp3LSrilcrGji/WfE5IzOBNyptnLulhBem5pDc/FHX\n2YIuHMel9WWW/djB5pdI06i4fUYG72/vbLIyiCLHmMxs8nmIG2TViEMRgbbWgY6KdqT++R3RUQHt\nLlC5OxIQKs6hBR3bjQUp6AnMogqtIKASBEarNYzTaFszSO2faGSSKZtxRm2nhbDE42PNFheJ6SoK\na7wkmVSoVQL/PC2L42bG8+eXS7nlnQoePiu7U0aqSJ6xIylweSVue6+CJp/MFYel8t7PNl5cXc8l\nB6cwc3T/5XgeweDGcCIFwtKbmL3ybi44MIXnv61DrxH406GpXRYgiwX25tiB25uJAICmWW7dPS6D\nOPXwtKiNoGvMtRhZ9lEdRckebhnbs/ROW5weNjh6lYW/HXpFK6xW66/AcW3+bgLu7EE7L4Y49jTw\ndG/6FynKlnkQgFLJgy0gkaZVk9smgPHZr2vZsDv40hOMKtLMagLpCnUNwYqnc+INnDsqMWy6zP0T\nTcxLMLCmsYnzs5ORFIU3KoM7yfaATJHbz3cFwa3Sael6/ldr5/bCav4zKYuEQS4s9ks08kmdk6dK\n6/H5ZUarNZ0KpLiVYIEbfZQ7INqoShm1R1vlvDv3oGgQqak8FAHo6u+26E0wY7hKuP1FClosJEkq\nFSeY4nnf5WCly84EjY4T4uJxGGTO3FRMvFrFsWlmRmnVLEwytY7zFpdJp1fmvGeLyU/X8cqlY0gw\nqlg4IY6/H5fB7SsqeXNdI2fslxT1MwUUJZh6d5mHsoMlbnyrgiq7n+uPySAvVceyb+vJTFDz1yNi\nXeFi+EK3F6UtH06k4Nqj0mjyyjz+ZS2JRlWnYHnjyIZ1zJDbxvL5u8vLnHgD8xMi22yIpcx2eyTK\nvpP3WlI2WHBdIBVvoszP9uiVeVlRWFXv5J1qO8ZuYiajwbAW425JxiHJuCUZtyyTolFT4fXzca2D\nOr+EAuxj1pOpVfOz3c3XDS48zT7qS4rNzDk6jjP2S2J6roGNJW4kGSypWvbLN1HjCGCt8fLWlRYS\nPqXbvPkHJpm4v6iWLU4P3ze6Wo8fm2pmvFFLCUGz4eLkOEZp1fx1eznPl9VzzZjolZKAooStPNwW\nsUiFuTDRxIIEI29X2ZCBNI+KQw0mEkUVBlHEq8g0SEFTWLkUYFZErQaR030a+j5BV9YBiM6HM1KX\npLaxCpEqGm3ba3t92zYG0lIwQavjWo2W1502dvi9PNhQi9ElsiQtHntA4tWKYJrbQrevNVuXxaDl\njem5vFbZSMZ8LW+sbeC6N8p46rxcVKLAafMSWbPDxb8/qWbprATqXwkTvRYCHknmoq2lJKpVjDfp\nWPGAnUkT9Lx5uYUp2cH3IgjBuRxJHYwRBJGTOLg3LEbQGcLSm2Dl3dx6Qiardzp588c95LoF++hj\n/117ax0ItWb1Z9rmnuKC7CDRerlZ5t1oSR8Q96C++KYjiB5GlYhZJWILyKyqd7LV6UECxhm0HJlq\nRi0IYbPyfdrkZJviI12r5oR0Mw/s7jq5QaSIKoB4sKIlgPjS7CTqAzLVvgA1vkCrYt8RiWoVYwwa\nArLCb82ZHxLUIouT4zCpxNYJqxcFPp5lIfsCPQ2uAB9vsvPJJgebS914AwpalcC3N41v9V/uSsHy\nyQpnbS7Gpygka1QUuf3ckZ/BoqTQaT2fL6vn1YpGcvQaFAUOSDJyVmYiFd4A9++uwS8rxKlEzhqV\nyP6JwTYkReGp0nreqbKRZ9Cw2+3nyFQz1+d1JhTRBL9GImw9ssw39S4eLaml2h1MBaBBwN9md/9o\no5nZekO4JjrBB0Qb6hRpPYEWRKqgt0VXLj7hApPb4t0dTk6aED52Itx9w7UXybWRpEuNFTqOLUlR\nSMvUsMHh5uCkOFySxGMldVR4g+PkgEQjd4VIm/tulY0n7cFA4w+uHsvY9GAMzcZiN2c+WcQjZ+cw\nZV3k6RFb5pRZLeIIyJyemcD5WclYLgqOSX9A4fD7dzE1W8/j5+aGbCPUO9vbd9n8koJGtXeRp+Fg\nGWiRCd/tcHLpCyXcdHwGB2/bs1vtkWX0feDa2d18CSeXuluzBjsp+KLOwd3WGsYbtTwzZWASE7T9\npnu73BpIlC3zcNOuSr5vzj6pFQRMapEGv8R5WUkk2kWK/X6+drtwyEEPFEUBiWAyjgMNJh6YOYr1\ndjenbCqGGAQQDysykKVVMyfBQJZOQ6pGTapWhVklYlCJGEWRYo8fs1pkUaIJTbN/ZFVzyrZUrRqV\nIOCTFS7aWkqJx89DE0cx07xHeW2ZPI1NAV75voFkk4o/7N/etNqVglXi8fFMaT2rmwfAtWNSOa5D\nDYIWeCSZFysasAdkmiSZ7xpcaEWBgKKQqFYxK95AQZOPArePfIMWjyzTGJBxSTJHp5rZ7vJS6PaR\nZ9DwwtT2ik1Pc85HImwb/BLbXV6qfAG+KnVgEkXSVGrSVOqoYwYizSYUrl9dCbueBBF3p6S3Rag2\n393hDHt+V+SgJwjV14EgBG2/TUBReLzKwcfldeTptRyREsd4k44JRi26EGPjo1o79xfV8q8Lszhh\ndmLr8TpngP2v38H1eWlhC5a1QFEUBEGgzOPnvN9KOCbVzPlZyTQGJPKa64O0jJP11ibOfWY3j56d\nw2FT97Qb6TvaWxfX4ZxNKByGAxmAPTLhzy+X8mOhi/9eYUH9ftAk2zGbUCwRbda6SNeswU4IbtpZ\nSYXPz7KpoTcb+hptv+neKq8GA8qWBeNNnQGJJlnBrBJJ1aq5q7CaDyrtTNJq2eLzkiyqGKPRICkg\nEPwvU61hjk6PW1EgCf6+qwpGyEAQLWRg+dQcbNWho7OXTkqIeGfhy3ondxVWc86oxFbzXltEOonC\nCbYGv4SsyNxbVItWEDg02YRHVohXq5ho1JKh67zbWez2sbLGjlmt4rDkOHL0GiRF4Z0qG5ucHhoD\nEj5JYZPTw6kZCVw1JpU/bgmmNX1t+ujWdnpTfKongrY390sfE0f17tAKdHd9iTQTTwsiJQQ9QVck\noCNiSQoGmhC0u4fHz992VlCiqJirF/nH2HTM3cTDeGSZUzcWc0iyiQdv37N4bnjKyRmbivm/vFSO\nSW1Pptc2NvGrw02Jx0+J10+510+KRo1KAEdA5pXpuZ3icFrGyoaiJs5+ejd3nDSKU+Ym9ui97I0L\n7AgZGLpokQeVNj9nPFGEKMD9CZlk6jQRkYHeuJp2nCu9JQKR3ncg8UxpHe9V2/lo37wBcUUcIQOD\nA+HGepXXz+KfrLgVGaMgcnKcGYOoCpIAlZp4UaQ44OfzJieVUgCfotAoyzBCBoJoIQNXJaYwVtP7\nHLqKovCoq56TMxK4Irdz7nzofc7kSq+fMzeXhDx/2dQcLIbunyOgKPxjVxXf29oXkTo9M4HLclI4\neoOVI1PMXDUm6I8diyq0/UEIWu7Rk52paAVcJIQgGmW+K7z95o8hj59y+ryw18SCHHQkBf1NCAKK\nwuW/l1Hrlzg5J52zUiJzFZMUheN/KeL4tHj+lJtCqcfPd40uvmtwsavJx/NTs8ltTtOrKAqvVjby\nfFkDJpVIrl5Drl7DKK2aGr+E1e3j5PR4DkvpbEmo9wf4eYqXl9bUoxIF3v2zBbNB1eN3srctsiNk\nYGijRR4U1Xo55+liTDqRZ5Ky+NEjt5O/PVk/equY96UVu7eItu5CqcfPX7aXk6xW8eyUztnQ+gMj\nZGBwINy4uXpDOWs8LhYbTGzz+ygNtM+urxWEoKu5qGKu3ohfkXnDaYcBqDMw7CErCl+4XTh8EuO6\nUMijyQUfKmgzQ6vmnnEZbHR4eLOqvcCr9PojIgMC8EMHInB8mpnLclKQFQWtKOCPMdnraSGqSNGb\ndrv6FuG+V9vsIC1Kcwsp6EgCwinzsEeh7+qccGi5JhQp6NiHnpCDjsXUugqK7gtSUOcLUNDkw6gS\nKfH4kRR9RMFzTknGLSuUe/3cXVjNqnonCpBn0HB9XmorEdji9PBCWT0bHB6WpJk5NSORtY1NbHa5\nKWzy4ZJlbshLY7RBS70/wMO7ayny+PHKCj5ZwRaQUBUJzLUYufH4DOyv+7H3osTJ3pxCcW9Bx/kz\nlMlBizzIS9Vx85IMrn6tjCecdah1OiarjSRqek70Vm6zDchOfV/cN9ScjoQQfFbn4J0qGzubfMSr\nRe4clzEoEhREo8OMoO9R5PbxvaeJmToD+xlMzNMbqZICaAUBSYFyyU+dJJEkqthHF1xDC/2RJ9Do\nDiOWgQ4o8Pt43dHIeI2OU+PiOXFyYvcXNaPtpIpGqVKaU292TMkZCQqavKyotqMVBY5ONTPOGAyy\nfLfKxn9K6rjZks7ilKACGQvLQFtEI2x7Yh1ou4sRKQmJ1PQc7pqWRT0UIeiJoh8purIOhEM0xCAS\n60BHtH13PR3bLShy+3ipvIEvXQGuyTSzND10rExHnP9bCbvdfhI1Ko5JNXNaRkJrJW+AFdU2HikO\nZlPIM2hQA2ttbgKKQkABjQhmlYpbx6aTb9Ryy64qXJLMgUkmdKKAVhRJ0ag4JMnErD+Z+8Vlargt\nvnujZSAchjIpUFbejdsnc+XLpazd5cLr1pIk+5kap+eS7GR+3d2znOa9Ucp7u2bFkhCEmrddyYtf\nHW6+qXexosbOVJOOg5PjOCjJRJp24PZgu7O2DzfZNBgRasz87vLwr6Iaiuw+LktIjjhwv9Dv4+HG\nOhhxEwoilmTAI8s80liHH4X5eiOHG4MK12D2Q+wItyRz6qZiZscbuG1seusuRKzJQAsieTc9uXdX\nAcTdFfnqSkiHqxbcIgj7ghD0RNmPBt0Rg56Qga7QU6X5jzuqUft9PBehmVxqJsodU+X+6nDzeZ2T\nj2odrcdkRaHeL2FUiehEgVStmnp/AGdAJkunwSMrZOrU3DUug9H63rsT9hbDZeEdIQOhMRSJQYtc\nCEgKb6xvYs3/6viyPij/LspOwtTYs7S7PV0/hyoZ+E9xLe9W2xGAUzMSuDQnuUebfbFGNK63w0U+\nDTa0HTO/Oty8UdnIOpubdK2aAwQDY6LQYUfIQAfEOmagTgrwvbuJjT4PqSo14zVa0lVq5mQbmWDU\nkWfoXFirKyiKQqnXT6HbR4JaRa5OQ7JG1SOhGolAeq2ikWfL6nl+Sg5jjXveR1+RAehe6MaaDERy\nz2ju3dJWLAlBXxOAcAhFDAYLGXiq2smbxdXcZEkL6b8fCer9AU7eWIxRJXB4spnDUuK4q7CaQrcP\njSCQrVNT6w8mEohXi9xkSeeDWgcqAa4ZnTqoqn4OhwV3hAyEx1AmBF/t9HHIeC3fPWbn9cpGbe/Z\ndwAAIABJREFUvqh3ctaoRNIc0X/r4WAdiGTt9UgyL1c08lplIyenx3NJTgraPq7sHA36Iw5vBF2j\nZcy8WN7A8vIG0rUqTslIQKjfU506UsSSDMQ+ifAwQIpKzTEmM8eb4kkSVfzkcbPSZeeWHZVcuLWU\n0zYV85/iWnY01yjoClW+AH/fVcm5W0q5raCaq7dXcMqmYs7aUkJhk49oyFi4Sdn2uKIovFNtY0Gi\nsR0R6Gus3GbrU7IR7p6xOKfteS0TtWVBbFGi2yrYA6XkDwfMiNMzO97Ao8V11PgCPWojSa1ifoIB\nAYEsnZrJJh3Hp5nRCMF6ITV+iUtyknl8chZPTM5mboKR2/MzuHVsxqAiAtA/mZxGMHDoLekeCHQk\nMGONWm60pHFUShzvVtnw9fMG4kAFIXeFsmWeTnN3k8PNH38r5fXKRo5PMw86ItBTtDzriKyKHT6p\ndbC8vIET0+N5dfpoTs1IjJoIxBojloEI4FeUYOEHBMqlANt9XirFAD5Z4ekp2a1++h3hkmQu+K2U\nJknmguwkZpkN2CWZYrePp0rrcUoy441axhl1TDHpODbVHJG1oCu/+B0uL5f+XsYtY9M5NLn9DnF/\nKOtdCe5o798ERFKwPdw9e5v9IvsCfScLAXRtJegrovD2mz9G1XZH60Bby0AsFJSeLgyNARmPJHHx\n1jIytGoemZSFqQcl1at9Ae6xVrPR4eGRiaO4r6iGXL2G2/Iz2OTwMCveEFEV7sGCobz75vYrGDRD\n510PJIaSpaDu9TtJNgbnZtkyT+vacvXoVJT6oN5gkyQkFJJV3fvBDwfrQDisbnBxW2EVY/Ra/paX\nxgRTaJ1goNEYkElU924PeCjLqsGAsmUertpejluSeWpy0F22p+N7xE2oA/qaDISCV5F5orGeGUkG\nLshOYpJRh76DUvN5nYN7rDU8OjGL6eb2E6jE4+OHxiZ+tLsp9/qp8AaIU4loRQGdKDDTbGBOvIG5\n8YbWfOy/Oty8VWmjxh9ARGCySceBSSYmm4L3VhSFi7eWUeD28eG+eRg79Ke/du5jSQh6ev9YLR6R\nuA0NRvQlGYjFDtFmh4drdpRzakYCl+SETt/bHZY3m1mvHZPKA7tru6zoPRQwssjuPRgKpKCjnGhR\nYmp8AU5WxyMCj9vqaZSDGU4maHUs1BsxdBH8OBCEoK+JQKnHz8VbS5lo0nHvuEwMPdjcGIoYkVfR\no2yZh2KPjz9uKeXSnGTOyEzsla4SSzIwklq0h9AJIouNcfyv3sEP9S5UCORpNIzX6Jii1WEURcoD\nfpw+iZWFNgp1nV2KdAgswoiiVvhRctMgSQgyeBSFH+UmPq51IAL7mPXIwEaHhyydmnFGHQFZYUWN\nnRU1djSCQL5Ri6QoFLh9nDMqsRMR6E/EKq1bpQiZcs/u31u0PENLmkhl5d2t6UetqqWtynZ3pCDa\nHf3hjm1emUk6kUSNiFEl4pJ68IGboRcFFOCB3bVMMunYPzESO9IIYo1Ku0xm/N6hAMUK3RHywUAW\ntow/l2k7X2p37PyspP9n7zzDJKnKhn2fqq7u6tzTk+PubE7skpacQSUjYAIEcQ0oKiD4ib5geFEx\noIjAi4KAGQMYyCBBAZEcN8fenZx7Oseq+n50zzA7O6Fnpidu39c1F0t39al06tSTH67a2kqqFBQ/\n9OqZ3JwS2cQr8SgNqRSfchcNO+ZE3g3jMfJMReGPp3vCpA2D/11QPuMVgb71Nx8USiiPjT4j2jPd\nYSTgjBLnlIdWj0RBGZgAqy0qy8wWWtMpfKkU21MJnoiGeDoaZrVFpSWdQkbglkaOUxZCcLi6tyBj\nGAYdssa2VIKgpqMb8KW6Ys4scfXHIf6prZfNkQS1qsLuWJKEbnDt/FLeX7xvAulkTbqgrtGSTrNE\nMaPxXgLMcIv+wM9GO6YoApg+z9VAhQCgmmweQTZiqE8pyEUh6GO6FYPpjmH26wZxTedTG5uRBRzs\nyq0B2VB8pNyN05RRKM4tc8+qsKChmK0v10hy9nuXZxoD+59MF13de69r1etUuBdOKLLz21Y/F1k8\n1Jsy6/5HnW4eiQTZlEyQNAzMIzyLU6UQ5LoPzTB4PRhjezRBWNMpVmRskkRXSiOm6RxTZGel3TJs\nCG9DPEmlRcE9gX4MU4V/hIIc46HQq2DseBUTOvDwtiD2HEuITgUFZWCCmIVgnmJmnmLmBOz4NY1X\n4lHeTsRxSBIfc7qpU5QxjyuEoNxkotw04Bb1gLnsvQXpYxUeXglEKTLJfLram4/TyZkeLc0jkRCd\nmkbM0FGFRMLQucDpyTlUayyKwXQxsL/BWL0EQ1UdGk8X4okyuJLQRMhHiJBFEiyymWmKpwinx+8Z\nEEJweklu/QpmC7NVISgwOcwEpWAwl9V4eXFDlDZ7Gm9MZn0yTkDTWGux8XYizuvxGEdZR/bSTbZC\n0De2YRjsiafYE09ilSTWOFVimoEQ4M6G3z7SGeKWhi4AHLJEOOutlEXGuPXn9gCnlzhZaDPzUEcQ\nRRJcVVfCSkfmOV1stfDvngihtNYf0ru/UVi3Rmbge3OhzUw4qdFmTrPQPP0lrvsoKAN5pkiWOdXu\n5GSbA5nxNRIbicGL4C3hTMOlpw6pxyQEacPgB75Ojiuy428fX7WWXIgaBg3ZVtln2p00pVPsSaV4\nKhrmM64ipAFJMflM8J0uRvMSAJy3ZO8k43wJ+EOVCp3pOQsjIYTg+gVlXL+jjbub/ZxROrcE+gIF\n5gqDu5X3eQc+VuHm9629HOOxke6A34V6udDpYZVZ5blYhEWKmTLT5IkXwykEA981D3UE+V2rv7/M\nMNDva1aE4LIaL8cU2dkdz3Rx/X/zSzi9xEVc14lrBi6TRMowuK+1l9+29gKZkN2elMa129u4ZWkl\ni2wWjvbYuKu5h2d6wnywbPb0IyowNQxUBCKazvWb2jAhcM2wkLKCMjBJTFWZKDkNAV3jhnfaOUS1\n8ko8ylPRMNt64pzvmLyFyZsNfTpGtXOgxcqBFis7kgn+FA7wTjLOQZb3wj9mk9A/EoO9BPCeUtDn\nKehjoGIAYxPec+ksPJKCMJbOxNNFlUXBKcv91rkCBQoMT583ciZQvU7lwrs9BNM6j3YFsZslepMa\nvw36+ajTzY5UkudjET7knFzBuE/wH/x+iWg6dzZ183BniMPdVj5X7GSpzUJ3Ks07oTjFisyboRi3\nN3Zze2N3/+/aEhnjmSpJqFk5zSIEn6z2stZtQ5UEi2wWelJpvrSlha9tb+P3q2qps5pZ5VB5oD3A\naSVOLDMo9KPAzOHVQJTrN7cR0nU+7HRTmkMFrqlkZh1NgTHzQbuL34T8PB4NscJs4cVYFIBV5sl1\n2dkkiYWKmfXJOMdZbUhCsFAxs0Ax80w0wkLFjGuUXInZykhKAeyrGAwMJRpMrgL84PGGYrKVgHzV\nmTYMg63RBBvCcS6pGj7ZcH+k4GovMBtY+GkbV90rcUqxg69vb8Njl2mLpLg32MvBFpU3EjGej0U4\nVrWNq7nmWOhTCqKazu0NXTzWFSKuG3y6uogLKzz9+69RFdY4M0aqM0pdnFMaoyOZxquYcMgSC0fo\ny7PK8d5z6VVMfGtBOZdtbuaZnjBnlLr4bI2XK7a08Nf2IBdWeibxbAvMJpruiRFM6/yzJ8TPG3uw\nC8E5Lg81prGHjk82BWVgllOrKDglmZCu8ZPerv7PH46EqDEpk5qgcrhq475QL5uSCVZZVIQQnGFz\nclfQz1/DQY5WbcxXzCMmk81mBlqkzrn3vc+HUwyGEuKHE+BHivPPRTGYyfy+tZd7W/wI4MRZXAp0\nuinE6e5fTJd3YHCoUB9J3WBrJIFdlnCaJC5f6uXrW9vwyjJrLVaej0XYnUpymGpjmXly6+4H0xrX\nbGtldyzJqcVOzit3U28dOR57tXP8xQv6Gq95sknDBzhU6q0KW6OjNyItsH/wj5/28MPdnXRnw9Tm\no3CmyzljC10UlIE5wKVOD7cFMu5ORQiOVG28FIvyYCTIxxzuvOct9FFvUqiQTTwYCRLSdY602nDL\nMufYnTwcCfGXcAC7kDjP4WLeoKRiwzDo1XXMQsyojPrxMqS3IJtsDPt6C2BfQX4kBWC4RMKRFI18\nkg+vQPU6leaf9PLrFj/vL3bwmWovJebCEpQrQ92DgkJQYLqInAq335h571wzr4K1bhuPFYd4tjvC\npS4PRbLMa/EYj0RCLFLMkyoE3dfay+5Ykp8sqZyQkJ8rfTV54tp71XnWOK080RUiounjaqRYYO7w\nr9sCfG17G4ttFj5ZVcTOtgR1JmXSvWQTodB0bI7wVDTMK/Eon3d7KZZNrE/EeTAS5ESrnaOtk2d9\njek6D0VC7EglOUq1crzVjiQEumHQnE7zZDREu5bOWIcUC8WyzBuJGG8m4oR0DQGcYnPsU1oVoFWC\nyvEXm5lWBidNDxTYcrXujVQGdLgxBioFQykX4yktmq/woIpLLZx87W60VIp7V9ZMay+MmczAuTKW\naz9dSkFrUKey0Gdgypgq78A7GxpYs6puxDXj58928dMHOnCZJFbaVb5QW8zV21ppj6T4UDZn7Tch\nP8sUC2fYnf0NyfJd//+qrS2YhODHSyrzOu5wGIbBF7dkGrD9ZlUtVlnCF0uybmMTV9WVcILXzv81\ndtOeSLM+HOeCCg+frC6adqvw5oTO8jz1GRhMwSiRWa81w+Dyzc30pnV+vbKGf24PTdr+Ch2IB1FQ\nBiCka9ze28MhFivvt2dCTx6NhHg7EeMzLu+kVnZIGgZPR8O8mYhxvsNNlWzCLkmYhCBlGDwVDfNO\nIo42oGfAIsXMzlQSA7ALiSs9xZPmwZhOhnrp5XPRHItgMJ2KAMBf2nr5eVMPP1qcsSIWyD/V69Qh\n71nhRT13mCplINf14uWdEX50ZzsbwnHcJonzy9w86w/jCya5wlPMu8k4j0fCyAIucnr4wqqSvB/r\nl7PKwE1TpAwAbI8muGxTMx+pcPO5bBf1c97ezanFTjTD4K8dQZK6QVcqTZVF4bM1Xi6omNv5BPvz\nOtN8b5wd0QQ37e5kWzTJdxeV09U2eRUdodCBuMAQOCWZg7KJWwepKqWyiffZHGxNJvhnNMzHnJPX\nlMmcDU16MxHjlXiUpnSKWpPCxU4PihCcbndyis1BQypJu6axSDHzdCzcX+ItYui8EItSbTJRLJvw\nSBJCCGLA5Dt8J5fBlS4GhhENZLyL6GTGEedTEWhPpPhVi5+jvfaCIjCJDHfPRgonGvib8czDeMpA\nVeaeIr+/0qcE9EZ1PLbRrchHLLRz/w/qefK2TAjgvS1+jnLb2K2kqKo0I7cJVCHx13CA1dWTs6JX\nmE38pzdKXNdRpyjsdLHNwnllLu5vC7DKrnKUx9YfIrTcbiFpGFRbFF7wR9gYSVA2A0Iig5o+qSUt\n99ewxeZ74yR1g2/vbCdlwHcWTr4ikG+mf3YWyBvHW+1sSSZ4LBLiEqcHsxCcYnPwUCTIs9FIv8dg\nMrAKgYKgKdt7oDGdolfX8GbLZ5mFYJHZQrWuc384SEM6U9v5BKudN+NxXohHBowlUWkyYbao1OsG\nJbKMBoR1nYiuo2OgAzoQ1XVC2T+zEJxtd+KWZ24Vo6GUA5hYOMhkNCYa6XiGsz4PR0o3+M6uDkxC\ncFxppnrQf/wROpJpzilzIc9Bj9BMZKhuoYPv41g7ijbfG2dPSueoywoK3lSRbwPAcB6At5rTnLg4\nN0+7LAlOv7KI1feoXLG1lUBao9xs4p6WHn6+vJoD4yme2hjm1oYuetMaZ5Y4h4yfHmre5bLWiGyP\nnaluWL+u2suWSIJv7mznIxVuNCPTVGqt28Zat43t0QTzVIWXAlGOnAFGkI1JgyMn2cK2vykEffPz\n9WCU5kSasy1Ouiexx9NkUVAG5hBWSeIUm4N/RIK8mYhziGpltUXFl0ryWiLG+2z2SUtgUSWJ0+1O\nHowE+z8b3GshoGncFwoQ0DXOd7j5WzhIRNf5nLuIuGHQq2t0ahrN6RQdWprWdIot8dg++xKAQCBl\nz9kpSbgkiYZ0it+FernY6ZnRCsFAhlMORmI4gW0kpWAsIUKjKQID/5vLi/oXTd1sjCS4cVE5mGQe\n7Aj2d/xsTqT4Ul3+wwYKDE8u92y0F/pElYgC08t4QgZzoeZTVrzXyeyOJbmqroSv72jjoY4g55W7\nuWVpFb9u6eHmPV1ENJ2P5RgyM5rxIa7p/KsnzKklTtQpzkOyyRLfW1zBJzc28ee2ALWqwhFZof+Z\nnjDf3dWBKgkeOnA+ilQwesw1Bs7LPfEU4aRGhW12itWz86gLDMtKs4UtKQtPRsOUyibqFIXF2X4A\nz8ejHG6xTpobdaXZwpsJhcasd2BTMoEJgSTAhOC5WISEYXCJy0OVSWGl2cKr8RjLzRYqTQpWSaLS\npLDakhEodkmCctVOt6ZhEuCQJBxCGja3oCOd5rehXp6OhSe14dpMYTiBbbpzA/oIpzXuaOrh8a4Q\nF1d6ONJj59ddUX7T0MVal5XXgrFC07EZzHjmxP5mFZwuxuMNnCwFYDDLj7Hy/MMRftGUqTR0W2M3\nlRYTR3rs/HhJJTft6eKuph40w+CCCk//ej7eebMhEieuG9NWpthtkrlvVS27YkkW2Mz9ns63gxlD\nVlw3+E9vhBO9M78ZZIHcGbg+PtUd4p7mHhYoZmyztDri7DzqAsMihOBsuwsdg7+FA+iGwTKzheVm\nCy/EItwT9NOjTY4LSxKCT7iKuMSZCQV5Khrm8WiIRyMhHowESRkGFzszigDA+20O7JLEvcFe7gv1\n8q9omNasItGHXZKoUxSqTAouSR4xybjMZGKtxcqWZJIHw0Ha07PPVTdWJiLEN98b7/8bz/5G+t1r\ngSjrNjbxVHeIT1cXcWlVEQld56HOAIttZo72ZF7cJxT6DMw5JkOxLDA0xoPfG1XIz2WbfHL1qaXc\ntKSC1IDiJNfvaOe1QBQhBFfVlXB6iZO7m/18c2c7SX302J6RFIWmeOadsdA2ub0MRkKVJVY41L0M\nbZdUFVFtydhbG+Kp4X5aYBYycI17LRDlB75OinWZs+zOaTyqiVHwDMxBzEJQIZvQoV94Pt/hpjWd\n4k+hAI9GwlzsmryqBnWKwmdcXlIYFEsyBpAyDKyStFcDMpsk8Vl3ES/GojSmU7wSj/FiPMpHHG6W\njLNJzRFWK3FDZ2MywaZkgpNsdg6zWGd0fd+JMlZr7ESFtZF+35FMc0djN8/5Iyy2mfnhksr+5j+v\n9kbpTulcP7+YX7X0sNRmoW6UxkAFZicTTUouMDamUtgfDSEEh7ps3LOihh/s7uQ5fwQduGFXB388\noBaHSeYr80tZ4bDw491d/MDXwfULykYdd7hwoahmIADbFIbhBFIajYkUnck0vWkNpyyhSILldrU/\nUbjUbOLT1V5ub+xmmX36FJWpZq4/7wPnoGYY3NrQjU0TfNjl3ic0ejZRUAbmKEWyzI5kkvWJOAdk\nw24qTQon2Ow8GgnhSyWpn8QyrOU5ljK1CImTbBn3adIwuC/Uy4OREB8SAiy5LaCD4+wtWySOt9p5\nIhrmqWiYxnSK8+yuGVu6NKrrPBIJkcago0ljXXXRmJNqc43ZzqfVNpTW0AzYGImzPhynK6nxYm8E\nAXyhtphzByUHu00Zq1ljPMWGcIIr64rzdiwFZi6D59xcFxYKvCe4f3NBGVdubWFDONFf+KGP00tc\npHX4aUMX5c093ED1uPYV1DRssjQlBp+3gjF+3+rnzdDQ66gADnKqXFFXgtMkcbDLygNr5k36cc1U\n5lIu0VDvzo3hOFtCcc5zuGa1IgBjVAbq6+sV4ErgEMANdAF/9Pl8j2e/twFXA0cCSeDvPp/vtwN+\nvxT4OmAGbvb5fK9nP78U+ARwq8/n+/uA7e8GHvD5fE+M9wT3V06zOXnQCPJgJEhzOsX7bQ4kIVht\nVvl3NMKGRHxSlYHxYBaC8x0ubu3t4Q+hXs5SShgpkm24ZNu+zz9iePjoaw1sSSbYZk6ybJzehsmi\nLZ3CKiSa0im2pTJt7O9r68UuS1xYOT7PzXBeglyUgIFdlEdjcyTO9Tva6cm2WnfIEiVmmRO9DtZV\nFQ3ZWXiJzYJTlvhZQxc2WXByIYZ2v2Qq8wrWN8Z4elOIL39gdMtzgfwjCcHnaor5W0eACyo8uAbl\nCJ1d5qIlkeJPbQHO3VPEQfNGrrgzlHegNZGmzDz5uUevBKJ8bXvbkN+dVeqkyqIQSuv8szvEpRub\nALBIgs/WeDm31DWnvdMwepWyuaAQDOaVQCYvpESa/Xb1sZ6BDHQD1wCtwHLgh/X19Z1Zwf5KwAV8\nFPAAP6mvr2/z+Xz/zP7+M8A3gAjwPeD1AWMHgUvq6+uf8Pl8+5aQKTAmbJLERx1uXohFeSEeocak\nsMqiIguBLMSMtZI7hEStSaFNS6EKachScbl2rxRCYLYKHAkZrzT9iao9WpqWdJpOTaNDS7M9qwD0\n8SV3MQ3WNH9o83NemWvclTHGa/0/Z5mbzZE4v2jqxmuSWe1UWWyzIAtBRNNpjqd4Jxzjqe4w26NJ\nVElQbTFxVqmLD5e7R51TqixxdqmTPzbE+Gi5B2cheXi/ZaqEg1/+u5unN4UossmsXWAjGNNZWa3i\nshbm3mQycA1a6VBZ6Rj+Xq+r9vJYV4g/vuwfVRmAfRWC+arCi70ROpNpSidQyz+m6bQn01gkQSit\n4zJJVFgy+W3/6glzw66O/m0X28x8vb6M//ZGuLvZz8OdmS6zTlniAKeKJ5mmTDGhSILbGrp5IxDj\nq/Wl+03BhMH3aC4qAg91BLmvrZeDLNZJbeo6VYzpDHw+Xxz41YCPNtXX178FHFBfX78eOAn4os/n\nCwPh+vr6vwNnAH3KgDTobyDrASfwYeC3FJgwkhAcZ7WxIRlnUzLBqmy4kFuS6JqkJOKJYBgGz8Yi\nNKSTnGl3Uj9IERhPC/tvLijnq9tbeVoP8wHNQbFsokdL82Yizq5Ukg853HinoAxpWNe5I9ADZCor\n2bKVkQ5RrXRrGkvNFi5Z6eXNYIxnesJsjyU5YIQXaL4JpTV+nq38o0qChG5gkGnmc2qJk1+3+Pu3\nXeNU+cq8Ek70OrCNUWG5uNjKCfZqFtpmlleqwPipNo3PsDAVIQTfPreCQ+qtnLHGzdf+0sJ/d0So\n8Sq4VJmfXFBNXbEy5y224+XweVMj4JglwTllLv70doBLjylmRfXo82FgaeMzS13c3x7gVy1+vjq/\ndEz7bk+keKQrxEu9UXbFknvZnkwC7lxewwKbmRV2C0e6bXgUmdNLnKzKrs31VjMXVRYR0XT2xJL8\nrSPIK4EoYU1nB5kutIe5rdzW0M0nNzZxlNvG0R4bR7ht0zLvDrJM3T7nogIwkHfDcWosCqeZ5oaH\ne0JPe319vZmMd+AZoC473o4Bm+wALhrw//cC381ud+sQQ94F/Ki+vv4hn8/XO5FjK5Chb8FpHFCl\nZ7nZwj+jYTYl4qywzIwHNmUYvJuI81I8ymGqjdVmtV9bHI8S0McSu4Vbl1bxlW2t/DLgp0iW6dTS\nyAg0DHamEnjlyW8Go2dfMydbHRyuWvexoved40KrGbMQPNEV2kcZGNiTYCLXZDBvBmN819dBKK2x\nrqqIj1Z4SOg6Z7+9B7MkeKQzyIFOlQsqPCywmocMAcoVqyyzxL5/WMf2Fyba2XwylQKvw8Qnjsnk\nplx9ahk80cGm5hhv7Ylx3I3b+dRxxXzzgxV53+9cwGaeumKDfZWHjDF2Datep1INnPRNB/8NRNEN\nI2evdzitcdnmZkJpnSPcNk4udlCnKqR0gw3hOH/tCNKcSLHAZqbconDj4uHniT1bTWiFQ0UzDNqT\nab62vY2f7OniCLeNddVFvBKI8WhXiEe7QtSqChdXejjJ65jSpotT1Z15fyBtGPSmNVpFmupshcTZ\nzLjf6vX19QL4f0AT8DxwABD3+XzagM3CQL+k5fP5NgAXDzemz+fbWF9f/ybwceD28R5bgb0xCUHl\nAOv3oRYrW5NJHomG6NQ1jlVt0xo2lDQMfhnowa9rVMoK77NmmqPNn+9gjTpxwbHOaub25dU82BHk\n+dYwq80qaywq9wT9tE+BhySgaTwaDSEhmK8oI15rtyJzQaWH37T4WWG3cEapC9i3OVm+FINtkQTX\n7WijVlW4ZUllf3UfsyRzRomTv7RnytN+sMzFYXnooPlOXMvLPS0wc2hN61SaJi5kjBbeNlFlYUW1\nyt2fqiOtGTz6ToBr/9zMH17qocQp8/GjvIXQoUG81pBibd3YhZyxhilqhsGf2wKY3IKV1eNrj3vu\nRUU8+rMQdzR284Xa4pys7lujSQJpnR8trmDtgLWtJ5XmZw3d1FgUDnKOfc7JQlBlUbhxUQX/19jN\n412ZEKLr6kv5eKWHOxq78cWS3Ojr5O8dQW5fVkVU07HKErIQxLWM6UiVRN69B4X1N398tsbL17e3\ncX8owOWe4r0qJc5GxqUMZBWBq4Ba4Bqfz2fU19fHAEt9fb08QCGwA9ExDn83cGd9ff0DYz2uZklg\nDBO2YDIM5ukGGrB7lNCGcl3HYUC7JAiPcIMthkGNbpAEGkcZs1rTUYEWSRAbYUy7YVChG0SB1lHG\nnKfpmIAGSZAaYUyH2Uw0mSQooDNrGTjI7UEk4vw3naQllWCtmim/uVDL1HvYJUsj2miKdR2PAT1C\n4B+hpJuMwXwtU0XCN8z5NKZTBM0KR8hWDlMsdMoSISHoiuuEhzgIhxCsUSWiusFbCX3fDQaw2iLh\nlAQdhsSKYjcrit280hSlFZBUlYAks1OWJuW6G+kUmyIRNmhpZEXhSLuDqGJm56DtDq9570X0ckxj\nkcfJirjOTa0h/uSP8+FyDzuzx+TVdYoM8AtBT/a637w9tNcYkHmwD7fK6IbBS/Ghr1FU0/lZQw8e\ns5mbllTQqQtejGlE0zr3tfeyI5pEM1uQBNzTGaXa6eAEh0JMN3hzlOt+gFnCJQs2JnT+uhDpAAAg\nAElEQVR6B9QR35LY+54WSYIVFomAZrAhOfKYh1gkVEnwVlwjOsLkrJAFC80SHWmD7amRxzxKzVQh\neTmmoY2w3TyToEaRaErp7EkPv3MZOMIqYxgG/x3muvexWJEoMwl2JnXatOHHtAo4WJVJ6Aavj3Ld\nV5olPLJgc0KnZ4T67R5JsNIiEdQM1o9y3Q+2SFglwdtxnYix75gR3SBpgEMSFMuCqG7QOcL5ANSZ\nMoJOU0of8bp7JIFbFgQ1gz13Rik/f+giALKA+cUZIWdn10gjQplD4pyDPQTjcOMjHdz2jJ8H3ghz\ny4XV2AcISmYZaotk0rrBnp6Rr1GlS8JmFrQFdSLJEe6lIqhySyTSBk29I49ZWyRhlgXNvTrxEeac\nSxWUOiTCCYP20Mhj1hdnmjbu6dFIj7BpkU3QGtB5szGNf5iHTRIwv1hinlfC162zu1un++nkkNt6\nJUGNIkga0JQyCA+aR3VuB01SmkfWx1ldo1DulNjTo9PoH/4gK1yC+mKZ3piBYrZwxvFlPPBSAEWN\nsmqAEG8VUGOSeH5HEL8kCAnB4TU2Xo+mMcwWOiUTL8Yyc6ZSFrzQFcJvwGfmlfNOCkjtO58cInM+\nFgGNKWOYZ01iVbGbl8JJ0NJ8t6EHZBMgQMnM415J4X+bgjznD4OhQzq7L5NMmcXMEruF4zx27AOU\nbQHUmAQ1JkFj2qBphLkx+Lq/Psw71QTUKoJSWdCUNmgZYcxSObPvsA5NaZ1YdtPirXv3UrAqmWfS\nYxXs7tFoDQw/Zo1HYn6xREdIZ3ePTnIYG53DIphfLGFTMmN2hIYfc36xxHyvRKNfx9etM9xy6LFm\nxpQE7O7R6YkMvWFPTKPGJKg1CRrSBk26xNHlXrYme3hXGBQNMLjaDIMi3UATmXd1Yhg5QSKznTP7\nTg+MIEs5smMmsmOmhKBFy58CMmZlIKsIXEkmPOgan88XyX7VAGjAQmBb9rNFwK6xjO/z+fbU19c/\nA3xyrMdWrRss0EZeDGXoF3hHo1w3KM/BbWkew5hVusGQWbGDsI1hzLpRxmxH4l9aGpOms3DAZqvM\nKq/oOk+Fg6wy6C9BCox6HfvwGgbeUV78kEkQGep8DMPAF4uhxeOsdRfjkmQuXpKxhr8U0zhyBGud\nTRIcnaM1b5XlvcX06MVOHtwSwJRIYJdNLDS/d975uu6NqRT3hXqRheBIi5XDVCt2SYJBYw+26h+R\nPZ9D5xfxnZ0d/DcQxuRVWTjoGhcZBkUDPuvaEx7SQyCJ4a/Rw50RItEYt62swW2ScZN5YP9neydt\noRhXVXs52GXlq9taiaTTLM0aCa1juO4rLXsrVhIMeU/dcu5jHpSjZavMJCjLMWHviBz3XaNI1ORg\nLBUjXPfBLDRLLMxhO8sYrvtyS26WetcYrvuB6tBj7knpzFPe+84mCeblWPO9Rsn9OF2ygIdSo3oI\nFpbkdj5uVSCMjPDV5tdoDyR4X82+TYNMksh5zApXbudjMeU+ZrUntzEdFoHDktuY87yjb+dUBQfX\n5iYiLCyRUR9KQQ5zqVjed27U1Xr43OZm7nqmhWvPKKPWY2dJmcySstHHs5kzCtYR84vZ/Govb3T5\nWVdSjTJoDn5omZsHtwT4zDIXTfEUd3f3Ui/DaY6980Z+E0siNI1jbPI+1Y+GYvkIcfgHKGYSZQ48\niozHlOm505vKlEKtURUW28z8aHcnIpkpJnFikZ2jPDYCaZ0XeiO82N7Di+09HOBQ+f7iCuyDDFXz\nFMG8HJ03xbIgahgjvlMB6hVBfQ5j2iT2Xl9fzDxLQz2fKypMrMghGq+2SKa2KLc5fEBVbnNzfrHc\nbygYjSLb8M9a84vvKYV91z0RM9DiceotNooZQm4wMgbeXGS+EsOgJAdZymyAMzumyKFhX66Mx7d7\nJZmQoK/4fL5Q34c+ny8BPAusq6+vt9fX19cA5wGPjmMfvwaOAwr14PLAErMZA9iY3Nd9e5jFSrFk\nYv2A7/IZjz4SAU3jt6FeXo5nnEcaxpTtGzKVi3r1ka2I46E1neKP4V6KZJnL3V5OtNkzisAgRjpX\nVZK4bkEZZWYTf2sPog1hkR3M4FCi0ehOaQhAHzR2VyrNIS4r55a5eC0QpSulcd2CMiotE4+LNAyD\nVwNRnvOH6Uml2R1LYuRwbgUK9JGvXhmr6zJCiyzBV04r46TlcyMRcDqY6D2pVc3cuKiCSIPGJ3/a\nwOW/aSI+ildvMEIILqvxsiOaZN3GJm7Z08W2SALdMNgZTfCcP0y7I80XNjdzyYZG/CmNL9WW7KUI\nbAjH2RKJU6sqOMdZzW0ge+Ip6q2ZBOSj3DZO9jo4v9zNaSVO2hMp7mzqYZVDpUjJCKvHe+2cUuzk\nzFIn/7uwnC9le7GsD8e5cH0DDfEkPak0T3WHeM4f5t1QLKd3w1Qy1q72s4HhzmdXSwKvJNOl5V+O\nmGrG2megHDgHSAF/rq+v7/vqKZ/PdzPwMzJlR+8HEmT6DPxzqLFGwufzddbX1/8D+NhYf1tgX4ol\nmTqTmSejYTo1jffZHP0NMoQQHKyqPBUNszOV5OoDMtUY+gTVsQqYuWAYBmFD55lYhDYtzZl2J9Um\nhU+unLomVOcsc/P8WxFejEVJGUbeGoY0plL8JRzAISQudHqwDZOwlYvSY5Mlrp1fyjXbWglXGbiD\nox/jg1sCOStU55e5eKIrxA27OrhzeXV/KdNSxcSLvVFOfsMHwGFuK0fmIV8AYEcsya98e9fq/vny\nKpbZZ0Yie4HZQT6SjueXWHj8moUosqCqaPYnAE4X+RL81jit/GplDY92hvjZq12c9e5OPni6h0Pq\nbayoyq0c7Llf9pL4icFT3SGe6QnzZHcIqyzhz4b6qJJghd3CZ6q9nF7ixK28N6ZhGHxpSwsAF1Q4\nJhyv/3R3iO/5Ovf67LM1Xi6oyPSQuauph85BIUjf3tnBVXU6v27x05ve+7tgWmd7JMnj3SHeCL5X\nff0DxQ6+Vj/z7KZzqeHYcHhkmR5dmxSj4lQz1tKi7cCJI3wfBb4z1oPw+Xy/HuKzO4E7xzpWgX0R\nQnCR081/4lH+E4tgExIn2Oz93x9qsfJOIs5D4SAnhRwc6HwvieucrGs1X+iGwV/DQbZma+yvNqt8\na830VPO4YKGH5zdEaEunqVUmJgzohsFbiTj/jIbxyjIXON04JqAI9HGwy8pRbhvP+yOcJe8bwjAU\nY7lf1y8o48otLdzR1M3V8zKK4DcWlPFvf4SQplFpVjjak78yeG8FYxQpMj9eXMnNezrZGEkQy8E1\nWqDAUAwniOYqgMwrKZS4nQj5tgDLQnB2mYsys4kHOgLc9rdOdKCsysSPL6jmiIX2Ucc4tsjOsUV2\nAmmNH/g6cZskjvLYqbcqVFqUYatfDVzjPlI+vqaPAykfwpN6V1MPHyl3I4tMM7I7mnoQZHoc2GSJ\n7pTGLQ1dACy1WVjusLDKoeKLJbFKgpO8dp7tCe81ZrEys2vcz/aGYyPNcRkQCNIzzDszHmb2LCqQ\nN2QhON5qJ6BpvJqIcrhqxZoVVmUh+JDDxf3hIN/a2c7f1szbq9xZPhSCqK6zKZkgpGtsTSVYZVZZ\nt9DL4XmyOI+H5VlrdLOWmpAyENZ1/h4OsiedZJli4SyHE4uYuCLQxwqHhZeao3zgYCdPbstE5hmG\ngV/X8MoTe4RXOVQ+UVXEr1r8HOaycUyRHVWWOLUkN8VjrDTGUxzqsrLAZuaGReV87N1G/huIcpBr\nfFVEChQYitkugMwGJjMU5AiPjSM8NsJpjc2RBLc3drPu7gZOWObg4qO9HLFwdAOF2yTz/RHKgQ6F\nKgmOL7LjUXKLMR+JAxwqf18zj8e7QjQmUhzgUKlVlf536ynFTk4p3nudbYqn+IGvgw+Wufb5ro+v\n15fSmm2OpgiRl/DNyWa2eQlyndu9uoaBgWcGNDWdKAVlYD/jGKuN9ckEbyZiHG19z8rilU283+bg\nD6Feftncw+dqJh6yE9N13k3G2ZFK0pRO9deSrpQVbj2wato70NpliZUuleZ4avSNh8EwDF6JR2lI\nJznX7mKF2TLkS2oiuRBtiTQuk4RZCM5Z5uZPm/w8EgmxNZXgfIeb5eahK6zkykWVHl4LxvjR7k5c\nJonVzskRzOOaTljT8WRrMnsVE9WqiZYJXP8CBYZitggds5GpjAd3mGTWum3c47LyYEeQP73Zy7+3\nhKnxKhw0z8aJyx28b6UTOZssPJFjSxsGcd2YUBfjwXiypaJzpUZVuH159YjbOEwyi2dpJ+OZrhSM\ndf68kYgjI6jZn/sMFJideGUT80wKW5PJvZQBgHrFzCqzyv1NgX2UgbF6BwzD4A+hXtq0TEOOA80q\na1UbJgEXLS/Kqa9B6RBVJ/LNcruFN/Wxv0A60mn2pFO8k4jRpqWplBVWDtPAbSKKgGEYvBqMsdSe\nEfj/3RPmL6kgLakULknmX9EISxXzuPpE9B2XLATXLyjja9tbuWprKxdUePh0dVHea1xfu72NWCqN\nVco05vlTWy+7YynOLZ26pPEC+ceRY+WgqWKmChqzjcpBlZGmMynUJATnl7v5YJmLl3qjvFIZ4w1f\nlIffCrCg1MxZB7lZvEFhic1MRNN5uDPEq8EoupHxMnyg2IF3hHAawzD4n+2ZXKYKs4mGeJJqi0Jr\nIs07oRjFiomIrnOQUx1xnJnOVLxTcyHXuTSVz/J45ndQ1/DKMm55dipnA5m9s7rAuFlqtvBkNMQL\nsQjHqHu7WxUhiOn6mBJRB9MXP9+mpTnD7uQgy945CLmyaAo6YB7otPJEd5htJFiSo4W9JZ3iV0E/\nBlBjUjjb7mLlML/NR3UkWcCrgRinvrmblGGwyqFy85JKfr/Nz4ORIL26jneMi9Hg4yo3m7hzeQ13\nNnVzX1svFRYTZ2UbnuWLLZEEkmFwoNPKv/0R7m72c7LXwfuLC1VcZjNDlYqcLgqKQP5YVv6eeDBT\nqsPIQnBMkZ1j4naMEoOXlSgPO8Lc/nQnCb9BmdlEMK2R0A1WO1V04JdNPdzX2stX5pdwfNG+a031\nOpXdXQle+2YmKffHezIx+4J9C0IK4MPlbi6tKsKah2pDU81UvFPzyVR4EsY7t9OGQVs6jXsOhAhB\nQRnYbxhYHegQi0qnlua5WITGdIqz7E6c2Qn9biKOll0Cx6IQaIZBUzrFjlSSLckEfl3DI8ksMI0/\nQa8zrVOah86mI3FKsYOHOoO8nIxRqyv9eRQj8XYijkVIfMZVNKJFIB+KgBCC366q5ZVAlK2RBDWq\nwileB5IQfGSRh2c2R1hZo9LamnuozXDHZZYEX6wtpiulcVtDN0d7bHm1gh3oUulIGRzksnJ7QxdO\nWeK6+tK8eyAKTC0R3cA+A7wDBUUgv7QGdSpd0oxRBAYjhOBIj50jsROdr/NSUZSXeqOUmWVOLXFS\np2bePW2JFD/c3cn/7uzgK/N1Ti/JGDkGzpf5JRZ+9rka/v56L2vqrKhvQnqtwGOTWVtvIxjXMMsS\nf7i7m7+0B3isK8Qdy6uoVWdXAvpUvFMng4FzMJ/P+Xjntm4Y3NPcQ8oEx1qmL+8xnxSUgf2MPkFQ\nbM5YtZ+Ihrgz4Ocsu5OlZgsHW1ReS8QI6houSe5XCEYKEVqfiPNkNEzc0FEQ1CtmTrE5WDwofGWs\nwvGOlEHpJM9QWQiunV/Kpzc10+hOsyQ++uIeM3RckjTpikAfJiE42mPnaM/eYV01aiZO0RdL8tFl\n78WlTiTZWwjBuqoinvdHeCsU52Rv/qz2x3ns/Kg1xImv76JEkUkZBlHdIJxOD1l5o8DsoEubGcpA\ngfyypT2N/sDYav1PFzZZ4mSvY8j1qsKi8MPFlXxnVzs37e5COVLmE8d499nutNUuTlud9YaeMvR+\nvvetatbcrHL9jnZeC8RmnTIwFe/UyWawAD+ccjCZSuyPdnfyZHeYU7wO5iVn1xwYjlk+LQrkwlCC\n6QeXexBbAswzKTwYCfJAOMC5DheHqTZeS8TYmUr2h/cMJVy2pVNsTyXxaxrvJuMsUMwcrtqYZxq6\ndNtUNhMbK3VWM+eUuXikM8iVB87n8W3BEbcvkUxsTybRDWPIWP2pOlebLLHYZuatUIyPVrynDAzc\n/8B7l+tx1akKHpPMO6FYXpWB00uc/LIzQiCZ+f+4bnDmW7sBWGG38KFyN8cV2feqZFWgQK6M9PIv\neA3GTvc/c+soPBswS4JvLyznpt2d3PjHdv71WIhji+yUKDIHOq37dCuG4efMio/Y4EZQZ1B43P7M\ndHiujvTYeLI7THAONBvro6AM7Oe4ZZkLnB7uDwf4ezjERxwuSmUT7ybie8X6R3SdNi1NXNfZk07x\ndiKOjoGC4DirnWNU27BJrDNZEejjMJeVB9oDbI8mRm245pVl0hiEdH3aE4cOcVn5R0eQQFrDbZIJ\npTVeCkRJGwYHO63juvZCCNY4Vd4O5XeRFUKw2qnyaDBMTNNRZYEgM2c2RRLcsKuDSyo9fLJ6X6td\ngQIToVBqtECfF3i5XeU3rX5eCkQBKFFk3l/s5Ai3jVUOC9ujyUwzsmHmzBPrQ6iS4Cj36D0PCsxN\nji9ycEWdxq0N3XxiWRHbmxLTfUgTpqAMzHFGEgb7wn8UIfiQw80fQ738NRxEkOmsB5nYuI3JBI9F\nQ/2lQVUhcZBF5QSrHVWIORHzvSRbrWdbNMFKR+YFMFx4lD2bVxA1dNzsrQxMteJzWomTf3QE+eme\nLr69sJy/dwT5VYsfgFOLHVw7zs6Ui2xmnvNHhvV+jJdN4TimbJL6KoeV7pS+V6fNlwNRLq3KfyWj\nAgUKCkHuzNQ8gYkihOCcMhdnljoJpDV2x1L8usXPXzsC3NfWi12WiGiZ0Kg/HVAL9+7rIdjVkWCh\n1ZyXXgQFZi+LbRmZIaTNjlC60Zh9mSQFJgWzEHzU4aZMNpHCoFNLc1egh+/7O3kwEqRIkvmEs4gv\nuIv5sqeY0+xOrJI0qtA2G7wCkGlQU2428eaANu+QOf7B59CWTgNgHdRYbDrOtU41c6TbRkM8E3vT\nk9IoUmQk4Dl/hE9vbOKxriDaGDskRjQduyzlVREAOLvUjV2W8ComPIqJu1ZU84mqIsrMJnTD4L+9\nUa7b0c5tDV3E9bmxyBaYOYxFyJ1Mgbj53nj/30xjJh5TvpGFwKuYONhl5dZlVTx84Hyury/jAwMq\nmz3alWnwOPh6SGLyBMAd0QQPdwYJpudO+MlcxZINLUvpxqyRc0aioAwU6EeVJD7u8qBmhVyvLHOE\nauN0m5N1riJqFYUiWR42pjum6/wzEuaRSJCkMfsekLNLXfynN8qWyL4vw75z0Q2D1xIxFiuWfu/J\nwO+ng5Cm48o2oWlJpChRZC6pKuJkrwObLHHT7i6+sq0VYwwKQTCt456EqhOL7RYuzDbhqTCbiGk6\nl1YV8ZuVNXyo3I3DJPFSIMrfOoI8ln0ZFyiQT0YTdqdaSJ9JwvdMOpapRJEEJxc7+FJdCQ8eOI+V\ndgu/a+3lsa5988febYrjnISyov/xR/jilhZu3pNZrzeE43xqYxPvhmKj/7jAlLPAakaVBA90BNDH\naGybiRSUgTnMQAG1ep3a/zfcNgAvxqLEjYzV40MONyfbHBysWodMCu5Ip3ktHuX5WITfB3u5NdDN\nq4kobyfirKidfe7488pdlJllbt7TRVLf9+E+Z5mbpGEQ0jUWKea9Pp9OqiwmtkQSbI8meCMY42iP\nnU9UFXHN/FJ+trSSq+pKeDsU5/Vgbi+V3pTGpkgcTx67XPamtH4la11VEd9dVM7TPWE+sbGJT21s\n4paGLj5X42WJ7b1+DQ91jJzIXaDAeBlO4B/82WQJxwPX4ZkSurS/KgKDcZlkfrq0iqPcNn66p4tn\nesL91yYc1+gKpXn/aRNf85O6wfpwnG2RBN/Y0cY3draTyL53zELwzZ3t7IoluWl3F3c399CSKHRq\nn0nIQvDxSg/vhOL8z442Vtap0y4LTISCMjBHGWlSjqQQrLGouLI9B7Yk30uKies6Hek0W5MJHg4H\nudnfxV3BHp6MhnkhFiVhGBxqsXKUmqm5m8+W7lOFKkl8ZV4p26NJHh3CIgRw+pJM6bk+1WgmPPxr\nXTYSusHvWvzosFcTLyEEZ5Y6qbaY+GVzz5BKzkBeDUS5bHMzbYk0l1QV5e0YP76hkc9vbunPQTja\nY6csO0c2R+L8tT3AyW/4eDWb1AewJ54ikCq4ywtMHgPDdaZDGJ4pikCBvVEkwXULylhqs/DdXR38\nuycMwIbmzBxxW6Vx37twWuN7uzo49U0fV2xp4bLNzbwRjHFFXTGOrMdhYySBP7v2JQ2D+9sCfH5z\nM5uH8FoPRDMMIpqOP6XRnUzTFEvyeFeI1wasqwXyx4UVHj5c7mZLJMEXNrfwt44AZy91zQi5YKzM\nPomtwKgMnohDLVrV69S9Xn59ybJeWeaLbi/3Bv08HAmxK5WkJZ2iTUv3b2sTEsvMFipNJpYplr2S\niF+KRXGYZWx5qDt+uGXqk0jXum2scqg83hXi3LJ9H+gd0WT/v2fKA/9Cb4RiRaYlkeYAh0rloJr9\nshBcWVfCtdvb+MSGRpbbLVxaXdTflAegKZ7iZw1dvB6MscBq5oeLK5hvzU/95LeCsf6kvHI9Td+y\n41VkGuMpulMaTllCFoKBkbinlTixz8IGOfsbdaa5new9WYnHQ405XUnOQylC07H+ziRsssSna7x8\neWsrT3SHOOFeB6svslLpUXhyfYgLjijqv1e5KpKGYXDNtlZ8sRSnFDvwKjKr7CrL7BZKzCbiusFd\nTT3Uqgqfqi7icJcNVZZoT6b5/OZmLt/cwnK7hVBax5/WWOuycvW8EmK6wWc3NRFI753L0JlMU2KW\nEQieOaR+v7+n+UYIweW1xVxc6eFHuzu5raGbWovCWrdtRPlgIr2AJov9UhkY7ibNxBs0VnJRBAZ+\nN5RCIAnBx5we/hEOsiOVpESWOdnsoESWcUkypbI8bGJpm5JmlVXFkYcQEymHbsCTwWklDm7a3YUv\nlqR+gED8dijG1dtasSkSKypmjkXPJATdKY3ulMZX5pUMuc1at43/XVjOC70RXg/G+NymZr69sJzD\n3DYa40mu3NqKYcBX5pVwaokzb7X+O5Nprt7WCkC52UT1gOt5rMeOP6UR13TUrEXs7BInr4fiaIbB\nxZWeIcPTCswsCpWfJs7AdXimVD2arvV3JlGbNaxsDCe4ZEMjN91bwRXvK+Hr97fy/Ufa+doZ5UiS\n2OddOhwdyTTbokmuqivhnDLXXt/FNZ1/qxEA3CaJYz32/vdsudnEVXUlPNMTRjcM5qkKqiTxaFeI\n5/x7kARoRsaAstRm4ZaGLmKaTkI3SOoGdllkxio8q5OC0yTz7YXlnP/OHu5o6uYbiokFtuGNaUPJ\noNMtf+43ykAuVtzR6svPdMZjqR5KIejjIvYNExnq2vT9pjOZ5pZ3u7mqLj8W89djGodOQ9ObYz12\nbhZdPOeP7KUM3NrQhSCTaPyLph66UxqX1XinvUlWbEBlC48ioxnGkMd0bJGdY4vstCdSXLC+kRd7\nI7hNMtfvaMME3LK8iqo8dgJuSaS4aH0jAHZZ4q4V1WxMGhyabV9xfrmbBzuDqLKEyyRhkySurS/l\n1WCMH/o6uXB9I99bVM5RnkI975lMU0qnRpnbguNMEdCnkulaf2cSXkXmWI+NF3qjhDWdz29p4Ycn\nVvH5k0r4xbOdmE2CL3+gDFkSVH3SwtO3BbiloYvGeAq3SebiSg+nFDuwSBK6YXB3c6bs80qHZd+d\nnSOz66dJvOUyW+JJ/CmN4gHhtscV2TmuaO+18JwyFy8HovSmNE4rcTIv+76ab1X47q4OwppOWNM5\nyGlFNwzejOv7/T2dLGQhuH5BGT/0dXLl1hZuX1bVfz9yYbrlzzmvDIxHQJ7smzJdnglxznUYD35v\nQmOMdD1fzsYlHuzKz0tzutKlnCaZQ1xW/tUT5uJKD2nD4PaGbnyxzBG9HYrhVWTubw/gkKW8xtaP\nlbRh8EowyoHZJmHX72jnw+VuLq8tHvY3nSkNA+hKanxhSzMVZhM/WFyZV0WgdYAiAHDPyhpcJpnU\noByAiyo8/GB3J70pjcvri5GFxBvBWL9F7Bs72vnHgfNw5jGZuUB+2V+yOiaiEPQZXIb7/XQqGsNZ\ntAvpqhmv1w2LKni6O8Q7oTgbInEu/00T3z2/kv93Wjk/eryDe5/v4bITi/nX5jDb2hKgQFcgTVjT\nuWlPJ493hbhmXgm/b+vl2Z4Ii2xmagastX333jAMVtWobGiKU1dsxptDH4P5VvOQ4ZxrnFbuXzOP\npG5wd3MP97cH2BxJkJLzt8YX2JdDXTZ+vryaL2xp5tu72rlzeQ3mMYZMT5dSIMZSbnCmIoQ4GvjP\nA6vrWOu25X38id6U8Sgk49nnSCFC4pzr+v89lEIw0eQ5wzD49KZmrJLg9uXVExqrj5diGkdOkxXj\nP/4I39jZzileB4ttZn7e1MNZpU4OcVr59q4OrplXwpZIgie6Q/xqZQ21an7i68fKjmiCz2xq5gPF\nDs4rc/Ontl5eCkT5y+q6YQXotGGwbmMTjfEUJ3sdXD2vBFseS+UZhsFJb/gAKDObuHVpJeXZl9/g\ne6oZBj/Z08ljXSGW2Cx4TDJvBGOsdqoc4FD5XWsvP1tayWqndch9FZh+9qR05s1xz8BAxiK4D15X\nZ6J3Ybi1fzrX35mKZhicumM3Zlnw+ZOK+ffWMG/tyRgv6ooVqjwK/90RocRhoqMlzUFOle3RJAYQ\n1XSuzIYHDTcPtrTGeXJ9kBOXOyl+Kj8e52Ba45y392ASsKLIzf/UuCmfhQU+ZhP/7Y1w3Y52frq0\nkgMn8d31WiDKh95tADjGMIwXJzJWYUbkwGjC/EihMxPd52Roh0N5CHKNeRyOt0NxdsWSXL9gfB1v\nZxrHFNm5oq6YWxu6eboHjnLbuHpeKc9kq0qscaocX2Tn2Z4wf2wL8NX5pdNynDEADaoAACAASURB\nVPFsdaAnu8PUqAqXVBXxL3+ElwJR3l/sHPI3JiH4zsJyGuMpjvbY8h7znTagRJGpVRVuWlI5fF8K\nTeeLW1rYFUvilGW2R5O4TRKX1Xj5SIWHQFrjd6297I6lCspAgRlDrh6C4UqXToVCMBuUkNmILATX\nnFrGT57o4GdPdQFgkQXnHuphR3uCl3dGOf9QDzecV8kB121hPQke+9YCNjTFqfQoHDJ/ZGPlskqV\nZZXZpGQmZqDru+fVwA9er2JrW4I/vhblkvWNXFFXzBmlrpEHmIMMfg4mq4LYKkdmPxvC8UlVBvJJ\nQRnIA5NZVaYvqTff5CNkaCC/b/VTbjZxfNHcie8+t8yNTZLYFk2wrtoLgCtrQY9pBrWqzIleB8/7\nI3y5rgQlDxWUxspyuwWzECQNg3ua/dhkCbdJ4u1QfFhlAGCe1TymeMaxoEiC+9fMG3W7R7tC7Iol\nUYSg79KttKucX+5GMwy2RBI4ZImmQn3tAjOM0YT6kYSMsSgEYxXqh9vvaGFKBXLnU8cXc/ZBbh59\nJ8CKapVr/9LCX17t7f++0qMgS4LLTy7hjme6iCR0zjxw6irPDXWPzzs00+hxRW2cp97p5Ccvd2GT\nJU70OvbZdraRS0WnqZ73LlMm1+R3Lb0stVkmJWIl3+w/vt1ZzFiSnyfCeB+YjeE4b4biXFgx96q/\nfKDEyZfqSrBnlYAKS0Z/bk1mBNSFVjNhTe8vnTnVyELwxMHzeeaQeo4rsnNPcw9Heew83hXijsZu\nNoantna6Zhj8oqmbG3a20xQfWYh/zp+pnGGRBIusZhyyxOvBGFFN5/u+Tr62va0/Aa5AgbnCeNfZ\ngiA/M2i+N06py8SlxxZz2AI7/7hiAWtqM9ZfNSxYvCkTEnnhEUUsr1T55l/baPFPvkFjqKaig3HZ\nJG65qIZVy63csKtj1DV6JjP4fIc7/+l6br46v5QFNjNf3d7Gt3e2sy2SGP1H08ic8wzENZ1dsSQt\niRQJ3cAgU3JrpcPCItsQGfyzhImGDRkPfm9S8gYMw+AXTT2UKjKnlgxviZ4rlJsVVEnwH3+U1Q4r\nb4Ri2LPW+OlCCIEAPl7p4cXeCM3xFKeVOPlbR4AH2gNcPa+EM0qcU1IC8h8dQf7cFkAC3gnHuXtF\nDUXDJMJ9rMLNP7tlLq8tptxs4sotLcR0HUUI/pUNxwJY65odbtYCc4fRrI2jCRhjrT8/2jiTtX2B\n8THQ0+K2yfzAUk5bbZoys6k/YdTrMHHbxdWcd6uPO57t4rvnV45pH6OF7o73XptkwftWOlm/Ocbn\nNzfz/cUV/WEts4XRSqbPBBwmmVuWVvK3jiB/bO3lpd4oNywq5/AZ6iWYU8rAbQ1dtCbTpIfIiVaE\n4FsLyzh6HGUKB06u6W7ZPtgDMBblIJ9hQX0864+wIRzn+vqyMWfNz0bMUqYF+d3Nfp7uCSOAz1R7\nZ0St9cU2C9fOL+VGXycfr/RwRV0x39nVwU/2dLE5kuCaeSXD9ofIBy/2RrirqYfji+xc+fkyzrvR\nx/81dnNJlWevBmd9HO2x7/U8vr/YwY/3dPH1HW3UqAoN8RRuk8RRnpm5eBaYmwy2Nk5kzR/8+5ki\nqBTID3331iQENaqy1+fV61SqiswcOM/GpubYXr8bzTA3EvmYQ587qYQjt6lcu72Na7a2crDLigS0\nJFP0pnSqLSbWOK0c5rayyqFOefnsqYrtn2wsksQFFR7OKnHy9R1t/M/2Nj5c4ebSqiLUGdbHY04p\nA1Hd4MIKDwc4VWotSn+FlJRhcMOuDr65o51frxq98stYtM6JvigmOtZI4UGTnawW1XR+0djNKofK\nSd785wrUzNDOph8u9xDSdEoVE4e5rVNWSejmPZ28GohRYTFRYTZRbjZxiMu6V3Jt7YAXkipJ3LCw\nnD+29XJPs59iRe7PfcgnmmHw57YAv4n4OWTN/2fvvOMjKcsH/p2Z7SWbTe/JXi9wIL0pvSqc9CoC\ngqA0UUSKgNSfAiJIFRBEpEnzAEEROKpIL9fr5tLbJtneZ35/bJJLctnNZrNJNrn5fj73ubvdmXen\nvOV53qeZuPMHVVgMIuccVciT/+rmrW4fC816bp5TSlUKC8qRRVYU4G+tvbRHYhxcYJkxAekzmfwZ\ntAmQTrX27WW3Plfn3+nG5s4weYat1tHBisDg/4+mFGSjH9UVbJ1/d7rAwt0PlfNoSw9r/WHqgxFK\n9RoOKjCzJRTlhQ43T7X1kq+ROLYkjzPK87O2mZTKcpZLrj7ZwqKRuH1uOQ83d/P3NjefuAPcOqeM\nsiTpvCOyQms4Slc0jgDMN+sHXJX7WeMP8aem7qxd43aTWtQbi3PSNw3sbzdzpWOrgBGRFT71BKhZ\namCv2ZkLtOkI8uNJSZcpmQaqjYaiKNzk7OC9Hj8PLqyc1i5Y04VzVzWxKRjhW1YDnZE47ZEYcUXh\n5jml7N23w+4MRjhnVRM/KM8fIvjftaWLVzo9PLq4KquBw+2RGNdubMOpjXLsrjauW1qGflCaycc/\ncHHLU+0A/GlhJfPMo/eTmKLwiTvATlbjNhOgiko2GauQMROLj03XXdfpQOU5Bk69vx6jTuDRcxNJ\nFYYrA4MZrhBMdH8b/O4VRRli4Y7ICp97ArzZ7ePtbj972oxc4ygZd82XXBk/U9Xvv/EGuW5TOwIC\nl9QU8h27GUkQCMRllnf7eKXTy/pAmMGSuUgiKHm2SccSi4EVvhCfe4IYRIFViVgENbVoulg1EqeW\n5fNYSw8GUUQvCnzkDtAdjRO2KPAI/Ovy2dQUZiYopTInZ9L5s+Vzmg6Z/MY/Oj0s7/ZzcU3hhCkC\nzVGZyu0of/lonFJm4xZnJyU6DXfMKyeiKFyxvo0bNndw+9xydrQacBh1HFJg4eVOzxBl4OxKO8s6\nPXzuDWZNGfDE4ly5oRVPnsxDp1az37yhmSm6vDHeWeNDYxM41WRjrkmX1jvVCIJadXga4Ykr5EnT\nZxd5PMJIrggyk4E6/2aHumId/9uUSJYwFkUAst/ftnTHqS0YWZgf7uqqEwX2zjezd76ZvWxe7qjv\n4pJ1LfxxfsW0LAKZK0rvEquRBxdW8ptNHdy4uYMynYZqg5YVvhAhWWGhWc+5lQVUGbSU6jREFYWv\nvUG6o3FW+EI81tLDHJOO8yoLqDFqOW1Qcc/xsN0oA5AIsGyLxHil04MC7JpnZNc8I1V6LY8He/jl\nM83ceFw588uzOwDHo92PRynINBf2aHzmCXBvg4sD7WaOncBcxQ0xhUq1YOIAhxRa6Y3J3NfoQhQE\nflZTxC1zSvnZulZ+uaGVXztK2M9uZonVwJvdPhpCkQFffX2fK0dYzo4lMKYo3Ly5gy5znL+cU8uS\n6qFBvpGYzA8f3kJzd5RT97JzZpcNQRBoiMnqO51h9MjTRxnYnoT5dEn2TNZviMD7aiavTOl/rp9s\nDtDujuHyxShKcuxExPONxGbXVmVgLGv/oYVWoorC7fVdXLy2hT8vrso4jmC4PDTemJp00ubmiiLQ\nT5leywMLK/jME+SlDg89sTjfK87j8ELLiJurgwO8Q3EZQ5/F/FN3IGvXtF0pA6IgcEVdMVfUFW9j\nEpuzq5H/e7Wdn/61iX/+fBaGMe6IjNbZxmvumwhLQSZtOYMRrt/UzlyTnivqinMicHZ74oRSG1FF\n4eGmbjYGwtw+t5y75pdz7aZ2rtvUzhnl+RxTbMWulfj1xnYeWFiJWRLxxBKLujkLQUveWJz7G118\n6gly1/lV2ygCAA+/48LZGeGJ82vZtc6Uc5OxyvaHqgiMnWzEtU0nIS3bKIqC3OeKHY6OvBEzWYrA\neDmqKI+oDPe6XWzeN8YBC6wZv79svfd02snVPiYIArvbTGOuQWCYINfZ7UoZGMxwIXanz3XcfXol\nZ/xpC/9Z5eXoCSgSkg3/v2woBZme2xiKcOWGNiySxK1zSyesU6qk5tSyfOaadFyzoZ0Hm1z8ylHC\n7XPLeaDJxROtvUQVhRtml3LJ2hbedPlYWpJHiU5DnkZkYzCzXMfeWJyHmrr5xBOgIxIH4JrTSjlq\np5EtQ183BLHoRarsqhlAZWpRlYDske1nOdIOcbLfSCfYNJcEvy5vjGc/7qHdHePXx5RSedZvtjlm\nqhSBTJW682U9T93ay39WejlggXXc2bZGuq5M4xxHOjeX+kOuM2XKgMPh0AAXAocACvAmcJ/T6Yw7\nHI5y4DqgEHjE6XS+MRnX1K8fFFtzX0dKNggnahfmE3eAGzd3YBAF7phXToE295/RTGa3PBNnlOfz\naEsPtUYdJ5XauKSmCL0o8EybG4MgUKrT8PstnbRGopxdYWdvm4lXOr3sYzOzVxrpOuOKgkDCovZM\nm5vXXV4OK7Qy16RjoVnPwfvlJz332qVlnHivkyuebeHxH49ejXg0FEUhoijocywdm4qKSnZIt3ZD\nOsdkmp8/nYrP6ayjJWfqOfquzTS4Ihy40MKpe9nTqvWTiwx+BpIocPBiK2+s9BKXFaQpyiSmCvnZ\nZyoluh8AOwJn9f3/d8DpwF+Bc4AHgI3AHQ6H412n0znh5dv+/YQbSYRFFdNjJ2kydrxCcZmHm7t5\nqcPDIrOeG2aXUqhTFYFc4JSyfBrDUf7U1M3nniBP3FrHb6gg+Hf429duZINCQJZ5tLuHj3sD3DC7\nlNX+MM+0946qDLhjcX62roVAXOHAAjPLu30ssRi4oq4YGL3vVRfqOGiRlX+t8BAfY5xCUyhKTFGo\nGxTo/GRfetRzK+0cX2JTrVIqKipJmeoqz69946bBFeG6pWWcvGf+gCfCdFEABjNc8P6WW8/TrT1c\ncG0Dl9QUZn1jMB3rQDoWCVVhGBtTuaIeCTzhdDpdTqfTBfwN+G7fdyIg9f0RgUlRP//rDrBjl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PznFUhUBlrBy9sw2dJHDZU83ssu9uSGL6rpUwtj6X7NipFiaTsafNRFhWWGzRM9uoY40/zOfe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8jwI98FiPsTb8BKbyCRHkA3MyelS0Fx4hH8wjTj99/A+P03NHxMGsncNlK5raprRH2wpAxY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\nY5qm9QBXJnNbP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f98cdc02160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vis.plot_map(da, central_longitude=-180) # center over ocean" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x7f98cd993860>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TLpIRP1Ii2iv8r97ionDh1RgdbmId9eksvFll0+jqaEHQ6VGkFIqU7H4g748s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"text/plain": [ "<matplotlib.figure.Figure at 0x7f98cd9932b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vis.plot_map(da, central_longitude=-90) # center over Americas" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<cartopy.mpl.geoaxes.GeoAxesSubplot at 0x7f98ce3a5c18>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mwTS58jA5sobstRyp9+qRzEhrUyUZZ+2/bkx/197vAsloN/Guxp5ZcUUsGYUY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\nx+6yvyLLrHr/VRY8dDc2Zyb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npk8G3T+zcjZFx3+DS6/Y83CGw4l9Kfr9nZ288dJzLFu0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\ndB5/Bvgo8MfA9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f98ce34b908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vis.plot_map(da, central_longitude=90) # center over Asia" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
jldinh/multicell
examples/06 - Growth and divisions.ipynb
1
821916
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import multicell\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem definition\n", "\n", "## Simulation and tissue structure" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim = multicell.simulation_builder.generate_cell_grid_sim(20, 20, 1, 1e-3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tissue growth\n", "\n", "We first enable growth and specify the number of growth steps to be applied over the duration of the simulation. Growth steps are spaced evenly.\n", "\n", "**Note:** this should not be used in conjunction with set_time_steps, as the two settings would otherwise conflict." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sim.enable_growth(n_steps=11)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then register the growth method we would like to apply. In this case, it is `linear_growth`, which requires a `coefficient` parameter specifying the scaling to be applied at each time step, along each axis." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim.register_growth_method(multicell.growth.linear_growth, {\"coefficient\": [1.1, 1.05, 1.]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cell divisions\n", "\n", "We first enable cell divisions and register the method we would like to use. In this case, we use a method called `symmetrical_division`, which divides a cell through its centroid, perpendicularly to its longest axis." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sim.enable_division()\n", "sim.register_division_method(multicell.division.symmetrical_division)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also register the division trigger, which is used to check if a cell needs to be divided. Here, it is a volume-related trigger, which requires a threshold." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim.register_division_trigger(multicell.division.volume_trigger, {\"volume_threshold\": 2.})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rendering" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sim.register_renderer(multicell.rendering.MatplotlibRenderer, None, {\"view_size\": 60, \"view\": (90, -90), \"axes\": False})\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization of the initial state" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.0\n" ] } ], "source": [ "sim.renderer.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation\n", "\n", "As the tissue grows, it maintains its rectangular shape. Cells grow in a uniform manner (they all grow by the same amount) and all divide at the same time when they reach the volume threshold." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Growth step #0\n", "Growth of the tissue: 0.689544916153 seconds\n", "Cell divisions: 0.952747106552 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.0909090909091\n", "Growth step #1\n", "Growth of the tissue: 0.55749297142 seconds\n", "Cell divisions: 0.514989852905 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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qa82AQInBQRAAdCPAgEC+QoIgPl6q0aAQA2B7X+5Peb+z+cn/7L/gQOx4uNpf7+Tr2V6/jXd1hTLMq8dfPBQLPtY2rfv8KOHR7RL2bNjb6z+TNreZfc/744VmX6Be+/dM6INTH/jQNKncPC1N6L7ie5Y8sfVdjYv9b8URx7vjEXvWTQ0t4HmgThv1bvi3EXnDr126JGOmL0pPcd4+F87YskHq30GK2/ec++eWJ3pb7jvp5WvitM+gIce6hjxFXjb3Xti7RfS3ogtP2yL9ZkWMq3/R3vc/+2f2acECExyAQFwki+w6RGYCAKX/8VlMf1zZ0RU/zhGHHmsM5a8P+0N2HZfe6z9dNoIueNfD0dd5mzbyaD052nYafpBUyz5kyUJx95Kr8GPp+Gp45eHY9OX07Nyjbc3R92V6Wstd7XGovcsHHre4CuvR19Dfyx4d/UCycvHXo7Xjr8eS/+oWrfSCHrw1cE4Z261EXTnE52x/rPpWcQDDx+M5ZekTa+bftAcG69Mexk239kSGy7LvHZ7cyweFkQrg9z/s32xfFjfwsprBx4aWaPrzu54+J8emQjbxhgJEDgNAQHwNPB8lACBsREY7VfAO2+oj21fTxsm17rIUOtyR/NtLbHhqvRiyGgbQde8BJK5pDH4ymB0Pt4ZFwxr+lzzEkh9T8ycnTaCrhXE9vx0b6z+VPrbyVqNoGtegrm9NTZckQbKxluao+7L2aA48n0ugYzNnvYUAuNdQAAc7ytkfARKICAAjvxNnABYgo1vigQKFBAAC8RXmgCB/19AABQA/SwQIJCvgACYr7dqBAjUELjqr66Ml7a9GFPPGNYIum0g5qxNL0b0NfeNaFzc09gb8+rSps99lcbNG7MNowdi3ob0tcpn52c+e7LZdKbpc2+liXLmed27emPBlup5vzdeG4xnDz0b56+ujvmlYy/FK8deivNXVC+4DOx7Js6eMzP+YNZZQxInG0Fnm09X5roxM6/Gvpibbea8qycWZptIN4106m3qrdEcui/mZhtB/+aFeFAjaD+nBCa9gAA46ZfYBAmMf4GafwquRiPktnvaYu3n1iYTqnWTdbSNkGudAWy7pz3Wfi69aLL3vr0jbgE/9U8746L/sG1oLDXPAPYcj4HWo7HsQ9Ubya0/bovlH10WZ846c+izO28c+afgajXCrjW2UZ8BvLkp6q5OL7I01zgr6Azg+P95MUICYyEgAI6FomcQIHBaAgKgAHhaG8iHCRD4vQUEwN+bzAcIEBhrAQFQABzrPeV5BAj8+wICoB1CgEDhAld844qYcdn0ZBzdT3SN+Lp33/37Y87m9Bxf56+PxKIPpP0CDz/aEUs+nDZRPviLA7Es01j60MOH4oKPpg2j++r7Y9Wn0t6ABx86GMsyPfme/IenY82wXoOVPoD9zf2x4MJqb8BXjr4Ur774etLPsNLMevH7FsWMWTOG5tt4W1PUXZV+PXvwwYNx3vq0OXbnb7ti/RfTr8Db7mqLtV/IfC1+Z2ss+kC1+XSl0IFfHIjl2fk/dDAuyMyr546eeODvf1H4njAAAgTeXgEB8O319XQCBEYhcOU3roh5/zkNdgcePBBzNqWvdfxbZ2zIBqAaZ/Yab2uOuqvSnne1zgo2394cG67I9Mb7YWss/ZM0UB585FDyVz8qU+rbPRArPlZt1Hxi8EQ039USdZdXg9yLfS9F5+NHYuHF1VBYafC88D0L48xzqgGw5cftsf6z6bnDw786Euu/kPby23PfvlhwcXoxpP3evbHm0rRf4IGHDsWmTHPsljtbYv1laR/Eth+3x9pM3ZZvtcXP/68HRrFq3kKAwEQWEAAn8uoZO4FJInDd314bi7+V/sZqX41LIO337ok1mT+FVivY1brc0XhLU9Rl/sLHaC+B1Kq79/59seqTK4dWoBIAd928O7Zdt2XoteM1LoG03NMaKy5ZPnaXQP65MTZ/pS7ZCbUuwTS6BDJJflpMg8DYCAiAY+PoKQQInIaAAHgaZwAFwNPYeT5KoLwCAmB5197MCYwbAQFQABw3m9FACJREQAAsyUKbJoHxLLD9r7fH8c0vxLQzpgwNs6+l0nx5bjLsnobemL85PQPXs7t2M+d5mQbHvY29MTf7vN19MX9TWqO/bSDmrkvPHtZqDt3fXmlUXf3sG6+/Eb3NvbFw8/yhMb/87CtR+Rp47rAG1AN7n4lz5p4dZ51b7QPY8fjhWPreJclcu3b1xsIt6Vz7Wwdi9pr0YkjP8Dm8GRFTInqb+kfMq7dxZDPr3pb+mLsubbb9/GPH48H/+tB43i7GRoDAGAgIgGOA6BEECJyeQK3fAO68qT62fXVr8uBdNzXElq9ufsvXmn/QEhuuTC88jPoMYI1btTtvqI9tX0/Hkj0DWLMRdO/x6G8aiOUfqV4W6dnZEzPnzIxZi2cNzSPbVLryDzu/syu2fa16nrDyWs1G0N/dHZuv25SYnN4ZwMbY8c17T29BfZoAgXEvIACO+yUyQAKTX0AATP+qiAA4+fe8GRIoWkAALHoF1CdAIARAAdCPAQEC+QoIgPl6q0aAQA2BSh/ANz95IqZUjwDGwYcPxbJMk+YDlYbMH61+nVp5VNdj3bEh098u+/Vs5X0nW7l8ZnVSveXO1lj43uqZvco/HvlVZyz+o0xLmgcOxIpME+Vn2o7Fio9XxzL46hvRu7M3Fr23+tmXBl6MgfajsfR91fN9PfW9ceKNN2Pm7LOGxrL/wcrz0+bT+x7YH6v+NH2t+3c9Mf/d6XgPPtIRyz+aNr0+8puuWPS+au/BSqGORw7H0g+n/Q07fnk4lv5Jevaw776BePj/ecQ+JUBgkgsIgJN8gU2PwEQQ2P6X22P+/5JevKi/qSG2Zs77VRohr/50tfdeZW6VELfsY+lf82i7uz3Wfj5trLz3vv2xOtMwec+OPbH5K+n5uco5uzWfTYNiw/cbY8u16fue+senY8Nl1SbSgy+/Ht1P98biYcHrpYGX42glAP5RNaDt//m+WPU/rEr7AN5UH1uH9Q+szGv3LU2xKdO3cM+9e0fMofHW5qjbnjazbrylOTZdnf5lkeY7WmLD5em5yKbbmmNjpmF20//aEvd/+2cTYdsYIwECpyEgAJ4Gno8SIDA2ArW+An76hp1x4de3JQVOpxF00y1NsXE0jaBrXAKp/059bP1aegkke3Gjcgmk63fdsfSPq79RO17jEkitRtBjfQlk9/ebYtM1aQBsurUpNmb+OkhDjR6CDX/nEsjY7GpPITC+BQTA8b0+RkegFAJ5BMDTuQUsAJZiG5okgVIJCIClWm6TJTA+BQTAsb0E4jeA43OfGxWB8SQgAI6n1TAWAiUV2P6frjrZCHrqtKlDAt27e2LBpvTCQ39bf8xZm54VrDSMnrs+27i5P+YNa75ceWhfa6U5ctpYua+pb0Rz6L7mvpibaSLd+eSRWLAlvVTR29QbC7ZVXzvx2mA8s/9YzB7WRPrlZ1+O5w8/F/OGzaPzqSMxb/3cmH729KG5Hvr14Vj87vTiSc/u7pi/Oa3Z11JpXJ3OoXd3T8zfkjp1N/TG3HWZBtctfTFnfbbp9chm28//5oX4xd8/WNKdaNoEyiMgAJZnrc2UwLgVGO2fgqvVCLnlh22x/ktrk7k13twUddlLELc2x4bMZYmWO9pi/eXpZ9tqnAGs1YC6ckt35Seqt3QHXxuMzse644Jht2prnQFsvactll+yLLkEMtozgLXGUescX/PtrbHhinWJSa0zgK0/aot1X0zn3/B3TbHjmzvG7V4xMAIExkZAABwbR08hQOA0BATA0X0FLACexibzUQIEEgEB0IYgQKBwAQFQACx8ExoAgZIJCIAlW3DTJTAeBa74iyvi7GvPiilTq52gazUprjSHnrWs+jd0K3Ppqe+L+VvTs20DjUdj9Z+n/QL3//xArPjE8mT6Bx48GMs/ljaWPvDQoVh+SdpXsHVHW6z7TPpVace/HU5avrz+6mAceuRQnL9h9lCNV46+HM93v5CcM+x+oitmb5yTnAHs+GVHbL02/RvHrXe3xbrPpzWb72qNhZlG0If/7Ugs+kDa4LlvV1+s+2zaB3Hfz/bHyj9LG0vvf+BALB/WzLoy8APfPRgP/8Oj43GbGBMBAmMoIACOIaZHESBwagJX//XVMfs/vyv58MEHD41o8Nx4c2Nc+D9emLyv9Uftse6Ladhp/kFLrLo0DYCtd7bFusvSQNV+955Y8/m06fPen+yLVZnw2Hhbc9RlGiZn/1LJG6+diL6Gvrjgg9Wmz/1N/THtzDPivFXVufXs7I2Zc2fGrEXnDs3jwIMHYtH700sgtdrWNN/WHFu/nvYjbLt7T6zNzKH59pYRc2j9YXus+1KmOXaNubZ9a0/8v9+6/9QW0qcIEJgwAgLghFkqAyUweQVqfgX88/2xMvOn0HbeUB/bMgGo1iWQWpcgagWqljtaY/3l6WWJWhdNav1Vkn3374uVn6yGzMolkCO/6YxlH6r+9rC3sS+mn1UJgOcNLV5PfU/MnD0zZi2u/ibzwMMHY3nmT9ztvLE+tl2fhr36G3fF1uu3ZALwyIsco70EUsvOJZDJ+3NmZgSGCwiA9gMBAoULCIACYOGb0AAIlExAACzZgpsugfEoIAAKgONxXxoTgcksIABO5tU1NwITRKDSCPrli19KGkF3Pd0dCy9ckMyg8+mumJdp0jzQfjRmrzk/eV9/20AsyDRH7tndG/MzjaV7G3tH2Qi6c0Sj5iO/64wFW6pNmQdfHYxjHc/F7FXVsTx35LmYNn1anDPvnKHx9bf2n/xKeMbZZyavzRnWQLryD931PTE308y6t7En5m1MG0FXnjeiwXNL5bW0OXZ/S18s2Jp6Vkxmr65eWqnUPf7U8Xjw/35oguwcwyRA4FQFBMBTlfM5AgTGTODav7kmln47vcm686b62PbVzIWHe9pi7efSixy1zrHt/l5jbLq2Lhlf7TOANRpB39Meaz+XXpao1X8v27x58JXB6Hy8My744LAzgA29Mf3sGXHeyuolkMqN4uUfSRtB1zoDuOene2P1p1Ylc6g1jlrNnGudAaxcjNlw5frkebXPADbGjm/eO2Zr60EECIxPAQFwfK6LUREolYAAOPIrYAGwVD8CJksgdwEBMHdyBQkQyAoIgAKgnwoCBPIVEADz9VaNAIEaApf9xy/FmV+cHjGl2gi67d72mFuXnmPr2dUbC7alZ+B66/tj3oXpa11PdMfCP0zPu/U+1RPztqUNo3t29cf8LWmNFw6/GEs/VO3lVxnunvv2xOpPp/0Cm25vjIXvrfbue+PVwXj9hddj/kXzh2Z4bN+xk2cAZy2ttnzp+FVHzN86P848d8bQ+4481hWL37swkTn86yNx9vzq2cHKPw409sfqP0/HUath9t7798X569JzkT31vTF/a+r0/KHnY9lH06bXR37QGQ//4yP2KQECk1xAAJzkC2x6BCaCwJf/+sux5H9PA1D9Tbviwq9vS4a/5969sfrS9Fxc293tsfbz6Zm9xu81Rt01mTOAtzZH3Zc3JM9rvaM11mX6ALb+sG1Ec+j6G+tja7YnX2V811fHd2LwROy6eVdsubbap6+3oS+mz0z7ALb8uC1WfDg9A7jvF/ti5Z+mjav33rcv1nwmDXvtP95To8FzW6z7UnousumW5tiUnf8tzVF3dTr/Xd/ZHZu+sjExafwvzfGT/+2+ibBtjJEAgdMQEABPA89HCRAYG4HRfgXcfu+eWHNpGopqBcBal0CabmmKjV9Ow07L7a2x/opMI+i72mLtF9JAVasBdbZRcyUANtzWGFuvqf5Jt95RXgLZ98D+WPmJ9M+01ToD2L5jb6z5TBqAK4E1GwAbv98Udddkgt3NTVF3dfpaw3d3x+brNiWLqBH02OxpTyEw3gUEwPG+QsZHoAQCAqAAWIJtbooExpWAADiulsNgCJRTQAAUAMu5882aQHECAmBx9ioTIPDfBL7819vjuY3PxdRp1UsglUbIS9+3JDHqqjYX7JgAABspSURBVO+ORRelZwV7Gnpj/ubMJZAnu2L2urTB8dH2ozE/0xy6t9Icemv10kal2JEnOkc0h+6t744F29JLJUf3PhPzhtWtfAV85LHDseR91Qskxw49G2ecdUbMWlC9zNH1ZHfM2Tgnps+sXgLp3d0zombX012xcFs6196mvhFzrVyMmT+sIXVlDl1P9yRNqiuv9TT0xOw1qcnJ59WlF2Oef+LFeOi/agTth5PAZBcQACf7CpsfgQkgMNrfAGbP3VWmVqsRcmON827NtzXHhqvSSxAtd4xsBF3rXFytuvvu3xcrP1m9uHHijROx63sNSfPqk2cAz5kR562oNoJuuac1VlyyPM6cVf1LIAceOhjLL1mWrNTO7+yKbV+rXiip/GNbjSbVoz0D2HRrU2zcnjkD+L3G2JxpmO0M4AT4gTFEAmMgIACOAaJHECBwegICoAB4ejvIpwkQ+H0FBMDfV8z7CRAYcwEBUAAc803lgQQI/LsCAqANQoBA4QKX/8XlcXzz8zFlanUo3fXdsfbS9G/X7n9gf6zItEs58m+HY+kH08bNh/6lIy7INHM++OihuCDzvn2/2B/nrz4vmX9fY1/MzZyLq5xHXJA5KzjQfjTOX11ttnzixBvRvbMnFl9UbQ79bMdzcd6qd8U7lr5jqMaBRw/GzNkzY8bM6UOvDbQdjdV/lvYB3P/ggZFz/fWRWPz+9G8mH/7V4VjyR+lZyQOPHDz594aH/7fnZ3tjzojm0H0xf2t6BvDFJ16OR258tPA9YQAECLy9AgLg2+vr6QQIjEKg1m8An75h54hG0LtuaogtX6322as8uvWutliX6dvXfHtrbMj092u+tTk2bM+cAbyzLdZflvb8a6hxLq7+pobYmqk74gzgyUbQu2PbddVze8d7jkcl3C37YPWvbbTc0xYrLkkbQdc6A7jvZ/tj5Z+lvQHrb9wVW69PzwWO9gxgrTOQtebqDOAoNqy3EJgEAgLgJFhEUyAw0QUEwJFfAQuAE31XGz+B8S0gAI7v9TE6AqUQEAAFwFJsdJMkMI4EBMBxtBiGQqCsAgKgAFjWvW/eBIoSEACLkleXAIEhgav+01Xx6ntejqlnVG+BHPr1oZi/KW3S3N86EHMyDZ6f2Xs0FlyYaQ69qzcWbE2bQx+pNGBeW720USne1zoQczPPq1zuWHhh2vT58GNHYsl708sXXU93x/mrqhdIKn0AK02ZF727OpaXn30ljvcdjznDGjBXGjJXLo9MH3YJpL+5f0Qj6P6W/lh0cfVCSWW8nZUG15k59DcPxJwNaYPnvqb+mJe5yFKxy352oPVoLLwoneuxf3k2HtQI2k8ngUkvIABO+iU2QQLjX+C6v702Fn8rDTu1LoG037sn1ly6OplQ213tsfYLa5LXmn/QEhuuTG8Q17wEckdrrL98Xfq8u9tj7efT59W6fFLrEkjDbY2x9ZrqJZXjvcejErKGXwJp/XFbLP/oW18CqXXxpH3HnljzmXT+p3MJpOXO1lh/WTp/l0DG/8+LERIYCwEBcCwUPYMAgdMSEABHfgUsAJ7WlvJhAgTeQkAAtEUIEChcQAAUAAvfhAZAoGQCAmDJFtx0CYxHgcv+4rJ4YdPzMXXKlKHhdT/dHQsyZ/GO7nnm5N/RHf7fkcc6Y/F706+PO351ON6xZFbyvr7KObsNc5LXnu94IZZ+MG2ifOS3nbH4fenzDj5yMJZlGivv+8W+tBF05QxgfW8surh6pu6V516NF3qOJ2cPuxt6Y3blDOAfnDE0lv6WZ2LO+rQhdffO3pi/JT3H2N/UH3M2pnN4Zu8zseJjab/Ajl92xNI/yTTH/teOWPrHbz3Xnh098fA/agQ9Hn9OjInAWAoIgGOp6VkECJySQK3fAGbP2FUe3P6TvbHmz1clNWqdAdz9vcbYdG1d8r5aZwBrva/Web+99+2NVZ9O6+57YH+sHPZXSU4MnogRZwArjaArZwA/NKwR9N2tseLjy+PMc88cGl+tRtA7b9gZ276+LZ3rPe2x9nPp+cTWH7XFui+mzawbv98UdddsTOd/W3NsuCpthK0R9CltVx8iMCkEBMBJsYwmQWBiCwiANf4WsAA4sTe10RMY5wIC4DhfIMMjUAYBAbCgAPjPu2PzVzYlW8wt4DL8xJkjgQgB0C4gQKBwAQFQACx8ExoAgZIJCIAlW3DTJTAeBbb/5fZ45QMvxdSp1UbQPQ29IxoyVxotZy9G9Ozsifnb0obRnb/rikUXp82hu57qivlb0vd17+yOhcMaN1dsKk2fF2caMFc+u+gPM02Zf9cVs9dUG0tXzgB2Pd0VCy+qvu/F/uNxYvCNmLXwnUPs3Q09sejdC2L6zBlDr/U19sXcTOPmQ7/qiPl1mUsgLX2x5P3pRY6enb0xb/PcZFl76nti/tZ0riftMs2xT160uSh1evaRY/ELjaDH44+JMREYUwEBcEw5PYwAgVMRqPUbwD337Y3VmYsXe36yN1ZnL4HUuBjReHNT1F2duQRxa3Ns2J5eghjtJZDRNoLedfPu2HbdliGC3obemH72jDhv5buGXmu+uyVWfnzFKV0Cqb9xV2y9vvr8ykNHfQnk9pbYcEXaHLv2JZDG2PHNe09lGX2GAIEJJCAATqDFMlQCk1VAABzdV8AC4GT9CTAvAvkLCID5m6tIgEBGQAAUAP1QECCQr4AAmK+3agQI1BC48n+6MuKTb8bUadV/7Hyicu4uPZ925LGuOHfhOckTntl7LM5bVf2KtfKPz+x7NpZ9KG2EfOQ3R2LxezINnn/ZkXw9W/lsrWbLPbv7YkHm/NyzB5+NJR+onsernAHc/9CBWPWnK4fGd3TP0Xjt+dfi7DlnD73W09ATyz68PGacPX3ote4nu0ecY9z/8IFY8dG06fX+h/bHnPVpI+ij7c/E+WvSJtK9jX0xL3Om8LlDz8WSTCPog48ejPOHnWOsDOiFJ16MR/9JI2g/qAQmu4AAONlX2PwITACBr/zttbHkW2k4e/qGnXFhphHyzhvqY9vXtyYzar2rLdZ9IW2E3PyDlthwZXrerfGWpqj7cnousOWOtlh/efrZWuf9ap09fOqfdsZF/6HaqLkSAEecAWzsi+lnnRHnraoGtNYdbbH8I8vizFnVRtDZptKVCdZqBD3ar4Cbb2+NDVesS5yabm2JjdszJjc3Rt3VacNsbWAmwA+MIRIYAwEBcAwQPYIAgdMTEADTvyoiAJ7efvJpAgTeWkAAfGsj7yBA4G0WEAAFwLd5i3k8AQIZAQHQliBAoHABAVAALHwTGgCBkgkIgCVbcNMlMB4FrvqrK+OFzc/FlGnVWyBdv+uMhZmGzH27+2LuprTp8UBzf8xeNzuZVl/zQMxeV23SXPnHoy0Dcf769H1H2wbi/LXpawPNAzG3Lr1oUet5lebNyz+0bKjuG4Mn4vCvD8eyD14w9Nqxg8di2vRpMWvRrKHXKo2m526YEzPOqV4C6WmoXDJJGzcfeeJInL8qnUOlOfbcjen8+1v6R1wMGWgbOFlj+H/9zf0xJ/NaX3NfzF6Xvu+lJ1+Kh//xkfG4TYyJAIExFBAAxxDTowgQODWB0f4GsK1G0+eal0BqXIJortEIuuXOtlh/WXoJpO2utlibuVTSfu+eWHPp6mRyO2+sj23XVy+kVC6BNNzWGFuv2Tz0vlqNoEd7CaRWI+xaF1QavtsQm6+r1qwUr30JpDk2Zhph13qfSyCntod9isBEExAAJ9qKGS+BSSggAI78ClgAnIQb3ZQIjCMBAXAcLYahECirgAAoAJZ175s3gaIEBMCi5NUlQGBI4LJvXBYvbnwhpkydMvRaz67emL9lXqL0zN5n4oI/Ths8H/pVpZlz2gi5cmZv7obMucCm/pi7MT3vVnneeZlGyANN/TE7c1awr7l/xJm67l29sWBb9dzem4Mn4uieZ2LFR6rnAo91PBdTpkyJdy6pngE89NvDMX/TvJgxs3oGsGtnTyy6aEEy184nu+PcBWnT6/6WgVh5SdocuuM3R2Lp+xcnnz04ygbXfTVMjj/5Yjx6w7/YnQQITHIBAXCSL7DpEZgIAtf+zTWx9NtpiKn1FWjbPW2x9nNv3bi55hm4W5piY7YRdK0zgDXOGdZqQD2qRtANvTH97BnJXxtpubs1Vnx8eZx57r/fCLrW/Nvv3RtrLl2VLGnD9xpj87VpM+fG7zdF3TVp0+umW0eeAWz47u7YfN2m9Hl/1xQ7vrljImwbYyRA4DQEBMDTwPNRAgTGRkAAHN1XwALg2Ow3TyFAICrfTlS/cvk9QE7pQ5Xnv/nmm2/+HnW8lQCBEggIgAJgCba5KRIYVwIC4LhaDoMhUE4BAVAALOfON2sCxQkIgMXZq0yAwH8TuOw/XhbxsRMxZdrUIZOep7tj1pJ3JEaVvnrzNqcXQ/ob+0Zc2ji2/1gses+i5LNdT/XEoj9cmL72eGcszLyv418Pxflr0wbMvY19I+p27+yJBcMuqVQaQR/vfiEWXFi9zFEZxxl/MD25zFFpBF1pDD39rDOGxvLMvmOx5H3pGciO3xwe8VpPfW8sfHd6WeTIY50j5tr52yOxKPO8w7/qGOHUu7vSWDr1fOHxF1wC8ZNJoAQCAmAJFtkUCYx3gZq/Afzp3lj9qfTCw84bd8W267ck06l5CeLmpqi7Or0EUasRdOP3G6PumvQCRa1my6O5BDL4ymB0Pt4ZFwz7SyC1GkE3390SKz++IrkEcuChg7H8kurt4coEa9Vs37E31nzm1C6BNN7SFHWZSzC7v98YmzLz1wh6vP+0GB+BsREQAMfG0VMIEDgNAQFQADyN7eOjBAicgoAAeApoPkKAwNgKCIAC4NjuKE8jQOCtBATAtxLy7wQIvO0C3/gv34ieDd0xdVq1VM+uvpi/ZW5S+/BjnbHkfUuS1zqf6IxFf5g57/dkVyy8KD3v1/1UVyzINFvuerJ7xJm6yte42fODlXN7S96b1uj49eFY+oHqWAZffSMqZ/mGN6B+7sgLMXXalOQMYNfT3TFn/ZyY/gczhubR29gb8zalZ/GO/KYjFmfm2rurN+ZlmmN3nTzHmDnb+GRPLHx3tUl1pVD3kz2xIPNa5++6Y9HF6ZnC1377evz4/7z3bV9zBQgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7gACYO7mCBAgQIECAAIFiBQTAYv1VJ0CAAAECBAjkLiAA5k6uIAECBAgQIECgWAEBsFh/1QkQIECAAAECuQsIgLmTK0iAAAECBAgQKFZAACzWX3UCBAgQIECAQO4CAmDu5AoSIECAAAECBIoVEACL9VedAAECBAgQIJC7QO4BMPcZKkiAAAECBAgQIDAmAlPG5CkeQoAAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEEBMAJs1QGSoAAAQIECBAYGwEBcGwcPYUAAQIECBAgMGEE/j/ruUSyNI8D8gAAAABJRU5ErkJggg==\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.181818181818\n", "Growth step #2\n", "Growth of the tissue: 0.497236967087 seconds\n", "Cell divisions: 0.546725988388 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.272727272727\n", "Growth step #3\n", "Growth of the tissue: 0.517410039902 seconds\n", "Cell divisions: 0.574597120285 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.363636363636\n", "Growth step #4\n", "Growth of the tissue: 0.48425412178 seconds\n", "Cell divisions: 1.86181807518 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.454545454545\n", "Growth step #5\n", "Growth of the tissue: 1.23253798485 seconds\n", "Cell divisions: 1.58813095093 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.545454545455\n", "Growth step #6\n", "Growth of the tissue: 1.17254805565 seconds\n", "Cell divisions: 1.68955206871 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.636363636364\n", "Growth step #7\n", "Growth of the tissue: 1.29916810989 seconds\n", "Cell divisions: 1.64202809334 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.727272727273\n", "Growth step #8\n", "Growth of the tissue: 1.26322984695 seconds\n", "Cell divisions: 1.6054008007 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.818181818182\n", "Growth step #9\n", "Growth of the tissue: 1.23599100113 seconds\n", "Cell divisions: 5.31329917908 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 0.909090909091\n", "Growth step #10\n", "Growth of the tissue: 2.88810992241 seconds\n", "Cell divisions: 4.3902258873 seconds\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width);\n", " canvas.attr('height', height);\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'];\n", " var y0 = fig.canvas.height - msg['y0'];\n", " var x1 = msg['x1'];\n", " var y1 = fig.canvas.height - msg['y1'];\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x;\n", " var y = canvas_pos.y;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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gAABAgQIEMgEBGBGb5gAAQIECBAg0AgIwMbdKgECBAgQIEAgExCAGb1hAgQIECBAgEAj8F/788LbJB8IzgAAAABJRU5ErkJggg==\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Time point: 1.0\n" ] } ], "source": [ "sim.simulate()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
