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#! /usr/bin/env python2
############################################################
# Program is part of PySAR v1.2 #
# Copyright(c) 2015, Heresh Fattahi, Zhang Yunjun #
# Author: Heresh Fattahi, Zhang Yunjun #
############################################################
import os
import sys
import argparse
import re
try:
import pyaps as pa
except:
sys.exit('Cannot import pyaps into Python!')
import h5py
import numpy as np
import pysar._datetime as ptime
import pysar._pysar_utilities as ut
import pysar._readfile as readfile
import pysar._writefile as writefile
###############################################################
def get_delay(grib_file, atr, inps_dict):
'''Get delay matrix using PyAPS for one acquisition
Inputs:
grib_file - strng, grib file path
atr - dict, including the following attributes:
dem_file - string, DEM file path
grib_source - string, Weather re-analysis data source
delay_type - string, comb/dry/wet
ref_y/x - string, reference pixel row/col number
inc_angle - np.array, 0/1/2 D
Output:
phs - 2D np.array, absolute tropospheric phase delay relative to ref_y/x
'''
if 'X_FIRST' in atr.keys():
aps = pa.PyAPS_geo(grib_file, inps_dict['dem_file'], grib=inps_dict['grib_source'],\
verb=True, Del=inps_dict['delay_type'])
else:
aps = pa.PyAPS_rdr(grib_file, inps_dict['dem_file'], grib=inps_dict['grib_source'],\
verb=True, Del=inps_dict['delay_type'])
phs = np.zeros((aps.ny, aps.nx), dtype=np.float32)
aps.getdelay(phs, inc=0.0)
# Get relative phase delay in space
yref = int(atr['ref_y'])
xref = int(atr['ref_x'])
phs -= phs[yref, xref]
# project into LOS direction
phs /= np.cos(inps_dict['inc_angle'])
# reverse the sign for consistency between different phase correction steps/methods
phs *= -1
return phs
def date_list2grib_file(date_list, hour, grib_source, grib_dir):
grib_file_list = []
for d in date_list:
grib_file = grib_dir+'/'
if grib_source == 'ECMWF' : grib_file += 'ERA-Int_%s_%s.grb' % (d, hour)
elif grib_source == 'ERA' : grib_file += 'ERA_%s_%s.grb' % (d, hour)
elif grib_source == 'NARR' : grib_file += 'narr-a_221_%s_%s00_000.grb' % (d, hour)
elif grib_source == 'MERRA' : grib_file += 'merra-%s-%s.nc4' % (d, hour)
elif grib_source == 'MERRA1': grib_file += 'merra-%s-%s.hdf' % (d, hour)
grib_file_list.append(grib_file)
return grib_file_list
def dload_grib(date_list, hour, grib_source='ECMWF', weather_dir='./'):
'''Download weather re-analysis grib files using PyAPS
Inputs:
date_list : list of string in YYYYMMDD format
hour : string in HH:MM or HH format
grib_source : string,
weather_dir : string,
Output:
grib_file_list : list of string
'''
## Grib data directory
weather_dir = os.path.abspath(weather_dir)
grib_dir = weather_dir+'/'+grib_source
if not os.path.isdir(grib_dir):
print 'making directory: '+grib_dir
os.makedirs(grib_dir)
## Date list to grib file list
grib_file_list = date_list2grib_file(date_list, hour, grib_source, grib_dir)
## Get date list to download (skip already downloaded files)
grib_file_existed = ut.get_file_list(grib_file_list)
if grib_file_existed:
grib_filesize_digit = ut.mode([len(str(os.path.getsize(i))) for i in grib_file_existed])
grib_filesize_max2 = ut.mode([str(os.path.getsize(i))[0:2] for i in grib_file_existed])
grib_file_corrupted = [i for i in grib_file_existed if (len(str(os.path.getsize(i))) != grib_filesize_digit or\
str(os.path.getsize(i))[0:2] != grib_filesize_max2)]
print 'file size mode: %se%d bytes' % (grib_filesize_max2, grib_filesize_digit-2)
print 'number of grib files existed : %d' % len(grib_file_existed)
if grib_file_corrupted:
print '------------------------------------------------------------------------------'
print 'corrupted grib files detected! Delete them and re-download...'
print 'number of grib files corrupted : %d' % len(grib_file_corrupted)
for i in grib_file_corrupted:
rmCmd = 'rm '+i
print rmCmd
os.system(rmCmd)
grib_file_existed.remove(i)
print '------------------------------------------------------------------------------'
grib_file2download = sorted(list(set(grib_file_list) - set(grib_file_existed)))
date_list2download = [str(re.findall('\d{8}', i)[0]) for i in grib_file2download]
print 'number of grib files to download: %d' % len(date_list2download)
print '------------------------------------------------------------------------------\n'
## Download grib file using PyAPS
if grib_source == 'ECMWF' : pa.ECMWFdload( date_list2download, hour, grib_dir)
elif grib_source == 'ERA' : pa.ERAdload( date_list2download, hour, grib_dir)
elif grib_source == 'NARR' : pa.NARRdload( date_list2download, hour, grib_dir)
elif grib_source == 'MERRA' : pa.MERRAdload( date_list2download, hour, grib_dir)
elif grib_source == 'MERRA1': pa.MERRA1dload(date_list2download, hour, grib_dir)
return grib_file_existed
###############################################################
EXAMPLE='''example:
tropcor_pyaps.py timeseries.h5 -d geometryRadar.h5 -i geometryRadar.h5
tropcor_pyaps.py timeseries.h5 -d geometryGeo.h5 -i geometryGeo.h5 --weather-dir /famelung/data/WEATHER
tropcor_pyaps.py -d srtm1.dem -i 30 --hour 00 --ref-yx 2000 2500 --date-list date_list.txt
tropcor_pyaps.py timeseries.h5 -d demRadar.h5 -s NARR
tropcor_pyaps.py timeseries.h5 -d demRadar.h5 -s MERRA --delay dry -i 23
tropcor_pyaps.py timeseries_LODcor.h5 -d demRadar.h5
tropcor_pyaps.py -s ECMWF --hour 18 --date-list date_list.txt --download
tropcor_pyaps.py -s ECMWF --hour 18 --date-list bl_list.txt --download
'''
REFERENCE='''reference:
Jolivet, R., R. Grandin, C. Lasserre, M.-P. Doin and G. Peltzer (2011), Systematic InSAR tropospheric
phase delay corrections from global meteorological reanalysis data, Geophys. Res. Lett., 38, L17311,
doi:10.1029/2011GL048757
'''
TEMPLATE='''
## 7. Tropospheric Delay Correction (optional and recommended)
## correct tropospheric delay using the following methods:
## a. pyaps - use weather re-analysis data (Jolivet et al., 2011, GRL, need to install PyAPS; Dee et al., 2011)
## b. height_correlation - correct stratified tropospheric delay (Doin et al., 2009, J Applied Geop)
## c. base_trop_cor - (not recommend) baseline error and stratified tropo simultaneously (Jo et al., 2010, Geo J)
pysar.troposphericDelay.method = auto #[pyaps / height_correlation / base_trop_cor / no], auto for pyaps
pysar.troposphericDelay.weatherModel = auto #[ECMWF / MERRA / NARR], auto for ECMWF, for pyaps method
pysar.troposphericDelay.polyOrder = auto #[1 / 2 / 3], auto for 1, for height_correlation method
pysar.troposphericDelay.looks = auto #[1-inf], auto for 8, Number of looks to be applied to interferogram
'''
DATA_INFO='''
re-analysis_dataset coverage temporal_resolution spatial_resolution latency analysis
------------------------------------------------------------------------------------------------------------
ERA-Interim (by ECMWF) Global 00/06/12/18 UTC 0.75 deg (~83 km) 2-month 4D-var
MERRA2 (by NASA Goddard) Global 00/06/12/18 UTC 0.5 * 0.625 (~50 km) 2-3 weeks 3D-var
To download MERRA2, you need an Earthdata account, and pre-authorize the "NASA GESDISC DATA ARCHIVE" application, following https://disc.gsfc.nasa.gov/earthdata-login.
'''
def cmdLineParse():
parser = argparse.ArgumentParser(description='Tropospheric correction using weather models\n'+\
' PyAPS is used to download and calculate the delay for each time-series epoch.',\
formatter_class=argparse.RawTextHelpFormatter,\
epilog=REFERENCE+'\n'+DATA_INFO+'\n'+EXAMPLE)
parser.add_argument(dest='timeseries_file', nargs='?', help='timeseries HDF5 file, i.e. timeseries.h5')
parser.add_argument('-d','--dem', dest='dem_file',\
help='DEM file, i.e. radar_4rlks.hgt, srtm1.dem')
parser.add_argument('-i', dest='inc_angle', default='30',\
help='a file containing all incidence angles, or a number representing for the whole image.')
parser.add_argument('--weather-dir', dest='weather_dir', \
help='directory to put downloaded weather data, i.e. ./../WEATHER\n'+\
'use directory of input timeseries_file if not specified.')
parser.add_argument('--delay', dest='delay_type', default='comb', choices={'comb','dry','wet'},\
help='Delay type to calculate, comb contains both wet and dry delays')
parser.add_argument('--download', action='store_true', help='Download weather data only.')
parser.add_argument('--date-list', dest='date_list_file',\
help='Read the first column of text file as list of date to download data\n'+\
'in YYYYMMDD or YYMMDD format')
parser.add_argument('--ref-yx', dest='ref_yx', type=int, nargs=2, help='reference pixel in y/x')
parser.add_argument('-s', dest='weather_model',\
default='ECMWF', choices={'ECMWF','ERA-Interim','ERA','MERRA','MERRA1','NARR'},\
help='source of the atmospheric data.\n'+\
'By the time of 2018-Mar-06, ERA and ECMWF data download link is working.\n'+\
'NARR is working for 1979-Jan to 2014-Oct.\n'+\
'MERRA(2) is not working.')
parser.add_argument('--hour', help='time of data in HH, e.g. 12, 06')
parser.add_argument('--template', dest='template_file',\
help='template file with input options below:\n'+TEMPLATE)
parser.add_argument('-o', dest='out_file', help='Output file name for trospheric corrected timeseries.')
inps = parser.parse_args()
# Calculate DELAY or DOWNLOAD DATA ONLY, required one of them
if not inps.download and not inps.dem_file and ( not inps.timeseries_file or not inps.date_list_file ):
parser.print_help()
sys.exit(1)
return inps
###############################################################
def main(argv):
inps = cmdLineParse()
k = None
atr = dict()
if inps.timeseries_file:
inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]
atr = readfile.read_attribute(inps.timeseries_file)
k = atr['FILE_TYPE']
elif inps.dem_file:
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
atr = readfile.read_attribute(inps.dem_file)
if 'ref_y' not in atr.keys() and inps.ref_yx:
print 'No reference info found in input file, use input ref_yx: '+str(inps.ref_yx)
atr['ref_y'] = inps.ref_yx[0]
atr['ref_x'] = inps.ref_yx[1]
##Read Incidence angle: to map the zenith delay to the slant delay
if os.path.isfile(inps.inc_angle):
inps.inc_angle = readfile.read(inps.inc_angle, epoch='incidenceAngle')[0]
else:
inps.inc_angle = float(inps.inc_angle)
print 'incidence angle: '+str(inps.inc_angle)
inps.inc_angle = inps.inc_angle*np.pi/180.0
##Prepare DEM file in ROI_PAC format for PyAPS to read
if inps.dem_file:
inps.dem_file = ut.get_file_list([inps.dem_file])[0]
if os.path.splitext(inps.dem_file)[1] in ['.h5']:
print 'convert DEM file to ROIPAC format'
dem, atr_dem = readfile.read(inps.dem_file, epoch='height')
if 'Y_FIRST' in atr.keys():
atr_dem['FILE_TYPE'] = '.dem'
else:
atr_dem['FILE_TYPE'] = '.hgt'
outname = os.path.splitext(inps.dem_file)[0]+'4pyaps'+atr_dem['FILE_TYPE']
inps.dem_file = writefile.write(dem, atr_dem, outname)
print '*******************************************************************************'
print 'Downloading weather model data ...'
## Get Grib Source
if inps.weather_model in ['ECMWF','ERA-Interim']: inps.grib_source = 'ECMWF'
elif inps.weather_model == 'ERA' : inps.grib_source = 'ERA'
elif inps.weather_model == 'MERRA': inps.grib_source = 'MERRA'
elif inps.weather_model == 'NARR' : inps.grib_source = 'NARR'
else: raise Reception('Unrecognized weather model: '+inps.weather_model)
print 'grib source: '+inps.grib_source
# Get weather directory
if not inps.weather_dir:
if inps.timeseries_file:
inps.weather_dir = os.path.dirname(os.path.abspath(inps.timeseries_file))+'/../WEATHER'
elif inps.dem_file:
inps.weather_dir = os.path.dirname(os.path.abspath(inps.dem_file))+'/../WEATHER'
else:
inps.weather_dir = os.path.abspath(os.getcwd())
print 'Store weather data into directory: '+inps.weather_dir
# Get date list to download
if not inps.date_list_file:
print 'read date list info from: '+inps.timeseries_file
h5 = h5py.File(inps.timeseries_file, 'r')
if 'timeseries' in h5.keys():
date_list = sorted(h5[k].keys())
elif k in ['interferograms','coherence','wrapped']:
ifgram_list = sorted(h5[k].keys())
date12_list = ptime.list_ifgram2date12(ifgram_list)
m_dates = [i.split('-')[0] for i in date12_list]
s_dates = [i.split('-')[1] for i in date12_list]
date_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))
else:
raise ValueError('Un-support input file type:'+k)
h5.close()
else:
date_list = ptime.yyyymmdd(np.loadtxt(inps.date_list_file, dtype=str, usecols=(0,)).tolist())
print 'read date list info from: '+inps.date_list_file
# Get Acquisition time - hour
if not inps.hour:
inps.hour = ptime.closest_weather_product_time(atr['CENTER_LINE_UTC'], inps.grib_source)
print 'Time of cloest available product: '+inps.hour
## Download data using PyAPS
inps.grib_file_list = dload_grib(date_list, inps.hour, inps.weather_model, inps.weather_dir)
if inps.download:
print 'Download completed, exit as planned.'
return
print '*******************************************************************************'
print 'Calcualting delay for each epoch.'
## Calculate tropo delay using pyaps
length = int(atr['FILE_LENGTH'])
width = int(atr['WIDTH'])
date_num = len(date_list)
trop_ts = np.zeros((date_num, length, width), np.float32)
for i in range(date_num):
grib_file = inps.grib_file_list[i]
date = date_list[i]
print 'calculate phase delay on %s from file %s' % (date, os.path.basename(grib_file))
trop_ts[i] = get_delay(grib_file, atr, vars(inps))
## Convert relative phase delay on reference date
try: ref_date = atr['ref_date']
except: ref_date = date_list[0]
print 'convert to relative phase delay with reference date: '+ref_date
ref_idx = date_list.index(ref_date)
trop_ts -= np.tile(trop_ts[ref_idx,:,:], (date_num, 1, 1))
## Write tropospheric delay to HDF5
tropFile = inps.grib_source+'.h5'
print 'writing >>> %s' % (tropFile)
h5trop = h5py.File(tropFile, 'w')
group_trop = h5trop.create_group('timeseries')
print 'number of acquisitions: '+str(date_num)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
group_trop.create_dataset(date, data=trop_ts[i], compression='gzip')
prog_bar.update(i+1, suffix=date)
prog_bar.close()
# Write Attributes
for key,value in atr.iteritems():
group_trop.attrs[key] = value
h5trop.close()
## Write corrected Time series to HDF5
if k == 'timeseries':
if not inps.out_file:
inps.out_file = os.path.splitext(inps.timeseries_file)[0]+'_'+inps.grib_source+'.h5'
print 'writing >>> %s' % (inps.out_file)
h5ts = h5py.File(inps.timeseries_file, 'r')
h5tsCor = h5py.File(inps.out_file, 'w')
group_tsCor = h5tsCor.create_group('timeseries')
print 'number of acquisitions: '+str(date_num)
prog_bar = ptime.progress_bar(maxValue=date_num)
for i in range(date_num):
date = date_list[i]
ts = h5ts['timeseries'].get(date)[:]
group_tsCor.create_dataset(date, data=ts-trop_ts[i], compression='gzip')
prog_bar.update(i+1, suffix=date)
prog_bar.close()
h5ts.close()
# Write Attributes
for key,value in atr.iteritems():
group_tsCor.attrs[key] = value
h5tsCor.close()
# Delete temporary DEM file in ROI_PAC format
if '4pyaps' in inps.dem_file:
rmCmd = 'rm %s %s.rsc' % (inps.dem_file, inps.dem_file)
print rmCmd
os.system(rmCmd)
print 'Done.'
return inps.out_file
###############################################################
if __name__ == '__main__':
main(sys.argv[1:])
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{
"blob_id": "9515dcdfc0ece1a6740d6e7075bbcd1c20977590",
"index": 9157,
"step-1": "#! /usr/bin/env python2\n############################################################\n# Program is part of PySAR v1.2 #\n# Copyright(c) 2015, Heresh Fattahi, Zhang Yunjun #\n# Author: Heresh Fattahi, Zhang Yunjun #\n############################################################\n\n\nimport os\nimport sys\nimport argparse\nimport re\n\ntry:\n import pyaps as pa\nexcept:\n sys.exit('Cannot import pyaps into Python!')\n\nimport h5py\nimport numpy as np\n\nimport pysar._datetime as ptime\nimport pysar._pysar_utilities as ut\nimport pysar._readfile as readfile\nimport pysar._writefile as writefile\n\n\n###############################################################\ndef get_delay(grib_file, atr, inps_dict):\n '''Get delay matrix using PyAPS for one acquisition\n Inputs:\n grib_file - strng, grib file path\n atr - dict, including the following attributes:\n dem_file - string, DEM file path\n grib_source - string, Weather re-analysis data source\n delay_type - string, comb/dry/wet\n ref_y/x - string, reference pixel row/col number\n inc_angle - np.array, 0/1/2 D\n Output:\n phs - 2D np.array, absolute tropospheric phase delay relative to ref_y/x\n '''\n if 'X_FIRST' in atr.keys():\n aps = pa.PyAPS_geo(grib_file, inps_dict['dem_file'], grib=inps_dict['grib_source'],\\\n verb=True, Del=inps_dict['delay_type'])\n else:\n aps = pa.PyAPS_rdr(grib_file, inps_dict['dem_file'], grib=inps_dict['grib_source'],\\\n verb=True, Del=inps_dict['delay_type'])\n phs = np.zeros((aps.ny, aps.nx), dtype=np.float32)\n aps.getdelay(phs, inc=0.0)\n\n # Get relative phase delay in space\n yref = int(atr['ref_y'])\n xref = int(atr['ref_x'])\n phs -= phs[yref, xref]\n\n # project into LOS direction\n phs /= np.cos(inps_dict['inc_angle'])\n \n # reverse the sign for consistency between different phase correction steps/methods\n phs *= -1\n \n return phs\n\n\ndef date_list2grib_file(date_list, hour, grib_source, grib_dir):\n grib_file_list = []\n for d in date_list:\n grib_file = grib_dir+'/'\n if grib_source == 'ECMWF' : grib_file += 'ERA-Int_%s_%s.grb' % (d, hour)\n elif grib_source == 'ERA' : grib_file += 'ERA_%s_%s.grb' % (d, hour)\n elif grib_source == 'NARR' : grib_file += 'narr-a_221_%s_%s00_000.grb' % (d, hour)\n elif grib_source == 'MERRA' : grib_file += 'merra-%s-%s.nc4' % (d, hour)\n elif grib_source == 'MERRA1': grib_file += 'merra-%s-%s.hdf' % (d, hour)\n grib_file_list.append(grib_file)\n return grib_file_list\n\n\ndef dload_grib(date_list, hour, grib_source='ECMWF', weather_dir='./'):\n '''Download weather re-analysis grib files using PyAPS\n Inputs:\n date_list : list of string in YYYYMMDD format\n hour : string in HH:MM or HH format\n grib_source : string, \n weather_dir : string,\n Output:\n grib_file_list : list of string\n '''\n ## Grib data directory\n weather_dir = os.path.abspath(weather_dir)\n grib_dir = weather_dir+'/'+grib_source\n if not os.path.isdir(grib_dir):\n print 'making directory: '+grib_dir\n os.makedirs(grib_dir)\n\n ## Date list to grib file list\n grib_file_list = date_list2grib_file(date_list, hour, grib_source, grib_dir)\n\n ## Get date list to download (skip already downloaded files)\n grib_file_existed = ut.get_file_list(grib_file_list)\n if grib_file_existed:\n grib_filesize_digit = ut.mode([len(str(os.path.getsize(i))) for i in grib_file_existed])\n grib_filesize_max2 = ut.mode([str(os.path.getsize(i))[0:2] for i in grib_file_existed])\n grib_file_corrupted = [i for i in grib_file_existed if (len(str(os.path.getsize(i))) != grib_filesize_digit or\\\n str(os.path.getsize(i))[0:2] != grib_filesize_max2)]\n print 'file size mode: %se%d bytes' % (grib_filesize_max2, grib_filesize_digit-2)\n print 'number of grib files existed : %d' % len(grib_file_existed)\n if grib_file_corrupted:\n print '------------------------------------------------------------------------------'\n print 'corrupted grib files detected! Delete them and re-download...'\n print 'number of grib files corrupted : %d' % len(grib_file_corrupted)\n for i in grib_file_corrupted:\n rmCmd = 'rm '+i\n print rmCmd\n os.system(rmCmd)\n grib_file_existed.remove(i)\n print '------------------------------------------------------------------------------'\n grib_file2download = sorted(list(set(grib_file_list) - set(grib_file_existed)))\n date_list2download = [str(re.findall('\\d{8}', i)[0]) for i in grib_file2download]\n print 'number of grib files to download: %d' % len(date_list2download)\n print '------------------------------------------------------------------------------\\n'\n\n ## Download grib file using PyAPS\n if grib_source == 'ECMWF' : pa.ECMWFdload( date_list2download, hour, grib_dir)\n elif grib_source == 'ERA' : pa.ERAdload( date_list2download, hour, grib_dir)\n elif grib_source == 'NARR' : pa.NARRdload( date_list2download, hour, grib_dir)\n elif grib_source == 'MERRA' : pa.MERRAdload( date_list2download, hour, grib_dir)\n elif grib_source == 'MERRA1': pa.MERRA1dload(date_list2download, hour, grib_dir)\n\n return grib_file_existed\n\n\n###############################################################\nEXAMPLE='''example:\n tropcor_pyaps.py timeseries.h5 -d geometryRadar.h5 -i geometryRadar.h5\n tropcor_pyaps.py timeseries.h5 -d geometryGeo.h5 -i geometryGeo.h5 --weather-dir /famelung/data/WEATHER\n tropcor_pyaps.py -d srtm1.dem -i 30 --hour 00 --ref-yx 2000 2500 --date-list date_list.txt\n\n tropcor_pyaps.py timeseries.h5 -d demRadar.h5 -s NARR\n tropcor_pyaps.py timeseries.h5 -d demRadar.h5 -s MERRA --delay dry -i 23\n tropcor_pyaps.py timeseries_LODcor.h5 -d demRadar.h5\n\n tropcor_pyaps.py -s ECMWF --hour 18 --date-list date_list.txt --download\n tropcor_pyaps.py -s ECMWF --hour 18 --date-list bl_list.txt --download\n'''\n\nREFERENCE='''reference:\n Jolivet, R., R. Grandin, C. Lasserre, M.