barakovic/COMMIT
doc/tutorials/AdvancedSolvers/tutorial_solvers.ipynb
1
8442
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "You can find the text version of this tutorial [at this link](README.md)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced solvers\n", "\n", "This tutorial shows how to exploit the advanced features of the COMMIT framework from the side of the **optimisation problem**. The general formulation is the following:\n", "\\begin{equation}\n", "x^* = \\arg\\min_{x\\in R^n_+} \\frac12 \\|Ax-y\\|_2^2 + \\lambda_{IC}\\Omega_{IC}(x) + \\lambda_{EC}\\Omega_{EC}(x) + \\lambda_{ISO}\\Omega_{ISO}(x),\n", "\\end{equation}\n", "where $A$ is the COMMIT dictionary, $n$ is defined in such a way that the product $Ax$ makes sense and $y$ is the datum that we want to fit. The three regularisation terms allow us to exploit ***distinct penalties for each compartment***.\n", "\n", "*Note*: before exploring this tutorial, you should follow the [Getting Started](https://github.com/daducci/COMMIT/tree/master/doc/tutorials/GettingStarted) tutorial.\n", "\n", "\n", "### Download and unpack the data\n", "\n", "Download and extract the **example dataset** from the following [ZIP archive](http://hardi.epfl.ch/static/data/COMMIT_demos/LausanneTwoShell.zip), which contains the following files:\n", "\n", "- `DWI.nii`: a diffusion MRI dataset with 100 measurements distributed on 2 shells, respectively at b=700 s/mm^2 and b=2000 s/mm^2;\n", "- `DWI.scheme`: its corresponding acquisition scheme;\n", "- `peaks.nii.gz`: main diffusion orientations estimated with CSD;\n", "- `fibers.trk`: tractogram with about 280K fibers estimated using a streamline-based algorithm;\n", "- `WM.nii.gz`: white-matter mask extracted from an anatomical T1w image.\n", "\n", "\n", "<span style=\"color:crimson\">**Make sure that your working directory is the folder where you unzipped the downloaded archive.**</span>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "path_to_the_directory_with_the_unzipped_archive = '.' # edit this\n", "cd path_to_the_directory_with_the_unzipped_archive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the usual COMMIT structure" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from commit import trk2dictionary\n", "\n", "trk2dictionary.run(\n", " filename_trk = 'LausanneTwoShell/fibers.trk',\n", " path_out = 'LausanneTwoShell/CommitOutput',\n", " filename_peaks = 'LausanneTwoShell/peaks.nii.gz',\n", " filename_mask = 'LausanneTwoShell/WM.nii.gz',\n", " fiber_shift = 0.5,\n", " peaks_use_affine = True\n", ")\n", "\n", "import commit\n", "mit = commit.Evaluation( '.', 'LausanneTwoShell' )\n", "mit.load_data( 'DWI.nii', 'DWI.scheme' )\n", "\n", "mit.set_model( 'StickZeppelinBall' )\n", "\n", "d_par = 1.7E-3 # Parallel diffusivity [mm^2/s]\n", "ICVFs = [ 0.7 ] # Intra-cellular volume fraction(s) [0..1]\n", "d_ISOs = [ 1.7E-3, 3.0E-3 ] # Isotropic diffusivitie(s) [mm^2/s]\n", "\n", "mit.model.set( d_par, ICVFs, d_ISOs )\n", "mit.generate_kernels( regenerate=True )\n", "mit.load_kernels()\n", "\n", "mit.load_dictionary( 'CommitOutput' )\n", "mit.set_threads()\n", "mit.build_operator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perform clustering of the streamlines\n", "\n", "You will need `dipy`, which is among the requirements of COMMIT, hence there should be no problem.\n", "\n", "The `threshold` parameter has to be tuned for each brain. Do not consider our choice as a standard one." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from nibabel import trackvis as tv\n", "fname='LausanneTwoShell/fibers.trk'\n", "streams, hdr = tv.read(fname)\n", "streamlines = [i[0] for i in streams]\n", "\n", "from dipy.segment.clustering import QuickBundles\n", "threshold = 15.0\n", "qb = QuickBundles(threshold=threshold)\n", "clusters = qb.cluster(streamlines)\n", "\n", "import numpy as np\n", "structureIC = np.array([c.indices for c in clusters])\n", "weightsIC = np.array([1.0/np.sqrt(len(c)) for c in structureIC])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that we defined `structure_IC` as a `numpy.array` that contains a list of lists containing the indices associated to each group. We know it sounds a little bit bizarre but it computationally convenient." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Define the regularisation term\n", "Each compartment must be regularised separately. The user can choose among the following penalties:\n", "\n", "- $\\sum_{g\\in G}w_g\\|x_g\\|_k$ : `commit.solvers.group_sparsity` with $k\\in \\{2, \\infty\\}$ (only for IC compartment)\n", "\n", "- $\\|x\\|_1$ : `commit.solvers.norm1`\n", "\n", "- $\\|x\\|_2$ : `commit.solvers.norm2`\n", "\n", "- $\\iota_{\\ge 0}(x)$ : `commit.solvers.non_negative` (Default for all compartments)\n", "\n", "If the chosen regularisation for the IC compartment is $\\sum_{g\\in G}\\|x_g\\|_k$, we can define $k$ via the `group_norm` field, which must be one between\n", "\n", "- $\\|x\\|_2$ : `commit.solvers.norm2` (Default)\n", "\n", "- $\\|x\\|_\\infty$ : `commit.solvers.norminf`\n", "\n", "In this example we consider the following penalties:\n", "\n", "- Intracellular: group sparsity with 2-norm of each group\n", "\n", "- Extracellular: 2-norm\n", "\n", "- Isotropic: 1-norm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "regnorms = [commit.solvers.group_sparsity, commit.solvers.norm2, commit.solvers.norm1]\n", "\n", "group_norm = 2 # each group is penalised with its 2-norm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The regularisation parameters are specified within the lambdas field. Again, do not consider our choice as a standard one." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "lambdas = [10.,10.,10.]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Call the constructor of the data structure" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "regterm = commit.solvers.init_regularisation(mit,\n", " regnorms = regnorms,\n", " structureIC = structureIC,\n", " weightsIC = weightsIC,\n", " group_norm = group_norm,\n", " lambdas = lambdas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Call the fit function to perform the optimisation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mit.fit(regularisation=regterm, max_iter=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save the results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "suffix = 'IC'+str(regterm[0])+'EC'+str(regterm[1])+'ISO'+str(regterm[2])\n", "mit.save_results(path_suffix=suffix)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
jquacinella/IS608_Project
.ipynb_checkpoints/IS608 Project-checkpoint.ipynb
1
16263
{ "metadata": { "name": "", "signature": "sha256:47dcac1f1f7021b0cabfdc81fc1049387953feb3760ca4ba3486debafbf6cf98" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IS608 Project -- James Quacinella\n", "\n", "## Goal\n", "\n", "The goal of this project is to create cloropleth map of energy consumption on a per-county level. Data from the EIA gives data on utility level demand and what counties utilities deliver power to. We use data, plus Census data on population, to come up with an estimate of per-county energy consumption. This methodology will be explained below.\n", "\n", "## Data Sources\n", "\n", " * [EIA Form 618](http://www.eia.gov/electricity/data/eia861/index.html) - This data folder has many Excel files, two of which are valuable\n", " * Service Territories maps each County to all the utility IDs that serves it\n", " * Retail data maps the amount of energy produced by each utlitity\n", " * Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2013 from U.S. Census Bureau, Population Division\n", " * Actually downloaded from [FactFinder](http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk)\n", " \n", "## High Level Code Overview\n", "\n", "Here is some pseudo-code decribing the whole process, from raw data to final map output\n", "\n", " * Build a mapping from county ID to its 2012 population (2012 being latest data from the EIA)\n", " * Build a mapping from utility ID to the total energy output of that utility\n", " * Build a mapping from utility ID to list of county IDs is serves\n", " * Derive a mapping from utility ID to the sum of county populations of all the counties it serves\n", " * For each county\n", " * For each utility that serves this county, calculate an estimate of the county's consumption based of the ratio of the county's population to the total population the utility serves.\n", " * Output the final county ID to consumption estimate to CSV. This will be served via a webserver to D3\n", " \n", "## Issues\n", "\n", " * The EIA data maps the Utility to a County by name, which did not eactly match the names of counties in the census data. This required some manual cleanup, which are being handled by the initial load data function.\n", " * The EIA data does not have more granular data about how much each county consumes of its total energy output. A methodlolgy needed to be developed to approximate this data. I have contacted the EIA for comment, with no response yet.\n", " \n", "## Code\n", "\n", "### Globals and Constants" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import csv\n", "\n", "# Column indicies for EIA data\n", "UTILITY = 1\n", "STATE = 3\n", "COUNTY = 4\n", "\n", "# Column indicies for EIA retail data\n", "UTILITY_ID = 1\n", "CONSUMPTION = 21\n", "\n", "# Column indicies for Census data\n", "ID = 1\n", "DESC = 2\n", "POP2012 = 7\n", "\n", "# Global variables\n", "countyToUtility = {} # Mapping from county number to a list of utilities serving it\n", "utilityToCounty = {} # Mapping from utility id to a list of counties it serves\n", "countyToPopulation = {} # Mapping from county number to a population from census data\n", "nameToID = {} # Mapping from county name to the county code\n", "utilityToConsumption = {} # Mapping the utility id to the total consumption in mWh\n", "utilityToPopulation = {} # Mapping the utility id to the total county population it serves\n", "countyToConsumption = {} # Mapping the final result of county ID to consumption in mWh\n", "\n", "\n", "# Constants\n", "YEAR = 2012\n", "PATH_TO_RETAIL_DATA = \"data/f8612012/retail_sales_%s.csv\" % YEAR # Path to the defined utility service territories\n", "PATH_TO_SERVICE_DATA = \"data/f8612012/service_territory_%s.csv\" % YEAR # Path to the defined utility service territories\n", "PATH_TO_POPULATION_DATA = \"data/PEP_2013_PEPANNRES/PEP_2013_PEPANNRES_with_ann_with_changes.csv\"\n", "STATES = { 'AK': 'Alaska','AL': 'Alabama','AR': 'Arkansas','AS': 'American Samoa','AZ': 'Arizona','CA': 'California','CO': 'Colorado','CT': 'Connecticut','DC': 'District of Columbia','DE': 'Delaware','FL': 'Florida','GA': 'Georgia','GU': 'Guam','HI': 'Hawaii','IA': 'Iowa','ID': 'Idaho','IL': 'Illinois','IN': 'Indiana','KS': 'Kansas','KY': 'Kentucky','LA': 'Louisiana','MA': 'Massachusetts','MD': 'Maryland','ME': 'Maine','MI': 'Michigan','MN': 'Minnesota','MO': 'Missouri','MP': 'Northern Mariana Islands','MS': 'Mississippi','MT': 'Montana','NA': 'National','NC': 'North Carolina','ND': 'North Dakota','NE': 'Nebraska','NH': 'New Hampshire','NJ': 'New Jersey','NM': 'New Mexico','NV': 'Nevada','NY': 'New York','OH': 'Ohio','OK': 'Oklahoma','OR': 'Oregon','PA': 'Pennsylvania','PR': 'Puerto Rico','RI': 'Rhode Island','SC': 'South Carolina','SD': 'South Dakota','TN': 'Tennessee','TX': 'Texas','UT': 'Utah','VA': 'Virginia','VI': 'Virgin Islands','VT': 'Vermont','WA': 'Washington','WI': 'Wisconsin','WV': 'West Virginia','WY': 'Wyoming'}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 93 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function reads in the census data, and loads a dictionary that maps the county name to its county ID (nameToID), and another dictionary that maps from the county ID to the population estimate in 2012 (countyToPopulation). The county name is constructed as \"state_countyname\", all lowercase" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def loadCensusData():\n", " ''' Loads the mapping from county number to population into \n", " 'countyToUPopulation' and a name to ID mapping from the PATH_TO_POPULATION_DATA file. '''\n", " f = open(PATH_TO_POPULATION_DATA)\n", " reader = csv.reader(f)\n", " reader.next()\n", " reader.next()\n", " for row in reader:\n", " # Grab the important parts of the data\n", " id = int(row[ID])\n", " desc = row[DESC]\n", " pop2012 = row[POP2012]\n", "\n", " # Derice the 'county key' from the description column\n", " (county, state) = desc.split(',')\n", " county = county.lower().replace(\"county\", \"\").replace(\".\", \"\").replace(\" \", \"\")\n", " state = state.lower().replace(' ', '')\n", " key = state + '_' + county\n", "\n", " # correction to Lousiana county names\n", " if state == \"louisiana\":\n", " key = key.replace(\"parish\", \"\")\n", "\n", " # Setup the two mappings\n", " nameToID[key] = id\n", " countyToPopulation[id] = int(pop2012)\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 94 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function loads a dictionary, countyToUtility, that maps the county ID to the list of utilities that serve it (and a reverse mapping)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def loadCountytoUtilityData():\n", " ''' Loads the mapping from county number to utilies into \n", " 'countyToUtility' from the PATH_TO_SERVICE_DATA file. '''\n", " f = open(PATH_TO_SERVICE_DATA)\n", " reader = csv.reader(f)\n", " reader.next()\n", " for row in reader:\n", " state = STATES[ row[STATE].upper() ].lower().replace(' ', '')\n", " county = row[COUNTY].lower().replace(' ', '').replace('.', '')\n", " key = state + '_' + county\n", " utilityID = int(row[UTILITY])\n", "\n", " try:\n", " if nameToID[key] in countyToUtility:\n", " countyToUtility[nameToID[key]].add(utilityID)\n", " else:\n", " #if key not in nameToID:\n", " # print \"key %s not found\" % key\n", " countyToUtility[nameToID[key]] = set([utilityID])\n", "\n", " if utilityID in utilityToCounty:\n", " utilityToCounty[utilityID].add(nameToID[key])\n", " else:\n", " utilityToCounty[utilityID] = set([nameToID[key]])\n", " except Exception as e:\n", " pass\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 95 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function loads the EIA data as a mapping from utility ID to the consumption data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def loadUtilityConsumptionData():\n", " f = open(PATH_TO_RETAIL_DATA)\n", " reader = csv.reader(f)\n", " reader.next()\n", " reader.next()\n", " reader.next()\n", " for row in reader:\n", " id = int(row[UTILITY_ID])\n", " consumption = int(row[CONSUMPTION].replace(',', ''))\n", " utilityToConsumption[id] = consumption" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 96 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define a function to map the utility ID to the sum of the populations of the counties it serves. This will help in the stimation of how much consumption each county was responsible for:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def deriveUtilityPopulation():\n", " for utility, counties in utilityToCounty.items():\n", " totPopulation = 0\n", " for county in counties:\n", " totPopulation += countyToPopulation[county]\n", " utilityToPopulation[utility] = totPopulation" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, for the main event: lets make our estimate for the consumption estimate of each county:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def calculateCountyConsumption():\n", " for county, utilities in countyToUtility.items():\n", " try:\n", " countyToConsumption[county] = sum([((countyToPopulation[county] / utilityToPopulation[utility]) * utilityToConsumption[utility]) for utility in utilities])\n", " except KeyError:\n", " countyToConsumption[county] = 0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 97 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, so lets load the data and take a look at what we have:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Start by loading the census data and the service territory mapping\n", "loadCensusData()\n", "loadCountytoUtilityData()\n", "loadUtilityConsumptionData()\n", "deriveUtilityPopulation()\n", "calculateCountyConsumption()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 98 }, { "cell_type": "code", "collapsed": false, "input": [ "# Print random sample of county name to list of utility IDs\n", "for county, utilityList in countyToUtility.items()[0:10]:\n", " print \"%s is served by utility IDs %s\" % (county, list(utilityList))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "15003 is served by utility IDs [19547]\n", "54065 is served by utility IDs [15263]\n", "42009 is served by utility IDs [40222]\n", "30007 is served by utility IDs [23586]\n", "30043 is served by utility IDs [23586]\n", "9003 is served by utility IDs [6207]\n", "41003 is served by utility IDs [40437]\n", "41005 is served by utility IDs [15248]\n", "41007 is served by utility IDs [28541]\n", "13095 is served by utility IDs [18305]\n" ] } ], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "# Print random sample of county name to list of utility IDs\n", "for name, countyID in nameToID.items()[0:10]:\n", " print \"%s (%s) had a population of %s\" % (name, countyID, countyToPopulation[countyID]) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "kansas_elk (20049) had a population of 2674\n", "alabama_wilcox (1131) had a population of 11406\n", "florida_washington (12133) had a population of 24854\n", "newmexico_mckinley (35031) had a population of 72726\n", "southcarolina_cherokee (45021) had a population of 55760\n", "texas_shackelford (48417) had a population of 3368\n", "westvirginia_grant (54023) had a population of 11814\n", "georgia_candler (13043) had a population of 11107\n", "illinois_pope (17151) had a population of 4271\n", "missouri_marion (29127) had a population of 28818\n" ] } ], "prompt_number": 100 }, { "cell_type": "code", "collapsed": false, "input": [ "# Print random sample of utility ID to consumption data\n", "for utilityID, consumption in utilityToConsumption.items()[0:10]:\n", " print \"County ID %s consumed %s mWh\" % (utilityID, consumption) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "County ID 8198 consumed 696574 mWh\n", "County ID 8199 consumed 102237 mWh\n", "County ID 57354 consumed 2060 mWh\n", "County ID 24590 consumed 423362 mWh\n", "County ID 8210 consumed 535614 mWh\n", "County ID 8212 consumed 307219 mWh\n", "County ID 57368 consumed 1349357 mWh\n", "County ID 16416 consumed 73074 mWh\n", "County ID 8226 consumed 145827 mWh\n", "County ID 16420 consumed 102694 mWh\n" ] } ], "prompt_number": 101 }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 102 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 102 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 102 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 102 } ], "metadata": {} } ] }
gpl-2.0
darkomen/TFG
medidas/20072015/FILAEXTRUDER/.ipynb_checkpoints/Analisis-checkpoint.ipynb
1
287636
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Producción del día 20 de Julio de 2015\n", "\n", "Los datos del experimento:\n", "* Hora de inicio: 16:08\n", "* Hora final : 16:35 \n", "* $T: 150ºC$\n", "* $V_{min} tractora: 1 mm/s$\n", "* $V_{max} tractora: 3 mm/s$\n", "\n", "Se desea comprobar si el filamento que podemos llegar a extruir con el sistema de la tractora puede llegar a ser bueno como para regularlo." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "#Importamos las librerías utilizadas\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numpy v1.9.2\n", "Pandas v0.16.2\n", "Seaborn v0.6.0\n" ] } ], "source": [ "#Mostramos las versiones usadas de cada librerías\n", "print (\"Numpy v{}\".format(np.__version__))\n", "print (\"Pandas v{}\".format(pd.__version__))\n", "print (\"Seaborn v{}\".format(sns.__version__))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Abrimos el fichero csv con los datos de la muestra\n", "datos = pd.read_csv('datos.csv')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Almacenamos en una lista las columnas del fichero con las que vamos a trabajar\n", "columns = ['Diametro X','Diametro Y','VELOCIDAD']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>VELOCIDAD</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>203.000000</td>\n", " <td>203.000000</td>\n", " <td>203.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.595262</td>\n", " <td>1.567753</td>\n", " <td>2.780788</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.253779</td>\n", " <td>0.229362</td>\n", " <td>2.128159</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.080699</td>\n", " <td>1.103673</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.396121</td>\n", " <td>1.413985</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.585374</td>\n", " <td>1.505929</td>\n", " <td>0.750000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.809036</td>\n", " <td>1.781762</td>\n", " <td>5.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>2.193277</td>\n", " <td>2.034608</td>\n", " <td>5.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Diametro X Diametro Y VELOCIDAD\n", "count 203.000000 203.000000 203.000000\n", "mean 1.595262 1.567753 2.780788\n", "std 0.253779 0.229362 2.128159\n", "min 1.080699 1.103673 0.750000\n", "25% 1.396121 1.413985 0.750000\n", "50% 1.585374 1.505929 0.750000\n", "75% 1.809036 1.781762 5.000000\n", "max 2.193277 2.034608 5.000000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mostramos un resumen de los datos obtenidoss\n", "datos[columns].describe()\n", "#datos.describe().loc['mean',['Diametro X [mm]', 'Diametro Y [mm]']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representamos ambos diámetro y la velocidad de la tractora en la misma gráfica" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-7f0904eecf00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#datos.ix[:, \"Diametro X\":\"Diametro Y\"].plot(secondary_y=['VELOCIDAD'],figsize=(16,10),ylim=(0.5,3)).hlines([1.85,1.65],0,3500,colors='r')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdatos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdatos\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'VELOCIDAD'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Modelo matemático del sistema'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1.6\u001b[0m \u001b[1;33m,\u001b[0m\u001b[1;36m1.8\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#datos['RPM TRAC'].plot(secondary_y='RPM TRAC')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Python34\\lib\\site-packages\\pandas\\tools\\plotting.py\u001b[0m in \u001b[0;36mplot_frame\u001b[1;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[0;32m 2486\u001b[0m \u001b[0myerr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[1;33m,\u001b[0m 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716\u001b[0m \u001b[0m__bool__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." ] } ], "source": [ "#datos.ix[:, \"Diametro X\":\"Diametro Y\"].plot(secondary_y=['VELOCIDAD'],figsize=(16,10),ylim=(0.5,3)).hlines([1.85,1.65],0,3500,colors='r')\n", "datos[columns].plot(secondary_y=['VELOCIDAD'],figsize=(10,5),title='Modelo matemático del sistema').hlines([1.6 ,1.8],0,2000,colors='r')\n", "\n", "#datos['RPM TRAC'].plot(secondary_y='RPM TRAC')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7dd2ed0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7e1a7b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Con esta segunda aproximación se ha conseguido estabilizar los datos. Se va a tratar de bajar ese porcentaje. Como cuarta aproximación, vamos a modificar las velocidades de tracción. El rango de velocidades propuesto es de 1.5 a 5.3, manteniendo los incrementos del sistema experto como en el actual ensayo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparativa de Diametro X frente a Diametro Y para ver el ratio del filamento" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x8d612b0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rl2MYMwCTu+/+D6qq5rNy5T1UVjbT1lbsWJQF9fVV8dSDJkuX3sArr5wkGj1F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\n6U/t/OfGFwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7e9a890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos['Diametro X'], y=datos['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Filtrado de datos\n", "Las muestras tomadas $d_x >= 0.9$ or $d_y >= 0.9$ las asumimos como error del sensor, por ello las filtramos de las muestras tomadas." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datos_filtrados = datos[(datos['Diametro X'] >= 0.9) & (datos['Diametro Y'] >= 0.9)]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#datos_filtrados.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Representación de X/Y" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x8da11b0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rpNGeVMvnuz7lcWA53POYRS+mcbnewOlso6cnvhh2OvLJqvilL/VH+oFt24bY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\n3/jGAJrWzObNF9i2LYCmmYU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"text/plain": [ "<matplotlib.figure.Figure at 0x8d761d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos_filtrados['Diametro X'], y=datos_filtrados['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analizamos datos del ratio" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 992.000000\n", "mean 1.016456\n", "std 0.135163\n", "min 0.618503\n", "25% 0.936906\n", "50% 1.005276\n", "75% 1.083876\n", "max 1.643703\n", "dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratio = datos_filtrados['Diametro X']/datos_filtrados['Diametro Y']\n", "ratio.describe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x8e552b0>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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h5tAkjh6ewvTWOEq2hcOHxzFmjSmfM7ZiosS8fR2bNFDa9n5WxV79YLumoXTb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\nLFvApLVn6PblsHEidxvys5NYYHsj9/UwjRs7Dfca3PZsMDfXq1g2+rvtq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W4w/I8QPN/W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\nIAiCGCv+f+wVFUFP+LMTAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x8dbde90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rolling_mean = pd.rolling_mean(ratio, 50)\n", "rolling_std = pd.rolling_std(ratio, 50)\n", "rolling_mean.plot(figsize=(12,6))\n", "# plt.fill_between(ratio, y1=rolling_mean+rolling_std, y2=rolling_mean-rolling_std, alpha=0.5)\n", "ratio.plot(figsize=(12,6), alpha=0.6, ylim=(0.5,1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Límites de calidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculamos el número de veces que traspasamos unos límites de calidad. \n", "$Th^+ = 1.85$ and $Th^- = 1.65$ " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_u = 1.85\n", "Th_d = 1.65" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_violations = datos[(datos['Diametro X'] > Th_u) | (datos['Diametro X'] < Th_d) |\n", " (datos['Diametro Y'] > Th_u) | (datos['Diametro Y'] < Th_d)]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>942.000000</td>\n", " <td>942.000000</td>\n", " <td>942.000000</td>\n", " <td>942.000000</td>\n", " <td>942</td>\n", " <td>942</td>\n", " <td>942</td>\n", " <td>942.00000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>64.429299</td>\n", " <td>151.112633</td>\n", " <td>1.641566</td>\n", " <td>1.620457</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.20265</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.698887</td>\n", " <td>0.745685</td>\n", " <td>0.302790</td>\n", " <td>0.324456</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.91965</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>60.700000</td>\n", " <td>149.700000</td>\n", " <td>0.977470</td>\n", " <td>0.000342</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>1.49750</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>63.200000</td>\n", " <td>150.500000</td>\n", " <td>1.413326</td>\n", " <td>1.436971</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.49750</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>64.700000</td>\n", " <td>151.000000</td>\n", " <td>1.585374</td>\n", " <td>1.563394</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.49750</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>65.900000</td>\n", " <td>151.700000</td>\n", " <td>1.872121</td>\n", " <td>1.885199</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3.50000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>66.900000</td>\n", " <td>152.900000</td>\n", " <td>2.583253</td>\n", " <td>2.436864</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>3.50000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO RPM EXTR \\\n", "count 942.000000 942.000000 942.000000 942.000000 942 942 942 \n", "mean 64.429299 151.112633 1.641566 1.620457 1 1 0 \n", "std 1.698887 0.745685 0.302790 0.324456 0 0 0 \n", "min 60.700000 149.700000 0.977470 0.000342 True True 0 \n", "25% 63.200000 150.500000 1.413326 1.436971 1 1 0 \n", "50% 64.700000 151.000000 1.585374 1.563394 1 1 0 \n", "75% 65.900000 151.700000 1.872121 1.885199 1 1 0 \n", "max 66.900000 152.900000 2.583253 2.436864 True True 0 \n", "\n", " RPM TRAC \n", "count 942.00000 \n", "mean 2.20265 \n", "std 0.91965 \n", "min 1.49750 \n", "25% 1.49750 \n", "50% 1.49750 \n", "75% 3.50000 \n", "max 3.50000 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_violations.describe()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x08E5AFF0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AE6BBB0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AE93FB0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AEB7670>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AEE2950>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AF00DD0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AF2AF90>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x0AF342D0>], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ffyCOjk4Gy0RGRrJgwXy8vFQqVapCnz4f4+Pjw8qVS+ndu1+s9Lu+vr78+usvvHjxHIDr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B888/n5NOOomdd945J++ZTzfddBNtbW3U19fz2Wef8aMf/ajbc95++23uvfdefvazn/GDH/wg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\nTK5Z7t5pPjWznO+a5RO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"text/plain": [ "<matplotlib.figure.Figure at 0x8e5dcb0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_violations.plot(subplots=True, figsize=(12,12))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
barbagroup/JITcode-MechE
module00_Introduction_to_Python/01_Lesson01_Playing_with_data.ipynb
2
431062
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Content provided under a Creative Commons Attribution license, CC-BY 4.0.\n", "(c) 2014 L.Barba, P.Bardet, A.Wickenheiser (The George Washington University).\n", "Thanks: A. Ahmadia, G. Forsyth, A. Golding, NSF for support via CAREER award #1149784 to LAB and GW Office and Teaching and Learning for seed grant to LAB and AMW." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Version 0.1 -- February 2014" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# JITcode 1, lesson 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is lesson 1 of the first *Just-in-Time (JIT) module* for teaching computing to engineers, in context. The first module lays the foundations for building computational skills. It is not meant to support a particular engineering course, so it can be used by freshman students. The context problems should be interesting to any science-minded student.\n", "\n", "Lesson 1 builds competency on these basic skills:\n", "\n", "* reading data from a file in comma-separated format (CSV)\n", "* plotting data\n", "* analyzing data with statistics\n", "* writing an image of a plot to a file\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Context — Earth temperature over time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is global temperature rising? How much? This is a question of burning importance in today's world!\n", "\n", "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n", "\n", "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced this video showing a color map of the changing global surface **temperature anomalies** from 1880 to 2011.\n", "\n", "The term [_global temperature anomaly_](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/lyb4gau3LyI\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7fc0040d1320>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('lyb4gau3LyI')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How would we go about understanding the _trends_ from the data on global temperature?\n", "\n", "The first step in analyzing unknown data is to generate some simple plots. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n", "\n", "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n", "\n", "Let's get started!\n", "\n", "The first thing to do is to load our favorite library: the **NumPy** library for array operations." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure you have studied the introduction to [_JITcode_ in Python](http://nbviewer.ipython.org/github/barbagroup/JITcode-MechE/blob/master/lessons/00_Lesson00_QuickPythonIntro.ipynb) to know a bit about this library and why we need it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Read a data file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is contained in the file:\n", "\n", "`GlobalTemperatureAnomaly-1958-2008.csv`\n", "\n", "with the year on the first column and 12 monthly averages of temperature anomaly listed sequentially on the second column. We will read the file, then make an initial plot to see what it looks like.\n", "\n", "To load the file, we use a function from the NumPy library called `loadtxt()`. To tell Python where to look for this function, we precede the function name with the library name, and use a dot between the two names. This is how it works:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.95800000e+03, 2.67300000e-01],\n", " [ 1.95808000e+03, 7.92000000e-02],\n", " [ 1.95817000e+03, -4.18000000e-02],\n", " ..., \n", " [ 2.00875000e+03, 4.07400000e-01],\n", " [ 2.00883000e+03, 4.51300000e-01],\n", " [ 2.00892000e+03, 3.51900000e-01]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.loadtxt(fname='./resources/GlobalTemperatureAnomaly-1958-2008.csv', delimiter=',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that we called the function with two parameters: the file name and path, and the delimiter that separates each value on a line (a comma). Both parameters are strings (made up of characters) and we put them in single quotes.\n", "\n", "As the output of the function, we get an array. Because it's rather big, Python shows only a few rows and columns of the array. \n", "\n", "So far, so good. Now, what if we want to manipulate this data? Or plot it? We need to refer to it with a name. We've only just read the file, but we did not assign the array any name! Let's try again." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T=numpy.loadtxt(fname='./resources/GlobalTemperatureAnomaly-1958-2008.csv', delimiter=',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's interesting. Now, we don't see any output from the function call. Why? It's simply that the output was stored into the variable `T`, so to see it, we can do:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.95800000e+03 2.67300000e-01]\n", " [ 1.95808000e+03 7.92000000e-02]\n", " [ 1.95817000e+03 -4.18000000e-02]\n", " ..., \n", " [ 2.00875000e+03 4.07400000e-01]\n", " [ 2.00883000e+03 4.51300000e-01]\n", " [ 2.00892000e+03 3.51900000e-01]]\n" ] } ], "source": [ "print(T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ah, there it is! Let's find out how big the array is. For that, we use a cool NumPy function called `shape()`:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(612, 2)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numpy.shape(T)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we've told Python where to find the function shape() by attaching it to the library name with a dot. However, NumPy arrays also happen to have a property shape that will return the same value, so we can get the same result another way:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(612, 2)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's just shorter. The array `T` holding our temperature-anomaly data has two columns and 612 rows. Since we said we had monthly data, how many years is that?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "51.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "612/12" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's right: from 1958 through 2008." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Plot the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will display the data in two ways: as a time series of the monthly temperature anomalies versus time, and as a histogram. To be fancy, we'll put both plots in one figure. \n", "\n", "Let's first load our plotting library, called `matplotlib`. To get the plots inside the notebook (rather than as popups), we use a special command, `%matplotlib inline`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from matplotlib import pyplot\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's this `from` business about? `matplotlib` is a pretty big (and awesome!) library. All that we need is a subset of the library for creating 2D plots, so we ask for the `pyplot` module of the `matplotlib` library. \n", "\n", "Plotting the time series of temperature is as easy as calling the function [`plot()`](http://matplotlib.org/1.5.1/api/pyplot_api.html#matplotlib.pyplot.plot) from the module `pyplot`. \n", "\n", "But remember the shape of `T`? It has two columns and the temperature-anomaly values are in the second column. We extract the values of the second column by specifying 1 as the second index (the first column has index 0) and using the colon notation `:` to mean *all rows*. Check it out: " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fbfdb926eb8>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3qYnDF3E5AzG6Y8bwsMinnvLXvM/lgJdeAt4oMVApnsG2bex99PVxvR1bDMTQ\n256BGO+ZM/0JXzFCEyfy2sRAcc/ATvjaxK0PbXsGdhvkPPZn04aJbETsat3IqmfA1PjXpKQVg97e\nyhVsE9J4BknEQMJEUWJgewZvvcV/c+YU7ivPS63TJO3aupWN5d57cy7ni18Mt3Pq1LBnIGEdSfS6\nRjGX4zkBLS3Be3YCuZScgQj+DjsUtl8ExG3D2rX8nm3MfcN2ixHVCbDnG9Qy6hkw6hnUOGnFYPXq\nZOvZpiGNZ2CHiexeteATA9dg2Z6B4PMM7Lh8KdiewejRwB57sCF1w0Qf+lChZxDVKwf4eqZM4bba\ns3fL8Qw6OznuL4ve20S14bXX+L0081COOQY45RT/e+oZ1Bc1/jWNLPr72eCMHx9sSysGq1Zxb3H5\n8uL7lktWnsEBB3Dy0Z5d29TEveeknoEgvXFbOORcUWGNKOyVwMaNC4aO2quWnXgisOOOfs9AjEzc\nvAAhiWdAVPjZzk5eJ8GXAPaJQVsbi8Fuu6UTg4cf9m+/995gydF6EYNab2elGXGewTHHAN/+tv+9\nJ54oXgysknzjG4W1a9KKwZo1PNa9kmQlBoJtnCRnECcG0gP3iYG9r5SmKLWd4hls2MDfjwyT3G23\nYJ8FCzhMNDAAfP7zwFe+Ep7UBSQTgzjPQK6zu5uHkLrrHkTVPfKJwZw5wNKlYc/gxhuL3orEnHAC\n3/t//udsZoFXEvUMmBEnBo88Atx+e+H2LVuAww/nH0i18K1qlVYMtm1jw7hkSeH6u1mRVZgIYOPn\nikF3d/IEsiACePPN/AcEYuC2s7MzMPjjxwP3OcVS5L2NG3mo76RJ/HrHHcP7NTZyW37zGxaHuDBR\nVGzeTiBHeQYr81M6F+Zn8yxfDlx3XTIxkN7vzjuzgIwaFXhK55zj/3wa/uM/uFxHLaOeATPixADw\nl02Qmd1ZVbosB1/PpJyEno3MKp01i8Mcl1+e7ng+svQMVq8OH6epKZhQFvXdSJhI4u4f+xhw7rnB\n+4sW8aMIjysGEycGwzG3bOGJUzZyXtczcL8bu5c9MBCeFwCExSDK8Ij4Rc1AbmwMRE3yE088wbPL\no3Ih0oYvfAH43Od4m4S60uYMhgPqGTAjUgx8RkV+WFlUuSwXn+HPYjRRc3PQa77ssnTH85HV0FKA\ne72uZwCwGMi+bijP9QyuvDJseGV7XJhIFqABCheCccNERx7p/x+SnrwY5bgwUdRiL0k8A7k/MglP\n7kuUwIgpWfofAAAgAElEQVQYXH89J7kBnvsg76kY8KN6BiMQ3w+5FkrtVqJn0tPDxlBEpRL5g76+\nwqJqSTCmUAx+97vwcpW2GMh4eNd4uQnksWPDSWJXDHzGb3AwEBlXDGzPQJaj9BlzCRPZYhDlGUSJ\nQRLPQJD7JP+zUf8/LS3BNUnbxDOwxWWkop4BMyK10BcmqoXqipUMEwlu5cws6O9nY12qUentDcpI\nyDj5n/0siMkDfs/AHQ3kegZjx4a9B7n+qDARwPuLcXXftz2Dvdxlmyxcz8ANE9k9z6iyD83NLHpv\nvun3DOxjSHvlvkQZs5/9LBADeZT8Qi3XDBoq1DNg1DPIIz+64RomAoA774xOMqahr69wIZYkSHJb\nxsgLbs4ACPfWXTFwPYO2trAYyHYRA187Bwe5fhBQOJvXzRlEISEXN0zk63lGeQYyQmm//cLrAdjH\nE5Ys4bYVCxNNnBjcx7Fjg7a89BJwcX6R2rSdjnpGPQNmxP0LRP0Ih3OYSIyT1O7JGimnXKpnIKNz\nAC4RcfbZ/DwqZyC453E9g5YWXldg7735tRg6txS1jS0GbujKzRlEMWpUuJqphIl8gn7nnf7F7WfM\nYCF78MHwSmHSbvt/5P77geOP59IYQLL/HxHVUaPYS5R7M5INoXoGzIi5/H/7tyC27RvfXathoiw8\nAzG4lRSDcjyDV1/l2byCGHOfGNjDRqM8g4aGwCOYMAG44grg5JMLF9+JEgM37CKIZ2CLl4/GRl5l\nTHDDOjZSajqO0aN5tba+Ph46KmLw9tvAo4/yyKCOjmAkXBKDbnsGbttHKuoZMHUlBps38wzQcgzk\nvHlsrCZPDtfqueaasMEYzmGiMWPY0N1xB/e0Tzwx3bHtc7S2li4GS5YAe1q1bsXg+8JEtkfnyxn4\njK4In4hAMTGQ7z7KM+juLu4Z+NqV9jt87jl+FDGYPZuF1KVUz0DYfvvwMpkjDfUMmLoKE02YEEy0\nKRc3cfcv/8L132vBM/D9My5bFqwHXA6uGPT08Lj0p54q/5g2fX1c8mLixNLDRC+/HIRypH2A3zOw\nvzffaCJf+E9GzIgoyGNUzkDEICpnAJQmBn/6UzoxkMllUnTPDhO5q5z5zu9DxMAWjsWL2dMYqahn\nwNSVGABBfNSHMVx+wYf8IH2jOBoa2ECMHVtdzyDqn/Hqq8s/pgwtBcKLv/jKQAh9fckrnf7TPwE/\n/jGPAFq1yl9sLoq//x344AeD1/Ld2MY3jWdw+OEcHly3jl/HjSYaHAyuWe7Nf/5nsBykECcG7vf3\nt7+lE4Ovf53vre3RyHUeeSRw1lnx5/ch99O+B5MnV252ej2gngFTd2IQx333Fa/QKb1kt7Rxfz//\n0KvpGdhhIns0jF24rlR8nkF/f3zu4ItfDA/vjOP11/lRxOCoo5J9rq+PQx3771/4ni3I0pN1xeCZ\nZ4JZxlGeARCs/GUfN5cDTj89nDsSz2C77QIxOPtsXnrUFqe4nIFtTGR9AiksVw7jxnEYzV5pzTb4\n7ndUSs+2ErmjekU9A6buxMAXV1+zhn9wnZ382q4c6eKbsi+eQbXFwP5ntEMZaYaD+nIGxTyDxYuT\nr44mPU0xTFGemYusa2wnhkUAbTGQeRG2sc/luNd+0038OsozsNsHhIeW/td/hRf9ETGYMiVsKI1J\n7hnYbWhpCbalGbbZ3Bz2DOz/EdfLVTEoD/UMmLoTA18vS0JHYjAmT47+nG1UJCwgnsH48dUNEwkD\nA+Ef67hx/Pjccxz6KAU7TGR7BnFiUEpPVoRGxCDpD0pWDbMRMejq4rLbL70EHHQQb3M9A1ss4zwD\nX65BHu1wky0GvtFEY8bwtcathWBfu9zztAnkUsSgFGPm3vuRjHoGzLDQQvkR+H4M3d3h3r5tNERE\niGrDMxAD19cXFgMJE/3lL5z8LQXbM5CcQV9ffM+/HDGQmLN8B4sXc1XPqJ60LVIuXV083n7ffVkg\n7rwz3Lv2iUESzwDgaxMxsENxxvB5p07lUWtyfyRn0Npa3NjaxkSuLU2YCKiMZ7BiRWHF1ZGMegZM\n3XkGPpfb92UefDDw4ovAZz/LP3DfEnyrV/NjreQMxMD19oYni0nc3DeCpBi2GDQ3s3GzZ636KMV4\nuWEigEs477MPV8mMwucZCF1dwXdKxPF9m1wuLAb//d/hRLSvfUJbW/BZ2zPI5bjjsOuuHOqS/4Mf\n/5ift7bG5wsAv2eQhRjYhQDjZjEnFYMZM7JZW3u4oBPvmLoTA98/sfyo7R/3s8/yZJwVK8Kfs8VE\nxMDOGVQzTGSLwbZtQTJcriupGKxZE/Qm7R44UbBYTFZiIL1radvKlby4CwC88EKwXy4X9na2bSv0\nDOycQdwPc3Aw7Nk8/jhw2mn+fd3enr1ITi7HnwW4vHdXF7D77uwxyv/BQw9xoru1tXjuxpczaGri\nFdzKxfYM3NxImpyBEkDENkA9g2GAGFE39OGOvgDCgiGJ5qEOEw0M+JdelOvo6eHJcGPHAlddFVxX\nUjGYPh345jf5uVuoTqpiJhGD3/ym+Lm6u4FdduE/FxHbhx/mH5rtCfT0xOcM4n6YdpjIGB44EDU0\n0p1T0NYWXn/hH/6Bn48fD7zyCveat20LSlMAQZioFDEQoWtq4tX1yl1BLy5M5HoGI92YpcGuITVS\nyUQMiGguES0hoqVEdHHEPj8joteI6DkiOjDJcRcvDv8o+Tjh53b82zXkAwOBJyCfs8dXy+gjO0w0\nFJ7BnDn+VaXEcK1bxzX5e3rCZYzlx+6ruuoihtgVg9Gj+RqTjCaRHn4c3d3ADTcEZQ5sxCDeemvh\nez7P4OCDg7bHtc8OE/X08PcXlX9w75UrBsIBB3DCurmZ4+kyZBbgUUdJxMA2JrZnkIY4MdCSEtlh\nr1U9UkktBkTUAOBqAMcD2BfAWUS0l7PPCQB2M8bsDuDLAK5Jcux99gmKlwXHCr/u6wt+1O5EKZ9n\nYIvBe+8FxxxKz2DZMn8iWK5Dlr+USUayXdpu1/uPQq7b5xls3ZpdmKiriw2s74ckPdeoUhGuZ/CZ\nzwQek6/cgmB7Bp2d8bF8t0KtvXymLQbiWYwezSOZbDHI5crPGVRSDFxGujFLw29/G4zaG6lk4Rkc\nAuA1Y8wyY0w/gFsAnOLscwqAXwOAMeZJABOIKNEyK25P3e3hS6/et6/tGQh2eEbEoKEhGFpa7QRy\nW1uw6lZfX9gzKEUM5LrdUTulhImSIOsTiyGUz8qcBiAczrjxRuDb3/Z7BvbnZRUvH3bOYNOmeCPt\negb28pkDA0E9f7vshesZAHyN5eYM0hAnBm7oScNE5XPqqZpUz0IMZgBYbr1ekd8Wt89Kzz5e3H94\n+cLEGBgTvRZBsTCRLQYDA8E8g3Lju1FceSXwiU+Et/nOMTAQNkQyft7tyZYiBlE5g7gwTDliYI/+\nAbj4mQirLQY33QT83//LpRSixuy3tsZ/B7lc8kqic+eGX48dG3x25Upe+e3RR4M2jh7tF4Nx44qX\nbPANLc1aDHwGX7apZ6CkoSb7EvPmzXv/eWdnO4D291+LsZEftN1LTOIZROUMBga4Nyc16eMmF5XK\nnXcWFgKLEoMdduDa/vLaXuDEXfs2jieeYCMZlTNIUlQuyczZ7m42sGKQ5Lqam4PvxhaDgw4KSi5H\nXUexcN3gYPD+mjXxYrDnnsCXvsQloIFwzmDuXB5KOmlSoWdw552ciJfcy8UXF18VzBcmipoMl5RR\no/h6JU/iG447bhx3bFQMRg4dHR3okB9SRmQhBisBzLJez8xvc/fZqcg+7yNiMH9+EMcTI+N6Bnb8\n2M0ZbNsWHSZqawuPJpKJS21tbKS23z6qdaXjrqkLRIvB9tsDTz7Jr6VNrmcgHk0cIihAoWfQ1xff\n85Z7nEQQ3TCR/T3JUpW2QbzyyuB5VNmQCRPiCxL+9a/B97NmTWnhG3toKcDGfty4cEG8HXZgr+GC\nC7jDcPPNLATFPINK5AyIAu8gquyGisHIo729He3t7e+/nj9/fupjZhEmWgRgDhHNJqImAGcCcAtN\nLwTwPwCAiA4D0GmMSVTF5pVXgFmzCucS+MJE1zhp6a6uwjCReA9TpgTVLMUzGD2aC6c980ySliVj\n5cqgHr2NzxhLolJGULlhIunNuyOs4mhqCod9xDj190d7B0nFoL+fv4+mJjZEt90WvNfQwNeydWt0\n7zhKDJIU5pPvbuXK4rX4bQNqewYAt2/cuHCYaPZsfj5zZtATTyKMlcgZyLl7e/nPl2eRkVyaM1DS\nkFoMjDE5AF8F8CCAlwHcYoxZTERfJqLz8/vcC+AtInodwLUA/mfS42/eDCxfXlhPxhcmclm6NBi1\n09/P7v+yZfx62rTwpDPphR95JC/+khVf+hKwdm3h9ijPoLU1iOdLAvm++zipLKKXRAzEG3GNmG2Y\no/IGcq+LGRfxCkQ8dt45eE88g3LEQNYBTsKKFcXFwO4xu2Ig2+wwkay+NmNGEK9PMsejEjkDgD2f\njRvDs9Jt5LseyesYK+nJ5N/HGHO/MWZPY8zuxpgf5bdda4xZYO3zVWPMHGPMAcaYkvveYrjEUPk8\nA5c//zkIHQ0MsJERI9zSEp6ZKy74hRcCv/51/GibUrBHLy1aFDy3J1jdf3/QRjsmbAwb0lwOOO+8\n4Nrj1gywY/ZAYejANk5R1+gayyhkWKnv2LYYRInKtdf6ty9YEIi2i/TahbSeAcD3yA4Tyczv1la/\nkCc5T5aewe67c2fA9Qzs/2VFSUtN9iV8vWaJIYtBdD0D6blFufODg+GeK8A1iwQJE22/Pf/gkhrE\nYthicMghQbxfrnHBAuCEE4I2yHXMnMkrnImBaWoKBCvOMxCRFEPv9v7FODU0hMXg1FOBp5/m59Ib\nLpb8FM/APTaQzDP45Cf928eO5dCgy5o1wK9+Fd720EPFxeCMM/ixoSFc68fGDhMR8frChx1WW2Lg\negannw6cf36Qh8l6FJwysqhJMfD9WJfnB6a6OQNJIH/0o/z6s5+NPq5bqVGMSC4Xrnxpx+nT4pad\nkGuTH667hoEYkS99iQu9iZFqbuZ2TpsWLwZi4KPEQI43fnz4vVdfDcJmfX1czsLthbvISCLBFYP1\n63lhmeXLCz9bDlOn+kcZFRODww4DfvELFnkZLebiLq/5m99wIvmgg4I1FYphh2myGk0E8Iint98u\n9Axmz2bvSmZuK0oaajLltG1bYQ9fCs75wkT9/YERjRtRYf+Qdt6ZV+YCCodAVlIMpNcdlzMACpd7\nFM9g0qT4oaXFwlty3PHjw/uuWxck13t7gUMPBR55JP5YrmdgGz4i4K23CnvyabGrkz72GNcW8pXC\ncGls5P+pxkb/d+tbXhNgz60csvQMZFRWVM5AGOmTppR01KRn4KsPJO66L0xk96jjxEB+SC+/zD0q\n8QRkYRN7icVKiYH0xo3hBdMvuih4z74OMSJ2mCiX4zbG1SZKKgbjxgX75nKcoJT7vnkzLxBU7B4U\nCxM9/3z858thxgwe8glwz/jNN7nnX4yGBu4MNDaGPQPpzbuegUBUnpHNMoHc1MTfRdRoIkXJgpoU\nA99i7LKcoi9MlNQzEDGYPJmfSy/QJwaVyBkAgQE2JqhBJPT2Br1caatd32fxYm5j3OxhGXIZhR0m\nkrZs2MDtkdnXmzalF4OGhuK1fMrF7sXvsksyYy2ewahRYTG94orgWPZjufT0sEhFiUs5NDXxfIcN\nG+I9A80ZKGmoSTFwPYNRo6I9AxlNZC8mAvgXO3FH2ESJgfTEsiBODHz5BJlk54Yt/vAHrg5azDNY\ntco/yU0QT2PcuEBU5N52d7MQt7SEa/hE4Y4msnutkkCuBOUY7sZGbl9bW7ich5T6Fg8h7fDM5maO\n79v5p7Q0NfFw5+efV89AqRw1KwZ2L2ennQrFwE0gi6En4pDHQnfaG4J95AcvP1SZ3Wn3xodCDOxJ\nX7LwuusZiFGRxGkSMZBRUxddBDzwQPh98bpaWoK2iDfR3c1ewYQJye6B6xnYQxyJCkXp/PPjj5eU\nNGIwcaI/AV+peHsWY/9t70JzBkqlqEkx6OoKG6JZswIx8M1AtpO/xvAP3tcrdb0H+cyWLeHJU0OV\nM7Dfk5iwiIEYAHfhHntSmo9Vq4KFZqZPBz7+8fD7MrQ1Sgw6O8sXA3etCbcX67alXOJKY0chYaKJ\nE+PnaWSFb/GicrFFTz0DpVLUpBh0d7NhtOvdSM4gKoHs1sbx/WiiwkQiBsJQ5AzWrgW+8pVge09P\nfM5AaGvjkVVnnuk/3+rVgWfg60WKGIwZE4jKunV8/8Qz2G67ZKEyd2ipjZt4/d73eFhsFpTjGUgC\nedKk7CYUxpFl/D6pZ6AoaahZMZD4+Q9+AHzrW4WTztwEsh3/B/wzcKPCRD4xKMczGBgA7rmH118W\nosTAZds2vmbXM9htN+CnPw32k3baq4cRAZdeyiOk+voKa/Tb+DyDtWvZ+3LDRMUE0fUMbNyQRZbL\nCpYT37c9g6EgyXDXpNjfo3oGSqWoSTHo6uK/1lbgsst4LQD5Qbi1iSSB7KuaCbDRdOv0+DwDd/JU\nOWLw+OPAySeHJwFFhYlctmwJeq92WwGejSy4xlfyBwsXslcwMBCUU/D1nEUM7BXU1q1jb+I//xN4\n6ikeaVROmMjGFQN7wfHddw8St0PFlCnxlUezjrfvumvxkV1JUc9AGQpqUgy6u7la6V7W4pkyezip\nZ2DjVuFMEiYqRwx8Pd+knsGmTdw+35BE2wC4dWhkTYaXX2ahyeUCQfHN17j+ejb6jY3BvVy3Lri/\nL74YFG7LUgxsz+Df/o0XuCmXcgz30UfztWcx1DMpxWZGJyWpZ7DPPtmcTxmZ1OQM5K4u7jked1yw\nbeJEHrIXNZrI9Qx8xImBXTa5XDFwVyB76SU2rjZxYtDUFC4/4bYbKLw+EQOAvQR7NSz7PeG00/jx\nmWfCJbEvu4xHLK1YwbF9EYvBwehwjDu01Mb9TGOjrshVLrYYRImZzjFQ0lKTnsGqVewZfOYzwTZx\n7/v7+R/fDhNt3hyEeXw/CulJyg/JHjUEsBG3Rx+Vm0B2y0TcfXfhPuV4BvZzt0KrbfB7evh9MbZx\nZStcz2D77TlvsHw5ex9ExUXR5xnIrGPJW8i8CTtMVIt19484gsuX1yLy/f/hDzp8VKkcNSkGmzZx\nkbDDDw+2iRj8+7+zYZEe9+AgGzCJq7thmR13LDT+grzeujXsfpfrGWzeDBx/fFANdc6cwn2iaviv\nXx+eFR0VJnLFQHIAjY1hz+CBB4BLLoluqy0G69dzSGPqVE7U23mLuDkNPjHYf38+xk038evFi4Pz\niUjVomewyy68glot4vMWFSVralIMOjsLY6Nu4u/VV/nRGBaDnXYKXgu9vSwoIgZuj1Re9/QU9sTj\nxGCnnfwTqDZvZoNqr0x2zDHhfaLq9L/zDp9Xwiu+UVByTJvOTu6Fn3tuMHmusZHH9Mct3SliYAyP\nu588Odhf7v2YMcXFwDdqZvr0QCTkOmzPoFbEYCjzB2mQdqoYKJWkZsXA/cc/77zwa+kR53JhMbA9\nA/fH7noGknBzq6QW8wxWrAhq/9ts2cJGVUJMssC9TZQYvP12uA2+pSrlmDadncBJJ7FHIgnkJMZW\nxGDTJg6RNTUFCU9bDOImuLmjsHzYApCVZ5BVqOS117I5TqVRMVCGgpoUg02bCj2DQw8Ffvaz4LWE\nWzZtYuMisem4nIHrGZx2Ghcq84lBkpzB5ZcDZ58dvN68mQ2q7Rm453z7bf+x4sRAhooC/jDRdtsF\nvfioRdNdRAwkRAQESXR73d+33gJOOcV/jI0bWfyKnQeovZzB5Mn+BXRqERUDZSioSTHwhYmA8AgV\nmYTmjmiJE4Np0woXuGlqYsNv976T5AyMAX77Wx6mKUjp576+YP6D2wsWj8ZmypSwGHz4w+G5BZMm\nBeEan2ew3XZBfL9Uz8AnBrZncMUV/jpPg4PBbOVi55HHWsoZuOG2WkbFQBkKalYMfP/4bnJ43DiO\nW9tj7+PmGbS28pq5NiIwpYSJhAkTwq83bw6M49Klfs/AZwinTePQk/zon3qqMPxi1yq6555A1Do7\nOZ/S3ByEicr1DMS7ssXANzwVYCEYO7b4uewwUS3lDOpRDHT2sVJJalIM+vv9//huMnPy5MKRQKWO\nt/aJQZIJV8YEhr+zkwVn8+bAoO61l98zsPMWn/scP0pyPEnPb2CAZziL6IlnYIeJsvIMmpvDVVZt\nkoSI5DwA3+esPIPDDgvPCymHehIDHU2kDAU1KQaAXwzcOP748aV5Bj7cla6A5DkDEQMZ2bR6ddhI\nSfy+vz9IJNtiIKN3ShGDtjY+hoSLJGeQRZhIhEzu55gxQclrVxw3bODwVZLzyGNWOYMjj2TPJA31\nJAZZrbWgKHHUQCrPj88wup5BSwt7BrYYZOUZ+MSAiBcZEcTwy0SrpUvDYiAhG9v42WIgAiSiUqyn\nvXQp5xJ6ewPj7IaJiMoPE0nuRT5vi8HAQFgwN24sTQyy9AyyoJ7EQIhbtEhR0pKqr0FEE4noQSJ6\nlYgeIKIJnn1mEtEjRPQyEb1IRF9LcuwkYaLGxkLPIC6B7EMMky0GsgC5j+eeC84jXsj3v8+P3d2F\nnkFcmEiMq+QHZLJaFLvvztcq3gZQfgK5oYHbv25dIAZyn8TriPMM7M/FYd/7WsoZuIn4WseYbCuh\nKopLWsfzOwAeNsbsCeARAL45rwMAvm6M2RfA4QC+QkR7efYLkSRM1NBQmTBRW5t/HWYg6FHKymR7\n783hIcHnGQDAP/8zcM45fjGQ9iXt+fnCRDInoNShpe+9VzihT+5zc3O0GKxeXTiHIg5jasszUBQl\nTNow0SkAjs4/vwlAB1gg3scYsxrA6vzzLiJaDGAGgCVxB/aFiVwxIAqHiaZM4RozLknEwD7f2LHR\nYiBGeHCQxUBCPBJakrg7wO+L4ftf/4sfd901eF/EQLyZpGsGy2gnYwLPoKurvJyBW6QPCMpojBkT\ntG04iYFdikNRFCatZzDVGLMGeN/oxwY6iGhnAAcCeLLYgZOEiRoawmKwbh1w4YW+80afJ0oMurvZ\nALa3A3/+c2EbBgb4ufSqP/QhfrQ9jM2bo0tguPtOmpS8UJq0+a67+HlzczhMVIpnsGVLWMCMAfbb\nj5/b38ExxwST/t55B/jJT8KT4YphTO0UWau2GClKLVLUbBDRQwDsBQsJgAHwPc/ukelbIhoL4A4A\nFxpjIvrdwjw8/jgwbx7Q3t6O9vZ2AMG4/mnTeBlMIjbaSYY4RhEXJlqzhoVg8WKuhw8E5RlkzWJJ\non70o8Bjj4WPvWlTYdvsMJEd0ip1Xd7Ro4HTTw9ey3DYUoeWxpWUsAVy8WLgttuAr30N+OMfeVsp\nMexaKrGsYqDUOx0dHejo6Mj0mEXFwBhzXNR7RLSGiKYZY9YQ0XQAayP2GwUWgpuNMZ7Czi7zMG4c\ni4HN5ZezMTr7bDbU4hnYs3X9549+z5dAljCRHRISxDPo7+ewkISJ/uEfCmcXb9pU2Eu3xeCFF/jR\nDh0lxR3+KqGjcsTA9gxsXO9scJBFQbYfdVTp7QaqLwwqBkq9Y3eSAWD+/Pmpj5k2Z7AQwLkArgBw\nDoAoQ389gFeMMT+NeD/EokX+ZOqYMVxPRoy0L4Hso9wwkZzHjpfbYmDnDFpaCkszSN0kGxGDH/yA\nK4v+/OfJcwU2rsjI+gPbtqULE9m4YvD441zcb8ECLhyYZGipUG0BsKmF2kiKUmukzRlcAeA4InoV\nwDEAfgQARLQDEf0h//wIAJ8D8DEiepaIniGiuXEH/dCHeJROFJL8kwRysWn6H/wgMHu2/724MJFP\nDCRMtGED8OabYTFweeyxaM/goou4+N7YseVNJvJ9pqmJxaBSnoHQ1VX6MMdaEgP1DBSlkFR9JGPM\nRgDHera/C+AT+eePAsj05ydikNQz+P3v/UNO5RhAfJjIHpPe08Pn27aNi+VJAtnXBp9hFjFIa5B8\n5TKamjhUldQz6O7mx6i6/lEzotOIwSc/GS3MQ4WKgaIUUpcT3M85B/j0p5OLwejR0YZNxMAuOhcX\nJurrC5/P5xl86lOc3wCiPYO0pQUkX/DrXwfbxKgn9Qw2bYr2CoDKeAYLF5YXFssSFQNFKaQuxeCC\nC4Bbby2cZ1AOYhjs2bRS5kJEwM0Z2MbMJwZ33MGLzdjHF7LyDESoPv/5YJuIQVLPoLMz3qiLGLjG\nW8NEijL8qOtUmjvPoBwkfGTPwm1o4D/fGgISJhLEo3ANpngilfIMBgcLE+Olegbuoj4u8l5rK99n\noRwxqCXuuce/roSijGTqWgyy8Ayk+qWvBy/1iWzP4O9/Dy+QIx6F2wbpVVdKDIgKe9uleB2NjSxs\nbikKG7kG99rq3TM48MBqt0BRao+6DBMJDQ1sZNKIgSyf6RIlBgCwahU/Xn99IAxufF161a5hltdp\nZ+P6jHipYaLe3vh9h2uYSFGUQupeDIBsPAOXpqYgNBJV4VLKQDz8cGFPP8ozyKomvW+Mv5wryTnE\nM7AnwblEeQbPPgvssUeydgoqBopS29R9mAhIJwYXXQR85COF2+M8A0GM5THHFL4X5RlkJQY+z6CU\nYyfxDOycgc20acAuuyQ/14knAscWDEBWFKWWqGsxyMIzmDjRb6iSiEGS5GuSNZDL4bjjgNdfL//Y\nDQ3s8STxDNzrLLUW1H//d2n7K4oy9AyLMFElFgq3xSAuTBSFvOf21rPyDC6/nFcpsylFDGTfJDkD\nt806NFNRhh917RlkESaKophnsPvuwAc+ULxtbt18ST6nhagwCV1qmAiI9wxE0NKcR1GU+qCuxSCL\nMFEUkkBuaGADLuskSPnspUuTHcf1Kt54o3LGtFKegVvKQz0DRRl+1HUfr9Kegcxh+MtfeGGXxkZe\nt6AUXDH44x+BZcuya6dN1p6BiME11wBPWssRqWegKMOPuv5ZixhUMmcwZkwwyziXA666CnjkkeTH\ncS9e5aQAAAkuSURBVMVgl12Kr79QLps3J9+3FDHYdVcuHe5+VlGU4UNdh4nsdQ2yRsSgtRVYay3Z\nM20a/yUlKvlcCV59Nfm+ScNEU6YUVjZVz0BRhh91/bOupKEVMWhpKVx7uRSGcuH1XC4+qW2TxDNo\nbASWLy/cT8VAUYYfw8IzqASSQE4bghrK2v1LlyYvD53EMwCC61fPQFGGN3UtBpXsddsJ5HLJ5YbW\ncJaSi0jiGdjY+2nOQFGGH3XdxxuqMFG51HIPOqlnINjXUsvXpShKedT1z3ooxKASI5VqgVI9A99n\nFUUZPqgYRJCFZ1DLiEeQ1DOwUc9AUYYfdf2zHooE8nAVg7Y2flTPQFEUoM7FoNIJ5G3b6nt5xzhE\nDMrp5atnoCjDj7r+WQ9FmEhmHw83xKD39JT/WUVRhg+pftZENJGIHiSiV4noASKKNJ1E1EBEzxDR\nwjTntBkKMRg/vnLnqAWkMmspqBgoyvAj7c/6OwAeNsbsCeARAJfE7HshgFdSni9EpXMGxgzfMJFQ\njhiccUb27VAUpbqkFYNTANyUf34TgFN9OxHRTAAnAvhVyvOFqGTOIGqVr+FGqWJw7rm8ypqiKMOL\ntGIw1RizBgCMMasBTI3Y70oA3wKQ6bLolfQMJMEqj8OVUsXAXdtAUZThQdFR5kT0EAC7TieBjfr3\nPLsXGHsiOgnAGmPMc0TUnv98LPPmzXv/eXt7O9rb2737VVIMpMZP0lo/9YqKgaLUHx0dHejo6Mj0\nmEXFwBgTGRQgojVENM0Ys4aIpgNY69ntCAAnE9GJAFoAjCOiXxtj/kfUcW0xiGMoxEA8g899Djj7\n7Mqdrxo0NPDynaWgYqAo1cftJM+fPz/1MdMWqlsI4FwAVwA4B8Dd7g7GmEsBXAoARHQ0gG/ECUEp\nDKUYNDcDc+dW7nzVoKur9ElnKgaKMjxJmzO4AsBxRPQqgGMA/AgAiGgHIvpD2sYVYyhyBiIK/f2V\nO1e1aGkpvRyFioGiDE9SeQbGmI0AjvVsfxfAJzzb/wzgz2nOaTOUnsFwFINyUDFQlOFJXU8fquTQ\nUlcMkq4gNpxpbgY++MFqt0JRlEpQ14vbDFWYaGBAZ90C5ZWuUBSlPlAxiMD2DLRKp6Iow526FoPz\nzy+vnEISZObxcF3cRlEUxYaMyXRScGqIyNRCm9avB7bfnusTKYqi1DJEBGNM0Qm9cWgkPIIpU4DN\nm6vdCkVRlKFBPQNFUZQ6Rz0DRVEUJRNUDBRFURQVA0VRFEXFQFEURYGKgaIoigIVA0VRFAUqBoqi\nKApUDBRFURSoGCiKoihQMVAURVGgYqAoiqJAxUBRFEWBioGiKIoCFQNFURQFKgaKoigKUooBEU0k\nogeJ6FUieoCIJkTsN4GIbieixUT0MhEdmua8iqIoSrak9Qy+A+BhY8yeAB4BcEnEfj8FcK8xZm8A\nBwBYnPK8NUlHR0e1m5AKbX910fZXl3pvf1rSisEpAG7KP78JwKnuDkQ0HsBHjDE3AIAxZsAYMywX\nlKz3fyZtf3XR9leXem9/WtKKwVRjzBoAMMasBjDVs88uANYT0Q1E9AwRLSCilpTnVRRFUTKkqBgQ\n0UNE9IL192L+8WTP7r7Fi0cBOBjAz40xBwPYCg4vKYqiKDUCpVl8nogWA2g3xqwhoukA/pTPC9j7\nTAPwuDFm1/zrIwFcbIz5ZMQxy2+QoijKCMUYQ2k+Pyrl+RcCOBfAFQDOAXC3u0NeKJYT0R7GmKUA\njgHwStQB016QoiiKUjppPYNJAG4DsBOAZQA+bYzpJKIdAPzSGPOJ/H4HAPgVgNEA3gTwBWPMprSN\nVxRFUbIhlRgoiqIow4OamYFMRHOJaAkRLSWii6vdHh9EdB0RrSGiF6xtkRPviOgSInotP9nu49Vp\n9fttmUlEj+Qn/b1IRF/Lb6+X9jcT0ZNE9Gy+/d/Pb6+L9gtE1JAfVbcw/7pu2k9EbxPR8/nv4Kn8\ntnpqf8Hk13ppPxHtkb/vz+QfNxHR1zJtvzGm6n9gUXodwGxwKOk5AHtVu12edh4J4EAAL1jbrgDw\n7fzziwH8KP98HwDPgvMyO+evj6rY9ukADsw/HwvgVQB71Uv7821qzT82AngCwCH11P58u/4VwG8A\nLKyn/598m94EMNHZVk/tvxEcoka+XRPqqf3WdTQAWAUOz2fW/qpfWL7hhwG4z3r9HfCIo6q3zdPW\n2QiLwRIA0/LPpwNY4rsGAPcBOLTa7bfacxeAY+ux/QBaATwN4MP11H4AMwE8BKDdEoN6av9bACY7\n2+qi/QDGA3jDs70u2u+0+eMA/pp1+2slTDQDwHLr9Yr8tnogauKde00rUSPXREQ7gz2cJ8D/SHXR\n/nyI5VkAqwE8ZIxZhDpqP4ArAXwL4fk49dR+A+A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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbffc155b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.plot(T[:,1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can add a semicolon at the end of the plotting command to avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness. Try it.