-P. Doin and G. Peltzer (2011), Systematic InSAR tropospheric\n phase delay corrections from global meteorological reanalysis data, Geophys. Res. Lett., 38, L17311,\n doi:10.1029/2011GL048757\n'''\n\nTEMPLATE='''\n## 7. Tropospheric Delay Correction (optional and recommended)\n## correct tropospheric delay using the following methods:\n## a. pyaps - use weather re-analysis data (Jolivet et al., 2011, GRL, need to install PyAPS; Dee et al., 2011)\n## b. height_correlation - correct stratified tropospheric delay (Doin et al., 2009, J Applied Geop)\n## c. base_trop_cor - (not recommend) baseline error and stratified tropo simultaneously (Jo et al., 2010, Geo J)\npysar.troposphericDelay.method = auto #[pyaps / height_correlation / base_trop_cor / no], auto for pyaps\npysar.troposphericDelay.weatherModel = auto #[ECMWF / MERRA / NARR], auto for ECMWF, for pyaps method\npysar.troposphericDelay.polyOrder = auto #[1 / 2 / 3], auto for 1, for height_correlation method\npysar.troposphericDelay.looks = auto #[1-inf], auto for 8, Number of looks to be applied to interferogram \n'''\n\nDATA_INFO='''\n re-analysis_dataset coverage temporal_resolution spatial_resolution latency analysis\n------------------------------------------------------------------------------------------------------------\nERA-Interim (by ECMWF) Global 00/06/12/18 UTC 0.75 deg (~83 km) 2-month 4D-var\nMERRA2 (by NASA Goddard) Global 00/06/12/18 UTC 0.5 * 0.625 (~50 km) 2-3 weeks 3D-var\n\nTo download MERRA2, you need an Earthdata account, and pre-authorize the \"NASA GESDISC DATA ARCHIVE\" application, following https://disc.gsfc.nasa.gov/earthdata-login.\n'''\n\n\ndef cmdLineParse():\n parser = argparse.ArgumentParser(description='Tropospheric correction using weather models\\n'+\\\n ' PyAPS is used to download and calculate the delay for each time-series epoch.',\\\n formatter_class=argparse.RawTextHelpFormatter,\\\n epilog=REFERENCE+'\\n'+DATA_INFO+'\\n'+EXAMPLE)\n\n parser.add_argument(dest='timeseries_file', nargs='?', help='timeseries HDF5 file, i.e. timeseries.h5')\n parser.add_argument('-d','--dem', dest='dem_file',\\\n help='DEM file, i.e. radar_4rlks.hgt, srtm1.dem')\n parser.add_argument('-i', dest='inc_angle', default='30',\\\n help='a file containing all incidence angles, or a number representing for the whole image.')\n parser.add_argument('--weather-dir', dest='weather_dir', \\\n help='directory to put downloaded weather data, i.e. ./../WEATHER\\n'+\\\n 'use directory of input timeseries_file if not specified.')\n parser.add_argument('--delay', dest='delay_type', default='comb', choices={'comb','dry','wet'},\\\n help='Delay type to calculate, comb contains both wet and dry delays')\n parser.add_argument('--download', action='store_true', help='Download weather data only.')\n parser.add_argument('--date-list', dest='date_list_file',\\\n help='Read the first column of text file as list of date to download data\\n'+\\\n 'in YYYYMMDD or YYMMDD format')\n parser.add_argument('--ref-yx', dest='ref_yx', type=int, nargs=2, help='reference pixel in y/x')\n\n parser.add_argument('-s', dest='weather_model',\\\n default='ECMWF', choices={'ECMWF','ERA-Interim','ERA','MERRA','MERRA1','NARR'},\\\n help='source of the atmospheric data.\\n'+\\\n 'By the time of 2018-Mar-06, ERA and ECMWF data download link is working.\\n'+\\\n 'NARR is working for 1979-Jan to 2014-Oct.\\n'+\\\n 'MERRA(2) is not working.')\n parser.add_argument('--hour', help='time of data in HH, e.g. 12, 06')\n\n parser.add_argument('--template', dest='template_file',\\\n help='template file with input options below:\\n'+TEMPLATE)\n parser.add_argument('-o', dest='out_file', help='Output file name for trospheric corrected timeseries.')\n\n inps = parser.parse_args()\n\n # Calculate DELAY or DOWNLOAD DATA ONLY, required one of them\n if not inps.download and not inps.dem_file and ( not inps.timeseries_file or not inps.date_list_file ):\n parser.print_help()\n sys.exit(1)\n return inps\n\n\n###############################################################\ndef main(argv):\n inps = cmdLineParse()\n\n k = None\n atr = dict()\n if inps.timeseries_file:\n inps.timeseries_file = ut.get_file_list([inps.timeseries_file])[0]\n atr = readfile.read_attribute(inps.timeseries_file)\n k = atr['FILE_TYPE']\n elif inps.dem_file:\n inps.dem_file = ut.get_file_list([inps.dem_file])[0]\n atr = readfile.read_attribute(inps.dem_file)\n if 'ref_y' not in atr.keys() and inps.ref_yx:\n print 'No reference info found in input file, use input ref_yx: '+str(inps.ref_yx)\n atr['ref_y'] = inps.ref_yx[0]\n atr['ref_x'] = inps.ref_yx[1]\n\n ##Read Incidence angle: to map the zenith delay to the slant delay\n if os.path.isfile(inps.inc_angle):\n inps.inc_angle = readfile.read(inps.inc_angle, epoch='incidenceAngle')[0]\n else:\n inps.inc_angle = float(inps.inc_angle)\n print 'incidence angle: '+str(inps.inc_angle)\n inps.inc_angle = inps.inc_angle*np.pi/180.0\n\n ##Prepare DEM file in ROI_PAC format for PyAPS to read\n if inps.dem_file:\n inps.dem_file = ut.get_file_list([inps.dem_file])[0]\n if os.path.splitext(inps.dem_file)[1] in ['.h5']:\n print 'convert DEM file to ROIPAC format'\n dem, atr_dem = readfile.read(inps.dem_file, epoch='height')\n if 'Y_FIRST' in atr.keys():\n atr_dem['FILE_TYPE'] = '.dem'\n else:\n atr_dem['FILE_TYPE'] = '.hgt'\n outname = os.path.splitext(inps.dem_file)[0]+'4pyaps'+atr_dem['FILE_TYPE']\n inps.dem_file = writefile.write(dem, atr_dem, outname)\n\n print '*******************************************************************************'\n print 'Downloading weather model data ...'\n\n ## Get Grib Source\n if inps.weather_model in ['ECMWF','ERA-Interim']: inps.grib_source = 'ECMWF'\n elif inps.weather_model == 'ERA' : inps.grib_source = 'ERA'\n elif inps.weather_model == 'MERRA': inps.grib_source = 'MERRA'\n elif inps.weather_model == 'NARR' : inps.grib_source = 'NARR'\n else: raise Reception('Unrecognized weather model: '+inps.weather_model)\n print 'grib source: '+inps.grib_source\n\n # Get weather directory\n if not inps.weather_dir:\n if inps.timeseries_file:\n inps.weather_dir = os.path.dirname(os.path.abspath(inps.timeseries_file))+'/../WEATHER'\n elif inps.dem_file:\n inps.weather_dir = os.path.dirname(os.path.abspath(inps.dem_file))+'/../WEATHER'\n else:\n inps.weather_dir = os.path.abspath(os.getcwd())\n print 'Store weather data into directory: '+inps.weather_dir\n\n # Get date list to download\n if not inps.date_list_file:\n print 'read date list info from: '+inps.timeseries_file\n h5 = h5py.File(inps.timeseries_file, 'r')\n if 'timeseries' in h5.keys():\n date_list = sorted(h5[k].keys())\n elif k in ['interferograms','coherence','wrapped']:\n ifgram_list = sorted(h5[k].keys())\n date12_list = ptime.list_ifgram2date12(ifgram_list)\n m_dates = [i.split('-')[0] for i in date12_list]\n s_dates = [i.split('-')[1] for i in date12_list]\n date_list = ptime.yyyymmdd(sorted(list(set(m_dates + s_dates))))\n else:\n raise ValueError('Un-support input file type:'+k)\n h5.close()\n else:\n date_list = ptime.yyyymmdd(np.loadtxt(inps.date_list_file, dtype=str, usecols=(0,)).tolist())\n print 'read date list info from: '+inps.date_list_file\n\n # Get Acquisition time - hour\n if not inps.hour:\n inps.hour = ptime.closest_weather_product_time(atr['CENTER_LINE_UTC'], inps.grib_source)\n print 'Time of cloest available product: '+inps.hour\n\n ## Download data using PyAPS\n inps.grib_file_list = dload_grib(date_list, inps.hour, inps.weather_model, inps.weather_dir)\n\n if inps.download:\n print 'Download completed, exit as planned.'\n return\n\n print '*******************************************************************************'\n print 'Calcualting delay for each epoch.'\n\n ## Calculate tropo delay using pyaps\n length = int(atr['FILE_LENGTH'])\n width = int(atr['WIDTH'])\n date_num = len(date_list)\n trop_ts = np.zeros((date_num, length, width), np.float32)\n for i in range(date_num):\n grib_file = inps.grib_file_list[i] \n date = date_list[i]\n print 'calculate phase delay on %s from file %s' % (date, os.path.basename(grib_file))\n trop_ts[i] = get_delay(grib_file, atr, vars(inps))\n\n ## Convert relative phase delay on reference date\n try: ref_date = atr['ref_date']\n except: ref_date = date_list[0]\n print 'convert to relative phase delay with reference date: '+ref_date\n ref_idx = date_list.index(ref_date)\n trop_ts -= np.tile(trop_ts[ref_idx,:,:], (date_num, 1, 1))\n\n ## Write tropospheric delay to HDF5\n tropFile = inps.grib_source+'.h5'\n print 'writing >>> %s' % (tropFile)\n h5trop = h5py.File(tropFile, 'w')\n group_trop = h5trop.create_group('timeseries')\n print 'number of acquisitions: '+str(date_num)\n prog_bar = ptime.progress_bar(maxValue=date_num)\n for i in range(date_num):\n date = date_list[i]\n group_trop.create_dataset(date, data=trop_ts[i], compression='gzip')\n prog_bar.update(i+1, suffix=date)\n prog_bar.close()\n # Write Attributes\n for key,value in atr.iteritems():\n group_trop.attrs[key] = value\n h5trop.close()\n\n ## Write corrected Time series to HDF5\n if k == 'timeseries':\n if not inps.out_file:\n inps.out_file = os.path.splitext(inps.timeseries_file)[0]+'_'+inps.grib_source+'.h5'\n print 'writing >>> %s' % (inps.out_file)\n h5ts = h5py.File(inps.timeseries_file, 'r')\n h5tsCor = h5py.File(inps.out_file, 'w') \n group_tsCor = h5tsCor.create_group('timeseries')\n print 'number of acquisitions: '+str(date_num)\n prog_bar = ptime.progress_bar(maxValue=date_num)\n for i in range(date_num):\n date = date_list[i]\n ts = h5ts['timeseries'].get(date)[:]\n group_tsCor.create_dataset(date, data=ts-trop_ts[i], compression='gzip')\n prog_bar.update(i+1, suffix=date)\n prog_bar.close()\n h5ts.close()\n # Write Attributes\n for key,value in atr.iteritems():\n group_tsCor.attrs[key] = value\n h5tsCor.close()\n\n # Delete temporary DEM file in ROI_PAC format\n if '4pyaps' in inps.dem_file:\n rmCmd = 'rm %s %s.rsc' % (inps.dem_file, inps.dem_file)\n print rmCmd\n os.system(rmCmd)\n print 'Done.'\n return inps.out_file\n\n\n###############################################################\nif __name__ == '__main__':\n main(sys.argv[1:])\n\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render