\n", "\n", "*Do you see a trend in the data?*\n", "\n", "The plot above is certainly useful, but wouldn't it be nicer if we could look at the data relative to the year, instead of the location of the data in the array?\n", "\n", "The plot function can take another input; let's get the year displayed as well." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T4MYE7/vGN4Bvf7u09tnCMHo03513dfmFYYOV3cvlgkqktuG1PQZ7u9g8hiTC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\nQeioJGW4SfuvdxqA2/LbtwE43T3AGLP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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb8ed080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.plot(T[:,0],T[:,1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The temperature anomaly certainly seems to show an increasing trend. But we're not going to stop there, of course. It's not that easy to convince people that the planet is warming, as you know.\n", "\n", "Plotting a histogram is as easy as calling the function `hist()`. Why should it be any harder?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb8764e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.hist(T[:,1]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*What does this plot tell you about the data?* It's more interesting than just an increasing trend, that's for sure. You might want to look at more statistics now: mean, median, standard deviation ... NumPy makes that easy for you:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0997959150327 0.07365\n" ] } ], "source": [ "meanT = numpy.mean(T[:,1])\n", "medianT = numpy.median(T[:,1])\n", "print( meanT, medianT)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can control several parameters of the [`hist()`](http://matplotlib.org/1.3.1/api/pyplot_api.html?highlight=hist#matplotlib.pyplot.hist) plot. Learn more by reading the manual page (yes, you have to read the manual sometimes!). The first option is the number of bins—the default is 10—but you can also change the appearance (color, transparency). Try some things out." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb7e5a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.hist(T[:,1], 20, normed=1, facecolor='g', alpha=0.55);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is fun. Finally, we'll put both plots on the same figure using the [`subplot()`](http://matplotlib.org/api/pyplot_api.html?highlight=subplot#matplotlib.pyplot.subplot) function, which creates a grid of plots. The argument tells this function how many rows and columns of sub-plots we want, and where in the grid each plot will go.\n", "\n", "To help you see what each plotting command is doing, we added comments, which in Python follow the `#` symbol." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BvvCxC/OWknPC7kBrGQ2RYETeaIiKG6obbs+ct8fbwcFRV1GH6nB13nOG/CE5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\nzs076x1CKs0UbzPFCs0VbzPFCs0X72hVmkBfAtwX3r4PuDR/gJlNBc5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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb7bb358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure(figsize=(12,4)) # the size of the figure area\n", "pyplot.subplot(121) # creates a grid of 1 row, 2 columns and selects the first plot\n", "pyplot.plot(T[:,0],T[:,1],'g') # our time series, but now green\n", "pyplot.xlim(1958,2008) # set the x-axis limits\n", "pyplot.subplot(122) # prepares for the second plot\n", "pyplot.hist(T[:,1], 20, normed=1, facecolor='g', alpha=0.55);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 3: Smooth the data and do regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You see a lot of fluctuations on the time series, so you might be asking yourself \"How can I smooth it out?\" No? Let's do it anyway.\n", "\n", "One possible approach to smooth the data (there are others) is using a *moving average*, also known as a sliding-window average. This is defined as:\n", "\n", "$$\\hat{x}_{i,n} = \\frac{1}{n} \\sum_{j=1}^{n} x_{i-j}$$\n", "\n", "The only parameter to the moving average is the value $n$. As you can see, the moving average smooths the set of data points by creating a new data set consisting of local averages (of the $n$ previous data points) at each point in the new set.\n", "\n", "A moving average is technically a _convolution_, and luckily NumPy has a built-in function for that, `convolve()`. We use it like this:\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/RezGBJ6ejaZatTSwjob584HRo6NzbiILmMEiIucZowFW/ozLlCnAAw8AP/0E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\nr9OEgTK1/jz8MPDYYzpNVhT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bY3YBGkiJyNnBDhaR2gCSAdhsTx2h0wvMXCYGpzJYv/4K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\nD9kug0ccWkQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb92fe10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = 12\n", "window = numpy.ones(N)/N\n", "smooth = numpy.convolve(T[:,1], window, 'same')\n", "pyplot.figure(figsize=(10, 4))\n", "pyplot.plot(T[:,0], smooth, 'r')\n", "pyplot.xlim(1958,2008);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Did you notice the function [`ones()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html)? It creates an array filled with ... you guessed it: ones!\n", "\n", "We use a _window_ of 12 data points, meaning that the plot shows the average temperature over the last 12 months. Looking at the plot, we can still see a trend, but the range of values is smaller. Let's plot the original time series together with the smoothed version:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T+j1Od7DqffVk5Y5j/xg7lhjMapSSEliNgUatSOYAc7D44APt30WLjLX5sp3Z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\nSaiEivwK/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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb8d4b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure(figsize=(10, 4))\n", "pyplot.plot(T[:,0], T[:,1], 'g', linewidth=1) # we specify the line width here ...\n", "pyplot.plot(T[:,0], smooth, 'r', linewidth=2) # making the smoothed data a thicker line\n", "pyplot.xlim(1958, 2008);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is interesting! The smoothed data follows the trend nicely but has much less noise. Well, that is what filtering data is all about. \n", "\n", "Let's now fit a straight line through the temperature-anomaly data, to see the trends. We need to perform a least-squares linear regression to find the slope and intercept of a line \n", "\n", "$$y = mx+b$$\n", "\n", "that fits our data. Thankfully, Python and NumPy are here to help with the `polyfit()` function. The function takes three arguments: the two array variables $x$ and $y$, and the order of the polynomial for the fit (in this case, 1 for linear regression).\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Z07+H/37Hf5fuPMNCoSPYgdVkpcY9fv4K0m0aEqlEVgbLZrYVL7AynLliG43O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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb6e15f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "year = T[:,0] # it's time to use a more friendly name for column 1 of our data\n", "m,b = numpy.polyfit(year, T[:,1], 1)\n", "pyplot.figure(figsize=(10, 4))\n", "pyplot.plot(year, T[:,1], 'g', linewidth=1)\n", "pyplot.plot(year, m * year + b, 'k--', linewidth=2)\n", "pyplot.xlim(1958, 2008);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is more than one way to do this. Another of the favorite Python libraries is **SciPy**, and it has a `linregress(x,y)` function that will work as well. But let's not get carried away." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Checking for auto-correlation in the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We won't go into details, but you will learn more about all this if you take a course on experimental methods—for example, at GW, the Mechanical and Aerospace Engineering department offers _\"Methods of Engineering Experimentation\"_ (MAE-3120).\n", "\n", "The fact is that in **time series** (like global temperature anomaly, stock values, etc.), the fluctuations in the data are not random: adjacent data points are not independent. We say that there is _auto-correlation_ in the data.\n", "\n", "The problem with auto-correlation is that various techniques in statistical analysis rely on the assumption that scatter (or error) is random. If you apply these techniques willy-nilly, you can get false trends, overestimate uncertainties or exaggerate the goodness of a fit. All bad things!\n", "\n", "For the global temperature anomaly, this discussion is crucial: _many critics claim that since there is auto-correlation in the data, no reliable trends can be obtained_\n", "\n", "As a well-educated engineering student who cares about the planet, you will appreciate this: we _can_ estimate the trend for the global temperature anomalies taking into account that the data points are not independent. We just need to use more advanced techniques of data analysis.\n", "\n", "To finish off this lesson, your first in data analysis with Python, we'll put all our nice plots in one figure frame, and add the _residual_. Because the residual is not random \"white\" noise, you can conclude that there is auto-correlation in this time series.\n", "\n", "Finally, we'll save the plot to an image file using the `savefig()` command of Pyplot—this will be useful to you when you have to prepare reports for your engineering courses!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YFBas0t1e/LIfm8dG3/i+f8q8rx2VO5Tei53U+Lpx2DGY8+U5JL4ae4lWHVzF\nT3k/UWYrIzk6meTo5CPyXB4JVY4qxr3XfJjM5DQRp43DEGHgqV+fYnfVbiodlQzuNjjksOOqwlUN\nVoLWp9BcyJayLfgWfIvBDVt6aAgf1j5ho1VpGZumCDZJknhywpOUP3oPn53eTQk73nYbHz/WXEOZ\nOs+XRg8WC+M2GTGZlApQT614issGXYbTVgP5+TB+PN3NPipH9if9mVntsq8zSY5O5r4l9/HUiqeO\n2PPV0USqI0nQJVBiLWl13LFQZFUgEPyFCIioUNugyLLcvOfLbaVXbK/Dvnlb3Bai1FGkGZqGvpxe\nJ3csuoP3L3r/sPbd1fj8PuweOzHaQz3t/ij6A6DTGl8HxVdUUtDzpVPrGni+GnuJalw1FJgLMDlN\nJEUlkRydzN6qvc3tvtMpthazvmQ9siw3KQprcpoUz1fdjd3msVFTU8aJqSeypngNACvyV1BqK+WK\nwVc0u3+r28rY9LHNer5eX/s6ucZcPvpSWWzw80nJpOlT22X/lAFTGJ02usG2yNhEnp6SwLRz7oOH\nHmLqf76Dy7bBkCENxtk8Noblu2DUKO7Zu5eDKTp694VRpWbOLv4ClcMF9Abg197hDF/yM1ptdLvs\n60wePvlhMgwZXPvttSHnmXUlmXGZbVa6F54vgUDQpQREVKhhR3etu8G8AFaPlaz4rAaer4+2fMTq\nwtUh7dfsMhOjjSE1OrWJ56vMVsYHmz84omT+rmT+rvnctLDhSrP1Jevpl9CPSnslc7bOwel1UuOq\nYfCbgzvkmHaPkvOlU+uIkMMY+N58nloXzYSPV9Lv5ocZWtY07KjbfxD7lnXEaeNQhamUsKO9Ydix\nq1Y/mpwmvH5vsI9jAFmWlZyvaidf/ZzEFytTGDBtOq9MfZcZb2zEZq6k1FrKlC+m8OXOL5vdt1/2\nN6n+X5+VB1eyY+8qDD//hl+Cnjc9wMk9Tm6X/dePuJ5B3QY12KZT65T/qwceIHfiCejcfuSLLwaT\nCWpr4eWXmX9hP4Zf+yBPPrIY9irCt0epg8d/hfP2Uie86q6FWs3Nk/xExye3y7bOJkwKY0y6Up2/\no8OOHUFmfCZ5ptbFl/B8CQSCLiUovkIMOwaWwTfn+RrSbUiDfK3FeYuxuq2clHFSm/s1u83ERMSQ\nok9p4vkKJElvK9/Gab1Cq7t0NCm3lzcJcxRZihiVOopKRyW3f387MdoYEnWJ7Kjcgc/va7P9SVsE\nSk0AzNhKBSg7AAAgAElEQVSo5ZJvN9S9orwf/yrS8OQoxfNlcVtw/vITLzywBB9+zn9KWQ3WOOer\n1FrKsP8No/S+UsLDwo/IvrYIhEkr7BWHVt6tWYOrqoL+VRLaUWPRVlZyqWIZAAOXbORGr4oNUzZg\n99iDn5N1xeuIi4wjKz4LOCRMu0V1ayK+HF4HW8q3cPMOFWEeL1uyE5hy5p0dck7dorphdpkps5fz\nxV1ncsGWDZywfz9ccYXSAmfGDAKBSK86HPV9D/DscCtl38zm8d9UJJWacVx4Hq+6V/DwpbOwdY+j\nfNv17Spc2lVkxmWiU+uOubAjQJ/YPsLzJRAIji3a6/kKNP1tzvPVI6YH1a7qQ9vc1jYLRgYIeL5S\nolOaeL4C3pBtFdtC2tfRxuQ0NcntMjqMZCdms7V8K1aPlRX5K4IhvtZW4IVKxK59DNlrhaoq7v6p\nbuVpWiy/nKRUKj9zjxdb6UHweln24p1EXTKNCK+fKC/c/pvyHjUWX0aHkUpHJZvLWq+R1BEEPk/B\n67ZoEYwbR+T5F7L1FTdUVkKfPqw+tTdrHpjGjBvTAbhug4+KmhJ6xvYMXsd3NrwTTLCHQwntgcUI\n9VlTtIah3Ydy/S5lZeO6U7M67Jx0ah1TB07loy0fUeG3Mvly8CTEwk8/KS1wgG/7w3WXqHjpyxnw\n/PN4uyfyeraNt2dPh4ICvHPn8OypwA03UDV+RINQ9rFEeFg4Q7oNOSY9X/GR8W3+jwnxJRAIupT2\ner4CrU+a83z1iOkRTOAH5aYXCFO2RWueL7NLEV+dVaahozE5TU1WbFY6KhmYNJBcYy5R6ihWFKwI\nluuof83aYuHuhU1rQP32G5OvepqHH14IiYnE2mspGt6H9cs+5rGb+lA0JhtVrcx3/y1BTk9n8qMf\nE233sqmXIjgmLymE9etJlqOgpAReeQXKyoJlHRbsXtDp1z5QGqPCXqEkl0+bBv56BXuTkuCPP5jz\n0HmsnXoSn/b34MvsQ7oFdN/9SK/YXsHPid1rbyDgA6UcEnWJGJ31PF+yzLaCtVxdksTQbZV4wmH3\naQ1Dh0fKNcOu4audX2FymTAmRrLk+ZsgXPEirjyjLzNu781Hg32EJyklI7Qq5T3RRydARgbRmmjs\nHjt+2Y/ZZT6iYqidzeQBk8lOyj7aZjRBp9a1mdPa6eJLkqSJkiTtkiRpjyRJDzXz+pWSJG2pe/wm\nSdKQ5vYjEByreGu9Ia+AEhwSUcXWYu7+4e42x7fL8+Vpv+crVZ/aJGRndpvpGdPzuPJ8md3mBkVN\njQ4j2bFKJfLJAyeTa8xlXcm64PhQcPlcXDj3Qn7Y98OhjQ4HXH454b56xUPVatI/WUCqQakKv+rm\nc7DERtLTDFJFBRXRYXw6Ts9FN0czd3wsGq8fTjwRQ1I6e19wwL33wjXXYHFbUIWpeOrXp/jbt38L\n+fwLzYUhjw0Q9Hw5KmH6dLBY2HpSJjd8fS33PjAEVq+GpCSiNdFY3BaqXCaYcS8AOe8uJVOXEfRu\n2L12SmyHPkMBz1eSLqlh2PHKK7lrwsPc8fhCJFnmuZNBndS93ba3Rt/4vhRZijA5TZyQegKP1i7h\njVev4Zv/u49LJ1q4Z+w9AMFSE4FK7gEPV3hYOJHqSPZU7SG/Jp+YiGPT8wXw0MkPcVbmWUfbjCZE\nqiPb7JfZqeJLkqQw4HXgHGAQME2SpAGNhu0HTpVleRjwDPBuZ9okEHQ0P+77kau/ufpom3HcYHKa\nMEQY2FaxjTnb2i606PK5kJAaCAZvrRdvrZdUfWoD977VbcXta6fnK7p5z9dJGScdk9XXmyNwbQI3\nepfPxZh9Lvpnn8pnX8Iow0DusQ6i12c/khoWG7L4CiS/P/7L44c2vvkmlJRQ1Lc7/170KMycCZ9/\nDoMGoY/QY/VYOdC/G//5+HbuvbUX6575O6MejuPG87yU1Zr59vYc9pzWTNL/0qWo127ggn4XMGfK\nnJAadA9+czCrC1eT/Wb7vR9VjiqlTIa5FJYtA+CyCZV8suMzDozsA5lKhXO9Rk+xtZgodRSqm28l\nP1lLcpmVGz7ZjtlVgyzL2D0NPV9WjzUYdgyKrwMHYO5cAHwRanx33M4LE1QdLm4CeWaV9kruGXMP\nd554J8X9U/g4Oo85U+YwInkEQIM6X0CDivOGCAP3L7mfJ1c8ecyGHY9ljgXP12hgryzLBbIse4G5\nQIPCI7Is/yHLcmC5yR9AWifbJBB0KLurdlNoaf8v72OFEmtJmy1QOhKTy0SGIYNCcyEmp6nNYzt9\nTrpFdWsgGAI3tzhtXIMQmtVjDT3s6FLEV6DWVP0ekTWuGnrH9sbitnTptQkFv+zn/iX3N9jWOH/J\n+uMCvvqsFqmmhit2wN1nPsrTz6/hre/hneXRDbyFrWH32Ek3pJNfk694l+pWzAE8cLKTkwefB088\nEWyLE62JDpaaUOtj2H3KQN4YZGdk5sloVVrCw8J55IyZRM5fpKy0q6jg1DdPpOh25cfL2PteIssR\nyVl9zmqyCrExsiyzs3InM5bMwOaxhezxDF4zl4kBiQNQ5e4Gp5OKZANXnn0fPr+P+Mj44LhoTTQH\nag4oPRfVal67aSjucBg1bw1Xbg8LFgyuL+Cb5HzJMvznPwCsGJfG9xvmonr9TYanHVmD6eZQh6uJ\n1cay17SXYcnDuPmEm3nujOeYd/k8zuhzBin6lOB5wSHxVV8E6jV6dhl3sbls8zHt+TpWiVRFBj32\nLdHZ4isNqH9XKqJ1cXUT8EMrrwsExxx7qvZQYi05blvUnPXxWVz5zZVdZr/JaSIjJoOD5oPIyK0K\ngfc3vU+huZA0Q1pD8VXXViRGG4PNYwsKpPbchM1uJewYoYpAH6FXVr/t3w+XX073VVuI08YRo43h\nQM0Bfjv425GddAdidBh5cfWLDXpdmpwmkqOTlfyl7dtJmHIVsU4Z4uOpNmiQJQnf0MH4wiXO/aUI\n9bqNIR3L5rFhiDBwdubZLM5brCRul5RQ0l3HeXe91qTGkl6jDxZZ1al1pOpTWXZgGdlJ2aTqU4nV\nxjIseRgZMRmQlQVJScTEdmfzrRfC+PFEl1dz6dytwQrmreGudSMjB+uZtSePLXDNBiQOIHar4t3c\n1EPFhF4TGNp9KHHauOC4aE00+TX5JEUlAVAyvA/Tz1Vem/2lF/X4U9BW1lBqKUH+7TcoLQ0WMU3U\nJWK0V8Jdd8Fbb4Ek8VFOLEkGpXTDwyc/TE6vnHbZHQrJ0cnYPLYGIjJASnRD8RWpbhh2BNBH6DlQ\ncwAZWYivw+BY8HyFjCRJE4DrgSZ5YQLBscyeqj34Zf9xWyW93FbOsv3L2F6xvUuOZ3Iqnq9Afa7W\n2q+8ue5NVhWuIjk6GafPyaK9iwCCbUXCpDD0EXrMbjOyLLcv4b7O8wXKDaly3xZlOf4XX3DLo1/T\no8xJfGQ8n2//nEd/fvQIz7rjCBSVrV8PKyAkKu0VcNddhHl9LBmfDBUVxJndSH4/qi3bUN3/IGEy\nnPzCp7hd9iZ1uBpj89iIUkdxTuY5/LjvR/jwQwDeG1bL5OwpTcbr1Do8tR4sbktQfBVZihiQOIA0\nfVqzzZQTdYlU+K3w4Yf4JRj5yy4ijIqQai2E7PA6CJPC0IRrSDekt7smW5WjigGJAwivE6LLEm0M\n7T6U8/qep4jDOvQRevJr8hXPFxAbEcu7J0DJ1LMB0KzfxAcv7uOH9z1Ip5wC48fjtFYTrYnGEBbJ\n9F8c8PrrEBEBX3/N8u6KJxfg4gEXMzBpYLvsDoXk6GQkpGaFU5QmCr1G32rYUa/R45f9aFVaEXY8\nDHRqXZs5X51d56sYqN/AKb1uWwMkSRoKvANMlGW5xZ8vM2fODP6dk5NDTk5OR9kpOM6ocdUQJoUd\nEytx9pr2khKdwoebP0SSJP556j+Ptkkh45f91LhqGJU6qkuLW2YYDt3cWhNfDq+DIksROrWOBVcs\nYPLnk6l8oJJqZzVpGCAvLxh6jAiPwC/72+35Ahhlj6HvKReD9dAKzPFvfUf8FfHkGnM7rUXP4RAQ\nrTaPjbjIOGRZxuQ00T+hP2HrNsAvv+A2RPHFTSdxdnijWlmPPYZ59juk7C1l7vPX8a/UPDbduqnF\nY9m9dqI10ZyTdQ5PzLsL+Vs3SBL7zx8fvHnXR5Ik4rRxFFoKGZcxjtS6qu0DEgeQqk9t9jOWpFMq\n5DMii+1jMxm6Og8+/RRDhAGz20w3VfONnB1eBynRKfx+4+9M+3payKFUUD5zJqeJM1NOpkeRHrCy\nv28CMdoYnj392QYV76M10bh8LpJ0iucrLjIOJCh59VluG2vki1dL6VVYSq/Ax/jAAYa89jmpXjPS\nxRE8C8iShPTJJzB5MhXPXxsUX51Fij6FWG1si7XSsuKzgufTbNgxQvlhk9MrR3i+2sHy5ctZvnw5\nRoeRg9ubbyofoLM9X+uALEmSekqSpAGuABbUHyBJUg/ga+AaWZZbrUo2c+bM4EMIr782z698ntfW\nvHa0zcDmsVHtrGZs+lje2/QeS/cvPdomtYsaVw3RmmhitDHt6rV4uMiyTKW9skFT3dbEl91rp8Ra\nQqQqknOyzuF0ox7/bbeS9N83efs/uTBwICNqtFS7qoONttubcA9w44/lqK12GD0a1qzBo5JI+3k9\ng8wRivhqpkXPN7nfUGw59Fuyq3oUBo4TOF+71446XM0ZG2u44iYlH2vbpDFEdmsmwyMqij3TlNVh\n0ncL2W3cjdVtbfFYgdyl5Ohk7j2YiuR2U3xif9S9M1uckxGTwW7j7qDnC6B/Qv9WPV+Bz8AfY5Va\nWixZ0mboMRDa7BHTo0nuX2vUuGpIfTGVcns5Az9dTGylFUv/XvQ8+zKAJq2GAiIz4PkKhCRjtbF4\nU7rx67ezePB8DS9dlsG2lx8BYOSnv3DWl4dCu8VPP8DjCVsZ+39j8dR6Or02VXJUcrMhxwBrblpD\nZrzyHgZWO9b/IavX6Oke1Z0ZY2cwMWtip9r6ZyInJ4eZM2fy4KMPEnVWVKtjO1V8ybJcC9wJLAF2\nAHNlWc6VJOlWSZJuqRv2GBAPvClJ0iZJktZ2pk2CPwcltpJWb9pdxfqS9QzqNogeMT0oMBdQYC44\n2ia1iypHFQm6BKLUUV3i+apx1RAeFh5M+tWEa9r0fBVbi5UbxOLFzHu1Av0Hcxjw+lz6FFjA6+Wi\nzS6qndVB+9sVdtTGQGkpJ/16AH+YxJKn/4Z75DB+HJOAJMtMW1rGLuMuqhxVTRLv31j3BmuLla8r\nv+yn/+v9u8RDFqjnFThfk9PESJuBqU9/BShelpXnDAjmKDXGepZSsf/8vRKjuo8I9ipsDpvHFqxi\nf/k2pdbXprMb5kQ1JsOQQX5NflB8JUcnE6ONIc3Qiviqq4W1JrtOlKxYQVKYvlXxFagiD0pRy1DD\njptKN+H1ezm5VI165tMAGF59mxfPe6XZ8QGhFAw71p1DrDaWWG0slSoP/z3Ry9pLx7FpwgCYPRuf\nKgxjjyRYvpwz3zuN2acY+Hjrx+wy7qJbVLcmAq+jSY5OJkGX0OLr6nB18G+tSkuUOqrBNkOEgVR9\nKmdlnsWIlBGdauufkUhVZJs5X53eXkiW5R+B/o22vV3v75uBmzvbDsGfi3JbOeFS57YfCYV5ufO4\nqP9FwV+PxZZiav21nd4apaMwOU0kRCYQpYkKueL8kVBoKSTDkEGUWrmhZ8ZlYnQYW2x3Y/fYMdWa\n0IVr4f77UfvBeMooojftQGtTcipO2lbDJlc1iW7l5tiusGNEDHz8CSqfn01jenLJhoeYnZHMe6dF\nc+EqI6f9cgDtUB8OnVKUs364yO6xB79gC82FmN1mLG5Li6KnowiEHQMeK5PTxMPLXIT5aqmO1bLo\nH5eyO66WoXVioTGa7CHsi4Osajcfv5zP3JSfObPPmc2OtXvsRKujwWql29Y8aiVYP6J7q16VDEMG\nMjI6tY4RySNYOG0hAFcOuZKzM89uMj4YdgSKtB4s2ZkYduZx6gF/sIhpc9RvbxSnjQs57LixdCOT\n+l7Av+b8Bt4aJRn+7KZ2BQh4vhqEHVHEV0xEDGW2MiJUEZyQcgLL85dz7bXv84huFT3Sspl+0mkk\nVHZnQ+kGRqWOwuf3cdDcejiqI0jVp7b6HtWnubwuvUYf9FoK2s9xlXAfCsfrajJQQiEbSja0PbAR\nTSpL/0WQZZnPt3/e4usV9op25Xh0BrIs882ub5gycArphnRS9akk6hKbFOw8lqlyKp6vaHV0l4Qd\nD5oP0iOmB1GaKDQ+OL86kQXbvuKk95r2YvTLfpw+J37Zzwl/FMD27RgTdCx7eTqvv34tP/39XFCr\n6ZtXjbRtO1aPlTApLPSwo8tMbERMMIn8oxPCsXqsfJX7FRviXbjOOh2N28eH34KqliYV5G0eW/AL\nNteYC9DmF25HEBRfdWHHvN1/cM4GM4SFUbT4S56I+J21xWvpl9Cv2fm94nrz3m2jkVNS6LmzhOz/\nmx98bUfFjgafg0DYkRUrCPP62NRDTbHKERQgzRFIVtepdYSHhTMqdRSgiJVA78P61A87WtwWzOfk\nAHDWxpqQwo6gCKJyWzlbyra0+Z25qWwTtzkGMXBvDcTHw7PPtjq+ubBjtCYaVZiKWG1ssAbYdcOv\nY96ueVQ7qzGqPURrlTBeYmQim8s2k65P58zeZ3Z6vhfApP6TmDVxVkhje8X24u+j/t5gmz5CiK8j\n4agXWe1ouiohuDNYW7yWv83/W7vmPLXiKS7/6vLOMegYx1Pr4Yqvr2hREJTbyzukP92RUG4vx+Vz\nMTBxIGPTx3LPmHvoGduTAnPBcfNDocpRRXxkPFGargk7HjQfJMOQgd5Ry+KP4T+PreS7GRs48/P1\n7KnIbTC2fp2cU+cp+TNLLz2ByloLe+L95F1/IdyiZC+Mfe4jrC4L8ZHx7fJ8JS5eCTt34k2I442E\n/fSJ68N3e75TPC4vvojToGPSHtj2rorSf97D22veDM6vL752Vu4EQhNfOyp2NKhED4rH9L7F94Vk\nd7mtHEOEIfh+1X7yEapaGSZOZPCJ5yNJElaPldN7n97s/HRDOs8/twbp228BOOf7XbBnDwAP/PQA\nX+d+3eAc00xeeOwxABb3rsXkNLXp+QKCwqgtGosv15QLARi3rhybreVQYn3xFR8Zz/ub32fE2yO4\n/fvbmx0/Z+sczC4z+/av5/QX6n7YTZ8O0U0XDtQn6Pmq82gGwo2Bv4utxURpougW1Y1zMs/hix1f\nYHVbG4i2AzUHSDOkcf2I63nlnObDmx2JTq1rUXw3Rh+h59FTG67mvST7Em4eKQJSh0uYFEZEeETr\nY7rIlg6htcTQYx2H19GuFhh7qvbw71X/5vfC3zvRqmOXwK/6xt4GUDwilfbKdtf16WiKLEVkGDKQ\nJImesT15YPwD9IzpydXfXM3TK54+qraFSpWzSgk7qrso7Ggu5LQ9bvpkjyenLj0u3gXPL4PkQWPg\nnnsgNxdWrUJ68EFuXg8nFEOP7YVgMLBv0jiqHFVBu3nmGRwxOjK2FaDZuIVEXWJIOV9+2Y+mxoru\nbqVYqefRh/GGw7iMcTx3+nMsuWYJ2iHD+eWNB7CpYUCZj7PeXYZv9gfBfdi99ibiK5RreOOCG/n5\nwM8Ntv2w7wdmb5kd0jWssFeQFZ+lfB+aTOR8qbQM4oYbkCSJB8c9yDMTniFMauPrffRoiqecpbT6\nue46kGUsbkuDqv52r52LX1sKmzdDRgazT9RQZClqPecrpn3iKykqKbigweK2oB0yAoYMIcruwbBq\nfYvzGni+tHFU2Cu4eeTNwXIkjXnsl8fYVrGN277KR5uXD0OGwP33Nzu2PoHQZsDz1T+xPw+OexCo\nE1+W4mAYffKAySzcszDY27H+vHRDOjq1jv6J/Rsf4pgjOymbE1JPONpmHNe09fk/vsSX5/gWX4Gc\nkFCotFcypPsQTE7TcS06D5fAr/rmVpmZnCZq5dqjHnYsthSTbkhvsK1njOL52mPac5Ssah9VDkXE\nBJrpdjZllfu58MXvkdxu8mNg0/y3uWCa0m7FYLTCrFmQnQ0nn4xu1pu88x2sDzQcu/lm9PEpVDmr\nDnlfYmPJmzgagG6LVyriK4Swo9Vm4t3vwpEqKuC009DddR+Rqkiy4rK4Y/QdnNzjZADk0SeSc7uW\n/QOU/nuTv9oOVuX/sXHYUa/Rh+T5qnRUNsn7WZ6/nCpnVUjex3J7OZlxmXiNFXimXkw3sw95/Di4\n+GLlMp1wM5cPDs1jXvTkfRhj1PDHH7B0KTaPjV1Vu4KvRxwsIXPNHqVG1bp1uFKS2Gva26Ger5iI\nGBxeB1vLtx5aBHGR0ggleWXLZTAcXgc61aGwI8BVyWfz8JeleC+7BIyHFnLU+msptBSi2riZq9e5\nkcPClJZIbXi9QPFiZBgy6B6lfAaiNdFMHzMdUPLA8qrzggJtYtZEfi34lX2mfU08Zo2/KwR/bgLF\na1vi+BJfx7EICcR/Q/V+WdwWYrWx9Evox56q4+NG3pEExVczq8fKbeWk6dOOetixyFJEmr7hcv7b\nup/P7H4PHTd5XyanSVnt2EVhx3Pf/43oUiPysGEs+fENEk6byI4xvdj79bvkpWhbnGfrHgePP06C\nLgGjwxhcpQlQec6pAAz8cjm3LTYxIK/1tjRUVRFx/oVcvMMHOh383/8hhYeTEZNB34S+DYbGR8Zj\n6pPCJ6/fwo4kSK10wZQp1Ho9uHyuoNjabdzNiJQRIQlYo8PY4HtAlmVWFKwgWhNNQU3bq2WNDiN9\n9D2Z9I8P0CxfSY1ejfThbGhc0ysEIpNSmHNanRfrlVcU8WWsE1/FxVz7rx+QZBkuvxy6K4n2Na6a\nVnO+0gxpSEjBRShtIUkS/RP6c+FnF+Lz+xTRct55APT5PVdpzdMMjcOOV2yDU866kb+v9qH+8mvc\np44HnxL+L7GWcMZuH6Om3YfKD9x0EwwMvbjpgbsPBD1Z9cmKz6LMVhb0fMVoY5g+ejpXDrmSMWlj\ngIaeL8FfB+H5OkrIstygDlXgSzrUlS5mtxlDhIEBiQMOfRn+hQgI7eY8XxX2CjLjM3F6nQ1arHQ1\nxdZi0vVpcO+90KsXPP00vU86l2uv/BdP/He90gfvGKcrw441z/yTy5aWIKtUSO+8wy1j/06PmB7s\numMX8adN5OzbonBq61Y85uayrnAN418axG3nw89vPwwGAwmRCVQ564UdAd/4sRxIj0LjcHPV5zv5\n5O1KMLciwC65BO3K1Rj1Kli6VGlzA1w37DrGZ4xvMHR48nD+e/Z/STQkc9EVUBkdBkuX4n1BSdJ2\neB04vU6sHiu9Y3u36fly+9xY3BYOWg59D5RYS3D73JyUflKbpUocXgdqr59rXltO780F2OP1vPLq\nVcFzaC9R6ihmj9FSG6GBRYuYtqycf8/KRR4zGvr2JXOPEXtqktI8G4KCtzXPlyZcw9eXfd1sWYmW\n2P737eTfk0/Vg1VKuHT0aFwGHQmlNXzzzoxmQ4n1xVdWoZ2P5ochmc0c7KkcNyJ3D188eyWfbfsM\n48rFfDsXVC4Pc0aqkWaFloweoKXVy4FaWQHPF8CzZzzLUxOeCpZuCIgvkcD+16KtHx/Hl/g6TM/X\nVzu/oshS1MHWtE61q5q7frwr6E0IJA+31ID5+vnXK73l6rC4LRg0Bvon9P9Liq/AdWsu56vcXk5y\ndDKx2liW7l/aJeGy5iiyFHHqrwfhlVegoAAefxwcys339B0O5M+VpN7LvrwsmBN0rFFmKyMpKkkJ\nO3am+Fq/ntjHFMEivfqqUsy0jghVBElRSRSFWbn9wWymzegBAwbg8DlRxSXw6Xg93r7KTS5Bl0CV\no6pB0ndcVCJ/u78vP/5NCRUaXDK88Ubzdhw4AMuXU6uL5KbHh8FJh1ZZ/uOUf9AztmeD4ZHqSKYM\nnEKSLgmpbxa3TlbEofbxp3hnATjqisCmRKeEJGCrnMr/eH3P1z7TPvol9KNXbK82PV9VjiqeWK1h\n0KL1uDXhvHXvySQPHtPqnNaI1kRTpHay7owBADy9wMb5e2SktevA6WTtsCRWf/Uy9O4NQEJkAuFS\neJtFQicPnHxYtayCoZrwcPZeppS/GP7Y63y9cU6TsUHxVV5Ot6tuQe3zwy23MOfjB7mzrvfiyI+X\nkfDWh2RfeTfaWth43ggevDIRtC17WdtDoABtc9X+A6Tp0+gb3xdNuKZDjik4PvhTeb7a27srwFvr\n32Jd8bojOvZ3e75rV9mHgGgIhJ/qe76qndUNKmMDLN63OLiEHOrEV53nK7CM/a9EwMvZUtixe1R3\nYrWxTP1iKisKVnS1eQAYK/IZ/apS2JKhQ2H4cJgwAf79bwD8/3oegK3lW8mtPPz3cPG+xR1ecuSO\n7+9g9ubZ7KjcQXZSNlGaKKxuK3O2Nr3JdQR5nyheYOeN18HtTVejhUlhpOpT+Vyzh0VJSjjZ7lWK\naCboEoI35URdIkWWImRZblBmoEiyMH/qYL5+ua528weHEuPr/6jhyy8BKJtwIp6U0Jf85/TK4dWJ\nr/J9Pyh75mH8ahU3b4Sxi3dSYi0hzZBGlCYq+H8uy3KzPxaNDiNalbbBj7C86jwy4zMV8dXI8zV/\n13w2lR7Ke6qylnPNGmU150v3j+eblBqyk7JDPo/GRGuisXlsLDw3E3+YhC8MPrl8IONvgE2rvua+\nu/qhST3UCio+Mp64yLhOLxIKEPnUcxh7daNPpY9Tv2xae9vhdRDjV8OkSZCfrwj6l19m2pBp6G++\nA6tORVaeibPfWkKE1cHuBJh9wwlEtSKUDoe+8X2DYcfmSIpKYvedu1t8XfDn5E8lvuqLk/ZgdVvb\nrLnRmFfXvBoUSLIsc/Hci6l0VHKg+kBI85sTXxmGDAothby78V3uWHRHg/F2r73BEvmA+MqMz+RA\nTWjH/DNh89iQkJoNO5bby+kW1Y24yDicPmeLqx47tUZaeTn/fGmD0gB4zBhlNdimTfDzz3D33TjV\nEoGfm8MAACAASURBVOFbt0NVFTWumsPOAZNlmUmfTepwz+3Gso18sPkDZFkOem32mvZy9byrO6VM\nhvN7pZZU5IVNmzEHSDekB/OoAsnsUeoo4iPjgy78QI5dgi4hKABitbFUO6updFTiyzmVmghg3z44\ncIAaVw29ZvVSqtPLMnz0EQB5Z45sV8PgpKgkzu17LgmRCdyetYvnr1dyw66Yu41SUwFp+jR0al3Q\nC7u+ZD2TPpvUZD9Gh5Gh3YdSaC4Mfj73mfaRFZcVXKxRn/c3v8+8XfOCz8MWLKSb2YetVyq/ZUeR\na8xlYOLhN2aOVEfi8rnYmuDlrn+MYNydkVz52XZ0OWdS0S0Ke917ECAhMqHVlY4dSVbKIBLfnwvA\nFfPzYN488PuDrxv2HuTyGe/BunVK2H/BAtDp6BXbi6lj/sa9t/fGrAunMlHH7NvHMXF6LMW11a16\nqQ7LzvisVsUXNG1ZJPjz86dKuG8uBBUKVo815No/AWZvmc3qotUAeP1eauVavtvzHed9el6Lcxxe\nB37Z38DWgIBz+pxkxWdRai2l1FrKT/t/CtokyzJ2T/Piq2dMz5CScP9s2Dw20g3pzYqvUlspqfrU\n4E2guVWPB80H/5+9845vql7/+PubpGkzupLuQVtoy95bZCkiooA4UJzgdV6uE7dexT2u84pcxInK\nlesWuIDgRUBAAUH2atndO+lI0jY9vz9OkrY0KS104e+8Xy9epOd8c3KSb5LzyfM8389D//dary2G\n9Le/cd6hCqTQEJg7F+p+uWq1HE5w9Un7/fezEl/F9mKqaqrO+IeHL9IK01h3fB29I3sjgKhtB7Gf\nPAY0jDDXLSw/IwoK6HbEgqTRyJFBH8QFxWHwM5AYkkimNdPTPub5sc97lr37a/x5a8JbJATXpgdD\nAkKwOCxklWYRE9qJn7u45mL1atIK0yirLJN9pH7+Gfbuhago9g1JOqOGwWa9mfXH1/Ne11L2hEOo\npRL9f1cRExiDwa828lViL/GkGOu9FBUFJAQnYNQaPe9td+QrISSBYyXH6o0/WnyUg4WuqElpKZ1f\n/BcA2TdfRVpROmqhPitHfZVQYdAaOGE5wY8RVk52CkYlVAT7B2N1WGtNVus8/6Y6p7cIY8dSetsM\n/KsluOIKOf15ww0wbBh//9uXRO88AnFxsGIFREZ67hauD+fHWBtD/h7Fzf84j0+HBRDXqRd55Xn1\n6rNagiGxQ+gc2rlFj6lw7tPukS8hxAQhxAEhxCEhxCM+xvxTCJEmhNghhOjn61hnFfmqal7ky+qw\neorj3b9mf8v4jZyyHEBOBb284eV697n+2+tZmb4S8B75SgxJJLc8l+yybGxVNo/XT6WzEqfk9Cq+\nIgwRlFeVt1tdU3tR6iglKTTJq+DOKs2SxZcuFJ1G5zXylVuWy67cXa3jOL5vH3zzDQ41iD92wKBB\nDYZkdJV7F1Zt/hWH00FmaWaDMU3B/X5ryabNxbZiHE4HnYI70TuiN7zyCilX3sbJN+DomxBwxTWU\n3n077/34IgDPrXuOMZ+MaWAM2iScTqQpU9DUgHP0SAj0XSsUFxhHbFAssYGxZJZmeiJfl6RcUq/p\n77W9rmXdjNpUs0alweBn4GDhQaIDo1mb4qqtWbaM9KJ0wPU5nD8fgJwbp/L0phcYkzim2U/HrDNT\nZCsiozSThefJv2y7frPWE/lyv9/s1fYGaccqZxX55fmE6cNICIzH+sG70KsX82d+y7hXvmLgbyc4\nlLVHNnjNzkbavRvbicNc/fZP7JkyHPt5QzFm5nMyyYz1lutIK0prkb577lWWGdYMj9CqK77qihV3\n2rEt8Zv7Lk+OUyHFx8OJE7BoEWzeTIVOQ/q142HLFujWrd59wg1yy6KMmmJyK4s4XnKcnuE9ySvP\na/HI152D7uT+4fe36DEVzn3ateBeCKEC5gIXAz2B6UKIbqeMuQToIklSCnAHMN/X8c70AtRY5KtG\nqvG6r574qqpNJZTYS+RWQdnbGqzAySrN8vxyzSvPI1Ab6BFftiobSSFJ5JblklOWw9iksR7x5S4u\n9ya+hBCeps3nOoeLDjd5bFllGV1Cu5Bdmu3ZZn36USy9U7n4m53EGKN5YuQT3D3kbq+WE6WVpUhI\n9Qwjz5THfnqM/x76b+2GF19ESBJfDw+ChASv9ynsJf8Sdm6Wo6dnGvlyi6/DxYeZ/7vPj0azSC9K\nJ9mUzBUpU7jj+wx4/HEANBIkWiBo1VoC577PsL++CA4Hq46sothezNu/NVwh9vz658kuzWbyF5O9\nP9imTYhNm8gJFGg++qTR84oLiiM2MJbYoFiOFh/11Hx5o24TYJDrvopsRUQZo1jdxyBH2ZYvJztN\ndsbPLj4Jq1YB8OWAAG7qcxPX9rq20fPxhjvqIyGxZkQMDo2gy86TdKkIqNcf0+F0NFid/cSaJ3hi\nzRN0qtSx/PUcku97FvbuJbSsmohF36O75noOvVlF2cVjITER0acPaS9XcNWGInot+Y2APfspiQrh\nP3OuItAgC6ABUQOa/RxOxag1YnFYsFfbPYX0Qf5BWByWeqtKQa59u33A7Wf9mM0hQKvnnQuNWA7s\ngI0b5Xq+pUu5ae6FHHjmboiObnAfd2ujiqoK8srzOGk9Sbewbq0ivhQUvNHeka8hQJokScclSaoC\nFgNTThkzBfgUQJKkzUCwECISL/hKO2ZaM/nLD3/xuk+SJMoqy3zWfP1w4AfuWHZHg+1Wh9UjeNy/\nZnfn7QZk+4PcstwGhfCFFYUesZBXnkffqL6eiEdFdQUJhljsJQVklWYxOXUyW7PkRQCeL+w65pBu\n8QWycWdbNGNtTSx2CwMWNP1CUVZZRs/wntRINaQVpkFaGsbnXyV4TxoPfJ9L5/+sok9kH1LNqV7T\njm5Buzd/71mf++bMzbXHOXwYvviCGo2abyb5Xt5f2kd2sVZv+wOVUJ21+Fp2aBnPrnv2jI5xKmlF\naaSYUnhzo5GeC74DScL+5KOEPALdZ8Hvc+7AZgqi75FyKt59m4MFB3n8/MfZnrO9wbHmbZ3H6iOr\nWXZomffI2IoVAKwcFAKdOjV6XsPjhzMpdRKXJF/C7FWzmf/7/CaniNz99oxaI9YQHbZLLgKnk94f\nLOW6fRpCPv63bD+RnMwajjA4dnCTjnsqZp2ZnuE9AdCHRbOmpw6VBIP/sx69Rlcv8nWqb9rmzM1Y\nHBYu+mYHkYdzsAQIst94lhG3qZFmz4YePTAXO4hd/wdUyq9llQp+S9Ly7mBYdesFvPbmNERyikdA\ntIQLed16Jfdxg/yDyLBm4Kfyq1e7khiSyNTuU8/6MZtLhCGCPFsBnHcezJgBl11Gkbqy0QtcmD6M\nQG0gGdYMTDoTJp2JYnvxaeuzFBRagtP18Gxt8RUL1PVWyHBta2xMppcxgO+048HCgyxP995Swt2Y\n11fkK6s0Sw7z16HSKRsonpp2dBci55blklOeQ0FFgacnGci1Mu6LbF55Hv0i+3n+djgqmHjX62S+\n6mT8ynSmBA9le/Z2nDVOz/G9Rb6Ac6bu60jxEZ8mshVVFVgdVk9N3OkorSwlyD+Iy1IvY+mhpfDc\nc6hqagvoDY8+BXv2EKoLbVR8tYTFQ4Y1wyOC+OEHqKkh8+LzkBIaERPJyVQYtPjl5nOeOvGs0o5+\nKj82ndxEoa2wRRYRpBWmcX5JMLz0kmzMuWwZmmeew6KD0s6xbJ3Yl58fmQaAeOUVRkUOIdWc2qAe\nCfA4k0tInvrGP7L/YNpX06ipceJYIheL7xvkPUJYlyGxQ7h/+P3c0OcG5k6cKzuHN/FCGaoLJdoo\nR0D8Nf4U3zIdgItWHGTRl9Wc95qrl9+FF7I9ezsDo89MtJj1ZkYljMJP5UeEIYJ/D5BTnPEffs3g\nVz6nvE4Uu9JZ6RGkkiSxK3cXrx3rysBFcsT7ngd78OuEnhxODUO89hrs3k3J//7LHVfrsO/Yxpe7\nFzP988t57OnzmD3Zn0WXxnHC34ZZb/ZEqM70edTFqDVi8DOgEqratGNAMIeLD59VPVlLEmGIaPDj\nu67PlzfC9eEkhiTip/IjMSTR815SIl8KbcEzY55pdL+mjc6jRShcXshTZU+hEirGjBnDmDFjANmO\nIK88D2eNs4EZnrvuwlfNV7G9uMEv9lJHKSqh8gieunVDBj8DeeV55JblohZq9ufvZ2TCSJw1Tkrs\nJWSX1Ua+JqZMZNuW7+GVV3jwy18J2y4X2M5dLiFtuoTuj0Swv2C/R3T5El/nStpx7pa5SJLEmxPe\nbLDPHXm0VdmaFM1w90ab3HUyX37zLCzaRpUKut4jeOlXPddsLocJExg0cQQ/hx2FU7qplDpKMelM\n7Mnbc1bPSZIkTlpPesTXH9/MpT9weEAS4XrfjVNNhjDSO4fQZ3ceFxYE83tYVoPi5aaQU5ZDj/Ae\n7MzdCchR0rO9eKQVpfHMd64U8F//CpdeigbZHHNgzEDyK/IpHBJPbCT0zS3iin2QeFWiV/Flq7Z5\nzu2E5QROycmhwkN8te8rHtpQw+C9BygJgMKBzbNDGB4n+281tUVNaECoR9gHaAIoGt6P2FdfxfnI\nwxTGh6EtqyCkqIKSyy7CumfxGRdI3zXoLiQklh5aSoQ+gkUpTu6dquOt5TUkfb6MR/aHQdT/sAfK\nn+Vv93/Lz0d/5u+j/87YozD7E1ca/KqryOtWRlphmse4FJWKkAsmknZyGKsCMkjPO0yCqTO9/YNI\nDk0mrSgNg9Ygm+JqDdw39L4WKfQ2ao2E6cOwOqz1Il/pRemE689h8WUIx15tx6w3kxiS6BmriC+F\n1mLt2rWsXbu2SWNbW3xlAnXDA3GubaeOiT/NGHnH+DDu+ttdRAfWz/HnV+RTI9WQV55Hoa2Qo8VH\nmdRVXubtrrvwFfkqtjUUX1aHlbigOPLK81i0axGhulD81f4YrQ76dulDbrlct3WLrRu2r/4ND46k\nxF6ChOQRXxUF2Yz/fjfTXs6Aykc5D5CEYHdKEH0OWRAlJTy8O5H1x9d7fHp8ia8B0QN4aPVDPDnq\nySZfjNqSnLIcSh2llNhLOFJ8xOsYt/gtrypvkvgqrSzFqDUyLG4Y1q93Qk0NCwdriO0zjPdSKrnG\nWgr799Pp/S95TSNg+wTZ6PT22+G++yirLOOCpAtYd2wdkiSd8VLvIlsR9mq7LL4kibhdxwDY2zWU\nCIPvL3GTzsSeTjr67Ia+Jx3Ed44nw5pBt7BuPu/jjZyyHPpG9fUInMKKwrO+eJQd3EPnVXtAo4HZ\nsz3bJ6VOYkD0ALJLs/HX+LNgILy7HM7/5RjRgdEU24qxVdk8aShnjZNKZyU7c+Rz+/fuf7M5czP3\nDL2H87P8GPj+t9QImDkFupkbTzmeSufQzoTpw5qVdnTXgfmr/ekzvw8HZh1gqOUxFkx/l8XbFvLt\n2Pmsr/iDwcWDz/j94DZhjTJGEW4Ip6yyjH/11/DWjK9xXjONUdsKYNw4Lh2SSrwF+n45m6SqEopu\n0fP1Aldt4jXXwGefYVo6g7SiNI8Dupsru1/Jwp0LySnL4YmRTzAxZSLpRelc9NlFhOvDMevNqITK\n64+cM8GoNXqO6Y6oBfsHc6T4CBckXdAij3G2ROgbiq9CW2Gjthfh+nAqqioI04eRGFwrvpS0o0Jr\nUTcoBPDMM76jX62ddtwKJAshEoQQWuBaYMkpY5YANwEIIYYBJZIkec0vfrWwgoLchp5XbiPO7LJs\nlhxcwmP/e8yzzxP58lHz5V7KXxerw0qwfzDxQfHc8N0NbM/8nYUrdRT8A1bevYVur33C+RtOsODl\nvYx/aD5fL3qSr/Z9RZDGgN+xk1BVxfMfHyfu2bcwVoLTT8PHEyLZv+JTXnxhArPuSwXgsh+Psnzb\nYsory9E4ofP36+HFF+G339DllxCcVQSSxMSUiQyMGUi/+f08RfodiU92fMKrG1/F4rCwI2eHxzqj\nborM/fp7W7VpsVsaRCbdUaKQGi2T98jieMXlPegb2RdTWDwsW+Zx3favluDHH+HAAXjgAZg3j9LK\nUnqEyeahnqX6Z8BJ60n81f7klOVQk3aI8DKJ0mAdB0KqG83pm3QmtsfKH6+uh60khiQ22SOuLrnl\nufSN7AvIFw1v9gXN5bKlBxBOJ0yfXm/BwNfTviYxJJH8inxK7CV82ROcKkHqlsOo7riT+KC4erWH\n7h8L+RX5aFQaVqSvwOqwErlyAys+daKSJBaPj2Ft/5Bm97UTQjA2cWwDYeKLumlHtydaWlEaDp2c\ncjphz4X4eH5M/5Hxncc361y8EW2MJjQgFLVKtnoQkyeTvmEJ3w2TW9skbTnElIOQuC+LoWkV9Hns\nLVQSsuB9803w88MUYCKtKK1eQTvAjH4z+C3jNw4UHGBcZ9nlPT5IFu+Hiw/TJ7LPWZ9/XYxaI2ad\nmZCAkHqRL3u1vcNEvnqE92D98fWev6trqskvzyfKGOXzPuH6cMw6M2adHPlyC3kl8qXQEWhV8SVJ\nkhP4G7AK2AssliRpvxDiDiHE7a4xy4GjQoh04D3gr76ON/RgOUkTrpUdxNPTPdvdfjk5ZTkcKT7C\n3vy9cpE2p498FdmKvEa+ggOCmdlvJlHGKLr9ezXXbJR/tfpVORm2aB0f/KdWLAx7+G2MT7/AsZcd\nbHm5ELRaJuyXBV3Web256/lhvHpZKAwYQKQhkpODU2HYMAIKLSy76xcSHnmJV1fDxS8uhieegOHD\nSXu5nKAe/WDoUERJCQsvX8jVPa5m6cGlTX3524zcslwKbYVY7BYsDgtHS45yxZdXsPbYWs+YupGv\nU3lyzZO8v/39etvKKssI1AaiWbkKYyXk9OiErltvxiaOZVjcMOjcGQ4fxlJWyF+narnpag1H/363\nfOf58z3ibVTCqHpf2s3lpOUkfaP6klOWQ8XPqwHYmqQlz5Z/WvG1LkZ+XyXvzyFVF9+oWe7rm15n\nwbYFDbbnlOXQP6o/WrWWAdED6ru1nwFFxVlc97trYcfDDzfYH64PJ78in2J7MQUGmHuxK7Lw/vtc\nmhfK8X2/Iu3YAW+/Tc28dwlxfQx6RfTipPUkVbYyLnh5MUZ7DVs6+zPnAhVLrl3CjX1vbPa5fnHl\nF0xKbWhU6o3JXSdzRXfZwNX9fXCk+AghASHEBMZwpPgI87bOY+XhlUxIntDsczmVgdED6RzaGb2f\n3iNQ/DonM/vaUDmyBSzuCU/OTCDbCOVawfHJo+GLLzyr80w6E4cKDzUQXwatgVfHvcpdg+7ytKTx\n1/gTrg9nZr+ZLS4e3JGvuuLLbUDbUcTXjH4z+PHwj/VWk5v15garXusyKGYQg2IGcW2vaxmVMKo2\n8tXCPl8KCmdCq/t8SZK0UpKkrpIkpUiS9LJr23uSJC2oM+ZvkiQlS5LUV5KkhkuqXORGBWI8fBIe\neQR69YLXXwdJko3z/Axkl2ZztOQoKaYUvj/wPdC0yJc38RXkH8RjIx9jQGhPxn79u7zj669Z/tlT\n7OkSSKm/IH/ShQDEZZVxw/IMQsvqO4NLCxagWfUT37JfTtdodEQZo4gKjJaNOTVy1rfX9xu5/zfX\nnSZNwtmlM4V6V1pk61b48ENUQkUXUxevxeXtTU55jiy+HBYiDZHsyNlBblluvRSkW/zWrZ8rryyn\n1FFKoa2wwYKCUkcpwUUVshgFNg6PI1wfzpU9ruTB8x6UBwlBoD6E9/pV81nPanZdNRJCQ2HPHoLS\nTmDUGhkRP8JjlnsmZFgz6BvZl7LKMhzr5ajjqugKjpUcO634Oqi1kpESidZRzegjzkYjX1/u+5IN\nJzY02J5dmi03V591gChj1FlHvrLX/Rd9FfLnp1evBvvD9GEUVBRQYi/BT+XH7GEWjv5FFjVvvfA7\n40fPRPTvD/fdR+D9j/C+K449KFr2Ohv3hxWd1UZuQhgjZ9RwvDKPIbFD6vl0NRW1St3k9OCohFGM\nShhVb9vhosME+wcTZYzivPjz+GD7B9ir7fSKaPi8m8vTY55mSrcp6P30nveBp7fjokX84/2ZTL8a\n3u1qofv9fiQ8oiXsq//CVVd5jmHSmcgpy6mt+arD9X2u5/kLnq+37W9D/sZ9w+4763M/FYOfAVOA\n7N9VN/IFdJiC++CAYC5LvYyfjvwEyCvc3d0OfDG993RuG3gbdw66k+7h3ZWaL4UOxTnlcL9w/p1s\nuHooXHABOBzw4IPwz3+SX5FPr4heZJdlE751H+9k9uXndDlK4a4dam7Nl/vL57LNxZiL7WQlmuGK\nK1ANG875t6oY9/YAyha+z9xxQWxMCeCfQ+C55y7iiZlyGmdNshpx662YdWYsDgtWhxW9n56Z/Wfy\n6PmPwsCBsHcvv13a1/O4/719DCxZwo71X3LB673llXUACxaAJBEaEOrV06q9yS3LpbBCjnz1iuhF\nVmkWJfaSeiv8vKUd3/j1DV7a8BKllaUNVgNesiGX5N6j4NAhTkT488lQrdcUlEqoCAmQUz25VcWe\ni9t1b68hpEZLUkiSzxWYTSGtKI2kkCQijZFoft0MQMXQAWzJ3NKo+ArUBmKrtrF1kBzlGLA1w2fk\nq9hWzO9Zv9dPj2ZlUZS2C4fTQaQxkqTQJMw681lHvuy/uNLW553ndb/bnLLEXkJCSAJOyUnpzdM9\n+x1aNcXRIWxOkiMOV+2HW7fB9B3VzF8KH38pR9X2XzESo38gwf7B+Gt8L0xoDSqfrOTREY9ypESO\nfGlUGpZdt4yfb/6Z7675rkVbvdQVXx6TVbWaEzFydKXEXkKfpGGM6HFxg4iL2zOsqanVx0c+3uz0\nbVPoFdGLAdEDMAWYPN97bvf/jhL5AogyRJFfnk+ls5LM0kxigxoXX6eirHZU6EicU6sdY5P6Mnfa\nCc6/ajF89BH85S/w9tsU3O/PiMSR5OUc4f0FOQRWfk3fQIHztzuouKEPEYaIRlc7nlrEbnVYCdIG\ngdPJ1GVyevOX6edzjcvw1OqwMj7lEqIDo5k92o5AAP7cMbA7ezo5+f7S25mdPpfDQqAWasw6M7nl\nuej8dPUjAKmpbHnsZv7l9wBHIjQMnNqXS5GtM7qau8LEiRAbC2lpMGcOoTPHdsjIV265LL4kJDqH\ndvZETur2I/SWdsywZlAj1WB1WGtFpSThzMrk6aVW+e+AAF75ax92lR9hko9f4SEBIRTZisgvz6f4\nkXuxfv0hyWmF2L7fhOr+YWfVV3HJwSV8efWXrNn6H4KP7sShVTNj5tvM/Xg4kQavdnSAXLMUGhDK\nDyk1TAXif9nJsQvjvY5de2wt/aP6c7DgoLw4YM0amDABU3U1H42LQfWo/BvJrDefdeRLt2WHfMOH\n+IowRFBkK0IlVPSK6EV6UTqBvQbC009DRgY/3DCARTk/suTgErZuG8igpdt4fymw9BPcpdlHk0I5\nPmkUsQfS0aja/ivGT+2HWW/mSPGRemIlOCCYIbFDWvSx6kW+XOKq2FZc78fec2Ofo4upS4P7usXX\nqWnHtsadEp6YMtHzHDpa5Avkc8kqzWLyF5PR+emIMcY06/5Kwb1CR+Kcinwlm5I97UK4+Wa5merR\no4zemEGfyD4E/bCCQFcQK6pUQj1/AePueYsY/zDfaUdbMVXOhgX3Qf5BsHw5UVlWjoUIjlwk++n0\nCO9ByaMlPHfBcwRoAjD4GQjQBNAzoicmnYmQgBD2hktoDLVtVCKN8kXaW7uBmNB4Pu0Hh1PDPV/Y\nBwtc4kujgXfeAZUKnn2WmP0ZPptItyc5ZTmemq8uoV3IK8/D6rDWF191Il81Ug0l9hJyynMoshdh\ndVhlj6iFCyE8HHVcPCYb0LMnlJeT30M2mfX1Kzw0IJQuoV3Ir8jniM7O/RfJlgNJ36whxhhNVmkW\nzhpnsxtG787bjVNy0j+qPxMOy9GSI90iGdBpKGl3p532wmTSmfhMtRtnbDTa3AKC9qZ7HffTkZ+4\npuc1qFVqCg7tkAvhq+VzvfKnLFgsNxc+68iXJBG955h824f40qq1pJhTyCzNJClEXtBg1pthzhz4\n4AMSkgd6Fn18N60P6/uGcDjKn5pBgyAqiptvNHLrnP5owyKJDYptlUhNUzDpTJ6ar9akrvhSCRUD\noweyJXMLdqfdc5HvFtbN6+vgEV9e0o7tQXRgtEd0GbQGBKJDRb7C9eEUVBRwrOQYy9OWNzvy5V6l\nq0S+FDoC56T4kiRJNod0LZN/58sybn/0ax7/xtWEed48PnzhKopCA+i0PZ0n/5OD3Uvkq9JZSXlV\nue+04/dy3diCARI6Xa2Yqhu9ig6MJjowmj6RfYg0RhLiH8JJ60nPkm2ASEMkGpXGa3FoTKD8682s\nN9eKr8KDtZYEU6d6nmfc3E/bPO140nISR7WDE5YTXg1Sq5xVWB1WtGotTslJfHA8R4qPyKabddOO\ndSJfPx/9mcsXX05uWS7FtmJKHaWk7syQnasLC6kJCGBHkl4WYyrVadMzw+OGM7nrZPIr8jluOc6y\nVMgzCoxpxwjZIqfunl33LM+vf77BfSVJYt7WebKfl+UkH27/0LPvl+O/cFHnixBCcPHv8uueNlqu\nF/IWyTiVUF0oQ+KGop58OQCX7LY1MPQF+N/R/zGu8zh6mLqive5GyM+H8eP55DaXC/szz0BNzdlH\nvo4dI7TETpUpBJJ9u/O7jTuTQpPwU/nVey+nmlM9BrZHdDbm3N+fLtl2VFu3QnY2aweYyC3Lxag1\nyr0aT1OX01qYdfLn6UyaZzeHuuILYGjsUDZnbsZebSdMH4ZA+BRXHSXy5Q2VUBHkH9ThIl/5FbKn\nY6WzstnvLZVQodPoFPGl0CE4p8SXSWdCCFHrKj9rFhn33oJaAu3P69A7nFRePgluuYVrHvyYx+/u\ngU0DF/98gluXZHqiCW6KbcUIRAPxZXFYCPIzwrffArAs1XeoOiYwhmhjNG9PeJuZ/WYSEhBSr0Et\nyKkcX/5c7i8Qs86MwynXyxwsPEjXsK61g2bPhoAA9MtXE32swNthWo3rv72eJ9c8Sb/5/diamPpa\nYQAAIABJREFUubXB/rzyPML0YUQYIgj2DyZcHy6bQfoZ6kW+3MKyvLKczNJMDhYeJKcsh2J7MVa7\nhWdXuaKPjz/O6l3f8fAzI+S6OGovUr4uBO9MfIfxXcaTX57PsZJjVGngXwNlmwtx333E66NZeXhl\nrUt9HQ4XH2bW8llsz97OrOWzeGnDS559VodVvjDu2kWPPzJwCsi+eESTXzuzzszUblPhWrmH4G1b\nayjKqV/3ddJykoKKAvpG9eXyjECCt++lIiIUFi3inb52KmMiZQuN558nssr/rMSXtHEjADXDhkIj\ndU8Dogdg1BoJ14fLAqLO2FBdqCcaUlBRUK/1DMhRhdzyXAxaAyM6jeC8eO8RttbG/Z5p7chXV3NX\nOUrtYmhcrfgKN4Rj0pl8pl6bW/PV1nxy+SckBJ++M0FbEaYPI9Oa6fkB2tzIF8hiWVntqNAROKfE\nlxCiXurxxQ0vMT7lV75f8g95Cfe2bWi/WwL+/hi1Ri6c9igzXZ0k715ZBAEBMGwYrFsHyPVeZr25\ngfiK+iON2yfPgZISyiJD2R3p22k72hjtCdf7a/y9iq9IQ6TPDuduw1h35KtGquFQ4SFSzam1gyIj\n4bbbALjvZ3uz02dniiRJ7MzdyWu/viZbD1TUF34FFQU8vuZxIg2RmHVmggOCZafsrGMsWqZl5loL\nNrscJfGkHavKyS/PJ6csh8zSTIptxfQ+ZGFIJlSFm+Hxx8kpz63n3+M2UmwsBeK2SHCvmnxlBFR3\nioOdO7l7C2zL2obVYW1wv40nZEHy0OqH2JW7i6zSLI8/WVVhHle9swaGDkVVVc3iPgJDfNMdxV+9\nSLYLYORIGDWKkIoa9PM/rDfmx8M/cmHnC1EJFTfIrUP579g4CAvjaFkGVbPvlzc+/TTjRs8k4Y/m\ne4W5cRfba88f3ei4gdEDCQkIwaQzeY3apJpTMWqNsvg65X1t8DNQUFGAUWtkRr8ZZ2Qx0RK4z7u1\nI1/zL5vPiE61grxfVD925e7yRL4aixyF6kLrnWtH4/JulzfoGNKehOvDOVh4kEhjJJ1DO3vS4s1h\n/mXziQ/yXnupoNCWnFPiCyAuKI6s0ixsVTbmrJ3DrMGzmHLZbDm6MKB+4+YxiWP4T2/47h9/Ic8o\nwOmEzZs5dO8NgCweooxR9UxWpUOHmP30j+gKrRAWxt5ZV4Pw7Q0TExhDlKFWKHgVX8ZIn+JNq9Z6\nzADt1Xbyy/Mx+BkaLs1/6CHw82P6bonypd826zVzY6uyNStteazkGEatkUdGPMKwuGEU2Yrq7d+T\nt4dPd35KlD6C0SdVLHkjh57jb+Dnj2qY8msxr61wUn7rTZCRga2yAo1KQ0VVhceHSZIkYtJyeGWF\n/PofmzwKDAayy7LriS+TzoRKqDwXK2+4V+kdtxwn0hCJTQu2N/8BwG3fneD6P5yUlReDJMnvAxeb\nTm5iWs9p/HzsZ54a/RR+aj+s6Xvh9de59y/vM3DJFrDb4Yor+M99FzWrnUuP8B4E+gfKUaY5cwAw\nz/9UbvDs4oeDPzA5dTKUlxP5oywE/5GYSUVVhdw+5b6H5JqvkSNRV9h4/r00OOK9i8DpqNkgW1mI\nEY1H74bFDWPRFYvoGtaVUZ1GNdg/a/Aspnab6jXy1VGMLNsq8nUqkYZICioKasVXIz8YNCoN/5zw\nT8+5KjROuCGcSmclkYZIdt+1mxRzSrOPcVWPqzqUoFT4/8s5J76iXQXUua7oyKwhs3wuHQ83hNMn\nsg/l48cw5jY/FveUt3fZlUF59gm2ZG5hWOyw2shXWRnWW2/Ev1pCuvpqyM6m6Fq5XsdX2vFU751Q\nXSiFtsKGkS8/75EvkMPnbvGVV57n3cIgPl5ecQboZ91XT0A0lcv/czmhr4Sy6eSmJo3flbuLvpF9\neXncywyKHtRgpWV5hYVLggby1QcWXn9+Kz2PV6Dds49e+bVjwhZ9B/HxjJu3ErPOTHlluacjwX2H\nzKyf72CQazHi4u7yc8opy2kgvsw6uf2JL9yRr2MlxxgaNxQA/ylXwK23EuBwsvB7+OaWH+Xo56hR\nUCnP+aaMTcwePpsnRz7JDX1u4JKCUIyDR8CDDxJcVE5u32TYswe++YYlM3488zTamDHsSA1CY7FC\nSAh060bV7bey9dBaJqZMlG1Fysth+HAKY0JYdXgVMYExCJVKNu38+Weqx44hrKwGacQI2LGjeY9/\n6BCGfWnYAzQwaFCjQ9UqNaMSRtEtrBvvXvpug/3Te09nQPQAn5Gvuv+3F25B4zYLbSvcP7KKbEXE\nB8UTH9x4lOXuoXc3+r5WqMW9uKmxMg4FhXOFc+5THxMYQ3ZZdoMLtC8WX7mYSamT2B9cyfSr4WD/\nTqglsH39BeuOr+PCzhdSXVNNzbbfITmZ4F+24FSrEK++ChqNJwLl68MeFxRX7wvW/Uu7qTVfAHNG\nz+H8TufLka+KfN+pisceIyNMi19mNqxvvmt7XnkeCcEJntqniqoKJi6a6LWQHmBn7k5PKxOTzlQ/\n8rV6NaMm3MHyB7YR+MsWnCrBsvFJ8MwzPH6xhns/mkbpnMc9w0d/u41DT+Vz28OLuf25/7L6Sx2v\nLcpD43pox4C+vFX9C45qR4O5DdWFnrbwV+enI0wfxp68PQyLHYZWrZXdwRcsYMVjV5MWJvBzSrLo\n2rQJnn0We7Wd9KJ0+kX147kLnkOLmn8sykdtscJ55/GvOwex/tPn5FWXZ4sQvH9jD6oNLrFy8CB+\n73/IvI0hckTv88/l7TfcwIj4EXx/4HvPYgwA1Go03//A2s4qRE4O0pjRPP7pDL7Y/UXTHv/jjwFI\nG9cf9Gd/4dL76bFX2xu8r93v+/aOfGnVWrk9VRtHvoQQntqkaT2n8dHkj9r08f/MuF9b9+pxBYVz\nmXNOfEUbo8kuyya7NLtBg21vdA/vTnBAMGohh5o/7y1f7Q1vzOXXI+sZlTCK+HIN4uIJkJvL0cQQ\nNr49W7axgHpLr5uCN/E1PH44j4x4xOd9pnSbQkxgDI5qB/nl+b5TFSoV64e7Lsj//neTzqcu7obh\n7hV3WaVZrEhfwbasbZ4xkiRRtflX2LiRnbk7PX0FQ3WhFNuKOfbB6xAYCOPHE3iytgXn949fwZd3\nng9PPcXnF0cjoqMJfOp57rynC1kP3kGVn5ogWw3ddmUxdFsu4/bJNWAvXm5myBvd8V+/kZ4Rvfjf\n0f81EF+DYgbxwgUvnPb5Hb/vONmzs+ke3r12hZ4QBNx2F68tmMkD001wnytK+cIL2CddwsysSLQq\n1yrUr74iPqeC0pgwWLuWJeeHYdA135ndF5nJkaxb9CI8+ii89RZOlWDqT5nwwguwapVsLTJtGv2i\n+rEifUV98QUQFMSsWUmUjzkPYbESM3dh01snff21/P/NM1rkubgjW74iX+0tvkBe8NDaNV/eCNOH\nYXFY0Pvp29xg9s9OuD68UX89BYVzhVZzQBRChAL/ARKAY8A0SZIsp4yJAz4FIoEa4H1Jkv7Z2HFj\nAmPIKs2SL9CG00e+3Oj8dJRVlvFSQgY3miD1WAaLF+mIuTuQ93+QEIWFcOGF3HiVlX9cOtVzv9NF\nvk7Fm/gKCQjhqh5X+boLIPduc6cdG6sT2TYqmeuWHpMbS0tSo6vWTqXUUcqQ2CGeui93+m9F+goG\nx8q2BvMX3M5tsz4CZw1vmzSE9MoG/48ZOKYzhwLSCHn8n+BqDfjbjHH83j2Yv0VPQTMokIEWudg9\nTB8mvw5CcHBAPAdHXcOjA4vRF1iIzbCSUXiEBwfdiza1O9+nvyivBjMYGJM4hk0nNzUQX0atkcu7\nXX7a56dRaQg3hBNljKr3+o9NGku/qH4kHfyGNx59E/r1g9tuI2TlWuYBhP4dZs2C++Xi9g3Xnc8l\nfn6e/pAthc5PR27nCJgiC8AFG9/irq+OwZNPygOuvx7Cwuhf2p+88ryG4gswhUaz77G/MGjdr9y+\nXeKePK896OuTlQXp6Vj9IeWym1rkubh/jHir+VILtacnYXtyVY+rzqgu6Gxxr14M0AS0+WP/2Qk3\nhDfaWUJB4VyhNSNfjwI/SZLUFVgDPOZlTDXwgCRJPYHhwCwhRLfGDhodGE12adPTjm4CNAH0juiN\nUw23XS4oNWq5YJ8NIiO5+JCTmtAQWLiQI+UZdAru5LmfJ/LVxBoWb+KrqefnTjs29uVSlppEhSkI\ncnJkC4JG+HzX5zyz9hnP31aHlbjAOCwOWQPnlecREhDCZ7s+Y2fOTk5uXMHkRz5C45Sjg3FF1RjX\n/wqrVzPyifeYN3sNIQ6wjBwMxcWsmjGSvME94MYbmdL9cu4ddi9QR3whrzYrsZdQKiqREhLY0CuQ\nLzpXEH7zXSSMvZxQXajnNR4SO4SV6SvJLssmMSSxWa9fXXpF9OLhEfWbRgf5B1FaWSqnWG++GY4e\nZcWNw+Wdb7wBV1wBOTlkDEhm5ShZ9Libe7cUeo3e09uy0lnJg/3ysL/+Su0Al/jrGyVHG735GEUb\nozkSo+NovwS0TojffbzBmAa4UtR7U4IJCGgZMdlY5MuoNbZoC58z5bXxrzXrO6KlcPt2KeKr5Uk1\npZJiantBraDQ0rSm+JoCLHTdXgg0CF1IkpQjSdIO1+0yYD/QqHlLc2u+3Og0OsYkjgGgaFBPrr7F\ndVG1yekv65sv44gMo9BWWO+4gf7yuLNJOzYFj/gqb6TmC0gMTeJgX9dLtGZNo8c8aTnp6RdY6azE\nKTmJMkZ50o555XncZRzLgrTu3LdgKqFTpxNrqWF3ShBvfXInt81Ohffe8/htAXzWB/77wgwICaHU\nUer1ecYFxXlSAyEBIVgcFmxVNsx6M0W2onrml6EBoZ7XeEjsELZlb2Nc53FnVVBr1Br56+C/1tum\nVqnR++k9BqHExvLaOB15YwbL74HffoPAQHa8/hAnK7IBWiXy5Tab3ZW7iy6hXQh44GFYuxaWLKnn\na9YpuJPXyFeUMYrssmwOdZcFetK+ht5lDVi7FoDSYQMaH9cM3PPjLfLVEVKO7YkS+Wo93pn4DlO7\nTz39QAWFDk5riq8ISZJyQRZZQKOxYiFEItAP2NzYuDB9GBa7heOW402q+XIToAlgRPwI1ELN4JjB\n/GgqZN/EIaBW8+zkYKyXX0JmaSbRxuh6S5E1Kg239LulyRcUg58BjUpzVpGvxtKOKeYUNia76kj+\n9z8AfjjwA2/99laDseVV5R5vrlJHKYHaQIIDgj2RL/9Nm3nq4eWMfWcpPz99FGO+hexusWz95CUe\nz1iIc+QIuP122LqV44vnk3I3zLhSxU67HG0prSz1Ghmad+k8pvWcBtRGvmzVNsL0YRwvOU64IdwT\nGQkNCJX7aCIvTEgMSeTqHlc367VrKkH+QVjsFo4WH6W8spzfs35HM3ceDHH1+3vpJUxdenl6QfoS\nl2eKTqPz+J3tzdtLrwjZLZ/Ro2HSpHpj7xp0lycVXJdoYzQ5ZTnsTpZf9x5p8grUpQeXejzO6uJw\nVGD/9ksAYi5vmZQj1P4Y8VZw///dxNItvvzVSr2XgoKCd86q5ksIsRq5XsuzCZCAJ70Mlxo5jhH4\nGrjXFQHzyhyXV5LhNwMbTm5gzpg5TT5Xk85E9/DuvHHxG+g0Oj7e8THH33iKHrGjWfhRX65zVpJh\nrZ9ydPPhlA+9HNHncyEkIOSMxJfD6ZBrvhqJfKWaU/kwpoy/AWzYAJLEzB9mUmwvZtbgWfVaGFVU\nVXjEl7tlkrnSjykfboD/3cO1732CtrLWsqJKLdj5xG3MGH4nXx1dyoj4Ee4nhf+lU0g/cCfDYoew\nv2A/4ErL+TcUX3V/8YcEhFBiL8FebSfFlEJFVQXJgbWtbUw6U71m20unL61trdTCBPvLwvOh1Q8R\nY5Q7E5h6DpKjXhYLhIQQXpjmec1aM/K1v2A/3cO6+xz76PmPet0eZYziQOEBjidokYSgxwkbZGTw\nzy3/5Jqe13DrgFvrjd/91VwG5Rdz1KyixyUtZ3h6urTj/2fc4qsj1L0pKCi0HWvXrmWtK9NwOs5K\nfEmSdJGvfUKIXCFEpCRJuUKIKCDPxzgNsvD6TJKkHxp7PLf4mpw9mSfWPNGs3P+am9eg99PTJ7IP\nK9JWAJAS3hWMRrRqLVXOKk5YTngVX83lTMSXv9q/SZGvZFMyazUnkSIiEHl5SOnpWB1WArWBWByW\neq1K6oqv0spSgvyDGPDeDyQsTQPS0AKHrrqA1MWrmPB6fw4XpPHhyLGohIrl1y2v97hul/lLki/h\ns12feY55uucZEhDCCcsJbFU2OgV34oPJH9RboXdZ6mWe1kNAbTSoFQgOCMbqsHK0+CirDq9iRt8Z\n8g4hZO8tZDFYaCukRqrBVm1r0SiO3k/vaYy+v2A/N/ZpvhiKDpQjXzZhI/ui4cSs2gT33EP2uGyP\nhUi/+f1YecNKooxRGBbLhryBN9+OqgXNJRsruFfEVxgBmoAOUfemoKDQdowZM4YxY8Z4/n7mmWd8\njm3NtOMSYIbr9s2AL2H1EbBPkqS3m3rgAdEDWHH9ima15aibHgk3hKNRaTxF3X4qPyqdlS0mvian\nTm6WEzrINUkqoSKrNKvRgnu9nx6zPgzb4H4AVG5Yh1at9aRj61I37Wh1WOlm8SP+i1pRteDSKI69\n9DCo1egTkkk32D2tN4QQ9S4e/hp/9H56xnUeR3VNNUPeH9KkgvTggGBKHHLaUeen49pe1zLv0nme\n/cPjhzM2aWwTX6Wzw50CPWE5gVqo67WFcRMSINeylTpK0Wl0LWqAWTfteKDgQKORL19EGaPILs2m\nxF5C4ctPY9eA9P331GRnkV0q16qdsJwgrzwPjhwhdeVWalSCsDsfaLHnAb4jX2H6sA7bq7CtcIsv\nBQUFBV+0pvh6BbhICHEQuBB4GUAIES2EWOa6PQK4HrhACPGHEGK7EGJCK54TAF1Cu3BDnxs8DW+1\nam2Liq/XL37da7H06QjQBKAW6tOKyhRzCpm95Ia31b+sI8g/qF4tl5uKqgps1Ta5VU1BNq//8yAq\nh4Nv+mgQT8Mdg3OIcBkWxgfFIxCNNqu9ue/N9I7ozf5Z+9mZuxOrw9qkyJfFLhfc++pv2VYE+QeR\nXpSO3k/Ps2Of5ZLkSxqMUavUBAcEc9J6ssUjOO60Y6WzkuMlx8/IBsFd81ViLyEoqStrkzUISWLE\nzmKyy2TxVV5VLvexfPZZ1M4aMi+/AFJadoWY+8fMqTVf4zqP4/Opn7foY51rmHVmRXwpKCg0Sqv5\nfEmSVASM87I9G7jMdXsj0OaNtkJ1oXw85WPP33XFV1P8pFoL96KA00Vboo3RpPfrRArg/8MywnuH\nyfVM9obiCyC3LJcuz88jPrucqp7dmTlxP0EBQVgdVk+UrVNwJyKNkY3WqdSNWOk0Ok5aTnqt+aqL\nu+bLHflqT4L9g9mRs4OEkASfNVUgXzzdfS1bEr2fHlu1jazSLMIN4WdUExSmD6PYXoyfyo+QgBDW\n9AtmwoFCpu6H58qyqXZWUemspHrvHvjsM6pUUPWEN5eXs8NP7Yefyq/BnKqEqt3nub1JCk2S+3Uq\nKCgo+OCcc7hvDbRqLVU1LVfzdab4q/0ZnTD6tONMOhNpiYHQvz+aohKm7nF66pnqUl4pF7L/67nJ\ndPluLVV+apyLPqc0AK7sfiVzRs/xWEJ0Cu7kSTk2hShjFLnluadPO7pSffZqe7tHvobFDePrfV+T\nEJzQ6Diz3szxkuOnFZbNRafRUVFVQYm9xFND11zUKjXh+nDs1XYC/QPZOiCKGrWKienw8L92o4qJ\nZd1HMGrSLKipYeEAFfF9GzbHbgkMWkO7z2lHJCQghPcmvdfep6GgoNCBUcQXsvhyVDvaXXyF6kK5\nsPOFpx1n1pkpshfDXXcBcM36Is9KPg9lZdz0dRo/fKXh8Xl7AFh503n49+mPn8qPruauPD3maY+t\nxoWdL+S5sc81+Vzd/dWaknYssZdgq7K1eypmWs9pOCXnacWXSWfihOVE66Qdq22y+NKdmfgCWfgG\nBwSjEiqk8DCPWezUbeWo8vIZdQJUzhpyw3T8fveV9VbAtiRB/kH/720lFBQUFM6EVks7nkv4qf3I\nLc9Fo6ptpN0ebL51c5PMRU06E4eLD8N1D1H1wH30TLfQPbMSS4xLfEkSTJrErWtrDTjXJsD26WOY\nJATBAcGkmlMbHPPi5IubfK5uI9rTCZTggGDyK/LRqDT1/NPag0D/QKb3mn5aKwuzzsxxy/HWSTtW\nyeLrbBo+RwdGe1pEBQcEM++CSiqrBtJt9R+YJkzljaxvuMHZk5k909h8QxMbb58BP93402mFrIKC\ngoJCQ5TIF3LkK70ovV2jXtD0/pFmvZlCWyEYDKRdOgyAMT+leyJf0urVHldzS6CW/b2jmH6TDqNO\ndpWPC4o7a0uHKEMU/mr/00ZVgv2DKass6zD92BZMWsDtA29vdExr1Xy5VzuerfiKMkTVa9+0p3Af\nf/xlIlP/nsKaB6/k1fPhhb/2oig5tlUFb4o5RbFTUFBQUDgDFPGFLL4OFx9ud/HVVEw6E0W2IgB2\nXdQHgB6/HcZiK2FXxjYO3C6333juEj27dv+E/7qNJCb09UT1tt629awbDkcaI5tUE+Wn9qN3RG8+\nv6JjrIBTCdVpBYNZL0e+WrKvI8hpR3fNV4j/2UW+3OIr2ZSMSqgY13kcUcYoOSIKHCo81KwOEAoK\nCgoKbYeSdkQWX2mFafSP6t/ep9IkTDoThRWFABxMCqI01EBwbgnDvviFoMp9JB6vwBETydzBVh6I\nHoBBa+CeIffQJ1IWam6LjbMhyhjVZHGy665dZ/14bYlJZyKnLIdkU/LpBzeDumnHs635couvp0Y/\nxVOjnwJk0XjCcgKQxVdz0sgKCgoKCm2HIr6QTVazSrMY32V8e59KkzDrzJ7Il7WqjGMjetB72Vau\nXLgFgCoVvH3fMPIrlniW/U/vPb1FzyHSEPmndTIP14cTZYzi7iF3t+hx3WnHYlsxSaFJZ3yc8V3G\ne43ShunCOG45jkBQXlVOtFGJfCkoKCh0RBTxhRz5yi3PPePl/22NuwUOyM71e2+dgkllpGr7VuKz\nK5hzYyzLg44TUBnQog7tdekb1Zep3aa2yrHbmyndpjAwZmDLW024046Os6v5SjWnNlgwAXLka/2J\n9YQbwskrz/MsilBQUFBQ6Fgo4otak9WzSQW1Je4WOM4aJxaHBdE9meNzx/Lgj7O5JG4sOY5c9u9e\n1KqRqbigOJ4Z67tv1bmMVq1tdnuopqDT6FpktaMvwvRhnLCcIMWUoogvBQUFhQ6MUnAPHqdxk87U\nzmfSNNQqNUH+QRTbi7E6rAQHBMs+X5VW8mpK6RXRixqpRvFg6mAEaAKorqkmvzy/1cRXRVWFp9Be\nSTsqKCgodExaTXwJIUKFEKuEEAeFED8KIYIbGaty9XVc0lrn0xhu8XWupB1BTjEV2YqwOCwE+Qdh\n0pkoqCigyF5EuCGchJCEJltXKLQNQgiSQpPYkbOjVd5rZp3cE9QtupTIl4KCgkLHpDUjX48CP0mS\n1BVYAzTWYO5eYF8rnkuj+Klkr6pzJe0ItSserQ4rQf5BRBmjKK8s51jJMUw6E0khSYr46oD0DO+J\nrdrWapEvqBVfitWEgoKCQsekNcXXFGCh6/ZCwGvHaiFEHDAR+KAVz6VRzsnIl85MQUUBFruFYP9g\nhBAkm5LZnr2d0IBQkkKSMPgpaceORs/wngCtIr7MejnyFW4IZ1TCqA5jbKugoKCgUJ/WFF8RkiTl\nAkiSlAP4uhK8CTwESK14Lo1yrtV8AcQHxXOs5Bj5Ffmei2yKOQV7tR2TzkRiSKIS+eqA9IzoiUC0\n+EpKqI18GbVG1s1Y1yJ+bgoKCgoKLc9ZfTsLIVYDkXU3IYuoJ70MbyCuhBCXArmSJO0QQoxx3d8n\nc+bM8dweM2YMY8aMafY5e0Or1uKv9vd4Yp0LJIUmsf7EeiINkfhr/AFIMcmu9SadiZ4RPdlX0G6Z\nXAUf9Azv6WmK3dIE+wejFmol4qmgoKDQDqxdu5a1rtZ+p+OsxJckSRf52ieEyBVCREqSlCuEiALy\nvAwbAUwWQkwEdECgEOJTSZJu8nbMuuKrJfFT+51T9V4AiSGJvLbptXo9Gt3iK1QXyqTUSVyWell7\nnZ6CD3pF9OLzqa3TakkIgVlvVla5KigoKLQDpwaFnnnGtx1Ta6YdlwAzXLdvBn44dYAkSY9LktRJ\nkqTOwLXAGl/CqzXRqrXnVL0XQFJIEoW2wnpO6SnmFIL8g9CoNAghWs1gVeHMUavUXJp6aasdP0wf\npkS+FBQUFDo4rXl1fgW4SAhxELgQeBlACBEthFjWio/bbLRq7TlV7wV4RFfnkFoz0N4RvbmpT5tr\nV4UOxLU9r6VrWNf2Pg0FBQUFhUZotYpcSZKKgHFetmcDDfJhkiStA9a11vk0hlatPefSjuH6cPR+\n+npO7MEBwbwz8Z12PCuF9ubvo//e3qegoKCgoHAalOVQwEWdLyLZlNzep9EshBA+e/wpKCgoKCgo\ndFyEJLWbw0OzEEJI58q5thUl9hKPx5eCgoKCgoJCx0EIgSRJXi/QivhSUFBQUFBQUGhhGhNfynI4\nBQUFBQUFBYU2RBFfCgoKCgoKCgptiCK+FBQUFBQUFBTaEEV8KSgoKCgoKCi0IYr4UlBQUFBQUFBo\nQxTxpaCgoKCgoKDQhrSa+BJChAohVgkhDgohfhRCBPsYFyyE+EoIsV8IsVcIMbS1zklBQUFBQUFB\nob1pzcjXo8BPkiR1BdYAj/kY9zawXJKk7kBfYH8rnlMD1q5d25YPp9DCKPN37qLM3bmNMn/nNsr8\ntS+tKb6mAAtdtxcCl586QAgRBIyUJOljAEmSqiVJsrbiOTVAeQOe2yjzd+6izN25jTJ/5zbK/LUv\nrSm+IiRJygWQJCkHiPAyJgkoEEJ8LITYLoRYIITQteI5KSgoKCgoKCi0K2clvoQQq4UDS2USAAAg\nAElEQVQQu+r82+36f7KX4d56A2mAAcC7kiQNACqQ05UKCgoKCgoKCn9KWq23oxBiPzBGkqRcIUQU\n8LOrrqvumEjgV0mSOrv+Ph94RJKkSV6OpzR2VFBQUFBQUDhn8NXbUdOKj7kEmAG8AtwM/ODlpHKF\nECeFEKmSJB0CLgT2eTuYryegoKCgoKCgoHAu0ZqRLxPwJRAPHAemSZJUIoSIBt6XJOky17i+wAeA\nH3AEmClJkqVVTkpBQUFBQUFBoZ1pNfGloKCgoKCgoKDQkD+lw70Q4kMhRK4QYledbX2EEJuEEDuF\nED8IIYxe9u1x7de6tg9wLSA4JIR4qz2ey/83mjN3QojrhBB/uFbK/iGEcAoh+rj2DVTmru1p5vxp\nhBCfuOZprxDi0Tr3UT57bUwz585PCPGRa47+EEKMrnMfZe7aASFEnBBijeuztFsIcY9ru0/DcyHE\nY0KINJfJ+fg625U5bG0kSfrT/QPOB/oBu+ps2wKc77o9A3jWdVsN7AR6uf4OpTYiuBkY7Lq9HLi4\nvZ/bn/1fc+bulPv1AtLq/K3MXQefP2A68G/XbR1wFOikzN85MXd/BT503Q4Hfq9zH2Xu2mf+ooB+\nrttG4CDQDbnu+mHX9keAl123ewB/INd+JwLpyrWv7f79KSNfkiRtAIpP2Zzi2g7wE3Cl6/Z4YKck\nSXtc9y2WJElyrdAMlCRpq2vcp3gxilVoWZo5d3WZDiwGUOau/Wjm/EmAQQihBvSAA7Aq89c+NHHu\nrnDd7oHcuQRJkvKBEiHEIGXu2g9JknIkSdrhul2G3C0mDt+G55OBxZJsbn4MSAOGKHPYNvwpxZcP\n9tbxH5uG/KYESAUQQqwUQvwuhHjItT0WyKhz/wzXNoW2x9fc1eUa4AvXbWXuOha+5u9rZG+/bOAY\n8JokSSUo89eROHXu4l23dwKThRBqIUQSMNC1T5m7DoAQIhE5ivkbECl5NzyPBU7WuVuma5syh23A\n/yfxdQswSwixFTAAla7tGmAEcuRkJDBVCDG2fU5RwQe+5g4AIcQQoFySJK82JQrtjq/5GwpUI6dL\nOgMPui4aCh0HX3P3EfLFeivwBrARcLbLGSrUw1WX9zVwrysCduqqOmWVXQegNX2+OhSS7CN2MYAQ\nIgW41LUrA1gvSVKxa99yZNf9RdT+ygP513pmm52wgodG5s7NtdRGvUCeJ2XuOgiNzN90YKUkSTVA\nvhBiIzAI2IAyfx0CX3MnSZITeMA9zjV3h4ASlLlrN4QQGmTh9ZkkSW5vzVwhRKRUa3ie59ru63tS\n+f5sA/7MkS/h+if/IUS4638V8CQw37XrR6C3ECLA9cYdDex1hWctQoghQggB3IQXo1iFVqGpc4dr\nbqbhqvcCT2hdmbv243Tz9y/XrhPABa59BmAYsF+Zv3alSZ89IYROCKF33b4IqJIk6YAyd+3OR8A+\nSZLerrPNbXgO9Q3PlwDXCiG0rtRxMrBFmcO24U8Z+RJC/BsYA5iFECeAp4FAIcQs5JDrt5IkfQIg\nycavbwC/AzXAfyVJWuk61CzgEyAAWF5nu0Ir0Zy5czEKOOEqGK2LMnftQBPnz138+y7wsRBij+vv\nDyVJ2uu6rcxfG9PMz14E8KMQ/8feeYe3VZ7/+z62ZUnW8N4zsRMnTkIII2EECDMJK7TQMsr4AS2l\nXwp0UVpaVqFQKJSVtpSyyyqzBUqAACEkIXsPZzle8pQtW3vr/P5QdGJZtiMnXoH3vi5fsY7ec/Tq\nHEfno8/zvM8jBQm7Ilf1OJS4dqOAJEknAz8AtkqStJHwNbuD8GrHNyVJuo79Bc8BZFneIUnSm4S7\nyviB/5NlORKSFNdwmBFFVgUCgUAgEAhGkG9y2FEgEAgEAoFgzCHEl0AgEAgEAsEIIsSXQCAQCAQC\nwQgixJdAIBAIBALBCCLEl0AgEAgEAsEIIsSXQCAQCAQCwQgixJdAIBAIBALBCCLEl0AgEAgEAsEI\nIsSXQCAQCAQCwQgy7OJLkqR5kiTtlCRptyRJt/fxvFGSpPclSdokSdJWSZL+33DPSSAQCAQCgWC0\nGNb2Qvubse4GzgSagbXAZbIs7+wx5reAUZbl30qSlAXsAnJlWQ4M28QEAoFAIBAIRonhdr5mAntk\nWa6XZdkPvAEs6DVGBgz7fzcAnUJ4CQQCgUAg+KYy3OKrEGjs8di0f1tPFgJVkiQ1A5uBW4d5TgKB\nQCAQCASjRtJoTwCYC2yUZfkMSZLKgcWSJB0ly7Kj5yBJkoYvPioQCAQCgUAwxMiyLPW1fbidryag\npMfjov3benIt8C6ALMs1QC0wqa+DybI85D933333sBxX/IzMj7h+R+6PuHZH9o+4fkf2j7h+w/8z\nEMMtvtYCFZIklUqSlAxcBrzfa0w9cBaAJEm5wERg3zDPSyAQCAQCgWBUGNawoyzLQUmSfgp8Sljo\nPSfLcrUkST8OPy0/A9wPvChJ0pb9u/1almXLcM5LIBAIBAKBYLQY9pwvWZY/Bip7bftHj99bCOd9\njQpz5swZrZcWDAHi+h25iGt3ZCOu35GNuH6jy7DW+RpKJEmSj5S5CgQCgUAg+HYjSRLyKCXcCwQC\ngUAgEAh6IMSXQCAQCAQCwQgixJdAIBAIBALBCCLEl0AgEAgEAsEIIsSXQCAQCAQCwUGIp3hqvAjx\nJRAIBAKBQHAQvvfW91hav3RIjiXEl0AgEAgEY5BWRysvb345atvti29n4ZqFozSjbzf11npa7C1D\nciwhvgQCgUAgGINsaNnAk6ufjNq2tH4p71S/M0ozig+3300gFBjtaQw5Ha4O7D77kBxLiC+BQCAQ\nCMYgTp+TDleH8jgYCrK1fSvrmtdh9VhHcWYDc/Oim3lt62ujPY0hp8PVgc1rG5JjDbv4kiRpniRJ\nOyVJ2i1J0u39jJkjSdJGSZK2SZK0ZLjnJBAIBALBWMfpd2J2mZXHuzp3ka/PZ1bhLJY3LB/FmQ3M\nXstezE7zwQceQXgCHhw+x5EhviRJSgAWEu7dOAW4XJKkSb3GpAJ/Bc6XZXkq8L3hnJNAIBAIBEcC\nTp8Tl9+Fy+8CYGPLRmbkzyBHl0O3p3uUZ9c/jbZGrN5Dd+bu/+p+HD7HEM7o8Ol0dQIcGeILmAns\nkWW5XpZlP/AGsKDXmCuAd2RZbgKQZbkDgUAgEAi+5Tj9TgDFRdrYupEZeTPQJGnwBDyjObV+Cckh\nTDbTYYmUJ1c/ybb2bUM4q8On031kia9CoLHHY9P+bT2ZCGRIkrREkqS1kiRdNcxzEggEAoFgzBNx\nvCJ5X2NdfDVYG9jQsgFf0HdYIsXus7PXsncIZ3b4RK7BUCXcJw3JUQ6PJOAY4AxAB6yUJGmlLMsx\nZ/6ee+5Rfp8zZw5z5swZoSkKBAKBQDCyOH37nS+XGVmWlbDj4n2Lx6T4enrd03xe+znAIYcdA6EA\nnoCHGkvNUE5t0Gxo2UBtVy0XV10MhMWXIdkwoKj88ssv+fLLL+M6/nCLryagpMfjov3bemICOmRZ\n9gAeSZK+AqYDA4ovgUAgEAi+yfQMOzZYG1AnqcnT56FN0o5J8eX0OVnTtAZNkuaQna9IrldN16GJ\nL1/Qx8nPn8yaH65BkqRDOgbAioYVrGpaFSW+xqWPG/B99TaF7r333n7HDnfYcS1QIUlSqSRJycBl\nwPu9xvwXmC1JUqIkSSnALKB6mOclEAgEAsGYxul3kqZJo8XRwic1nzAjbwYAmiQN7oB7lGcXSyRM\nWpVddcjiy+4Nh/UOVXx1e7pZ17yOdmf7Ie0fweV3RZXz6HB1MD59/JGR8yXLchD4KfApsB14Q5bl\nakmSfixJ0g37x+wEPgG2AKuAZ2RZ3jGc8xIIBAKBYKzj9DkpSyvj4RUPc/tnt3PehPMAxmzOlysQ\nFl9TsqdEiZSP934cd09Eu8+OUW2kxlJzSH0UI+Ktwdow6H174vK7okKnna5OxqUN7HwNhmGv8yXL\n8seyLFfKsjxBluU/7d/2D1mWn+kx5hFZlqfIsnyULMtPDfecBAKBQCAY6zj9TkpTS+l0d/Lighe5\naeZNQN/iyx/08+yGZ0djmrTYW9hh3oHL72J+xXwurLxQcY2CoSAXvH4BjbbGgxwljMPnYELGBGxe\nGzmP5NDl7uKKd66Iey6RhPh6az3BUJD67vqYMR/t+QiTzTTgcVx+V1Q5jzprHVXZVYq4O1xEhXuB\nQCAQCMYgTl9YfGmSNJw1/ixlu1YVm/Nlspm4/bM+65gPO//e/m/+/PWfcfqc/OyEnzG3fK7iELU5\n2wiEAnH3RLR7w87XfaffhyfgYbt5O69vex23P74wa0Qc1XfX8+HuD7n07Uujnt/XtY+L37yYlza9\nNOBxeocdq83VzCqchd1nj3LknD4nf1n5l7jm1hMhvgQCgUAgGCOE5JDSmsfpd3Jyycn8dvZv0SXr\nlDF9OV89i7GONFaPFZvXhsvvIkWVgi5ZhzvgJhgKKuG/Fkec4stnx6A28MuTfkm+Pl9Z9fhu9bv8\n9rPfDryv1x7lfK1oXEGT/cAav02tmzj7X2dzaumprGhcMeCxXIEDYUdPwEODtYHKrEq0SVqcfqey\nEnWHeQd3fH4HwVAwrvcXQYgvgUAgEAjGCC32Fn7w7g+wuC24/C6m5kzlrtPuihrTV8K90+/EE/AM\nWgQMBVavFavHqoivBCkBQ7IBu89OozUcbuztfO3u3M271e/GHMvutWNINgCQpklTEu9f2PQCr297\nvd85tDvbKfhLAXXddSQnJlNvrefrxq9pdbTiD/rpcndx1XtXccfsO3j5opdZaVpJSA71ezyX34XN\nayMYCrKncw/j08eTnJiMQR0uN1HxVAVmp5lmezPeoJfa7tpBnTMhvgQCgUAg6MXdS+5md+fuEX/d\niFOzrnkdTp8TnUoXM6Yv5yvixIzUKsjdnbu5+M1wGQarx4rVe0B8ARjVRqwea7/O17L6ZTy38bmY\n4zp8DvTJeiBafC2tX0q9tR6L29LnfJ5a/RQOn4M9nXuYnDWZjS0b2di6EU2ShoVrFpL952yMaiPX\nzbiOXH0uWSlZVJvDhRWueOcK/EF/1PEiYU67z84O8w4mZ09W3pfJZqLV0UqjrVG5XpFjxYsQXwKB\nQCA4JJ7d8Cz/3vbv0Z7GsLBo7yJ2duwc8ddtsoVv5mub1uL0O6PCjRH6qvMVqQk2UqHHrxu/5r87\n/4vD56Db2x0VdoSwSLF5bTTaGpmYOTHG+bJ5bX2Wg7D7Djhf6dp0aiw1SEgEQgFS1alsbNnY53ze\n2P4GObocarpqOKXkFG6eeTM/P+HnlKWVsWjvIm6eeTNvf+9tpfbXtJxp7DDvwBPw8Pq216m3Rifm\nR86j1WOluqOaqqwqAAoMBaxsXAmE3bwmWxOJUiLVHUJ8CQQCgWAE2NK2hfUt60d7GsOC1WsdspVt\ng6HZ3kx2SjbrWg7N+Yr8O9xsadtCUA6ypmlN2PnyWMNicf98jWojF75xIR/u/pBZhbNodjRH7W/z\n2mhztMUc1+4N53wBpKnDztfEzIlISFw8+WL+s/M/MS6TP+inwdrAiUUnUtNVQ6omldtOvo37z7if\nfH0+yxqWcca4M8g35Cv7VGRUUNNVo6xo7F1RPyK+uj3dbG7bzNScqQBMypzE4n2LgbCb12RvYmbh\nTHaYB1chS4gvgUAgEBwSLr8Ls8s82tMYFqwe65D18RsMTfYmLph4AatMq/CH/GiSNDFj+hRfI+x8\nbW7bzLScaaxoWBHO+eoVdjyl5BQWVC6g3lrPrMJZ/TpfvWt59XS+0jRpdLg6OL7weCoyKphdMpuF\naxfy8NcPR+1T111HoaGQImMR+7r2KfsD5Bvy8QQ8TMmZErVPeXo5ey17lRWNvYu6uvwudCodVq+V\n1abVzCqaBcDk7Ml8WfclsN/5sjdx1vizhPMlEAgEgpHB6Xdidn5DxdcoOV9N9iZml8xGnagmRZXS\nZ4scTZImpvRCxPEaCfElyzKbWzfzf8f/H8sbl2P1WPEEPHgDXkUsPnT2Qzw+73G2/mQrCyYtiMn5\nsnlteIPemKKlPXO+0rXpAMyvmM8X13zBpVMv5c5T74zZZ49lDxMyJ5Cjy8ET8CjOGUC+Pp8UVQpl\naWVR+8TjfOUb8tnevp2gHKQ0tRSAyVmTcfqdZGgzws6Xbb/4MlcPqiisEF8CgUAgOCS+qc6XL+jD\nE/CMjvNla6LQWMj5E8/vM+QIsXW+3t/1viJIIg5YvPQsJBovDdYGEqQE5pbPpdpcrZRk0Kq0MWJx\nUtYk8vVh92lz62Zu/uhmAGy+8Hx7531FSk1A2PkCyErJoshYRIoqhVNLT42qv2V2mqk2VzMhIyy+\ngGjnS5/P5KzJJEjRcqc8Y7/z5Y12vtqd7dz5xZ24/C4KDAV8UvMJswpnKe8rkng/s3CmEnacljMN\nrUobVdbiYAy7+JIkaZ4kSTslSdotSVK/FeAkSTpekiS/JEnfHe45CQQCgeDwcfld30jnKyJkRivn\nq9CwX3z1kWwP0WFHq8fKxW9ezOa2zcDgnC+b10bFkxVxjX3k60dotofzthbtXcQ55edQZCyi1dFK\nl7uLPH2eEnLsTWJCIueUn8MNH97AP9b/g0AooJzbGPHVq9QEQIY2Q3k+ksgf4Zr/XMOdS+6kIqOC\n7JRsgCjna1bRLL4/5fsxcyo2FmN2mmmxtyguGMDWtq28tPkl3AE3+fp8Pq/9nBOLTlT2KzQUok/W\nM7NgJmua1pCcmEyaJo3JWZMHteJxWMWXJEkJwEJgLjAFuFySpEn9jPsT4R6PAoFAIDgCcPlddLg6\nRnsaQ07EWRlp50uWZUw2E4XGQs4efzZvXPxGn+N6iq/Paz8nEAooq/UGI75a7C10ujvjCpc9v/F5\n1jatBeDD3R9y/sTzUSWqyNPnAZCry+1XfAGcN+E81jStwR/ys69rHzavjUxtJm3O6KT7bk93jPOV\nqc1Unu8tvixuCxnaDI4rOK5P5+uEohP49cm/jplPYkIipWmlbGzdyDH5x7Cvax+yLNPqaKXd2Y7T\n5yRfn4/D5+Ds8rOV/SRJ4tqjr+Wc8nNotjdz88ybkSSJyVmT2da6bcDaYT1JimvUoTMT2CPLcj2A\nJElvAAuA3ut3bwbeBo4f5vkIBAKBYIhw+pzYfXa8AS/qJPVoT2fIiISiRlp8mV1mVIkqRXQcX9j3\nLbFnkdWP934MHGgkPZjVjq2OVgC8QW+fif09aXe202hrxBf0sbR+Kf/6zr8AKEsrwx1wk6pJxRv0\n9rv//Ir5jE8fT5GxiGpzddh1y6iIcr66Pd1sa9/G0XlHA5CuCed8Zab0L77sPjufXPkJU3KmsKtj\nFxDtfA3E+PTxbGzdyPEFx6NT6Wh1tNLiaFHeR64+l0xtJjPyZkTtd23etfz76X9j+NzA519+zmst\nr1HbUEvCjAQ237aZFy968aCvPdxhx0KgZzdN0/5tCpIkFQAXybL8dyA2s1AgEAgEY5KIy/JNy/sa\nybCjzWvDF/QBsNeyl4qMg4cBNUkavAEvsizz2b7PmFU4i1ZHKzqVblDOV0R89V452ZtAKECnu5NG\nayObWjdRnl6uJMOXpZWRpknDqDb2m6MGkK3LZu/Nezku/zh2duzE5rUxIXMCX9V/pZSc+GDXB5w+\n7nSMaiMQdr6SEpKinCyj2qiIY4guTdGX8zUQpamlbGnbgjakJdeWy4v/fpFFry2CzyDxvUQ2vbuJ\n+RPmk5iQGLXfjh07eOjBh7CvsfPVl1+xa9cufG4ffpufTa2b4nrt4Xa+4uFxoGcuWL8C7J577lF+\nnzNnDnPmzBm2SQkEAoFgYFx+FxnaDMxOM0XGokHt+8z6Zzi19FQmZcVkoow6Vo+VNE3aiDhfP//4\n58wumc21M65lT+eeuMRXgpSAKlFFt6ebFkcLCyoXsLppNdm67LjF17L6ZXGLr0houcHWwCrTKk4o\nOkF5riytjB3mHaSqU6MS4ftCkiQmZ09mecNybF4b10y/hr+v+zunvngq1x59LU+ufpIn5j2hjM/T\n50UluwPoVDq8AS+BUICkhCTsPntURfykhCRFjAWDQdra2jCZTJhMJlJSUpg3b17U3Ls93bRsamHb\n/du4gzuU54IE6crr4oM/fxDzPo477jjuueceioqKon4+X/E5lz16GXe33N3nKtWeDLf4agJKejwu\n2r+tJ8cBb0jhmWYB8yVJ8suy/H7vg/UUXwKB4NtNs72ZVaZVfHfyoa/R+cPSP5Cry+XHx/14CGf2\n7cHld1GWVqY4X7d9eht3nHKH4ooMxJOrn8Tld41N8eW1UmQsGhHny+wK9wdcUruELW1bmJAxIa79\nNEkatrVvY3z6eHL1uQBkp2THvdpx7itzmVcRFiK9y1b0JhIajPRpnFs+V3muLK2MVE0qqerUAXO+\nIlRlV/H0uqexeW2cVnoaZ40/izu/uJOdHTv58IoPOSb/GGVsqiaV5dctj9pfkiQMagNdzi4krxSV\noC9JEg+e+SB1W+s46fKTaG5uJhg80OvylFNOiRJfkfIRZePKyC3PxZhtxKlx0pHUgT5Lzz233ENy\nYnLMe6isrOTuu++O2f7dc7+LYZuBm266iRxdDvfee2+/52G4xddaoEKSpFKgBbgMuLznAFmWx0d+\nlyTpBeCDvoSXQCAQ9GRl40oeX/34YYmvuu66Q1pqLwjj9DvD4mv/iseXt7zMD476wUHFl9vvZmfH\nTra3bx+JaQ4aqycsvnr3duxZRHTIXstrpd3ZzlXvXYXZZea5C2P7HfaFJknD1vatTMiYoCSk5+hy\n4nK+gqEg7oCbT2s+BQ7ufLU72ylLK6PR1ojJZuKe0+5Rnjtz3JmkqFLY3Lo5rnMzPXc629q3oUpU\noUpUAXDfGfcNuI/JZOKRRx6hsbERk8mEfZedvDvymH70dJK+k6QcB+BXJ/2K7du309gYFoo5OTmK\nMzVjRnTuVqT21/Sjp/PwOw/zSc0nbGzZSKmmlE53JyeeeCKDpTy9nBpLjRIC7Y9hFV+yLAclSfop\n8Cnh/LLnZFmuliTpx+Gn5Wd67zKc8xEIBN8c7D57n+1JBoPVax2xRsTfNAKhAIFQgEJDIR2uDmRZ\nptPVGZdbtK19G5Iksd08RsWX10qhoZD1zQdaJ9m8NsqfLKftV20xNaMO67U8VlocLbQ520iUEuN2\nvrRJWja3bmZi5kSyUrKAcF5VPAn3EXcs8m884mtG3gz+s/M/VGZVMiHzwBxL00opTSulrrsuLvGl\nS9ZRbiynubqZV155RQkJmkwmkpOTefPNN2P28fl8PPHEE1HbJEnC6/P2mVw/ceJE9u3bR0FBAWp1\n/wtBStPCzleqJpUMbQY1lhpaHC2cVnoaq5pWHfS99EV5Rjn7uvZxYvHAwm3Yc75kWf4YqOy17R/9\njL1uuOcjEAi+Gdi99phl6oPF6rESCAWGaEbfLtx+NymqFLJ12ZhdZqxeK0E5GFee1KbWTcyrmMdX\n9V8hy/JB82NGGpvXFg479ngvbY42OlwdtDpaKTAUDOlrbWvfRoY2gxXXraA8vTyu/TRJGra0b+H6\nGdcrqwFzUnLiKv3h8DmU35MTkwf8AmJxW2i2N1NsLKYsrYxHz3m0T/FZYCjA1GFi165dipiyWq3c\ncsstMWOn6Kew4+kdXPX0VVHbjUZjn3MoLCzk0UcfVRysW1bcwiPffYTi9GLO/tfZMeNVKhXjxo3r\n9z1FyNPnKXW68vX5VHdU4wl4mJQ1iS3tWw66f1+MTxsf06qoL8ZCwr1AIBAMGrvPjs1rwxPwHHSZ\nfH9YvdaYViWC+HD6nWHxlZLNptZNyk3f4XPg8rs446UzWHn9yj6F1db2rZxedjrrmtdhspkoTi0e\n6ekPiNVjZXz6ePxBv5LYHXl/tV21cYuvlY0rD+qAWL1W6q31TM+dHleyfQRNkoatbVtjnK8GW8NB\n93X4HOTp82h3tlOSWjKg83XjhzfyVf1X/GjKj3jnjHeYMWFGzBin08nPTv8ZXV1d/JW/KttVKhU/\n/elPSUiIFmuzq2azqGIR5x17HkVFRRQXFyvCqi8xrlar+cUvfqE8zqnNwRVyRVXDPxQSpASuOuoq\nio3FpGvTuWjSRXxZ9yW5+oFrlg1EeUY5S+uXHnScEF8CgWDEufD1C3n94tf7reDdm3NfPZcXL3ox\nKo+iZ4XsktSS/nYdkEjI55vKxpaNTM6efMjidCAijYcjzldEnNi9dqweK6ubVtPt6e4z/6vJ3sQp\nJadQkVHBvq59Y058dXu7SdOkoU/WY/faSdemK4sKartrObnk5IMeY2fHTk56/iRcd7jQqrR9jpFl\nGavHSkgODdpN0yRpcPldHJ13tCKeslPiCztGxNe/vvMvHl35KDavjbd3vM0lVZdg94a/1Nz9i7tp\nbGxk+bbluDpc3O+7n/u5H4/HExPK0+l0BAIBkpOTY8SUz+dDo4n++5tbMZeWv7fw4FkPDuo9RzCq\njVg9VgzJhrjLSvTHsxc+q/z+xzP+yHvV73F62emHfNzy9HKe3/j8QccJ8SUQCEaUkBzif3v+R6e7\nM27xtaFlAyabKVp87Q8JtTnaDl18ea04fI6oZr7fJC5/53Iem/sY8yfMH/JjR5LPs1PC4qvT1QmE\nr0sk6bu2uzZKfNm9dkJyiDZHG7n6XPL0eYcdOh4OIlXTDWoDdl9YfEXE5b6ufXEd463tbwHQ6e6k\nSNV3GQ6X30ViQiKhYIhCQ2GfY/pDq9JSlV2FUW0kRZWCNkk7YML9okWLqKurw2QysX7nemqra7np\nLzcx8TcT2dS6iYdXPMzMwpnMf3U+R+UexRcffEF7+4ECqMmaZMpKyujq6iIvLy/m+HV1daSnp8cV\nQq7Mqjxk4QUHCq0ervPVmyJjETfPCveePG/ieYd0jPKMchF2FAgEY4/IDXgwlbjdATcWtyVqWyRc\neDg3b6vHSr4+nxZ7S1QS8TeBLncXuzp3DVsBVEV86bIxO6OdL0V8ddVGlQ54YljTJUUAACAASURB\nVPUT2Lw2Wh2t5OnzyNPlKbWmRpqQHOKL2i84a/xZMc91ubtI16RjSDZw4nMnsur6VZidZjK0GdR2\n18Z1/Hd3vktyYjIdro5+a6BZvVbSNekE5eCgnS91gprp+umsW7cOk8nE79S/47XHXsNaGa615fK7\naLI1KX/XP/nJT6ivr49+faxUWCtodbTiDri58cMbCYaCdHu6+dvf/oZWq+XHS3+MPlvPW1e9xdTc\nqf3OJyMjo9/nhhqj2sgnNZ9wTP4xh+18DTUFhgK6Pd0H/XwT4ksgEIwoERE1mErcbn+s+LL7wvV9\nejfmjRdvwEtIDjEufRwtjljx1WhtHHPhsMGwpmkNwLA1vnb6wjlfWSlZdLg66HSHna9IzhcQI1Qi\nCettzjZydWHna7TEV21XLd9763t03d4V81xP56u6o5pWRysdrg6OLzhecb5kWR6wLU99dz1V2VV9\nnv93drzDHsseFlQuIFWTSlJCEoXGA85XMBiktbUVk8lEVVUVBkOswFh15yqs9VZe47Wo7RW/CeeN\nLdqziIVrF7LkmiUAXHLJJVitVoqKimhNaGWbdxv/uOIfPLzjYeULzKK9i3jgjAf4357/cfEPLgbA\ns93D+p+sP2jphJHkgokXcOeSO3lw+YNcNuWy0Z5OFAlSAuPSxh3UIR3u9kICgUAQRURExVsMMhgK\n4g/5Y8WX105FRkVUuQmb14Y30H9/uZ5YvVZSNakUGAposUfnfbU725n818kEQ8F+9h5b1FhqeHN7\n9BL91U2rUSeqo5yvkBzig12xFbsPBZffhS5ZR6Y2k25PN22ONvL0eVFhx7ruuqh9uj3d7O7cTSAU\nwKg2jqr46nR30u3pVlr79KTL00WGNoNpOdPI0Gbg8Dkwu8zMyJtBk60JWZa5/v3rufmjm/s9vsPn\nYFzauD5XH767813e3vE2Nq+NVHUqRcYiPnvmM0466SSKi4tRq9UUFRVxwgknsHHjxj6Pf3TF0WRm\nZjJ9+nTOO+88brzxRn52x8/wa/zK+9vTuUcZ/8gjj/DPf/6Ts649i4lnTaTi+AqqqqrQacM9DVUJ\nKrJSsjin/BzFVY7kpKWqUwd1boeb08pO4w+n/wGX3zWkYcehIp7QoxBfAoFgRFHEV5xhx0gycV/O\nV0VGRVTY8deLf82rW1+N67iRm0q+Pj8m6d7ituD0O+PK3RgLfFX/Fc9tjC7Oua19GycWn4jZaebU\nF06lxlLDvq59XPLWJcjy4ZdUjIQdExMSSdOksduym3Fp4xTxlSglxjhfkcbJubpcJEmKEV/egJe/\nrvlr75caFiJ/T73FUTAUxO61k6pJ5dkLn+Wk4pNw+Bx0uDqYmDmRLk8Xq5tW88KmF/pdrOEL+gjW\nBGn9tJV/3P8PLr74YmbNmkVhYSGLFy9mWf0ytrRtodXRSqomlTcveRN3q5uVK1diMpkIBoPk5uZy\n7LHH9nutPvv4Mzo6Oti0aRMffvghf//737ntN7fh0DqUmmtN9qYoh1mWZa5870r+u+u/6FXhHEdN\nkoZWRyvfnfxdnr3g2ai2Sp6AhwQpYUw2TZ+eOx1gTOZqRgqtDoQIOwoEghFlsM5XpAZRX87XuLRx\nNDualW2d7viKfMIB5yuS8xX13P4edZGl/GOdLk9XTPjV7DIzNXsquy27WdG4gsX7FlOaWoov6MMd\ncB92pfae1d6zddns7NjJjLwZSs5XRUaF4nytaFjBDvMOujxd+EN+8vThhO3e4qvB2sDNi27mimlX\nxNWi6HCILBAwO81R+Vbdnm6MaqNSy0qfrA8X9O1uQ2PT0F3dzauvvIputY71X61n1fhVLPEvYXz6\neC6deikQdr0SNyWycvPKmNfduHMjvqCP6XnT+aL2C1LVqaRqUrnz93fyq1/+iqKiooMWBwVISoq9\nfefr88nT5/FV/VfK/5d9XfuYmhPO1drZsZO67jp8QR8nFoVLYGhVWtocbYxLG8eCSQtod7Yr/4es\nXqvS5Hqska5Np9hYPOZyvgBKUktosA5c8kOIL4FAMKIM1vmK9J3ry/kqTSuNqpLu8DnwBuMMO0ac\nL0M+Ozp2RD0XaTm0pW0LF1ddHNfxRhOL2xKTW9Th6qAqu4pXtr5CSA6xtH6pcsO1uC2HLb6cfic6\nVXi1aklqCV/UfsE1069h8b7FuPwuJmROYHlDuC/fWzveYq9lr3JeI70Ic/W5UeLL5rUhI7O0fikX\nTbrosOZ3MCJ/T+3OdhwOB01NTZhMJqR0KUr46VV6HD4Hu/61iytWXQHAQhaGzwFO1q5dy67iXTh8\njijxZZhiYHrVdLwpXm4951al9MIS8xJm18ymwFDAxzUfM7t4NhBu1ny4SJLED4/5Ic9ufJbkhHBP\nwr2WvYr4+mjPRyRKiTTbmxXHSJOkwR1wk6ZJA8CQbFCcL6sn/AVlrDI9b/qYDDvqVLqD5rSKsKNA\nIBhReibcN9maDjp+IOerJLWELk9X1LaDtUqJMFDOl9VrRZWgYmv71riONdpY3BbMLnNUiMrsNDM5\nezLdnm6m5kxlad1SJQeoyx2bZN4XITnEnBfn9Dm+p/P1weUf4P6dmzllc5SE+0JDIW6/G5ffxUrT\nSprsTXR7wvWz8nRh5ytHl4PZZSYkh4ADK1j/tPxP/Oaz3xz6CelBz8bKPfn49Y/hb3DRjIswGAxM\nmjSJs846i5dfe5kM7YGVewa1ISzqDV5KSktIHpfMhNMmMPGCiaRelMqcOXNw+p1RDqzdayf3pFxu\n/N2NFM4t5Pvf/z4nnXQSJSUlmJwmJmZO5MSiE9nduXvInaW55XNZ07QGi8dCSWoJm1s34wv68AV9\nvLzlZS6ovAAgSnwBisjSJGkIhoL4gr7w/5Exlu/VkwfPfJCLJ4+9L0cpqpTRF1+SJM2TJGmnJEm7\nJUm6vY/nr5AkafP+n+WSJE0b7jkJBEPJvq59PLzi4dGexhGDxW0hQUpgU+smTnnhlIOO78v5kmUZ\nh89BsbE4ShjYffb4E+4HyPmyeqwcX3j8ESW+fEGf4liE5BCd7k4mZ00GYH7FfNRJat7f/b4yPh6c\nPidL65fy0IqHYp7rKb6SE5NJSkgKuyb7w446lY4CQwH7uvaxsWUjzfZmuj3dTMmeojhfyYnJpKpT\nlRCgzWujIqMCm9fGB7sPvjDgw90f4va7uePzO1i9ejV33XUX1113Heeccw5VVVUYjUbuu6/vps3d\ntm5oB5fdhVqtpnRcKcnjk1nUuoh0TQ/nK1lPp6sT+RSZuto6Km+rpPLHlVz40wsJzgwybdo0nD5n\nlICP1I2LrATtidllJjslW6l8P9TOUr4hn1ZHKxa3hbPHn80jKx8h5885HP300ZSmlnL9jOuV9wU9\nxNd+kSVJEga1gafXPc07O94Z087X1Jypyt/SWEKr0h60Z+ywii9JkhKAhcBcYApwuSRJk3oN2wec\nKsvydOB+4J/DOSeBYKhZVr+Mlze/PNrTOGKweCzk6/Op7a6Nq0ZXpH1QT8HgDrhRJarI1mXHOF9x\nhx29B8KOfTlf03On02htHJLk9KEmkpMWoWcILfJ8pPp8opTIpKxJXFR5EQ3WBiZlTYpbfDl8DlJU\nKTy97mn8Qb+y3e1383Xj10qoKkIkPyoizAoMBXyw6wOm5U6jy92Fw+fg5OKTqcw80O43T5+niF+b\n18YJRSfwyZWf0G3tZuPGjXzwwQf8/e9/53e/+x3XXHMNzzzzjLLvJW9ewhOrn+ChFQ+xevVq7rvv\nPl544QUWL15MdXU1drudlpa+k+JzTsyh9PZSfvbOz3C73Vz7/LXM+v0s2ie0Rzlf+mQ9ddY6MlMy\nkaRwSLK2q5YiYxEuv4tAKIDT74wS8A6fA4Pa0Kf4ane2k6PLoTS1lDx93pA7S6nqVHxBHyabiVtm\n3YL9t3Z2/XQX/zj/H7xxyRtKfltEfGmTwtX3e15Lo9rIG9ve4PlNz49p52usEo/zNdw5XzOBPbIs\n1wNIkvQGsADYGRkgy3LP1uGrgMGV+RUIRpndnbsx2UyjPY1D5s8r/szcirkclXvUiLyexW2hOLWY\nRlsjLr8rykHpTUgO4Q64KTQURgkGuzdc4ytdkx7lfDl8jvjDjvvzWTK1mTj9zqgekZHiq5IkDUly\n+lDi9rspfbyUrtu7lGriFrcFVYIKs9NMRUYFZpeZrJQsEqQEslKymJQ1iYqMCp5a8xTH5h8bJVgH\nwuFzkK/Px6g2ssq0ilNKw07ln5b/icSERG449oao8QZ12PlyB9wYkg0UGApYvG8xR+cerSRyP3jm\ng7S3tyvFQVWNKiVfzea1YUw2YlQbsayzcMwdx8TMye/3c8MNNxAMBfEGvTyw7AFCcojpx0/nrrvu\nUnKrIj9paWkxxwDwpHg4+uijcSQ5kCSJzW2bufG4G9nStiXK+TIkG6jtqlX6J6Zr0lnXvI4MbYZS\naX0g56t3kduI+JIkidkls8nWZcd1LeIlsoq0tqtWEZG5+lzFIcrX5wP9hx0j73lX5y4sbsuYdr7G\nKimqFMWx74/hFl+FQGOPxybCgqw/fggsGtYZCQRDzG7Lbqxea1gQjMHkz4Px1JqneHD5g1TfVD0i\nFr7FbaHYWMyWti1AeNVZSmrf4mbeK/M4rfQ0Co2FrDKtUpru2rw2DGqD0jPP7XejVWkHFXbs9nRT\nZCyKKnlQllamPDchcwIZ2gw+rfmUL+u+5PF5jx/+mx8C2pxtWL3WKFFocVuYkDlBudF3uDqUm/qT\n85/k2PxjSUpI4q3vvcXyhuXxhx39TvTJeuZVzOOTmk84pfQUZFnm1a2v8u9L/h2zzF+bqMVus+P0\nOcnV5VJgKOD9Xe9z7oRzWbF0BS0vtaC5S4Pff8BFKziugI4fhN0hm9eGUW1En6zHa/Qybdq0GDE1\ndWo4eTziLNh9dhKlREomlXDviffGfR47XZ2cMe4MPtj9Ac9vfJ5NrZt48MwHObX01Fjnq7uOSVnh\noE2aJg2X30WaJo00TVq4mrnfidllxhf0kZyYjN1nR5+sJ1ObGQ5Z9mgWbXaZlWvzwoIXUCcOfRmH\nPH0edd11Ue8jQo4uhwQpod+wI4RFdGQhizF5bK52HMtok7Sj7nzFjSRJpwPXArP7G3PPPfcov8+Z\nM4c5c+YM+7wEgoOxu3M3CVICO8w7yNXnKjfwI4WIE1VvrR8x8TWzYKby4dTh6ui3kny7s53dlt2k\na9KZljONea/OY9EPFik3aQjfDLs8XagSVXgCHjzB+BPup2imACjlJiLXLhKSzNBm8FX9V3zd+PVh\nvuuhI1JUNhIShPA5PSPvDMVBMjvNilPz/SnfV/b9zuTvsMO8A4vbwqbWTSyuWcxtJ9/W72tFHJy5\n5XP55ae/5P4z7mdDywYkScLoNPKLX/yCxsZGTCYTJpOJlpYW5HEyncd1MiVnCgWGArxBL5OyJpFj\nzGGXZRdBgmRlZSliypx5oDVR5LomJiSim6hj+dPL+01Id/ldGJINXFB5Advbt9Pl6WIc4+I+jxa3\nhak5U3l81eP88tNf4g/6qcio4Jcn/jKqEbY+WU+Lo4XZJeFbU8QV6ym+HD4HCVICbY42ilOLw+dN\npUedpEar0oaLqe53kCLOV+TYw0GePg91oloJKfYkMSGRHF3OgbCjKjbsGCnfMC5tnHC+BsGXX37J\nl19+SZujjbrqugHHDrf4agJ6drwt2r8tCkmSjgKeAebJstyvH95TfAm+3Xy892PUiWpOH3f6qM4j\nJIfYa9nLcQXH8Yev/oA6Uc27l747qnMaDN6AF1/QR64ud1C9Fg8Hi9sS1euurwrgEZx+J832ZrJS\nslj1w1Xk/DkHi9uitH+BcL2fLneXcqMZVIX7/d/28w35NNubo5/ThMVXdUf1IbcwGg4ieXJ2r50c\nXQ6BUACHz0FFRoUyzw5XB9kpfYez0rXpNNoa+dnHP2OHeQe/OulXSJKEy+Vi/fr1mEwmRVCt37me\nen89J11zEo22RmosNeww72BW4SysViuPPfZYzPHVITV7LXuZVzFPuSaTsiZRNb0K38M+lvx0CVrt\nAVFw95K7FcfO6rUqfxup6tQokd0bl99FZkomr373Vc58+cy43byQHOIPS/+A2WVmfsV8am6p4b6v\n7mNj60YSExI5rey0qPERkRI5nxGRkqZJI1WditVjxelzUpJaQoujRRFfERc8kvflC/rY1LqJDleH\nIoyHi3x9PhnajH6bXP/m5N8o7bT6DDuqDehUOs4pP6ffvyNBLBFTaK9lL5++8inmj/pv7TXc4mst\nUCFJUinQAlwGXN5zgCRJJcA7wFWyLB8Z5aQFo85/d/6XdG36qIsvk82EIdlAVXYVr299narsqlGd\nz2DpdHeSmZKJPjlcy2i4CYQCdLo6GZd+wKEYSHxFylEUG4tJSkiK6iOYqc0Ewk5El6dLuUkPqs7X\n/htOkaGIJntT1HNpmjQytBlsbNnYZ3PqH7z7A2489kYlD+qPX/2R206+jeTE5Lhe/1Dp6XxBOESa\nqgm3qFnbvBZAyfkCcLvdijNlMpnYXLuZ/+n/hz5ZjypRRU1XTbggal0dp556aszrpWSnoEpUcemU\nS3llyyvKeamoqODhhx9WHKzi4mIKCgo469Wz2Nq+lRRVCqnqVJITkylLK6M0q5Q2X1uU8IKwONnV\nuQsgSmxF8qn6o2euYO/cv4GosdRw79J7kZBI06SRmJDIfaffx17L3j7H9xRRgFIDLF2bHhV2PLbg\nWEXARxzDyH7VHdXcvOhm2p3t6JP1w/43kqfPIzMls9/nbz3hVuV3TZKGRClRqdkGKPl6f5n7F5IS\nxkyA7Ihh1HO+ZFkOSpL0U+BTwisrn5NluVqSpB+Hn5afAe4EMoC/SWGZ7pdleaC8MIGAdtfYcCI+\nrfmU08pOo8hQhDfopd5aP9pTGhSdrk4ytBnoknVxV5w/HJrtzeTochTHqa/VYD1x+pw4fA7FQclM\nCefQ9OV8RR4Pqs7X/nkUGYsw2UzMf3U+fzv3bwfCjpoM5Zo6fU50yQduUI3WRqVAqMvv4s4ld3L1\n9KuHvRl3xN2y++w4HA4279pMhjaDK4+6kgeWPcD65vV0uDrQuDRkZWXR2dkZtX9aZhrdN3fz1Pyn\nWNawjK8bv6Yio4KioiJOPPHEqPyqmmAN+0LhBsHfnfxdfvPZb5hXMY90TTppaWncdltsyLI4tZhl\nDctIUaVQmVXJeRPOIykhiWPyjyEQCsSMz0rJYkXjCiBWfPVe1dmTGPEV5yKCDS0bSFWnkpiQSGJC\nIhCu0N9f4rvifO1/vnfYscPVQSAUoDy9XKno7/A5lCr+WSlZfLz3Y8anjydFldLnORhq8vR5feZ7\n9YU2SUuqJjXKJTMkG8g35I+phSZHEmNhtSOyLH8MVPba9o8ev/8I+FGcx+rXRhV8u2hztA37t8d4\neLf6Xa6Zfg3dnm5UCSpcflfUt96xTsRB0qv0IxJ2bLA2UJxarIiYiZkT2d25m/eq3+M7k78TM97l\nd+EP+ZW8lKyULDrdnXS6op2vbk+30hJlUHW+NAfE15rmNXxa8ylvbn9Tea7nDczsMkeJr0gxUYBd\nHbuQkQ/6gXs4uFwubr31Vj7d+Ck0wLxH5+G0O0nRp3DU40eRoc3gzlPv5MHlD6JOUnNa4Wl0dnai\nUqkoLCxU3ClVuorXE17nimlX4A/6WW1azdXTr8ZoNPL119G5bX9d81dkc7jURqGhkA5XBxa3hfL0\n8n7nWWwMi88UVQpFxiIlDD+3Yi5zK+bGjM/WZSvO4qE6XxnajLjDjhtaNnDLrFs4vuD4uMb3dLAg\nLLoiTlGqOpVmezM6lY4FlQv4/ZLfc+usW5Wm7xAOV25q3cSEzAlMzZ7K+pb1cb3u4TA9dzr7uvbF\nNVafrI8Raka1MarlkmBwaJO0B40kHFEV7uP9RjsWqeuu45ef/HJQ+7TYW1hWv2yYZjS2CckhZv5z\nJsFQ39Wp253tcYcZhgtPwMNX9V8xf8J8StNKmZozlZLUEuq7jxz3q9MVDjuOlPPVaG2kJLVECXFU\nZlbyytZXuPa/18aIJn/Qjz8UXhUXyUuJrB6LhEvhgOth99nRJGkGXecL9rs1+/+vvbXjLbo8XUrC\nPYA6UU19d32US+f0OxWxtcO8Q9kWL7Is89FHH/HMM8/w+9//nkuvvJSzzz6bqVOnEgqFYsZrNBpe\neuklGtY3gBmcdicajQZtmpajM44G4PJpl7N432IW7VnEvEnzaGtrw+PxUFtby7Jly3jttdd45oln\neO/S98jQZpCnz6PDfeA9PbDsATa1bop6jxHxkZmSSae7ky5P14B9F3uKr3jo6X72TEwfVNhRm86a\npjVc/d7Vyira/tjQuoFZhbOUSu8HI5J8Hsl9ioQbJSkctmyyN6FL1jG3Yi5d7i7WNq/F4Xco+2Wl\nZLG5bTNFhiIuqbpESdwfTmYVzeJPZ/0prrEVGRV8cfUXUdvy9HlUpFcMx9S+FSQnJiMzcH3AIyqY\na/fZo1ahHEns69rHO9Xv8OjcR+Ma7/A5OP2l00lRpbDhxxuGeXZjD6fPydrmtVjclj7DAW3OtmFv\nvHswTDYTufpcjGojZ40/i8lZk7nhwxt4f9f7eAIeji04dlTnFw8Wt4VMbSY6lW5Ecr4arA2UGEui\nnK9Iv7/F+xZz/sTzlbE9XSQl7KjNVNyXo/PCgiMSdnT4HGSnZA+6zheEnS+zy8yZ485ka/tWpmRP\nUXKbACZnT+aB5Q9QaCjk+QXPA9HOlyK+fE7MZnNUjpXJZOKuu+5SGiXfveRurjn6Gsanj+fyyy/H\nZosVGO3t7eTl5UVtS0hI4LnnnuPh9Q/ToergN/N/wy2n38KFb1zImZVnAmEH6JSSU9CqtJSklsQc\nF0CdpOa8iecBYdejp+P5ee3n6FQ65dw6fA5FKKdp0rB77Zid5gFDWpGw66GKr4jzlapOxeqNP+z4\n/q73KUsr44fv/5A1P1oTM/6cf53DE/OeoNpcrfQ6jIfezleuLlf5TErTpLG+ZT06lY4EKYErj7qS\nt3e8HZPz5fA5KDIWcUrpKUqO4FhBkqSYUPlPZ/70oOJB0D+SJKFN0uKk/y9jR5b42r+650jE7XfT\nZG8iGAoqeQYDsbNjp1KlOCSHSJCOKJPysIkIgZ41cSJ4Ah5sXpty0x4tTDYThYZwTeCkhCRK00op\nTS3lji/uoN5af0SILyXsmDxyYcfKrMoo5wvgkqpL+O/O/3LmuDMJysHwfHq4SJEvXRH3pXfCfb21\nHrvXTlZKVlziyxvwEpSDiqiLXMfJWZN599J3MSQbkCQpvGIMiarsKt7d/i4nZ57MunXrmDZtWpT4\nqu6oDr+Pky6hvTk2H/H6669n/PjxALy38z2m5ExhfPp4vv/97xMKhdgb2MsK6wre//H7lBSXkJnZ\nd7L0VVddxYO2B6kyVCHpJEJyiOUNy3n2gmeVMQvPXag4hQej90ILh8/Bzo6dUY8j5yZBSiBVk8q+\nrn1RRUh7c6jOlyzLhxV2DMpBbp55M79f8vs+PzM3tGygzdmGzWuLqcw/EJHWSRHxNSFzAiuuC+eo\npWnSaLQ1Kl8mLqy8kKveu4oCQ0GMaOu5wnesI0kSEiLF53BIUaV8g8TX/r5lRyKRNhStjlYKjQcv\n4m/z2ihNK8UdcIdXew1zEu9YI3KtzU4z9DK+2p3taJI0ox52NNlMMR+oR+Uexfj08VFlC8Yyna5O\nslKyUCep+1zRN9Q02ho5u/xspfVKWVoZiVIiVx91NX9Z9RcWrllIi6OFv8z9Cy6/i6SEJAKhQJTz\nVdtVq4RLIex8bWrbhN1nJ1uXrTSPHohIyDGSQ6pOUpOjy2FC5oSo0gZvPPIGiV8m8qH7QzxmD5+H\nPud4jmfr1q04fU5FIO4w76AiowI5Xcbv9ketACwqKkKnO5Ar1uHqoNEarj39z3+Gu6nNeXEOwfog\nM2bPIN+QP+Dc253tnFx8Mnavne3m7eTocqLqsw2mzlyf4qvzgPhy+pxR+YuZ2kxqu2uH1PnSJGlI\nTkzm//33/0WtWj2Y+HL6nYqIj7jg55Sfw8NfP0yjtZHStFJlrN1rp9PdqVyznrl7B0OSJB4444Go\ncxx5/yWpJezu3M2MvBkAHJN/DDavDZPNpDiPkXBlPJ/7gm8OB4vSHVniy3tkiy8I33ziFV9GtZFJ\nWZPY2bHzWye+IjeEvuortTnamJAxgeqO6lFdhNFka4oRXzcdfxMzC2fyk//9ZFTmNFg63Z1UZlUq\njaqHE1mWqe6opiytDE2ShoafNRCSQ/zxjD8yMXMiJpuJuu46Wp3hFYROn5NCQyH11nrFyYkk3Pdc\n7ZimSaPL3aU4X9vat/U7h08++YRt27axbc82PKs9nPjeiZhMJt577z1KUkuYmDkxanzDzgYCdQFs\nhEVAoj6RaRXTsLvsBOUgLr8LWZap667j3AnnsuClBVxz7DUDnoMOVweNtgONP7wBL+ua1zEpaxL1\n1voBxVcwFKTL00VJagl2n50tbVuUG/+hEBFfbr8bGRm71x6VuO7wO6KESmZKJnssewYM+WdqMylP\nL1dynuLhobMeIhgKkp2SrVR8N6qNNNmbCMkhZFmOiRj0Djsakg1UZlUyJXsKt3x8CxdMvICTik8i\nVZ2qrITsdHcqTtZg6K8QbUVGBZ6ARzlHCVICn1/9OZnaTMWxPxKdL8Hhc7AvH0eW+DqCnC9vwEvl\nwkr23bqPBClB6XDeYG3ghKITYsbv6tjFxMyJipCIiK8CfQE7O3ZydvnZIzr/0aZn2LE3bc5wFem9\nlr24/K5BfYsdSkw2k7KiKYIkSRQaCqOcr68bv+aY/GPiDgWNJJHwnTvgHvaE+w92f4BOpWN67nTg\nwDfD22ffjsPnwGQz0WRviirfkKfPw2Qz9Rt29Hq9uNvd1G2uI3FbIuYWM5ZdFrads01pQ9OTxx57\njE8++UR5vIpwa9nGxkZev/j1GNfosT8/hj/gZ41tDXdvuBt9ip6Nv94Yw7C3fQAAIABJREFUzlFa\nFJ5jp7uTFFUKWSlZeBk42d/mteEP+WmwNijb9lr2UmQsYmrOVOq66/r8fIjQ7ekO97TUptPmbKPa\nXH1YteUi4uuxVY/hCXhw+BzYvDYlH673yl2lvMcAYUdJkth7S981s/rj/47/v5htqZpUXt36KhOf\nmsiCygUx+bI9xde03Gm8dvFrJEgJVGVX8cTqJ8jX5/N149dUZVcprYHane1R9awOlyJjEcmJyVHH\njLxWhKyULNSJaiVMLvh2cDDxdUQlEg1kQQ/EZW9fxtK6pUM8m4Exu8zUW+uVdh+K82Vt7HP8ua+d\nS03XgRqzkQazEefr20bE5Yycv55E2nOka9O57v3rqDZXj/T0AGiyxzpfEG5i2+nqVOr53PTRTWNu\n1epq02oarA3UdtVSkloy7Dlfbr+b337+W+467a4+nUp9sh5Nkoat7VuVYqedtk4ki4S+SY+rM/z/\nJ1ObSbuznW5PN+nadC677DKuOO0Ktj60lf888B9WvLACz9ceNm/e3Oc8FixYwK233soNv7mByTdO\nZtmyZdTW1nL++edTkVER44jMnj2b0+eczsxpM5lTMYduTzchOaScK5ffRbO9mQJDQVy1fSKJ5T2d\nr72WvZRnlFOaWhqzUjYQChCSD6x87FkU1+6zs6NjB5OzJg/4mgMRya1rsbfQ7mzH4XNQlV3Fua+d\nS5OtKVzbrIewyNRmkqJKQZ009P0IexMJcc6vmN9n/bye4is5MVlZrHFs/rEY1UbMLrNSliRyXiNF\nToeKxIREytPLBzxmWVoZD531kCiT9C2jr9ZOPTmixFdfN+J4aLI3Dbo9iNPnRJYPrPY451/nxF0/\nCA6EyyI3ErffjTZJS4O1gS9qv+ClTS9Fjbd5bVEf3BHnqzKrUqn+/G1iQOfL0UauLpd0TTpvbn9T\nSXYeafrK+QKU5NxIJfJuTzctjpZDfp3zXzt/yIXRH5f9kb+u+St7LHuoyq5Cp9Jhdpk5/p/x1T4a\nLHctuYtpOdP4zqRwLa++SikUGYvY9+E+TA+ZyMzM5IKpF7Dq9lVYn7GydcVWIFwTqrarFkOygaSE\nJMaPH09hUSGqMhVlJ5dx5hVnkjAvgWOOOQYIh+keXPag8ho/+clPePzxx5l79VwmnjaR2bNnU1ZW\nhkqlGnD+c8rm8L8rwlXhn1r9FH9b+zfgQAX+QmNhOMF2/3XqdHXyypZXYo7T4eqgyFgU5XzVdNVQ\nkV5BWVpZjMi4ZdEtPLfhOeVxpL6ZIdmAw+c4bOdLlxxe5drp7lTaFv129m8x2UzsteyNcb4ytZkD\nul5DyQWVF7Dn5j1cUnVJn5/fPcVXT6486kre+f47mJ1mOl2ddLg6qOuuI0FKCDtfQ+yUV2RUDOim\nqZPUURXlBd8OvlHOV+TDYbDYvfZB1wi78I0LWd6wHAjXnFq8bzFb27dy39L74to/8mERCT+5/C4m\nZE7AZDfxZd2XPLoy2kJ3+pxRc4yIr74+kL8NRJoG9/Wh2+poDYuv/Xknfa169Aa8vFf93rDOscne\n1G/+XoGhQBHe3Z5ummwxLU3jwhvw8r89/8NkMx3yPPtij2UPL2x6gdLUUrQqLbpkHdvbt7Oued2Q\nCr3ly5dz77338uJ9L1K3sI5p06aRlpbGwoULY8YWGYvABbSBxWIhMSkRXY6O9Mp0CrLDBR/z9HnM\nq5inJNs/+uij7KzZSdIPk5j0k0n8/O6fI58gM7EynLvVYG3gji/uiHGkIu2D4kWSJBITEslMyeSV\nra+weN9iINr50ql0yutsbd/KY6ti+x52uDqYmjOVbk+38mUuyvnq9X+9uqM6qm6VxW0hMyUTg9pA\np6uTuu46pUffoZCcmIyERLO9OdwqS23g8mmXMy1nGjavrc+wY7yV0w+XyJeYHF3OoMSXJEnk6/Mx\nu8xKK6o6a53S+3KoCyBXZFSMWuqDYOzyjUq4jzgJg8Xusys5V/HS7mxnr2Uvp5SeovRo+njvxzyz\n4RnuPO1Ouj3dWD3WqBU171W/x8zCmRQaCw84X/tvuu6Am3Fp42hztJGpzWRr+1bqu+spTSslJIdw\nB9wx4qvYWExJagmN1sZvXbkJu8/OuLRxfTpfLY4WTi45Wbl59rXqcVfnLq57/zoumnTRsNj9gVAA\ns9OstBDpTYGhgGZ7MyE5hNVjPeTVj5G/ozZnG5VZlQcZHR/BUJDarlqCclBpIhwJY0HYbUxRpSgF\nNlc2rqTd2c6CSQuw2+3U1tZGNV82mUzMmzePSy+9NOa1Pv/8c+655x4AOjhQzLO5OfZ8FBmKyDo1\ni4zTMlh46UJqfbWsblrNcwueixr31PyneH/X+8pjnUqHP+SnvjucrK5OUuMNeklJSFH69bXYWyjP\nKFfe/4rGFYfk4GRoM9jQsgGdSqd0NGiyN1FoCDtfkb/XSP5UbzpcHeTocigwFGCymSjPKKemq4bz\nJ55PWVoZ1eboRSS1XbWoElTYvDZ0Kl1UaZANLRsYlz7usDs96JP1NFgbcPldijBJ1YRrbPVeGZiZ\nkjni9fUGK75gf9V8pxlJkuhwdeAOuJmcNRmTzRTVQHoouPboa+Mu7Cv49jDqCfeSJM0DHudAb8eH\n+hjzJDAfcAL/T5blTb3HwKH384us5hkMNq/tQE+3/YnIa5vX0u5sR5ZlXt3yKkvqlvD2999W9nly\nzZNcP+N6rjzqyj6dr3Fp49hu3k6GNoMcXQ4f7v6Qm2bepHxb7hnWjDhfKaoUjGojbY62gy5B/ybh\n8DkYnz6+zxYZEaehKqsKk83UZ083u9euhPsOt03G9vbt5OpzlVVLEA6BZ6Zk9rtqKiK+bF4bMjLN\njkMTX5Hk81ZH65Ct7GywNpCty6Y0tZSjco4CICUpBRyADd75zzsYMgy82f0mn139GU+teYr3dr7H\n2h+t5aMXP+L222+POaZbcvcpvk4//XR8fh9/2vIn3v3hu5SVllFUVERGRqx7UmQsoqSshHx9Pm61\nG4/b06ejUJxazE0zb1IeS5JEuiadPZY95Ovzw1XuA15SVCnssYTLTjTbmxXx9drW11jfsp4PLv9g\n0OcuU5tJSA5h99kpMBQoYcfpedNJSkhS/i97Ap6Y1dmrTatZUreELG0WlZmVLGtYRnlGedj5Si9n\nYuZEgnKQ7ebtTM2Zij/op9HWiIzMgjcW8PMTfh4VdrT77Bybf/i15PTJehptjQRCASWEaUw2Kon3\nPVct/n/2zju+reru/+9jy7ZkyZYteY94ZSeETAKEkTDCJjRQ9iqU8hR4Ci1QCoUnycNDn0IpZTx0\n0AX8yihtWaUpMyTMAIFAyLQT73hvW16yfX5/XN1ryZI84iWH83699LJ8dXR0dM/Vvd/7/X7P55tq\nSw16wzFexFviae1upbu328fQHMz4clqcNHU2IZHUd9RT46rh1NxT+aLyizEvm3NE8hFj2p/i8CDa\nNIlhRyFEGPB/wGnAPOASIcTsAW3OAPKklDOA64HfBusvmOerT/YNmnQ9WNixpKmEjQUb/ba3dLUY\neRn6CXVbxTa6e7tp7mqmsq3SL9eovr2eylYtt6fGVUNOXI6P8ZUdl011WzVVbVWcN+s8Pi7/GOjP\nbwoUdgTIisvyyRGZirh73dz+ZuDl2oFo7WplTsIcSptLDaP0rQNv8d1Xv2sYX/efej/XLro2oOdL\n36e68vhouOfde3hh1ws+2/Sk/2AkRCdQ315vhEQPNeyoG18fl33M0t8vPaQ+ent7aWvr98IUNBQw\nwzGDe1fdS3xRPHl5ecxJnQMPAk/Abd+5jb8+9Vcq2yqRUrKpaBNXLLiC33z2G6ZPn87cuXNZvXo1\n11xzDc4znPz45z/mFcsrPjmSOieccAI3/PgGkk5MYs25azjyyCNxOp0BjchMeybpMelkxGbw5JdP\nsr9h/7C1ouIt8ZpUgVWTKtB/S4bnyyvn7pODn3DFgisOaem/M9pp5PckWZO0sGNbheH50m/UAnm+\nfrX1Vzz11VMkRCewfuV67nrnLtq62wxNKiEEa2evNcLlpc2lpMWkUeOq4aOyj9jfsN8n4R40XanR\nYou0GYtD9H5jo2Kpb6+npavFJ8x47qxz+eO5fwzYz3gRJsICFmBvd7cHzbUKDwsnzhxHn+yjtLnU\nuJkbj7CjQhGIocKO4x3HOgookFKWSCndwPPAmgFt1gBPA0gpPwHsQohkAhAs5+vzis8576/nBXyt\np6+Hjp6OoGHHTUWb+ON235OJrrSsGzx6DoxuSOkGVEF9gU+F+oaOBqNNjauGRamL+hPuezpIjUml\nu7eboqYizpl1Dp9VfObTf1DjK0AuyFSjuauZRz55ZNjt27rbyLRnckTyEWwu3gzAu8XvsqVkCxWt\nFaTaNC9gnDkuoOdLv/Dtqtk16rGXt5T7Gf41rhpDPDEQeqHfps4mo/juoaAbX28WvjmsQrm7du3i\ntttu4+KLL2bFihVkZWVhNpu57rr+2vUF9ZrxtSpnFRlxGRQWFtLd3Q1mIBmOPO5IknKSaOxoZFft\nLqyRVs6ZeQ7FzcWsXbuWXbt28cYbb/DHP/4RsUqQfEIynUmdhqHZ2dPpo7Wlh+WG4oK5F/DL1b/k\np8f/FInk/z79v2HLAsSb40myJmEKMxlhR9AMzczYTJ/9/0XlF4fsMXJanEaoNjE6MWjOl258eRuk\nX9d8TWR4JAnRCRydcTQOi4PtlduJMkUZMiRr56zlxb1aIeqipiLy4vPIi8+ju7eb0ubSfs9XlOaN\nGivPl65m7h12LGoqIt4S76OvFR4WPinGS6DQo8vtGtQ4T7QmkhmbSVt3G1n2LKwRVrp6u5TxpZgQ\n1s5ZO+jr4x12TAe8tRXK0Qyywdoc9Gzzs7SCrVisdlUbHqeBBPIqedPQ0UB3b7fPtnZ3O32yzy/s\n6D2Oalc17j43BxoOGLk49R31xh12jauGYzKO4R97/mH0GR0RTZI1ibKWMlZlr6KitYKmziafu2Ud\n7wKzU6VY81NfPoXZZOai+f7hpw53B+4+N+5eNxHhg68sA4zCtOfOPJdX973KadNPY1vFNvY37CfO\nHGfcVcSb4wMm3Ld2tyIQY+L5KmspM4ygosYisuOyh/R8OSwOvqr+iqbOJuYkzuHzis8PKW+vtLYU\ne42d3Tt3QzP8oOIHVBysIDc3lwceeMCv/cGDB/nlL/3rh7a29ofA9jfsN/TJTjrpJPbt20daWhqx\nv4xlUeoiLp1/KQAvbXqJzys+55iMY8iOy6a4qdiv3w53B19Va7IOZS1lxFvieWP/G1z58pXsuXEP\nm4o28cKuF4YlLBwbFWvccNy07CZe3vvysD1fceY4IyxvNpl9PF8nZp9oGF89fT3sqN5h1C4cKfMS\n5zHTOZMPSz8kyZqklaxpqybFlkJDR4OP8SWRlDaXUtteyxFJR1DYWMiza581vFW6YKl3OPvYzGOp\naK2gsLGQosYicuJzcEY7aXe3U9JcQkRYhJZw71ntuSj10AVWdayRVhKiE2jtbjVCjLFRsexv2D/o\nDcZEEsj4GizsCJpxHBsVS3OXlpurtx1LnS+FIhgn5Zw06OtTKuG++51u7uq4i8jwSFauXMnKlSsB\nLf+mtbsVV7d2JySRxkVOz7sIlvPV2NnoZ3zpXic90d17pVR2XDbVLs3zlRidyJ66PcxKmEWHW0uY\n9za+FqYs5LFPHzM+32KykGxLprW7FWuklUUpi/js4GfGnZi38dXc1ezj+fq65uvR7r5x59ODn9Lc\n1RzY+PJ4Hl1uF3HhQ68ya+1qxRZp46yZZ3He8+chpWRbxTaSrEk+F6t4S3xQz9f8pPl8WR0wfXDY\ndPd2a55Oj+r66r+s5sk1Tw7L+NI9X8nWZOxmO7WuWqNESWdnJwcPHjQS1sPDw7n44ov9+tm1YxfN\nv+4vLvzYW9rxpEspDGT+/Pn8/Oc/N8rbZGRkkJ6ejtncL/Ba0FBgeG9iY2OJjdWOM2uklcUpi6lt\nryUiLIKu3i7KWspIsiaRFZdFcVOxT96ZlJKOng6+qtKMr20V23h578vkxufS0tXC/R/cT0dPB//M\n/yffXzoyxf+j0o8iTIQNexVZvCXe+M1HhUfx3NfPccdxd3Cw5SA3LL2BTw5+Amg1U9Ni0g456fr6\npdcD8H+f/h9J1iRcbpdR1sjabPXzYj+/83leP/A6vzrtV+TF53H+3PONvhwWB/n1+T7im+Fh4ayZ\ntYYX97xIYWMhcxPmcmL2iRQ1FnH/h/cTb4nHaXESZYpi3037fEohHSq2SBvOaKePV8seZdckMAaI\nCE8WgYwvfUV0MPQ6n06Lk2x7tnEsKc+XYrzYvHkzmzdvHlbb8Ta+DgLTvP7P8Gwb2CZziDbaC+dm\ncO0V1xqJszr6CqPKtko+KvuId4re4anzNB0tfQVXMM9XY0cj7l63z7aWrhbjTvao3x/FjctuJDE6\nkdr2WhanLqbGVUNVWxUnZp/Into9rMhcYRgA3mHHeUnzNLVnjxJ7dEQ0ydZkwxt3cs7JbCzYyJkz\nzgTwWTHjHXZcO2ct971/H3/d+Ve+Nedbo17dNNZsLNhIWXMZzV3NfFH5RcA2uvHr6nYNa4l/W3cb\nMVExZMdlU9lWSXFTMZYIC8dmHuvj6Yo3x9PY0WgkLeuGQWtXK6uyV/GH7X/wWy4/EipaK5BIn8T3\nj8o+oqGjIajx1d7ejrvRbRhfceY4suya8VJfWs/KlSuprfVdxTlnzpyAxlenrZPU2alUikqIhdvP\nuJ0ls5eQk5MT8LPT0tICJsR7o+d8DeTVi1+loKGATw9+aggE5tfnMzthNrFRsZhNZura64yyKe4+\nN32yz/AuPr/zecpayrh5+c2k2FLY39ivcj6csKM3MVExzE+aP/ycL3O8MWYhBP/93n+zOm81LreL\nWQmzeHnfywC8W/Qux007bkRjCUSKLcW4uMdExmA2mX1EVvU8xcLGQnbV7GJH9Q6/xGyH2UFBQ4Eh\nm6Fz9cKrufQfl9LZ08kH13zAdIemAfb9f32fuvY6w4uYG5876u8BmjGi53V553xVtFZwTMYxY/IZ\noyXZmuwnUF3VVjVo8n9idCLtPe1GDVHD86VkIRTjhLdTCGDDhg1B2453ztdnwHQhRJYQIhK4GHh1\nQJtXgSsBhBBHA01SyoDJXcnW5IChR118tbK1kl01u3hpz0vGyc/wfAXJ+RrM85Udl83nlZ9T0FBA\nniOPqPAo5iXOo6qtihpXDSdmnUh+Qz53vH0HP3v/Z0yzTzPCn81dzTgtTi6ZfwlPf/U0HT0dWCIs\nJFuTjRPGBXMv4O97/j5kwn2mPZPnzn+ODVs28IsPfxHwe0wmH5d9zAdlH9Dc1cy++n20u9v5686/\n+syVvv8DKYC/lv8a2yq2+WzTDSZrhBV3r5vPKj5jYcpC5ibM9VmtpHu+Vj21ysc72NbdRqI1kUUp\ni9havvWQv1tZcxlpMWlUtVUZeTxbD241PF/19fVcf/31nHXWWSxYsACHw4HVauUHF/6Axo5GGjsa\niTPHkROfQ1FTEU6nk9raWkwmE9OmTePYY4/loosu4uQzTw5Y/aDF3MIDf30ALoKcS3JY8501XHTR\nRRx11MDo/fDo6euhpKnE7wYGYFXOKuMmQ7+ZKGgoMDwzA0OPukHt7nPjtDh5r+Q92rrbcHW7mOmc\nycGWg5S3lPPtud9mecbyEY/1p8f/dNgX/2RrMpmx2j2crotV1KSJsWbEZhg3Ra8feJ0zpp8x4rEM\nJDUmldioWCwmi2GED0y418dQ217L6/tfN8oq6TgsDgrqC/zKzhybeSzzk+aTHptueJ4SoxPp6Okg\nxZYyKkX7QNgibIZ4qnfOl/65ocC5s87lya+eNNT+27rbcPe6B72RS7Qm4rQ4SbImkROfYxhfyvOl\nCAXG1fMlpewVQtwEvEm/1MQeIcT12svyCSnlRiHEmUKI/WhSE98J1l+yLdnwQHije76q2qoobCrE\n5XaxqWgTZ8w4Y2jP1yDG1xNnP8GlL15KZVsls5yz+NVpv+Lzis95r/Q9LCYLefF5/DP/n0gpKWgo\nIDsu21gh1NatFaS9fMHlXP7i5fTKXs3zZUumvUczQOYlzSMmMoZNRZt8xtjV04WU0igwC3By7snc\nuOzGQYsGTxbVrmrq2+sNI3JH9Q4e/uRhTGEmI8yif7dA9QNf3PMi2XHZLE3rX83X2q2FHYUQOCwO\n9tTuIc2WxjWLrvHxfMWZ46hrr6NP9mlJ8cn970+yJnFi1olsKd7CKbmnDOu7uN1u3nnnHSMcuGXH\nFtwH3FQ2V1LznRoiwyP5uOxjFqcuJsmaRGRkJE888YRPH5GRkVjMFmO1Y5w5jsjwSIoai7hw7oVU\nVFSQlJREeHh/IvPlL17OI588woOrH/Tpq6qtioUpC7l4/sW0dLVQ31E/rO8RjJKmEpJtyUHrTOr6\nSL19vYCWnK+HebPjsnns08f4n5P+h4aOBh+drKVpS3njwBu4ul20dbcx2zmbF/e+SJ/s4+0r3ja8\nZSPhwnkXDrttoMLHhY2FxJnjSItJI78+n+mPaiKbf/mWv/L8SLl5+c0kW5NZv3m9YXwNTLjXxwDw\nwq4XuGOFr0fSYXGwv2E/K7NX+vX/u7N/53OuE0KQE5fDLUffMua6dbZIGz2yh56+Hh/PF3BI8zYe\nnJh1IrFRsbx54E1On366UVVgsH1x7aJrcfe5iY6IJjE60VhApYwvRSgw7jlfUsrXgVkDtv1uwP83\nDaevabHTAq76q22v1bxObZUUNRaxds5aXst/TTO+ulp9ClsPJFDCvW585TnySIxO1AQa4/M4OuNo\nylvK2VG9gxRbCmkxaVS2ViKRFDcVsyhlEakxqRQ2FhIZHokpzGTIJcRbtLDIkclH+izdPn7a8bxf\nqtX900/YNa4aEqIT/E4s8ZZ4mrr8k8snm2pXNXXtdXT1djE/aT576/bS1Nnko8ruHXbUea/kPbp7\nu2npavGTYmjrbjOSf+Mt8eyr30d2XDZZcVlk0S9sazaZiQyPpLOn00eQVfecpcem88yOZ6ivrzcM\nqvLyciorK1m3bp3fPpZScuaZZwaUTdhVsYs5CXOoaqtiW8U27j7hbmJiYvjtb39LamqqkWOVmJhI\nd283Mf8bQ2NnI7nxuSRbk9letZ2wsDBSU1P9PvOdond8jE/QxEArWivIjsvmufOf46qXr6Kho2FY\ncxKMYCFHHd3zFSbCcFgchrQBwKrsVdz3/n0sT1/Ob7b9htuOvc14n2F8uV243C5y4nNo6WpBIHxy\n9MYL7/Dk42c+zkt7X6KwsRC72U6cOY63rniLvXV72VS0aUxEQvXi1/oiGv35wJyvkuYSUm2pWCIs\nLEhe4NOHw+Kgo6cjYMHl9Nh0v0UKGy/byDT7NL+2o8UWaSM8LJyuni7jN2ePCi3PlxCC5enLKagv\n4PTpp1PRWjFkKDsn3jc0rxLuFaHElEq4n+6YbogmelPrqmVB8gIqWyspbCzkvpPu44dv/BDQLsJ6\nbkYgGjsa/VbfeSe7x0bFsrt2N0ckafkaydZk8uvzOX7a8aTGpFLRWmFcwB0WB4nRiZQ0lRh3V9ZI\nq6GyHB0R7ZeMnh6bTsHX2nfSx7ivfh8znTP9xqrnN4Ua1W3V1HfU09vXy5K0JdS119HY0ehrfHkl\n3Ou8uu9Vunu7ae3212Grb683LpJ6YvKyNP+6g319fcS6Y+ms7KS6tZrevl4e+/Qxw/hKs6Xxz2v+\nSUKHvwHwwx/+ELvdN/E6MjKS888/H5vNRkZGBq9VvcbKBSt56eBL7GnYQ6I1kfNmn8eGLRuMi+71\n11/v13eUKYrI8EgOth5kcepikq3JhoTAQHbX7qa5s5l9db41PPPr80mLSTOOJafFSX376DxfusxE\nMBKiE6h11RIVHkVufC4NHQ2G8XTTUTfR3dvNvvp9FDYWGsd0u7udpWlLCRNh9PT10NTZRJY9ixRb\nCqYw04QXFL5h2Q3Uump5p+gdIyx1Us5JnJRzEjcsu2FMP8vb+LJGWnG5XfTJPuN47unr4dIjLiXV\nluq3H/SbsIE5X8HIjsseu4F7cdXCq+jt6+WtwreMYyPUPF/Qf2Pw8t6XKW4qHtYKWm9U2FERSkw5\n4+vf+//tt722vZZTc09ld91uunu7OSX3FKraqqhqqzLCT4OtdhyYN9DS1UJsZL/xVdlWaSRpzkua\nx3cXfZf/WPofJEQnGArmKbYUnBYnceY4ylvKfX7gydZkipqKAoqupcek0+5uJzYq1shT21e3j9kJ\ns/3axpnjAsoqTDZVbVU0dTYhhCAvPo+69jqtnmFrvzcrkOdL121r6WrxMb5c3S7cfW7j7tthcbCl\neIthBFx//fXs2bOH8vJyDh48qGlUAcWnFFPSXMKP3vgRJ+eeTExUjHaCtoA90u6zAjAjIyOgdwvg\nb3/7mzGORx96lHe++w5f/PULdtXtIjE6kduPvZ0d1TsMrbFgOCwOtpZv5cZlN5IWk0ZRY1HAdm8X\nvs2F8y7k+Z3P+6h4f1n1pY8kgtPiHHXYcX/D/kFrATosDsJEGIWNhZw982y2VWzz8VzNdM40chjr\n2uvIsmexp24Px2Qcw6/P/DW3v3U71a5qzesYkx60AsB444x2cqDxwJjoYA2Gt/FlNplJj0knvz6f\nzt5OBAKJ5PtLvx8wx84wvgJ4viYSPa/Mu3yVYXyFiOcLtBuDnTU7ue/9+2jubGbNrIGSkYOjn8NV\nwr0iFJhyxpeuWO1NrauW46YdxyOfPEJufC7hYeGckHUC7xS+Q2tXK4nRibR0tfi9T0pJY0ej34oq\nb42t2KhYal21RhuHxcHvzumPmibbkumTfcxLnIczWjO+ylrKfI0vm2Z8Bcqz0e/evL1z++r3Mcvp\nX8cvmKzCZCKlpNpVbYRuc+Nz+aD0A7p6u4J6vipaK3jrwFtUtVURFR7Fwc8P0lDWwK35t1JeXs7+\n4v24C9wUXVJEbm4u8eZ4Yzk/wIcffsiuXf3iqSariShHFFX1VZSZMFoZAAAgAElEQVQ0lSCRFDUW\nYYu0kWJLIeymMDZ/fzPuXjfL0v29Z9709vUaopJvHniTo9KPwmFxkGJLYUfNDo7NOBZrpJUXLwrs\nxfLGYXFQ0lzC8dOOp1f2UtZS5tO/zttFb3P5EZfzYdmHHGg4wJxELaH6y6ovWZTSr+PkjHZSWjm6\nSgcFDQWcnHty0NeFECxOXcw7Re8Yq+m8w+QznTMNXa+69jocFgd/XvNnkqxJXL/0etZvWU91WzXW\nSCsZsRmTZnw5LA4qWis4OSf4dx0LvI0vgOUZy/mk/BNN4iDaadRyDDZGYELCsiMlyhRFVHhUaHm+\nrJrnq7qtmrKWshF7viLCIggXkyMSq1AMZEpVas6Oy6aspcxHGqLd3Y67z83p00/nyfOe5OqFVwNw\nzaJruPXNW/m4/GMSrYkBc77autvolb1Bc75Ay32QyKB5Aqm2VFJtqVy+4HKOzTw2qOfLbDIHFNjU\nV+45LU46ezXja2/d3oBFlIMJio4nj33yGNsrt7Nh84aA5Z1au1sJF+E4LU4sJgspthTya/KhEfZ/\nuZ/nn3+eBx98kGceeAYaNW/S1vKt/OKjX1DVVkVjZyPVr1Xjes3FQw89xAsvvMAXn35Bb2MvZWXa\n6j/9IqXfhT/88MNs2rSJ/Px8XC4X7+x6h0dfeRR3vNvICSxuKiYmMoYoUxR2m50HPnyAZ79+1m/8\n7e528h7No6Wrhed3Ps+p/+9U47XCxkIj3LwkdQmfV3w+ootRvCWes2eeTUR4BGaTmXhzvN+CEXev\nm/dK3uOknJOY5ZzFs18/a+zn7VXbx9zzNVTOF2gla8JFOJmxmcRGxfpIm+TE5RgGlR52vHrh1UZI\nzRZp8/F8jVRiYqzQvUm693S8GGh8HZV2FJ8c/ISuni4SohOICo8KerEfadhxopnhnDFp8xcIPeyo\nr6Ie6diEEERHRCvjSxESTCnPV5QpirSYNIqbipnhnIGUkke2PsKpuacihOCCuRcYbc+ddS7bK7ez\nfst6blp2U8Ccr8bORswmc0CdL/2uXzfCgrmq02LS6JW9XHnklYCmebW7dref8RVMr0g/gTgsDh/P\nV7CwY2NH45gVWB4Oj3/2OL//4vd8XfM1x2cdj91kN8RB582bR3VfNcm2ZCLDI4noiiAhOoHtD2yH\nIqikkksevaS/s8s8OV8uzbCJjoimp6+HsHlhRGRE8KPTfsSCGQs40HuA9xvf59hjjzX2DfR7CE45\nxXfl4glZJyClpLa91pBC6JW9xhykxaSxuXgzp08/3e/7bavYRmFjIX/b9Td+uumnPq9564OtzlvN\nHW/fMaIwzLK0ZazOW238HxsV61fv79ODn5ITl0OiNZH5SfO57/37CBNhbFi1gfz6fMMLBtpFejTG\nl7vXTWlz6ZD6UEtSlxBnjsNhcfiFxCLCI8iNzyW/Pp+69jo/w8EaYSW/Ph9bpI0bj7pxxIr+Y4U+\nruFoyo2GW4+51cdAXp6xnL98/ReSrVoh9rbutqC/1VAJOwbj6++HlrBzojWRwsZCwkQYEWERI/Z8\ngVa83duTq1BMFlPK+ALtzruoqYjpjukk/iKRXtnL9uu3B2x7+vTTWb9lveb5cnfQ0NGAw+KgsaOR\neEs8DR0NJFuT/VaQVbZVssq8Cug3voIZT6m2VHplr/F/vDme8pZyH0HFJGuSIQA5EIfFQVR4FM5o\nJy1dLXS4O6hx1ZBlz/JrG2WKwhRm0grKjkPewkCjTi9pIt+ViN2Cbz36LVoa+sO3v/zjL/nJwZ+w\nNG2pEUpLiE6gJ6aHyPhI+mL6OGPxGeRl57G7azdvRrxJu7uddne7UW8zPCyc7mO6WZy6mLNPO5vj\nph3Hrz/7NXnVeUREaAshdEmDwbxOukRCSXMJ4SKcXtlr1L9Li0ljR/UOmrua/d6nl4m55Y1bOGP6\nGby09yWjBFJbd5vh1ViQvIBka/KIPF8PnOpb/scSYfHzwL6W/5ohsrvuxHUcm3ks9394P+tXrqey\nrdJH02y0CffFTcWkxaQRZYoatN0xmcewJG2JZnwF8MrMSZhDb18vde11fsWprZFWOno6sEZYJ1Ud\nXb/AHqqS/XA5bfppPv9Pd0ynpKkEe5TdqP0YDF3cNBTDjqFIQnQC5S3l5Mbncu2ia5mfNH/Efey+\ncfTlxhSKsWBKhR0BQ96hoaOBnr4eKn5UEXQV0JK0JcRExmgJ9z0dzPq/WeTX55P1cBZ9ss+o0+cd\ndmzpamFz8WZDF8rwfAUJOy5NW+qT1Bsw7GgL7vkSQpAWk0aCJYGuni6q2qpItib75QV593+ooce/\n7/47//Xuf/HSxpe49957uf766znzzDMNcdBnnnnGp/2uml3Mcs7i3NRzkZWSloYWTCYTWVlZrFix\ngg46MJvMLEldgtPixB5l1y4k34KVj65k5f+s5JL/voRf/epXLF67GFuKDVe3yxDFjTPHGSvr5iXO\n48sqrRTQQOVqh8WBxWQZso6b7vnSDV/D82XTDJjmTn/j66Pyj7j7+LvpcHdw9wl3k2RNMhYCeHu+\nwkQYj5z+CMdmHntI+x7AYrL4Lfx4Zd8rRuKwJcLCKbmn8FXVVxQ3FWOLtPnkCQYqsTIShhNyBK2W\n6BuXv8Fx047jodUP+b3+pzV/4jsLv0Nde53fIhL9dzLZoR3dmzTenq+BOCwOo15rQnTCoJ5SIQTF\nNxerBPBhos9psjWZu46/a0xKKykUk8WU83ylxaRR2VZJVVsVqTGpAVcQ6pjCTFyx4ArmJc6jubMZ\nieStA2/R2t1KQ0cD75e+z8k5J/NeyXuG1+fFPS+yKnuVccc/lOfr2sXX+vwfZ46jq7cLW8Twwo6g\n3S1n2jPZXrWdGlfNoN4VPene2+Xe2tpKSUkJ5eXllJWVGVpWF1xwAWec0a/m/cuPf8mO6h2csu8U\nXn16YKEBKC8v9/n/q+qvODLlSO786Z1EHhVJ9rRsNpy9wRAHfXHPi5y842QeO/Mxrn3lWjp6Oog3\nxyOEIM4cx6XzL+W+9+/j4vkX09HTQUJ0Ai63i9p2zeDKsmeR35NPTFQMF8y9gJ9/8HNuOuomqtqq\nfAxah8UxpMfJYXHQ3NnMnto9rJm1hi+rvjT2ue49Guj5klKytXwrvz3rt1w0/yKSrEmkxaRR0VpB\nRmyGIfSqE6hm5UjwLj8DmuxDU2eTzyKA6IholqYt5W+7/+bj9QLNu6dLeoSHhVPfXk94WPiwDYzd\ntbsDhrODYY20cnzW8X7bHRYHdrOdho4Gok3Rfu+ByTe+YqNiCRfh457zNRBTmInYqFiq2qq4efnN\nhiJ7MHTvrGJoIsIjiDfHG/VRFYqpzJQzvlJtqRQ3FQ9Z10vn8bMep0/2IdFkBTYVa2ryNa4a3it5\nj4dPf5j1W9bTK3sxCRMfl33sl6cDw1+erF8IvS8+2XHZgybVbrxsI59XfM4Lu16gtr3WuFuWUvqI\ng2ZlZQVMur/vvvu4//77/fpNT0/3Mb5aulrIicth1vJZ3Jl+J/ZEO3d9dhfv3PQO82bMIyHBN/zx\nVdVXLEhawOzZs5lXO4/mrmYfVXZvIVRntFNLvg8LJ94ST1xUHGfPPJsbNt7AgcYDdPZ0asaXx/O1\nOHWxtpK0XVtJujpvNVe/fDWlzaV+c5sRmzGkxlF4WDj3rrqXffX7WJ6xnGd3PmvkG6XFpJEXn+e3\n4lVPfk+LSTPCrbrxpX+/sTQiBoYd3yl6h9Omn+aXF7U0bSmv5b/ml1BsCjPhsDiocdVQ2lzKqqdW\nceOyG/nF6uGVnNpavpVvzf7W6L8ImodLIv1ufrz17SYTvTLCRHu+QAuPFTUVaeWwEudO+OcfziRa\nE0mKDl7QXqGYKkzJsGNFW8WwjS/QQkZ6qZ53i94F4EDDAfbW7WVZ2jIiwyONpPvSllKfC/1QYceB\nBDK+lqUv49+X+eqT9fX10dGhXYhNYSYsERZNpd1VS8snLcyYMYPo6GgSExNZtGgR55xzDk8//bSR\ndO/N9OnTmT17NqeccgpXX301d999N7/73e84//zzfdq1dLWQac8k96hcfvazn3HmpWfSN6uPWnst\niYmJCCHYV7ePtwvfBvo9X6B53Ora6/jOK98x7uZbu/o9QwnRCYaXISE6gThzHOFh2oq5WletoeSt\ne77OmXkOy9OXE2+ON1bUnTnjTF7f/7rf3B6RfATvXvXukPv+zuPv5MnzniQzNtNn/18w9wIeP/Nx\nv7DjjuodHJF0hE+eW5otzVDb14t7jxUDw44flX3EiswVfu0Wpizko7KP/DxfoN18VLVVsb1qO129\nXcNWvJdS8mHZh6MKm3qjG1cDcxn130koqIjr0i8TTUJ0Aj19PUFLOCkOnYToBOX5UhwWjJvnSwgR\nD/wVyAKKgQullM0D2mQAT6NV5OsDfi+lfHSwflNjUqlsraSyrXJIkUtv9DI0uk7WlpItzE6YTZQp\nioiwCLp7u7FEWChtLvUp4TFU2HEggYyvL774gueff94nLHjw4EGuu+46Hn/8cWN8eokcW4SND/d/\nCIDdbiczM5OMjAxmzJhBlaXKz/P13e9+l+9+97t+YyltLmVnzU4jMbW1q5XM2EzDCNHzh/69/998\ne9636ZN9XPXyVbS72/nqP75iR/UOoxiww+JgV+0utpZvZd2J68iOy6a1u9XwfF0y/xIjrKcbX/r+\naO5qpsOthR2bu5qpddVy5ZFXkh6bzqbiTYQLzZt2dMbRfFL+CaXNpaTG+M7tSFbNZdozfVaQJVoT\nOT7reL+w447qHX4lXybS8/Vh2Yf85Lif+LVbmLKQXtkb0PhKsaVQ2VZJU2cT2XHZwy43pUtwjJVK\num5cBcr5spgsQXMWJ5Lnzn/OkAqZSHQvt3dtVsXYkBidGFQ3TaGYSoxn2PEnwNtSygeEEHcAd3q2\nedMD/EhK+aUQwgZ8LoR4U0q5N1in3jlfw/V8gXaRWJy6mI0FG4kzx/Fh2YeG0ndkeCTdvd1IKYMa\nX95hlJaWFnbt2uWXY7Vw4UKuu/k6wDeXIz8/n1/8wj801NjY78Eym8x09XZR66pl+cnLefi6h0lP\nTycmxtfzkv9mPpVtlcP6zi/seoG9dXv5w7l/QEpJa3crGbEZhhFS46rhmIxj+FfBv/jxWz/muGnH\n4e5zU+2qZkvJFq0grSfXKt4cz1dVmrjmnto9ZMdl+3iGMu2ZZJIJ+BpfdrOdps4mOno6SLWlcrD1\nIHXtdcYKL4fFYSjNL09fzp3v3ElidCKZsZnD+o6BmJ0wm/e/877PNovJgrvX7aMgv6NmB6uyV/m0\nS49NN2ptDsz5Gi3Rpv6cr6q2Kho6GgLmYM1OmE1UeFRgz1dMqlFRIDsuO+AigkBsKd7CcdOOGzOJ\nkqCer0jrpIccdbwlICYS/dhWnq+xJ1i1AIViqjGextca4ETP86eAzQwwvqSUVUCV53mbEGIPkA4E\nNb5SbVo9xcq2Sj+vxWBYTBZOzDqRjQUbWZq2lC3FWzg1VxPU1I2vmpYaRJNgx2c7KC8vx2q1cvbZ\nZ2MKM/mEUd59913OO+88v89obGzk9jtuB3w9X0uXLuVnP/uZX3kbi6X/whUVHkVnTyc17TWszFrJ\n7NmBE6OnO6bzecXnw/rO7e52QxfK5XZhNplxWBzsqd0DaMbXktQlPH/B8yx9Yin2KDun5p5KrauW\ndZvXGSFH6C8CDLCnbo9RtDzQMvmL513MvKR5AMRFxdHc2e/5qmytxBJhMeQO4s3xuPu0kO8RyUfQ\n1dPFt+d+e9RGwsDiyUII7GY7zZ3NhkG5vXI7tyy/xafduHu+PGHHL6u+ZHHq4oAePVOYiSOSjwgo\nIpliTaGy1eP5smcbavND8a+Cf3Hm9DNH9wW80D3BAz3C1gjrpCfbTzYJFmV8jRcDpT0UiqnKeBpf\nSVLKatCMLCHEoL5iIUQ2sBD4ZLB21kgrkeGR7K3by1VHXjXswUTKSKaFTyPJmsTy9OW8Xfi2sexe\nlkkWzlhIXU0dAMc/oK3wWrFiBeeccw6fXfeZz918bm4uy5Yt8zGkMjMzmTFjBhHhEX4XoOnTp3Pn\nnXcOOj4j7OiqHXRl3wzHDJ7f+bzPtjOeOYOdNTspvaXUT6errl37Tq1dWojQHmU3PF+17bUkWZPI\niM2gpauFgoYClqUt44oFV7DkiSXcvPxmoy/dmHFanIbx5p1w780lR/QLq+qer86eThKtiRQ3Ffto\nQ8Wb4w1x2cjwSC6efzFXHHnFoPvqULFH2WnpauG5nc+xIHkBJc0lfgZ8ik0zbiD49ztULKb+sOOe\n2j3MSZgTtO2za5/18cDqpMakUlBfQGNnI/MS5/Fe6XsAXPvKtVww9wLOmHGGT/uixiKu++d1fF75\nOY+eMWhEf0QECzvaIm3K+PLckAylp6ZQKL65jMr4EkK8hZavZWwCJHB3gOaBqxhr/diAvwM3Synb\ngrVbv349ANN2T+OL6C9IWRM47FheXs59991nhAPLy8upq6vjZ0f+jPyt+bx54E2A/rCjNZKqmirC\nwsKIjItk4cyFZGZmcuSRmudnYPjiiCOO4NNPPw02TOLMcSO+AHnnfA2W0zDDOYOChgKfbe+XvI/L\n7cLldvl8rqvbZRhfeskk3RgCzfOle18yYjP4qOwj1s5Zy7ykebzw7Rd8lNB10crVeavZU6cZX8MJ\ny+m6ZB09HSxKWcSy9GX09vWL0ibbkn1U358878lB+xsNdrNmeD638zl+u+23LEtbRkR4hE8bp8Vp\nJLGPtefLW2piT90eI58uEMGKX6fYUniv5D1au1u1nC/PXO6p20N+fT5nzDiDl/a8xDmzzsEUZuKz\nis+MItkjCdMPxaBhxxBItp9MEqITMIWZJq2upUKhmBw2b97M5s2bh9V2VGcHKeWpwV4TQlQLIZKl\nlNVCiBQgoDqkEMKEZnj9PynlK4N93rRp0ygvL+do+9FYCizc/b27efUVf72q3t5efvvb3/psM5lM\nRIRHYDfbDc+SrsBtTbHy9pdvs8u1i72Ne/n1Wb8e9HsPRbwlfsQXbX3FZY2rZlBhxozYDBo7Gg3D\noKevh46eDlJsKTR3Nvt8rrfnyzC+PJ6vHdU7qHZVG4Zepj2TzcWbjVyrc2ed6/O5sVGxCARnTD+D\nm/59ExsLNg5rNaAuOtvh7sAWaeNv3/4be+v6o8rD0UIaK2KjYmnubKakqYTKtkrOn3O+Xxu9hE93\nbzd9ss+nruFosURYfIyli+dfPOI+9NWO7j43WfYsTb9OSiMPEuD7//o+85LmMdM5k/z6fC6adxH/\ne8r/jtn3gMET7pXnK0GFHBWKbyArV65k5cqVxv8bNmwI2nY8b81eBa4G7geuAoIZVn8CdkspHxmq\nw2uv9RU0tVqtAescpqWl8dhjj/mEBZOSkggL0/JrkqxJxJnjjBVxUZFRxCfFc3DnwYChnpHy9HlP\n+5QXGg5CCKJMUVS2Vg7q+QoTYeTG57K/YT8LUxYa4UR9VWE6/XlC7T3tNHQ00NvXq61MjIrBbrZr\nOlu/03S2bj9Wy1HTv3emPXCie5gIw2FxcHTG0aw7cR1XvHQFcxPnDnmhtUf1J9xbIrTC294emMFE\ncscae5Sd2vZa6jvqyYzN5Lhpx/m10XOYalw12CJtY1pDcyRhx2Doqx1NYSaSbVolhI6eDqraqoyF\nGC63y9A0y6/P91tUMBbo+2mg52tx6uJDMioPJ5zRTmV8KRSKQRlP4+t+4AUhxDVACXAhgBAiFU1S\n4mwhxArgMuBrIcR2tNDkXVLK1wN1eOWVV/olrQciIiKCm266KejAZifMZuOlG40Lq+51Km0p9Uky\nP1QWpS46pPdFhUeR6cgccrVYbnwuRY2aiGNzVzN2s93IZ/LG1e2iT/bR1NlkeL7izHHk1+cjkTR2\nNhpetmmx0zCbzIMW+X37yreZ7pjOLUffwh1v30F9e/2QOVHeUhPB6ltOFHazna+rvyY9Jp0tV28J\nWpjXYXFQ0lQypvleoBksHT0d1LfX4+5zH1IYUF/taI2wEmeOwx5lp7S5lM6eTirbKpFS4up2Gasg\n8+vzuX7J9WP6PaDfaB6YcJ/nyPvGr0ZLjE5UxpdCoRiUcTO+pJQNwCkBtlcCZ3uefwgMWxDoqaee\nGpOxhYkwjsk8xvhfX+1Y2lw6KomD0WI2mTkx68Qh2yVEJxh5Sd65XANlB/T8oo0FG9lRvcMIO0ok\ny9KW8XXN14ZgYaY9k8zYzEE9PXrum0CQZE2iqKloyLCjt9TERHq5ApFsTWZzyWay4rKCevhAy/sq\nbS4d8/CZJcJihIKTrcmH5FWzRdoQCOra64gzxxFnjmNv3V4EgsrWSjp7OpFIWrpakFKyr34fM50z\nx/R7gPYbio6InvQ5DUVmOmfy4oUvTvYwFApFCDPlFO7HA11kdaDG10QzXOPLYXEYEhLNnc3Yo+xa\nPlOXv/EVJsL40Zs/4rFPHyMmMsbQLTsh6wRKbyk19LhmJ8xmVsKsYY812ZpMZ0/nsBPuO3s6J93z\ndcHcC/ig9AOy7FmDtnNGOylpLhl748ujcN/U2TQq5fXUmFSiTFGYTWbsZjt76/Yy0zmTyrZKXG4X\n0F8gPlyEB5QDGQt0QVWFL0IIn3qdCoVCMRBlfKF5vtrd7VS3VQcUtpwovrfke5yaF3QNg4H3ijyf\nRPoBni+X20V6TDp17XV09XZpxYbDwrFF2pjpnOkjaXFC1gn885J/DnuseshsOGHHho4G3L3uMU1e\nPxSWpy9ndsLsoY0vi5PipuJx8Xx19Ize+EqxpfSL2EZpxtcRyUfQ1NlkJPQ3djZy3l/P4/kLnh/T\nvDVvzpp51piuoFQoFIpvCmotNJrxVdxUTLIt2U96YCK56/i7htXOYXFQ2FgI4JPzFcjzlRWXRUxU\nDLtrdxteL3uUfdShqBRbCgIxZNkle5SdGlcNZpN53IyA4SKE4KHVDw1pMDgtmudrLOs6gifnayw8\nX7ZUYxWrHnY8OuNoEqMTjeOisLGQyPBITsn1i/yPGX9e8+dx61uhUCgOZ5TnC8342t+wf1JDjiPB\nGe2kodPL8xXZn/PV1t3G6/u19Qrt7nYWpyzme4u/R2ZspuGl+vGKH7MkdcmoxpBsTR7WakDde3TW\nzLNG9XljxRkzzhhyQYTD4hifnC+TlvM11p6vr2u+Ji0mjdSYVPY37Ae0RHvllVIoFIrQRHm+gIjw\nCPY3Th3jy2FxUN/ulfPl8XwVNhby6cFPueCFC6i8tRJXt4sNqzYQZ45jc8lmQyj1B8t/MOoxpNhS\nhuUZEkLwxNlPTCn5AWe0FnY8LtNfimI06GHHxs5G4s3xQ78hCN7G19ULr+bIlCO58sgr2VS0iZIm\nrYD2vvp9RgUHhUKhUIQWyviiP+x4et7pkz2UYREw58uj3t7Q0YDL7eKFXS/Q7m43woJ/OOcPY+rJ\nSbYlD7u/axdfO3SjEMJpcdLubufb8749pv2OVdgxy57FPus+AFZMW8GKaSsAbRVsaUspACVNJRw/\n7fjRD1qhUCgUY44KOwKRYZFUt1XjjA6ucRVKeK92bOlqwR7Vn/PV2KF5Vf6x5x/0yT4iwrQcNme0\nc0xrzaXaUo0cssONhSkL+Y8l/zHm+VK6yOpoja+L5l/E42c+7rc9ITqBkqYSzCYzEqnCjgqFQhGi\nKOMLzfNV31E/qlDQROKM7vd8NXc1++h8NXQ0sCx9GTtrdmKNtI5bkvuKaSt4+rynx6XvyWZR6iJ+\nc/ZvxrxfXedrtMaXKcwU0Ouo65Ol2lIBjL8KhUKhCC2U8QWGBEK8ZWoYXxaTBSklHe4OI+yo63w1\ndDSwJHUJZS1lQ65EHA2mMBNzEkdeHuebzFjpfAUjITqBitYKUmM0o0t5vhQKhSI0GTfjSwgRL4R4\nUwixTwjxhhDCPkjbMCHEF0II/yrZE4AuLzFVPF9CCKMAtI/UhMfzlR2XjcPiGFfjSzFyIsIjiI6I\nprCxcFwMfWe0E4ns93zFKM+XQqFQhCLj6fn6CfC2lHIWsAm4c5C2NwO7x3Esg6J7vvTVgFMBh8VB\nQ0eD4flKi0mjorWCuo46HBYH2XHZWCMGrxGpmHjmJs6loKFg3Dxf0B9uVJ4vhUKhCE3G0/haA+jF\nGJ8CzgvUSAiRAZwJ/GEcxzIoUy3sCP1yE3p5IWuklXhLPDuqd+CwOMiJy1GerxBkXuI8gHExvvSi\n6LrHS+V8KRQKRWgynsZXkpSyGkBKWQUkBWn3K+B2QI7jWAbFML6mSNgRIMmaRLWrmoaOBuNCPsMx\nQwtpmeOV8RWizEsaP+NL93zFmeN4+aKXD9vVqAqFQjHVGZXOlxDiLSDZexOaEXV3gOZ+xpUQ4iyg\nWkr5pRBipef9QVm/fr3xfOXKlaxcuXLEYw6ELscwHhfE8SLbns1XVV/R3dttXHRnOGawpWSL5vmK\nz2Fv/d5JHqViIPMS5xEVrhXFHmt0qRRrhJU1s9eMef8KhUKhCM7mzZvZvHnzsNqOyviSUgatAi2E\nqBZCJEspq4UQKUBNgGYrgHOFEGcCFiBGCPG0lPLKQH16G19jSWR4JPYoO+Fh4ePS/3iQE5/D0189\nTU58jiEnMcOpKZo7LA5OyzuNxOjEwbpQTAILUxYyP2n+uPRtNpmxRlixRqpcP4VCoZhoBjqFNmzY\nELTteIYdXwWu9jy/CnhlYAMp5V1SymlSylzgYmBTMMNrPIkMj5xS+V4AOXE5fFbxGbnxuca2GY4Z\nhgZUniNvzBXaFaMn2ZbMtu9tG7f+E6IT1EILhUKhCHHG0/i6HzhVCLEPOBn4OYAQIlUI8do4fu6I\niQyPnFL5XqB5vvpkH7lx/cbXTOdMnBbnuAmrKkKfRGvisGpuKhQKhWLyGLfajlLKBsCvPouUshI4\nO8D2LcCW8RrPYExFz1eWPQvQjDCduYlzefOKNydrSIoQ4Jm1z/h4QxUKhUIReqjC2mh6SLOdsyd7\nGCPCEmEhxZbic6EVQrAgecEkjkox2cx0zpzsISgUCoViCBX/zJ0AACAASURBVISUk6bwMCKEEHKq\njHWi+J/3/ofrFl9Hsi156MYKhUKhUCgmDCEEUsqAeUDK+FIoFAqFQqEYYwYzvlRhbYVCoVAoFIoJ\nRBlfCoVCoVAoFBOIMr4UCoVCoVAoJhBlfCkUCoVCoVBMIMr4UigUCoVCoZhAlPGlUCgUCoVCMYEo\n40uhUCgUCoViAhk340sIES+EeFMIsU8I8YYQwh6knV0I8TchxB4hxC4hxPLxGlMgNm/ePJEfpxhj\n1PxNXdTcTW3U/E1t1PxNLuPp+foJ8LaUchawCbgzSLtHgI1SyjnAkcCecRyTH+oAnNqo+Zu6qLmb\n2qj5m9qo+ZtcxtP4WgM85Xn+FHDewAZCiFjgeCnlnwGklD1SypZxHJNCoVAoFArFpDKexleSlLIa\nQEpZBSQFaJMD1Akh/iyE+EII8YQQwjKOY1IoFAqFQqGYVEZV21EI8RbgXdVZABK4G3hSSunwalsv\npXQOeP8SYCtwjJRymxDiYaBZSrkuwGepwo4KhUKhUCimDMFqO5pG2empwV4TQlQLIZKllNVCiBSg\nJkCzcqBMSrnN8//fgTuCfFbAL6BQKBQKhUIxlRjPsOOrwNWe51cBrwxs4AlLlgkhZno2nQzsHscx\nKRQKhUKhUEwqowo7DtqxEA7gBSATKAEulFI2CSFSgd9LKc/2tDsS+AMQARQC35FSNo/LoBQKhUKh\nUCgmmXEzvhQKhUKhUCgU/hyWCvdCiD96cs52eG1bIIT4SAjxlRDiFSGELcBrOz2vR3q2LxZC7BBC\n5HsWAyjGmZHMnRDiUiHEds9K2e1CiF4hxALPa0vU3E08I5w/kxDiSc887RJC/MTrPeq3N8GMcO4i\nhBB/8szRdiHEiV7vUXM3CQghMoQQmzy/pa+FED/wbA8qeC6EuFMIUeAROV/ttV3N4XgjpTzsHsBx\nwEJgh9e2T4HjPM+vBv7b8zwc+AqY7/k/nn6P4CfAMs/zjcBpk/3dDvfHSOZuwPvmAwVe/6u5C/H5\nAy4BnvU8twBFwDQ1f1Ni7m4A/uh5nghs83qPmrvJmb8UYKHnuQ3YB8wG7gd+7Nl+B/Bzz/O5wHa0\nhXfZwH517Zu4x2Hp+ZJSfgA0Dtg8w7Md4G3gfM/z1cBXUsqdnvc2SimlZ4VmjJTyM0+7pwkgFKsY\nW0Y4d95cAjwPoOZu8hjh/EnAKoQIB6KBLqBFzd/kMMy5W+t5PhetcglSylqgSQixVM3d5CGlrJJS\nful53oZWLSaD4ILn5wLPS03cvBgoAI5SczgxHJbGVxB2CSHO9Ty/EO2gBJgJIIR4XQixTQhxu2d7\nOpoUhk65Z5ti4gk2d95cBDznea7mLrQINn9/B9qBSqAYeFBK2YSav1Bi4Nxlep5/BZwrhAgXQuQA\nSzyvqbkLAYQQ2WhezK1AsgwseJ4OlHm97aBnm5rDCeCbZHxdA9wohPgMsALdnu0mYAWa5+R44FtC\niFWTM0RFEILNHQBCiKMAl5RSyZSEJsHmbznQgxYuyQVu81w0FKFDsLn7E9rF+jPgIeBDoHdSRqjw\nwZOX93fgZo8HbOCqOrXKLgQYlcjqVEJKmQ+cBiCEmAGc5XmpHHhPStnoeW0jsBh4hv67PNDu1g9O\n2IAVBoPMnc7F9Hu9QJsnNXchwiDzdwnwupSyD6gVQnwILAU+QM1fSBBs7qSUvcCP9HaeucsHmlBz\nN2kIIUxohtf/k1Lq2prBBM+DnSfV+XMCOJw9X8Lz0P4RItHzNwyt/NFvPS+9ARwhhDB7DtwTgV0e\n92yzEOIoIYQAriSAUKxiXBju3OGZmwvx5HuB4VpXczd5DDV/v/G8VAqc5HnNChwN7FHzN6kM67cn\nhLAIIaI9z08F3FLKvWruJp0/AbullI94bQsmeP4qcLEQItITOp4OfKrmcGI4LD1fQohngZWAUwhR\nCqwDYoQQN6K5XF+UUj4JIDXh14eAbUAf8C8p5euerm4EngTMwEav7YpxYiRz5+EEoNSTMOqNmrtJ\nYJjzpyf/Pg78WQix0/P/H6WUuzzP1fxNMCP87SUBbwghetG8Ild4daXmbhIQQqwALgO+FkJsR5uz\nu9BWO74ghLgGj+A5gJRytxDiBbSqMm7gBimlHpJUczjOKJFVhUKhUCgUignkcA47KhQKhUKhUIQc\nyvhSKBQKhUKhmECU8aVQKBQKhUIxgSjjS6FQKBQKhWICUcaXQqFQKBQKxQSijC+FQqFQKBSKCUQZ\nXwqFQqFQKBQTiDK+FAqFQqFQKCaQURlfQoh1QohyIcQXnsfpnu0OIcQmIUSrEOLRQd4fL4R4Uwix\nTwjxhhDCPprxKBQKhUKhUIQ6Y+H5ekhKudjz0EsQdKLVAbt1iPf+BHhbSjkL2ATcOQbjUSgUCoVC\noQhZxsL4EgM3SCnbpZQfAV1DvHcNoNd5ewo4bwzGo1AoFAqFQhGyjIXxdZMQ4kshxB8OIWyYJKWs\nBvBUUk8ag/EoFAqFQqFQhCxDGl9CiLeEEDu8Hl97/p4D/BrIlVIuBKqAh0Y5HlXlW6FQKBQKxWGN\naagGUspTh9nX74F/jvDzq4UQyVLKaiFEClATrKEQQhlmCoVCoVAopgxSSr/ULBj9ascUr3/XAjsD\nNRuki1eBqz3PrwJeGezzpJRj/li3bt249KseE/NQ8zd1H2rupvZDzd/Ufqj5G//HYAzp+RqCB4QQ\nC4E+oBi4Xn9BCFEExACRQog1wGop5V4hxO+B30gpvwDuB14QQlwDlAAXjnI8CoVCoVAoFCHNqIwv\nKeWVg7yWE2T7dV7PG4BTRjMGhUKhUCgUiqnEN17hfuXKlZM9BMUoUPM3dVFzN7VR8ze1UfM3uYih\n4pKhghBCTpWxKhQKhUKh+GYjhECOR8K9QqFQKBQKhWJkKONLoVAoFAqFYgKZUsbX5ZdvoKioZLKH\noVAoFAqFQnHITKmcL2gjL28db731n+TkZE32kBQKhUKhUCgCchjlfFk5cGAD99zz5GQPRKFQKBQK\nheKQmGLGF4CVioq+yR6EQqFQKBQKxSExBY0vF2lpU3DYCoVCoVAoFEw548tFXt467r336skeiEKh\nUCgUCsUhMaWMr8sue1Al2ysUCoVCoZjSTKnVjlNlrAqFQqFQKL7ZHEarHRUKhUKhUCimNqMyvoQQ\n64QQ5UKILzyP0z3bHUKITUKIViHEo4O8/wEhxB4hxJdCiH8IIWJHMx6FQqFQKBSKUGcsPF8PSSkX\nex6ve7Z1AncDtw7x3jeBeVLKhUABcOcYjEehUCgUCoUiZBkL48svnimlbJdSfgR0DfZGKeXbUkpd\ntGsrkDEG41EoFAqFQqEIWcbC+LrJEzb8gxDCPop+rgH+PQbjUSgUCoVCoQhZTEM1EEK8BSR7bwIk\n8FPg18B/SymlEOJ/gIeAa0c6CCHETwG3lPLZwdqtX7/eeL5y5UpWrlw50o9SKBQKhUKhGHM2b97M\n5s2bh9V2zKQmhBBZwD+llAu8tl0FLJFS/mCQ910NXAecJKUMGqZUUhMKhUKh+KZQVFTCPfc8ycGD\nfaSnhxni4gO3Kd3L0GUwqYkhPV9DdJwipazy/LsW2Bmo2SDvPx24HThhMMNLoVAoFIpvCkVFJZx6\n6mMcOLABsAIu3nvvhwhhobT0Z8a2rVvXKeHxKcqoPF9CiKeBhUAfUAxcL6Ws9rxWBMQAkUATsFpK\nuVcI8XvgN1LKL4QQBZ7X6z1dbpVS3hDks5TnS6FQKBSHPZdfvoFnnrkNzcjSuQf4yYBtLi677EH+\n8pd1Ezo+xfAYN8+XlPLKQV7LCbL9Oq/nM0bz+QqFQqFQHG4cPNiHr5EF2vo4K1ACPInm8wjjwIHG\niR2cYkwYlfGlUCgUCoVibElPDwNc+BpgfcAe4I9Afzhy587/pKioRIUepxiqvJBCoVAoFCHEvfde\nTV7eOjQDDMBFSsoBwsNvod/wArDS1vYY99zz5CSMUjEappTna9WqdVNyhUegVStTafwKRaihflOK\nw5mcnCzeeus/ueeeB6mo6CM2toXt2xPp7U3DPxxppaKiL1A3ihBmShlfmzdvYKqt8Ai0amUqjV+h\nCDXUbyo0UAbw+JKTk2Uk0l9++QZKS28DHsQ/HOkiLS10gljquBgmUsop8QAkSM+jTV522Xo5HAoL\ni+Vll62XK1f+l7zssvWysLB4WO8bKy67bL2ENq+xD2/8kz1uhSJUOdTf1EDUb+zQKSwslnl5t3rN\nQ5vMy7tV7cNxYuXK//Ls52IJobvf1XHhi2ZiBbFpgr0Qag9f40vKVav+a8gvHgoHQv+PRg57/Fu2\nfCBttu+oA1ihCMCh/KYGEgrnhqnMWBnAiuHhu7+LJayX8FOZnb02pI5ZdVz4MpjxFTq+yhExPDfr\nPfc86RWaALBy4MCGCU1O7F+14k3w8RcVlXDWWffT1vYYkzluxfAoKirh8ss3sGrVOi6/fANFRSWT\nPaSQZ7T7bKS/qUCEwrlhKhNYCkHlHo0Xvgn4WcBt5OV1smnTQyEV0lPHxfCZUjlfGi7y8tZx773/\nOWTLyToQvGPednsL06bd5aNKPNj477nnSdraFkzKuBUjQ+UejZxD2WcDc0i+971T2Lp1nU8fwz0n\n6KiLxOgILIUQWrlHhwtFRSX88IcP09i4l6iob2GzJbJiRRoPPxx655nD+bgY81y2YC6xUHsActWq\nkeVmjFW+1ZYtHww7NyRQOCMz8zp57rm3DWv8WkhFuW4HI1RydZSLfeSMdJ8FCw/qv8mRnhMOdRwK\nX1TYdmIoLCyWmZnXSfihz76eNu0HIbmvx+K4CJXz+8AxHcr34nDJ+ZqIHeb/nt3SZLpKwm4jzm6z\nnSO3bPkg4PsP5aTufbBlZ6/1fJbvuG2274TEQTjZhNJJfyxyj75pjHSfjZeRFErH0VRFP28dqgEc\nyoSKAaAd/3dPiRsFfZ8tX36zzM5eK48++vYR77tQ/V0e6nloMONrCoYdfRnMFThQKyUtLYx77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JI3ypOHjwEC1atIbmzNlAixatMQaOGS2lO+WbTH/mzKuCF4NkQJbru1CW4NAM2B1GL8nVHy4G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\nKHH5IIA/j0/7cyT0GADwl0T0KkW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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb700b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure(figsize=(10, 8)) # the size of the figure area\n", "pyplot.subplot(311) # creates a grid of 3 columns, 1 row and place the first plot\n", "pyplot.plot(year, T[:,1], 'g', linewidth=1) # we specify the line width here ...