from django.http import JsonResponse
from knowdb.models import Knowledge
import random
# Create your views here.
def answer(request):
ret = {}
data = Knowledge.objects.all()
num = random.choice(range(1,int(data.count())+1))
ret['name'] = data[num-1].name
ret['answer'] = data[num-1].answer
print ret
return JsonResponse({'exec':'true','ret':ret})
def edit(request):
name = request.POST.get('name')
answer = request.POST.get('answer')
print name,answer
try:
adddata = Knowledge(name=name,answer=answer)
adddata.save()
return JsonResponse({'exec':'true','ret':'提交成功'})
except Exception as e:
return JsonResponse({'exec':'false','ret':'提交失败'})
|
normal
|
{
"blob_id": "eb558644283d992af2c324d457dbe674b714235f",
"index": 735,
"step-1": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\nfrom knowdb.models import Knowledge\n\nimport random\n# Create your views here.\n\ndef answer(request):\n ret = {}\n data = Knowledge.objects.all()\n num = random.choice(range(1,int(data.count())+1))\n ret['name'] = data[num-1].name\n ret['answer'] = data[num-1].answer\n print ret\n return JsonResponse({'exec':'true','ret':ret})\n\n\n\ndef edit(request):\n name = request.POST.get('name')\n answer = request.POST.get('answer')\n print name,answer\n try:\n adddata = Knowledge(name=name,answer=answer)\n adddata.save()\n return JsonResponse({'exec':'true','ret':'提交成功'})\n except Exception as e:\n return JsonResponse({'exec':'false','ret':'提交失败'})\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
# Copyright © YXC
# CreateTime: 2016-03-09 10:06:02
"""
Example of functions with arbitrary number arguments
"""
def optional_argument_func(arg1='', arg2=''):
"""
Function with two optional arguments
"""
print("arg1:{0}".format(arg1))
print("arg2:{0}".format(arg2))
def arbitrary_argument_func(*args):
"""
just use "*" to collect all remaining arguments into a tuple
"""
numargs = len(args)
print("Number of arguments:{0}".format(numargs))
for i, arg in enumerate(args):
print("Argument {0} is : {1}".format(i, arg))
if __name__ == "__main__":
optional_argument_func("Hello", "World")
arbitrary_argument_func()
arbitrary_argument_func("hello")
arbitrary_argument_func("hello", "world", "again")
|
normal
|
{
"blob_id": "061a78650e2abf6a9d1e4796dd349174a8df5cb8",
"index": 8747,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef optional_argument_func(arg1='', arg2=''):\n \"\"\"\n Function with two optional arguments\n \"\"\"\n print('arg1:{0}'.format(arg1))\n print('arg2:{0}'.format(arg2))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef optional_argument_func(arg1='', arg2=''):\n \"\"\"\n Function with two optional arguments\n \"\"\"\n print('arg1:{0}'.format(arg1))\n print('arg2:{0}'.format(arg2))\n\n\ndef arbitrary_argument_func(*args):\n \"\"\"\n just use \"*\" to collect all remaining arguments into a tuple\n \"\"\"\n numargs = len(args)\n print('Number of arguments:{0}'.format(numargs))\n for i, arg in enumerate(args):\n print('Argument {0} is : {1}'.format(i, arg))\n\n\n<mask token>\n",
"step-4": "<mask token>\n\n\ndef optional_argument_func(arg1='', arg2=''):\n \"\"\"\n Function with two optional arguments\n \"\"\"\n print('arg1:{0}'.format(arg1))\n print('arg2:{0}'.format(arg2))\n\n\ndef arbitrary_argument_func(*args):\n \"\"\"\n just use \"*\" to collect all remaining arguments into a tuple\n \"\"\"\n numargs = len(args)\n print('Number of arguments:{0}'.format(numargs))\n for i, arg in enumerate(args):\n print('Argument {0} is : {1}'.format(i, arg))\n\n\nif __name__ == '__main__':\n optional_argument_func('Hello', 'World')\n arbitrary_argument_func()\n arbitrary_argument_func('hello')\n arbitrary_argument_func('hello', 'world', 'again')\n",
"step-5": "#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n# Copyright © YXC\n# CreateTime: 2016-03-09 10:06:02\n\n\"\"\"\nExample of functions with arbitrary number arguments\n\"\"\"\n\n\ndef optional_argument_func(arg1='', arg2=''):\n \"\"\"\n Function with two optional arguments\n \"\"\"\n print(\"arg1:{0}\".format(arg1))\n print(\"arg2:{0}\".format(arg2))\n\n\ndef arbitrary_argument_func(*args):\n \"\"\"\n just use \"*\" to collect all remaining arguments into a tuple\n \"\"\"\n numargs = len(args)\n print(\"Number of arguments:{0}\".format(numargs))\n for i, arg in enumerate(args):\n print(\"Argument {0} is : {1}\".format(i, arg))\n\n\nif __name__ == \"__main__\":\n optional_argument_func(\"Hello\", \"World\")\n arbitrary_argument_func()\n arbitrary_argument_func(\"hello\")\n arbitrary_argument_func(\"hello\", \"world\", \"again\")\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
import unittest
import sys
import os
#Add project root to path
sys.path.append('../..')
from speckle.SpeckleClient import SpeckleApiClient
class TestSpeckleStream(unittest.TestCase):
def setUp(self):
self.s = SpeckleApiClient()
self.user = {'email':'[email protected]','password':'testpassword', 'username':'testuser'}
self.test_stream = 'RKWgU-oWF'
self.test_object = '5bcf2c7e3ff66c15abac431d'
login = self.s.UserLoginAsync(self.user)
assert login, 'Test User Login was not successful'
self.user['id'] = login['resource']['_id']
self.stream = self.s.StreamGetAsync(self.test_stream)
obj = self.s.StreamGetObjectsAsync(self.test_stream)
#for o in obj['resources']:
# r = self.s.ObjectDeleteAsync(o['_id'])
self.s.StreamUpdateAsync(self.test_stream, self.stream)
def tearDown(self):
self.s.StreamUpdateAsync(self.test_stream, self.stream)
def none_msg(self, header):
return header + ' responded with None'
def test_get_object(self):
r = self.s.ObjectGetAsync(self.test_object)
self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))
self.assertTrue(r['success'])
def test_create_object(self):
r = self.s.ObjectCreateAsync([{"owner": self.user['username']}])
self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))
self.assertTrue(r['success'])
self.assertTrue(r['resources'])
#Check created object ID is in response
resource = r['resources'][0]
self.assertTrue(resource['_id'])
print(resource['_id'])
self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])
def test_create_point_object(self):
obj = {
"owner": self.user['username'],
"type": "Point",
"hash": "hash",
"value": [0,0,0]
}
r = self.s.ObjectCreateAsync([obj])
self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))
self.assertTrue(r['success'])
self.assertTrue(r['resources'])
#Check created object ID is in response
resource = r['resources'][0]
self.assertTrue(resource['_id'])
print(resource['_id'])
self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])
def test_create_mesh_object(self):
obj = {
"owner": self.user['username'],
"type": "Mesh",
"geometryHash": "Mesh.66ec936fc8eb1844581db685e5672f79",
"hash": "2e4d67853709316f17e3745cd700a9ed",
"properties": {
"center": {
"type": "Point",
"value": [
-2.326136578802356,
7.41377889150433,
0.01525474415516414
],
"hash": "318e1a3b9bf16bf5711170b61b4cd144",
"geometryHash": "Point.8012f72d1fd49795101ab099b7dff3cb"
},
"area": 1.6718884716988291,
"revitFamTYpe": "undefined"
},
"vertices": [
-2.6709675788879395,
7.420193672180176,
0.007017634343355894,
-2.6617817878723145,
7.910780906677246,
0.016628438606858253,
-2.6525962352752686,
8.401368141174316,
0.026239242404699326,
-2.6434104442596436,
8.891955375671387,
0.03585004433989525,
-2.6342246532440186,
9.382542610168457,
0.04546085000038147,
-2.507732629776001,
6.9263834953308105,
0.005644594319164753,
-2.498547077178955,
7.416970729827881,
0.01319583784788847,
-2.48936128616333,
7.907557964324951,
0.02074708230793476,
-2.480175495147705,
8.39814567565918,
0.028298325836658478,
-2.47098970413208,
8.88873291015625,
0.035849571228027344,
-2.3444979190826416,
6.432573318481445,
0.004271554294973612,
-2.3353121280670166,
6.923160552978516,
0.00976323802024126,
-2.3261263370513916,
7.413747787475586,
0.015254922211170197,
-2.3169405460357666,
7.9043354988098145,
0.020746605470776558,
-2.3077549934387207,
8.394922256469727,
0.02623829059302807,
-2.181262969970703,
5.93876314163208,
0.0028985145036131144,
-2.172077178955078,
6.42935037612915,
0.006330638192594051,
-2.162891387939453,
6.919937610626221,
0.009762762114405632,
-2.1537058353424072,
7.410524845123291,
0.013194886036217213,
-2.1445200443267822,
7.9011125564575195,
0.016627009958028793,
-2.0180280208587646,
5.444952964782715,
0.0015254743630066514,
-2.0088422298431396,
5.935540199279785,
0.002898038364946842,
-1.9996565580368042,
6.4261274337768555,
0.0042706020176410675,
-1.9904708862304688,
6.916714668273926,
0.00564316613599658,
-1.9812850952148438,
7.407302379608154,
0.0070157297886908054
],
"faces": [
1,
6,
1,
0,
5,
1,
7,
2,
1,
6,
1,
8,
3,
2,
7,
1,
9,
4,
3,
8,
1,
11,
6,
5,
10,
1,
12,
7,
6,
11,
1,
13,
8,
7,
12,
1,
14,
9,
8,
13,
1,
16,
11,
10,
15,
1,
17,
12,
11,
16,
1,
18,
13,
12,
17,
1,
19,
14,
13,
18,
1,
21,
16,
15,
20,
1,
22,
17,
16,
21,
1,
23,
18,
17,
22,
1,
24,
19,
18,
23
]
}
r = self.s.ObjectCreateAsync([obj])
self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))
self.assertTrue(r['success'])
self.assertTrue(r['resources'])
# Check created object ID is in response
resource = r['resources'][0]
self.assertTrue(resource['_id'])
print(resource['_id'])
self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])
def test_line_object(self):
obj = {
"type": "Line",
"value": [
-5689.317811503128,
-13716.87365524665,
3448.9999880790538,
-5688.317811503128,
-13717.87365524665,
3539.9999880790538
],
}
r = self.s.ObjectCreateAsync([obj])
self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))
self.assertTrue(r['success'])
self.assertTrue(r['resources'])
# Check created object ID is in response
resource = r['resources'][0]
self.assertTrue(resource['_id'])
print(resource['_id'])
self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])
def test_line_objects(self):
objects = [
{
"type": "Line",
"value": [
0,
0,
0,
1,
1,
1
],
},
{
"type": "Line",
"value": [
-1,
-1,
-1,
2,
2,
2
],
},
]
r = self.s.ObjectCreateAsync(objects)
self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))
self.assertTrue(r['success'])
self.assertTrue(r['resources'])
# Check created object ID is in response
resource = r['resources'][0]
self.assertTrue(resource['_id'])
print(resource['_id'])
self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])
def test_update_object(self):
geometry = {
"vertices": [0.0, 1.0, 2.0, 3.0],
"faces": [1,2,3]
}
props = {
'type': 'RCSlab',
'material': 'Concrete'
}
data = {'properties': props}
data.update(geometry)
r = self.s.ObjectUpdateAsync(self.test_object, data)
self.assertIsNotNone(r)
#Todo: Look into why user is not authorized to update
self.assertTrue(r['success'])
if __name__ == "__main__":
unittest.main()
|
normal
|
{
"blob_id": "b39403171ed264c8fae5ea4ae9d17f77cfcab497",
"index": 9122,
"step-1": "<mask token>\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n self.s = SpeckleApiClient()\n self.user = {'email': '[email protected]', 'password':\n 'testpassword', 'username': 'testuser'}\n self.test_stream = 'RKWgU-oWF'\n self.test_object = '5bcf2c7e3ff66c15abac431d'\n login = self.s.UserLoginAsync(self.user)\n assert login, 'Test User Login was not successful'\n self.user['id'] = login['resource']['_id']\n self.stream = self.s.StreamGetAsync(self.test_stream)\n obj = self.s.StreamGetObjectsAsync(self.test_stream)\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def tearDown(self):\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def none_msg(self, header):\n return header + ' responded with None'\n\n def test_get_object(self):\n r = self.s.ObjectGetAsync(self.test_object)\n self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))\n self.assertTrue(r['success'])\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def test_line_objects(self):\n objects = [{'type': 'Line', 'value': [0, 0, 0, 1, 1, 1]}, {'type':\n 'Line', 'value': [-1, -1, -1, 2, 2, 2]}]\n r = self.s.ObjectCreateAsync(objects)\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_update_object(self):\n geometry = {'vertices': [0.0, 1.0, 2.0, 3.0], 'faces': [1, 2, 3]}\n props = {'type': 'RCSlab', 'material': 'Concrete'}\n data = {'properties': props}\n data.update(geometry)\n r = self.s.ObjectUpdateAsync(self.test_object, data)\n self.assertIsNotNone(r)\n self.assertTrue(r['success'])\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n self.s = SpeckleApiClient()\n self.user = {'email': '[email protected]', 'password':\n 'testpassword', 'username': 'testuser'}\n self.test_stream = 'RKWgU-oWF'\n self.test_object = '5bcf2c7e3ff66c15abac431d'\n login = self.s.UserLoginAsync(self.user)\n assert login, 'Test User Login was not successful'\n self.user['id'] = login['resource']['_id']\n self.stream = self.s.StreamGetAsync(self.test_stream)\n obj = self.s.StreamGetObjectsAsync(self.test_stream)\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def tearDown(self):\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def none_msg(self, header):\n return header + ' responded with None'\n\n def test_get_object(self):\n r = self.s.ObjectGetAsync(self.test_object)\n self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))\n self.assertTrue(r['success'])\n\n def test_create_object(self):\n r = self.s.ObjectCreateAsync([{'owner': self.user['username']}])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_point_object(self):\n obj = {'owner': self.user['username'], 'type': 'Point', 'hash':\n 'hash', 'value': [0, 0, 0]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_mesh_object(self):\n obj = {'owner': self.user['username'], 'type': 'Mesh',\n 'geometryHash': 'Mesh.66ec936fc8eb1844581db685e5672f79', 'hash':\n '2e4d67853709316f17e3745cd700a9ed', 'properties': {'center': {\n 'type': 'Point', 'value': [-2.326136578802356, 7.41377889150433,\n 0.01525474415516414], 'hash':\n '318e1a3b9bf16bf5711170b61b4cd144', 'geometryHash':\n 'Point.8012f72d1fd49795101ab099b7dff3cb'}, 'area': \n 1.6718884716988291, 'revitFamTYpe': 'undefined'}, 'vertices': [\n -2.6709675788879395, 7.420193672180176, 0.007017634343355894, -\n 2.6617817878723145, 7.910780906677246, 0.016628438606858253, -\n 2.6525962352752686, 8.401368141174316, 0.026239242404699326, -\n 2.6434104442596436, 8.891955375671387, 0.03585004433989525, -\n 2.6342246532440186, 9.382542610168457, 0.04546085000038147, -\n 2.507732629776001, 6.9263834953308105, 0.005644594319164753, -\n 2.498547077178955, 7.416970729827881, 0.01319583784788847, -\n 2.48936128616333, 7.907557964324951, 0.02074708230793476, -\n 2.480175495147705, 8.39814567565918, 0.028298325836658478, -\n 2.47098970413208, 8.88873291015625, 0.035849571228027344, -\n 2.3444979190826416, 6.432573318481445, 0.004271554294973612, -\n 2.3353121280670166, 6.923160552978516, 0.00976323802024126, -\n 2.3261263370513916, 7.413747787475586, 0.015254922211170197, -\n 2.3169405460357666, 7.9043354988098145, 0.020746605470776558, -\n 2.3077549934387207, 8.394922256469727, 0.02623829059302807, -\n 2.181262969970703, 5.93876314163208, 0.0028985145036131144, -\n 2.172077178955078, 6.42935037612915, 0.006330638192594051, -\n 2.162891387939453, 6.919937610626221, 0.009762762114405632, -\n 2.1537058353424072, 7.410524845123291, 0.013194886036217213, -\n 2.1445200443267822, 7.9011125564575195, 0.016627009958028793, -\n 2.0180280208587646, 5.444952964782715, 0.0015254743630066514, -\n 2.0088422298431396, 5.935540199279785, 0.002898038364946842, -\n 1.9996565580368042, 6.4261274337768555, 0.0042706020176410675, \n -1.9904708862304688, 6.916714668273926, 0.00564316613599658, -\n 1.9812850952148438, 7.407302379608154, 0.0070157297886908054],\n 'faces': [1, 6, 1, 0, 5, 1, 7, 2, 1, 6, 1, 8, 3, 2, 7, 1, 9, 4,\n 3, 8, 1, 11, 6, 5, 10, 1, 12, 7, 6, 11, 1, 13, 8, 7, 12, 1, 14,\n 9, 8, 13, 1, 16, 11, 10, 15, 1, 17, 12, 11, 16, 1, 18, 13, 12, \n 17, 1, 19, 14, 13, 18, 1, 21, 16, 15, 20, 1, 22, 17, 16, 21, 1,\n 23, 18, 17, 22, 1, 24, 19, 18, 23]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_object(self):\n obj = {'type': 'Line', 'value': [-5689.317811503128, -\n 13716.87365524665, 3448.9999880790538, -5688.317811503128, -\n 13717.87365524665, 3539.9999880790538]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_objects(self):\n objects = [{'type': 'Line', 'value': [0, 0, 0, 1, 1, 1]}, {'type':\n 'Line', 'value': [-1, -1, -1, 2, 2, 2]}]\n r = self.s.ObjectCreateAsync(objects)\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_update_object(self):\n geometry = {'vertices': [0.0, 1.0, 2.0, 3.0], 'faces': [1, 2, 3]}\n props = {'type': 'RCSlab', 'material': 'Concrete'}\n data = {'properties': props}\n data.update(geometry)\n r = self.s.ObjectUpdateAsync(self.test_object, data)\n self.assertIsNotNone(r)\n self.assertTrue(r['success'])\n\n\n<mask token>\n",
"step-3": "<mask token>\nsys.path.append('../..')\n<mask token>\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n self.s = SpeckleApiClient()\n self.user = {'email': '[email protected]', 'password':\n 'testpassword', 'username': 'testuser'}\n self.test_stream = 'RKWgU-oWF'\n self.test_object = '5bcf2c7e3ff66c15abac431d'\n login = self.s.UserLoginAsync(self.user)\n assert login, 'Test User Login was not successful'\n self.user['id'] = login['resource']['_id']\n self.stream = self.s.StreamGetAsync(self.test_stream)\n obj = self.s.StreamGetObjectsAsync(self.test_stream)\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def tearDown(self):\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def none_msg(self, header):\n return header + ' responded with None'\n\n def test_get_object(self):\n r = self.s.ObjectGetAsync(self.test_object)\n self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))\n self.assertTrue(r['success'])\n\n def test_create_object(self):\n r = self.s.ObjectCreateAsync([{'owner': self.user['username']}])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_point_object(self):\n obj = {'owner': self.user['username'], 'type': 'Point', 'hash':\n 'hash', 'value': [0, 0, 0]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_mesh_object(self):\n obj = {'owner': self.user['username'], 'type': 'Mesh',\n 'geometryHash': 'Mesh.66ec936fc8eb1844581db685e5672f79', 'hash':\n '2e4d67853709316f17e3745cd700a9ed', 'properties': {'center': {\n 'type': 'Point', 'value': [-2.326136578802356, 7.41377889150433,\n 0.01525474415516414], 'hash':\n '318e1a3b9bf16bf5711170b61b4cd144', 'geometryHash':\n 'Point.8012f72d1fd49795101ab099b7dff3cb'}, 'area': \n 1.6718884716988291, 'revitFamTYpe': 'undefined'}, 'vertices': [\n -2.6709675788879395, 7.420193672180176, 0.007017634343355894, -\n 2.6617817878723145, 7.910780906677246, 0.016628438606858253, -\n 2.6525962352752686, 8.401368141174316, 0.026239242404699326, -\n 2.6434104442596436, 8.891955375671387, 0.03585004433989525, -\n 2.6342246532440186, 9.382542610168457, 0.04546085000038147, -\n 2.507732629776001, 6.9263834953308105, 0.005644594319164753, -\n 2.498547077178955, 7.416970729827881, 0.01319583784788847, -\n 2.48936128616333, 7.907557964324951, 0.02074708230793476, -\n 2.480175495147705, 8.39814567565918, 0.028298325836658478, -\n 2.47098970413208, 8.88873291015625, 0.035849571228027344, -\n 2.3444979190826416, 6.432573318481445, 0.004271554294973612, -\n 2.3353121280670166, 6.923160552978516, 0.00976323802024126, -\n 2.3261263370513916, 7.413747787475586, 0.015254922211170197, -\n 2.3169405460357666, 7.9043354988098145, 0.020746605470776558, -\n 2.3077549934387207, 8.394922256469727, 0.02623829059302807, -\n 2.181262969970703, 5.93876314163208, 0.0028985145036131144, -\n 2.172077178955078, 6.42935037612915, 0.006330638192594051, -\n 2.162891387939453, 6.919937610626221, 0.009762762114405632, -\n 2.1537058353424072, 7.410524845123291, 0.013194886036217213, -\n 2.1445200443267822, 7.9011125564575195, 0.016627009958028793, -\n 2.0180280208587646, 5.444952964782715, 0.0015254743630066514, -\n 2.0088422298431396, 5.935540199279785, 0.002898038364946842, -\n 1.9996565580368042, 6.4261274337768555, 0.0042706020176410675, \n -1.9904708862304688, 6.916714668273926, 0.00564316613599658, -\n 1.9812850952148438, 7.407302379608154, 0.0070157297886908054],\n 'faces': [1, 6, 1, 0, 5, 1, 7, 2, 1, 6, 1, 8, 3, 2, 7, 1, 9, 4,\n 3, 8, 1, 11, 6, 5, 10, 1, 12, 7, 6, 11, 1, 13, 8, 7, 12, 1, 14,\n 9, 8, 13, 1, 16, 11, 10, 15, 1, 17, 12, 11, 16, 1, 18, 13, 12, \n 17, 1, 19, 14, 13, 18, 1, 21, 16, 15, 20, 1, 22, 17, 16, 21, 1,\n 23, 18, 17, 22, 1, 24, 19, 18, 23]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_object(self):\n obj = {'type': 'Line', 'value': [-5689.317811503128, -\n 13716.87365524665, 3448.9999880790538, -5688.317811503128, -\n 13717.87365524665, 3539.9999880790538]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_objects(self):\n objects = [{'type': 'Line', 'value': [0, 0, 0, 1, 1, 1]}, {'type':\n 'Line', 'value': [-1, -1, -1, 2, 2, 2]}]\n r = self.s.ObjectCreateAsync(objects)\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_update_object(self):\n geometry = {'vertices': [0.0, 1.0, 2.0, 3.0], 'faces': [1, 2, 3]}\n props = {'type': 'RCSlab', 'material': 'Concrete'}\n data = {'properties': props}\n data.update(geometry)\n r = self.s.ObjectUpdateAsync(self.test_object, data)\n self.assertIsNotNone(r)\n self.assertTrue(r['success'])\n\n\nif __name__ == '__main__':\n unittest.main()\n",