\n", "pyplot.plot(year, smooth, 'r', linewidth=2) # making the smoothed data a thicker line\n", "pyplot.xlim(1958, 2008)\n", "pyplot.subplot(312)\n", "pyplot.plot(year, T[:,1], 'g', linewidth=1)\n", "pyplot.plot(year, m * year + b, 'k--', linewidth=2)\n", "pyplot.xlim(1958, 2008)\n", "pyplot.subplot(313)\n", "pyplot.plot(year, T[:,1] - m * year + b, 'o', linewidth=2)\n", "pyplot.xlim(1958, 2008)\n", "pyplot.savefig(\"TemperatureAnomaly.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Generating useful output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we'll use our linear fit to project the temperature into the future. We'll also save some image files that we could later add to a document or report based on our findings. First, let's create an expectation of the temperature difference up to the year 2100.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb707f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spacing = (2008 + 11 / 12 - 1958) / 612\n", "length = (2100 - 1958) / spacing\n", "length = int(length) #we'll need an integer for the length of our array\n", "years = numpy.linspace(1958, 2100, num = length)\n", "temp = m * years + b#use our linear regression to estimate future temperature change\n", "pyplot.figure(figsize=(10, 4))\n", "pyplot.plot(years, temp)\n", "pyplot.xlim(1958, 2100)\n", "out=(years, temp) #create a tuple out of years and temperature we can output\n", "out = numpy.array(out).T #form an array and transpose it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, that estimation looks reasonable. Let's save the data that describes it back to a .csv file, like the one we originally imported.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "numpy.savetxt('./resources/GlobalTemperatureEstimate-1958-2100.csv', out, delimiter=\",\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, lets make a nicer picture that we can show to back up some of our information. We can plot the linear regression as well as the original data and then save the figure. \n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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srGa1t2V0yXBeKygTEen4FJR1EiFBmStTdsI/TgjIjNnTlzctv4l5b8xr0f18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\ncE+Vvv7V687rodlDQx5yaGo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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbfdb52c198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure(figsize = (10, 4))\n", "pyplot.plot(year, T[:,1], 'g')\n", "pyplot.plot(years, temp, 'k--')\n", "pyplot.xlim(1958, 2100)\n", "pyplot.savefig('./resources/GlobalTempPlot.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nice! Now we've got some stuff that we could use in a report, or show to someone unfamiliar with coding. Remember to play with our settings; I'm sure you could get an even nicer-looking plot if you try!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Dig Deeper & Think" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. How is the global temperature anomaly calculated?\n", "2. What does it mean and why is it employed instead of the global mean temperature to quantify global warming?\n", "3. Why is it important to check that the residuals are independent and random when performing linear regression?\n", "4. In this particular case, is it possible to still estimate a trend with confidence?\n", "5. What is your best estimate of the global temperature by the end of the 22nd century?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### What did we learn?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should have played around with the embedded code in this notebook, and also written your own version of all the code in a separate Python script to learn:\n", "\n", "* how to read data from a comma-separated file\n", "* how to plot the data\n", "* how to do some basic analysis on the data\n", "* how to write to a file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Please ignore the cell bellow. It simply loads a style to make this notebook look pretty." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<link href='http://fonts.googleapis.com/css?family=Fenix' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n", "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' type='text/css'>\n", "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " }\n", " h3{\n", "\t\tfont-family: 'Fenix', serif;\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", "\th4{\n", "\t\tfont-family: 'Fenix', serif;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\t \n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 135%;\n", " font-size: 120%;\n", " width:600px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\";\n", "\t\t\tfont-size: 90%;\n", " }\n", "/* .prompt{\n", " display: None;\n", " }*/\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 50pt;\n", "\t\tline-height: 100%;\n", " color:#CD2305;\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\t\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #CD2305;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"../styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.4" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
gvanhorn38/nobal
index.ipynb
1
274118
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook implements the decision making algorithm presented in the paper [Near Optimal Bayesian Active Learning for Decision Making](http://www.ri.cmu.edu/pub_files/2014/4/javdani14hec_extended.pdf) by [Shervin Javdani](http://www.cs.cmu.edu/~sjavdani/) et al.\n", "\n", "tl;dr Go to the bottom of the notebook to see some working examples of the algorithm. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from itertools import combinations_with_replacement, permutations, combinations\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "import random\n", "import time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problem Motivation\n", "------------------\n", "\n", "Imagine you are a robot that desprately needs to push a button.... \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problem Introduction\n", "-------\n", "\n", "In the domain of active learning for decision making, our task is to find the true hypothesis from a set of finite hypotheses $\\mathcal{H}$. At our disposal is a set of finite, deterministic tests $\\mathcal{T}$ that we can conduct. Each test results in a piece of evidence, allowing us to rule out hypotheses that are inconsistent with the evidence. The challenge is to create an algorithm that will find the true hypothesis while conducting as few tests as possible. Put another way, the goal is to gather the necessary information while minimizing test cost. For the examples below, we will assume unit cost per test.\n", "\n", "A prototype for such an algorithm would be the following:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "def hypothesis_determination_problem(hypotheses, tests):\n", " true_hypothesis = None\n", " while true_hypothesis == None:\n", " # 1. Use a policy to pick a test to conduct.\n", " # 2. Observe the outcome of the test.\n", " # 3. Remove hypotheses that are inconsistent with the outcome. \n", " # 4. Is there only one hypothesis remaining? \n", " pass\n", " return true_hypothesis\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is provided the set of hypotheses $\\mathcal{H}$ and the set of tests $\\mathcal{T}$ and outputs the true hypothesis. We will take the Bayesian point of view and we will require a prior probability $P(h)$ for each hypothesis $h\\in{}\\mathcal{H}$. \n", "\n", "Lets make things a little more general. Rather than requiring our algorithm to return the one true hypothesis in $\\mathcal{H}$ we will allow our algorithm to return a \"decision region\" that is guaranteed to contain the true hypothesis. Decision regions can be thought of as groups of hypotheses for which you will perform the same action regardless of which hypothesis is the true hypothesis. \n", "\n", "Our prototype function would be updated to:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "def decision_region_determination_problem(hypotheses, decision_regions, tests):\n", " correct_decision_region = None\n", " while correct_decision_region == None:\n", " # 1. Use a policy to pick a test to conduct.\n", " # 2. Observe the outcome of the test.\n", " # 3. Remove hypotheses that are inconsistent with the outcome. \n", " # 4. Is there only one valid decision region left? \n", " pass\n", " return correct_decision_region\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function is still given the set of all hypotheses $\\mathcal{H}$ (with prior probabilities) and the set of tests $\\mathcal{T}$. In addition, we provide the function with a set of available decision regions $\\mathcal{R}$. We require that all hypotheses are contained in at least one decision region. The output of the function is now a decision region that is guaranteed to contain the true hypothesis. \n", "\n", "This function nearly writes itself, except for the fact that we don't have the \"policy\" that was used in step one of the while loop. Javdani et al. contribute a policy that is relatively efficient and has good guarantees in the number of tests we have to conduct.\n", "\n", "Before implementing this function, we need to define the hypotheses, the decision regions and the tests. These inputs are specific to particular problem domain. If you need to use a decision making algorithm, then you must be able to define these inputs. For our case, we will keep things easy to visualize and we'll use points, circles, and euclidean distance:\n", "\n", "* Hypotheses will be defined as 2d points. Our goal is to find the correct point (the \"true hypothesis\") from a set of points. Each hypothesis will have a prior probability of being the correct hypothesis. \n", "\n", "* Decision Regions will be circles, and a hypothesis will be a member of a decision region if it is contained within the decision region's circle. A hypothesis may be a member of many decision regions.\n", "\n", "* Tests will be conducted between two points. The point closer to the true hypothesis (euclidean distance) will be returned as the evidence. \n", "\n", "The following classes (Point, Hypothesis, DecisionRegion, Subregion, Test) will be used to encode the hypotheses, decision regions and tests." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# GVH: Consider renaming to Value so that we aren't doing point.point in the code.\n", "class Point():\n", " \"\"\"\n", " The value of a hypothesis. \n", " \n", " In this example, it will be a 2d point.\n", " \"\"\"\n", " \n", " def __init__(self, name, point):\n", " \"\"\"\n", " Args:\n", " name (str): A name for this point.\n", " point (numpy.ndarray): A 2d numpy array for this point. \n", " \"\"\"\n", " self.name = name\n", " self.point = point\n", "\n", " def __str__(self):\n", " return \"%s (%s)\" % (self.name, self.point,)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Hypothesis():\n", " \"\"\"\n", " A representation of a hypothesis. \n", " \"\"\"\n", " \n", " def __init__(self, point, prior, decision_regions):\n", " \"\"\"\n", " Args:\n", " point (Point): The value for this hypothesis.\n", " prior (float): The prior probability for this hypothesis. Should be between [0,1].\n", " decision_regions (List[DecisionRegion]): a list of DecisionRegions that this \n", " hypothesis belongs to.\n", " \"\"\"\n", " \n", " self.point = point\n", " self.prior = prior\n", " self.decision_regions = decision_regions\n", " self.consistent = True\n", " \n", " def test(self, test):\n", " \"\"\"\n", " Run the test under the assumption that this hypothesis is the correct hypothesis.\n", " \n", " Args:\n", " test (Test): The test to run.\n", " \n", " Returns:\n", " Point: The point closer to the value of this hypothesis. \n", " \"\"\"\n", " \n", " return test.run(self.point)\n", " \n", " \n", " def conditional_probability(self, evidence):\n", " \"\"\"\n", " Compute the likelihood of this hypothesis given the evidence. In this case, the value\n", " will either be 1 or 0 depending on if our point passes all the tests.\n", " \n", " Args:\n", " evidence (List[(Test, Point)]): A list of test, outcome pairs.\n", " \n", " Returns:\n", " float: 1.0 or 0.0 depending on if this hypothesis is still consistent with the evidence.\n", " \"\"\"\n", " p = 1.\n", " for test, outcome in evidence:\n", " if test.run(self.point) != outcome:\n", " p = 0.\n", " return p\n", " \n", " def render(self):\n", " \"\"\"\n", " Render this hypothesis on the current figure.\n", " \"\"\"\n", " x, y = self.point.point\n", " plt.plot(x, y, 'bo')\n", " plt.annotate(\n", " self.point.name, \n", " xy = (x, y), xytext = (5, -20),\n", " textcoords = 'offset points', ha = 'right', va = 'bottom'\n", " )\n", " \n", " def __str__(self):\n", " return \"%s\" % (self.point,)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class DecisionRegion():\n", " \"\"\"\n", " A circular region of the hypothesis space. \n", " \"\"\"\n", " def __init__(self, _id, center, radius, color):\n", " \"\"\"\n", " Args:\n", " _id (int): An identifier for this decision region.\n", " center (numpy.ndarray): The center of the circle.\n", " radius (float): The radius of the circle.\n", " color: The color of the circle. This is passed to pyplot. \n", " \"\"\"\n", " \n", " self._id = _id\n", " self.hypotheses = set()\n", " self.center = center\n", " self.radius = radius\n", " self.color = color\n", " pass\n", " \n", " def generate_random_point(self):\n", " \"\"\"\n", " Generate a random point within this decision region.\n", " \n", " Returns:\n", " numpy.ndarray: A 2d numpy array.\n", " \"\"\"\n", " t = 2*np.pi*np.random.rand()\n", " u = np.random.rand()+np.random.rand()\n", " r = 2-u if u>1 else u\n", " p = self.center + np.array([r*np.cos(t), r*np.sin(t)]) * self.radius\n", " return p\n", " \n", " def contains_point(self, point):\n", " \"\"\"\n", " Does this decision region contain this point? \n", " \n", " Args:\n", " point (numpy.ndarray): A 2d numpy array. \n", " \n", " Returns:\n", " bool: True or False depending on if the point is within the circle\n", " of this decision region.\n", " \"\"\"\n", " return np.sum((point - self.center) ** 2) <= self.radius ** 2\n", " \n", " def contains_hypothesis(self, hypothesis):\n", " \"\"\"\n", " Is the hypothesis within this decision region?\n", " \n", " Args:\n", " hypothesis (Hypothesis): The hypothesis to check.\n", " \n", " Returns:\n", " bool: True or False depending on if the hypothesis is in the decision region.\n", " \"\"\"\n", " return np.sum((hypothesis.point.point - self.center) ** 2) <= self.radius ** 2\n", " \n", " def contains_all(self, hypotheses):\n", " \"\"\"\n", " Does this decision region contain all of these hypotheses?\n", " \n", " Args:\n", " hypotheses (List(Hypothesis)): A list of hypotheses to check.\n", " \n", " Returns:\n", " bool: True if all hypotheses are contained in this decision regions, \n", " False otherwise.\n", " \"\"\"\n", " for h in hypotheses:\n", " if not self.contains_hypothesis(h):\n", " return False\n", " return True\n", " \n", " def add_hypotheses(self, hypotheses):\n", " \"\"\"\n", " Add a list of hypotheses to the decision region.\n", " \n", " Args:\n", " hypotheses (List(Hypothesis)): a list of hypotheses. \n", " \"\"\"\n", " for hypothesis in hypotheses:\n", " self.add_hypothesis(hypothesis)\n", " \n", " def add_hypothesis(self, hypothesis):\n", " \"\"\"\n", " Add a single hypothesis to the decision region.\n", " \n", " Args:\n", " hypothesis (Hypothesis): The hypothesis to add.\n", " \"\"\"\n", " if self.contains_hypothesis(hypothesis):\n", " self.hypotheses.add(hypothesis)\n", " \n", " def render(self):\n", " \"\"\"\n", " Render this decision region on the current figure.\n", " \"\"\"\n", " circle=plt.Circle(self.center,self.radius,color=self.color, alpha=0.4)\n", " plt.gca().add_artist(circle)\n", " plt.annotate(\n", " \"Region %d\" % (self._id), \n", " xy = self.center, xytext = (self.center[0] + self.radius / 2., self.center[1] + self.radius * 4./5.),\n", " textcoords = 'data', ha = 'right', va = 'bottom'\n", " )\n", "\n", " def __str__(self):\n", " return \"%s\" % (self._id,)\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Subregion():\n", " \"\"\"\n", " A container for all hypotheses that are within the same decision region(s). \n", " \"\"\"\n", " \n", " def __init__(self, _id, decision_regions):\n", " \"\"\"\n", " Args:\n", " _id (int): A unique id for this subregion.\n", " decision_regions (List[DecisionRegion]): An array of decision regions that this\n", " subregion belongs to.\n", " \"\"\"\n", " \n", " self._id = _id\n", " self.decision_regions = decision_regions\n", " self.hypotheses = set()\n", " \n", " # Create a tuple of decision regions ids for convenient comparing\n", " decision_region_ids = [decision_region._id for decision_region in decision_regions]\n", " decision_region_ids.sort()\n", " self.decision_region_ids = tuple(decision_region_ids)\n", " \n", " self._weight = None\n", " \n", " def add_hypothesis(self, hypothesis):\n", " \"\"\"\n", " Add a hypothesis to this subregion.\n", " \n", " Args:\n", " hypothesis (Hypothesis): A hypothesis to add.\n", " \"\"\"\n", " self.hypotheses.add(hypothesis)\n", " \n", " def add_hypotheses(self, hypotheses):\n", " \"\"\"\n", " Add a list of hypotheses to this subregion.\n", " \n", " Args:\n", " hypotheses (Hypothesis): A list of hypotheses to add.\n", " \"\"\"\n", " self.hypotheses.update(hypotheses)\n", " \n", " def contains(self, hypothesis):\n", " \"\"\"\n", " Does this subregion contain this hypothesis? \n", " \n", " Args:\n", " hypothesis (Hypothesis): The hypothesis to check.\n", " \n", " Returns:\n", " bool: True or False depending on if they hypothesis is within the\n", " subregion.\n", " \"\"\"\n", " return hypothesis in self.hypotheses\n", " \n", " def weight(self):\n", " \"\"\"\n", " Return the weight of this subregion.\n", " \n", " Returns:\n", " float: The total weight of this subregion.\n", " \"\"\"\n", " if self._weight == None:\n", " self._weight = sum([h.prior for h in self.hypotheses])\n", " return self._weight\n", " \n", " def __hash__(self):\n", " return hash(self.decision_region_ids)\n", " \n", " def __eq__(self, other): \n", " return hasattr(other, 'decision_region_ids') and self.decision_region_ids == other.decision_region_ids\n", " \n", " def __str__(self):\n", " return \"%d (member of decision regions : %s)\" % (self._id, \", \".join(map(str, self.decision_regions)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Test():\n", " \"\"\"\n", " A test decides the closer of two points to a specified point. \n", " \"\"\"\n", " \n", " def __init__(self, point1, point2):\n", " \"\"\"\n", " Args:\n", " point1 (Point): The first of two points to compare.\n", " point2 (Point): The second of two points to compare.\n", " \"\"\"\n", " self.point1 = point1\n", " self.point2 = point2\n", " \n", " def run(self, correct_point):\n", " \"\"\"\n", " Test which point is closer to the `correct_point.`\n", " \n", " Args:\n", " correct_point (Point): The point to compute distances from.\n", " \n", " Returns:\n", " Point: Either `point1` or `point2`, which ever is closer to the `correct_point.`\n", " \"\"\"\n", " \n", " dist1 = np.linalg.norm(self.point1.point - correct_point.point)\n", " dist2 = np.linalg.norm(self.point2.point - correct_point.point)\n", " \n", " if dist1 < dist2:\n", " return self.point1\n", " else:\n", " return self.point2\n", " \n", " def __str__(self):\n", " return \"%s vs %s\" % (self.point1, self.point2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our inputs defined, we can move forward with implementing the algorithm to determine a decision region. For this purpose, Javdani et al. designed an algorithm they call Hyperedge Cutting (HEC). \n", "\n", "A quick review of terminology:\n", "* <a href=\"https://en.wikipedia.org/wiki/Set_(mathematics)\">Set</a> : for a mathematical set (all unique items) we will use the Python set() class \n", "* [Multiset](https://en.wikipedia.org/wiki/Multiset) : for a mathematical multiset (a set with non-unique instances) we will use the Python list() class\n", "* [Hypergraph](https://en.wikipedia.org/wiki/Hypergraph) : Javdani et al. define 2 different hypergraphs in their paper. The first is region hypergraph $G^{r} = (\\mathcal{H}, \\mathcal{R})$ and the second is the splitting hypergraph $G^{s}(\\mathcal{G}, \\mathcal{E})$. The region hypergraph is used to recast the decision making problem as a hypergraph, and is only used to determine a suitable cardinality for the hyperedges of $G^s$. The second splitting hypergraph will have nodes defined as Subregion instances and its hyperedges will be lists of Subregion instances. \n", "\n", "\n", "Here is the gist of HEC:\n", "\n", "1. Pick a suitable value for $k$ based on the region hypergraph $G^{r} = (\\mathcal{H}, \\mathcal{R})$.\n", "1. Construct the *splitting hypergraph* $G^{s}(\\mathcal{G}, \\mathcal{E})$\n", " * The nodes $\\mathcal{G}$ are groups of hypotheses that share the same decision regions. These groups of hypotheses are called subregions.\n", " * The hyperedges $\\mathcal{E}$ are *multisets* of subregions, each of size $k$. The only restriction is that one decision region cannot contain all of the subregions comprising a multiset.\n", " * Note that if no hyperedges can be constructed, then it must be the case that all remaining subregions are contained in one decision region.\n", "2. Pick a test $t\\in{}\\mathcal{T}$ that will cut the most hyperedges, in expectation. \n", " * To do this, compute the expected marginal gain of each test, and greedily pick the test with the largest gain.\n", "3. Observe the outcome of the test. \n", "4. Remove inconsistent hypotheses. \n", "5. Remove subregions that no longer have consistent hypotheses.\n", "6. Update $G^{s}$. \n", "7. While there is still a hyperedge in $G^{s}$ go to (2)\n", "8. All uncertainty will be contained in one decision region, return that decision region." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subregions\n", "----------\n", "The following functions all deal with subregions. Subregion memberships in decision regions do not change over time, so they can be constructed once. As the algorithm progresses, hypthoses are trimmed from subregions unitl the subregion can be completely ignored. \n", "\n", "When constructing the hyperedges of the splitting hypergraph $G^{s}(\\mathcal{G}, \\mathcal{E})$, we will need to generate all multisets of size $k$ of the subregions. We will also need to check if each of these multisets is valid (i.e. the subregions composing the multiset are not all contained in one decision region). Since subregion membership do not change, we can precompute a hash table of invalid multisets. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def construct_subregions(hypotheses):\n", " \"\"\"\n", " Construct a set of subregions for the hypothesis space. \n", " \n", " A subregion is a region of the hypothesis space where every hypothesis\n", " is within the same set of decision regions. For each hypothesis, test if \n", " it shares a decision with each remaining hypothesis (forming a subregion). \n", " Return the unique set of subregions.\n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): A list of all hypotheses in the hypothesis space.\n", "\n", " Returns:\n", " List[Subregion]: A list of subregions for this hypothesis space.\n", " \"\"\"\n", " \n", " subregions = {}\n", " subregion_num = 0\n", " num_hypotheses = len(hypotheses)\n", " \n", " hypothesis_pairs = combinations_with_replacement(hypotheses, 2)\n", " \n", " for h1, h2 in hypothesis_pairs:\n", "\n", " # Are these hypotheses are in the same subregion?\n", " if h1.decision_regions == h2.decision_regions:\n", "\n", " decision_region_ids = [decision_region._id for decision_region in h1.decision_regions]\n", " decision_region_ids.sort()\n", " decision_region_ids = tuple(decision_region_ids)\n", " \n", " # Have we already seen this subregion?\n", " subregion = subregions.get(decision_region_ids, None)\n", " \n", " # Create a new subregion if necessary\n", " if subregion == None:\n", " subregion = Subregion(subregion_num, h1.decision_regions)\n", " subregion_num += 1\n", " subregions[decision_region_ids] = subregion\n", " \n", " # Add the hypotheses to the subregion\n", " # This operation is idempotent\n", " subregion.add_hypothesis(h1)\n", " subregion.add_hypothesis(h2)\n", " \n", " return subregions.values()\n", " " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def restrict_subregions(subregions, consistent_hypotheses):\n", " \"\"\"\n", " Restrict the original subregions to only those that contain consistent hypotheses.\n", " \n", " Args:\n", " subregions (List[Subregion]): The original subregions for this hypothesis space.\n", " consistent_hypotheses (List[Hypothesis]): A list of consistent hypotheses. \n", "\n", " Returns:\n", " List[Subregion]: A subset of `subregions` that contain only consistent hypotheses.\n", " \"\"\"\n", " \n", " # Go through each subregion and restrict its hypotheses to the consistent ones.\n", " restricted_subregions = []\n", " for subregion in subregions:\n", " restricted_hypotheses = []\n", " for hypothesis in consistent_hypotheses:\n", " if subregion.contains(hypothesis):\n", " restricted_hypotheses.append(hypothesis)\n", " # Ignore empty subregions.\n", " if len(restricted_hypotheses) > 0:\n", " # Copy over the original subregion id so that we can still access the \n", " # precomputed shared region hash table\n", " restricted_subregion = Subregion(subregion._id, restricted_hypotheses[0].decision_regions)\n", " restricted_subregion.add_hypotheses(restricted_hypotheses)\n", " restricted_subregions.append(restricted_subregion)\n", " \n", " return restricted_subregions\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def construct_all_multisets(n, k):\n", " \"\"\"\n", " Construct all multisets of size `k` over the objects `n.`\n", " \n", " Args:\n", " n (List): An array of objects.\n", " k (int): Size of the multiset.\n", "\n", " Returns:\n", " List[List]: A list where each element is a multiset of size `k.`\n", " \"\"\"\n", " return [x for x in combinations_with_replacement(n, k)]\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def subregions_contained_in_one_decision_region(subregions):\n", " \"\"\"\n", " Are all of these subregions contained in one decision region? \n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions.\n", "\n", " Returns:\n", " bool: True if all subregions are contained within one decision\n", " region, False otherwise. \n", " \"\"\"\n", " \n", " hypotheses = set()\n", " decision_regions = set()\n", " \n", " # Gather all the hypotheses and decision regions that make\n", " # up these subregions.\n", " for subregion in subregions:\n", " hypotheses.update(subregion.hypotheses)\n", " decision_regions.update(subregion.decision_regions)\n", "\n", " # Check if one decision region contains all of the hypotheses.\n", " for decision_region in decision_regions:\n", " if decision_region.contains_all(hypotheses):\n", " return True\n", "\n", " return False" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Subregions that are contained within one decision region\n", "# will be stored in this set. \n", "precomputed_shared_subregions = set()\n", "\n", "def precompute_shared_subregions(subregions, k):\n", " \"\"\"\n", " Precompute which subregions are contained within a single decision region.\n", " \n", " The regions that are shared will be stored in `precomputed_shared_subregions`,\n", " for O(1) lookup at runtime. \n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions.\n", " k (int): The size of the subregion multiset. \n", " \n", " \"\"\"\n", " \n", " # clear the set\n", " precomputed_shared_subregions.clear()\n", " \n", " # We'll create the multisets using the subregion ids, so create a map\n", " # that takes us from the subregion id back to the subregion.\n", " subregion_map = dict([(subregion._id, subregion) for subregion in subregions])\n", " \n", " # Create & test multisets of size 1 to k\n", " for i in range(1, k + 1):\n", " \n", " multisets = construct_all_multisets(subregion_map.keys(), i)\n", "\n", " hyperedges = []\n", " for multiset in multisets:\n", "\n", " # grab the subregions for this multiset\n", " s = [subregion_map[i] for i in multiset]\n", " \n", " # check to see if they are contained in one decision region.\n", " if subregions_contained_in_one_decision_region(set(s)):\n", " # These subregions are contained in one decision region, so\n", " # add them to the set.\n", " subregion_ids = list(set(multiset))\n", " subregion_ids.sort()\n", " precomputed_shared_subregions.add(tuple(subregion_ids))\n", " \n", "def subregions_shared(subregions):\n", " \"\"\"\n", " Check if these subregions are contained in the same decision region.\n", " \n", " This function simply checks the precomputed list of shared subregions.\n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions to compare.\n", " \n", " Returns:\n", " bool: True if the subregions are all contained within one decision\n", " region, False other wise.\n", " \"\"\"\n", " subregion_ids = [s._id for s in set(subregions)]\n", " subregion_ids.sort()\n", " return tuple(subregion_ids) in precomputed_shared_subregions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hyperedges\n", "----------\n", "The following functions deal with the construction of hyperedges and the calculation of the weight of a collection of hyperedges. \n", "\n", "Constructing a valid set of hyperedges (of cardinality $k$) for a set of subregions requires constructing all multisets (of size k) of the subregions. Then, for each multiset, one must check to see if the subregions comprising the multiset are all contained in the same decision region. If so, the multiset should be excluded. \n", "\n", "Computing the weight of a collection of hyperedges involves computing a sum of multisets, where the multisets correspond to a product. Javdani provides two algorithms for this weight computation. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Naive Hyperedge Weight Computation\n", "\n", "In the naive algorithm for calculating the weight of a collection of hyperedges, one builds the hypergraph and sums the hyperedge weight. \n", "\n", "$$\n", "\\begin{align}\n", "{e_1, \\ldots, e_n} &= \\text{Build hypergraph and extract hyperedges} \\\\ \n", "\\text{weight of a collection of hyperedges: } w(\\{e_1, \\ldots, e_n\\}) &= \\sum_{l=1}^{n}w(e_{l}) \\\\\n", "\\text{weight of a hyperedge: } w(e) &= \\prod_{i=1}^{k}P(g_{i}) \\\\\n", "\\text{weight of a subregion: } P(g) &= \\sum_{h\\in{}g}P(h) \\\\\n", "P(h) &= \\text{Prior probability of hypothesis } h\n", "\\end{align}\n", "$$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def constuct_hyperedges(subregions, k):\n", " \"\"\"\n", " Construct the hyperedges for the given subregions.\n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions to form hyperedges.\n", " k (int): The size of the hyperedges.\n", " \n", " Returns:\n", " List[List[Subregion]]: A list of hyperedges, whose elements are subregions\n", " \"\"\"\n", " \n", " # We'll construct the multisets based on the subregion ids.\n", " subregion_map = dict([(subregion._id, subregion) for subregion in subregions])\n", " multisets = construct_all_multisets(subregion_map.keys(), k)\n", " \n", " hyperedges = []\n", " for multiset in multisets:\n", " \n", " # grab the subregions for this multiset\n", " s = [subregion_map[i] for i in multiset]\n", " \n", " # If the subregions in this multiset are not shared, then it as a\n", " # hyperedge.\n", " if not subregions_shared(set(s)): \n", " hyperedges.append(s)\n", " \n", " return hyperedges\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_weight_of_hyperedges(hyperedges):\n", " \"\"\"\n", " Computes the weight of a set of hyperedges.\n", " \n", " Args:\n", " hyperedges (List[List[Subregion]]): A list of hyperedges.\n", " \n", " Returns:\n", " float: The total weight of the collection of hyperedges.\n", " \"\"\"\n", " # We are computing the entire collection weight.\n", " collection_weight = 0\n", " \n", " # Go over each hyperedge.\n", " for hyperedge in hyperedges:\n", " hyperedge_weight = 1.\n", " # Go over each subregion that makes up the hyperedge.\n", " for subregion in hyperedge:\n", " # Compute the weight of the subregion.\n", " subregion_weight = 0\n", " for hypothesis in subregion.hypotheses:\n", " subregion_weight += hypothesis.prior \n", " hyperedge_weight *= subregion_weight\n", " collection_weight += hyperedge_weight\n", "\n", " return collection_weight" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def slow_hyperedge_weight(subregions, k):\n", " \"\"\"\n", " Compute the weight of the hyperedges for the subregions. \n", " \n", " This function first constructs the set hyperedges for the subregions, \n", " then computes their weight.\n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions.\n", " k (int): The size of the hyperedges.\n", "\n", " Returns:\n", " float: The weight of the hyperedges.\n", " \"\"\"\n", " hyperedges = constuct_hyperedges(subregions, k)\n", " return compute_weight_of_hyperedges(hyperedges)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Utilizing Complete Homogenous Symmetric Polynomials for Hyperedge Weight Computation\n", "\n", "Javdani et al. present an alternative algorithm for computing the weight of a collection of hyperedges. They note that the algrebaic structure of in computing a sum of multisets, where a multiset is a product, is equivalent to computing a complete homogenous symmetric polynomial. \n", "\n", "In this version of the algorithm, Javdani et al. first compute the weight of all multisets of size $k$. They then iteratively trim off the weight of invalid multisets of size $1\\ldots{}k$. They efficiently compute the weight of a collection of multisets using the following complete homogenous symmetric polynomial identity\n", "\n", "$$\n", "CHP_{i}(\\boldsymbol{x}) = \\frac{1}{i}\\sum_{j=1}^{i}CHP_{i-j}(\\boldsymbol{x})PS_{j}(\\boldsymbol{x})\n", "$$\n", "\n", "where $PS_{i}(\\boldsymbol{x})$ is the i-th power sum\n", "\n", "$$\n", "PS_{i}(\\boldsymbol{x}) = \\sum_{x\\in{}\\boldsymbol{x}}x^{i}\n", "$$\n", "\n", "In the previous equation, $\\boldsymbol{x}$ is a set of subregions. \n", "\n", "To see the effect of this speedup, you will need an experiment where the number of decision regions is much greater than $k$. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def power_sum(subregions, i):\n", " \"\"\"\n", " Compute the power sum of the weight of the subregions.\n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions.\n", " i (int): The exponent value in the power sum.\n", " \n", " Returns:\n", " float: The power sum. \n", " \"\"\"\n", " ps = 0.0\n", " for subregion in subregions:\n", " ps += subregion.weight() ** i\n", " return ps\n", " \n", "def complete_homogenous_symmetric_polynomial(subregions, i):\n", " \"\"\"\n", " Compute the complete homogenous symmetric polynomial of degree i over \n", " the weight of the subregions.\n", " \n", " Args:\n", " subregions (List[Subregions]): A list of subregions.\n", " i (int): The degree of the polynomial.\n", "\n", " Returns:\n", " float: The CHP.\n", " \"\"\"\n", " if i == 0:\n", " return 1.0\n", " \n", " chp = 0.0\n", " for j in range(1, i+1):\n", " chp += complete_homogenous_symmetric_polynomial(subregions, i - j) * power_sum(subregions, j)\n", " \n", " chp /= float(i)\n", " \n", " return chp" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fast_hyperedge_weight(subregions, k):\n", " \"\"\"\n", " Compute the weight of the hyperedges for the subregions. \n", " \n", " This function computes the weight of all hyperedges of size k,\n", " and then iteratively subtracts the weight of hyperedges whose \n", " subregions are completely contained in one decision region.\n", " \n", " Args:\n", " subregions (List[Subregion]): A list of subregions.\n", " k (int): The size of the hyperedges.\n", "\n", " Returns:\n", " float: The weight of the hyperedges.\n", " \"\"\"\n", " \n", " # This is initially too big, and will iteratively trim it down\n", " hyperedge_weight = complete_homogenous_symmetric_polynomial(subregions, k)\n", " \n", " # This is our queue, that will hold the sets of subregions whose weight we need to subtract from the overall weight\n", " Q = {}\n", " \n", " # Each subregion by itself is completely contained in one decision region.\n", " Q[1] = [[s] for s in subregions] \n", " \n", " for k_hat in range(1, k+1):\n", " \n", " # Create a new queue for the next level\n", " Q[k_hat+1] = [] \n", " \n", " # For each set of subregions, check if we need to subtract its weight \n", " # from the total weight.\n", " for subregion_set in Q[k_hat]:\n", " \n", " # Are the subregions contained in one decision region? \n", " if subregions_shared(subregion_set):\n", " # Yup, lets subtract the weight from the total weight.\n", " subregion_set_chp = complete_homogenous_symmetric_polynomial(subregion_set, k - k_hat)\n", " subregion_set_weight = 1.\n", " for subregion in subregion_set:\n", " subregion_set_weight *= (subregion.weight() * subregion_set_chp)\n", " hyperedge_weight -= subregion_set_weight\n", " \n", " # Add all supersets to the next level of the queue\n", " for subregion in subregions:\n", " subregion_super_set = subregion_set[:]\n", " if subregion not in subregion_super_set:\n", " subregion_super_set.append(subregion)\n", " Q[k_hat+1].append(subregion_super_set)\n", "\n", " return hyperedge_weight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Utility\n", "-------\n", "\n", "To compute the expected marginal gain of a test, we need to be able to compute the utility of a set of test, outcome pairs. Javdani et al. define this utility as the total mass of hyperedges cut in $G^{s}$ as a result of the test, outcome pairs\n", "\n", "$$\n", "f_{\\text{HEC}}(S) = w(\\mathcal{E}) - w(\\mathcal{E}(S)) \n", "$$\n", "\n", "where $w(\\mathcal{E})$ is the original hyperedge collection weight of $G^{s}$ and $w(\\mathcal{E}(S))$ is the hyperedge collection weight after removing hypotheses inconsistent with the test, outcome pairs in $S$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def utility(hypotheses, evidence, k, initial_hyperedge_weight, weight_computation_func, original_subregions):\n", " \"\"\"\n", " Compute the utility of a set of evidence. \n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): A list of all hypotheses.\n", " evidence (List[(Test, Point)]): A list of test, outcome pairs.\n", " k (int): The size of the hyperedges.\n", " initial_hyperedge_weight: The weight of the initial collection of hyperedges for this problem.\n", " weight_computation_func (Function): The hyperedge weight computation function.\n", " original_subregions (List[Subregions]): The list of subregions for the hypothesis space.\n", "\n", " Returns:\n", " float: The utility of the evidence. \n", " \"\"\"\n", " \n", " # Determine the consistent hypotheses given the current evidence\n", " # The evidence could contain \"hypothesized outcomes\" so compute the \n", " # consistent hypotheses each time rather than checking the \"consistent\" attribute\n", " consistent_hypotheses = []\n", " for hypothesis in hypotheses:\n", " consistent = True\n", " for test, outcome in evidence:\n", " if hypothesis.test(test) != outcome:\n", " consistent = False\n", " break\n", " if consistent:\n", " consistent_hypotheses.append(hypothesis)\n", " \n", " # Restrict the original subregions to only those that contain consistent hypotheses.\n", " subregions = restrict_subregions(original_subregions, consistent_hypotheses)\n", " \n", " # Compute the weight of the hyperedges using the specified function.\n", " hyperedge_weight = weight_computation_func(subregions, k)\n", " \n", " return initial_hyperedge_weight - hyperedge_weight" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "k: Cardinality of the Multisets\n", "------------\n", "\n", "Javdani et al. provide a practical way of setting $k$ rather than treating it as a hyperparameter. It is chosen to be\n", "\n", "$$\n", "\\begin{align}\n", "x &= \\text{the maximum number of regions any hypothesis lies in} \\\\\n", "y &= \\text{the maximum number of subregions in any region} \\\\\n", "k &= \\min{}(x, y) + 1\n", "\\end{align}\n", "$$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_k(hypotheses, decision_regions, subregions):\n", " \"\"\"\n", " Compute the size of the hyperedges for this problem. \n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): A list of hypthoeses.\n", " decision_regions (List[DecisionRegion]): A list of decision regions.\n", " subregions (List[Subregion]): A list of subregions.\n", " \n", " Returns:\n", " int: The hyperedge cardinality. \n", " \"\"\"\n", " \n", " # What is the maximum number of decision regions that overlap \n", " # a single hypothesis?\n", " max_regions_overlapping_hypothesis = 0\n", " for hypothesis in hypotheses:\n", " if len(hypothesis.decision_regions) > max_regions_overlapping_hypothesis:\n", " max_regions_overlapping_hypothesis = len(decision_regions)\n", " \n", " # What is the maxium number of decision regions that overlap\n", " # a single subregion? \n", " max_sub_regions_in_decision_region = 0\n", " for decision_region in decision_regions:\n", " num_sub_regions = 0\n", " for subregion in subregions:\n", " if decision_region in subregion.decision_regions:\n", " num_sub_regions += 1\n", " if num_sub_regions > max_sub_regions_in_decision_region:\n", " max_sub_regions_in_decision_region = num_sub_regions\n", "\n", " return min(max_regions_overlapping_hypothesis, max_sub_regions_in_decision_region) + 1\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hyperedge Cutting Algorithm\n", "--------\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=True):\n", " \"\"\"\n", " Run the Hyperedge Cutting Algorithm.\n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): The list of hypotheses.\n", " decision_regions (List[DecisionRegion]): The list of decision regions.\n", " tests (List[Test]): The list of available tests.\n", " gt_point (Point): The ground truth point.\n", " go_fast (bool): Should we use the faster implementation of the hyperedge weight\n", " function? \n", " verbose (bool): Print debug information. \n", " \n", " Returns:\n", " Object: Either a DecisionRegion instance or a Hypothesis instance depending on if\n", " uncertainty was driven to a decision region or only one hypothesis remains \n", " consistent.\n", " \"\"\"\n", " \n", " # Select which hyperedge weight algorithm to use\n", " if go_fast:\n", " weight_computation_func = fast_hyperedge_weight\n", " else:\n", " weight_computation_func = slow_hyperedge_weight\n", " \n", " # Remove tests as we use them, and keep track of the current evidence\n", " available_tests = tests[:]\n", " evidence = []\n", " \n", " # Compute the subregions. We'll do this once since they don't change. \n", " subregions = construct_subregions(hypotheses)\n", " \n", " if verbose:\n", " print \"Number of hypotheses: %d\" % (len(hypotheses))\n", " print \"Number of decision regions: %d\" % (len(decision_regions),)\n", " print \"Initial number of tests available: %d\" % (len(tests),)\n", " print \"Number of subregions: %d\" % (len(subregions),)\n", " \n", " # compute k\n", " k = compute_k(hypotheses, decision_regions, subregions)\n", " \n", " if verbose:\n", " print \"Setting k=%d\" % (k,)\n", " \n", " # Precompute the shared subregions. We'll do this once for all multisets of size [1, k]\n", " start_time = time.time()\n", " precompute_shared_subregions(subregions, k)\n", " end_time = time.time()\n", " if verbose:\n", " print \"Subregion sets contained in 1 decision region:\"\n", " for regions_ids in precomputed_shared_subregions:\n", " print \"\\t%s\" % (list(regions_ids))\n", " print \"Shared subregion computation time: %f seconds\" % (end_time - start_time, )\n", " \n", " # Compute the weight of the initial collection of hyperedges. We'll use this value \n", " # when computing the utility of the evidence. \n", " initial_hyperedge_weight = weight_computation_func(subregions, k)\n", " \n", " if verbose:\n", " print \"Initial hyperedge collection weight: %0.3f\" % (initial_hyperedge_weight,)\n", " print \"\"\n", "\n", " # As we mark hypotheses as inconsistent, we'll store which points are inconsistent too. \n", " # This will help filter the available tests.\n", " inconsistent_points = set()\n", " \n", " # Repeat until we have cut all hyperedges.\n", " num_iterations = 0\n", " while True:\n", " \n", " num_iterations += 1\n", " start_time = time.time()\n", " \n", " if verbose: \n", " print \"Starting iteration: %d\" % (num_iterations,)\n", " print \"\\tNumber of consistent hypotheses: %d\" % (sum([h.consistent for h in hypotheses]), )\n", " valid_subregions = []\n", " for subregion in subregions:\n", " hypotheses_consistent = [h.consistent for h in subregion.hypotheses]\n", " if any(hypotheses_consistent):\n", " print \"\\tSubregion %s still in contention\" % (subregion,)\n", " valid_subregions.append(subregion)\n", " \n", " multisets = construct_all_multisets(valid_subregions, k)\n", " print \"\\tRemaing Valid Multisets: %d\" % (len(multisets), )\n", " \n", " # Uncomment to see the hyperedges at each time step.