"step-4": "import unittest\nimport sys\nimport os\nsys.path.append('../..')\nfrom speckle.SpeckleClient import SpeckleApiClient\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n self.s = SpeckleApiClient()\n self.user = {'email': '[email protected]', 'password':\n 'testpassword', 'username': 'testuser'}\n self.test_stream = 'RKWgU-oWF'\n self.test_object = '5bcf2c7e3ff66c15abac431d'\n login = self.s.UserLoginAsync(self.user)\n assert login, 'Test User Login was not successful'\n self.user['id'] = login['resource']['_id']\n self.stream = self.s.StreamGetAsync(self.test_stream)\n obj = self.s.StreamGetObjectsAsync(self.test_stream)\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def tearDown(self):\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def none_msg(self, header):\n return header + ' responded with None'\n\n def test_get_object(self):\n r = self.s.ObjectGetAsync(self.test_object)\n self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))\n self.assertTrue(r['success'])\n\n def test_create_object(self):\n r = self.s.ObjectCreateAsync([{'owner': self.user['username']}])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_point_object(self):\n obj = {'owner': self.user['username'], 'type': 'Point', 'hash':\n 'hash', 'value': [0, 0, 0]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_mesh_object(self):\n obj = {'owner': self.user['username'], 'type': 'Mesh',\n 'geometryHash': 'Mesh.66ec936fc8eb1844581db685e5672f79', 'hash':\n '2e4d67853709316f17e3745cd700a9ed', 'properties': {'center': {\n 'type': 'Point', 'value': [-2.326136578802356, 7.41377889150433,\n 0.01525474415516414], 'hash':\n '318e1a3b9bf16bf5711170b61b4cd144', 'geometryHash':\n 'Point.8012f72d1fd49795101ab099b7dff3cb'}, 'area': \n 1.6718884716988291, 'revitFamTYpe': 'undefined'}, 'vertices': [\n -2.6709675788879395, 7.420193672180176, 0.007017634343355894, -\n 2.6617817878723145, 7.910780906677246, 0.016628438606858253, -\n 2.6525962352752686, 8.401368141174316, 0.026239242404699326, -\n 2.6434104442596436, 8.891955375671387, 0.03585004433989525, -\n 2.6342246532440186, 9.382542610168457, 0.04546085000038147, -\n 2.507732629776001, 6.9263834953308105, 0.005644594319164753, -\n 2.498547077178955, 7.416970729827881, 0.01319583784788847, -\n 2.48936128616333, 7.907557964324951, 0.02074708230793476, -\n 2.480175495147705, 8.39814567565918, 0.028298325836658478, -\n 2.47098970413208, 8.88873291015625, 0.035849571228027344, -\n 2.3444979190826416, 6.432573318481445, 0.004271554294973612, -\n 2.3353121280670166, 6.923160552978516, 0.00976323802024126, -\n 2.3261263370513916, 7.413747787475586, 0.015254922211170197, -\n 2.3169405460357666, 7.9043354988098145, 0.020746605470776558, -\n 2.3077549934387207, 8.394922256469727, 0.02623829059302807, -\n 2.181262969970703, 5.93876314163208, 0.0028985145036131144, -\n 2.172077178955078, 6.42935037612915, 0.006330638192594051, -\n 2.162891387939453, 6.919937610626221, 0.009762762114405632, -\n 2.1537058353424072, 7.410524845123291, 0.013194886036217213, -\n 2.1445200443267822, 7.9011125564575195, 0.016627009958028793, -\n 2.0180280208587646, 5.444952964782715, 0.0015254743630066514, -\n 2.0088422298431396, 5.935540199279785, 0.002898038364946842, -\n 1.9996565580368042, 6.4261274337768555, 0.0042706020176410675, \n -1.9904708862304688, 6.916714668273926, 0.00564316613599658, -\n 1.9812850952148438, 7.407302379608154, 0.0070157297886908054],\n 'faces': [1, 6, 1, 0, 5, 1, 7, 2, 1, 6, 1, 8, 3, 2, 7, 1, 9, 4,\n 3, 8, 1, 11, 6, 5, 10, 1, 12, 7, 6, 11, 1, 13, 8, 7, 12, 1, 14,\n 9, 8, 13, 1, 16, 11, 10, 15, 1, 17, 12, 11, 16, 1, 18, 13, 12, \n 17, 1, 19, 14, 13, 18, 1, 21, 16, 15, 20, 1, 22, 17, 16, 21, 1,\n 23, 18, 17, 22, 1, 24, 19, 18, 23]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_object(self):\n obj = {'type': 'Line', 'value': [-5689.317811503128, -\n 13716.87365524665, 3448.9999880790538, -5688.317811503128, -\n 13717.87365524665, 3539.9999880790538]}\n r = self.s.ObjectCreateAsync([obj])\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_objects(self):\n objects = [{'type': 'Line', 'value': [0, 0, 0, 1, 1, 1]}, {'type':\n 'Line', 'value': [-1, -1, -1, 2, 2, 2]}]\n r = self.s.ObjectCreateAsync(objects)\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n print(resource['_id'])\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_update_object(self):\n geometry = {'vertices': [0.0, 1.0, 2.0, 3.0], 'faces': [1, 2, 3]}\n props = {'type': 'RCSlab', 'material': 'Concrete'}\n data = {'properties': props}\n data.update(geometry)\n r = self.s.ObjectUpdateAsync(self.test_object, data)\n self.assertIsNotNone(r)\n self.assertTrue(r['success'])\n\n\nif __name__ == '__main__':\n unittest.main()\n",
"step-5": "import unittest\nimport sys\nimport os\n#Add project root to path\nsys.path.append('../..')\n\nfrom speckle.SpeckleClient import SpeckleApiClient\n\n\nclass TestSpeckleStream(unittest.TestCase):\n\n def setUp(self):\n\n self.s = SpeckleApiClient()\n self.user = {'email':'[email protected]','password':'testpassword', 'username':'testuser'}\n\n self.test_stream = 'RKWgU-oWF'\n self.test_object = '5bcf2c7e3ff66c15abac431d'\n\n login = self.s.UserLoginAsync(self.user)\n assert login, 'Test User Login was not successful'\n\n self.user['id'] = login['resource']['_id']\n\n self.stream = self.s.StreamGetAsync(self.test_stream)\n obj = self.s.StreamGetObjectsAsync(self.test_stream)\n\n #for o in obj['resources']:\n # r = self.s.ObjectDeleteAsync(o['_id'])\n\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def tearDown(self):\n self.s.StreamUpdateAsync(self.test_stream, self.stream)\n\n def none_msg(self, header):\n return header + ' responded with None'\n \n\n def test_get_object(self):\n r = self.s.ObjectGetAsync(self.test_object)\n\n self.assertIsNotNone(r, self.none_msg('ObjectGetAsync'))\n self.assertTrue(r['success'])\n \n \n def test_create_object(self):\n\n r = self.s.ObjectCreateAsync([{\"owner\": self.user['username']}])\n\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n\n #Check created object ID is in response\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n\n print(resource['_id'])\n\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_point_object(self):\n obj = {\n \"owner\": self.user['username'],\n \"type\": \"Point\",\n \"hash\": \"hash\",\n \"value\": [0,0,0]\n }\n\n r = self.s.ObjectCreateAsync([obj])\n\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n\n #Check created object ID is in response\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n\n print(resource['_id'])\n\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_create_mesh_object(self):\n obj = {\n \"owner\": self.user['username'],\n \"type\": \"Mesh\",\n \"geometryHash\": \"Mesh.66ec936fc8eb1844581db685e5672f79\",\n \"hash\": \"2e4d67853709316f17e3745cd700a9ed\",\n \"properties\": {\n \"center\": {\n \"type\": \"Point\",\n \"value\": [\n -2.326136578802356,\n 7.41377889150433,\n 0.01525474415516414\n ],\n \"hash\": \"318e1a3b9bf16bf5711170b61b4cd144\",\n \"geometryHash\": \"Point.8012f72d1fd49795101ab099b7dff3cb\"\n },\n \"area\": 1.6718884716988291,\n \"revitFamTYpe\": \"undefined\"\n },\n \"vertices\": [\n -2.6709675788879395,\n 7.420193672180176,\n 0.007017634343355894,\n -2.6617817878723145,\n 7.910780906677246,\n 0.016628438606858253,\n -2.6525962352752686,\n 8.401368141174316,\n 0.026239242404699326,\n -2.6434104442596436,\n 8.891955375671387,\n 0.03585004433989525,\n -2.6342246532440186,\n 9.382542610168457,\n 0.04546085000038147,\n -2.507732629776001,\n 6.9263834953308105,\n 0.005644594319164753,\n -2.498547077178955,\n 7.416970729827881,\n 0.01319583784788847,\n -2.48936128616333,\n 7.907557964324951,\n 0.02074708230793476,\n -2.480175495147705,\n 8.39814567565918,\n 0.028298325836658478,\n -2.47098970413208,\n 8.88873291015625,\n 0.035849571228027344,\n -2.3444979190826416,\n 6.432573318481445,\n 0.004271554294973612,\n -2.3353121280670166,\n 6.923160552978516,\n 0.00976323802024126,\n -2.3261263370513916,\n 7.413747787475586,\n 0.015254922211170197,\n -2.3169405460357666,\n 7.9043354988098145,\n 0.020746605470776558,\n -2.3077549934387207,\n 8.394922256469727,\n 0.02623829059302807,\n -2.181262969970703,\n 5.93876314163208,\n 0.0028985145036131144,\n -2.172077178955078,\n 6.42935037612915,\n 0.006330638192594051,\n -2.162891387939453,\n 6.919937610626221,\n 0.009762762114405632,\n -2.1537058353424072,\n 7.410524845123291,\n 0.013194886036217213,\n -2.1445200443267822,\n 7.9011125564575195,\n 0.016627009958028793,\n -2.0180280208587646,\n 5.444952964782715,\n 0.0015254743630066514,\n -2.0088422298431396,\n 5.935540199279785,\n 0.002898038364946842,\n -1.9996565580368042,\n 6.4261274337768555,\n 0.0042706020176410675,\n -1.9904708862304688,\n 6.916714668273926,\n 0.00564316613599658,\n -1.9812850952148438,\n 7.407302379608154,\n 0.0070157297886908054\n ],\n \"faces\": [\n 1,\n 6,\n 1,\n 0,\n 5,\n 1,\n 7,\n 2,\n 1,\n 6,\n 1,\n 8,\n 3,\n 2,\n 7,\n 1,\n 9,\n 4,\n 3,\n 8,\n 1,\n 11,\n 6,\n 5,\n 10,\n 1,\n 12,\n 7,\n 6,\n 11,\n 1,\n 13,\n 8,\n 7,\n 12,\n 1,\n 14,\n 9,\n 8,\n 13,\n 1,\n 16,\n 11,\n 10,\n 15,\n 1,\n 17,\n 12,\n 11,\n 16,\n 1,\n 18,\n 13,\n 12,\n 17,\n 1,\n 19,\n 14,\n 13,\n 18,\n 1,\n 21,\n 16,\n 15,\n 20,\n 1,\n 22,\n 17,\n 16,\n 21,\n 1,\n 23,\n 18,\n 17,\n 22,\n 1,\n 24,\n 19,\n 18,\n 23\n ]\n }\n\n r = self.s.ObjectCreateAsync([obj])\n\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n\n # Check created object ID is in response\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n\n print(resource['_id'])\n\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_object(self):\n obj = {\n \"type\": \"Line\",\n \"value\": [\n -5689.317811503128,\n -13716.87365524665,\n 3448.9999880790538,\n -5688.317811503128,\n -13717.87365524665,\n 3539.9999880790538\n ],\n }\n\n r = self.s.ObjectCreateAsync([obj])\n\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n\n # Check created object ID is in response\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n\n print(resource['_id'])\n\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n def test_line_objects(self):\n objects = [\n {\n \"type\": \"Line\",\n \"value\": [\n 0,\n 0,\n 0,\n 1,\n 1,\n 1\n ],\n },\n {\n \"type\": \"Line\",\n \"value\": [\n -1,\n -1,\n -1,\n 2,\n 2,\n 2\n ],\n },\n ]\n r = self.s.ObjectCreateAsync(objects)\n\n self.assertIsNotNone(r, self.none_msg('ObjectCreateAsync'))\n self.assertTrue(r['success'])\n self.assertTrue(r['resources'])\n\n # Check created object ID is in response\n resource = r['resources'][0]\n self.assertTrue(resource['_id'])\n\n print(resource['_id'])\n\n self.s.StreamAddObjectAsync(self.test_stream, resource['_id'])\n\n\n\n\n def test_update_object(self):\n \n geometry = {\n \"vertices\": [0.0, 1.0, 2.0, 3.0],\n \"faces\": [1,2,3]\n }\n\n props = {\n 'type': 'RCSlab', \n 'material': 'Concrete'\n }\n data = {'properties': props}\n data.update(geometry)\n r = self.s.ObjectUpdateAsync(self.test_object, data)\n self.assertIsNotNone(r)\n\n #Todo: Look into why user is not authorized to update\n self.assertTrue(r['success'])\n\nif __name__ == \"__main__\":\n unittest.main()\n",