\n", "# for multiset in multisets:\n", "# if not subregions_shared(multiset):\n", "# print \"\\t\\t%s\" % ([subregion._id for subregion in multiset])\n", " \n", " # determine the best test to perform \n", " best_test = None\n", " best_expected_marginal_gain = 0\n", " start_time = time.time() # Time the iteration\n", " \n", " # Compute the current utility. We'll subtract this from the utility of conducting each test to determine the most\n", " # advantageous test. \n", " current_utility = utility(hypotheses, evidence, k, initial_hyperedge_weight, weight_computation_func, subregions)\n", " \n", " if verbose:\n", " print \"\\tCurrent utility = %0.3f\" % (current_utility, )\n", " \n", " # Go over all available tests and compute the expected marginal gain of conducting that test.\n", " for t in available_tests:\n", " \n", " # Ignore tests on already inconsistent points.\n", " if t.point1 in inconsistent_points or t.point2 in inconsistent_points:\n", " continue\n", " \n", " expected_marginal_gain = 0\n", " \n", " # Observe the effect of the test outcome given each hypothesis is consistent. \n", " for h in hypotheses:\n", " \n", " # Ignore inconsistent hypotheses. The conditional probability will be 0. \n", " if h.consistent == False:\n", " continue\n", " \n", " # Assume this hypothesis is the correct hypothesis.\n", " outcome = h.test(t)\n", " \n", " # Compute the expected marginal gain. Add this (test, outcome) pair to the evidence.\n", " additional_utility = utility(hypotheses, \n", " evidence + [(t, outcome)], \n", " k, \n", " initial_hyperedge_weight, \n", " weight_computation_func, \n", " subregions)\n", " cond_prob = h.conditional_probability(evidence)\n", " expected_marginal_gain += cond_prob * (additional_utility - current_utility)\n", " \n", " # Keep track of which test will gain us the most (in expectation)\n", " if expected_marginal_gain > best_expected_marginal_gain:\n", " best_test = t\n", " best_expected_marginal_gain = expected_marginal_gain\n", " \n", " # Has everything been cut? \n", " if best_expected_marginal_gain == 0:\n", " if verbose:\n", " print \"\\tAll edges cut, breaking out of loop.\"\n", " break\n", "\n", " if verbose:\n", " print \"\\tBest Test: %s\" % (best_test,)\n", " print \"\\tLargest Expected Marginal Gain: %f\" % (best_expected_marginal_gain,)\n", " \n", " # Conduct the test and observe the outcome\n", " outcome = best_test.run(gt_point)\n", " evidence.append((best_test, outcome))\n", " available_tests.remove(best_test)\n", " \n", " if verbose:\n", " print \"\\tOutcome: %s\" % (outcome,)\n", " print \"\\tNumber of tests remaining: %d\" % (len(available_tests),)\n", " \n", " # Update the consistency of each hypthoses\n", " for hypothesis in hypotheses:\n", " if hypothesis.consistent:\n", " if hypothesis.test(best_test) != outcome:\n", " hypothesis.consistent = False\n", " inconsistent_points.add(hypothesis.point)\n", " if verbose:\n", " print \"\\tMarking hypothesis %s as inconsistent\" % (hypothesis,)\n", " \n", " end_time = time.time()\n", " if verbose:\n", " render_example(hypotheses, decision_regions, gt_point)\n", " print \"End iteration: %d (%f seconds)\" % (num_iterations, end_time - start_time,) \n", " print \"\"\n", " \n", " if verbose:\n", " print \"\"\n", " print \"Ran for %d iterations\" % (num_iterations,)\n", " print \"\"\n", " \n", " # Gather the consistent hypotheses\n", " consistent_hypotheses = [h for h in hypotheses if h.consistent]\n", " \n", " # The best decision region will be the region that contains all the consistent hypotheses.\n", " best_decision_regions = [d for d in decision_regions if d.contains_all(consistent_hypotheses)]\n", " \n", " # All uncertainty should have been driven to a single decision region\n", " if len(best_decision_regions) != 1: \n", " # Perhaps we have discovered only one valid hypothesis? This hypthosis could be in multiple decision regions. \n", " if len(consistent_hypotheses) == 1:\n", " print \"Found one consistent hypothesis: %s\" % (consistent_hypotheses[0],)\n", " return consistent_hypotheses[0]\n", " else:\n", " # There is probably an issue with the way the decision regions have been sepecified.\n", " print \"ERROR: %d decision regions found to be viable, each with consistent hypotheses, expected 1\" % (len(best_decision_regions),)\n", " for d in best_decision_regions:\n", " print \"\\tDecision Region %s viable\" % (d,)\n", " print \"\\tConsistent Hypotheses:\"\n", " for h in consistent_hypotheses:\n", " print \"\\t\\tHypothesis %s consistent (in decision regions: %s)\" % (h, \", \".join(map(str, [d._id for d in h.decision_regions])))\n", " \n", " # In typical cases, there will only be one decision region to choose from.\n", " best_decision_region = random.choice(best_decision_regions)\n", " \n", " return best_decision_region" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_tests(hypotheses):\n", " \"\"\"\n", " Create all possible tests for the given hypotheses.\n", " \n", " A test should compare hypotheses that are not in the same decision regions.\n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): A list of hypotheses.\n", "\n", " Returns:\n", " List[Test]: A list of tests.\n", " \"\"\"\n", " tests = []\n", " hypothesis_pairs = combinations(hypotheses, 2)\n", " for h1, h2 in hypothesis_pairs:\n", " # We only want to create tests for points that don't share a decision region\n", " if len(set(h1.decision_regions).intersection(h2.decision_regions)) == 0:\n", " test = Test(h1.point, h2.point)\n", " tests.append(test)\n", " \n", " return tests" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def render_example(hypotheses, decision_regions, gt_point):\n", " \"\"\"\n", " Render the hypotheses and decision regions. Place a star on the \n", " ground truth point.\n", " \n", " Only consistent hypotheses will be rendered. Only decision regions\n", " that contain at least one consistent hypothesis will be rendered.\n", " \n", " Args:\n", " hypotheses (List[Hypothesis]): A list of hypotheses to render.\n", " decision_regions (List[DecisionRegion]): A list of decision \n", " regions to render.\n", " gt_point (Point): The ground truth point.\n", " \"\"\"\n", " fig = plt.figure()\n", " \n", " for d in decision_regions:\n", " if any([h.consistent for h in d.hypotheses]):\n", " d.render()\n", "\n", " for h in hypotheses:\n", " if h.consistent:\n", " h.render()\n", "\n", " plt.plot(gt_point.point[0], gt_point.point[1], 'r*', ms=20)\n", "\n", " points = np.vstack([h.point.point for h in hypotheses])\n", " min_x = np.min(points[:,0])\n", " min_y = np.min(points[:,1])\n", " max_x = np.max(points[:,0])\n", " max_y = np.max(points[:,1])\n", " \n", " l = min(min_x - 1, min_y - 1)\n", " u = max(max_x + 1, max_y + 1)\n", " \n", " axes = plt.gca()\n", " \n", " axes.set_xlim(min_x - 1, max_x + 1)\n", " axes.set_ylim(min_y - 1, max_y + 1)\n", " #plt.axis('off')\n", " plt.axes().set_aspect('equal')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A test will be comparing two points. \n", "The set of all tests will be all pairs of points.\n", "An observation will be selecting one of the points from a test.\n", "Consistent hypotheses are those which are closer to the the selected point." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_one_hypothesis_per_decision_region_nonoverlapping_test():\n", " \"\"\"\n", " This test constructs one decision region per hypothesis. There\n", " is no overlap between the decision regions. \n", " \"\"\"\n", " \n", " points = []\n", " p1 = Point(\"x1\", np.array([1, 0]))\n", " points.append(p1)\n", " p2 = Point(\"x2\", np.array([0, 1]))\n", " points.append(p2)\n", " p3 = Point(\"x3\", np.array([0, 2]))\n", " points.append(p3)\n", " p4 = Point(\"x4\", np.array([1, 3]))\n", " points.append(p4)\n", " p5 = Point(\"x5\", np.array([2, 2]))\n", " points.append(p5)\n", " p6 = Point(\"x6\", np.array([3, 2.5]))\n", " points.append(p6)\n", " p7 = Point(\"x7\", np.array([2, 1]))\n", " points.append(p7)\n", " p8 = Point(\"x8\", np.array([4.5, 3]))\n", " points.append(p8)\n", " p9 = Point(\"x9\", np.array([4, 1]))\n", " points.append(p9)\n", "\n", " # Create the decision regions\n", " decision_regions = []\n", " r1 = DecisionRegion(1, np.array([1, 0]), .1, 'r')\n", " decision_regions.append(r1)\n", " r2 = DecisionRegion(2, np.array([0, 1]), .1, 'r')\n", " decision_regions.append(r2)\n", " r3 = DecisionRegion(3, np.array([0, 2]), .1, 'r')\n", " decision_regions.append(r3)\n", " r4 = DecisionRegion(4, np.array([1, 3]), .1, 'r')\n", " decision_regions.append(r4)\n", " r5 = DecisionRegion(5, np.array([2, 2]), .1, 'r')\n", " decision_regions.append(r5)\n", " r6 = DecisionRegion(6, np.array([3, 2.5]), .1, 'r')\n", " decision_regions.append(r6)\n", " r7 = DecisionRegion(7, np.array([2, 1]), .1, 'r')\n", " decision_regions.append(r7)\n", " r8 = DecisionRegion(8, np.array([4.5, 3]), .1, 'r')\n", " decision_regions.append(r8)\n", " r9 = DecisionRegion(9, np.array([4, 1]), .1, 'r')\n", " decision_regions.append(r9)\n", "\n", " # Create the hypotheses\n", " prior = 1. / len(points)\n", " hypotheses = []\n", " h1 = Hypothesis(p1, prior, [r1])\n", " hypotheses.append(h1)\n", " h2 = Hypothesis(p2, prior, [r2])\n", " hypotheses.append(h2)\n", " h3 = Hypothesis(p3, prior, [r3])\n", " hypotheses.append(h3)\n", " h4 = Hypothesis(p4, prior, [r4])\n", " hypotheses.append(h4)\n", " h5 = Hypothesis(p5, prior, [r5])\n", " hypotheses.append(h5)\n", " h6 = Hypothesis(p6, prior, [r6])\n", " hypotheses.append(h6)\n", " h7 = Hypothesis(p7, prior, [r7])\n", " hypotheses.append(h7)\n", " h8 = Hypothesis(p8, prior, [r8])\n", " hypotheses.append(h8)\n", " h9 = Hypothesis(p9, prior, [r9])\n", " hypotheses.append(h9)\n", "\n", " r1.add_hypothesis(h1)\n", " r2.add_hypothesis(h2)\n", " r3.add_hypothesis(h3)\n", " r4.add_hypothesis(h4)\n", " r5.add_hypothesis(h5)\n", " r6.add_hypothesis(h6)\n", " r7.add_hypothesis(h7)\n", " r8.add_hypothesis(h8)\n", " r9.add_hypothesis(h9)\n", "\n", " # Create the tests\n", " tests = create_tests(hypotheses)\n", " \n", " # Randomly choose a ground truth point\n", " gt_point = random.choice(points)\n", " \n", " render_example(hypotheses, decision_regions, gt_point)\n", "\n", " best_decision_region = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=True)\n", " print \"Result: Choose Decision Region %d\" % (best_decision_region._id,)\n", " print \"\"\n", " print \"Speed Test\"\n", " print \"Fast Version\"\n", " %time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=False)\n", " print \"Slow Version\"\n", " %time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=False, verbose=False)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examples\n", "--------\n", "\n", "\n", "### One hypothesis per decision region, no overlap between decision region." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SSilb07PDSilb0xBUStmahqBSytY0BJVStqYhqJSyNQ1BpZStaQgq\npWxNQ1ApZWv/P1h7PQaOBv0jAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103edb7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of hypotheses: 9\n", "Number of decision regions: 9\n", "Initial number of tests available: 36\n", "Number of subregions: 9\n", "Setting k=2\n", "Subregion sets contained in 1 decision region:\n", "\t[0]\n", "\t[1]\n", "\t[2]\n", "\t[8]\n", "\t[3]\n", "\t[4]\n", "\t[5]\n", "\t[6]\n", "\t[7]\n", "Shared subregion computation time: 0.001776 seconds\n", "Initial hyperedge collection weight: 0.444\n", "\n", "Starting iteration: 1\n", "\tNumber of consistent hypotheses: 9\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tSubregion 1 (member of decision regions : 2) still in contention\n", "\tSubregion 7 (member of decision regions : 8) still in contention\n", "\tSubregion 2 (member of decision regions : 3) still in contention\n", "\tSubregion 8 (member of decision regions : 9) still in contention\n", "\tSubregion 3 (member of decision regions : 4) still in contention\n", "\tSubregion 4 (member of decision regions : 5) still in contention\n", "\tSubregion 5 (member of decision regions : 6) still in contention\n", "\tSubregion 6 (member of decision regions : 7) still in contention\n", "\tRemaing Valid Multisets: 45\n", "\tCurrent utility = 0.000\n", "\tBest Test: x1 ([1 0]) vs x2 ([0 1])\n", "\tLargest Expected Marginal Gain: 3.086420\n", "\tOutcome: x2 ([0 1])\n", "\tNumber of tests remaining: 35\n", "\tMarking hypothesis x1 ([1 0]) as inconsistent\n", "\tMarking hypothesis x6 ([ 3. 2.5]) as inconsistent\n", "\tMarking hypothesis x7 ([2 1]) as inconsistent\n", "\tMarking hypothesis x8 ([ 4.5 3. ]) as inconsistent\n", "\tMarking hypothesis x9 ([4 1]) as inconsistent\n" ] }, { "data": { "image/png": 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Wvr8G+Do1F1ObRFNNkYn5idXW2gvANOBtaq6qrbLWfuhsVQ0zxqwA3gf6GmM+\nNsZMdrqmKzAcmEDNVdaC2lcsX+HuAvyl9pzg34A3rbXvOlzT1YiH0zw3Ahvq7OM/WmvXNVXnmiwt\nIq6mq8Mi4moKQRFxNYWgiLiaQlBEXE0hKCKuphAUEVdTCIqIqykERcTV/j8FkdBkDXvZxAAAAABJ\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x103ff6e90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 1 (0.092612 seconds)\n", "\n", "Starting iteration: 2\n", "\tNumber of consistent hypotheses: 4\n", "\tSubregion 1 (member of decision regions : 2) still in contention\n", "\tSubregion 2 (member of decision regions : 3) still in contention\n", "\tSubregion 3 (member of decision regions : 4) still in contention\n", "\tSubregion 4 (member of decision regions : 5) still in contention\n", "\tRemaing Valid Multisets: 10\n", "\tCurrent utility = 0.370\n", "\tBest Test: x2 ([0 1]) vs x4 ([1 3])\n", "\tLargest Expected Marginal Gain: 0.246914\n", "\tOutcome: x4 ([1 3])\n", "\tNumber of tests remaining: 34\n", "\tMarking hypothesis x2 ([0 1]) as inconsistent\n", "\tMarking hypothesis x3 ([0 2]) as inconsistent\n" ] }, { "data": { "image/png": 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uNDssMdH4K0FF2ptmhyVmdu3axfDhw6murqaiooKMjIw2f6+JSHvSSFDa3X333UdVVRWV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10405da50>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 2 (0.009503 seconds)\n", "\n", "Starting iteration: 3\n", "\tNumber of consistent hypotheses: 2\n", "\tSubregion 3 (member of decision regions : 4) still in contention\n", "\tSubregion 4 (member of decision regions : 5) still in contention\n", "\tRemaing Valid Multisets: 3\n", "\tCurrent utility = 0.432\n", "\tBest Test: x4 ([1 3]) vs x5 ([2 2])\n", "\tLargest Expected Marginal Gain: 0.024691\n", "\tOutcome: x4 ([1 3])\n", "\tNumber of tests remaining: 33\n", "\tMarking hypothesis x5 ([2 2]) as inconsistent\n" ] }, { "data": { "image/png": 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7J+6ZOsPM8syswswScQHTzHaZWVXzzP+Ty33n6haZHn9jtZnlSXpQ0mWSRkq60sxOi3eq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\nEDQiCCBo/wfxe1+AL1Yv0AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1041bda90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 3 (0.002330 seconds)\n", "\n", "Starting iteration: 4\n", "\tNumber of consistent hypotheses: 1\n", "\tSubregion 3 (member of decision regions : 4) still in contention\n", "\tRemaing Valid Multisets: 1\n", "\tCurrent utility = 0.444\n", "\tAll edges cut, breaking out of loop.\n", "\n", "Ran for 4 iterations\n", "\n", "Result: Choose Decision Region 4\n", "\n", "Speed Test\n", "Fast Version\n", "CPU times: user 27.8 ms, sys: 3.23 ms, total: 31 ms\n", "Wall time: 28.8 ms\n", "Slow Version\n", "CPU times: user 22.9 ms, sys: 41 µs, total: 22.9 ms\n", "Wall time: 22.9 ms\n" ] } ], "source": [ "run_one_hypothesis_per_decision_region_nonoverlapping_test()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_multiple_hypothesis_per_decision_region_nonoverlapping_test():\n", " \"\"\"\n", " This test places multiple hypotheses in the same decision region, \n", " but decision regions do not overlap.\n", " \"\"\"\n", " \n", " # Create the points\n", " points = []\n", " p1 = Point(\"x1\", np.array([1, 0]))\n", " points.append(p1)\n", " p2 = Point(\"x2\", np.array([0, 1]))\n", " points.append(p2)\n", " p3 = Point(\"x3\", np.array([0, 2]))\n", " points.append(p3)\n", " p4 = Point(\"x4\", np.array([1, 3]))\n", " points.append(p4)\n", " p5 = Point(\"x5\", np.array([2, 2]))\n", " points.append(p5)\n", " p6 = Point(\"x6\", np.array([3, 2.5]))\n", " points.append(p6)\n", " p7 = Point(\"x7\", np.array([2, 1]))\n", " points.append(p7)\n", " p8 = Point(\"x8\", np.array([4.5, 3]))\n", " points.append(p8)\n", " p9 = Point(\"x9\", np.array([4, 1]))\n", " points.append(p9)\n", "\n", " # Create the decision regions\n", " region1 = DecisionRegion(1, (0.5, 0.5), 1, 'red')\n", " region2 = DecisionRegion(2, (0.5, 2.5), .85, 'blue')\n", " region3 = DecisionRegion(3, (2.5, 1.75), 1.1, 'pink')\n", " region4 = DecisionRegion(4, (4.5, 3), .75, 'green')\n", " region5 = DecisionRegion(5, (4, 1), .5, 'cyan')\n", " \n", " decision_regions = [region1, region2, region3, region4, region5]\n", "\n", " # Create the hypotheses\n", " prior = 1. / len(points)\n", " hypotheses = []\n", " h1 = Hypothesis(p1, prior, [region1])\n", " hypotheses.append(h1)\n", " h2 = Hypothesis(p2, prior, [region1])\n", " hypotheses.append(h2)\n", " h3 = Hypothesis(p3, prior, [region2])\n", " hypotheses.append(h3)\n", " h4 = Hypothesis(p4, prior, [region2])\n", " hypotheses.append(h4)\n", " h5 = Hypothesis(p5, prior, [region3])\n", " hypotheses.append(h5)\n", " h6 = Hypothesis(p6, prior, [region3])\n", " hypotheses.append(h6)\n", " h7 = Hypothesis(p7, prior, [region3])\n", " hypotheses.append(h7)\n", " h8 = Hypothesis(p8, prior, [region4])\n", " hypotheses.append(h8)\n", " h9 = Hypothesis(p9, prior, [region5])\n", " hypotheses.append(h9)\n", "\n", " region1.add_hypotheses([h1, h2])\n", " region2.add_hypotheses([h3, h4])\n", " region3.add_hypotheses([h5, h6, h7])\n", " region4.add_hypotheses([h8])\n", " region5.add_hypotheses([h9])\n", " \n", " # Create the tests\n", " tests = create_tests(hypotheses)\n", " \n", " # Randomly choose a ground truth point\n", " gt_point = random.choice(points)\n", " \n", " render_example(hypotheses, decision_regions, gt_point)\n", "\n", " best_decision_region = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=True)\n", " print \"Result: Choose Decision Region %d\" % (best_decision_region._id,)\n", " print \"\"\n", " print \"Speed Test\"\n", " print \"Fast Version\"\n", " %time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=False)\n", " print \"Slow Version\"\n", " %time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=False, verbose=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple hypotheses per decision region, no overlap between decision regions" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rhZWDxw+yp2QPB48fxCM9KsJKdaZHrYQPi9lCh8QOdGrXiZSEFJJjk2ke3bxJ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\nhRfy8ezZjHrwQRwVFexctYrUXr0aZUs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"text/plain": [ "<matplotlib.figure.Figure at 0x1042f6650>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of hypotheses: 9\n", "Number of decision regions: 5\n", "Initial number of tests available: 31\n", "Number of subregions: 5\n", "Setting k=2\n", "Subregion sets contained in 1 decision region:\n", "\t[2]\n", "\t[0]\n", "\t[3]\n", "\t[1]\n", "\t[4]\n", "Shared subregion computation time: 0.001045 seconds\n", "Initial hyperedge collection weight: 0.383\n", "\n", "Starting iteration: 1\n", "\tNumber of consistent hypotheses: 9\n", "\tSubregion 1 (member of decision regions : 2) still in contention\n", "\tSubregion 4 (member of decision regions : 5) still in contention\n", "\tSubregion 2 (member of decision regions : 3) still in contention\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tSubregion 3 (member of decision regions : 4) still in contention\n", "\tRemaing Valid Multisets: 15\n", "\tCurrent utility = 0.000\n", "\tBest Test: x2 ([0 1]) vs x9 ([4 1])\n", "\tLargest Expected Marginal Gain: 2.814815\n", "\tOutcome: x2 ([0 1])\n", "\tNumber of tests remaining: 30\n", "\tMarking hypothesis x5 ([2 2]) as inconsistent\n", "\tMarking hypothesis x6 ([ 3. 2.5]) as inconsistent\n", "\tMarking hypothesis x7 ([2 1]) as inconsistent\n", "\tMarking hypothesis x8 ([ 4.5 3. ]) as inconsistent\n", "\tMarking hypothesis x9 ([4 1]) as inconsistent\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x103f2f9d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 1 (0.059247 seconds)\n", "\n", "Starting iteration: 2\n", "\tNumber of consistent hypotheses: 4\n", "\tSubregion 1 (member of decision regions : 2) still in contention\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tRemaing Valid Multisets: 3\n", "\tCurrent utility = 0.333\n", "\tBest Test: x1 ([1 0]) vs x4 ([1 3])\n", "\tLargest Expected Marginal Gain: 0.197531\n", "\tOutcome: x1 ([1 0])\n", "\tNumber of tests remaining: 29\n", "\tMarking hypothesis x3 ([0 2]) as inconsistent\n", "\tMarking hypothesis x4 ([1 3]) as inconsistent\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x104655410>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 2 (0.006631 seconds)\n", "\n", "Starting iteration: 3\n", "\tNumber of consistent hypotheses: 2\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tRemaing Valid Multisets: 1\n", "\tCurrent utility = 0.383\n", "\tAll edges cut, breaking out of loop.\n", "\n", "Ran for 3 iterations\n", "\n", "Result: Choose Decision Region 1\n", "\n", "Speed Test\n", "Fast Version\n", "CPU times: user 25.5 ms, sys: 1.22 ms, total: 26.8 ms\n", "Wall time: 25.8 ms\n", "Slow Version\n", "CPU times: user 22.4 ms, sys: 2.06 ms, total: 24.5 ms\n", "Wall time: 24.5 ms\n" ] } ], "source": [ "run_multiple_hypothesis_per_decision_region_nonoverlapping_test()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def run_multiple_hypothesis_per_decision_region_overlapping_test():\n", " \"\"\"\n", " This is the example is similar to Figure 1 and 2 in the paper. \n", " Decision regions can have multiple hypotheses, and decision \n", " regions can overlap.\n", " \"\"\"\n", " \n", " # Create the points\n", " points = []\n", " p1 = Point(\"x1\", np.array([1, 0]))\n", " points.append(p1)\n", " p2 = Point(\"x2\", np.array([0, 1]))\n", " points.append(p2)\n", " p3 = Point(\"x3\", np.array([0, 2]))\n", " points.append(p3)\n", " p4 = Point(\"x4\", np.array([1, 3]))\n", " points.append(p4)\n", " p5 = Point(\"x5\", np.array([2, 2]))\n", " points.append(p5)\n", " p6 = Point(\"x6\", np.array([3, 2.5]))\n", " points.append(p6)\n", " p7 = Point(\"x7\", np.array([2, 1]))\n", " points.append(p7)\n", " p8 = Point(\"x8\", np.array([4.5, 3]))\n", " points.append(p8)\n", " p9 = Point(\"x9\", np.array([4, 1]))\n", " points.append(p9)\n", "\n", " # Create the decision regions\n", " region1 = DecisionRegion(1, (1.5, 1.5), 2, 'r')\n", " region2 = DecisionRegion(2, (3.5, 1.5), 2, 'b')\n", " decision_regions = [region1, region2]\n", "\n", " # Create the hypotheses\n", " prior = 1. / len(points)\n", " hypotheses = []\n", " h1 = Hypothesis(p1, prior, [region1])\n", " hypotheses.append(h1)\n", " h2 = Hypothesis(p2, prior, [region1])\n", " hypotheses.append(h2)\n", " h3 = Hypothesis(p3, prior, [region1])\n", " hypotheses.append(h3)\n", " h4 = Hypothesis(p4, prior, [region1])\n", " hypotheses.append(h4)\n", " h5 = Hypothesis(p5, prior, [region1, region2])\n", " hypotheses.append(h5)\n", " h6 = Hypothesis(p6, prior, [region1, region2])\n", " hypotheses.append(h6)\n", " h7 = Hypothesis(p7, prior, [region1, region2])\n", " hypotheses.append(h7)\n", " h8 = Hypothesis(p8, prior, [region2])\n", " hypotheses.append(h8)\n", " h9 = Hypothesis(p9, prior, [region2])\n", " hypotheses.append(h9)\n", "\n", " region1.add_hypotheses([h1, h2, h3, h4, h5, h6, h7])\n", " region2.add_hypotheses([h5, h6, h7, h8, h9])\n", " \n", " # Create the tests\n", " tests = create_tests(hypotheses)\n", " \n", " # Randomly choose a ground truth point\n", " gt_point = random.choice(points)\n", " \n", " render_example(hypotheses, decision_regions, gt_point)\n", "\n", " best_decision_region = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=True)\n", " print \"Result: Choose Decision Region %d\" % (best_decision_region._id,)\n", " print \"\"\n", " print \"Speed Test\"\n", " print \"Fast Version\"\n", " %%time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=False)\n", " print \"Slow Version\"\n", " %%time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=False, verbose=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multiple hypotheses per decision region, overlap allowed between decision regions\n", "#### Similar to Figure 1 and 2 from the paper" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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lBTy20amsSybRHrhQDbh7m9mw22Uim6o4rStC9O1ZE2h0+FPCmXBdVyf/pUHY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\ntxun201bR47h4bY25jzxBA9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"text/plain": [ "<matplotlib.figure.Figure at 0x1046c8e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of hypotheses: 9\n", "Number of decision regions: 2\n", "Initial number of tests available: 8\n", "Number of subregions: 3\n", "Setting k=3\n", "Subregion sets contained in 1 decision region:\n", "\t[0, 1]\n", "\t[2]\n", "\t[0]\n", "\t[1, 2]\n", "\t[1]\n", "Shared subregion computation time: 0.001489 seconds\n", "Initial hyperedge collection weight: 0.030\n", "\n", "Starting iteration: 1\n", "\tNumber of consistent hypotheses: 9\n", "\tSubregion 1 (member of decision regions : 1, 2) still in contention\n", "\tSubregion 2 (member of decision regions : 2) still in contention\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tRemaing Valid Multisets: 10\n", "\tCurrent utility = 0.000\n", "\tBest Test: x1 ([1 0]) vs x8 ([ 4.5 3. ])\n", "\tLargest Expected Marginal Gain: 0.395062\n", "\tOutcome: x1 ([1 0])\n", "\tNumber of tests remaining: 7\n", "\tMarking hypothesis x6 ([ 3. 2.5]) as inconsistent\n", "\tMarking hypothesis x8 ([ 4.5 3. ]) as inconsistent\n", "\tMarking hypothesis x9 ([4 1]) as inconsistent\n" ] }, { "data": { "image/png": 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, "text/plain": [ "<matplotlib.figure.Figure at 0x10489b710>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "End iteration: 1 (0.017425 seconds)\n", "\n", "Starting iteration: 2\n", "\tNumber of consistent hypotheses: 6\n", "\tSubregion 1 (member of decision regions : 1, 2) still in contention\n", "\tSubregion 0 (member of decision regions : 1) still in contention\n", "\tRemaing Valid Multisets: 4\n", "\tCurrent utility = 0.052\n", "\tAll edges cut, breaking out of loop.\n", "\n", "Ran for 2 iterations\n", "\n", "Result: Choose Decision Region 1\n", "\n", "Speed Test\n", "Fast Version\n", "CPU times: user 20.3 ms, sys: 745 µs, total: 21 ms\n", "Wall time: 20.5 ms\n", "Slow Version\n", "CPU times: user 17.5 ms, sys: 59 µs, total: 17.5 ms\n", "Wall time: 17.5 ms\n" ] } ], "source": [ "run_multiple_hypothesis_per_decision_region_overlapping_test()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_random_test(num_hypotheses, num_decision_regions, min_radius=1, max_radius=3, do_speed_test=True):\n", " \"\"\"\n", " Create a random set of hypotheses and decision regions.\n", " \n", " Care is not taken to ensure decision regions \"make sense.\" In some\n", " cases it will be possible that multiple decision regions are \"valid\"\n", " at the end of the HEC algorithm.\n", " \n", " Args:\n", " num_hypotheses (int): The number of hypotheses to create.\n", " num_decision_regions (int): The number of decision regions to create.\n", " min_radius (float): The minimum radius of a decision region.\n", " max_radius (float): The maximum radius of a decision region.\n", " do_speed_test (bool): Also run a speed test using this random setup.\n", "\n", " Raises:\n", " AssertionError: If num_hypotheses < num_decision_regions\n", " \"\"\"\n", " \n", " assert num_hypotheses >= num_decision_regions, \"Need at least as many hypotheses as decision regions.\"\n", " \n", " points = []\n", " hypotheses = []\n", " decision_regions = []\n", " num_points_created = 0\n", " prior = 1. / num_hypotheses\n", " \n", " # construct some random decision regions\n", " for i in range(num_decision_regions):\n", " \n", " # random center and radius\n", " center = np.random.rand(2) * 10\n", " radius = max(min_radius, np.random.rand() * max_radius) \n", " region = DecisionRegion(i, center, radius, np.random.rand(3))\n", " decision_regions.append(region)\n", " \n", " # Try to put one hypothesis in each decision region to start with.\n", " for decision_region in decision_regions:\n", " p = decision_region.generate_random_point()\n", " valid_decision_regions = [d for d in decision_regions if d.contains_point(p)]\n", " point = Point(\"x%d\" % (num_points_created,), p)\n", " num_points_created += 1\n", " points.append(point)\n", " hypotheses.append(Hypothesis(point, prior, valid_decision_regions))\n", " \n", " # Randomly place the remaining hypotheses.\n", " for i in range(num_hypotheses - num_points_created):\n", " \n", " # sample a random decision region\n", " decision_region = random.choice(decision_regions)\n", " \n", " p = decision_region.generate_random_point()\n", " valid_decision_regions = [d for d in decision_regions if d.contains_point(p)]\n", " point = Point(\"x%d\" % (num_points_created,), p)\n", " num_points_created += 1\n", " points.append(point)\n", " hypotheses.append(Hypothesis(point, prior, valid_decision_regions))\n", " \n", " \n", " # add the hypotheses to the decision regions\n", " for d in decision_regions:\n", " d.add_hypotheses(hypotheses)\n", " \n", " tests = create_tests(hypotheses)\n", "\n", " gt_hypothesis = random.choice(hypotheses)\n", " gt_point = gt_hypothesis.point\n", " print \"Ground truth point: %s\" % (gt_point,)\n", " print \"\\tContained in decision regions: %s\" % ([d._id for d in gt_hypothesis.decision_regions])\n", " print \"\"\n", " \n", " render_example(hypotheses, decision_regions, gt_point)\n", "\n", " result = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=True)\n", " if isinstance(result, DecisionRegion):\n", " print \"Result: Choose Decision Region %d\" % (result._id,)\n", " else:\n", " print \"Result: Choose Hypothesis %s\" % (result, )\n", " print \"\"\n", " \n", " if do_speed_test:\n", " print \"Speed Test\"\n", " print \"Fast Version\"\n", " %%time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=True, verbose=False)\n", " print \"Slow Version\"\n", " %%time _ = HEC(hypotheses, decision_regions, tests, gt_point, go_fast=False, verbose=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run a random test\n", "\n", "Note that some random configurations of decision regions don't make sense, and the code may complain about it.\n", "\n", "To see a benefit from the faster weight computation function, you'll need a situation where the number of decion regions is much larger than $k$. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ground truth point: x38 ([ 10.26262229 5.15622114])\n", "\tContained in decision regions: [15]\n", "\n" ] }, { "data": { "image/png": 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c/CO4eYmEZiHNCX2iGa/NnGjTkYzt/RJtG7SlLXA6/ld2bIvGP8CXiMjHSE1P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\nqkV1ECoqKtWiOggVFZVq+X/0S5SBUokoqgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1029204d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Number of hypotheses: 40\n", "Number of decision regions: 30\n", "Initial number of tests available: 761\n", "Number of subregions: 29\n", "Setting k=4\n", "Subregion sets contained in 1 decision region:\n", "\t[21]\n", "\t[23]\n", "\t[11, 28]\n", "\t[9]\n", "\t[11]\n", "\t[13]\n", "\t[24]\n", "\t[15]\n", "\t[26]\n", "\t[1]\n", "\t[28]\n", "\t[14, 18]\n", "\t[3]\n", "\t[5, 6]\n", "\t[5]\n", "\t[16]\n", "\t[7]\n", "\t[18]\n", "\t[20]\n", "\t[14, 28]\n", "\t[22]\n", "\t[8]\n", "\t[10]\n", "\t[18, 28]\n", "\t[12]\n", "\t[14]\n", "\t[25]\n", "\t[0]\n", "\t[27]\n", "\t[2]\n", "\t[14, 18, 28]\n", "\t[4]\n", "\t[6]\n", "\t[17]\n", "\t[19]\n", "Shared subregion computation time: 1.404898 seconds\n", "Initial hyperedge collection weight: 0.053\n", "\n", "Starting iteration: 1\n", "\tNumber of consistent hypotheses: 40\n", "\tSubregion 6 (member of decision regions : 5, 6) still in contention\n", "\tSubregion 9 (member of decision regions : 9) still in contention\n", "\tSubregion 11 (member of decision regions : 11) still in contention\n", "\tSubregion 13 (member of decision regions : 13) still in contention\n", "\tSubregion 23 (member of decision regions : 24) still in contention\n", "\tSubregion 15 (member of decision regions : 15) still in contention\n", "\tSubregion 25 (member of decision regions : 26) still in contention\n", "\tSubregion 1 (member of decision regions : 1) still in contention\n", "\tSubregion 14 (member of decision regions : 14, 18) still in contention\n", "\tSubregion 3 (member of decision regions : 3) still in contention\n", "\tSubregion 22 (member of decision regions : 23) still in contention\n", "\tSubregion 5 (member of decision regions : 5) still in contention\n", "\tSubregion 16 (member of decision regions : 16) still in contention\n", "\tSubregion 7 (member of decision regions : 7) still in contention\n", "\tSubregion 18 (member of decision regions : 18) still in contention\n", "\tSubregion 28 (member of decision regions : 11, 18) still in contention\n", "\tSubregion 20 (member of decision regions : 20) still in contention\n", "\tSubregion 21 (member of decision regions : 22) still in contention\n", "\tSubregion 8 (member of decision regions : 8) still in contention\n", "\tSubregion 10 (member of decision regions : 10) still in contention\n", "\tSubregion 12 (member of decision regions : 12) still in contention\n", "\tSubregion 24 (member of decision regions : 25, 28) still in contention\n", "\tSubregion 0 (member of decision regions : 0) still in contention\n", "\tSubregion 26 (member of decision regions : 27) still in contention\n", "\tSubregion 2 (member of decision regions : 2, 21) still in contention\n", "\tSubregion 4 (member of decision regions : 4) still in contention\n", "\tSubregion 17 (member of decision regions : 17) still in contention\n", "\tSubregion 19 (member of decision regions : 19) still in contention\n", "\tSubregion 27 (member of decision regions : 29) still in contention\n", "\tRemaing Valid Multisets: 35960\n", "\tCurrent utility = 0.000\n" ] } ], "source": [ "run_random_test(num_hypotheses=40, num_decision_regions=30, min_radius=0.5, max_radius=0.5, do_speed_test=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ecervera/Baxter-Vision
00 00 Lowercase Folder and File Names.ipynb
1
1306
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import glob, json, os.path\n", "\n", "with open('parameters.json', 'r') as infile:\n", " params = json.load(infile)\n", "\n", "ITEM_FOLDER = params['item_folder']\n", "os.chdir(ITEM_FOLDER)\n", "\n", "items = glob.glob('[A-Z]*')\n", "\n", "for item in items:\n", " os.chdir(item)\n", " files = glob.glob('*')\n", " for filename in files:\n", " os.rename(filename,filename.lower())\n", " os.chdir('..')\n", " os.rename(item,item.lower())\n", "os.chdir('..')\n", "print('Done!')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
amueller/nyu_ml_lectures
Unsupervised Transformers.ipynb
1
8022
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib nbagg\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"figures/unsupervised_workflow.svg\" width=100%>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "from sklearn.cross_validation import train_test_split\n", "import numpy as np\n", "np.set_printoptions(suppress=True)\n", "\n", "digits = load_digits()\n", "X, y = digits.data, digits.target\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Removing mean and scaling variance\n", "===================================" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Instantiate the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scaler = StandardScaler()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) Fit using only the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "scaler.fit(X_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) `transform` the data (not `predict`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_scaled = scaler.transform(X_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_scaled.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The transformed version of the data has the mean removed:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_scaled.mean(axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_scaled.std(axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_test_transformed = scaler.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Principal Component Analysis\n", "=============================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "0) Import the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Instantiate the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pca = PCA(n_components=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) Fit to training data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pca.fit(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) Transform to lower-dimensional representation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(X.shape)\n", "X_pca = pca.transform(X)\n", "X_pca.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize\n", "----------" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()\n", "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pca.components_.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.matshow(pca.components_[0].reshape(8, 8), cmap=\"gray\")\n", "plt.colorbar()\n", "plt.matshow(pca.components_[1].reshape(8, 8), cmap=\"gray\")\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Manifold Learning\n", "==================" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.manifold import Isomap\n", "isomap = Isomap()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_isomap = isomap.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.scatter(X_isomap[:, 0], X_isomap[:, 1], c=y)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exercises\n", "* Visualize the digits dataset using the TSNE algorithm from the sklearn.manifold module (it runs for a couple of seconds).\n", "* Extract non-negative components from the digits dataset using NMF. Visualize the resulting components. The interface of NMF is identical to the PCA one. What qualitative difference can you find compared to PCA?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load solutions/digits_unsupervised.py\n", "from sklearn.manifold import TSNE\n", "from sklearn.decomposition import NMF\n", "\n", "# Compute TSNE embedding\n", "tsne = TSNE()\n", "X_tsne = tsne.fit_transform(X)\n", "\n", "# Visualize TSNE results\n", "plt.title(\"All classes\")\n", "plt.figure()\n", "plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y)\n", "\n", "# build an NMF factorization of the digits dataset\n", "nmf = NMF(n_components=16).fit(X)\n", "\n", "# visualize the components\n", "fig, axes = plt.subplots(4, 4)\n", "for ax, component in zip(axes.ravel(), nmf.components_):\n", " ax.imshow(component.reshape(8, 8), cmap=\"gray\", interpolation=\"nearest\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
airanmehr/bio
Scripts/MultiLociSelection/.ipynb_checkpoints/MultiLociSelection-checkpoint.ipynb
1
722912
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simuPOP Version 1.1.7 : Copyright (c) 2004-2016 Bo Peng\n", "Revision 5000 (Jan 21 2016) for Python 2.7.11 (64bit, 1thread)\n", "Random Number Generator is set to mt19937 with random seed 0xf923c4e567973891.\n", "This is the standard short allele version with 256 maximum allelic states.\n", "For more information, please visit http://simupop.sourceforge.net,\n", "or email [email protected] (subscription required).\n" ] } ], "source": [ "%matplotlib inline\n", "import numpy as np;\n", "\n", "np.set_printoptions(linewidth=200, precision=5, suppress=True)\n", "import pandas as pd;\n", "\n", "pd.options.display.max_rows = 20;\n", "pd.options.display.expand_frame_repr = False\n", "import seaborn as sns\n", "import pylab as plt;\n", "import matplotlib as mpl\n", "import os, sys;sys.path.insert(1,'/home/arya/workspace/bio')\n", "import Utils.Util as utl\n", "import Utils.Plots as pplt\n", "import Scripts.MultiLociSelection.Util as mutl\n", "prop=[0.89, 0.1, 0.01, 0.]\n", "N=10000;L=1000;gen=500\n", "r=1e-7\n", "sa=0.05;sb=0.09;sab=0.1;saabb=0.15" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.98, 0.01, 0.01, 0.0]\n" ] }, { "data": { "image/png": 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4d+6ceJC+7777cPDgQYfznMWn1Wrx3nvv4dChQxg+fDjuuOMOqNVqfPDBB6isrERUVBSm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\nt0vfQXuDa+zhxkK3hcPnc54kK3wKcpmcaG0k44M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9O1caPUj0jetXmXOrrJTPbw1Gb+4i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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e83c39950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;r=0;prop=[0.98, 0.01, 0.01, 0.]\n", "reload(mutl);mutl.simulate(N,L,r,gen,sa,sb,sab,saabb,prop)\n", "plt.suptitle('No Recombination (no 11 hap) and sa=sb, a0=b0. Absolute Equilibrium!',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.69, 0.3, 0.01, 0.0]\n" ] }, { "data": { "image/png": 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rR1bKPI9JrysnWyqw68sr1MOVbnpxTOsLVeH8nZ0onzZ9jg90H6PP1g95rBw3\nZV4LVcp82ASbXXs8dEfaF3edw6k9dlZXV+3xgG0A+86+hbOGWjR9eWHKmZ/m/xDTpFMc1tnYeQnv\n13+MI5eO4dqMlfhQXw4AWD55MW7KvBbpCZPwccMRZMqmIzE6ERdNOiydfBViIqPxP5//L84bL+Br\n0wtw14LbR13P7oEe7Dv7FjRNn7ld7l9Ud2GeYjb+6+iv0D3QPaoyZsiUeGTJA4iOiMblHgN2VvzR\n6QU6AFiavgg1HWexNH0RrIIN12asxHTp1FGVN+St8wftuvUPCbf2mMIPk7cg5M8f0AHbAPqs/YiQ\nSBATGYO9Z97ABVM9Nsy9Bb/5osSumxwAJMcq7LqnDZFAgh8tfxhK2XRx2tChNJpnj0LtZKHX2oc/\naXcjNS7Z7XNdNsEGCSQ43XEOL5zcBVmMFD9evhXnu8/h7Zr3sCbzelS11+BkawWmS6firgUbMFs+\nA8Bgl8K368pQ0aa1W2fhjOtROOM6fHbpBPaefVO8wvnI4gcxWz4DEokEpy+fg/ri+zhrqHUaV/E1\nTyHew/MIfdZ+tPdcxpSE9DE/RyYIAqoslThw+kNcMNWL0+cqZiEtfhLS4lNxU+a1iIyIhLnPgi9a\nK9Hc1Yr3dR+7XOf1GavRZ+tHSlwybsq8xu5umk2wYd/Zt3BIfxixkTGYljgFA4IVOnOD03VNl07F\nltxvi1ewzX0WvF2rRmt3O6YkTsbyyVdh1pf7AwC6+rvxvu4jlF08BKtgBTD43ZgUn4I5ilm4b8Ud\nY9pOoSqUvrPj5c82qt/aj15bH2IjY2G1WbGr4s+IjozC1VOWOb1III9JgrHP5DBdGp2IJ65+DNLo\nRHHaUJI3GqHWHrd3d+CP2v9DwdTlWDltucvlhrbFxw0a7D79OvImZeP7q+7Bm6few+HGo7g7+w78\n7cybuNQ1OAjI3Vl3YFJ8CgCgqr0GB+reRd2wdiwqIgqb5t+G5ZMX443aA/hA9wkAIC4yFo8u/R6m\nJk7GgG0Ale01eLXmb+hykgxNik/F9qsf97iPOvu70D3QI8YzFv3Wfhzt+AwHz3xo12V+SfpCREqi\nsCB5Dgq+3H7NnS04Z6xDVVsNvmircrnOb8y7BRdNOmSlzMfVU5ba/VZ0D/SgpOIV1HSchSxaiknx\nKejoNTr06hhyVVou7s66Awlfdp1v6mzGgbp30T3QgwzZNBRMXWZ3t7Gt+zLerf8QHzdoxGmTE9Ih\nj5FhcXoSTiy7AAAgAElEQVQevrG4aGwbiihEMHkLQv74Aa1qr8G79R+JDzG78k+zCrFy2gocbjyC\nd+r+4XG91ytXo627HVnJ83HOWIcTLaewaFIObpu73q6xHdIz0INISSQu93Sgc6AbK+bmjqmu7u5Y\n+VqvtQ9/O/MmLpp1aOpsFpOmSEkk/n3Z95EhmwZgMOk5pP8EH+k16Og1ICUu2eOzZUNiImPw0/wf\noqu/CztO7ESPtRfS6ETMSFKiYOpyLE7Ps1teEAQ8f+J3OGeoc7veNZnXodZ4EeeNdXhk8YOYlzx7\nDFtgbIaO4z5rPyK/vFMggcTtfjP2mnD00nEUTF0OG2ywCTYoYuWjKnf4sdHRY0B502cw9hpR3viZ\nQ1fRJekLERMRg4o2LToHuuzmfSd7I5ZNvgr/+8XvcXrY92b19Ktx+5z1dg/lp6XJRhVjqAuXthjw\nT3t8uOFTfNx4xOXFhSH35XwLqtQFeEVb6vZEesj6mTfhglmHq6cshfriB2jpasPS9EW4Y/7XnV60\n6ezvQlxkLC6a9UiMikfuzDlj6ko4kYMFGXqN+Ev1XnQP9NhdGJqRpMQDefdAHjv4XbT0d+Kd2n/g\n6KXP0WPtxRz5LJw3um8vh0yKT8VP83+IijYtfl/1VwCALFqKrJT5uDZjJWbJM+2W77X24ccfP4k+\nJ10fh/v67LXQNH0GY68JPy/4EeSxSaOp+rg4a489JY56cyPOGmqxevrVMPaakBAV79BV3pPhx4fO\n3IATLRW41NXitNvl0vRFkEgkON5ySvydBQa7lW5d/K9IiUvG/xx/0a4r6W1z1uPGzGvs6hJu7TGF\nHyZvQcjXJwsdPQaHB4qHm5yQjuauFiyclIPNuXcjKiIKgiDg4wYNNE3HEBcVh7zULDR1tiAlToGV\n01bgzdoDONJ0zOU6s1Pm47uL7hMbVEEQcNGsw44TL6HP2icu94ubfoRzTTrUmeqxfPJizEhSOqzr\nUmczkmKScKmrGR/pNfis+QSAwYb+3pxv+eTEoWegF580HkF2ynyHLhwvnfqT2xOnHy9/BBESCX5x\n9FdelSWNTkRsZAwmJ6Zj/cybcLjxqEOXlusyVuHO+bd6XNfvK/+Kz1u+EP+eIVNicXoeEqLisTAt\nB7IYKQRBEJPJiRRsV/GNvSYkxchwqk2Llyr+5DB/unQqvp19Jypatdh/4V2nzwX+06xCrJt1k8Nn\nw+1kIZj2q7/5+jiuuXwWL5zc5XRelCQSA1/e2V2TeR1um7sewOCdkzdrD0JnboBEEoHlk69CVXsN\nclOzkZUyDztP/RE6S6PLMm/MvAYb5v6T+LcgCDh66Tj+XL1HnBYhicCv1z8JzfkvcKmzBV+bXoC0\nhFS79QiCAJ2lAdMSp6CyrRofNWjECxu3z70ZN2VeO7aNMkJHjwHHmk9i+ZTFdhdv+q39ePLIs057\ngwBAQlQ8niz4EWqNF/HiqT94VZY8RobIiCjMls/A+llr8GftHrukEAC+nXWneGfKlX7bAJ757Ncj\nBhDJxlzFLCTHyrEwLQcxkTHotw2gq79bTDInSjC1x4IgwNRnhixGisONR7H79GsOyyyalIN1s9ZA\nffF9nGg5hdjIGPQOO3cAgPty7sbSyYscPhtu7TGFHyZvQcjXjeyrNX/D4cajAAbvFm3O/TaiIqJw\npOkzFM64ARnSqeiz9SM20vnIgM4IgoATrRX4v5p9dl1CfrHqZ9hx4iU0d7Vgafoi3Jd7N8x9Fjz3\n+W8dHop25kfLBrtiSiQSdPQYPJ6UfH32WqyYuhR91n5Mik8ZdTehzv4uvHByFxosTXZX+qYlTsG/\n5v0L/n7+HZz88gphdsp8NFqakByXjO9ftQV/qfkbTrScclinBBLcOmcdLph06LX24tY565AQlYDq\ny6dRlLManQb7rqk9Az149KNt4t+b5t+G1dOv9qouVpsVPdZevFqzD/JYGe6Y9/VRbwN/CaaThZHM\nfRZ8oPsEqfHJ6OgxYEHyPLu7kp82fY5XqkshQEBURBSeLPiR21cahNvJQrDuV3/w9XH8y89+LT7H\nOjkhHXctuB3tPR04ffksvjHvFkijE0fdHg/YBnBIfxhvnD8gtmNxkXH4j/yt+O/PdqB7oAe3zl6H\nwpnXo96kxzPHXnA6YJFDrF/bLnbFrDVeREnFK067bQ65P++fMUM2OGhHcpzC6/iHNFiasKviz3a/\nFRJIME8xG/fmfgu/+vxFtHS3AQBUqQtwzlCH/MmLUTjjBuyq/LPTO5nS6ETclHktTnecQ3xUHO6Y\ndytMfSaYJB3ITlQ5tJeXOpvxn58+J5b94MJ7vH5Gts/ah15rH1784g/In7oE12WsGvU28Jdgbo+b\nOpvxadPnSE9IQ3t3O/K/HORqyGtn38Z7uo8ADN5h/bdFmxEXFefyty7c2mMKP0zegpAvG9mOHgN+\nrvklEqMTsL3gR7AJNo/PPI1GV383oiOjca6jFnFRcZglz0RXfzeKP/8NmrtaIY1OFEe6AoD5ijm4\nN/dbEATg7+ffwdFLxx3WeW3GKtw57+v45Wc7xMRNAgliI2MwIFhxVVou+qz9ODXiblhidAKWpi/C\n7XNvRsywEx9BENDc1YKKtmrMTMrEvOTZsAk2HGk6hr/W/M1jHaMiovDT/B8gPSENgiBAgCCOqvVS\nxZ/FZ9PS4lPxb1fd7/bZBFf79qJJh2eOveByAJJQFMwnC95otFzCBZMOOalZHq+Sh9vJQijv19Hy\n5XFcffkMfnOyBNkp87E599uIlEQiZpTvOHTH3GdBYnQCjjWfxGz5TEyKT0FzVyt++dkO9Fr7HJ5h\nvinzWlyvXI0oSRSe+/y3YmI03D9nb8KitBz89PB/iUPax0REY0CwIjEqAQvTctBgaXK4W5Ucq8A1\nGQVYk3mdXe8Iq80KvaURle01WD75KqQnpKHP2o8DF95F2cUPPNZRKZ2GrUseQHxUvJioRkgi0NXf\nhV8d/5145ysreR625H0b8VHOu/m5268fNxzB3jNv4IdLH8LMpEyny4SaUG+Pzxnq0N59GVel53m8\nsBFu7TGFHyZvQchXjazVZsW+c2/jQ/1hbJp/O67JKPBBdN65aNLh+eO/E58BmBSfiusyVqFg6jLx\nWSGbYMPFvjpkRGUiQhKB18+9gw/0n9itZ/DZgx/YJWNDTH1mvHVejY5eAyIkEahqrxHnxUfF4aq0\nPCxOz0NTZzNeP/eOOO/qKctQ2V4tJpU5qVm4N+dbiIuMxQWTDg2WRuyve1e8wvxA3r9gYVqOy7p2\n9XcjPirOq+6b7vat1Wa9ot5TFuonC6MRbicL4bJfAd8dx8MHcXhs6ffsBsTxt6OXjuNP2t3i33Pk\nM1EwLR8rpiwR7170DPSi2daIzOiZ6Ozvwoun/uCQkOWmZuGBhfc4vePRaLmEd+r+gQHbAHqtvXaD\nJsVHxWPl1OVYNvkqfKD/xO6i3eK0PFS0V4uDZd2YeQ1umb0W0RFRqGjTwtRnxqs1+8Tlt1/9uNPn\nqYHB35Reax/iImM9tsee9ivb49AVbu0xhR8mb0HIF42s1WbFr0++hHOGOkRIIvDL1dvEkZwmiqHX\niNaudnT2dyI7dYHTq2Uj6/qHqldxrPmk+PePlz8C5ZcDgnjS3t2BbZr/53V8EZII3DyrENdlrEJc\nVKzdvEbLJXzUoMGKKUt8epIVbj+g4VTXcBIu+xXwzXFs6jPjv4/ugLHPhPSESdi24vEJHeQDGHwf\nlqW/E90D3chJzXKagA2vq02w4T8++U/xIleUJBL/ueonSIrx7lg/03EOO0685HV80uhE3D73ZuQP\nSyiH1Fw+i5OtlViTeS1SxzHq4nDh1D4B4VXfcGuPKfwweQtC421kDb1GvHBiFy51tQAAbpldhLUz\nb/RVeD7lrK7Hmk9C234ad8z7+qhHthqwDeBUmxaVbdX4orVS7OazfuZNmC2fiZer/gJVygLMlGdi\ncVremJ7LGI9w+wENp7qGk3DZr8D4j2OduRH//dnz4t/3534HV40YPTZYOKvrgbr30G3txm1z1o/6\nedqu/i4cbzmFMx3nxYGVIiWRuCfnmzD0GPB2XRlWT78ak+JSsHzKYpddHP0hnNonILzqG27tMYUf\nJm9BaLSN7IBtAL+vehWnWqvwb1dtwevn3oH+y2fFfrbiUUxNnOyvUMfN3z8oXf3dAIQJv+voSrj9\ngIZTXcNJuOxXYPTHsbnPgl8d/x16rb3417x/xrPHfgMBAmIiY/DL1ducdgEPFv7+zpr7LIiOiHbo\n6RAI4dQ+AeFV33Brjyn8RHlehIKVTbBh9+nXcbjxU3Ha8CGo/0V1V1AnbhNhtHfuiIjGos/ahxdO\nlqDWeEGc9syxFwAMdtF+aOG9QZ24TQRZjDTQIRARhTwmbyGqz9qPbZr/B3OfxW56alwyVk7Lx02Z\n1yIqgruXiMjfLvd04Ilyx+dtpyZOxprM65A/ZcmEP+NGRERXJp7dT7Beax/erlXjfd3HmKeYjc25\n30ZidALqzXrER8ZBAHDK9AWO66pxy+wipMY7f7Gy+sJ7YuI2XToVP8n/wQTWgogo9LV3d2B/3T9w\n5NIxrJqWj43zb4MAoM54AalxqbAKVrxX8QHajAbcNvdml0OU/1n71cuuR74Qm4iIyJeYvE2wj/Tl\neF/3MQDgrKEWP/7kKZfL6iwNeHTJd512/btg0gEAHl36PfGlqERE5L03aw+Io9sebjyKw41HXS5r\n6DVhS+63HYaPFwRBfDn00yt/Anlskv8CJiKisDe6oaNozHoGenDecAF/P78fAPDgwnuQGOV6EI3Y\nyBhc6mzG4x9vx8cNGnF6e3cHSir/gpqOs5BGJ2K2fMYV9S4aIiJ/M/dZ8HnzSRxrPomYiGjcl3O3\ny2UlEgmiI6Jxqq0KDx/6D9RcPivOqzPW4z8/fQ491l7kTVIhOU4x6hEZiYiIRoN33ibIb7/4vfgg\n+/LJS5A3SYUnV/4IrV3tSI5TIC4yFgOCFZGSSEybnIzmFiP+XL0Hx5pPYvfp1yGNlmJxeh5+d+oP\naOy8BADIkHr3/jMiIho0YBvAzzW/FF8jsmHeLVg6eRHmJ8/B5Z4OpMVPQnREFAQMDsQ8fUoqahua\n8MLJXWiwNGFnxZ/w2NLvITlWjt+c3CWuZ7p0asDqRERE4YPJ2wTQNB2zG4Fs04JbAQDxUfHITPqq\ny2M0ogEMXumNjIjEvTnfwg3Kr+H5EztRUvkK4iLj0GPtgTxGhuzUBbhB+bUJrQcRUah7/dw7YsKV\nnjAJq6etADA4EqKr0RBlMVL8JP8HOHbpBP6g/T/84uivxHnTEqdgtmImrpm+0v/BExFR2GPy5kO9\n1j6c6TiHOfKZ6LdZERMZhfioeBxvHnw56fpZa6BKmT+qF5HOSFLiXtU3UVL5F/RYewAA38z6BvIm\nqfxSByKiK4Gh14hLnS3IkE0DBCAqIgqxkTH4vOULSCDBxvm3YUHK3FGNArlsymJc7jHgjdoDAIDo\niGhsyfsOJiek+asaREREdpi8+cDJ1kq8dvYttPd0OMzbkvsd1BovIi0+FTfPWjOm9S9My0HxNU/B\n0m9BZ38XlLLp4w2ZiOiK9F79R/hQX472nssO8x5ceA/MfRYsTsvDNRkFY1p/4czrcZ1yNdq62xEd\nEY20hNTxhkxEROQ1Jm/jJAgCdlX82eX8kspXAAD5KUvGVU5MZDRSIpOREuf81QFEROGuvbsDr517\n2+X83536IwAgO3X+uMqJiYzGNOmUca2DiIhoLJi8jVODpUn89+1zb8ZcxSy8dV6NJekL8erpfeK8\n3ElZgQiPiChsnDWcF/+9OffbiI6Iwkd6DRamqbD79OviPFXKgkCER0RENG5M3sbpwIX3AAD3qL6J\n5VMWAwC+v/h+AIPdHdUX38dc+SyeLBAR+dGAbQBlFw9BAgn+I/8RcfTHoeeD5ynm4JD+MFZMWYLk\nOEUgQyUiIhozJm9j1NLVhrdr1TjZWoGpiZOxdPIih2VkMVLcMe/rAYiOiCh81Bnrsef0a2juasGy\nyVc5HbZ/SmI67lpwewCiIyIi8h0mb6PQ3NWK6vYzsAlWlF08BHO/BQBw25z1fDErEdEEqjVeQK3x\nIuIj4+y6qN82Z30AoyIiIvIvJm9earRcwn8d/R+H6arUBVClskskEdFE+bz5JH5f9arD9LUzbmCX\nSCIiuqIxefNSZXu1w7T7cu522l2SiIj85+ilEw7TnljxKKYkTg5ANERERBOHff28YO6z4I3zgy9l\n/e6i+zAzKRM3KL+GJekLAxwZEVF4abA0iRfTfrDkIUxLnIJvLtjAxI2IiMIC77x5YWiIaWl0IhYk\nz0VOKof9JyKaaAO2Abxc+VcAQG5qFuYqZuGnK34Y4KiIiIgmDpM3D9q62/FFayUmxaXgZ1c/hqgI\nbjIiokCoaq9Bc1cLclOz8NCi+wIdDhER0YRjt0kP/nHxEAQIKJp5A6KZuBERBYz64gcAgPWz1gQ4\nEiIiosBg8uZGrfEiyps+gzwmCVdPXRbocIiIwlZ542e4aNJBlbIAM5KUgQ6HiIgoIHgryQVznwXP\nff5bAMA/zS7ie9yIiAKkwdKEv9bsBQAUzbwhwNEQEREFDjMSF96qVQMAZsiUWDlteYCjISIKXy+d\n+hMAoGjGDZirmBXgaIiIiAKHyZsL5wy1AIB/UW0KcCREROHLJtjQ1nMZALBmxnWBDYaIiCjAmLw5\nUWesR3NXK5TSaZicmB7ocIiIwtZ79R8BAK6eugzxUXEBjoaIiCiwmLw58bezbwIAZslnBjYQIqIw\nJggC3jh/AAAwh+0xERGRfwcsUavVSEpKgk6nw8aNG13O1+v1uPPOO/0Zyqhc7ukAAHx9ztoAR0JE\nFL7M/RYIEJAQFY8VU5YGOhwiIqKA89udN61WC4lEgoKCAgBAdXW1w3ylUomCggJkZGQ4zA+Urv4u\nmPrMWJA8l110iIgC6FJnCwCgYOpyREZEBjgaIiKiwPNb8rZ//37IZDIAgFKpRHl5ucMyxcXFAACd\nTofs7Gx/hTIqHzVoAAALkucGOBIiovAlCAI+1A/+bixIYXtMREQE+DF5M5lMUCgU4t8Gg8Fuvkql\nQkZGBvLz8+2WC6SegV4cvPAepNGJWDV9RaDDISIKW/VmPU62ViBTloHslPmBDoeIiCgoBGzAErPZ\njBkzZuDpp5/GE088Ab1eH6hQRNrLp9FvG8DXpl8NaXRioMMhIgpbJ1srAQy+lDtCwrG1iIiIAD8O\nWCKXy8W7bSPvwgHAnj17cNddd0EqlUImk+HgwYPYsmWL23Wmpcn8FS4AoPpcDQDg+vkrkJbs37I8\n8Xddg0k41RUIr/qGU13DyUTs16rPqhETGY1r5i9FbFSM38tzJ5yOY9b1yhVu9SW6UvkteVu3bh2q\nqqoADD7TtmrVKgCDd9xkMhkkEgmkUikAoKCgwKs7b62tZn+Fi37bAI43VCA1LhkJ/XK/luVJWpos\noOVPpHCqKxBe9Q23uoYTf+/XS53NaDBfwqK0XJg6egH0+rU8d8LtOGZdr0zhVN9wa48p/PitL4pK\npQIAaDQayOVycUCSe+65BwCwefNmlJSUoKysDHv37g34qwLOdJxDj7UXi9JyIZFIAhoLEVE4+6J1\n8MLfVWm5AY6EiIgouPj1PW/OErJ9+/aJ//bUTXIi1RkvAgAfjCciCrA6E9tjIiIiZ/gU+Jcumge7\nbU6TTglwJERE4csm2KAzN0IWLYUsRhrocIiIiIIKkzcAxl4Tai6fRYZ0GhSx8kCHQ0QUtk53nIOh\n14icSVmBDoWIiCjoMHkDUNGmhU2w4eqpywIdChFRWBt63q1g6vIAR0JERBR8mLwBOGeoAwBkpcwL\ncCREROHtnKEWMRHRmJWUGehQiIiIgk7YJ29WmxWnO85BGp2IKQnpgQ6HiChsdfQY0NTZjNnymYiM\niAx0OEREREEnrJM3m2DD/xx/EaY+MxanL+QrAoiIAqSzvwtPHnkWALAkfWGAoyEiIgpOYZ28NVou\n4YKpHgBwbcbKAEdDRBS+Ktq06Lf1AwCWTl4U4GiIiIiCU1gnbxdNOgDANxdswNTEyQGOhogofA21\nx/++7PuIi4oLcDRERETBKayTtwtfnizM5IPxREQBdcGkQ5QkEtOlUwMdChERUdAK6+TtolmH6Iho\n3nUjIgqgfms/GixNyJBNR1REVKDDISIiClphm7z1DPSi0XIJmbLpHNWMiCiAdJZGWAUrZiQpAx0K\nERFRUAvb5O2iSQcBAmbLZwY6FCKisFZnvAgAmM0u7ERERG6FbfJW++XJwiz5jABHQkQU3r5qj2cG\nNhAiIqIgF7bJW0NnEwAgUzY9wJEQEYW3RksTEqMSkBKnCHQoREREQS1sk7fmzhbERsZAESsPdChE\nRGGr3zaAtp7LmJyYDolEEuhwiIiIglpYJm82wYaW7jZMTuDJAhFRILV2tcEm2DAlIT3QoRAREQW9\nsEzedOYGDNgGMCWRJwtERIFUZxp83o3tMRERkWdhmbxVttcAAPImqQIcCRFReKtsG2yPF07KCXAk\nREREwS8sk7eWrlYAwAxZRoAjISIKby1drYiPisOk+JRAh0JERBT0wjJ505kbEBsZg2SObEZEFDDd\nAz1o6W7DlITJfP6YiIjIC2GXvLV3d6C5qxXzk+cgQhJ21SciChpnOs7DJtiQlTI30KEQERGFhLDL\nXt6tPwQAUKUsCGwgRERhzCbY8G79hwAAVWpWgKMhIiIKDSGTvPX1W8e9Dptgg6bpM6TGpaBg6nIf\nREVERGPR3NWKWuMF5KRmYVZSZqDDISIiCglRgQ7AW9/48dv4zSNfQ0Jc9JjXYew1od82gJlJSkRH\njn09REQ0Pq1dbQCAuYpZfN6NiHzCarXizJkzgQ5jQlitgzc1IiMjAxzJxAi3+s6ZM8dlXUPmzhsA\n/PUf4/tCnjPUAQAmJ6T5IhwiIhqj88YLAIDJfDk3EfnIhQu1qKurC3QYE6K8vBz19fWBDmPChFN9\n6+rqcP78eZfzQ+bOGwDoWizj+nz15cHk76r0PF+EQ0REY1R9+QxiIqKhSuXzx0TkO7NmzcL8+fMD\nHYbf1dXVhU1dgfCrrzshdeets2dgXJ+/aNYjNjIGUxMn+ygiIiIarT5rH5o6m5Ehm47oiJC6hkhE\nRBRQIZO85aumoMPcC2Nn35g+b+ozo7mzBRnS6XxFABFRAJ03XIBNsCFTNj3QoRAREYWUkMli5mbI\nAQBvH74wps9/3vwFBAjsokNEFGDHmk8CANtjIiKiUQqZ5O3aJRkAAH3r2J57a+lqBQDk8GSBiCig\nWrpbESGJQFbyvECHQkREFFJCJnmbliZFenI8Gto6IQjCqD/f1n0ZADApPtXXoRER0Si0dV9GcqwC\nkRHhMeQzERGRr4RM8gYA0yclwtLdD1NX/6g/29bdDml0IuKj4vwQGREReaPX2gdTnxlpvJBGREQ0\naqGVvKUlAgAaRtl10ibY0N7TwbtuREQB1tbdDgCYFJ8S4EiIiLyj0+mwdetWbNiwAWVlZVCr1Xju\nueeg0Wi8mh+KPNVJo9FgzZo1AY7Sd9zVJ9jqGlJjNE+fJAUANLR2QjXT+x/+jh4DrIKVJwtERAH2\nVfLGi2lEFBqUSiXWr1+P8vJyFBYWAgCKioqQn5+P999/3+N8qVQayPDHxFOdCgoKkJSUFOAofcdd\nfYKtrqF5561tdHfeWnmyQEQUFNgeE9GVQi6XQ6fTjXl+KBpep7GMQUHjF1J33qakJCAyQoKG1s5R\nfa6dg5UQEQUFtsdEdCWoqqpCUlISsrOzxzQ/FDmr01A3Sq1Wi8LCQiiVykCFN26CILisj7t5Ey2k\nkreoyAhMSUkQR5yUSCRefW7oSi8fkCciCqyv2mN2Yyei0GI0GlFdXQ2DwYCDBw/i6aefHtX8UOSu\nThKJBAUFBQAGuxZu2LABr732WqBCHTd39QmmuoZU8gYMdp1saOvEZVMvUuXejRzJB+SJiILD0Mi/\ncRz5l4hCjFwuF+86DZ3AP/jgg+IzYZ7mhyJ3dRrZbbKhoSEQIfrMyPro9XqX8wJZ15B65g0YfF0A\nMLrn3tq62xEdEYWkGJm/wiIiIg+sNitH/iWiK0Zubi4qKirGPD8UDa/TyB5wGRkZgQjJZ0bWZ3i3\nyGCqawjeeRscsUfXYsHCOZM8Lm8TbGjpbkNa/CRESEIuVyUiumK093TAJtiQFu+57SYiChY6nQ77\n9++HxWJBWVkZBEGATqeDyWTCU0895XF+KPKmTitXroRGo4FcLkdVVRV27NgR4KjHx119gqmuIZe8\nZaQPJm+fn27FzQUzPS7f3t2BXmsfpkmn+DkyIiJyp8HSBACYzvaYiEKIUql0e7LuaX4o8qZOjz76\nqPhvlUrl75D8zl19gqmuIXcrKk0eh4y0RFy4ZEZnT7/H5Rs6h04Wpvo7NCIicuOr5I3tMRER0Vj4\nNXlTq9XQaDQoLS11Ol+r1UKtVruc74xEIkH2jMGBR5ovd3tcnicLRETBoZHtMRER0bj4LXnTarV2\nw2pWV1c7LLNz504UFRXBbDY7ne/KpC9Hmbxs6vG4LE8WiIiCg97SBGl0IgePIiIiGiO/JW/79++H\nTDb4A61UKlFeXm43X61WY+HChQCAzZs3j+olhkmJMQAAU1efx2UbLE1IjE6APCbJ6/UTUeAdOvQe\njh07ijfffN3tNGefG+nFF19AZ+dXI9SeOVODzZu/g9/97jc4dOg9vPjiC+LnLBYLjh076sOaEAB0\nD/Sgvecypkunev2OTiIiIrLnt+TNZDJBoVCIfxsMBrv5FRUVMBgM0Gq1KCkpGdW6h5K3sqM6h/cu\nDNcz0Iu27suYnsiTBaJQcuZMDSQSCZYtyxf/Hjnt7NnTDp9rbGyATOZ4oWYo6Rsyf34WsrNVuPHG\nNbjuuhvx0EPfxy9/+V8AAKlUapfokW80dV4CwF4QRERE4xHQ0SYVCgVUKhXKy8uhVqtRVFTkdvm0\ntHBOJNYAACAASURBVME7eUJkJACgxdCNLiswc6rzLjhn2lohQMDctEzxs6Ei1OIdj3CqKxBe9R1r\nXf/0pw+xatUqpKXJoFLNQ1XVFzAYDHbTtNovsHLlMrvPvfHGJ9iyZYvdNK1Wi4ceehAff/w+7rjj\nVnF6bGwUkpMTxRhTUpIRHy+BVCpFUdENOHDgADZu3Dim+K90Y9mvJ4wdAICsqbNC7jsQavGOB+t6\n5QqH+qalLcGZM2cCHQaRX/kteZPL5eLdtpF34YDBxG3o5XdJSUmorKz0mLy1tpoBABIAq/Km4HDF\nJdScb0NilPO7alUN5wEAyZGTxM+GgrQ0WUjFOx7hVFcgNOtb+v45fFbTMurPRUZKYLU6vzO+PCsd\nG2+Y6/KzLS3tEIRotLaaYTB0obGxBZ2dFodpI7flmTPnHaZptWdx3XVr8cwzz9rN6+npR0dHJ1pb\nzWho0CM+PhHd3QK6uweXOXbsBK6/fp1XdQ2Hk6LhxnIM11yqAwAkCckh9R0Ixe/sWLGuV65grK/V\nasWFC7U+XeeFC7UwmztQV1fn0/UGo6NHj6K+vj4s6gqEV331ej1Wrlzpcr7fkrd169ahqqoKwOCL\n/latWgUAMJvNkMlkKCoqQllZGYDB5C4vL29U679q7iQcrriENqPrEScbLEPddPhOIaJw4Kx79FDX\n6qVLl+Pzzz/D0qXLxXk1NdUwGo04dOg9/OhHP7X7nNkcXCc6oa7R0oQISQSmJKQHOhQiCgIXLtTC\naGzFrFmzfLbOqiojMjMzfbrOYKXX65GRkREWdQXCr77u+C15U6lUqKqqEt9GPjQgyT333IN9+/ZB\nqVQiKSkJarUaRqMRhYWFo1r/JHk8AKDN4HrEyfaeywCA9IS0MdaCiDbeMNftXTJXxnOlVyZLgslk\nAgBYLGbI5QpIJBK7aUlJco/raWxsQE1NNSQSCeRyOT744F275C0rKxvz5i3AsmX5+MEPvofvfvdh\nzJu3AMBgjwDynfbuy0iJVSA6MjrQoRBRkJg1axbmz5/vs/XV1dX5fJ3BKpzqCoRffd3x6zNvd955\np8O0ffv2Ocz31F3SmTTF4OsCWt3ceevs70KkJBJxkbGjXj8RBc4NN9yE06drsHTpcjQ2NmD58hUA\nBu+UjZzmzpkzNXjwwX8DACxdmo/Nm7/tclmpVIaammoxeTMajT6oCQGDdz87+7swXTYt0KEQERGF\ntIAOWDIeCXHRiI+NQrvR9Z23zv5OJEYncKRJohAzf34WTp+uwbFjRyGTJYkJVU1NtcO04aTSr549\nO3bsKP7ylz9h+vQMzJu3AI2NepjNZrz66itYtmw5Tp+uwXvv/QONjQ1oaNBDLpfjlltuEz8vl3u+\ns0fe6bP1Y0CwIjE6IdChEBERhbSQTd4AIE0eh0sdXRAEwWmC1tnfBXksuz4RhaLhiZS7acNNn54h\n/nvZsnyUlPxZ/Hv+/Czs3//VO+CGzxvJ2zt75J3O/k4AQGJUYoAjISIiCm1+e8/bREhPSUBfvw1N\n7V0O87r6u9E10M2XcxOFkeuvv8npS7pH68yZGlx77Q0+iIgAoL178DUBCl5MIyIiGpeQTt4WzUkF\nAFTWXXaYp7c0AgCUsukTGhMRBY5UKoVMljSul2w3NjbY3cGj8dNZGgAAGXxBNxER0biEdPI2e9rg\nVVxds+OIdnrzlycLfECeKKwsXbociYnSMX9+2rTpTp+no7HTmwcvpmXwYhoReWHr1q0+WSbYaLVa\nlJaWupyvVquh0WjE/webscY/9HdxcTHUarU4vbi4GDqdDmazWXx9WLAb6zbwZV1DOnlLUwy+LqDd\n5DhoiW7ozpuUyRsRUSDpzA2IiYhGesKkQIdCREFOo9HgyJEjsFhc96DwZplgo9FosHPnTpfvENXp\ndDh8+DAKCgpQVFSEXbt2TXCE7o01fq1Wi6SkJBQUFOCxxx5DcXGxuN+0Wi02b96M4uLiUb8yLBDG\nsw99WdeQTt6iIiOQlBiDy+Zeh3l6cyNiImOQxpMFIqKA6bf241JXC6ZLpyFCEtI/Of+fvfuOb7O6\nFz/+keRtDe/Y8Yid4cTOImSAE0YggBNWIZBBKS0lcC+dtCUd97a0l27a9JaO+6MU6KBAIZRAAyQ4\nIeAQEmUyMmzHGU4secdDw1vS8/tDsWLF8owcWdH3/XrxInrmOZL86Pk+55zvEUJcBFarlaVLl/Ly\nyy9f0DZjTUFBAYsWLep3fc+8yD30ej2lpaUXo2hDMtLym0wmdu3a5Vmu0+kwmUwArF69mi1btvD4\n44+PXsH9aLjvgU6n83yG/qxr0P+SxmsjabF1oiiKZ1nX2ZuFDG2a3CwIIUQAVbfW4lJcZEoXdiHE\nIGw2G3q9nlWrVvHKK6+MeJtgZLVaiYuL87zW6/WeICcY9Ff+wsJCHn30Uc82VVVV5OXlAe75VEtK\nSigqKvLqThmszn8PDAaD5zP0Z12DPrKJ10XS5XDR2uHwLKs5e7OQoZXxFUIEq+Libezfv5eNG1/3\nWv7UU38YdL/zPfXUH7ySmBQXb+Oxx77XZzu73c7+/XtHWGLhy7nxbhK8CSEGtnnzZgoKCsjPzwfw\n2fI0lG3E2LRu3To2bNjgeb1ixQry8/MpLCzk6aefDqpusMPlz7peEsEbQEuvrpOms8lK5EmvEMGp\nvLwMlUrFvHkLADh27CgAGze+zvbt7/W7X3V1FTpd33T0PYFgj8WLl/icG1Kr1V5QpkrR17nxx/Iw\nTQgxsMrKSrZs2UJRURHTp0/32S1yKNsEI73e+7fLYrGQmZkZoNIM32DlLyoq4p577iE9Pd3z+rnn\nnvOsj4uLC6qWRl/6ew/8XddLJnjrnbTktNUMyJNeIYLVtm1b0Wp1gDv747597sDr9tvvZPz4/oOA\n4uJtzJ0732tZeXkZn/vc/bz7rnd2p95drXubO3dBn9Y+MXKnrSbUKjVp2tRAF0UIMYaVlJRwyy23\ncNNNN1FYWMhPfvITNm/ePOxtgtWyZcuorKz0vLbb7Z7uhcFgoPIbjUby8/PJy8vDZrNhMpnIyspi\n4cKFnu0tFktQ1deX/t4Df9c1bCQ72Ww27rrrrjGR1jPJEAXAGYs7eGvrbmNf3cdEaiJIi5WbBSEu\n1Ibjb/Fx/aFh76dRq3C6fAdIc1Jmsnzyrf3ua7fbvJ5gWa2WIZ2zqsrcZ1lNTTW33XZHn+6W1dVV\nHDiwD5vNilar87TyabVajh4tBe4c0jlF/05bTVTazEzQZxKuHtHPjRAiBJSUlPDYY4+xdu1azzKT\nyYRKpeJHP/oRDz30EFarddBtxjKj0cjOnTux2+3k5+dTUFAAwPLly3n++efR6XQsXbrUk17+wQcf\nDGRx+xhp+UtKSvjhD3+IXq9HURSqqqrYs2cP4G59q6ysxGw2e32uY9VI34O8vDy/1nVEv6Y6nW5M\nBG5wbrqAuqY2AA6dKaXb1c0tOTfKzYIQIcZXV8ieFra5c+dz4MA+T8ucwWDw/Pub3/yKJ3gD+k0D\nLIZnf90nACydcH2ASyKEGKn17x1nX1m9X485f1oKcyece52fn89rr73mtY3JZPIkusjIyADos01+\nfr4nEAA4cuSIX8vpTwUFBZ6b/d56jwHztX6sGGn58/Pz2bp1q89jFhYW+q+AF8GFfIb+rOsFRzdG\nozGgX7ascVrCNCqOV7mfzJ+0nAJgRmJwN70KMVYsn3zrgK1k/UlO1tHQMLIgSKfTY7VagZ5WOMMg\ne/hWXV1FWVkpKpUKg8HA+++/6wnYek/krdXqqKmpJi3N3dX6/H7rYmROWE6hUWmYlpAb6KIIIYJI\nSUkJZrOZNWvWsHz5clauXBnoIgkxZgw5eHv22Wd55ZVXyMrKorm5GZVK1af5MxDCwzSMS4ihrrkN\nl8vFccspItThpGvTAlYmIcSFuf76Gzh6tIy5c+dTXV3F/PlXeNb1N1bNl/LyMh5++KuAeyzbmjWf\n86yz288Flq2tdk/gBu7+6OLCdDq7MNmqyNJlEKEJD3RxhBAjtPL6yay8frLfj3vixLF+1+Xn52Ox\nWDAajcTHx/v93EIEsyEnLJk+fTpbt27lueeeY8OGDaxdu5YNGzbw5JNPjmb5hiRJH0V7p5PjTSZq\nW+uYmjAZjVoT6GIJIUYoN3caAPv370Wn0zNlylTAnZDk6NEy3nzzDZ/79SQ56dn3hRf+7slUWV1t\nxmaz8dJL/wAgPT2DAwf2UVy8jXvv/YLXcXpPsilG5qO6T3EpLvKk1U0IMUzr16/HbDZTUFCAoiiY\nzX3HMwsRqobc8tbfGJCx0D838WzSkhON7ikC8hKmBrI4Qgg/uO22O/osW7x4CYsXL+l3n/T0DM+/\n581bwLPPPu95nZs7jU2bzs0Bt3btf/k8xvktfWJkatrqAMhPlOBNCDE8mZmZlJSUYDQaPf/uGfcm\nRKgbcvB28OBBTCYTmZmZmEwmWlpaxkTgBpBkcCctqbadASAxSprYhQhF1113A8XF2wYM8AZTXl52\nQfsLt8b2ZgASoxICXBIhRLDpnRhirNxrCjFWDLnb5Nq1a8nIyODDDz9Er9d7MgCNBZ7pAtoa3a+j\nEwNZHCFEgGi1WnQ6/Ygn2q6urvJqvRMj19jRRLg6DH2EbvCNhRBCCDEkw8o2abFYWLZsGTNmzMBu\nt6PVagff6SJIS4oFlZNKRykgLW9ChLLzJ+kejoEmABdD19jehMlWRXRYtM/pG4QQQggxMsPKNpmZ\nmQm453kL9BQBvaUmRKPRt3heh0tmMyGECJi9tR8BEKWJDHBJhBBjldPpZMeOHVRUVPjtmHv37qWy\nstKvxxyrQqmuEFr1NZvNLFy4sN/1Qw7epk+fTkFBAaWlpX4pmD9p1Gois8pRgFW5dwa6OEIIEdLe\nqtgCwH/M+nyASyKEGLtUZGRkkJOT47cjms1mvx9zrAqlukLo1XcgQw7edu7c6UnVajKZMJlMY6bl\nDUATpuAAZsTNCnRRhBAiZHW7HJ5/Z2qlG6oQwjeNRk1OTg65uf7LSFtRUeH3Y45VoVRXCL36DmRY\nCUssFgsffvghFouFNWvWjGa5hk0Jb8fVpmV/aVOgiyKE8JOnnvqD1+vi4m3s37+XjRtf73ef4uJt\n/a4rLy9jzZr7+NOf/khx8TaeeuoPnu3tdjv79+/1T8FDmKXTPcH5FalzZbybEGJEioqKeOSRRwJd\nDCHGpCEHb2vWrGH16tX87ne/Y8WKFaNZpmGzddlx0o3SGcPL244FujhCCD/YuPF1tm9/z/O6vLwM\nlUrFvHkLADyTb/dWXV2FTqfv95i5udPIy8tnyZIbWbx4CV/60td44omfAe5MlSPNUinOaWh3Z/2V\nxFFCiJEqLCyUhz9C9GPIwduDDz7o9XrLli1+L8xI1ba6J4N1tccC0O1wBbI4Qgg/uP32O72yP27b\nthWt1p12fvz4dPbt69tKVly8bdBsk4qieL02GAyeoG3u3AUDtuqJwdW21gOQGjsuwCURQgSz86/V\nQgi3IY95+/Wvf43dbiczMxNFUThy5Ag33XTTaJZtyOrb3ZNzT0ocz1EzNNs7SYmLDnCphLg0NLz6\nMrb9+4a932mNGqfT94MU3bz5JK9YPegxev942+029PpzrWpWq6XP9lVVZq/XxcXbsFqtgDsY9LW9\nVqsjNtY97YlWq+Xo0VJAEh+NVH2b+3qcEpMU4JIIIfxhw/G3+Lj+kF+POSdlJrNVUwfcxmQyYTQa\nsVqt6PX6MZVnQYhAGjB4e/XVVwHIyMjg29/+ttcfTklJyeiWbBga25sBSIpO4CjdNFs7JHgTIgT1\n7mZTXl5GdXU1n/3sfaxZc59X8FZWVorFYqG4eBvf/e73vY5hs9kuWnkvRY0d7nHHSdEJAS6JECKY\nxcfHe+47H3jgAQnehDhrwOBt8+bN/OUvf/G5Lj8/f1QKNBI9NwtpuiSghiZrZ2ALJMQlJHnF6iG1\nkvXZL1lHQ8OFBUK9gzGdTu9pRXO3whkG3Dc3dxo2m439+/diMHhvO21aHlOmTGXevAV885tf4ctf\n/jpTprifAvdu3RPD19jeRExYNNFh8gBNiEvB8sm3snzyrX4/7okT/ecoeOSRR9BqtZ7XOp3Okyp+\nrCkpKeHw4cOsXLnS5/qioiL0en2fFsSe5Tt37mTmzJkUFhYCsG7dOlatWkVcXBxGo3HM9HIbyEjf\nA6nryAw45m3p0qWef69fv5677rprTI1163GmvQmNSkOaPtH92toR4BIJIfyhd7fJ66+/gerqKsCd\nmGT+/AUD7rtx4+tUV1cxb94CFEWhpqba53ZarY6ysnPzV1osfbtjiqFRFIXGjiYSpdVNCDFCRqOR\n3bt309zc7Flmt9vHZOBmNBp5+umn++2xYTKZ2LlzJwUFBRQWFvLMM88A7gCg58Z+7dq1rFu3Drvd\n7lm3Zs0a1q1bFxTBzEjfA5C6jtSALW9xcXGef69cuRKr1eo5odFoHBNN2C7FRX1bA4lR8WSnGlAB\npaeauG1hdqCLJoS4AMXF2zh6tIw333yD2267g9zcaRw9Wsb+/XvR6fSelrLeehKagDupSXn5Ufbv\n30t6egbl5WXYbFaOHi1j27atVFdXUVVlxmAwcNttd3j2O7+VTgxdU0cz3S4HSdGJgS6KECJIWa1W\nli5dSmlpqWfM2/lJ88aKgoICTCZTvzfzRqPR6zdFr9dTWlqKyWTi8OHDnvtonU6HyWQiLy+P1atX\nB0Ug02O474FOp6O0tFTqegEGDN5eeeUVTCaT5/Xhw4d57rnnANi1a9eYCN6q7bW0OdqZlTydeF0k\n6claTtZYcbpcaNRDTqYphBhjFi9ewuLFS7yW9Q6yfElPP/dkdt68BZ5pBXr+D/Dss8/3u7+7Re+K\nkRRXAMdaTgIwyZAd2IIIIYKSzeZOTLVq1SoeeeSRPveZPTfHmzZtYtWqVWRmZgaopENjtVq9GkL0\nej0mk4nCwkJPN0mr1UpVVRV5eXmAu/dHSUmJ5/67Z7tgdf57YDAYPIGq1HVkBoxumpubvf7LyMjw\n/HuspHCta3Onpc7QjgcgO01HV7eLmsa2QBZLCBEA1113w4CTdA+mvLyMa6+93o8lCi11bQ3Aueux\nEEIMx+bNmykoKPDkVSgtLfVa/8orr5Cfn8/NN9/s1SUtmK1bt44NGzZ4Xq9YsYL8/HwKCwt5+umn\nPd0pL0VS15EZsOXtpz/9ab+JScZKtsmGdneykuSz3XRyUnV8eLCGimorGcnagXYVQlxitFotOp2e\n1la7J/3/UFVXV3m13Inh65mgOzlGuk0KIYavsrKSLVu2oCgK06dP5+WXX+bxxx/3rH/00UcpKirC\nYrEExSTeer3eq4udxWLxai0sKirinnvuIT093fPabDazZs0awD18qaflJlj19x5IXUde1wFb3gbK\nKDlWsk02d7YAkBAVD8CkdHdf079uLsPpksm6hQg1c+fOH3bgBu4xcr7G0Ymha+loQaPSoI/QDb6x\nEEL0UlJSwi233MJNN91EYWEhP/nJT9i8ebNnvdFo5JlnnqGwsJCCggIURcFsNg9wxMBbtmwZlZWV\nntd2u91zw240GsnPzycvLw+bzYbJZCIrK4uFCxd6trdYLEEdzED/74HUdeR1HfIk3WOVpdOdGS4u\n0p3eOyPl3E3bqRqbJ5gTQggxupo7LRgi9ahVMt5YCDF0JSUlPPbYY6xdu9azzGQyoVKp+NGPfsRD\nDz2EwWBApVJRWlqKoihYrVZMJlNAs1AajUZ27tyJ3W4nPz/fM0Zv+fLlPP/88+h0OpYuXYrRaATw\nJF4pKSnhhz/8IXq9HkVRqKqqYs+ePYC79a2yshKz2ez1foxVI30P8vLypK4jpFLGyuC1IfA1Z9Qv\n9z5JbVsDv732p54m9A8P1vCXTaV8fulUFl+WfrGLecH8MT9WsAilukJo1TfU6hpKfH2uLsXFI8X/\nTbY+k0fnfiUApRodofY9lrpemsZifU+cOEZCgpbc3Fy/HbOoqIicnBy/HnOsCqW6QmjVt7y8HKDf\nugb949GWTivxkQavvs9piTEA1ErSEiGEuChsXXZcigtDpPR2EEIIIUZLUAdvDpcDW7cdw9kukz1S\ne4K3JgnehAhmTz31hyEt622gbJPFxdt47LHv9Vlut9vZv3/v8AsoPFrO68IuhBBCCP8L6uCtsaMZ\nOJespEdsVDj62Agq62y4gqdXqBCil40bX2f79vcGXdZbdXUVOl3/wcPixUt8ZijTarW0tl66KYov\nhjNnM02efz0WQgghhP+MavBWVFSE0Whk/fr1A2737LPPjuj4lVZ3lqHx2tQ+66ZnJ9Bi78JUJzdk\nQgSj22+/k/Hj0wdd1ltx8Tbmzp0/4HH7G+Y7d+4CNm58ffgFFQCctp29Hsf2vR4LIYQQwj9GLdtk\nSUkJKpWKgoICTCYTpaWlPtNiGo1GjEajJyPLcHzScBiAvIS+A/qyU3UYj9RS19zGhNTQSiYghD/t\neu8EJ8vqh72fWqPG5fQ9XcfEaSksvH7ShRatj6oq77TRxcXbsFqtgDvwA3fr3IED+7DZrGi1OubN\nWwC4W9+OHi0F7vR7uULBpw1HiNJEkq3PCnRRhBBCiEvWqLW8bdq0CZ3OHTRlZmaya9cuv5+juaOF\nMJXG55Pe5PhoABpa2v1+XiHE2NS7S2R5eRnV1dXcfvud/PvfGzzLDQYDc+fOZ/HiJbz44t+99u89\nuaYYOkVRaO5oITV2HFFhkYEujhBCCHHJGrWWN6vVSlxcnOd1S0tLn21KSkooKCjgmWeeGdE5Ws7O\nKeRrDEtKnDt4q2+W4E2IC7Hw+kkjaiULdGrq3Nxp2Gw29u/fi8FwLgNi7wm8tVodNTXVpKWNB0Cv\nl2QbI2HvbsWpOPskjxJCCCGEfwV0km6LxTLifbucXVi7bEw0ZPtcnxwXhQppeRMimPkanzbUqSk3\nbnwdlUrFbbfdwYsv/t0TpNnt5wLK1la7J3CDC7smhbLGjiYA4mWaACHEEDmdTnbs2EFFRYXfjrl3\n714qKyv9esyxKpTqCqFVX7PZzMKFC/tdP2rBm8Fg8LS2nd8KB+da3QCfLWeDMdurUVDI0vlOXhAe\npiEmKgxrW/ewjy2ECLzi4m0cPVrGm2++wW233dHvst602nPjW8ePT6e8/Cj79+8lPT2D8vIy0tLG\nk56e4Rnzdu+9X/Dav3cLnRi6nuRRGf1cj4UQoi8VGRkZ5OTk+O2IZrPZ78ccq0KprhB69R3IqAVv\ny5Yt48iRIwCYTCYWLVoEuMeU6HQ6TCYTZrOZlpYWmpub+01o0lty8rkbs9JW9xxuk8dleS3vTa+N\npL3T0e/6sSwYyzxSoVRXCK36XkhdV6y4gxUr7hh0WW/Tpk32nHPZsiUsW7bE8+8eTzzxc5/7mkwm\nlixZHFKfz0id/x61mt2tmXnp2SQnXnrvXyh9J6Sul66xVt/k5Hl+P2Zubi7l5eXk5vZNZHepqaio\nICcnJyTqCqFX34GMWvCWn5/PkSNHMBqNGAwGT2B2//3389prr1FYWAjA+vXrsduHls6/9/iZg+Zy\nAMK6I/sdVxMdoaGhuY36euuIWvcCJdBjhS6mUKorhFZ9A1HXefOu4tVX32Dx4iWDb3ye3bsPsHjx\nkhGVeazdFI223u+RoiiU1B0HQNUeccl9v+Vv9tIUSnWFsVnfEyeOkZCg9evNeFFRkbTMiEveqI55\nW7FiRZ9lr732mtfrlStXsnLlymEdt6XTwgdV7uyV42JS+t0uNioch1Oho8tJdGRAh/cJIS4CrVaL\nTqentdXulZhkMNXVVaSnZ4xiyS5dR5uPc9JyGo1Kgy5i6O+5EEIIIYYvKCOahrZGANK1aSTHJPa7\nXZIhCnBnnJS53oQIDYNN0u3LQBN/i4HVtNYBcG3GQtSqUZt9RgghhBCM4jxvo8nW7e5mWZA28E1a\nRnIsAOaGoXXLFEIIMTy2Lvf1dXbyjACXRAghhLj0BWXwZj97s6AfpItORop7valegjchhBgNPcGb\nLjw2wCURQgghLn1BGbw1djQDYBhkTqH0JHfwJi1vQggxOpqGeD0WQohQUlJSwvr16/tdX1RUhNFo\n9Py/x7p16zCZTNhsNrZs2XIxinrBpK6Db+PPugZd8OZ0OXm3cjsAqQMkKwGIiQojyRCFWVrehBDC\n71o6LZQ1HyMu0kBUWGSgiyOEEGOC0Wjk6aefxmbzneHTZDKxc+dOCgoKKCws5JlnnvGsKykpYc2a\nNaxbt46bbrrpYhV5xKSuQ9vGn3UNuoQlPYPjAbQRg3fTGRcfzZFTzXR2OYmM0Ixm0YQQIqSUNh0D\nIDm6/8RRQggRagoKCjytLL70TKPVQ6fTeeY7Xr16dVAEMj2krkPbxp91DbrgrazZfbNwz9TlQ9o+\nQe/OONlk6yAtUcZkCCGEv5Q1uefbvHPyLQEuiRBCBA+r1UpcXJzntcFgwGQykZeXh8VioaSkBJPJ\nBOCZFzlYhVJdB+LPugZd8Has+QQw9MxmPdMF1Da1SfAmhBB+dLylAkOEniydzJEnhLgwPS00mzZt\nYtWqVWRmZg64/FLVM0dyfn4+y5cvZ9GiRWi1l+YcmlLXkdU16Ma8NXY0Ex0WNeTJYCenu5tqj5st\no1ksIYQIKQ6XA0unlZSYJFQqVaCLI4QIcq+88gr5+fncfPPNXuOi+lsezPR6vddri8VCZmYmRUVF\nPPfcc57lcXFxnpaaYBVKde2Pv+saVC1viqLQ1NFM0jDGV6SfnS6gtqlttIolhBAhp7nDgoJCQlR8\noIsihLgEPProoxQVFWGxWLweCPW3PJgtW7aMdevWeV7b7Xby8vIAyMrK8iy3WCye5cEqlOran6ys\nLL/WNaiCt1ZHG53OrmHdLOiiw4mO1FDf3D6KJRNCiNDS2NEEIMGbEOKCGY1GNm/ezI9//GNMJhOH\nDx/GbDZjMpl8Ls/IGNtdtY1GIzt37sRut5Ofn09BQQEAy5cv5/nnn0en07F06VJP2vwHH3wQHKY3\nWwAAIABJREFUgLy8PIqKiqisrMRsNrN27dqA1WGopK7n6qrVan1u4++6BlXw1tTunk8ocRg3CyqV\nipS4GKobW3EpCupL5KmNEEIEUs/8bsO5HgshhC8GgwGVSkVpaSmKomC1WjGZTP0uH+vBW0FBgefG\nvrcNGzZ4beNLsCXtkLr2rauvbfxZ16AK3k5Z3f1Dx8emDmu/lPhoTtfZsNi7iNfJXERCCHGhPNdj\n7fCux0IIcb78/Hwef/xxz+snn3zS8+/+lgsRqoIqYYnJVgVAjmHCsPZLiY8GoL5Zxr0JIYQ/mGxV\nhKk0ZGjHB7ooQgghRMgIquCtvr0BFSqSY5KGtV9KnDt4q5Nxb0IIccEURaG+7QzJMUlo1JpAF0cI\nIYQIGcEVvLWdISEqnnD18Hp7Jp0N3s5YOkajWEIIEVJs3XY6nB2kxCQHuihCCCFESAma4K2xrRlr\nl42UYba6ASTo3ePcmq0SvAkhxIWqsJwGICV6+NdjIYQQQoxc0ARvO07vBWByXM6w9004m6Skydbp\n1zIJIUQoOlD3KQC58ZMCXBIhhBAitARN8Haiyf2kd0Hq5cPeNzxMgy4mnCZpeRNCiAt22mYmNiyG\nvITcQBdFCCGECClBM1VARXMl2vBY4iPjRrR/gi6K6sZWFEVBJXO9CSHEiLR2tXGmvZFp8VPkWiqE\nuCAVFRV+PZ7ZbPbr8cayUKorhFZ9KyoqyMnpv6dh0ARv9a2NZOnSR3yzkGiI4nSdDWtrFwatzPUm\nhBAj8Y3N7jmXMnXpAS6JECKYZWdP5NQpaGqy++2Y06bNIiFB67fjjWULFy4MdBEuqlCqb05ODpMm\n9T8sIWiCN4AoTdSI9x2XcG66AAnehBBiZCwdVgASokbWC0IIIQA0Gg2TJk3x+3GTk3V+P6YQY0nQ\njHkDuDdvxYj3zUx2P4kpq2z2V3GEECJkXZE2L9BFEEIIIUJO0LS8PX/Xk9iau0a8/6xJiQCUnW7m\n9kXDz1gphBACfnDt1xmnHo9aFVTP/oQQQohLQtD8+kaFXVhXx5iocFITYjhdZ8OlKH4qlRBChJZZ\nqXkSuAkhhBABElK/wDlpOto7ndQ2tgW6KEIIIYQQQggxLCEVvOVnJwDwUXlDgEsihBBCCCGEEMMT\nUsHblAwDALVN0vImhBBCCCGECC4hFbwl6KNQqaCqoTXQRRFCCCGEEEKIYQmp4C1Mo2ZKuoHTdTZM\n9f6bFFIIIYQQQgghRltIBW8Aiy9PB+DjYzLuTQghhBBCCBE8Qi54mzUxCbVKxcETjYEuihBCCCGE\nEEIMWcgFbzFRYeSk6ThVY6Pb4Qx0cYQQQgghhBBiSEIueAPITNHiUhRqZL43IYQQQgghRJAIyeAt\nO00PwP6jMu5NCCGEEEIIERxCMnibNzUFgIpqS4BLIoQQQgghhBBDE5LBW0xUGAn6SKrOyHxvQggh\nhBBCiOAQksEbwPjEWFrsXbR1dAe6KEIIIYQQQggxqJAN3jJTtAAcr7IGuCRCCCGEEEIIMbiQDd5m\nTkwE4EhFU4BLIoQQQgghhBCDC9ngLSdNj1qloqJWWt6EEEIIIYQQY1/IBm+RERrSk2OprLXhdLkC\nXRwhhBBCCCGEGFDIBm8AOWk6uhwuqhok66QQQgghhBBibAsbzYMXFRWh1+sxmUysXLmyz/r169cD\nUFlZydq1a0ezKD5lp+n54NMaTtXayBqnu+jnF0IIIYQQQoihGrWWt5KSElQqFQUFBQCUlpZ6rTca\njSxcuJCVK1diMpkwGo2jVZR+9WScNNfbL/q5hRBCCCGEEGI4Ri1427RpEzqduzUrMzOTXbt2ea3v\nHbBlZmZiNptHqyj9ykjSEhWhYefhGs5Y2i/6+YUQQgghhBBiqEYteLNarcTFxXlet7S0eK1fuXIl\nK1asANytdDNmzBitovQrMkLDPUum0N7pZJPx9EU/vxBCCCGEEEIMVcATlpSUlDB9+nTy8vICcv5F\nM9OIjQrj4MlGFEUJSBmEEEIIIYQQYjCjlrDEYDB4WtvOb4XrzWg08uijjw7pmMnJo5NU5PJp49jx\nSRXtLpiQOjYSl4xWXceiUKorhFZ9Q6muoSTUPtdQqq/U9dIVavUV4lI1asHbsmXLOHLkCOAe37Zo\n0SIAbDabZyzc+vXrWbNmDeAO4nqSm/SnocE2KmXNTdez45MqPthvYukVWaNyjuFITtaNWl3HmlCq\nK4RWfUOtrqEkVD5XCL3vsdT10hRK9Q2167EIPaPWbTI/Px9wB2UGg8HTLfL+++/3LP/Nb37DjTfe\nyBVXXDFaxRiSGRMTAfjk+JmAlkMIIYQQQggh+jOq87z1JCTp7bXXXgOgoKCAPXv2jObph8wQG8Hk\nDAPlphbaOrqJiQoPdJGEEEIIIYQQwkvAE5aMFZPG6wGoOtMa4JIIIYQQQgghRF8SvJ3Vk6jk0Mmm\nAJdECCGEEEIIIfqS4O2sOVOSiY7UYDxcE+iiCCGEEEIIIUQfErydFRmuYUpGHI3WTuzt3YEujhBC\nCCGEEEJ4keCtl/SkWABO1VoDXBIhhBBCCCGE8CbBWy/TcxIA+PRYY4BLIoQQQgghhBDeJHjrJTcz\njpjIMD461oCiKIEujhBCCCGEEEJ4SPDWS5hGzezJSTTbOik3tQS6OEIIIYQQQgjhIcHbeRbOSAXg\nk+NnAlwSIYQQQgghhDhHgrfzTM4woFGrpOVNCCGEEEIIMaZI8HaeyHANGSlaKmpsHDklE3YLIYQQ\nQgghxgYJ3nwYFx8NwO//dTDAJRFCCCGEEEIINwnefLhnyRQAwjXy9gghhBBCCCHGBolOfDBoI5k1\nKZG2TgdHKqTrpBBCCCGEECLwJHjrx5QMAwC7S2oDXBIhhBBCCCGEkOCtX8uunIA+Jpz9Rxuoa2oL\ndHGEEEIIIYQQIU6Ct36oVSruvGYinV1O/v1hRaCLI4QQQgghhAhxErwN4JrZ4zHERlBW2YyiKIEu\njhBCCCGEECKESfA2AJVKxZQMAy32LhosHYEujhBCCCGEECKESfA2iKlZ8QB8euxMgEsC7ceO0bTp\nLVydnYEuihBChLS9pXUUf1IlvTKEEEJcVGGBLsBYN29aCv989xi7jtRy4/zMgJRBcThoeX8bDetf\nBkXB1d1N4i23oQqTj08IIS6mtg4HG3dWsGWfCQB9TASzJiUSJvOCCiGEuAjk7n8QhtgIZkxM4OCJ\nRmqb2khNiLmo51cUhbp//B3rzh2eZU1v/pumtzaScOvtJH3mzotaHiGECFVd3U5+++onnKiyepb9\nccMhAL5y50zmTk0OVNGEEEKECHlUOASzJycB8PN/HMDlurhdZFreexfrzh1EpI0n+xe/wnDtde4V\nioKl+L2LWhYhhAhViqLwwpZyTlRZmTExgd8/cjWTz84HCnCgvD6ApRNCCBEqJHgbgivyxmGIjcDe\n3k31mdaLdt76l1+k4Z8voo6OJv2ba4lITiHlc58nY+13UWu1uNrbUZzOi1YeIYQIRd0OF99/Zg8f\nHqohI1nL15bPRBsdzrdXz+Fry2cCUNMo84EKIYQYfRK8DUFMVBifuSoHgBPVFr8eW3G5cHV24rBa\naT9+jPYTx6l86WWsxp20vLsVgKS7VxGekAC4M2DGTMtDe9kcFIeDY/+5hpYPiv1aJiGECEVOl4uu\nbid1TW1UnWnlwNF6Xi8+zlu7TlHb5A7OPr90KuFhGgDCw9TMyU0ma5yW07U2HvjlexytbA5kFYQQ\nQlziZMzbEE1Kd3eP2by7khNVVm5ZOIFx8QOPf3N1d1P31+dABSmfvQ9NbKzX+vZjxzD/769Qurt9\nH0ClIu0/v4R27vw+qwzXXIf1Q/c4uPrn/0b9838jdtZsus80EH/TMgxXXT2CWgohxKXJ0trF0/8+\nTHaanruvnYRarfJa/8Gn1fxtc1m/+8dEhrH2nsvITtX3WXfjvEyee7sUgCde+hiAGTkJtHY4WH7N\nRKbnJPixJkIIIUKZSgmiPMcNDbaAnVtRFH789/2crj1Xhj9+42piosK9tnN1tNPw6isoDqdXkpHw\n5GTC4tzTDkRPy8O+by9dtTX9nk8VFkbyytXEXX9Dv9s4rFZOfuvr/a5Pfehh9FdcOWjdAik5WRfQ\nz/ViC6X6hlpdQ0mwfK71zW28saOCqMgwij+u8izPSXMHYLqYcFITYvjg02o6uvp2QVepQFHcgduX\n7pgxYBBW09jK95/Z43NdvC6S/7x9OrmZcRdYo9EVan+zoVJXCK36htr1WIQeCd6GYcen1fy115PZ\nWxdms/yaiZ7XisvFsf94YFjHjMmbTvI9nwWnE43egOJwMH5aNvV1FlTqofVqbSsrxbzuCQCiJk6i\n01Tpac1LWrEKw6Kr0Wi1wyrXxeLrB8XV3YXTaiM8MXHEx3V1d+NqtWPZ8QGujg7iFl+PWqvFtnsX\nsbPneLqhXmyh9gMaSnUNJcHwubZ1dPPVJ3cMvmEvhQsymTc1BW1MOBqVipioMCZkJlBXZ+3TUueL\noijsPFTLXza5W+GunD6O3UfqAFABD92ez9zcFMLDxuaIBV9/s/b2bhRFQRcTMeLjtnc6aO3oZus+\nMzFRYdw4L5O2zm4OnWyiYPo4oiIufiegULo+QWjVN9SuxyL0SPA2DIqi4HQp2Nq6+e6fdkF3N/cv\nTOHynDgixqXStPltmt5+072xRoMqLIyU1Z8lPGUc9S8+jzomlpi8fGz79hCZkUVkRgaGaxYTpvfu\nhjOSi6yiKHRWniYyawLdDQ00vPwirQc/BSBq0mSy/usHfnkP/O38unbV1mD65c9x2m3E37SUpBWr\nUKkGv2nq0bK9mPp//K3P8rD4eKInT8G2b69nWfyyW0hafne/x3fa7ahjY4d1/sGE2g9oKNU1lIzF\nz9Xe3o29vZvOLifJcdH87Z0y9pe5M0BGhLuDpW+vnkNtUxsbPjhJbmYcUREayipbyMuKY1xCDNdf\nnu4Zz9ZjJN/jboeT+uZ20pO1HK+y8LfNZZ5kV9dfns7nbprqhxr73/l1PXSykd//6yBOl8KX75jB\nvGkpQz6Woij8a/sJNu+u7LMub0I81rYuqhrOJQB74OY8rpqV1u+xWjscaKPDfa4fiVC6PkFo1TfU\nrsci9EjwNgKd1dUcfeYvRJmO91mnMRjI+q8fEJ408vl+/HWR7R3IZP/8V0SkDP2H90IpDgdn3tiA\nff8+nO1tpKy+F33Bwj7b9a6rs7WVE498xWt90vK7Sbj51n7P093cjNNmRaVS0fj2W9j37/Var4qI\nQB0ZhdNm9bm/KiKC8V/+KrEzZnktb9z4Bo0b30AdE8vEX61DHRU9pHoPJtR+QEOprqFkLH2uJaea\neHFruc9sj1njtPz35+YSEa7xsefQ+ON7rCgKL717jG0HzMREhvG7R65CM8SeFf5gb+/mlW3H+PjY\nGQzaCL6wdJrPLpy961rVYOex57yvp99YMYtZk5L6PU9tUxsOh4uObqdXwNrDEBtBe5eDrm6Xz/1T\n4qN55O5ZpCV6jw//44ZDfFTeQG5mHN/57BzUfnigFkrXJwit+oba9ViEHgnehqGrvp76l16g7fBB\nn+u1c+cx7vNf7JOYZLj8eZG17NhO3d//iuHaxYy7736/HHMozrz+2rlWyF7SH/kWsTPdgVKn2cSZ\nf/wN7dXX0n2mgaa3zm1vWHy9Zx671DX/gaurk/CkZDSxWiLS0lCFhdG06S0a39oI502XEH9jIbFz\nLicqawLqqCg6TSbqX3mJ9rJSdAuuJOVz99FVXU3VH57E1eq+uYi9bA6GRVfTvG0r7WWlXsfTXXEl\naQ897LOe7SdPYineRlhCAvE3LUUTM/Bn399n6+rupuXdregXXdWnJTZYyc3CpWssfK7HzC385e1S\n6prbfa6/pWACn7kqhzDNhQVJ/vwev7ilnG0fmbmvcCrXzUn3yzEHoygKv/vXQQ6eaPRaro+N4Luf\nneMJlA4cbeDdj8zcODeDvaV17C09N29dTpqOihobGrWKryyfSaOlg+w0HSpUZKfp6Oxy8s9tx/jw\nYN9x3KuXTGFaVhzj4mOIjNDw6fEzvLb9BOaGVpZfM5ElczP49PgZ/vxmiWefG+Z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cvW+PE5uvrqa3XllUt0662fVUVFOZ/BQBcIbwD6vZKSYn3603do7do3un1Ofv7mHvWiXco1YF5/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\nLqK7qqo6vs+sL+QVVOpPrxRq8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1e82804e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;r=0;prop=[0.69, 0.3, 0.01, 0.];reload(mutl)\n", "mutl.simulate(N,L,r,gen,sa,sb,sab,saabb,prop)\n", "plt.suptitle('No Recombination (no 11 hap) and sa=sb, a0>b0. Imbalance Equilibrium.',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.08 , Ns=800.0 prop=[0.8, 0.1, 0.1, 0.0]\n" ] }, { "data": { "image/png": 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nn8fXv/51fOtb3wqra2+g8sL5ffO+p7t378Z3vvOdoOWEel+tViuWL1+OtrY2SJIEp9OJ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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e8283f990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.08;sb=0.05;r=0\n", "prop=[0.8, 0.1, 0.1, 0.]\n", "reload(mutl)\n", "mutl.simulate(N,L,r,gen,sa,sb,sab,saabb,prop)\n", "plt.suptitle('No Recombination (no 11 hap) and sa$>$sb, a0=b0. Equilibrium.',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.12 , Ns=1200.0 prop=[0.8, 0.1, 0.1, 0.0]\n" ] }, { "data": { "image/png": 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YyhM/bDZb2JOWExERxRve80ZEk2a1WiEIApOSEOrq6sIacZGIiIgoHEzeiIiIiIiIEgC7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\ngooAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e817e2790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.12;sb=0.05;r=0\n", "prop=[0.8, 0.1, 0.1, 0.]\n", "reload(mutl)\n", "mutl.simulate(N,L,r,gen,sa,sb,sab,saabb,prop)\n", "plt.suptitle('No Recombination (no 11 hap) and sa$>>$sb, a0=b0. a dominates.',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.98, 0.01, 0.01, 0.0]\n" ] }, { "data": { "image/png": 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IKCycb2zFl8KHkEX2YM2c+5GimCZ1SEREHoVc8paSEI9fb8qBXCbD30834lhV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LT1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\nakIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e81887d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;sab=0.1\n", "saabb=sab\n", "gen=1000\n", "r=1e-6\n", "prop=[0.98, 0.01, 0.01, 0.]\n", "reload(mutl)\n", "mutl.simulate(N,L,r,gen,sa,sb,sab,saabb,prop)\n", "plt.suptitle('Recombination (11 hap will appear) and sa=sb$<<$sab, a0=b0. Absolute Equilibrium!',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.98, 0.01, 0.01, 0.0]\n" ] }, { "data": { "image/png": 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jIwvr1n2E22+/KxTFIyLqUoo2H8COqhNB3eaEMQMwa8rIoG6TqDvoVkkeAGSM\nTsDka5Lw5Z5abNX/ABuAh28fK3VYRF2eFD/uVqsFCsWVFjmTyYSmJqvTNLPZtdW+ttboMu3YsTrc\ndtudWL78dY/7q601QiaTo08fGQB7a96+fZUAmOQREUmlpGQTzGYzAPCiG9Fl3S7JA4DbcobCWG/F\nfqMJO6tO4P4ZY9CrR7TUYRFRJxEEwWWao8UuI2MCdu3agYyMCeK8qqpKmEwmlJRswjPPPOe0nsVi\nCW2wRERdxKwpIzu91a26ugp1dXX46U/vw5w59zHJI7qsWyZ5Klkv/O7nGfjXxiqUfFuH4u01uC1n\nmNRhEXVpUvy4y+UK8eqt1WqBUqmCIAhO0xQKpc/t1NXVoqqqEoIgQKlU4ssvv3BK8saMScOoUaOR\nmZmFJ5/8FX75y8fFLqCtWw2JiKhzpaaOgcViwc6d26FU+q7vibqLbjHwiiczJ49EXK9oaLcbYG66\nIHU4RBSgKVNuRl1dLQB7ojZhQhamTp3mMs2X6uoqzJv3KG66aQrmzXsMO3Zs87isTCZHVVWl+Npk\n4iBORERSWbfuI9TV1SIzMws2mw3HjtVJHRJRWOjWSV5crxjkTUjBmfPNWPzOdpw51yx1SEQUgNTU\nMQCAnTu3Qy5XYNSo0WILW+tpbclkcvHvnTu34/33/ymOwllXZ4TFYsGHH76H6uoq7NtXhU2bPseW\nLZvx4Yf/glKpxG233SmuzyvHRETSSUxMElvykpKSUV1dJXVIRGFBsLUdOi4C1Nf7f4+M5cwFPPW3\nUjS32N+Gv8z/keSjbSYkyAMqQ7iKhHKwDOEjmOX49NOPnRK19qqrq8X+/fucRuP0JiFB7nuhCNPV\nj71I+P5EQhmAyChHJJQBiIxydMf6mLqXbt2SBwDy3j2x+IEr3bmWf1yOlkuXJIyIiEJt8uSb3T4M\nPVDV1VV+J3hEREREnaXbJ3kAkNS/Dwp/Mxnxil6oPNqAt9bppQ6JiEJIJpNBLld06GHmdXW1SEpK\nDmJURERERMHBJO+yqCgBD96qAQDsqDrh8gBkIoosGRkTxOfdtUdiYpLb+/2IiIiIpMYkr5W0IX1x\nvWYgAOD46TMSR0NERERERBQ4JnltjEiyj5R3sNYscSRERERERESBY5LXxvBE+4ONDx9nkkdEREQU\nzkpKNmHhwt9KHQZR2GGS10ZyggxxvWKwa189mls4yiZRuCsp2YSdO7dj3bqPnKYvX/66z/XaWr78\ndafBWDyqI9o9AAAgAElEQVSdPFitVuzcub2dERMRUbBMmjQVgiBIHQZR2ImROoBw0yMmCtljB2Lz\n7lrsPXgK16QmSB0SEXlQXV0FQRCQmZmFdes+wv79+zBq1GisW/cRtmzZjEceecztenV1tZDLFS7T\nS0o2QaMZKz4WYdKkqdi8+QuX5WQyWYdG5iQiikRrD/wXe07sDeo2rxmQjvyRP/a6DAfLI3LFljw3\nxg2PBwCs2nwAF5tbJI6GiDzZtOlzyGT2B9omJiZhxw5769rtt9+FxMQkj+uVlGxCRsYEp2nV1VX4\n+c/vxxdfFDtN93TykJGR5dJ6SEREna+urha7du0Qe3YQEVvy3Bo/sj+yxw6CruI4/rFhHx66TSN1\nSERhT4oruFarBQrFlRY5s9nk13Zra40u044dq8Ntt93p0s3TcfJgsZghk8mRmZkFwN6at29fJYC7\n/NonEVGkyx/5Y5+tbqGgVCrFC3dPPvkrsZ4m6s7YkufB7TcMBQAcPsYBWIgijbv7NxwtdhkZE7Br\n1w5xuuPkYdKkqfjgg386rWOxWEIbKBER+dT6macymRzHjtVJGA1ReGBLngcD+/ZG2pC+qDzaAOvZ\ni5DF9ZA6JKKwJsUVXLlcAbPZfiHG3qqnbNd26upqUVVVCUEQoFQq8eWXX4hXhd2dPAwenAgATq2I\nREQkDav1ygW3piarWEcTdWdsyfOivzIWAPD2p3qJIyEid6ZMuRl1dbUA7InahAlXuugEciN+dXUV\n5s17FDfdNAXz5j2GHTu2ifO8nTyYTP51DyUiotBJSkoW78n72c/+V+pwiMICkzwvHM/M23volMSR\nEJE7qaljAAA7d26HXK7AqFGjAdgHVtm3rwqffvqx2/Ucg7U41n3//X9i//59AIC6OiMsFgs+/PA9\nAN5PHpTK9rUcEhFR8CxY8DuxW33bQbWIuit21/TiR1cn4p8b9yGuVzRsNhufw0IUhm677U6XaZMm\nTcWkSVM9rpOUlCz+nZmZhcLCf4mvU1PHYP36K8/QW7Dgd263YW85vK49IRMRERGFFFvyvIgSBGSO\nGYCz51vQYDkvdThEFCSTJ9/s9mHogaiurhKfp0dEREQUTpjk+ZDUvw8AoPZkk8SREFGwyGQyyOWK\ndj/QvK6u1qk1kIiIiCicsLumD2KSV9+E9MsPSSeirq8j9214e9A6ERERkdTYkudDUoI9yTtQy1H0\niIiIiIgo/DHJ82FQv95IUMWi8mhDQEOyExERERERSYFJng+CICCpvwxnzzfDfOai1OEQERERERF5\nxSTPD0MH25+p9fHXhySOhIjcWb78dafXJSWbsHPndqxb95HHdbyNrlldXYU5c+7DG2/8FSUlm7B8\n+evi8larFTt3bg9O4EREREQhwCTPD5qh/QAAW76t46MUiMLMunUfYcuWzeLr6uoqCIKAzMwsABAf\nct5aXV0t5HKFx22mpo5BWpoGU6dOw6RJU/HII4/hlVdeBmAfmbO9o3ISERERdQYmeX4YkahAXC/7\nQKRb9ccljoaIWrv99rucRrvctOlzyGT21vfExCTs2OHa6lZSssnn6Jpt78FVKpVicpeRkeW1lZCI\niIhISnyEgh8EQcDcW9Pw+tq9+P7AKcy4bojUIRGFnfrVK2HZuSOo25RnTkDCzHt8Ltc6IbNaLVAo\nrrTSmc2uI+PW1hqdXpeUbILZbAZgTxrdLS+TydGnjwyAvTVv375KAK7LEhEREUmNLXl+Gj+qP2Rx\nPXDkBwsucZRNoi5NEATx7+rqKtTV1eH22+/CJ5+sdVquqqoSO3dux7///R6eeeY5p3kWi6VTYiUi\nIiIKFFvy/CQIAtKHx0NXcRw/nD6DwfF9pA6JKKwkzLzHr1a3UGidtMnlCrFVzt6qp/S6bmrqGFgs\nFuzcuR1KpfOyY8akYdSo0cjMzMKTT/4Kv/zl4xg1ajQAOLUWEhEREYUTtuQFYOgg+30+R4/zCj5R\nOGndXXPKlJtRV1cLwD7AyoQJWV7XXbfuI9TV1SIzMws2mw3HjtW5XU4mk6OqqlJ8bTK5dgMlIiIi\nCgdhkeRptVrodDoUFRV5nb969epOjszZkMtJ3hEmeURho6RkE/btq8Knn34MwN4yBwA7d26HXK4Q\nW95acwzMAtgHZ3G05CUlJaO6ugrV1VXYt68KmzZ9ji1bNuPDD/8FpVKJ2267U1yvbasfERERUbiQ\nvLumXq+HIAjIzs6GwWBAZWUl0tLSnOar1WpoNBrodDqX+Z0pZaAMAtiSRxROJk2aikmTpjpNa52M\nuZOUlCz+nZmZJT5uwfE/ABQW/svj+vYWwuvaEy4RERFRyEnekrd+/XrI5far6mq1GmVlZS7LFBQU\nAAAMBoNkCR4AxPaMQT9FLE40npUsBiLquMmTb/b6MHRfqqurcNNNU4IYEREREVHwSJ7kmc1mqFQq\n8XVjY6PTfI1Gg+TkZGRlZTktJxVFn55osJxHtaHR98JEFJZkMhnkckW7HmpeV1fr1BJIREREFG4k\nT/J8sVgsGDJkCF566SUsXLgQRqPR90ohpOjdAwDwfx/sljQOIuqYjIwJ4nPvApGYmOT2Pj8iIiKi\ncCH5PXlKpVJsvWvbqgcAq1atwj333HP5yrscGzduxNy5c71uMyFB7nV+RwyI7wMcPAUAiI+XISpK\n8LFG+4SyDJ0pEsrBMoSPSClHdxIJnxnLED4ioRyRUAYgcspBFKkkT/JmzJiBiooKAPZ77nJycgDY\nW/DkcjkEQYBMZr/anp2d7VdLXn196AZGGT+8Hz7fXgMAOHDkFPrKewV9HwkJ8pCWobNEQjlYhvAR\nCeXojidFkfCZsQzhIRLKEQllACKjHN2xPqbuRfLumhqNBgCg0+mgVCrFgVXuv/9+AMCcOXNQWFiI\n4uJirF69GjNnzpQqVABA2tB+yMtSAwCOnWqSNBYiIiIiIqK2JE/yAGDmzJnIzs52SuDWrFkj/j13\n7lzk5uZKnuA5JMb3AQCUHz4tcSREBADLl7/u17TWvI2uWVKyCQsX/tZlutVqxc6d2wMPkIiIiKgT\nhUWS19VcNbI/AKC+gY9SIJLaunUfYcuWzT6ntVZXVwu5XOFx/qRJUyEIrvfbymSydo3ISURERNSZ\nmOS1g6J3D8RER+Gk6ZzUoRB1e7fffhcSE5N8TmutpGQTMjImeN2uzWZzOz0jIwvr1n0UeKBERERE\nnUTygVe6IkEQMHSQHAfrTDh7vhlxvfg2EpVtPohDVSeCus3hYwZg4pQRQd0mANTWOg/gVFKyCWaz\nGYA9QQTsrX27du2AxWKGTCZHZmYWAHtr3r59lQDuCnpcRERERMHAlrx2GjpYDpsNqOPgK0RdTuuu\nmNXVVairq8Ptt9+FTz5ZK05XKpXIyJiASZOm4oMP/um0vsXStUeVIyIiosjGJqh2SupvH3ylrr4J\nIxKVEkdDJL2JU0aEpNUt1FJTx8BisWDnzu1QKq98l1s/KF0mk+PYsToMHpwIAFAoPN/PR0RERCQ1\ntuS1U6IjyWNLHpHk3N0/5+meurbWrfsIdXW1yMzMgs1mw7FjdQAAq/VKa11Tk1VM8ADAZDJ1MGIi\nIiKi0GGS106Olrzak0zyiKRUUrIJ+/ZV4dNPP/Y6rTWZ7MpDcBMTk8SWvKSkZFRXVwEAkpKSsWvX\nDpSUbMLPfva/Tuu3bvEjIiIiCjfsrtlOvWN7QNG7B07wMQpEkpo0aSomTZrqc1prSUnJ4t+ZmVni\noCqO/wFgwYLfuV23rq4WEyZc15GQiYiIiEKKLXkd0E8RixMNZ9Fy6ZLUoRBRACZPvtnrw9C9qa6u\nwk03TQlyRERERETBwySvA4YMsnf52lrxg8SREFEgZDIZ5HJFwA82r6urdWoFJCIiIgpHTPI6YMb1\nQwAAX39XJ3EkRBSojIwJTiNo+iMxMQmjRo0OUUREREREwcEkrwMGqOKQqlah2mh/KDoREREREZHU\nmOR1UGJ8bwDAact5iSMhIiIiIiJiktdhfRWxAIDjfF4eERERERGFASZ5HZQ+vB8AQMfBV4iIiIiI\nKAwwyeugIQPlSE6Q4dv9J3H+YovU4RARERERUTfHJK+DBEHA0MFyXLLZ8M+NVVKHQ0RERERE3RyT\nvCDIGjMAAFBtaJQ4EiIiIiIi6u6Y5AXBuOHxGJmsRKPlAppbLkkdDhERERERdWNM8oJkUL/euGSz\n4UTDWalDISIiIiKiboxJXpAMvvy8vOOnz0gcCRERERERdWdM8oJkUD97kneMz8sjIiIiIiIJMckL\nkoF97Ukeu2sSEREREZGUYqQOIFIkqGIhAKhvZJJHREREFK5aWlpQXV0tdRidoqXF/gzn6OhoiSPp\nHN2tvCNGjPBYVrbkBUmPmGio5L3wA1vyiIiIiMLWkSOHcPjwYanD6BRlZWWoqamROoxO053Ke/jw\nYRw8eNDjfLbkBdGgfr1RebQB5jMXoOjdU+pwiIiIiMiNYcOGITU1VeowQu7w4cPdpqxA9yuvN2zJ\nC6LRKSoAwMFak8SREBERERFRd8UkL4iS+ssA8DEKREREREQkHSZ5QTTI8ay8U0zyiIiIiIhIGkzy\ngmhg3zhERwmo+cEqdShERERERNRNMckLopjoKIxIUuLoDxacv9gidThERERERNQNMckLssT+fQDw\noehERERERCQNJnlBNqhvHADgBw6+QkRERNRtGAwGzJ8/H/n5+SguLoZWq8XSpUuh0+n8mt8V+SqT\nTqfDtGnTJI4yeLyVJ9zKyufkBdmAfpcHX2GSR0RERNRtqNVq3HLLLSgrK0Nubi4AIC8vD1lZWdi8\nebPP+TKZTMrw28VXmbKzs6FQKCSOMni8lSfcysqWvCAbdDnJ+6GBSR4RERFRd6dUKmEwGNo9vytq\nXSabzSZxNN0TW/KCrL8yFgKA+sZzUodCRERERBKqqKiAQqFAWlpau+Z3Re7K5Oi+qdfrkZubC7Va\nLVV4HWaz2TyWx9u8zsYkL8hioqMg690D5qYLUodCRERERJ3MZDKhsrISjY2N2LhxI1566aWA5ndF\n3sokCAKys7MB2Ls05ufnY+3atVKF2mHeyhNOZWWSFwKKPj1x2nxe6jCIiIiIqJMplUqxFctxoj9v\n3jzxnjVf87sib2Vq212ztrZWihCDpm15jEajx3lSlpX35IWAsk9PnD3fjIvNfFYeERERUXc2btw4\n7N27t93zu6LWZRIEwWlecnKyFCEFTdvytO6OGU5lZUteCCj69AQAmJsuIl4ZLXE0RERERBRqBoMB\n69evh9VqRXFxMWw2GwwGA8xmM5YsWeJzflfkT5kmTpwInU4HpVKJiooKLFu2TOKoO8ZbecKprEzy\nQkDR257kmZouIF4ZK3E0RERERBRqarXa60m9r/ldkT9leuqpp8S/NRpNqEMKOW/lCaeysrtmCChl\njpY8Dr5CRERERESdKyxa8rRaLRQKBQwGA2bNmuUyX6/Xw2AwwGQyuZ0fbhwteeYzTPKIiIiIiKhz\nSd6Sp9frnYYbraysdFnmzTffRF5eHiwWi9v54UYl6wUAaLBwhE0iIiIiIupckid569evh1wuB2Dv\n11tWVuY0X6vV4qqrrgIAzJkzp0s8LDJBZb8Pr77xrMSREBERERFRdyN5kmc2m6FSqcTXjY2NTvP3\n7t2LxsZG6PV6FBYWdnZ47RKvjEV0lIATDUzyiIiIiIioc4XFPXm+qFQqaDQalJWVQavVIi8vz+vy\nCQnyTorMswH9euOk6Vy7YwmHMgRDJJSDZQgfkVKO7iQSPjOWIXxEQjkioQxA1y5HQsK1qK6uljoM\nopCSPMlTKpVi613bVj3AnuA5HjKoUChQXl7uM8mrr7eEJtgAxCt6ofxkE2qMDYjrFdjbnJAgD4sy\ndFQklINlCB+RUI6ufFLUXpHwmbEM4SESyhEJZQA6txwtLS04cuRQULd55MghWCwNOHz4cFC3G462\nb9+OmpqablFWoHuV12g0YuLEiR7nS57kzZgxAxUVFQDsD1TMyckBAFgsFsjlcuTl5aG4uBiAPQlM\nT0+XLNZADFT1RjlO4/jpMxg2WCF1OERERERdzpEjh2Ay1WPYsGFu558+b0LdmR8wVjUKgiD4tc2K\nChNSUlI8bjOSGI1GJCcnd4uyAt2vvN5InuRpNBpUVFSIT4d3DKxy//33Y82aNVCr1VAoFNBqtTCZ\nTMjNzZU4Yv8MS5QDu4H9hkYmeURERETtNGzYMKSmprpMP26tx+8/+7P4+p27CiDr2QcAcMl2CQAQ\nJTgPP3Gh5SK2VJd53GakOXz4cLcpK9D9yuuN5EkeAMycOdNl2po1a1zm++qmGU6GJyoBADUnrBJH\nQkREFHotl1oQJUT53ZpC3deFlgvYVPMVRqiGIrXvSL/XO9d8HvVNp6BWJgIAXte94zT/wY8W4A83\nP4OhqmT89D+PYeyAVCye/KTTMv/YXYQvmrbhJnju5kYUCSQfXTNSDVDFAQDKyo+j6dxFiaMhIiIK\nvku2Szhz8QxqzEY8XvI7PPrlMzBfsKC0dhs+qPwPfmg6AcCeANpsNgDArh++w7cn9jptR39qH57a\nshBr9n+KU2cbOr0c1LlKjKX47+FiLNvzFqwXmvxeb+GmAjy18UV8f7wSP1jrsf/0EZdlnv3iFRSU\nvgkAqDhRjVmrHsGuur042XQa73+3Fl8c+iZYxSAKa2HRkheJoqKuXMk8fMyMccPiJYwmMrVcasGS\nbQU4efYUZqbegb69lNhs+BrTh05FWr9UnG+5gCgI6BHdA0ZLHY6fOYHMgePF9c81n0N1w0GMjR+D\n6KhoCUtCRNQ1vV+5GtuO73Ka9rtvXhT/Lju2HX/IWYiXty2FIAiYd9X9eKfiA3G+Jn40pg+Zir99\ntwIAsNnwNTYbvsb/au5B1qBrO6cQ1On2N14ZSOWg6TCuThjnc52GsyYcbTQCAF7a8hdxemr8cPxk\n7C34w1d/FaftPlbutO4rX//d6fXEuKvbFTdRV8IkL4Tuvmk41mw5BMuZyGjJO3PxLMwXzIiPi0eP\nqMAPHZvNhs2GrzFMOQSJfQYiNia2XXGcaz6PQ6YjiI3phZNnTwEAVld/Is7/67eFeCH7t/jbd4W4\n2NKMX179IP7fjtcAAB8fWI/fX/dr9Iruhdf2vAmDpRYAkDXoWtwzOh+9onu2KybqGk6cOQkAGNC7\nv8SREHXMhsNfYO+pSoxQDsWdI24J+ELVqbOnsXr/JxipGo5kWSLG9BvVrjjaJnjuvLStAGea7c+N\nLdj1N6d5+lP7sL/BdeTEEmMpk7wIZblgReWpK48v0B3b4TPJ232qAmv3/NntvEWTn0DP6B54+ebf\n4Lkv/ug0r1dML5xvPu+yTnLMgHZETtS1MMkLoaT+MgCAsT4y7st7v2o1vqu3Xx17dPxcpMiT0adH\nb7/X/6ZuG9Ye+K/4+qWJz6JvrMrLGu6tP/w5Nhm+8rrM87pXYIO9a9DL2/8kTm8434invlqEFHmS\nmOABwPbju3Gs6Qf8dsL8gOOhrqHGYsQrO/6CGCEaz133awzondDubZ04U4/YmFgoevr/SITyk5WY\nnJDV7n0SOTRfasZ/D9tHnT5qNmD78d0YPyAd947O92v9MxfPYJHu/wAAe09WAgCW3rgk4Atv55rP\n+be/ywlea/GxfXHqnL1b5sVL9guhfXup8Oj4OXhx21IcNRugq9uB7MQJAcVE4e1Y0w94adtSAMA1\nCenYU78Xe09W4mLLRfSI7oFPDm5A8dEvsST7d4gSBCh7KaBr3A2d6crFhCcnzsXZi+fxxo73AAA9\no3sAAEbFD8N9V9+N976zj+kQG9ML/7r7NfzPmidwrlWi9z/jf4IeR5o7q8hEkmGSF0JpQ/oiOkrA\nvppGqUMJCkeCB9hby3pExeCPP3oePf1o/ao/cwor9611mlZ+qhI/SsoOOI7W3Twc4mLicLbViYQj\nwfOkplWC52Cw1OJs81nExcQFHBOFv29P2I/fZlsLVu37GI9d85Bf69lsNvxw5gQG9h4AQRBw1GzA\nH3e+DgD45dUPYmz8GHHZ5kvNaL7UgtiYXk7b2HZsF/5VuQqT05jkUcedPHva6bX1YhO+qd2Ke1Lv\n8mvQk9X717lMq7EY3Q6AsfHIJnx6SIunMn6F4cohaLnUgk8ObUBCXDwOm2oAADclT8T0oVPRI6oH\nYoRofH+yAmn9RuOIuUbshgkAj1z1AJZ//y5yEq/DvaPzsfX4LsTH9sWyPfb7p0b3G4lBfQZiXPwY\nlJ+qwqrqj9E3VoURyqHocflEHgBOn2tAdcNBZA261mXkRApvnx8tEf8e3W8Uyk9V4uKlZnxeUwJ5\nTzmKj34JAHh5+1Kcb7ngdhtj+o9E3zglRvcfjpf/8Adg9pV5t425GT801aP4wFfo3cP+W67QXUL8\n1EEYLBuAp2+YB0EQoD2iDVkZg0Gv16O8vByzZs1yO1+r1UKhUMBsNkOhUCA7O/BzqVBqb/yO6aWl\npUhPTxcHXCwoKMDs2bOhUqmg0+m6xEj77X0PgllWJnkh1KtnNAb2641jp85IHUqHXXBT2V681Iz6\ns6eQJBvsdp0Pv/8YMc290C9WJZ4MRAvRaLG1AADqrMfdrldiLMV/D2nx2PiHMEShtu+r5SIuXmpG\n7x5xOHGmHgAwTJGCocoU3DpsGnpF90KUEAXzBYvT/SD3a+7FP/T/BgD8dfIrKCx/HzbYxIRV0VOO\n/3fDQrxT/gF2nfgOf979Bp7NehIUWRrONeKbuq3i66qG/TjXfM6vlovC8vfxbb19kIiCG5fgg6r/\niPP+/t07+H83LBRb9JZ/9y6qGvbjmczHkaJIFpfb6keXNiJ/nT7nfmCSE2fqMbCPaze0MxfO4rnS\nl3FjUjZiomLwfb392bQzU+8Qu7pXNxxySfJaLrXg00P2k+Glu/6GO0fcgqNmA/bUOw+ackPi9U6t\n2hmX733WxI/GkuzfwgYbBAiIj+uHl3Oeg6xHHwiCgOzBmU7bGaZIAQD8r+YePFv6Ei5euojXv30b\n1w3KwM/G/ERc7rNDn2Pr8Z14r7IIcTGxeD77Gch69PH5vpnOm3Gm+SwG9xnoc1kKjiPmGry99z38\neFguekb3wBFzjTjvmgHpiIuJxbsVH+Kzw587recuwbs2MR0Lch5GzOWuyTUVh7Hry22wWq2QyWTi\ncjcOuQ6HT9fg/mtnQafTYcfX27Dp+U1Oy4QznU6HlStX4qqrrnI732AwoLS0FEuWLAEAPPjgg2GV\n5LU3fr1eLyY72dnZmDZtGnJyciCTyaDX6zFnzhxkZ2fjhRde6MzitEtHPsNglpVJXogp+/RE3ckm\nNLdcQkx0173i2PqkIj62H06ds19JPmQ66jbJ2/XDt/i40vlKWZQQhVd+tNjeXa7sZXxVq8MI5VBk\nDrrGablNNV/hbPM5/HHn67hj+AxMGzIJS3f/HfVnTuL6wZk413IeqX1HYv41v3DZr6KnHMsm/QFn\nm8+h8bwJankSUuRJ6BndE4Ig4KH0+wAAv9r8GwBAjyj71WFN/GjsOvEdaq3H0HKphQOxRJDT5xqw\nsOz/AQAS4uJRf/k+zrf3vofHrnkIh01HUbDrb3h8/C8wut+Vk9z6M6fwVW2ZmOABwIKvFrls/3ff\nvIgnrpkHec8+qGrYDwB4ZedfMHfcfbhmQDrKT1aiuuEABsTxPkAKDkc3xynqHyF3yGR8V1+Of+9b\niz/tXo4Xsp9xuXjxyjd/R+N5E9Yd2ihOu6r/WExKzkHmgPFYWPYHbDjyBdTyJFydMFZc5ljTD07b\n+fjgepdYRqmGI1E2yGOs8XH9nF6reildlnntppehP70PV/W377t3j964d/Td+FflKgD2+/62Hd+F\nW1On4pbkPOw9qRfXPdt8DntOfO+2V0jLpRa8U/EBTOfN+NX4OVj+/bswWGqx+PrfuNyXu8VYhuZL\nzZiacqPHslDgVu77CI3nTXi/arU4bWDvBDyb9SRiomKQMeBqvFvxoc/tPD12Lq7RjBcTPAAwm82Y\nPn06Vq5ciblz54rTU/sPx8vTngEAHDJXu10mnGVnZ8NgMMBisbid73iutINCoUBlZaX4nGmptTd+\ng8GA8vJyMdmRy+UwGAxIS0vDPffc0yVa7xwCfQ/kcrn4GQazrF036+gi+sTa8+gz57p2/2/HScVt\nw6djycTfYvH1T0OAgJX71mLbMedWCpvNhncr/u2yjQkDr0FcTCx6RPfAnSNuAQC8q/83dp/4HoC9\nZbDlUot4fwYAfHJoAx798hkYLLU413IeJcZSAMCPkq73GGtMVAzkPWVQy5MAAAP7DHC59+8POb/H\n+IR0PDp+DgDg+sGZuP7yVeXHS36HL2q2OC1vuWDFJwc3wHTe7OOdonDTelAHtTwJPxtjf+5mVcN+\nvL33X+JAEH/59i08+82LeHFrAX61+Td4fusr2Gz4GgAQG+180vxQ+v84vX5tzxvYYixzmlZY/h5+\ntfk3WP79uwCAawe4v6JHFCjHRber+o+FvKcM2YMnoG8vFawXm/BtvfOogpYLVlTWH3Ca1i+2L+4Y\nMR0AIOvZB3eNvBUA8Nbef2L78d0AgN0nvnc5pt3xZ1REX3pE98DVCeOcuppeNzjDZbnPqjfhxJl6\nNDU7947Z13DQZVnTeTOWf/8uvq0vx2FzDRZ8tVi8D/uFrVcG5zh59jSsF5tQVP0x1h74r1MCSe13\nyXYJe07sRVSb00wBAh4Y+zPEXB68TRAEzBg6FQAwqPcAvD75/zBxsHO39vsTZ0LZU+6U4FksFigU\nCsyePRurVq1yG4M/y3RFZrMZKtWVcxqFQgGDwSBhRIHxFH9eXh6eeuopcZna2loxcTWZTNDr9dBq\ntdBqw7urrT/avgdKpVL8DINZVrbkhVifOHtLkfXsRSj6dN2RGx3PLYqP7QsAGNA7Afkjb8WaA/+F\n/vQ+px9k0wWzyz1xv5vwhNPV3uzBE1BiLEWt9RhWlL+PcTe9jCe3POdXLL1j4jp8wqzspRBb9Rwy\nB+npZgIAACAASURBVI7H1mM7AQAfHfgM4xPSgaYLAHqitG4bio9+if0NBzE3/T63V6Pd2fnDt9Ae\n2YyH0u9zGejjeNMJGK11To91oOBoONcIeU8Zqk7vF1sDAODuUbdB1UuJsrptOGyucTkhNl2wwHTB\n9cpbdmIm4mP74T+X72UapkjByznP4bnSl8VlvqrVeY1phGpYR4pEJHIkefFx9vo4Oioa9465G3//\nboXYUu1wyHTE6fWPh+VixrCbnabdmDwRq6o/BgD8U78SFaeqsPOHb8X5j4//BT6vKcFhUw3uGDED\nGQOvxr6GA0iSDQ5pC/UT18xDibHUqTX9ha2vArD/hvxszE/w8vY/Yc+J7/H6nrehiR+NqSk34kLL\nRTxb+pLXbTu66ANAxoArw+m/8f0/sPTGF13uqw215pZLiI4SwvZB8o3nTTh59jRG+lmPFR8twaet\nWo4B4DeZjyFZlujSU+bHw/NwY/JEyHr0QZQQBUXPK90q/zbljzh4cL/L9jds2OB0r5O7lix/lqHw\nVFBQgLVrr4zjMHOm/eKsRqNBfn6+2I0zEgWzrGzJC7F+cvsPxYkG19HFupLjZ+zddvpdTvIA4Kbk\nHEQJUTjVZhAAx712t6ROweLrn8bfpvwRyfJEpxvkBUHAdYOuJIZtE7zr29yr0T8uHn1i7CN55g2d\nEoQSuUrrl+r0erHu/zB//fMAAKP1GADgsLkGz5W+7HQC1NY3tVvxq82/wcYjm/CefhXqmo7jha2v\nig8CdnhxWwHerfgQX9du9bAlag+DpRa/L/sD5pc8K7aiDeydgL9OfkVMzlMu3+sJ2C8axLc6rttK\nliXixqSJmKy+Ac9mPYn/1dwDZS8FVL2UKLhxicvyL+c8hxvbdB2bMfRmaOJHB6N41M1daLko1j/K\nngpx+qDe9nvxTrZJ8qovt3LNGfdzLL1xiUuC5zBF/SPx77b123DVUDw6fi6W3rQENyZno0+P3rh2\nwFUY2DshpEnJqL7Dcb/mHswYejMS4q48a7Z3TBzuHHkLBEHA1JSbANhb5tce+C+Omg3QHtnkcZvp\n/TUAICZ4bf8GgANtBve6ZLuEGrPRpQ4P1Nnz7nv0nDSdxaN//gobttnvVzt2qglrvzqEr7+rE/d5\nsflSwPs7f7Gl/cG2sbp6Hf68ezl2HN/je78tF1wSvEevnoshCrXHWyEUPeXiOcIw5RAAwPWDMt0u\nCwA1NTUoLi6GVqvF2LFjsXLlynYt0xUpFAqn1yaTCWq12sPS4cdX/FqtFvfeey+SkpLE1ytWXBnA\nSaVSdamWS3c8vQfBLitb8kIseYA9+6471YTxo7rmPTn6U/vEbjuOK8eA/epxfGxfcaQ3m82GA42H\nxdHUMhOvwoAoz8PU5yRmQXt0M5ouug5MkzdkMn425ic40HgIybIkcZSsUJuWMgmf15SIr5svNaOw\n/H3sudyl1GHrsZ0uLXBNF89g94nvsHLfRwAgDljg8OiX9nsE+sT0xn2aK1cXV+5bi+sHZTiNHkf2\nk1mjtQ7DFCl+n0jWWY9jz4m9LtN/fe0vnbYxRX0DKk5VYXCfAZh31QMord2GD/etEef/bMxMJMsH\nw2aziYP/AECSbLDTPahxMbH4TeZj+KDqP6i9fCFA2VOB/JE/Flv2fjvhCajliYEVnsiD1s8EbX3C\n3DdW6XTR7cSZkyiq/hiVp6sh69kHaf1SvQ40dNvwPFSerna5D2/eVfe367mowdIjugd+PDwXtw6b\nhgPn9sN2LhqpfUeI868flAHtkU1iC6Zj5FuHeVfdLyZ2gD1he+zL37rd1w2J1+Gbum1Y/v27eOWG\nxTBdMCNJNhjrD3+BDUe+wLj4MZiZeif6t7nP0BebzYaX/rULh4+Z8cz/ZGKAvBcO1JowdJAcCao4\n7K4+iQvNl/CfkoPo1SMaH3x+5Rly726oAgBECQLm/DgN+2oaMHHcYKSqvT9+SH/kNP5c9B1mTRmJ\naZnuE4A9J/YiLibWr2ck6k/vA2C/L3O8jy66m2u+dpmWFp/qZkn3xvVPw1MZv4Ranux2vl6vx623\n3iq2yk2cOBFTp051GqTCn2W6qhkzZqCgoEB8bbVau1QLpbf4dTodNBoN1Go1LBYLGhsbkZKSgpSU\nFHF5k8nUpcrrjrf3IJhlDWnNbbFYcPfdd6O4uDiUuwlr/eT2H9VGi+vDOMPV2eazON50As2XWjCw\nTwJK67aJ81pfOQbsLWyVp6tx5uJZ7DnxvdOJ8riBo1Ff7/6mUwCIjYnFQ+Puw2uXh88WIOCx8Q+h\nT4/eYtdGd0N6h9Jtw/Nw/eAMvHj5OT4AnBK8+df8Asv2vIX9jYdw1GyA5YIV4/qnoeVSC373zYvi\nyKHeNDWfwRvf/8Npmu7YTtyYHD6jY4WDLcZSfHxwPfKGTMHtl+8f8ua7+gq8tfefTtNuTJqI2aPv\ndFm2f1w8Xsh+Rnw9qNVoe6/+6IWALioMUahx+/DpWP79uxihHAZBENAjugeWZP8W9WdPMcGjDjFY\n6tB4vhH1Z08hc+B47PjB3pLS9iJTlBCF3jFxOGyuwYWWi1i25000njcBACamZCDOx0iyPaN7YvrQ\nqeIgGPkjfxxWg5AIgoCJKRkuvymCIOA3mY+h+GiJ0wW6MX1HuX1MSpQQhRR5MmosRqhliTBY68R5\n04ZMxjeXf++e+caeDPx4WB6+NHwDACg/VYVy3f/hiWsexqj/z955BkZR7X34mW3ZbDbZ9N4bpADS\nCSBgo6ooKNjue1VsWK4Nr167XstVsXexXfWqoNgFA1Za6DUklPRNI303m77l/TDJbpYkECANMs8X\nds6cnTlnsszM//zLr52heTQtZgt/7Cpm8rBAahta+DT1ILklYj73c59sd+rr56mmvMahN9jewGuP\n1WZj2Y9ivuC6PSX29pdvn4ROK0YMmRpacFMrEASBHzbkYrHa2HagrFMjz9BUy/vpn9q3F8ZfSoxn\nJIEafw5UZ5Fdk4tMEJgddQECAkqZgmZLMzVNBtYXpXGJ7wXYbDanxTNTcx1fHvrW/sy8KFq8b8d7\nRXd5rboiWhfZaXtGRgaPPPIIS5Yssbfp9XoEQeCxxx7jxhtvxGg0HrfPQCYtLY2NGzdiMplITEy0\nFyKZN28en3zyCe7u7sycOZO0NHEhcaAVlDnZ8WdkZPDoo4/i4eGBzWajqKiILVvE/4+pqakUFBRQ\nWFjo9HcdqJzsNUhISOjRuQq2U40/GIAcy7Doawx1zdz9+gZ0WhUv3TapWx4JPz/3fptDo7mRezup\nIAiixlGyr/OKwjNbX7Z7MNpz9dDLmDvivOPOw2azsWTdozRamrgkZjYXREw76bH3JPlGPY3mJlL1\nv3KwMocRvkn8LXEhrgo1a/L/4Pvs1fa+d468GavNyuu7l9nbVHIVc6IuYLT/CHQuHry150Myqzo+\nvN0UGurM9cgEGS9PfQqrzWYXdu0pTuT3ZDGZkGu12Mxm6vanoxmagMyle7kpNquVmj9/Rx0WgWvc\n8VeGj8d7e//Lngqx1PttIxYxdeiYY87jkU3POlWBHRswkoVDLumW7mF9Sz33rX+cYLdAHhp/zwmP\n1WazsadiPwne8bgcQzfSz6/74ulnCgPpfnwy9Of9uMhUwjNbX+7QPsIvmeuTrrIXr2ijrWpwexK8\n43no3NsxVB1fuNxqs/LP9U/QYG7giZT78W0XIjkQON7fom2hx8vFk/vG3IHOpfP/b/srD/LFgZXM\niZ7OrrK97K8UvWVvnvs8Xx/6gT8KN9j7apVumFrqnL4/KXgcV7WTdGjDZrOhLzOx7McMiirqOuw/\nFueMDGHT/lKamsWFwjkpEfycln/c78WH6njgmtHkl9by9Kc7MFusJER4kZnfmrfp4cJziycia/fu\n0WBuZOmONyk9ymvbGbGeUfi7+rGpZCtxntHk1xbaJZUuj5/LtNBJ9rm/uONNcttJJPTUbyg7+zDe\n3lri47vvDTweqampREVF9egxByqDaa4wuOZ76JD4XtnVXPssBiMtLW1A6Xj0FR4aJR4aJQZTMzkl\nRmKCu1ewoy/Jqsnl59y1zIudw+ftNMDac+fImzr1qk0OnsDyQ9/atwUEXjvn2W4L1AqCwNIpT2Jo\nNnbwEvYnbSF6k+LPovhItZPhNSUkhS0lOyitLwPEcMsAjUObalbkeVwYPcPpeDG6SLuRp5IpaW6t\nILogfi4fZXyB1WbloY1PY2qpY/Hw64j1jGbFoe84J2yyvUpob1KVupqKr8QCJQovLywmE7aWFjwm\nT8FcXYV29Bjckocj12qRqTo3Ygzr/6L8889QR0cT/mDnCwUHq7KobqqhvqWeySETULUziKw2K6V1\nZfYCPQ0Wh/f7x5xUxsckdylvkV2T10E77O+JV3Q7zFOj1PDYhPtwV51ccrMgCMcNYZKQ6A7fZ69m\na+lObhuxqFMDD+CaoZd3MPAArhgyjy8POooVnBM2mcviLm69fx3fyJMJMpZOOX3D2Ub4JfHmuc8f\nt1+SzxCemvQgAClBYyg2ldq18yYEjXEy8tobePPjLuLbrJ9Jr8js9F70y9YCvvqjY6XPznjn3qms\n2pzPDxvzxGNPjSEy0J2PVh/gvNGhzJ8aw7wp0Xy29hB/7Czq8ji5pbVU1DTwx64izBYxb6/NwAOo\nNDbx6zY908c5QsDW5P/RLQMPxPeDrJpcAJJ8huKu0torYn916HuUMgWTgsdT1lDhZODdOfKmAbdI\nICEx2OhxI+/9999n+fLlhIeHU11djSAIHdyugwlBEJgxPpyv/sgmt3hgGXk2m40XdrxBvlFM6nx+\n++tYbZ0nd3cVNnl2yAQyqw6xt9XjciIGXhuCIHS7WmVfIwhCB8+aWqHm5uHX2stwH6kv50irQPvS\nKU92GhY1PeIc6s0NBGr82V910C7GHu0ZSYR7GPm1evvLxNt7P+LCqBl2bah7R9/aZejKsbBZj52o\n35CTg0KnQ6bRUPH1Cnu7udrxgmDcsA6A+v2OKpThDz+OOjISm81Gza9rQBCwNTdjWC/2bczJwWax\nIMidX4BsNhtv7/2QFqtYfGBl1k8sGX0b3mpv/izcwLbSXVQ31Th9J0DjT31LPQW1hfzfN3cT4RHG\n7SMWoVGKRXhqmgy8s+cje8hVjC6SbEMecZ7RJ1wQ4ujqpxISfUmbN7mNp7e+1Gm/S2PndBlO3HY/\n3lOezsXRM3utSNWZRvvKz11p/rUtdGZV57CnYj/fZa9iXuyFTveZYxljL90+iVWb8/l1eyGxoTpU\nSjnTx4ajUSsZHe+HRq3g7BHBnD3CEd4tCALzp8TQ3GJh+thwLFYrkYEe1De2sCe7ki9+PYypoYV/\nvtN5Zd9h0T7sy6nky9+znIy8tuePWu5Co6X7qSQRHmGcHZJClC6ClYd/BODzAytJCRrLs60LEpfG\nzmGk37AO+ogSEhJ9T48beUlJSaxdu9a+3ebBa4s7HYy0GXaGuuZ+HokoDvtN1k+o5S40WBrtBh7g\nZOA9kXI/9eYG/F39gK4jegVB4NqkK9lfeYDhvoknbOCdrvhrfHlp6lPsLNvLZ5migTQ1dGKXeS9y\nmZz5cRcBMDF4HCuzfsRX7YO32ourEy7rsGL/U66jaMuLO97iuqSrjiu1UH/oIC5BwcjdxRCl7Ltu\nx1pfj8szT4J/uFNfS309+mfEypBuw4bDCURtFzz1OLFvvUdzaQnlyzvqIQI0HDqIJsFR7KDB3Mhz\n2161G3httGnUdUW0LgIftbf9euQb9byw/Q0eSxHD0t7c/QHFdaX2/lcNnU+Axn/AliGXkGiPsbmW\n5Qe/JdF7CD/ndp67ftWQ+QzxjkUtV6NVuR33mNcmXkltc630kn2SyAQZNyb/jf2VBzgvfIo9P7vN\nKxXhEcaeiv38rl9PoMaflOBxVBka8fV0RSZz3HeSo72ZmBzImq16rpudgKfWhQXnxDIqIZDYQDFi\nQKNWMH3ssasiatQKFs1JPKpNSUpSIPWN5g45fEtvnchrK/firlFxxXmx7MsRC9K05c/tKtvHkfpy\nYnRR3DN6MQW1hfi5+rKvIoOz/IZRWn8ElUxlb99QtJmqphqqGqqI9AhDJVdxbtjZuLmp+GS3mIP/\n0f7P7ff2eM8Y6bcnITFA6HEjryt198EYqtmGrlUfz2DqfyMvvTLTLijenpSgsaSVbAPgyZQHTugm\n7SJXDUqhZxe5igmBozlQdQg/V18ujJ7ere8JgsBlcRfbt0O0QajlahotXYdTrSvcdEwjr3brFkre\nexsA93ET0CQmYq0Xq5amP/w4kU88Rf2BTDwmnY1MpcK0Y5v9u3X7xNCboMW3oUlIwrDuT9GzJ5eD\nxYImKRmbxULDgUz7d2p+XUPFNx1De13jh9Bw6CCFLz5P/Psf29t3le3roN/VHQLd/JkWOgl3lRtf\ntIahlTVU0GJp4VBNtpOBd3ncXKcCKhISA53Vub+xuzzdSa9xbMAogt0C+D5HzPudFDL+hI6pkiul\nl+xT5Cz/YZzlPwwQq+PmGfPt8kGTQsbzQ6s8wOcHV7JtG+w9YCJ2RA1ltRpAzGG+7dJhuCjlTEh0\neAYVchlnnxXSYzmeEQGOnMMgHw1/nzkUbw81j183zm7UnRXry+6sCjbuK2Xy8CB7sZURfkkAhLdW\nsBwXOMppO9BNTEGI0jkvELZx4ZDz7UZeW/jm1UMvJ9yj84qYEhISfU+PG3l79+5Fr9cTFhaGXq+n\npqZmUBt4AJ7uLggClFZ3lAroa8rqK5y2w7TBPDDuLgASvOOoba6TXhBOAEEQuC7pqlM+zoL4uazK\nXcuEoDH81LqiPz/2Qob7JfPC9tepPCrfrA2b1Ypx43pq/vrT3la7dTO1W9tp71mt5D0i5p+U/e9T\nvC6YgWHTBo7GNX4Ico0G75mz8UiZhEzjikzpyJlrqaqk8rtvMG7a6GTguY+bYD9fwN+vI+8hsTy5\npaEBuasYVpbfmqsR5xnN4aM0qNpzdkgKIdpAgtwC2VW2lykhKShkCiaHTGB8zHDuatUtvKudruLc\nmFkkeg9xkjaQkDgdMDQbnbYvjZ3D+a26b34aX7zVxy6TL9H7hLkHO1XI1SrdWJR8DR+kfwbAQeUv\nuCTKKXIx4pLoStPeqTx27VhclJ3rwfUkkUHuJEV6UdvQwoPXjEbV7pxt0Qxnjwhid1YFH67KxIJj\nofloLdqT4e5Ri3l559v27YnBY0/5mBISEj1Hjxt5S5YsITU1lQ0bNjBs2DAWLVrU06c47XBRygn1\n05JfWovZYkUh77+Qxn0VGU7b94y+zf559HHCASV6j/FBoxkfJIrD7yrfR5GpBK1Ki6+rN+HuoWRU\nHaSsvgJ/jbPWonHTBo7896MujysoldhaWpzaqteKoY+ahCR8519O9dpf8J2/AIW7o/CNQtcxR1Lp\n7YP3rDkYNzl7ggNvvBmfuZdiMRpQBQSijo2jMeswzcVFuMbEoq8tZkPxFgQEbj/rBtJKthGji6LI\nVEK0LpKSulLkgryDjlKsZ5TTdrB7gL0aaXsmBo9Dqzx+GJuExEDCZrPZc6MA/Fx9OC/MIVkwstWT\nJDHwGOU/nI1uwzlQtxeZq6Mwi0zdwJypfkQE9k0VXYVcxr1XjDxmn+ExbcVPbCwvfxNBgPPCpuDW\nmtd8KsR6RnGWXzK7y9MZ0sdyRxISEsenV6wNg8HArFmzmDlzJiaTqTdOcdoRG6KjxWwlu8jQb2P4\nJusnsg15ADwy/l6enfxIj5frlzh17hx5M5fHz2W0/wgAEn2GAPDE5uc7CBU35jhXcvO52KEJF7T4\nNrxnX2jfjn3zXVwiIu3bgTfejDoykqAbb0Hp3T3vrTLAEXrkNWMmkU8/JxanCQjANU400rxnzwFA\n/+xT1KXv5Z29ohEa7h6KQqbg7JAUgrWBjA0ciY+rF8m+Cd0Wyn1g3J1O269Ne1Yy8CROS/618d8A\nRLiHcf/Yf/CvcXdLuaSnCYXlJnb9EYy5smP0wC7rj/0woq6Ry2QsviQZmXs1bT8vN8Grx46/KPka\nrhoyn+uTr+6xY0pISPQMvVJdMyxMTCR2d3cftNIJR5MU5c0fu4rIKjIwJLznbrAnwm8FYvXDs0NS\npNylAYybUmPXHgKcJBSe2vIiL09+ksZdu3AbMRKz0RHupUlKxmvWHCz19bglD8MteRiWxAbkdUZc\nRo9H5uJC6JL7KXrpBTQJiSg8TlyyQpDJCHvgIeoz9uM1aw4yZcdFArdhI+yfi155Ce0Nw6nBwKIe\neAnwVnvZQ4SmR5zTqZyChMRAp9nSQm2zuAB6UcwMex6UxMCnqdnCox9sBaAlezi+gU3UtFTZ91c3\n1fB99mrmxszq1vGsNmuvFyxr0GbhkiCO2WLwoTzHB3rI8SYTZAz3GombovcWjC0WC+vXryc3N7fH\njrl161YKCgp69JgDlcE0Vxhc8y0sLGTixIld7u+V6popKSlkZmYev/MgIsBbDI0or2no83M3W1pY\n0k7gfGH8JcfoLTHQiNFFolN52PN30p/4J9ojzrk8AdctwmPiZARBwP8KR46g3NWVuDtutSf6y11d\nCX+ocw277uIaG4drbNdi54Ig2AuwAJjz9WiDPXss1zPWM6pbWlgSEgORioZKHkt7DoBQbTAJ3me+\nYO+ZxB+7HDIJF0+KYs6kqeQb9cR6RvHopmepbKxmTf4flNdXcF74FJbueJPzwqcwL/ZCsmvyeH7n\nD1wSNYd4rxjyjXpe27WMi2JmOC3snQi/5P2GqbmOeXEXdmosmq1mlh/6zr7dfGg0v9uKuWb60JM6\n39Es//0wqVv1zJ8azZyUyB45ZkcEQkNDiYqKOn7XblJYWNjjxxyoDKa5wuCb77HocSNv48aNFBYW\nAqDX69Hr9ZInD/DxECtuVdV2X5PmVFl5+Ed+1693ars8bq4UEnSaIQgCT6Tcz11/Psj0NCPaIx1/\nQ7pJZ/fDyLom7J//Yve3H6L5eR0L11azc15Ejx3bZrNRveYXXGNij2lsSkgMFMxWM0u3v2HXc2zj\nbwkL+mlEEt3lYEE1BwtquGhSJIIgkF0splz46tTMmhCBUia35w9b20nR7CrfR3rlAUCMoonzjOab\nwz9R1lDBq7veZX7cRXatuZWHf2RMwFloFK7YbLZuRygUm0r5MUfMsS6pO8L8uIs66PzlGPLtn6eG\nTuKXrTL7vE41quiv3UWkbhVlmFb+ldNrRp5cLiMqKor4+J5bEMnNze3xYw5UBtNcYfDN91j0eIzA\nkiVLMBgMbNiwAYPBIBVeaUWtUuCikmPsIxmFFqu5g4E3JuAspoWd3GqhRP+ilCv5V14MCXkdDbwd\nV43r07FYbVaaLcf+Hb+y8x0+1Dq8+WM3Fh+j94lhXL+Oiq+Wo//P0z12TAmJ3qTQVNypgRfarmqj\nxMDBbLFiamjBbLHy3Oe7+G5DLuWGRmw2G4f1NWhdlTx784QOFTSTWvOn22ixOopevbP3Y8oaHNWt\n2ww8EO+pP+ak8tKOt/nHn/9ix5Hd3RpnXjud2wPVh3l660sANFmaabG0OJ3nrpG3sCB+LklRYkRF\nm3FWVFGH1WbDWH/i7yb//eWg0/bdr2/Aau2+5qqEhETv0uOevEWLFvHqq69yww039PShT3s83VTU\n9JEgemldmdN2gne8tGp8GtOQnUV9mihTUO6pYGuyhjkbjLTIYQN5eOSsYVbU+X0iRv/Wng85XJ3N\nvWNu6zKX6HBNDsgFvj7Pk8t+q8FypAxLfT1yzalVdDPXVHPkE0c1UZvFgiCX8vIkBjZFphKn7fPC\npvRICXuJ3uGNb/ZxoKAaHw+1va2sqh5jXTPG+hZGxPggl3W8114WdzFDveP58uA3mFrqOuwHcHfR\nUtsk5mOGaYOZH3cRr+x6lw1FDtmbD/d/zjDfJFRyJWarmT/0G0jyGUpFQyWJPkMQEPgxJ5W1BX92\nOP7a/D/5rWAdXmpPrhhyKYWtiwtxXtEA3LNgBPe8sZGcEiPbD5Tx1nfpRAa6k1dayzXT49l+oIyR\n8X4o5DKGhnuilMtQuyioMTUR6qe1n6eiXerJhKQANu8/gqGumcyCanQaFaH+WiQkJPqXHjfyjjbu\n1qxZw/Tp3ROJPtPRuakoqzFgtdqQyXo3ZLK49aViQfwlTA2daBdGlRj4NBUVUZ26Gr8rr0bu6oq1\npQX9s0/Z96tvvZ6c/B84ED2KP+vFEuyr8n5FJVdxQcQ06lvqcZG79EpRElNzHZlVhwB4bttrTI84\np0OBgQNVhwEQELjpkkep+u0eACpWriDgb9ee0vn1zz3jtF36wTICb7gJoZMXLgmJgUJhrfiifd+Y\n24n06FxcWqJn2H6gjJgQHV7uLif1/frGFvZmVwJQUumQa3lpxR77Zz9P106/q5QrGek/DLkg4919\n/8VNoeG2sxZhsVl4ccdbAEwKG8Os0Ok0mBtxV4mG0NyYWXyfvdrpWH/qN5BWus2ubftd9qpOzykT\nZNw58mZWHPqOIlOJvV9ti4k1+X926C8IAlFBHuzOquCt78TnR16pmLP92Rrx3n6goKbTc7142yQ0\nagXfrc+xewIBbrooCYVMxoZ9Jbz4peiFfODqUcSHSTqPEhL9SY+/Gb3wwgvMmzePRYsWcf311/PI\nI4/09ClOWzRqJTYbZORVHb/zKXKoWiytH60Tc6EkA29gU7ttKzkPLMG4JY38xx7CuGkDph3bAKjf\n79DS0k2dxujoibw45d9cPOVakkMdGknfZa8irWQ7961/nFd3vYfNZsNsNbNy/ypKj5JeOFkKagud\nttfk/4Ghycim4q0sP/gtZquZ13cvA+C6pCvxdfUm6KbFANS1mweAtbEBW7scFpvVis1moyE7i8aC\nfI58+jHWxkbHNTp4iJbycgDcx40X27ZupnZLWo/MTUKitzhUk4NKriJUK4Vn9ibr9xTz1nfp3Pvm\nRg7pOzdUjqausYXHPtzK+r2iIf7Dxrzjfmf0EL9j7h/ul8QLZz/O81MeJ8IjjAj3MIb5JnKW4br5\nggAAIABJREFUXzJXDZ+LQqawG3gAo/1HoJQpCdMGc2msKEHzfc5qu4F3LEb5DyfWM4qLomd02Le7\nfB8A/xxzh1N7eMDJedky8qr44OdMJwNv7FB/AC4527nIxX/+t5MqYyOHC2swmPquFoGEhISDHvHk\nffXVVwCEhoZy3333ORVaycjI6Oprg442O0tfZiI52ufYnU+BfKOezaXbAQjRdtTx6Wka8/Oo/PF7\n/BZcicrfv9fPdyZSvuILzNXVlC57197WUiE+4Jv0BQC4jx2H/5XXANj1Db3Uziuln2WuACDbkMvP\nuWvQKDX2nIyXpz6NTBBIzfudGM8ohnrHYbPZKK0vw9/V1+75azA38OCGpxjmm0h5QyXTQidhaqnj\nm6yfOh37gxuf6rS9Td/Pfdx4qn9bS2N2FnXp+yhf/gUuYeHU7tiG57nno/T1xVxVSV16OuaqSqwN\njjAghZc33rMvpH5/OodefcneHnjDzdisNkzbt1J/6CAuEZGogoKlxQyJAceOI3sorTti14nsbVZt\nzuf3nYXcu/AsgnwGh4akvszEh6syyW/1SAF8vvYQj1/fdb6ysa4ZrUbJHa+IuesfrTrAxORA1mzT\nO/U7K9aX8poGiirE8MtH/j6GqKDjy89o2omNy2Vybhl+LQBqpZpaWpz6+rh6s3TKEyhkCmqbTXyb\n9XOnx1TKlE55fiN8k/h74hUAJPskcNOwv1NkKmZfRQYFtWIV0ACNX4ew+tkTItCXmdh12NmIDPPX\n4u/pyo5D5Z2ef292JdsPOFJBdFoV189JAMDbQ81NFyfy3g+Od74lb20CxCI1zy/uusy7hIRE79Aj\nT5zVq1fz4YcfdrovMTGxJ05xRjBrfAS7Dldgamw5fudTIDXvdwB8XX36JEer9KMPaC7UU7d7FxFP\nPo1LcMjxv9QOa1MT9Qcy0SQmdaq7dibSmJeLXOeJ0ssLs8GAubq6Q5+qn37AYjTSkJ0FgoDfwisR\nFM7/ZUcHjODXgr86PcfqvN+ctjcWb8HP1YdVeb8C8OC4u8msOmR/oXhq4oNYbVY+P7CSZmsLO8rE\n8KRPMpd3OPZTEx+kyFTC23s/cmpfV+TwqrkqHCFN6ugYGrOzKHrlRQCaS8RV85q1qZ2OvY2WsiNU\nr02l4ivHGDxSJiHIZATdcBNZu3diXL8O4/p1+F1xNV7nX3DM40lI9DV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NZmunfY+ujFhu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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e8157fe10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;sab=0.1\n", "saabb=sab\n", "r=1e-7\n", "prop=[0.98, 0.01, 0.01, 0.]