"step-ids": [
7,
11,
12,
13,
14
]
}
|
[
7,
11,
12,
13,
14
] |
def lucas():
yield 2
a = 2
b = 1
while True:
yield b
a, b = b, a + b
l = lucas()
for i in range(10):
print('{}: {}'.format(i, next(l)))
|
normal
|
{
"blob_id": "4745c00ca0f3ca4316117228a9d44bdb5df02877",
"index": 7799,
"step-1": "<mask token>\n",
"step-2": "def lucas():\n yield 2\n a = 2\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\n\n<mask token>\n",
"step-3": "def lucas():\n yield 2\n a = 2\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\n\n<mask token>\nfor i in range(10):\n print('{}: {}'.format(i, next(l)))\n",
"step-4": "def lucas():\n yield 2\n a = 2\n b = 1\n while True:\n yield b\n a, b = b, a + b\n\n\nl = lucas()\nfor i in range(10):\n print('{}: {}'.format(i, next(l)))\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
def solution(S):
# write your code in Python 3.6
# Definitions
log_sep = ','
num_sep = '-'
time_sep = ':'
# Initialization
from collections import defaultdict
# defaultdict initialize missing key to default value -> 0
bill = defaultdict(int)
total = defaultdict(int)
calls = S.splitlines()
maximal = 0
free_number = 0
for call in calls:
# Parsing values
hhmmss, number = call.split(log_sep)
hh, mm, ss = hhmmss.split(time_sep)
hh, mm, ss = int(hh), int(mm), int(ss)
number = int(number.replace(num_sep,''))
# Call duration calculations
minutes = mm + hh * 60
seconds = ss + minutes * 60
# Free number Rule
total[number] += seconds
if total[number] > maximal:
# new maximal
maximal = total[number]
free_number = number
elif total[number] == maximal:
# in case of a tie...
free_number = min(number,free_number)
# Billing Rule
if minutes < 5:
bill[number] += seconds * 3
else:
if ss > 0:
started = 1
else:
started = 0
bill[number] += (minutes + started) * 150
# Free number Rule enforcement
bill[free_number] = 0
return sum(bill.values())
|
normal
|
{
"blob_id": "bf8bbeb408cb75af314ef9f3907456036e731c0b",
"index": 294,
"step-1": "<mask token>\n",
"step-2": "def solution(S):\n log_sep = ','\n num_sep = '-'\n time_sep = ':'\n from collections import defaultdict\n bill = defaultdict(int)\n total = defaultdict(int)\n calls = S.splitlines()\n maximal = 0\n free_number = 0\n for call in calls:\n hhmmss, number = call.split(log_sep)\n hh, mm, ss = hhmmss.split(time_sep)\n hh, mm, ss = int(hh), int(mm), int(ss)\n number = int(number.replace(num_sep, ''))\n minutes = mm + hh * 60\n seconds = ss + minutes * 60\n total[number] += seconds\n if total[number] > maximal:\n maximal = total[number]\n free_number = number\n elif total[number] == maximal:\n free_number = min(number, free_number)\n if minutes < 5:\n bill[number] += seconds * 3\n else:\n if ss > 0:\n started = 1\n else:\n started = 0\n bill[number] += (minutes + started) * 150\n bill[free_number] = 0\n return sum(bill.values())\n",
"step-3": "def solution(S):\n # write your code in Python 3.6\n # Definitions\n log_sep = ','\n num_sep = '-'\n time_sep = ':'\n # Initialization\n from collections import defaultdict\n # defaultdict initialize missing key to default value -> 0\n bill = defaultdict(int)\n total = defaultdict(int)\n calls = S.splitlines()\n maximal = 0\n free_number = 0\n \n for call in calls:\n # Parsing values\n hhmmss, number = call.split(log_sep)\n hh, mm, ss = hhmmss.split(time_sep)\n hh, mm, ss = int(hh), int(mm), int(ss)\n number = int(number.replace(num_sep,''))\n # Call duration calculations\n minutes = mm + hh * 60\n seconds = ss + minutes * 60\n # Free number Rule\n total[number] += seconds\n if total[number] > maximal:\n # new maximal\n maximal = total[number]\n free_number = number\n elif total[number] == maximal:\n # in case of a tie...\n free_number = min(number,free_number)\n # Billing Rule\n if minutes < 5:\n bill[number] += seconds * 3\n else:\n if ss > 0:\n started = 1\n else:\n started = 0\n bill[number] += (minutes + started) * 150\n # Free number Rule enforcement\n bill[free_number] = 0\n return sum(bill.values())\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
}
|
[
0,
1,
2
] |
from data_structures.datacenter import Datacenter, urllib, json,
URL = "http://www.mocky.io/v2/5e539b332e00007c002dacbe"
def get_data(url, max_retries=5, delay_between_retries=1):
"""
Fetch the data from http://www.mocky.io/v2/5e539b332e00007c002dacbe
and return it as a JSON object.
Args:
url (str): The url to be fetched.
max_retries (int): Number of retries.
delay_between_retries (int): Delay between retries in seconds.
Returns:
data (dict)
"""
pass # the rest of your logic here
for i in max_retries:
while True:
try
time.sleep(delay_between_tries)
response = urllib.request.urlopen(url)
data = json.loads(response.read())
print (data)
break
except Exception:
continue
def main():
"""
Main entry to our program.
"""
data = get_data(URL)
if not data:
raise ValueError('No data to process')
datacenters = [
Datacenter(key, value)
for key, value in data.items()
]
pass # the rest of your logic here
if __name__ == '__main__':
main()
|
normal
|
{
"blob_id": "e56a7912b9940b1cab6c19d0047f1f60f0083f66",
"index": 4911,
"step-1": "from data_structures.datacenter import Datacenter, urllib, json,\n\n\nURL = \"http://www.mocky.io/v2/5e539b332e00007c002dacbe\"\n\n\ndef get_data(url, max_retries=5, delay_between_retries=1):\n \"\"\"\n Fetch the data from http://www.mocky.io/v2/5e539b332e00007c002dacbe\n and return it as a JSON object.\n\n Args:\n url (str): The url to be fetched.\n max_retries (int): Number of retries.\n delay_between_retries (int): Delay between retries in seconds.\n Returns:\n data (dict)\n \"\"\"\n pass # the rest of your logic here\n for i in max_retries:\n while True:\n try\n time.sleep(delay_between_tries)\n response = urllib.request.urlopen(url)\n data = json.loads(response.read())\n print (data)\n break\n except Exception:\n continue\n \n \n \n\n\n\n\n\n\ndef main():\n \"\"\"\n Main entry to our program.\n \"\"\"\n\n data = get_data(URL)\n\n if not data:\n raise ValueError('No data to process')\n\n datacenters = [\n Datacenter(key, value)\n for key, value in data.items()\n ]\n\n pass # the rest of your logic here\n\n\nif __name__ == '__main__':\n main()\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
#!usr/bin/env python
# -*- coding:utf-8 -*-
"""
@author: Jack
@datetime: 2018/8/31 13:32
@E-mail: [email protected]
"""
def isValid(s):
stack = []
for ss in s:
if ss in '([{':
stack.append(ss)
if ss in ')]}':
if len(stack) <= 0:
return False
else:
compare = stack.pop()
if (compare == '(' and ss != ')') or (compare == '[' and ss != ']') or (compare == '{' and ss != '}'):
return False
if len(stack) == 0:
return True
else:
return False
if __name__ == '__main__':
print isValid("{[]}")
|
normal
|
{
"blob_id": "607f0aac0d6d2c05737f59803befcff37d559398",
"index": 5117,
"step-1": "#!usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\n@author: Jack\n@datetime: 2018/8/31 13:32\n@E-mail: [email protected]\n\"\"\"\n\n\ndef isValid(s):\n stack = []\n for ss in s:\n if ss in '([{':\n stack.append(ss)\n if ss in ')]}':\n if len(stack) <= 0:\n return False\n else:\n compare = stack.pop()\n if (compare == '(' and ss != ')') or (compare == '[' and ss != ']') or (compare == '{' and ss != '}'):\n return False\n if len(stack) == 0:\n return True\n else:\n return False\n\n\nif __name__ == '__main__':\n print isValid(\"{[]}\")\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
version https://git-lfs.github.com/spec/v1
oid sha256:7f0b7267333e6a4a73d3df0ee7f384f7b3cb6ffb14ed2dc8a5894b853bac8957
size 1323
|
normal
|
{
"blob_id": "f1972baee8b399c9a52561c8f015f71cb9922bb0",
"index": 4875,
"step-1": "version https://git-lfs.github.com/spec/v1\noid sha256:7f0b7267333e6a4a73d3df0ee7f384f7b3cb6ffb14ed2dc8a5894b853bac8957\nsize 1323\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
from flask import Flask
from flask import render_template
import datetime
from person import Person
import requests
from post import Post
app = Flask(__name__)
all_posts = all_posts = requests.get(
"https://api.npoint.io/5abcca6f4e39b4955965").json()
post_objects = []
for post in all_posts:
post_obj = Post(post["id"], post["title"], post["subtitle"], post["body"])
post_objects.append(post_obj)
@app.route('/')
def home_page():
year = datetime.datetime.today().year
return render_template("index.html",
current_year=year)
@app.route('/guess/<name>')
def guesser(name):
person = Person(name=name)
return render_template("guess.html",
name=person.name,
gender=person.gender,
age=person.age,
country=person.country,
)
@app.route('/blog')
def blog():
return render_template("blog.html", posts=post_objects)
@app.route('/post/<int:id>')
def blog_post(id):
requested_post = None
for post in post_objects:
if post.id == id:
requested_post = post
return render_template("post.html", post=requested_post)
if __name__ == "__main__":
app.run(debug=True)
|
normal
|
{
"blob_id": "895ece0b8d45cd64e43f8ddc54824f7647254185",
"index": 2547,
"step-1": "<mask token>\n\n\[email protected]('/guess/<name>')\ndef guesser(name):\n person = Person(name=name)\n return render_template('guess.html', name=person.name, gender=person.\n gender, age=person.age, country=person.country)\n\n\n<mask token>\n\n\[email protected]('/post/<int:id>')\ndef blog_post(id):\n requested_post = None\n for post in post_objects:\n if post.id == id:\n requested_post = post\n return render_template('post.html', post=requested_post)\n\n\n<mask token>\n",
"step-2": "<mask token>\nfor post in all_posts:\n post_obj = Post(post['id'], post['title'], post['subtitle'], post['body'])\n post_objects.append(post_obj)\n\n\[email protected]('/')\ndef home_page():\n year = datetime.datetime.today().year\n return render_template('index.html', current_year=year)\n\n\[email protected]('/guess/<name>')\ndef guesser(name):\n person = Person(name=name)\n return render_template('guess.html', name=person.name, gender=person.\n gender, age=person.age, country=person.country)\n\n\[email protected]('/blog')\ndef blog():\n return render_template('blog.html', posts=post_objects)\n\n\[email protected]('/post/<int:id>')\ndef blog_post(id):\n requested_post = None\n for post in post_objects:\n if post.id == id:\n requested_post = post\n return render_template('post.html', post=requested_post)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-3": "<mask token>\napp = Flask(__name__)\nall_posts = all_posts = requests.get(\n 'https://api.npoint.io/5abcca6f4e39b4955965').json()\npost_objects = []\nfor post in all_posts:\n post_obj = Post(post['id'], post['title'], post['subtitle'], post['body'])\n post_objects.append(post_obj)\n\n\[email protected]('/')\ndef home_page():\n year = datetime.datetime.today().year\n return render_template('index.html', current_year=year)\n\n\[email protected]('/guess/<name>')\ndef guesser(name):\n person = Person(name=name)\n return render_template('guess.html', name=person.name, gender=person.\n gender, age=person.age, country=person.country)\n\n\[email protected]('/blog')\ndef blog():\n return render_template('blog.html', posts=post_objects)\n\n\[email protected]('/post/<int:id>')\ndef blog_post(id):\n requested_post = None\n for post in post_objects:\n if post.id == id:\n requested_post = post\n return render_template('post.html', post=requested_post)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-4": "from flask import Flask\nfrom flask import render_template\nimport datetime\nfrom person import Person\nimport requests\nfrom post import Post\napp = Flask(__name__)\nall_posts = all_posts = requests.get(\n 'https://api.npoint.io/5abcca6f4e39b4955965').json()\npost_objects = []\nfor post in all_posts:\n post_obj = Post(post['id'], post['title'], post['subtitle'], post['body'])\n post_objects.append(post_obj)\n\n\[email protected]('/')\ndef home_page():\n year = datetime.datetime.today().year\n return render_template('index.html', current_year=year)\n\n\[email protected]('/guess/<name>')\ndef guesser(name):\n person = Person(name=name)\n return render_template('guess.html', name=person.name, gender=person.\n gender, age=person.age, country=person.country)\n\n\[email protected]('/blog')\ndef blog():\n return render_template('blog.html', posts=post_objects)\n\n\[email protected]('/post/<int:id>')\ndef blog_post(id):\n requested_post = None\n for post in post_objects:\n if post.id == id:\n requested_post = post\n return render_template('post.html', post=requested_post)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-5": "from flask import Flask\nfrom flask import render_template\nimport datetime\nfrom person import Person\nimport requests\nfrom post import Post\n\napp = Flask(__name__)\nall_posts = all_posts = requests.get(\n \"https://api.npoint.io/5abcca6f4e39b4955965\").json()\npost_objects = []\n\nfor post in all_posts:\n post_obj = Post(post[\"id\"], post[\"title\"], post[\"subtitle\"], post[\"body\"])\n post_objects.append(post_obj)\n\n\[email protected]('/')\ndef home_page():\n year = datetime.datetime.today().year\n return render_template(\"index.html\",\n current_year=year)\n\n\[email protected]('/guess/<name>')\ndef guesser(name):\n person = Person(name=name)\n return render_template(\"guess.html\",\n name=person.name,\n gender=person.gender,\n age=person.age,\n country=person.country,\n )\n\n\[email protected]('/blog')\ndef blog():\n return render_template(\"blog.html\", posts=post_objects)\n\n\[email protected]('/post/<int:id>')\ndef blog_post(id):\n requested_post = None\n for post in post_objects:\n if post.id == id:\n requested_post = post\n return render_template(\"post.html\", post=requested_post)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n",
"step-ids": [
2,
5,
6,
7,
8
]
}
|
[
2,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def main():
reader = csv.reader(row for row in fileinput.input() if not row.
startswith('#'))
circles = lps.parse_lps(reader)
for circle in circles:
circle.r = R
print(circle)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('inputfile', help=
'if specified reads a *.lp formatted file otherwise standard in')
<|reserved_special_token_0|>
def main():
reader = csv.reader(row for row in fileinput.input() if not row.
startswith('#'))
circles = lps.parse_lps(reader)
for circle in circles:
circle.r = R
print(circle)
if __name__ == '__main__':
main()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser = argparse.ArgumentParser(description=
'Takes an input of *.lp format and sets all radii to the same value')
parser.add_argument('inputfile', help=
'if specified reads a *.lp formatted file otherwise standard in')
R = 1
def main():
reader = csv.reader(row for row in fileinput.input() if not row.
startswith('#'))
circles = lps.parse_lps(reader)
for circle in circles:
circle.r = R
print(circle)
if __name__ == '__main__':
main()
<|reserved_special_token_1|>
import sys
import csv
import math
import collections
import argparse
import fileinput
import lp
parser = argparse.ArgumentParser(description=
'Takes an input of *.lp format and sets all radii to the same value')
parser.add_argument('inputfile', help=
'if specified reads a *.lp formatted file otherwise standard in')
R = 1
def main():
reader = csv.reader(row for row in fileinput.input() if not row.
startswith('#'))
circles = lps.parse_lps(reader)
for circle in circles:
circle.r = R
print(circle)
if __name__ == '__main__':
main()
<|reserved_special_token_1|>
#!/usr/bin/env python3
import sys
import csv
import math
import collections
import argparse
import fileinput
import lp
parser = argparse.ArgumentParser(description="Takes an input of *.lp format and sets all radii to the same value")
parser.add_argument("inputfile", help="if specified reads a *.lp formatted file otherwise standard in")
R = 1
def main():
reader = csv.reader(row for row in fileinput.input() if not row.startswith('#'))
circles = lps.parse_lps(reader)
for circle in circles:
circle.r = R
print(circle)
if __name__ == "__main__":
main()
|
flexible
|
{
"blob_id": "00f62fec7f5372c5798b0ebf3f3783233360581e",
"index": 2987,
"step-1": "<mask token>\n\n\ndef main():\n reader = csv.reader(row for row in fileinput.input() if not row.\n startswith('#'))\n circles = lps.parse_lps(reader)\n for circle in circles:\n circle.r = R\n print(circle)\n\n\n<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('inputfile', help=\n 'if specified reads a *.lp formatted file otherwise standard in')\n<mask token>\n\n\ndef main():\n reader = csv.reader(row for row in fileinput.input() if not row.\n startswith('#'))\n circles = lps.parse_lps(reader)\n for circle in circles:\n circle.r = R\n print(circle)\n\n\nif __name__ == '__main__':\n main()\n",
"step-3": "<mask token>\nparser = argparse.ArgumentParser(description=\n 'Takes an input of *.lp format and sets all radii to the same value')\nparser.add_argument('inputfile', help=\n 'if specified reads a *.lp formatted file otherwise standard in')\nR = 1\n\n\ndef main():\n reader = csv.reader(row for row in fileinput.input() if not row.\n startswith('#'))\n circles = lps.parse_lps(reader)\n for circle in circles:\n circle.r = R\n print(circle)\n\n\nif __name__ == '__main__':\n main()\n",
"step-4": "import sys\nimport csv\nimport math\nimport collections\nimport argparse\nimport fileinput\nimport lp\nparser = argparse.ArgumentParser(description=\n 'Takes an input of *.lp format and sets all radii to the same value')\nparser.add_argument('inputfile', help=\n 'if specified reads a *.lp formatted file otherwise standard in')\nR = 1\n\n\ndef main():\n reader = csv.reader(row for row in fileinput.input() if not row.\n startswith('#'))\n circles = lps.parse_lps(reader)\n for circle in circles:\n circle.r = R\n print(circle)\n\n\nif __name__ == '__main__':\n main()\n",
"step-5": "#!/usr/bin/env python3\nimport sys\nimport csv\nimport math\n\nimport collections\nimport argparse\nimport fileinput\n\nimport lp\n\nparser = argparse.ArgumentParser(description=\"Takes an input of *.lp format and sets all radii to the same value\")\nparser.add_argument(\"inputfile\", help=\"if specified reads a *.lp formatted file otherwise standard in\")\n\nR = 1\n\ndef main():\n reader = csv.reader(row for row in fileinput.input() if not row.startswith('#'))\n\n circles = lps.parse_lps(reader)\n\n for circle in circles:\n circle.r = R\n print(circle)\n\nif __name__ == \"__main__\":\n main()\n",
"step-ids": [
1,
2,
3,
4,
5
]
}
|
[
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if d == m:
print(a[0])
elif 0 < d < m:
for i in range(hmin, hmax + 1):
fin1 = a[0] - i + m
if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:
print(a[0] - i)
found = 1
break
if found == 0:
i = 0
while i < n - 1:
found = 0
invalid = 0
d = a[i + 1] - a[i]
print(a[i], a[i + 1], d)
if d < hmin or d > hmax:
i = i + 1
continue
for j in range(i + 1, n):
d = a[j] - a[j - 1]
print(a[i], a[j], d)
if d < hmin or d > hmax:
i = j - 1
invalid = 1
break
if a[j] - a[i] > m:
invalid = 1
break
if a[j] - a[i] == m:
found = 1
invalid = 0
break
if invalid == 1:
i = i + 1
continue
if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:
print(a[i])
break
i = i + 1
if n == 1:
print(a[0] + hmax - m)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
n = int(input().strip())
a = list(input().strip().split(' '))
H = list(input().strip().split(' '))
a = [int(i) for i in a]
m = int(H[0])
hmin = int(H[1])
hmax = int(H[2])
pos = 0
found = 0
d = a[-1] - a[0]
if d == m:
print(a[0])
elif 0 < d < m:
for i in range(hmin, hmax + 1):
fin1 = a[0] - i + m
if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:
print(a[0] - i)
found = 1
break
if found == 0:
i = 0
while i < n - 1:
found = 0
invalid = 0
d = a[i + 1] - a[i]
print(a[i], a[i + 1], d)
if d < hmin or d > hmax:
i = i + 1
continue
for j in range(i + 1, n):
d = a[j] - a[j - 1]
print(a[i], a[j], d)
if d < hmin or d > hmax:
i = j - 1
invalid = 1
break
if a[j] - a[i] > m:
invalid = 1
break
if a[j] - a[i] == m:
found = 1
invalid = 0
break
if invalid == 1:
i = i + 1
continue
if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:
print(a[i])
break
i = i + 1
if n == 1:
print(a[0] + hmax - m)
<|reserved_special_token_1|>
import sys
n = int(input().strip())
a = list(input().strip().split(' '))
H = list(input().strip().split(' '))
a = [int(i) for i in a]
m = int(H[0])
hmin = int(H[1])
hmax = int(H[2])
pos = 0
found = 0
d = a[-1] - a[0]
if d == m:
print(a[0])
elif 0 < d < m:
for i in range(hmin, hmax + 1):
fin1 = a[0] - i + m
if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:
print(a[0] - i)
found = 1
break
if found == 0:
i = 0
while i < n - 1:
found = 0
invalid = 0
d = a[i + 1] - a[i]
print(a[i], a[i + 1], d)
if d < hmin or d > hmax:
i = i + 1