\n", "reload(mutl)\n", "mutl.simulate(N,L,r,1200,sa,sb,sab,saabb,prop)\n", "plt.suptitle('Low Recombination (11 hap will appear after eq.) and sa=sb$<$sab, a0=b0. Sweep-Equilibrium-Sweep!',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.8, 0.1, 0.1, 0.0]\n" ] }, { "data": { "image/png": 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2DBUVJ3Ho0Kt+l/l6X38vvfQiWlud0uu6uhps2vQ1/OY3/42ysmN46aUXpfc5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\nV1dXDyi8BLMNRaaioiK88sor0uv169cDAPR6Pda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63FrG/NQ5tHYqvf09rh6va56wmPhL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HAAAg\nAElEQVQ/uN+foQoRMpwuFzuOHQPgqomrBoyCEEIIIcTgQuYvptvtpuuzzwEomLl8yA8LN6+aCkBH\nVw/2zp5Ri0/4ltPpYtPrB6mpbCM9KxZDXORZXyMtOpXUqGSOZ4QBUP3Mf+Hq7vZ1qEKEnO0H6lBF\nKOst5sTK2m5CCCHEcIVM8ta6+X3P48TMvCHPC9NqWLlIWfvtRL3V73EJ/6g83sLxw8rQ1/zpg1eW\nHI7EyAQ+nhmOLicHgLYtm3wSnxChymJ38Mf3DqHWtwIqMmLSAx2SEEIIMWaERPJmP3yIpldfBuD1\nC2OZlnD6ymaT0pU1wCpqLX6PTfhH6d5az+PJBSkjvk5iRAKoVOy5eCIAbR99iNvlOuf4hAhVv3vt\nAKhcaGIsZOsnEB0WFeiQhBBCiDEjJJK3qt88CcCuWXrseRnE6KJPe35uuh6Asmqz32MTvudyuamt\nagPgu/+xFF24dsTXSgiPA+CDLiMxixbT09qCo7bGJ3EKEWqsdgdHq8xoUipB5SIzZvAlO4QQQggx\nuHGfvLl7+uet7SiMGNaHhURDBClxkZSeaMXtdvszPOEHrU3tOLqc5M9IJTwi7JyulRrd32vXkKQs\nItzVW7VUCHF26ls6AIhMrwYgV6pMCiGEEGdl3Cdv3c3NAFTmJ+JWq5gSP+mMz1GpVGSlxNDpcGJp\nd/g7ROFjB79QPhhmZMWd87VmJhVwXspsAN52KNUmO8rKzvm6QoSiRnMHqggbTp0yn7goY0GAIxJC\nCCHGlvGfvDUqpeKrw5WFt5emLxzW85LjleqEjW2yYPdYUlbagLF3vlt6Vuw5X0+tUnNZzoUANCSE\n0R0Rhvnjj6RHVogRqGltI2LWVgAmx02UJQKEEEKIszTu/3KWH98HgDlGw4LUeYRphjeMLrm3tHxD\nm91vsQnf27fbBEBKhp64BN8UQsiMyeD6yVfh1Kho7B06ad210yfXFiKUfNbRX631lhnfDGAkQggh\nxNg07pO35mplIdikzDxunn7TsJ+X0pe8tXb4JS7he+22LhpqrKROMHD9t87z2XVVKhUXZy9navxk\nDmYq/2U6yo767PpChAqbqhGAu2bfhl4XE+BohBBCiLFnXCdvTmcP8SWVuIEvL/7GWT1Xhk2OLVUV\nLbz4P8UAZGSf+1y3wUyLn0JZVjgA3Q31frmHEOPVZyfKcKocqDvimZY49FqbQgghhBjayGuoD8Om\nTZswGAyYTCZWr1494Pgrr7wCQGVlJWvXrvXpvV1uF/987SmmdzrpCQ8jJjbxrJ6foA9HrVLR2CY9\nb8Gu8ngL77yy37Odkqb3y32mJkzmzTA13VHhOGqqcbvdqFQqv9xLiPGk2lbLH489h0oFaa7pgQ5H\nCCGEGLP81vNmNBpRqVQUFRUBUFpa6nW8uLiYJUuWsHr1akwmE8XFxT69/96GA+R9eAgA7fKis36+\nVqMmMTacBknegt7Bz6s9j7MmJZA7Jckv90mPTiU6LIryVA09ra10Hj/ml/sIMd68WvqO5/HtF14a\nwEiEEEKIsc1vydu7776LXq/0gGRlZbF9+3av4ycnbFlZWVRVVfn0/vbinUR0KxUBs68Z/ly3k6XE\nRWJpd9Dp6DnzySIg3G43tVVmtGFqvnnnYq5aPQu12j+9YTqNjvNSZlOarRS9kaIlQgxPdbOyNEB8\n23kk6n1TSEgIIYQIRX5L3iwWC3Fx/XOP2travI6vXr2aG264AVB66WbMmOGzeztdTjo++wyAsPOL\niAgf2YeFvoqTTTLvLWhVn2jD0dVD3rQUYgwRfr9fXHgslek63NGRtH34AR3S+ybEaZW3VmHXKct3\n3Hf5NQGORgghhBjbAl6wxGg0Mn36dAoKCnx2zWM1JWTVO6hP0JL9zVtGfJ2+oiUydDJ4HT5QB8DE\nfP8MlTxVWnQqLrWKQ8smgduN6Ymf4+qWhdyFGMrbhz8BQI2G2JjwAEcjhBBCjG1+K1gSGxvr6W07\ntRfuZMXFxdx7773DumZy8pkLURxqPMbOf/yBRW7QnTeTtNSRVx6cnJ0AHMPe7RrWvYfDV9cJtGBo\nh73dgam8hahoHQsW56I6y+GSI2nDxUkL+ajmEzZTTt/XDQZ3F5HJZ1cQx1eC4efgC+OlHaFiuD+v\nv+9/k0P2vQDcv+AnQfdzDrZ4RmI8tAHGRzvGQxtg/LRDiPHKb8nbqlWrKCkpAZT5bUuXLgXAarV6\n5sK98sorrFmzBlCSuL7iJkNpbLSe9viR1mM8V/x7vndQWVh7/hXfPeNzTie8t1+yvKrtnK7TJzlZ\n75PrBFowtMPpdPHcb5Rv9Bcun0hTs+2snn8ubSiIncrR5nJ6LlyM9t87aKioIUo3+n/sguHn4Avj\noR2h9mFnOD+v9ys+5K3j7wOgbk8mIyY2qH7O4+V9N9bbAOOjHeOhDTA+2hFqv49F6PHbsMnCwkJA\nScpiY2M9wyJvvvlmz/6nnnqKyy67jEWLFp3z/VxuFxsO/oVLd1oAcKclo9Gf23/gvjlvslxA8Kk1\n9c+hnDEvY1TvnRGTBsBu+xEAelpbR/X+QgQ7q8PGW8ffx+1S0dOUwcVJV8qyGkIIIYQP+HWdt76C\nJCfbuHEjAEVFRezc6btqfdtqdtJltzKpWpl/NPGu4Q3FPJ3IcC36qDAOlrfQ2NbhSeZE4B071AjA\n4gsnER4RNqr3ztZnAmCKdVME1K3/A9EzZ6GJkip6QgA8u//PALgsSXQfn8XVX/HdnGYhhBAilAW8\nYIkvuNwuXjnyJoltTgCi585Dl5rmk2tPnhALwCf7anxyPXHutm45inGvUr1u5vwJo37/2HADk2Jz\nqU/o/+6j/Mf34HJI4RIhOns6KbecAMBRPp1brirw2/IdQgghRKgZF8nbFw37cbldJJqV9dhiZs32\n2bVvuUoZ/nnU1HaGM8VocLlcHOhdlDs7LwGtVhOQOKbGT8alUeH66tVKXJ2ddFaUByQWIYLJ+xUf\nAhBNAnRHkBIvPdJCCCGEr4yL5O1wSxkAC1zK3KfwrGyfXTsyXEt2agzHa61097h8dl1x9rodTl7/\nvz0AJKfpueSqwA3F6pv3VjMlkdSbv6vE11AfsHiECBbGlsMAJFuUucwp8TLcXAghhPCVMZ+8uXt6\nqKs8RFKHlviSSlCp0KX7toBFflYcPU4XZdVmn15XnJ1DB2ppqFWqYOVPTyUicnTnup0sI1pJ3ky2\nGrQJyjIB3c3NAYtHiGDQYLFQY6sjIzKT0kNuoiO06AP4/1QIIYQYb8Z88lb9j7/zpVfL+PrrNbg6\nOsDtRh3u24VgZ0xMAODAMflwHkhbt5R5HhfMTg9gJJASlUSkNpLddXvQxscD0NPaEtCYhAi0p97+\nEDduTpQpv4OT4yKlyqQQQgjhQ2M+ebN/8C+vbcOy831+j6nZ8URHaPl0fw09Thk6GQg1lf1zDm+5\nZxlhusDMdeujVqnJi83BjZt93SYAelokeROhq7vHRatTGTrsssUBcOHc0S8oJIQQQoxnYzp5c1q9\nF5I0nL+clK9+w+f3CQ/TsLAglfbOHirrz24xaOEbb/5tLwBzFmURpvPrChfDdkn2cgC2N+1BHRMj\nyZsIaYcrW1GFK2tiujuiueOaGZw/K7A95EIIIcR4Exyfgkeo/cB+ALbOiebiohtJnb0EldY/TcpO\njQGgrNrMpAyDX+4hBmezdHoeL1iWG7hATpEfP5kpcZM42nYcVayB7sYm3G63DBMTIemLsnq0STWo\nUPGjaxcxY2JyoEMSQgghxp0x3fNWsUMZMnl8QjhZc5f6LXEDmD05CYADx2Xe22irMSmFYs5bkoM2\nLLDDJU81L0VZlsJuiMDtcOA0y5ISIvS4XC52d74LQGpUiiRuQgghhJ+M2eTN1mXDXVaOJVrN3OkX\nolH790N9XEw4iYZwqhpl2ORo6el28vGmI/zrrVIAJuYnBTiigdKjUwEwp0QDYNu7J5DhCBEQn1cf\nxhXTAMBXp10X4GiEEEKI8WvMJm8VZV8Q6XBTnaxjQfq8UblnZnIMZpsDi90xKvcLZZ0d3ax/6lOM\ne2o8+5LT9AGMaHApUUoPQ0WOkrx1HDkcyHCECIgvag55HucYsgIYiRBCCDG+jdnkrdGo9HBkzFxA\ntj5zVO6ZmaLMe6uss57hTHGujhq9F7wOjwjO6ZkGXQwRmnCKu4+BTkdXdXWgQxJi1FVYTwDwvck/\nIEwdnP9XhRBCiPFgTP6VbbY3M2GTkrwVzL901O6bNyEWgB3GemZMShy1+4aiqopWAK5cPZPaKjP/\nn737Dm+rvvcH/j4aljwkeW8nTrydTaYTsggkhFUoTaCUUlpoL6WTjktLS3+05XKhQDdQbqG0bMKm\nNJBABiTEJHF2vFe8E29L8pA1zu8P2bIVy0OKpSMp79fz8CDprM+xHVtvfVf2nESJK3JNEAQMWE2A\nIKBVAyScOwubyTTtaw0S+auDDSehlzXD1heBuam++SCNiIjoYhWQLW/vffx/jsfhyTN8dt0FGTGI\nCFWitK4Loij67LoXG1EUcaayA5HRoUibFY3la2YjKiZM6rLGdWvuFgBAXYICosWCvpLTEldE5BuD\n1kG8VPkaACC0Kw9yWUD+SSEiIgoYAfWXVrTZUPmXR7HqPfu4osQf3uPTadkFQUDOjEh0GUxo6xmY\n/AByi2nAjP6+QWx/4xQAICY+IiCm3S9IXoq1qSvRFK8EAJgaGyWuiMj7OvUD+O+3/gUrzLD1ReCB\nG78gdUlERERBL6C6TTb+/lGIZfaZB21hamhy5/i8hswUHY6Ut6GmuQfxkaE+v34wGjRZ8I8/7sf5\njZm5AbTAb25UFo5E7gMAmBobJK6GyLtsNhE/fWo/1EsrAQCzLauhDQuRuCoiIqLgFzAtb4byCvSX\nlcImAB8t1yD1l7/y6rpu4xke91bTpPf5tYPR2aYevP5c0ZjgNm9xCmbMjpamKA9kRs6CMVSGXrUM\nxiNFsBo4qQ0Fr11HGyFPrXA8/8HVqyWshoiI6OIRMOGt/UgRAOBYbhjyr7wJEfHJktQxMyECcpmA\nIxVtsNpsktQQLERRxM53iqHvHumCmr8wCRuuzUPBZRkSVua+MGUYNsxYi/pEe+tDwx8elbgiIu85\n1lgFZdIZAMBPl3wXIcqA6sRBREQUsAImvDW/8TZEAIfzw5ATlSlZHUqFHEkxYegymPD7105IVkcw\n6GzrRa/BvmbekkvTcde9a7H2yhxkz0mAXB4wP5oOixMW4Ei+fWKVwfp6iAz3FISqG7tRZRhpdUsJ\nD5zuzURERIEucN4hW20whMlQkLEayRHSTht//erZAIDSui5YrHyD7onTR5uw7R/21tSN18/B0kvT\nA2JykonM1KZh65pvomS2GgAw2NIicUVE0+8nrzwHZUo1AODuBd+AUq6UuCIiIqKLR+CENwCdM6Lx\nxcxrpC4Dl2THoWBOAgDgkZeOSlyN/7NabBBFEaeKGvHUw3txqqgR+3ZWOrbPzAicsW2TyYycjdao\noVkn685IWwyRFyhn2FvdMtXzMCcmV+JqiIiILi4BM1DBlBSNy773IOQyudSlAAAuW5yKwuJzqG7W\n41xnHxKi/XcdMikdOVCHQ5/WOr22/+Mqx+OsOfFQKP3jezod1AoVbKkJwBED9DUV0K5cJXVJRNNK\ntAnItK3BD1ZslroUIiKii07AhLf1T/0f2tuNUpfhkJGsQ1JMGFo6+vD2vhrc9YW5Upfkd95/7QQa\nartcbouKCcNVW+YhXKPycVXel56zFDahClXFBxBvvRUh7FZGQeSFL/0Jhm6uc0lERCSFgOk26Y/j\noR74+jIo5DIcKm2Fvm9Q6nL8hs1mw1MP73UEN7lChvTMGAD28W3f/tk63PzNZdBGhgbkxCSTmZc8\nH51aOeI7zDhQ+rHU5RBNK7WSH0YQERFJJWBa3vyRUiHD3FnROF7VjlPVHVg1j7OuAcAnH47MRLdi\n3WwsWJYGmcz/wre3pEQkwbBuA2Tv7sTg6+/AnH8FlDL+UyMiIiKiCxN8zR4+du2qdABAdVOPtIX4\nibNNPagqbQUA3HTnUixaMeOiCm7Dcq75MvoiQ5HWYsKppuNSl0NEREREQYDh7QKlxUcgRClDSV0X\nRFGUuhxJlRxvxtsvHIPFbENGbhyiY8OlLkkygiAgYslSyG1AbeFHUpdDREREREGA4e0CKeQyzJ0V\ng9aufrT1XLyD+M2DFuz/aGT6/6WrZ0lYjX+Ysc4+G9/MwmrYrFaJqyEiIiKiQMfwNg0yU3QAgPc/\nOyNtIRJqqO2C1Soib0ESvvXTNYiK4dIJIYlJ6J4RA63Bgvc/fwlmq1nqkoiIiIgogDG8TYOVcxMB\nAPtPtaCpzX+WM/AVURRx+mgTACB/YVJQziDpqZQNVwMALJ8ewA8/+QX6zP0SV0REREREgYrvsqeB\nNjwEm1fMAADc/+whnOvqk7gi7zINmHHo01q88c8ivPz0QfztkU/QVNcNAIhL1EhcnX9JLlgHWVws\ncmsHILeIOHT2qNQlEREREVGAYnibJl9cM9vx+KWdFRPsGfhefuYQjhyoQ9tZI3q6RlqSll6a7pfr\n8UlJkMmgW7QEChuQX9uPj+s/QY9JL3VZRERERBSAGN6miVwmwxP3rIEqRI7TtZ34xd8/x6HSc1KX\nNSGL2YrKknMY6DfDbLbi8L5avPhkIV75v4N4/olCFH12BhaLFYMmCwDAaDDhvVeOo6G203GONZuy\nsHpjFr542yVYcmm6RHfi3yI3XA4IAlbWK9E10IXfH31K6pKIiIiIKABx5eBpFKpS4NYrsvHsf0rR\n0tGHv71bjMgIFbLTIqUuDTabiOJjTTjyWR36+8yQyQTYbCNLG2h0ahjOmy3z8L4zOLzvDAQBSJsd\njfrqkdC2+ca5mJERDZmM+X8yyphYhM2ZC5w+hbVHQvHJEgF1+gbM1KZJXRoRERERBRC+855mq+Yl\n4a8/XIONS+1vzB9+6Sjqzxkkrgo4cbgB+z+qQn+ffcbD0cENgFNwO39RbVGEU3D72t0rkZ4Vy+Dm\nhsTbvwEAWFBvg8Ii4r3qDyWuiIiIiIgCDVvevCBMrcDNG7IgEwR8eKgeDzx32Gn7fbcuxtHKNuTP\njMLc2TFun7+jzYhegwkhKgU6Wo1ISo1EVGwY+vvMONfUgxkZMRBFERWnz0EmEyAIAj7fUwMAyFuQ\nhJ6ufshkAhavnAmbTcQHb5yCxWLDVVvmYWaGvR5RFCGK9vXbqsvbULi7GsvXzsacRcmIj9eirU36\nQBpIFJFRiNq0GV07PsCqxhB8qqhC10A3otTSt8oSERERUWBgePOiLesz0NhmxOlRY8QA4KEXjwAA\nPjxYj+tWpWNJbjwSokKhVMhdnsc8aIG+ewCnjzah5HiLx/Vk5MZh3eacMa/f+ePVsNlEpyn+BUGA\nIAAqtRL5C5KRvyDZ4+uSXeS6y9C14wMsPNCEwwkx2NOwH1/MukbqsoiIiIgoQDC8eZEgCPjBlvnY\nd7IFr35ciR/efAn+81kNikeFufc+O4P3hhb3fvJHa6AOGfmWGHoG0HbWgI/eLRnTzdFdMzNisGZT\n9rh1yuWcJdLblHFxCM3OQX9FOW7Y14vX1fuxNnUlYkKjpS6NiIiIiAIAw5uXyWUyrFuYgnULUxAX\np0FWcgRKz3QhI0WHg6XncLqmE0cr2gAA3//TPsxO0uLmy7MQIQh4/bkjY86niwrF1VvnIUKrhs1q\ngzJEgUGTBUcO1OFckx7zl6ZCGSJHanoUAHDqfj+T+pN70fSn3yO2+DQya3rx78SduH3OzVKXRURE\nREQBgOHNx+QymWOc23Co6zKY8PBLR9DRPYCaxh785Z9FyBJGujAuWzPLHsqUzt0qh7s5hqgUKFif\n4bubII8JMhniv3wrzvzqPlxx0IB9g/tRnbwcGVGzpC6NiIiIiPwcpwucRjabiEGTBYf31aK5vhui\nKE7a3dE0YEbt6bO4NDIciyHDIsiQCRlEEZBpQpB9RQbyF6eMCW7nuvrwwo5yPPTiEXxU1ABjvxk2\n8cK6VpJvhCQmIvbGLQCA1ceMKH/2zzBZByWuioiIiIj8HVvepsmxz+vx+d4ax/Oiz+ocj2fnxCIx\nRYeo6DAc2FONro4+yOUCrFbXYUsE0AQbWgwDOPhRJd7ZV4uvXZmLnBmRCA9VoqqxB3996xSM/fZp\n/6sae/DKx5UAgCuXzcCN62ZDzmn8/Vr0ps2wdHSge/fHyCzrwtGHfoYVv3yc3VyJiIiIaFxeDW87\nduyAVqtFQ0MDtm7d6vZ2f9TT1Y+m+i6ER6hQuLsaiak66Lv70VTX7dgnLDwE/X2DGG4IqylvR015\nu9N5hoObOlSJ9KwYJKRoHdP0h0eocLyqHX9+4yQAoHfAgiffOT2mlnULkxGlUaHkTBfKG+zX//BQ\nPRpaDZiXEQulXMDKuUlQhbiexXI6WG02VDfpERcZCl1ECGQMH1MWf8ut0F52Gep/eR9i6jrx1t/v\nw9XfeABqhUrq0oiIiIjID3ktvJWUlEAQBBQUFKChoQGlpaXIy8ub8nYpiKKIjlYjdNFhUChkTq0g\nA/1mHC2sw4lDjU7HdHX0AQCS0nRYsykb0bHhAOxdKAf6zRBFEaXHW2Cx2qBWKaHv6cecRckQZAJk\nMgGR0WEua1mYGYtn710PEcAz/y5BU3svGlqNEARgSU48FufEYWluPARBwLWr7OOl+k0W/PmNkyg+\n04XiM10AgBd2VgAANGFKRGvVqDtrwKwkDUIUcqQnadA7YIFMEKCUy3D50lQkRLmuZ1jdWQOefLcY\nx8pbEaNTo7Wr32l7rE6Nmy7LREJ0GGJ1auw72YLm9l4AQGaKDsvzE6CQO7cKNrYaUdHYDblMgNli\nQ3FtJ9QqBZbnJcDYb4ahbxAZKTqkxkUgTD3yI6vvG8Sp6g6EqRTITNVBExYCURRxtrMPSrkMYWoF\n1CEKp0XHdx9tREfPABbmJmB2QviYFsq+ATPUKoXPQqg6MRkxP74Hbb//A+YdasFb1vugvnw9RIhI\n16ZhUfx8yAS2ohIRERERIIiidwZKPfbYY1i1ahUKCgpQWFiIkpIS3HHHHVPe7sp0LQw9vAB1f98g\nzjXpcbZJj5ryNhh6Bhz7CAIQGh4CmUyAUW+CMkQO86DVsV0XHQqVWoGMnHgkz9AhLlEzaZe3uDjN\nBd2DTRTRb7IgXK0cdx+L1Ybntpei2zgIQQDOdfahQ2+a8jVUSjnSEzWIiwqFJkyJMy0G6CJCEKKQ\n4UR1B3qMrsdmzUrSoLZl8ntTKmQIVSlwSVYsbKKIg6WtMI36uk5EIZdhYWYM4iJD0TtgRmHxOZgt\ntgmP0YQpkRIbjsgIFaqaetA+6nsMANowJWJ0alhtIvS9g/avG4D1l6Qgb2Y05mdEQ6mQO35mIAAn\nqzrQZRjA4px4hKoUUCrGhqu+ATP6BizQhIVMqeXzbMlRdDzxBJQmKz6fF45Dc8IgswGJegCJcdBp\nYjA3Jg/p2jT0WfoRo47CzKQE9PdMfP+B4EL/XfiDuDiN1CX4VKB/v4Dg+bkL9HsAguM+guEegOC4\nj4vt9zFdfLzW8qbX6xEZGel43t3d7db285WdakF3dx9sNhGiTYRMLnO8oRZtouOx1WqD1WKDxWxF\nT/cARFFEVEwY2s8Z0XbWgLAIFVqb9S4nEhEEICYuAooQGQb6LTDqB2Ax298cW8xW5M5PxJJV6dDo\n1BfypfGYTBAmDG6APeB889o5Tq/ZbCIGBi0oKm+DUi5DWkIEjpS3od9kwdnOPsRFhsI0aEVNix76\n3kGUN3Q7umGeLyJUiR/evAhGownhoQokx4QjVGX/MbJYbSg8fRb6vkEU13aisa0XGclarJiTCBEi\njle2o6ZZj/aeAew93uw4pzY8BAszYyETgBmJGsxO0qKpvRf7T7bAbLFBMxSwjle2o6i8zXFcrE6N\nFXMSAAg4WtHmaOHLStWhp3cQEaFKNLf3oqx+5F5itCosy0tAd58ZJyra0GeyQD8qdKpD5BgYtGL3\n0SbsPtqEKI0KoSoF2rv7YbWJkA21DgL2Vk25TEC4WgG5XIaIUCU69QMQBMExHhEAQlUKzEyIsIdE\nq4iIUCU0YUrYRHtYVofIEaZOQcjXfgLh5Sew4pQRiystkA0OQm4TAbShOU6JLs1hVGvlMMsFDIYI\nMClliFRGI0yhQZg8HEpBBaVg//mwwQYZZBAEGWQQAEGAAP/s0qpSKWAyWaQu44LcNMkHT0RERETT\nIWAmLNn2z6JpOY++ewCR0aEIi1BBpVZAGxmKqJgwhIYpMTMzxqn1zN6NsheCAERGh0HuooUlEMhk\nAsLUSqxZkOx4LTUuwuW+Vpu922KIwh5iTGYr+gbM0IaHICJUiVlJWqQkR7r8ZE4hl2H10DWuLkgf\ns31FfiIAoMtgQv05Azr0A5g/OwbROvWYboozEjQomJPo9NqWdZmoP2eAbWgWz6zUSEeXyBtWz4LF\nKo5pBbNYbTBbbOjoGYAIIDUuHIIgOD5d7B0wo6mtF2nxEeg0mJAQFQp97yCKz3TidE0nisrsLYNx\nUaEw9pkhlwuIjwyFSilH/6AVZosVHT0DGBi0oMtgQqxODblMQFp8BCIjVGjv6Ye+z+wUICeiidmE\nNcJxzOxrgVmugSAXEWo1IbltEMltZhdH9EzpvORlDG9ERETkA14LbzqdztGadn4r21S2n+9Xj1/r\nnUInER+vndbzBUJzfmKCbtJ9LuQ+4uI0yJ4d69GxyUmT1+bKjFTXdcQBSE+Ltu8z9HoSgJyMOHxx\ng0eXmga3SHVhoikJhN9jUxEM9xEM9wAEx30Ewz0AwXMfRMHKa01JmzdvRmOjfXKPhoYGrFy5EgBg\nMBgm3E5ERERERERjeS285efnAwAKCwuh0+kcM0nefvvtE24nIiIiIiKisbw22yQRERERERFNn8Cc\ngYOIiIiIiOgiw/BGREREREQUABjeiIiIiIiIAgDDGxERERERUQBgeCMiIiIiItuEx5cAACAASURB\nVAoADG9EREREREQBgOGNiIiIiIgoADC8ERERERERBQCGNyIiIiIiogDA8EZERERERBQAGN6IiIiI\niIgCAMMbERERERFRAGB4IyIiIiIiCgAMb0RERERERAGA4Y2IiIiIiCgAMLwREREREREFAIY3IiIi\nIiKiAMDwRkREREREFAAY3oiIiIiIiAIAwxsREREREVEAYHgjIiIiIiIKAAxvREREREREAYDhjYiI\niIiIKAAwvBEREREREQUAhjciIiIiIqIAwPBGREREREQUABjeiIiIiIiIAgDDGxERERERUQBgeCMi\nIiIiIgoADG9EREREREQBgOGNiIiIiIgoADC8ERERERERBQCGNyIiIiIiogDA8EZERERERBQAGN6I\niIiIiIgCAMMbERERERFRAGB4IyIiIiIiCgAMb0RERERERAGA4Y2IiIiIiCgAMLwREREREREFAIY3\nIiIiIiKiAOD18PbYY4+Nu23Hjh0oLCzEtm3bvF0GERERERFRQPNqeNu2bRt27tzpcltJSQkEQUBB\nQQEAoLS01JulEBERERERBTSvhretW7ciLS3N5bbt27dDo9EAANLS0nDgwAFvlkJERERERBTQJBvz\nptfrERkZ6Xje3d0tVSlERERERER+jxOWEBERERERBQCFVBfW6XSO1rbzW+FcEUURgiD4ojQiIhrH\ntT9+1/H4rz9dj+TYcHzx3vcBAFetTMf2A2cAAJsL0vHljTmI0qqlKJPII8+8exrvflrt0bFzZsfg\n4e9cOs0VERE583p4E0XR6bnBYIBGo8HmzZtRXFwMAGhoaMCqVasmPI8gCGhrM3itTl+Ii9ME/D0A\nwXEfvAf/EQz3ERenkboEn/m/n1+ODz+rwVuf1uDF/5Rg3aIUx7bh4AYAHxSewQeFZ3Drxmwsy0tA\nRKgSVU09MJmtmJMe7fO6zxcsP3eBfg+Af92HwTgAALj7+rmIjwqd8nG/+VcRTCaL39yHp/zpe+Gp\ni+n3MV2cvNptcseOHSguLsbrr7/ueO32228HAOTn5wMACgsLodPpkJeX581SiIhoGiTFhuPS+UkA\ngKLyVjz9XvGE+7+4swJPvn0KfQNmPPTCETz+6nG09/T7olQitw1/3pwUE4YZCZop/ydjxyAi8hGv\ntrxt2rQJmzZtcnrtzTffdDzesmWLNy9PREReEBmhwvpLUrDnaBO6DKZJ9y+r70Zty8in+b969hCe\n/NFab5YYlERRRHFtJ3JmREKpkEtdTlBy9BXiMA0i8lOSjXkjIqLANTPBddekzctn4IOD9WNeP9vZ\n53g8MGiFsd+MiFCl1+oLBsZ+M3Yerse82TF4d38tyuu7YbWJ0IWH4P99fem0dQ+radZDEID0RA3H\nlg81vXnSkiZCnHwnIqILxPBGRERuC1WN/Pm4bVMOZDIBVY09+NK6DKQlRKD+rBEfHhoJcS99VOF0\n/EsfVWB5fgIWZMQwMIzjqXdOo7SuC+8fqHN6vad3ED/662fYtGImblqXcUHXeG9/Ld7ZXwsA2Lo+\nE1cun+HW8cW1nQgPVSA9UXtBdUzmVE0H3tlXg7u+MBdxkVMfi+YuG/MXEfk5LhVARERuUypG/nys\nW5SCNQuS8Y2r8yAIAlbkJ2LrZZl4+K4CZKbonI5bkhsPADhYcg5/fuMknt9RPuVrHq9qR22Lfnpu\nwI/Vtujx86cLUVrXNeF+Oz6vGzMpmCvGfjP6TRan1yxWG17YWe4IbgCwbU8VSs90TqnGqqYe/Oiv\n+/H4a8fxm38WwTaFOi7Etj1VqG0x4N6/FU6pq67n7Pfh/gcK/ACCiHyD4Y2IiNxmsdgm3Sc+MhQ3\nrp3t9Nr5Ye6T481469NqdOoHJjzXkfJW/PmNk/jtv4pwuKzV/YL9jNliHRN4jP1mmMxW/O6VYzjX\nZZ/UZcv6DDxxzxo8e+96fP/G+XjyR2tw9/VzHceM7o7qik0U8bO/FeI7f/gUu440Ol7ftrsKe442\nAQDmZ8Q4Xn/01eOTBkJRFPHwi0fRbRx0vFbV2DPJHXtOFEWc6xyZ5Ob9UbOaTv+17P9nYzAR+SuG\nNyIictv8jBgsyIjBj29aOOF+OTOisH7UcgIr8hPG7PP+gTr85MkD+MubJ3Gusw8nq9udtvebLHji\n7dOO50+9cxoVDd0XeAfSMfab8f0/78e23VUoq+vCNx7ejW88vBvf/9M+fPvxT2AatAIALl+SivWL\nUhCqUkAQBCzMioU6RIElufG4fXMuAOB0Tee4YUvfO4g7H9mDvqFWt+Guqz1GEz4eCnLxkaH4wZfm\nY+v6TMdxtS0G6PtGgplp0IpPTzRD32t/rbmjb0zwLK8faSUsKmvFk2+fcjmraL/Jgjf2VqOycerf\nv0GzDRbryIcFe4414b+fOjBpy6QnHOHNo4OnsxIiItc45o2IAsJTT/0Ft932dYSHRwT0NYJFiFKO\nH2xZMKV9r1mZjj3H7K08mrCRSUrkMgHWUYOMjlW241ilPbj9+QerHROalLl4k360og3ZaZEe1y8F\nmyjiaHkbnnzHHkR3Hm7AzsMNLvf9+lW5WD0/edxzzZsdA0EAXtlViVd2VWJxdhzuvmGuo7tfcW0n\nHn/t+JjjCovPYtvuKgDAZZek4AuXzoIgCNi4NA2FxWfR0GrEg88XAQBu35yLNQuSce/ThY7g9tWN\n2WhoNQIA7vrCHOSkReJnT3+OXUcasWFxGlQhMsf9Ha1oxzP3rgdg76b55ifV2HHIfr/bP6/D/35r\nBRKiwyb9ug2Hz2V58ThUam91be8ZwKOvHMOz966f1jGTjklH2PTmdRUVZbj//p9h/frLkZeXj6am\nRkREaHDddTdIXRqRX2PLGxEFhL17d6Go6NCU9zcajV6/Bk1NlEaFr27Mxi9uWwxBEBCutn9uuHnF\n+JNjdBtNjv//5a1TAID/um4OfndXAQCgZIpjs6RmsdogiiL6Bsy485E9jmAzmZxJgmmURoWV80bC\n3ZGKNtzxyB5H98XRwe2Oq/Nw/aWzAAB//3cJeoaC2FUrZkITFgIAkMkE3Lox2+ka//ygDP8pPOMI\nbgDwws4K7D3eDG14CBbnxEEXocLahcnQ95lxvKoN//7sjGNfmyjicFkrTtd04MOD9Y7gNqy62bmr\nZf05g8sW1eHxeqMnyRk2HCSnzVB2c/fNEbOe+7Kzc5GTk4cNG67A2rWX4ZZbbkNzcxN/BxNNgi1v\nROT3KirKcOutt+Pjj3di7drLpnRMUdFBLF26fMqtaJ5cg6Zu/SWpjsf3374UNpuIWJ0ahj4z5qRH\nI0ytwO9fO+Hojvfg80W479bFeOC5w47j5syKdrTGNbb14rNTLVg1L8m3N+KG5vZe/Pqfh2F2MT5w\nSW48iobG7n3h0llYmBmLmYkadPQMoL2nH/FRk7dIbV6Zjs9ONju99tCLRxyTwgDAE/esQahKgXOd\nfU6TkyzLi0e0Vu10bFZqJK5fPQvv7BvZ781Palxee9W8RMhlMse97DzcgHf21aK9x3ns4lMThNXS\nM11YOdf+/es2mhzf6x/ftBBzZkU79hsd3q5Zme405m10sJwOjoZghjGfOL/L73XX3YD77/8Znn32\nBYkqIvJ/DG9ENCXbdldN+0QRS3Pj8Z2bFk26X0tLM6699no89dRfnF7fu3cX9Hr77IPnd7UZryvV\neMeMdw2afvGjpnr/2pW5jsdP/3Qtnn6vBEVlrRg02/C/Lx51bLtmZbojuF25fAY+PFiPvcebsDw/\nATabiBCl/y1aXdOsHxPclubG4ysbs6ENC0HvgBkqpRwK+Ug7T4xOjRid+vxTuTQ/MxY3rp0NpUKO\nV3dVOl4fDoW68BBHa1VCdBie+vFafFzUgEuy45AUE+7ynNeuTMfCzFjE6NS45y+fOcaa3XfrYkRr\nVfjwYD0K5iYiLX7kQ5EZQ4+Hg9uSnDhcsTTN6fs37Idb5iMuMhS/+PtBfHb6LG7akIUPtpfgWPnI\n75a/v1+CcLUC92xdgOb2Pjz1rj0AakKVuGxxqlN4a2zrxdzZMedf5gIMzTbpQXoL5CFvw7/f5XIB\nVuv03MnS3HhsvSxz8h1HSU5OQXOzvYv13r278MIL/8Tdd38fTU2NSE5OwZIly6alNqJAxm6TROT3\nhj+dXbx4KY4csX86X1FRhubmZlx33Q149923XB5z/jwOEx3j6hrkW3KZDNeuTHc8N5ntE3f84rbF\n+OKakVkrt67PRGJ0GKqb9PjWo3tx1+Of4L3PamGx2mC1TT4L5nQ4UdWOgyXnJtynsc25S9//fHM5\nvn39XGiHuiqGq5VOwc1dgiDg6oJ0bFyahmfvXY8YrcqxLVytwMP/VeC0v0opx9UF6eMGt+FzzkjQ\nIFytxG/uWIaVcxPxs69cgsxUHaK1atxyRTZmJWmd6g5RynHNypkA7CHxy5dnIys1Er/7tvP17/rC\nHMzPiHW6/pkWPV7fVek0W6W+dxAtHX3Yc7QJf3z9hGMClxidGiqlHD++aSGWD018c7Syzd0v24Qc\nDW9seZNMb6/93826dRuQkpKKxYuX4rrrbsCjjz4kcWVE/oEtb0Q0JVsvy3T7U9Tp0NzchLKyUgiC\nAJ1Ohz17PsbixUuRnZ0Lg8GAoqJD0Ol0jn337t0FACgrK0VzczPsb8cE3HLLV10eM9E1yPfS4iNw\n9/VzHWPDLp2fhIxk3Zj9Rs8+CADv7Kt1dPf724/Xeq0lzmyxoss4iD+9cRIAkJ0WiSiNasx+A4MW\n7DnWBLlMgCAIuHR+0oSh6UIJgoD7vroEHxyswzUF6dCGh1zwOROjw3DnNflT2veG1bNx5bIZCFOP\nTEgTqwvFP37mugvy1vWZ2LanCr/fdsLp9axUHSrHWXZgdpJ9IfA5s6KROzPSHp5dNBJVNHTjZHUH\nblw72+3JTEaWCnDvuEDPesO/3+PiNGhrM0hWh9FoRHb2SGv86G6VyckpaGlpRlLS+BP5EF0MGN6I\nyK9VVJThrru+CwBYvHgZ7rjjVgDAe++9DUEQcO211+Oll/6FlpZmJCen4JZbbgMAfPLJbixZssxp\nzJurY5KSkse9BkljSW48Vs5NxIHTZ7FpaZrLfZbnJ+A/hXUut52u7cQl2XEutw2/Gfz4UB1CZALy\nZka5VduDzx9xmiTjnx+U4Ydb5o95s1931gCzxYaNS9PwpXUZkMm8//Y+SqPCLZdnT76jFwiC4BTc\nJhMZMTZc/uwrl6Ch1egIbxVDywnMSIjAT25e5Og2C9hbabXhITD0jR3z9vBL9u6aS3PjMTNR49Z9\nTGXRc/Ke9957C1/96u2O50bjSJA0GAwMbkRgeCMiP1ZUdAgvvvgvpKSkIisrB83NjTAYDHj55ReQ\nk5OL8vIyFBUdQkpKKioqypz+sLt6E5acnIKKinKnY5qaGse9xi23fNWXt0uj3HlN/oStPjeuzUCn\nfgCFxefGLDnw3me1LsPbsYo2x8yVw8ZrGXJF3zs4ZnbDUzUd+OR4M9aNWssOsI93A4CMFN0FdY0M\nVpERzq2Vf/r+pdCEhTgtJVHdZP8apsZFOAW3YdowJTr0Joii6LKlzHIBXWg9ydrMfe6pqChDZWU5\ndu36CM3NTWhqaoRGo3WaMMpgMKCyshylpSX49re/J2G1RP6D4Y2I/NaSJcvwzDPPO55nZ+di+/Zd\njufDXRtdDWLXaLQuzze87+hjJroG+a87r8nHV67IgVwmYOfheqyYk4hfP3cY9eeMaGozIiXOeabR\nd0fNtjjs188dRu+AGesWpeCqFTNdXsdqs8E0aMNHRSNT3d9xdR5yZ0ThgecO4dVdlSiYkwhViL2r\npk0UUTy0lEFW6tgunwSnbp2/umO5Y8mCpJhw/PF7l+KxV487xgyqQ1x3gRVhn4nyr2+dwrHKdkcA\nvBA2DnrzmezsXLz66tsT7pOUlIysrBxkZeX4qCoi/8fwRkRBiWPWgp+9q579z9i1q+zrmG1cmoZ3\n9tdi38kW3LwhCwDw8kcV+PhIo+O4UJXCMf183Tl7t6w39lZDIRNw6fxkxzmHvbCjAp+eaEbmUBD7\n6ZcXObpbLs9PwO6jTWjp7EV6otapdS8lLnxMCxPZJceG446r85CZqsPc7ASncVba8BCoQkZaK1Xj\nhLemtl4AcCzs/ux/SrEgM9ax3aNZE8Xh2SbdxLA37YqKDqGyspzj3IjOw/BGRERBY9OyGXhnfy12\nHra3kqXEhTsFt/REDe7/2hIo1CH42q93OB376u4qtPcM4JYrnMeNfXrCvpZaVWMPwlQK5M4YWUB7\neBKSlvY+WCyiU7fMOenRoPFNuEbfqNyVk+Z6XKI2PMRpnbeT1R04Wd3heD5osbpdExve/MeSJcsm\nbZkjuhgxvBERUdAY3UozHOBGu2JJGgRBQJRGhcXZcdCEKbH3+MhC1x8facTAoBVb1me47IIXplY4\nja9KibWHt+d3lDuWNhh23VBrILlvYVYsqpv1mJ8Rg3mzXYfgb12bj8dePT7uOQbN7o9583S2yaGj\nPTiGiMg9DG9ERBRUbrk8Cy9/XOn02o1r7evEFcxNBGB/c/6dL84DANw2tFD4/zxfhOpmPfafasH+\nUy2Ykx6Fb1ydD22YEvo+MwD7WLfRsmdEOnXDBIDv3TgPCzJjIWPzjcc2Lk1DVmokslJ14wYpV5OY\njOZqJsrJcLZJIvJ3DG9ERBRUNixORYxWjb3Hm3GqpgMhShmuLkif9LhNy2Y41pcDgOIzXfjxE58B\nAObNjsE9WxeMOUYmCFiYGYPC4nOOay/Kcr1MAU2dUiFHdlrkhPtMtpZf3VnP1ytzd7ZJ5nQi8hWG\nNyLyWxUVZXjkkf/B0qXLkZubh9LSEuTl5WPdug3Yu3cXdu36CL/97cNunXO84ya6FgUWQRCwKDsO\nNtE+lf+Vy2ZM6bglufF49t71uOORPWO2rRxqsXPlxrUZOFXTidXzk7Blve8Xsr9YKeQTJyaz1fNu\nk4G/7Lb/Kyo6hEcffQjr11+O5OQUNDc3Oc0I/N579vFuTU2NXCaAaBSGNyLyW9nZucjLy8eGDVcg\nKysH69ZtwObNl2Hdug1Yt24Ddu/+eMLjjUYjIiKcp4sf77iJrkWBaXFOHP7w3VWICJv64tGCIODr\nV+Xiue1lAID4yFA8+M3lE67VFq1V488/WH3B9ZJ7ojQjM3nOnRWNr1+Vhz9sO47GoVkoR6/9N1XD\n3SY9aUljj0v3LFmyDDk5eY7fuQCwevVS7Nt3GEVFh7B06XIkJSXj/vt/hiNHDnMGYaIhDG9E5NfO\nH4Oi1WrR22tEeHjEpONTiooOYunS5QgPdw5w4x13/us6nc5xLQpMOg+m6l89Pxmr5yejpaMXURoV\nF9n2U3LZyPdlaW48ojQqpwDlyVIBnG3St0b/zm1qakRKSioAoLm5CS0tzbj22usdrXIMb0R2DG9E\nNCVvVb2PY62nJt/RDYvi5+G/4r485f2bmhqh0WgdYaq5uQlHjhyGwaBHRIRmzGLd4010MNlxw9eK\niNAwuF3EhpcBIP+VnapDRWMP0hLs/07lo7pSetbyZv+/4Ga3yUDPesO/3+UywaOvmyuL4ufhi5nX\nTLpfWVkpenp6sGfPx46lAa677gbH9oqKMlx++cZpqYkoGDC8EZHfG/7jvnfvLtx77y8cr+t0Osen\nsffc850xIUwURZddmSY6brxrEZH/+f6XFuBsZx/SE7UAgMXZcag/ZwQAWD0Z8wZHeiMfyc3NQ1ZW\nDg4fPoi9e3c5dVWvqChDTk6eo1slETG8EdEUfTHzmil9iuoNw3/clyxZhnvu+Q7uvvv7yMrKcWoV\ni4jQoKWlGaIoYu/eXQDsQay5uRn2zlACbrnlqwDg8rikpOQJr0VE/idMrcDsZK3j+dUr0zErSYvf\nbzsBqyeD0IYOcXe2yVGHBqTh3+9xcRq0tXk+S+eFyMvLR1HRIafwVlR0GHfd9V1J6iHyVwxvRBRQ\nIiI0KCsrRVZWDozGkTcZvb1GRwC75ZbbAACffLIbS5YsG9P1cbzjJroWEfk/mSAgd2YUAA/HvDkC\nH5vefG349y1gn2xq9+6PHB+4FRUdctm9nehixPBGRH6roqIM5eVl2LXrIzQ3N6GpqRE6nQ7XXns9\nACAlJdUxdu0rX/namOPHm5jE1XGTXYuIAoN8qNnMozFvQ/93d8ISTnDivuFJSXbt+sjR2yE5OQWf\nfLIbMpkMf/vbX/HSS/+CwWBwe0kYomDG8EZEfis7OxfPPPP8uNt/8pOfT3i8RqN1+bqr4ya7FhEF\nBkEQhibe8HydN4Yx70tOThnzO/c3v/lfx+PVq9f5uCKiwMDwRkRBi1NLE12c5DLhgrpNujvbpP1g\n9w8hInIXF68hIiKioCKXX+CU92x5IyI/xfBGREREQUUmCLB5EN5sHq8UwLRHRL7B8EZERERBRS6X\nweLBOm/Dg94EDnojIj/F8EZERERBRelheLOvCOkZkYPeiMgHGN6IiIgoqIQoZRi0eDbbJFvdiMif\nMbwRERFRUFHKZTB7Et4gerRMAPMeEfkKwxsREREFFaXSs/DGno9E5O8Y3oiIiCioKOUyWG2i2wt1\n2y6k2ySDHxH5AMMbERERBZUQpRwAPGh986zbJBGRrzC8ERERUVBRKuxvb9ydtMQ+YYn712PeIyJf\nYXgjIiJywWqz4pWyN3G6vVTqUshNmlAlAEDfO+jWcaIICIxiROTHGN6IiIhcON1Rhv3NB/HUyefG\nbBuwDMBkdTcYiKjTN8AmejCRBrklLjIUANDW3e/WcSJEj5vROOSNiHyB4Y2IiMiF9v4Ox+M/Hv0b\nzFaz4/lPPv1/uP+zhyY9hyja39K39XXgu3vuxe+K/oInjj/LAOdlkREqAECPmy1vEAEZG96IyI8p\nvHnyHTt2QKvVoqGhAVu3bh13e2NjI7Zs2eLNUoiIiNzyVtX7jseV3TVo7W/H8dZTON1RBhEiei19\nsNqskMvkLo/fXf8p3qx6HzO1aUgMi3e8XtZViZ11e3Fl+mVu19Rv6YfZZoE2ROP+DV1ENGH2bpOG\nPvMkezqziYBHTW+c5YSIfMRrLW8lJSUQBAEFBQUAgNLS0jHb09LSUFBQgNTU1DHbiYiIpLKv6fMx\nrz106A/YfuZj1BsaHa91m3rGPce71R8AAOr0DTh49ojTtrLOCo/q+u3nj+Pn+3/LlrtJRAyFt3c+\nrcG5zj43jhQ54o2I/JrXwtv27duh0dg/GUxLS8OBAwfG7PPYY48BABoaGpCXl+etUoiIiNzyavlb\nAICZmjRkR2aMu19FV7XL1602K2wTjILypOWsta8dPYN6AMCx1pNuH38x0YSGALCPQ/vDthNuHevx\nMm8c9EZEPuC18KbX6xEZGel43t3d7bQ9Pz8fqampWLZsmdN+REREUjnX14bv7P5vx/NwZRhuy79p\n3P1fLHt9zGtdA90o6SyHTbRhtm6m4/VLk5fjR5fcDQAw2ywuzzdoHXS57c3Kf+PXn//O8fwfxS/D\nYnV9DhppeQOAbqNpyscxfxGRv/PqmLeJGAwGzJw5Ew8++CDuv/9+R5gjIiLylq6BbugHDYiLy3e5\n/Z/Frzg9v2rWFYhSR+L3ax/EH4/+DfWGRsSGxuDK9A14sXQbAHsQU8pG/pz+8djTjslOcqKycGny\nCiRHJCFNkwxRFCEX5DjZXgzjYC8iQsIdx4miiB99cj9EiPjL+ochQMDexs/wRuV7Lmst76hBvJB0\nQV+PYKVSjoxDDA9VTrDneTxMb+xqSUS+4rXwptPpHK1t57fCAcBrr72Gm2++GREREdBoNPjwww9x\n5513TnjOuLjAH6AdDPcABMd98B78R7Dcx8UikL9f33nN3qr2wsw/ocF8Bi+deBvrZ6/C88ffcNrv\n6eseRlSozum1x676hdPz4u5iHGspxhlTNVbOWAIAOGdsc5qlck5KBpalLnQ6bni82gMHH8HWOdfg\naMtp/NfSW/HA7t/bp6oH8O+G7ZALcnxQuWfMPaxLL8DeM4U43HQCty/K9uTL4He8+TMVqlJM+fxy\nhQwymcyjehQKz47zN8FwD0TBzGvhbfPmzSguLgZgH9O2atUqAPYWN41GA0EQEBERAQAoKChAY2Pj\nuOca1tZm8Fa5PhEXpwn4ewCC4z54D/4jGO7jYnuzE+jfLwD4yY4Hcc7YBgBjghsAWIwytBknvs/0\n8HQcQzH+WPgsskJzAAA/2POA0z5hlvF/vvvNA/jX0LW/+/4vnbbtrPrU5TFquQor41dg75lC1HU3\nBsX3wtu/A+QyYcrnt1hsEEXRo3osFmvAfz/4+5jI/3ltzFt+vr1LSmFhIXQ6nWNCkttvvx0AcMcd\nd+CZZ57Bzp078frrr3OpACIi8qqzva2Ox8PBzZVIlW7cbaMtipvneFynb0DXQPeYWSAj1WPHdE80\nhm4i9y27B4+u+TVSIpIQoQxHe1+XR+eh8YmcdYSI/JxXx7y5CmRvvvmm4/Fk3SSJiIimy2sV74y7\nTSbI8Od1/4uW3nPQhERM6XwxodFYlngJDp09it8V/QUL4+YCABLC4rAicQmae89CJQ8Zc9yyxEvQ\nYGjC7oZ9U649MSweKREj49uSwhNQ2V2Dqu5aZEbOmvJ5LiZb1mfg9T3ViNWp3TrOk9kmucwbEfmK\nZBOWEBER+VJFV9W42yKU4RAEAckRiW6dMz86B4fOHgUAHG87DQC4Mn0DliVeMuFxaZoUl69fOfMy\nXD5zHf5V8go6B7pxc84X0W3qQX6089i2y9JWo7K7BiUd5Qxv41g9Pxmv73G9lAMRUaBieCMioqA3\nYLFPF58XnY1FcfNQ31+Plp52RKq0ONJ6Amab2aPzLklYiHerP0CXaWQ5nOTwyQPgbF26o54bs66F\n2WZGRVc11qauglKmwF3zvz7h8cPhr62/3aO6LwZymb05zN2ekJ42orHDJRH5AsMbEREFPf2gfRIG\nXYgWq1KW4/q4y9HWZkBFVxWOtJ7AutRLPTqvIAhYlbwM79fuBAAsjl+AU0rF9AAAIABJREFUVE3y\npMfFhkbjNwU/hyYkHCFDXStnaKa+XI5OpUWIXIljrac8qvtiIBsKb1bb1GMVh7wRkb9jeCMioqA3\nHN60KueZ6LKjMvHgyvugU2k9PndMaLTj8Q2ZV7txXJTH15QJMkSqtWjt7UCz8azb3T0vBrKhgWg2\nm22SPUeIgIcD2DjojYh8g+GNiIiCXmlHOQBAGzJ2GvEoFzNCumNJwkIYzb1YFDfvgs/ljuWpi/Dv\n8o/R0svw5orcg5Y3gDGMiPyb15YKICIi8hcf1u0GAKjkqmk/t0yQ4bK01T4NbgAwLyEXAPCP4pfx\n7OkXfXrtQDDcgOZWdruQfpPscklEPsDwRkREQc1o7nU8XpywQMJKpleiJt7x+GjrSQkr8U+CIEAu\nE2BzZ8wbwKY3IvJrDG9ERBTUqrtrHY9drbsWqOLCop2eD1o9mzEzmAmC4JNuk1znjYh8heGNiIiC\n2pFzJwAAa1IKJK5kesllcqfnJ9uLJarEf8llAmzudIVk10ci8nMMb0REFNTO9rUCAK5Mv1ziSqbf\nr1b8FDLB/qf8ueKXcd/+36K2p17iqvyHzM1uk4C9tc4TzH1E5AsMb0REFLQOnT2KJmMLIlU66FRj\nZ5oMdAlhcXhgxb2O5z2DBjx25K8SVuRf5DIBFqs7SwUwghGRf2N4IyKioGQTbTjYcgQAsC51lcTV\neE9MaBQeX/Nbp9cMg0aJqvEvKqUcg2brlPf3dLJJjnkjIl9heCMioqD0fMk2lHVVAgCumLlO2mK8\nTK1Q4cGV9zmev1b+toTV+A+1So6BwamHN4BBjIj8G8MbEREFpcPnjkpdgk9FqSORH5MDALCI7gWW\nYKVWuh/ePCVeyBpxRERTxPBGRERBTbiIFu761tzbAAB95j6JK/EP6hA5rDYRZsvUxr2JIpd5IyL/\nxvBGRERBx2obaW0JpoW5J6OUKxEfFouW3nNsCQKgClEAAExujHvzJL5dTB8QEJG0GN6IiCjonGwv\ncTy+dvYmCSvxvaSwBPRZ+mE090pdiuTUIfa18AZMlikewcBLRP6N4Y2IiIKKxWbBM6dfAADckHk1\nYkNjJK7It6LUkQCALlO3xJVIzxHeRrW8fXaqBS/sKB/3GE5YQkT+jOGNiIiCSrPxrONxdmSGhJVI\nQ6fSAgD0JoPElUhPNRzeRk1a8ux/SrHnWBPMlrFdKdnuRkT+juGNiIiCin5wJLQMt0JdTHQh9vB2\nou00BiwDElcjLfXwmLcpzjjp8TBBttYRkY8wvBERUVA529cKANg4cz00IRESV+N7WpUGAHCg5TAe\nOvQHiauRllo53PI2dszbeEGN3SaJyJ8xvBERUVDZcWY3AGBJwkKJK5FGpErneNwx0IU6fYOE1UhL\n7aLb5LDpnoyTk3sSkS8wvBERUdA40VaMPks/lDIFksMTpS5HEolh8U7Pa3rqJKpEemqVvdukq/Bm\nc5G2RFHktP9E5NcY3oiIKGgcaz0JANiS/QUIF2n/N0EQcEPm1Y7n7f0dElYjLdWE3SbH6zfp/nUu\nzp80IpICwxsREQWNXksfAGBJwiKJK5HWhrQ1+N7CbwIADINGiauRznC3SVeLdNtcZDf2fCQif8fw\nRkREQaPf3A+ZIEOITCl1KZISBAGZkbMAAEdaT4zfyhTkJh7z5vpr4mkr2sX5FSYiX2N4IyKioNFn\n6UeYIvSi7TI5mkKmcDwuOndcwkqko1TY3+ZYLLYx21xmNyYwIvJzDG9ERBQURFFEt6kH2hCN1KX4\njeHWt3+WvCJxJdJQyu1vc8wuwpvLCUsAj9YK4GcFROQrDG9ERBQUes19MFkHERsaI3UpfmOWdqbj\ncedAl4SVSEMx1PJmttrDW++A2bFtGucrISLyGYY3IiIKCh0DnQCAmNAoiSvxH+vSVjkeX4wLdg93\nmxxuedt7rMmxzeWYtwsZG3iRjiskIt9ieCMioqDQ3j8U3tTRElfiPyJVOnxr3tcAAP2WAYmr8T1H\nt8mhlrfR3SfH6zbJLpBE5M8Y3oiIKCgMt7zFhjK8jTYnJsfxeNBqhmHQiN31n8Im2lDYUoT3qj+U\nsDrvUpw3YclwiANcLxXgOSY+IvINxeS7EBER+b8Otry5pJApsChuHo61ncLJ9mLsb/ocld01KOuq\nQnFHGQBgflw+ZmhSIROC6zNdmSBALhNctry56jbJno9E5O+C67c0ERFdtDqGJuSIVnPM2/lKOysA\nAM8Vv4zK7hoAcAQ3AHi06K/4d80OSWrzNoVC5ghtFutIOht3whIP+00y9xGRLzC8ERFRwLParGg0\nNEOjjIBaoZK6HL+zPm31pPvsrv/UB5X4nlIuc4Q2m23iljciIn/H8EZERAGvvb8DBrMROdGZUpfi\nl66adTmUsrEjJcIUoY7HFtHqy5J8RqmQwWyx39voVjWbi0Fvoih6NHqNk5wQka8wvBERUcDTDxoA\ngGu8jUMmyDA3Nn/M699e8A0JqvGt0S1vo8PbuA1vDGJE5Mc4YQkREQW8bpMeAKAN0Uhcif9an3op\njrWexJUzL8Oa1FUIkSsQqgjFLO1M1OrrpC7Pa5QKGfpMFgCAbFQwc7VUwAVhL0wi8gG2vBERUcBr\nNDYDABLD4iWuxH9lRKbj0dUP4OrZG6FTaRA61GXytvybIECATJDBJtomOUvgUchHJiyRTdLyJops\neCMi/8bwRkREAe90RxmUMgVmaFOlLsWvhSnDxiwHEB8Wi4Xx82ATbY7up8FEqZDBYrXBJorO3SbH\nbSpzP74x8BGRrzC8ERFRQOsx6XG29xxEUUSoQi11OQEpSqUDYP9aBhuFXIDVJuLOR/ag/txIOLW5\naGRkz0ci8ncMb0REFNDertoOIHhnS/QFnUoLAOgy9UhcyfRTKuSOx+UN3Y7H4y0V4OnMkeO35BER\nTR+vTliyY8cOaLVaNDQ0YOvWrWO2l5SUoKGhAT09PS63ExERTcZoNkpdQsCLHGp56x4IvvCmkLtO\nY66zGwMYEfk3r7W8lZSUQBAEFBQUAABKS0vH7PP0009j06ZNMBgMLrcTERFNZrir5PzYORJXErhi\nQ6MBAG397RJXMv2UCtdvdVzNNunxhCVc6I2IfMRr4W379u3QaOxTNqelpeHAgQNO23fs2IH58+cD\nAO644w7k5eV5qxQiIgpSNtGG2p56qOVq3Dn3VqnLCVgJYfEQIKCmJ/iWDBgvvI3XbZKzjxCRP/Na\neNPr9YiMjHQ87+7udtp+6tQpdHd3o6SkBM8884y3yiAioiD2WfMhdJm6kRmZDrlMPvkB5FKoQo28\n6GzUGxqxvfYjqcuZVkr5eC1vY1+7kE6T071sHBGRK5JOWBIZGYn8/HwA9pY4IiIid7xa/hYAoMl4\nVuJKAp9WZe8t858gC2/dxkGXr7tseRMBgU1vROTHvDZhiU6nc7S2nd8KB9iDW1paGgBAq9Xi9OnT\n2LRp04TnjIvTeKdYHwqGewCC4z54D/4jWO7jYuFP3y+lXAmz1YwfrPyG23X50314ajrvYUvIZnze\nUgQA+POJv+E3G37itC6aN3nze1F3zvXadVpt6JjrCgKgVMrcrkcQAJnM/eP8UTDcA1Ew81p427x5\nM4qLiwEADQ0NWLVqFQDAYDBAo9Fg06ZN2LlzJwB7uJs3b96k52xrC+zFQ+PiNAF/D0Bw3AfvwX8E\nw31cbG92/OX7JYoiFIIc0WFRiBMS3aorWH7upvMe1NBg48z12Fm3B+UdNbhp29343sJvIjc6a9qu\n4Yq3vxcqpevutF3dfWOuaxNFmC02j+qxWj07zp8Ey78LomDmtW6Tw90hCwsLodPpHBOS3H777QDs\nk5hotVrs2LEDPT092Lhxo7dKISKiIGQwG9FvGUBCWJzUpQSNULnzIucHmg9JVMn0GW9iknHXefNm\nMUREF8ir67xt2bJlzGtvvvnmmO2TdZckIiI6X2uffVp7hrfpY7I5jw870noCt1q3IEQeIlFFFy5n\nRhTOdfWPed3VhCVc5o2I/J2kE5YQERF5qttkX1A6Sh05yZ40VVFDi3WP9k71BxJUMn2+fLnrbp+u\nWt5EeLZkG1vriMhXvNryRkRE5C0miwkAoJarJK4keKxMXoZQhRpR6ii8UvYmmnvPoiXAZ/Icb8zb\n+FP7M4oRkf9iyxsREQWkAetQeFMwvE0XmSDD4oSFmK2biZ8v+yEAoKK7Gn3msd0OA53NRb/JC1mr\njeu8EZEvMLwREVFAGg5vKra8eYVMGHmL8HnLYQkruXCZKWO7g44zjYlH3SaJiHyF4Y2IiAKSo9sk\nW968JkplH09ospolruTCfHVTzpjXpnW2SSY+IvIRhjciIgpIBrMRABChDJe4kuD1zXlfBQD0Wfok\nruTCyGRjw5XN1YQl7PpIRH6O4Y2IiAJSt0kPANCFaCWuJHhpQ+wLHtf21EtcyYVRuAhvom2cnT1u\nRGPyIyLvY3gjIqKA1GPSI1wZBqVcKXUpQStKHYn4sFg0GZvH7WYYCORTbHkjIvJ3DG9ERBSQekw9\niHSxLhlNr8SwBAzazOgN4K6TcvnYtzuuspsoAoIHTW8c8UZEvsLwRkREAaff0o8Bq4ldJn0gQhkG\nANhxZrfElXjOVcvbuC2JTGJE5McY3oiIKOA0DS0cnRSeIHElwa970D62cHfDPnxn93+jsqta4orc\nN+UJSy5g3Bo7YRKRLzC8ERFRwGkwNAEAUjXJElcS/G7IuNrp+VtV70tUiedct7y52FFkwxsR+TeG\nNyIiCjj1hkYAwExNqsSVBL/kiESsSl7meB6ILUwK+dS7TXoS3rjMGxH5CsMbEREFnGbjWYTIlIgL\ni5W6lItCckSS43GoXC1hJZ6Ry8a+3bG5mrDEB7UQEV0IhjciIgo4PSY9IlU6yAT+GfOF5YmX4MqZ\nlwFAQM466WrM2/gTlnjWjMaVB4jIF/hXj4iIAorVZoXR3AutSiN1KReNUEUors24EunaGWg2nkWf\nOfAC3LB1C+3jJF21vBER+TuGNyIiCigGsxEiRC4TIIEZmlSIENFl6pG6FI8lxYYDGNvyNvzcozFv\nnOaEiHyE4e0CdeoHUF7f5dYxoijinX01KKtz7zipma1mWGwWSa79Ud1eHG09Kcm1ici/6E0GAIA2\nhC1vvqYbau3sMeklrsRzaqUcwPjdHDn5CBH5M4Y3NxTXduIbD+/GfwrPYNeRRoiiiF8+cxCPvHwM\nXQbTuMeJogirzeZ43tLRh38fP47fvf75uMdYbVaUd1aN3yffVX0d5SjvqMIPPnwI333lGbToO3C6\nvdRpn8quatTrG7Gzbg8eP/IEBq2DaDA0O+3Tb+mHcbAXVpsVhkGj4/VfHngIv/n80Unr6Db1oM/c\nDwBoMragrLPSafvp9lI8eeIfONVe4vJ4i82Cfks/RFFEn7kfrX3teKd6O549/eKUvg5EFNw6B+wf\nfOlUbHnzNe1Qa+fpjjKJK/GcKmQ4vJ3X8iZFMUREblJIXUAgefy14wCANz+pAQD8p/AMBgatAIA3\n9lbhzmvysftoE1RKOZo7enHD6tlQKmR4v7AOb39ag7/fdznkANr6uqCeMxzcNjtdo66nCeUd1ahu\nP4vTxiJEh8Ri46zV6OjvQlV3DdanrcYl8fNhE20QBAF/PPQPZESl40BjEYzotJ8kBEBCNx4sesT+\n9P+3d+eBUd91/sef3zkzySSTkxwQbkI5S1sKBnrZ2iI9rK2WovXoWtddz5/a7m9dXXV1qz91rbeu\nR911PXYt3aqrbivV1tqDFGgL5Uig3ElICLmvSTLn748JA4FAIGTm+/1OXo9/mO93JjOvLzP5Zt7f\nzxXO55+v+xhNwaN8c9sPR7zex/7yjyO2v7D6U3zqhS+ccewz8irpC/fTF+5nIDxINBbl94eepK59\nL/csuItHX/stB7oP8eFlf823t/8IvzuHj13+fr645esAvOOSu5ieN42OwU6+v+MnAOxu38N3r/8K\nr7buYsuxbaydeQNbWl7hqfpnz/oexONxDF0WFZnUjgwvEzBdywSkXZ7HD8BfGl9gXdXtJqe5MPff\nvYz6ll6yvYmvPmcs0q3qTURsQMXbeYqNMrK5qy+UvF2zu4Wa3S0j7i/we7lifgm/eXE3rrJmvviT\nLbxn7SV8+zcv4V2ceMyfduzlTzUdRL3t/O3NK/n6ju8Rd4STz9ERauOXe3+d3D60+xc8cfgpmvuP\nJfcd6H/tnNlD7i7+/oXPntdxjla4ARzpaUjefvevPobP5WMgkmhd+9LWbybv+/b2HwHQF+7nnzd/\nNbn/53seHfV5nzv6Ir/c+ysAtrfuHDPfH+uf4aYZrycWj/Hg5oeozJ1KU98xlhYv5LY5bxzz50XE\n/npCiW6TBVn5JieZfLLdvuTt5v4WynNKTUxzYRbNKmTRrEJ2H0pc6DzbhCXjukCoa4oikiYq3s6h\nub2fHQfaqW/pTZ7sx+QM47viKQB++UIf//WUn6xlNRieEE19LXxh269xlpxcL2fDq8/gnncAgK9u\nfRmHLzzq047IdUrhZpYThdvFOlG4na//OfAE2S4f7YOdtARbaQm2AtDUf4xgZJBnj26iuvxK7p5/\nBy7DqVY6kQzUH+4HwO/ONjnJ5DMrb0by9oObH+K713/FxDTjc2LVgDO7TarpTUSsT8XbaZ57tYnW\n7kHuvGY2n/rR5rM+7oqqEtp6BqmoDLO1oY542IsjpwtXWX3yMVlLnyfaWYLhSbTQOfyJ2blcpScf\n4556IHnb4euf6MOxpKG6KyHuwLvw7P+/5/JfZyn4nj26CYCa5q3UNG8F4CPL3sf8wrnjCyoilhOK\nhtk5PJY3y2W/xaLtzjAM7lv8juQY5PqeRqbn2av76omLehO9LtuFjFEXERkvTVhymn9/Yg+/33SY\nbz822syGcd72NgfvvMdDfPZm3vOWCl5zb8Q9fS+eOTtGFG4nOAtax50l2lmSvB1pqyB8dA7hplmj\nPjYecRMbzGZV3hu5rfRtyf2Dr15NtLtw9OfvOXN/uKGKcOPJYicW9J/yGmfW+qH9lyaeq7uI0JFL\niHZOSd43tPcKwvXzT3v8UmK9RcT6Ckbsj7ROHTXjVRUred+Sd7Ewa+Wo94/lW9t/yPFgK20D7YSi\nobF/QEQsrbZjLwAOw6EFuk1y+ZSlydtffulbJiYZnxMLdp86kRhokW0RsQe1vJ3i1Ktm2/a1nXF/\n1cIwvzmwMbld13HusWbj9a4Fd/OH/c/zzmvW89DOhwAIHzz5x9JdcSiRN+QFZxjDGeOmyhu4rnI1\n+X4vQ5EhnmktYEXJCkpuWMDjL5bQ7ftfDM8Qi2Jr2dG3mfChRdyxcjG/3bkF18ydhI8swBPLwzsY\noH8wQmzATzyURbw/H/fMXRB3EO0oxbtgazJHPOog2lHOwNYpEHcABtHWaTjzW4l2lAEGse4Sol0l\nZC19PvFDjpN/LMONc3FP28/grlXEg7mJ55//CgChA0tw5LVzuHkGd65bwDefPY5vxfj+Pz932gyZ\nd1fdwVtKbhrfk4mIqdoG2gFYV/Vmk5NMbldVrOT5pkTvid5QH7ke/xg/YR05WYmvPv0Doy99oyFv\nImJlKt5O0T945om8OJDF294wj8FQlN3xP9Fw/NzP8emV91OaPYUP/fnvx3y9HJef/kgfvo7FPLDm\nFn6+9xFKc4pZUXY5K8uvAGB95b00HBskuDiLprZ+3nLtHL796hYc/k6+ccM/8VpzKyF3G8vKFiav\nQntdXr503T8kX+fqpRV87ucR6jtaecd7VzM4tJLC27JwOgxuXTWLP2+7ikajj3felGgle2FnMz/+\nX3A6DKLECR9enHyugS1vBEcEV9lhIseGWwHjzpMHFXMR7Tg5pg8gPugnHjcwjPhwkQcfvGMx3/11\nnEjz7OS+WHcJ4eaZRDvKiPfnE22fyj56eOLFIwCEDi3CM2s3Q7Urx93lEuCR137NLUuuHffPi4h5\n2gYS449n5U03Ocnk5j+lWNvXdXBEa5zV+X1uAHoHRh9jrkJMRKxMxdspOnoGz9h33y0LmD+9gD8c\nfpptB8deJNrv8WMYBh+7/P009jbhdDh5tXUXb5l3G2SF+dXOjdw+Zy3FviKyXF4ON/dQnO/D73Pz\nwPIPnPF8V89bCPNG7st/8hqO7xnAfYODxdPLgLIxc/3D3dUMhKLkZXvIy/aMuO/1l43ssrh6STmr\nFpdhGAa/fvYgwcEIt189i30NXXz7Vzsh5iLSlOhauf6GefzyqZHruI1maNcqXCWNRNvL+Ke/upLp\npbnccc0cfv3swVMeZRBpuOSMn/3tC4cBiLZWMtA6DTAY2HYdRF0Qd1BYHqQvrxZn3nlOKgP88KX/\n5G1z3nrejxcRa2gfLt6KfKN3B5f0uHH6dWxq2kJPqDc5gYxd5PjcGEBX38j1WS+226R6XYpIOqh4\nGxYKR/mnf986Yt9da0voch/kay9v5kD34bP+7NqZN/C68uW0Btvxu3MAmJs/i7n5iZapq6e+DoCS\nklzKnSMHds8sv/BFZj//npVEYxe23pnH7cTjdo79wGEnnvuOa2Yn911WVcI/37eCT/94S3LfDVdM\npb17kOrFpXT3hTjQ1I0/y83yS6bg87r4v/+6if7BCPGBXML1CwConJK4Yntr9QxuurKScCTGk1sb\n2LrnOC0dwXPmumxeSaJLa/jkRAUdTX5oWoGR1Yez4DjuyrG7sz5/ZAszs2cyM6/SVlNdi0x2bYOJ\n86xPk5WYKsvl5V0L7+Y72x+mL3Tu87bVuJwOppbksL+xm46eQQrzTnyWhssvzVIsIham0d5Ae/cg\nH/z6aQtDu4b4ffvP+GndI8nCzeP08MXV/8jamTckH3b/FR/g1tlrKPYVsaCoKi15PW4nPq85dffU\nEj+/fPBmbls1k+99/BqcDgdve8M8ZpblcencYu68Zg43rZhOYV4WPq+LKy+ZMuLnVyyYkiwMDcPA\n63bi97m585rZfODNie6ZM0pzueHyabicI/+AfuVvq5k7NZDcXja3eMT98UE/kebZIyZcOdUDV3yQ\n9fPvSG7/vG4DD25+aPz/GSKSVrF4jI6BToqy1OpmBQFP4uLjK8dfNTnJhTtRsD3wvU08+uf9vLz3\n5JiI8ZRuqvdEJF3U8gb8x8Y9RE9ZrfN9ty1ke0sdpy8Zfd201QS8edw6ew2RWJSnGp6lImfsLouZ\nJsfnHtEidy7rb5jHtcum4nAYPPtqE3dff/Zp+yun+PnUu66grDCbnCw399xUxQe+9hcGQ1FWLymj\nON/HqiXlvFjbwvrr57JgZiGxeJz3fvnPI54n0jSXyLGZuCoO8s319/J3z36WOHFyPX6qCs58/QNd\nh5mTP/OC/g9EJP26h3qIxKMUq8ukJZTlJC7ONfUfY3Pzy8mx2naQfcoF0Cc2J2aK/v79GgstItan\n4o1EyxvA/euXUZjrpbwoh87s3ew8dPIxH1r2Xqry5yS33zz3Zt489+Z0R7Udj9vJjLJcAO65ceyW\nyTkVgRHb3/nYNQA4hi9rBnI8fO49J6eddBgGH7pzCd/51WmldsxFpLGKb2+o5WM3fpSg0UnAXcD+\nxi5unXUTvz/0ZPKhX3vle3zo0vemreVURMbnxEyTGu9mDacu1bCrvc5Wxds5e6+MtxVNg95EJA0m\nfbfJ7r4hmtuD+LwuFs0spLwoh9c69/P7QyeXBFhVvoIFhVU4Hec/ZkwmhsMwkoXb2Vw2r5jCPG9y\nO+D3MHO4YNxT38WXf7KX4PEi/uarz/Avv9zOo484uDQwct2477z68MSHF5EJ9Y1tPwCgMCvf5CRy\nwuerPwFgu3U0RyveVHuJiB1M+uLtpxsTC776vInC7Ps7fsI3t/0wef9nVj7AuqrbTckm58cwDF63\n8GT31Q/fuTS5CCtAJBrjB7/dPeJnXvxjAVX5Z+/CKSLWcuo6nLlu+6wplumKfIX4XFl0DHaZHeWC\neNyjfP05MV/J+Ea9XVQeEZHzNemLt47exFTBn7jncgB2ttUm77tpxuspzZmC2+k2JZucv1uGZ678\nyFuXMrsiL7lm3blcHbiVL6z+FAA+ly/VEUXkIrQOtCVvLy1ZZGISOV2BN992xZvbefavP5p8RESs\nbNIXbwODEQJ+D8UBH72hvhH3zR9lcguxJp/Xxfob5iVnoJxRlsvqxaNPJnPF/BIAvvPoHvK9Aabn\nTiMSG32xVhGxhuPBRPF22+w3jhhrJeYrzMpnMDpIMGyfJQOcoxRv8YvsOKlulyKSDpN+wpLeSBcs\n/DMffPq3I/bff8UHmR2YYVIqmQhvv7GKKQU+fv1cYuaZ6kWldPYOUVaYnXxMW9cA2S4f4ViETzz/\nef5++Uco0HgaEcsJRgYA8Luzx3ikpFvh8NIN7YNdZNvk/XE7z2xeu9hFukVE0mHSFm+RaIwPf+M5\nomWHOL1TpNfpUeGWAXxeF7etnsXrL59GR88g00sTk5jsOtjO/9YcAeD/fr+GtbdPYw/76A318bkX\n/4VvXPcFM2OLyCiC4UTxZpfiYDIp8hUA0D7QTmVuhclpzs9oLW8naJ03EbGySdv35FBzD0PhCO7y\nw2fc9+Y5t6Q/kKSM3+dOFm4Ai2cX8aG7Lk1uP/G7k9cwwrEwP697NK35RGRs/ZFEl7xsjU+1nAJv\nYomXH+36mclJzt+5xryJiFjZpD17PfVyI0ZO94h9f7/8I3z3+q9wzbRqk1JJuqx53cyTG7GRS0DU\nNG8lEoukN5CInNPAcMtbjlreLGfWKT1V4jbpe+gcpdvkCcZ4m9FscuwiYm+TtnjbUnccwzOY3H7z\nnJuZnjfNxESSbp9+9/Kz3tcx2JnGJCIyFrW8WVdhVgGLiy4BoCfUa3Ka8zNay5tqLxGxg0lZvMVi\niTP0qcXbVVNXnu3hkqFmleclb0faEzNTRrsSM1G2DrSbkklERndiJkONebOmytzExc8XmjabnOT8\nuFyjff0Zf/WmMW8iki4pLd42btxITU0NGzZsOOfjHn744VTGOEOtPkzpAAAgAElEQVTfQBgcETwz\n9gCQ5fSS5cxKawaxhvKixBfB8IFlDGx5I9H2cgBagyreRKwkGBnAYTjIcnrNjiKjqC5P9GTY13XI\n5CTnx+U4V7fJNAYREblAKSveamtrMQyD6urE+LG6urpRH1dTU0NNTU2qYoxq+4HjuMqOJLc/ueLj\n4+/jLrb2mXuvHLEdG8wB4Pm9++joGRztR0TEBP3hxLIeOldbU5GvkIAnl/aBDrOjnJfRWt4uttek\nel2KSDqkrHh7/PHHyc1NzPBXWVnJpk2bUvVSF+znr2zEPW1fcrsgK2BiGjGT1+3kM/cu5yvvr+b2\nq2YRH0gUb82OWn725F6T04nICcFIkGy3xrtZWUFWAe2DHfSG+syOMiaXxryJiE2lrHjr6ekhP//k\nYsddXV1nPKa2tpbq6uq0zk4Vj8cxsnuS25+48v/gMCbl0D8ZNrMsj+KAj9uvmsXSWaXJ/bv7XqKn\nP2RiMhGBxHk7GB4gx6Xxbla2oLAKgL2d+01OMrbRircTxte6qxZhEUkPU6uW7u7usR80wf64tQGi\nJ5flLvEVpz2DWNdbr52TvO2evpcHfv1jthx7hVBURZyIWYaiIaLxKD61vFna3PxZADT3t5icZGyu\ncywVICJiZa6xHzI+gUAg2dp2eiscnGx1g/O/ylVSkjv2g8bwy6f341tRD8Dnr3+AypL0Fm8TcQxW\nkAnHMdoxlJTk8q0Zn+cj//sZANwVh/iP2kP0LlzL+iVvSnfEMWXC+wCZcxyTRbrfr7ZgGIBCf2BC\nXzsTPndWOgaXfw5shz8cfoq4K8L6JW8ix3N+raXpPo6o48xr14WFiW7zXq9rXHkMw7DU+zFemXAM\nIpksZcXb2rVr2b17NwANDQ2sXr0agN7eXnJzc2loaKCxsZGuri46Ozupq6tjwYIF53zO1taLXz/G\nl3WyUMyN5k/Ic56vkpLctL5eqmTCcZzrGJxk8cnlD/Dgc/+Kw9cPwMuNu7ih7PXpjDimTHgfIDOO\nY7J92Un3+9XY2wqAM+qesNfOlM+dlY4hHj/593Xj/r+wuWE7X1j9qTF/zozj6OkdOmNfe3tirF5o\nKDKuPLFY3FLvx3hY7TM1HpPtfCyTT8q6TS5cuBBIzCYZCASShdm9994LwJo1a7jpppsA6OtLz+Dm\n/Ue7iM19DoBKfwUepyctryv2MzVvCuFDi5PbdugGJJKpglqg2xYMw2BGbmVyu2so/UMjztdo3SaT\no+/H0aNSk6CKSLqkrOUN4K677jpj32OPPTZie926daxbty6VMZK+9D9/xHtJolC8onRZWl5T7Mvv\nySE8fDsUDXG0r5mp/nJTM4lMRsHwAAA5WqDb8t6z+B4+W/Ol5Hb7QAdFvkITE43unBOWpDGHiMiF\nmjTTLB5s6sE1JTHWbXr2TK6rvMrkRGJ1//DWqwk3zcKIJBZw/+KWr/PI3t+YnEpk8ulXy5ttFPsK\n+cCl70lutwRbTUxzdqMWb1oqQERsYFIUb0eO9fLgT1/CWZjo+vbORXfidqS00VEyQGlhDoV9yxjY\ntTK579mjmzimLpQiaXWi5U3rvNnDoqJLWD//TgD6wv0mpxndubtNqu1NRKxrUhRvn/vJ1hHbpTkl\nJiURu5lVkUc85GNg23XJfaFY+Ow/ICITLhgZLt60zptt+N2JmRv7LLpgt2EYZ+0eqVXeRMTKJkXx\nBoARAyDaXYTT4TQ5jNjFzLLhWavCWWTHCwAYipw5S5mIpE4wPNxtUi1vtlHhLwNgX9chk5OcncNx\nWskVV79JEbG+jC/ewpEoAM6CRFe3wuKYmXHEZgI5J2ck7W5MrAm4o63WrDgik5Ja3uynNLuEXLef\nA12HiMWt+Xc3dlqxdmJrvL0m4yr+RCQNMr54Cw5GwBHBM/dVALrDnSYnEjspCmQlbxvexBfIpxue\nMyuOyKSkMW/2NCd/Fv2RIP+973dmRxmVai0RsaOML94ONPXgXfJ8cruqYK6JacRu5lQEkrcjx2Ym\nb+9U65tI2gQjQTwOtyaaspk3zV4DwHNHa4jGoianGdvFFHOGJjkRkTTJ+OLteGcQh3cwuf03S95t\nYhqxG4fD4OsfWk1FcQ7xQX9y/5/q/2JiKpHJpT88QLbWeLOd0pwpLC9dRiwes/SC3adTISYiVpbx\nxVv74MluksW+IrJcXhPTiB0F/F7++b4VI/Zp0hKR9AlGBrTGm02V+IoAaBvoMDmJiEhmyPjirX5g\nPwCrSq7iUys+bnIasSvDMHjT6pmEDi4BwKcvkiJp0T7QwUBkgIA3z+woMg5FyeKt3eQkZ/q7t102\nYvvEhCNqdxMRK8vo4m3jrldp9GwBYEXZ5XicbpMTiZ1NL80l2jaVbCNAQ99Ry86gJpJJDvXUA7Cw\naL7JSWQ8yrKnAHC0/5jJSc60YEbB6HeoehMRC8vY4q1zsIvfHv9Fcntu8VQT00gmOLHmW6y3gIHI\nIIeHv1SKSOq0BFsBKM8uNTmJjMdUfzlep4dXW3eZHUVEJCNkbPG2t/VI8nbe8WoNQJaLVpiXxazy\nXPpaEjNQHulpNDmRSObrGuwCoDAr3+QkMh4ep5uqgrl0DXXz9Vf+1dKzTp6YbXK83xa09ICIpEPG\nFm+7mk5+sb59xUITk0gmqZziJzKYGO/2PweeIByLmJxIJLN1Ds9SGPAGxnikWFV5TqLVdH/XIQ50\nHzY3zHnRxV4Rsa6MLd66h//gx0M+Lp8+2+Q0kimqKvOJBxMTJ4RjYV7r3G9yIpHM1jXUjc/l00zB\nNnaieAM43G3d7uZxxt90ps49IpIuGVm8xeIx9vfvAeCvL3kvHpcmKpGJkZfjgbiDoT3LgUTrm4ik\nRl+on+b+FgrU6mZry0qWcH3l1QAc7DkyxqPNp0JMRKwsI4u3H736n8mFucvyNE5CJo7X7QQg1lPM\n1OwKjvY1MxAZMDmVSGb6Wd0GAAqzzjIroNiCx+nmLfNuo8Cbz/6uQxzpaaDeimOGL3LMmoa8iUg6\nZFzx9scjz7CjY0dyu7ww18Q0kmnmTg3gdCQuy8b6ExcG2gY6z/UjIjJOu9rrgMRSL2J/swLTGYgM\n8JWXvs2XX/pWciZRqzhRfKnhTUSsLKOKt2caXuA3Bx5Pbt9Vca95YSQjGYbBh+5MLNRd35CYNa1j\nUMWbyESLnzJ132VTlpiYRCbKVH/5iO1vbfuhSUnGMI7qzVDJJyJpklHF2x8OPzVie/VcLeoqE8/p\nTPyRjoeyAHipZZsKOJEJNhQNAYnFuR1GRv2pmrSuqngdq8qv5K6q24HEZDTH+o+bnOokdXsUETvI\nmL+I4WiY/kgwuZ03NBO3y2liIslUc6cmJk+IDSWWDHjl+A4+ven/mRlJJOP0hxPn8xxXtslJZKL4\nPTncs+Aurp26KrmvN9RnYqLTDLf2jr8VTeWfiKRexhRv9b1HicVjRI7NYGDrjXx4xbvMjiQZKsvj\n4rJ5xcT780bsj2uFVpEJ0x/pByDbreIt0xinTOd46kVXy1APSBGxsIwo3rqHevnaK98DINpTyKyy\nAiqK/CankkxWFMgCHNxQuja5rzdsoSvIIjbXPdQDQL4nb4xHih29Z9E9APxo509NTnLSRV1+U8En\nImmSEcXbqWPdYj3FLL+kxMQ0MhlUFOUAUBSZm9zXNtBhVhyRjNM52AVAQZaWe8lEc/JnJm8391pn\n3BuoDhMRa8uI4i0Wj56y4SQv22NeGJkUKooTxdvxjiHWz78DgLaBdjMjiWSU48E2AIp9RSYnkVTI\n9wZ444zrAdhxrM7kNMMudp039ZwXkTTIiOJtKBoGYLn7NgACOSreJLVOFG9Nbf0UZyW+XLaqeBOZ\nMC0DiTXAynKmmJxEUmVJyUIAmvvMa3k79ftCcp03Nb2JiIVlRPHWPtiBAwedzYlxbgG/1+REkun8\nPjd+n5vmjiD5WYnZJ3tCvSanEskcvUO9eJwefK4ss6NIipT4igE4ZmK3yU+8I7EAfGHeqd8bLrx6\nU8EnIuli++Jt16F2GjqPExnysutgYq2tqSU5JqeSyaAokEVn7xDZzsSSAcGwBWdNE7GheDxOT6iX\nXLcmnspkOe5sclzZpra8lRZkU5jnxWEYmjFYRGzB9sXb1361mbAjSHx4zS0Ahy6BSRqUBLIIR2I8\n9ud6AILhAZMTiWSGL7/0LbpDvQS8uWZHkRQryS6mpa+NaCw69oPTZLxfIVT6iUg62Lp4i8fjOHMT\nrW2x3gIAPvWuK8yMJJPIsnmJLj/Pv3ocj9NjzfWKRGwmGovS0HsUQC1vk8BUfzmxeIxnj9aYlsFg\n5GQjuvwrIlZm6+Ktqy8E7lBiYzCXL/z1SuZUBMwNJZPGlZeUJm/7HD61vIlMgM6hruTtG2dcZ14Q\nSYs3zkzMOLm7fY+JKQwgflGzRargE5F0sXXx9ofN9bgrDgDwoTddQXmRxrpJ+rhdDm5dNRMAl+El\nqJY3kYu2ufllAFaUXc6swAyT00iqFWYVUJRdwNG+ZtMynNFNUkMvRMTCbF28NXa2Ywy3vM0sqDA5\njUxGpQWJsZaOmIeByKClxm2I2FH38Kytq8pXmJxE0mVG/jR6Qr30hvpMyxBnAsasadCbiKSBrYu3\n1tAxABYXLSDPo4Htkn5TThRvkcS/rQNtZsYRsb0Ti93PyJtmchJJl5n5UwFo7GsyLUM8TnLgm9rd\nRMTKbF289ZdvAmBR0SUmJ5HJakp+omgzgokJcw5115sZR8T2WgfaCXjy8Dg9Yz9YMsKM/EShblbX\nyTO7TU7Ek4iIpIZti7fO/v7kbZfDaWISmczycjx43U4GOhMtv4d6VLyJjFckFqFzsItiX5HZUSSN\nThRvh3saTHl9g8Qab+r1KCJ2YNvibXPD7uTtSCxiYhKZzAzDoDiQxbEmJ07DSWOved1+ROyufbCT\nOHFKVLxNKmU5JQBsO76DvR370x/gRKNZfOTmhVL5JyLpYNvi7cVjW5K3ZwVmmhdEJr2m9n6IOwgP\nuukLmzfgXsTuWoOJMaMl2SreJhOH4+RXkd8d/IMpGU4tuwyNehMRC7Nl8RaNxTjW3QPA+6reT2Wu\nZpoU8+T63AAYzgjtg52EomGTE4nYU1NfYhIqtbxNPm+eczMAOe7stL+2ARC/uMkiVe6JSLrYsnh7\n+tBmnLldxGMGC0qnmx1HJrkP3LEEAMOV6L67vXWnmXFEbOtgzxEA5ubPMTmJpNsbpl+L2+GibbAz\n/S9uGCMLN1ViImJhtizefnPk1wAYvVPwuDVZiZirqjKfy6tKCDfNAiAYGTA5kYg99Yb6cBpO8jx+\ns6NImhmGwZzALI71t9AzvNZf2l57+N94/CLHrGnIm4ikge2Kt5cOHknefv9l7zQxichJJflZxLqm\nANA91GNyGhF76gv14XfnYGja9Umpwl8GQOdgV/pf/JTCTZ8+EbEyWxVv7d2DfP/JxNpu4foq5lVo\nXIRYQ2lhNvGQF4CuoW6T04jYTzQWpSvUQ8CbZ3YUMUmBNwCkv3gzDC6626SuN4hIurhS+eQbN24k\nLy+PhoYG1q1bd8b9GzZsAKC+vp4HHnhgzOf7u3/dhLM00SUtNpSN16Muk2INVdPyiYezAOgcMOGq\nsYjNtQ92EIlFKM8pNTuKmCQ/Kx+AThMugMXjIxrfREQsK2Utb7W1tRiGQXV1NQB1dXUj7q+pqWHV\nqlWsW7eOhoYGampqzvl83X1DQBzPjD0AvPPaK1KSW2Q8KopzmFtRQDziYl/3QV5u2W52JBFb6Qkl\nltlQy9vkZV7L28hms/EuFaDaT0TSIWXF2+OPP05ubi4AlZWVbNq0acT9pxZslZWVNDY2nvP5/ril\nHkduBwBOw8m1l8xPQWqR8ZtdkZeccfLfdv+nyWlE7KU/3A+YM1W8WEP+cPHWHUrvuOHTSzV1gRQR\nK0tZt8menh7y8/OT211dI6+kndqNsra2lltuueWcz3esowfvgq0A3DZ7zQQmFZkYc6cGeGb3FJwF\nxwGIxWM4DFsNKxUxTX84CECOO8fkJGKWPE/igq8Z44bj8Tjxi2g708LeIpIuKR3zdj5qa2tZtGgR\nCxYsOOfjjgYbIDEfBNdULackLzcN6SZeSYk9c58uE45joo9hbUkuOxreyMv8FAC3P05hdmr/nzLh\nfYDMOY7JIhXvV2dDOwBV5ZVp+zxkwucuE44BTh5HSXYhLcHjFBXnpO3il8vlwDAMCvITFw6ysz3j\n+n81yIz3IxOOQSSTpax4CwQCyda201vhTlVTU8P9998/5vMdaW+BClheugzvkJ/W1vSuAzMRSkpy\nbZn7dJlwHKk6hhVzy3lx00zc5YfZ33SUWYHUTaqTCe8DZMZxTLYvO6l4vw61HwUgJ5Kfls9Dpnzu\n7H4MMPI4qvLn8kLTFv6y5yUWF5/7ou5EiUZjxGJxOjoTXXeDwdC4/l9j8dT8bqRTJnymJtv5WCaf\nlF3WWrt2bXIcW0NDA6tWrQKgt/fkSWHDhg3cd999AGNOWBIqSkxUsqp8RSriikyIKfk+4qHErJNa\nMkDk/HUOdZPt8pHl8podRUx07bTVADxV/2waX/W0CUvUA1JELCxlxdvChQuBRFEWCASS3SLvvffe\n5P6HHnqIG2+8kZUrV44d1DsIQJGvIDWBRSZAUSALI6LiTeRChGMR2gbaKfYVmh1FTDbVX05V/hxe\n6zqQ1nPoqaPdxjV+TQWfiKRJSse83XXXXWfse+yxxwCorq5m8+bNF/ycBd7Ru1+KWIHT4SDgDhAk\n/dNdi9jVkZ4GIrEIswMzzY4iFjC/cB6vdR2gvqeR/JJAyl8v0dIW1zpvImILtpoK78FVn8Tp0MLc\nYm3leUUAHO1uMzmJiD009jYBMCtvuslJxAoKhxfrTteSAQbDi3SfaH8bdyuaqj8RST3bFG/vXvZW\nCrLU6ibW97r504nHDHYfbSIciZodR8TyTnxJz9c5XoCAJ7FQe9dQmtZ7M07vNikiYl22Kd5urrre\n7Agi5+WKeaXEQz4cWUGe29FsdhwRy+se/pJ+4ku7TG4V/jIADnfXp+X1kmPcLqLhTAWfiKSLbYo3\nQ9M/iU14PU4CzkIMd4itza+aHUfE8pr6mnEYDgqyUj++Sawv1+On2FdEY19T+l5UTW8iYhO2Kd5E\n7OTmhVcCcDi4j0g0ZnIaEevqGOykoa+Jqf5yXI6UzqElNlKeU0pfuJ/NzS+n/sWMxHi3ix2xpglP\nRCQdVLyJpMBV01bijHkgp5OuviGz44hYVmuwHYBFRZeYnESspCx7CgA/rXsk5a81PNkkJ+crUdOb\niFiXijeRFHAYDvIdZTiyBjjYctzsOCKW1TmUWFKjwKsuk3LS1VOrk7fjKW7SOn1UxnhGaWhoh4ik\ni4o3kRSpKpwJwHMHas0NImJhx4OJJTWmZBebnESspMhXwNLiRUB6lgxINLyp36OIWJ+KN5EUWV45\nH4DjQ8dMTiJiXfW9jQCU55SZnESs5sR6b//4whdT/EoG8bgW6RYRe1DxJpIi5cPTXff7DpsbRMTC\njvQ0MMVXTK7Hb3YUsZgspxdItIjV9zSm7HUcxsjJRtQDUkSsTMWbSIrkefwYkSzi7gEGwoNmxxGx\nnI7BToKRAXWZlFFdM2118vbzTS+m7HU0Xk1E7ETFm0iKGIaBPzQNgOf2HDA5jYj17GyrA2BGXqXJ\nScSKAt5cvnXd/8PvzuHF5pcJhoMpeZ14PE40Fic4FBneo2JORKxLxZtIClWVlgPwWM0uBpJfDEQk\nFo/xu4MbAVhYNN/kNGJVToeT6vIricajfPXl7xKNRSf8NQ40JSZE+fHvL25yKY2ZE5F0UPEmkkJL\nK6cDEM/u5INff5aY/rqLAHCs/zgDkQFAk5XIud086w1U+itoCbayr+tgyl6nfzBxgU3tbiJiZSre\nRFJoQdE8vI4sXMVHAWg83mdyIhFr6BjsBOC22WvwOj0mpxEr8zg93DnvVgBebH455a83vnXeJj6H\niMhoVLyJpFCOO5tZgRkYniFwhjnYlPr1ikTsoHWgHYDirEKTk4gdzAnMosRXxEst2+gc7DI7joiI\naVS8iaRYsS+xVpHhDbL/aLfJaUSsobGvCYAKf7nJScQOnA4n105bTZw4zx9N3cyTF0OLfItIOqh4\nE0mx2YGZAHgK29m06xi7D3eYG0jEZPF4nMPd9bgcLkqzS8yOIzaxrGQxAPu6DqX0dbR0gIhYmYo3\nkRSbVzAbgPziIQB2HWw3M46I6doGOjgWPE5VwRycDqfZccQmCrLyyfPk0hOyXvdzFXwiki4q3kRS\nrMCbj8+VRY/nCBgxuvtCZkcSMdWxYAsAs/Kmm5xE7CbfG6B1oJ2WYGvKXkNlmIhYmYo3kRQzDIOA\nJw8A/+z9vPJaK71BFXAyOUVjUZ6ufw6AGSre5AKdWBPw8y/+C+0DnSanOY2GvIlIGqh4E0mDt8y7\nDQB30XFCkVhyUViRySQai/KNbd/nta4DBDx5LCysMjuS2Myq8iuTt7e2bEvNi6jpTUQsTMWbSBos\nGP6SGjWGgDgv7zlubiARExzta+Zg9xHyvQHef+l7NE5ILliRr5AvXfUZAF7r3J+S19CnUkSsTMWb\nSBoYhsGyksWE42FcOf0cOtZrdiSRtNs7/GX7TbPfSGVuhclpxK5yPX6m+ss52H2YcCxidhwRkbRS\n8SaSJnPyZwFQWNnJsfYg4UjM5EQi6XW4px6A+YVzTU4idjc3fzbhWITG3qaJf3K1CIuIhal4E0mT\ny0qWAOD2B4nF4xxq1rg3mTxi8RiHuuvxu3OSE/iIjFfZ8PqALcGJ74I+3tJN85WISDqoeBNJk3xv\ngCxnFh2OQ+AMs7m2xexIImmzr/Mg3aEeFhcv0Fg3uWgzh2cq3dG62+QkCfpIi0i6qHgTSRPDMJg7\n3HXSPeUo9S0a9yaTQ2+oj++8+jAArytbbnIayQTT86aR7w0ku+JOJBViImJlKt5E0mj9/DsA8JQ2\ncvBYF529QyYnEkm93x/cSCyeGOM5OzDD5DSSKWbkTqM71EvbQIfZUURE0kbFm0gaFWTls6LscmKe\nPoycTrbvbzM7kkhKRWIRnm/aDMCq8hU4HU6TE0mmWDC8YPf21p0mJxERSR8VbyJptrAw8YXDyO6l\nQV0nJYPF43H+cPhpAKry5/D2S95iciLJJPPyZwNQ1/7ahD7veMZkGlodTkTSRMWbSJpNG17fylPY\nzkt7W4nFNUeZZKYf7vwpTxz+E26Hi/WX3KmJSmRCTckupiKnjL2d++kN9ZkdR0QkLVS8iaRZaXYJ\nFTllkNtK0HuUl/ZM/FTXImY70tPAjrbETICfXPFxSoendheZKA7DwcryK4gT57F9v5uw59UlBhGx\nMhVvImnmMBxcPfV1AHirXuGJndtNTiQy8Z44/CcA3nHJXUzJLjY5jWSq1RUrKMspZWvLNo72NU/M\nk15E9RZXTwoRSTEVbyImWF2xMnm72b2dprZ+E9OITJzByBA/2f1f7GyrY05gJq8r19IAkjo+l483\nVF4DwLbjO0zLoR7BIpIuKt5ETOB0OLmr6vbE7UA7j+99weREIhcvFA3x/R3/ztaWbbgdbt4y7zaN\nc5OUWzZlMX53Dk/VP0t/OHjRz6fJR0TEylS8iZjkummruXv2egC2Df3J5DQiF+/R137Lvq6DVOXP\n4StXf5YZeZVmR5JJwOfy8Ybp1xKKhflZ3SPJNQVFRDKRijcRE10947Lk7b1NLSYmEbk4f254nk3N\nW8j3Bnj/pX+Fx+kxO5JMIjdMv4b5BXPZ2VbH80c3m5ZDI95EJNVUvImYyDAMluVWA/BI3cTNliaS\nTvs6D/Df+34LwLsX3q3CTdLOYTh498K3keXM4ncH/0BPSGtoikhmUvEmYrI18xOTlxyLHeDZHQ0m\npxG5ME19x/jxrl8AcPvstVQVzDU5kUxWAW8ut8x6A8HIAP+26xfjnvlxT33nBCcTEZk4LrMDiEx2\n0/MqmO5cRD27+fnO33G4+QamFuewcmEpfp/b7HgiZ4jH47zWeYBNzVt4qSWx1MXdVXdwzbRqk5PJ\nZHdd5VVsat7Kvq6DvNyyneVll439Q6cJhaMpSCYiMjFS2vK2ceNGampq2LBhw7juF5ksPrL6blx4\ncE1p4Jmdh/jFH1/jI998jsdfPEJzu5YREOuoa93H93f8O9/a/kNeatmOy+Hi3QvXq3ATS3AYDv5q\n0dtxGA6erH9mXK1v71wzf/wBNOhNRFIsZcVbbW0thmFQXZ34g15XV3dB94tMJj5XFjfPugHDGaVw\nbn1y/38/c4BP/WgzH/7Gs3T1DZmYUCThs09/jV3tewh48nj7/Lfw9WsfZEXZ5WbHEkma6i9nafEi\njvY18/Cun9M91HPeP/s3b1pEeVHOBb+mVsQQkXRJWbfJxx9/nNWrVwNQWVnJpk2bWLBgwXnfLzLZ\nXD/jajYd20Ib+/noe67huT37qQu+RDTson/vcj7+nRfwup0Ecjz0BEPcetVsbl6hqdglva6fvZpC\nZxEry64g2+0zO47IqO6e/2ZagsfZ3rqT9oF2PrjsveR6/GP+nLqqi4jVpax46+npIT8/P7nd1dV1\nQfeLTDZuh4s7597KD3f+Bz/Y9W+JnR5weCBryfNEO8qIDPhpG8ghFsvlv5/ex2NP72PZvGIunVvM\njNJcygqzicZihCIxsjxOvG6nFkmWCfW3V76D1lbN5CfWlufJ5ZMrPsYPd/4HO9vq+PyL/8Lc/Nks\nLKoiHoc8j59ZgZlku7JI9HU0wIiR7dP5UkSsTROWiFjIpSWLeP/Sv+KZxheIx+NU+Mt4uuE5DHcI\nV2n9yAfHHMTjBnVxg7oWA86yTJy+iqTehnu+ZXYEETmNw3DwviXv5k/1f+EPh59iR9tudrTtPuNx\nvhUQjzownDG+Wvsknr0e4vEYBgaGYWDgGLNb5FAgStblcT781J9SdDQpEj/twAxsP25P52PJdCkr\n3gKBQLI17fRWtvO5fzQlJbkTHzTNMuEYIDOOw6rH8PqSFS/RYwEAAATbSURBVLx+wYrk9t/ydhPT\niJzJqr87FyoTjiMTjgFSexz3THkT9yx/U8qeX0QknVI2YcnatWtpbGwEoKGhgVWrV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1e8153ae50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;sab=0.1\n", "saabb=sab\n", "r=1e-6\n", "prop=[0.8, 0.1, 0.1, 0.]\n", "reload(mutl)\n", "mutl.simulate(N,L,r,1200,sa,sb,sab,saabb,prop)\n", "plt.suptitle('High Recombination (11 hap will appear before eq.) and sa=sb<sab, a0=b0. Fast Sweep!',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.5, 0.25, 0.25, 0.0]\n" ] }, { "data": { "image/png": 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I18GJdpsEQn/t+8r/RPJlsVjc3oMzGAxYvny5W1fekd/zl5bFYhlzg2g2m9HR0YH29nZp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k8aGBzi1V2NHDnqJsZ26mN50fRsirJTyEj1rWsryEq2KsULplmTcialpTOYPr2MFSuu46qrruH2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\nREQkDfw/uHqJ/6n++NgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e83be9b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;sab=0.1\n", "saabb=sab\n", "r=1e-7\n", "prop=[0.5, 0.25, 0.25, 0.]\n", "reload(mutl)\n", "ha,hb,hab=0,0,0\n", "mutl.simulate(N,L,r,800,sa,sb,sab,saabb,prop,ha,hb,hab)\n", "plt.suptitle('Recessive Allele Medioum Recombination (11 hap will appear at eq.) and sa=sb<sab, a0=b0.',fontsize=18);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N=10000 ,s=0.05 , Ns=500.0 prop=[0.5, 0.25, 0.25, 0.0]\n" ] }, { "data": { "image/png": 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L+uijj2LPnj1+IzmN//xU8U9nqrsEWq3W1zVgurjG/x1KDN73zzRer/HJB+A56Xv66af9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QAAAg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\nInFPyVuCuKnoep6veYlXTu7mmsxruOO6ORyqbedQ7fDab0fqO/j2T98C4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1e817ef990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sa=0.05;sb=0.05;sab=0.1\n", "saabb=0.15\n", "r=1e-7\n", "prop=[0.5, 0.25, 0.25, 0.]\n", "reload(mutl)\n", "ha,hb,hab=0,0,0\n", "mutl.simulate(N,L,r,800,sa,sb,sab,saabb,prop,ha,hb,hab)\n", "plt.suptitle('Recessive Allele Medioum Recombination (11 hap will appear at eq.) and sa=sb<sab<saabb, a0=b0.',fontsize=18);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit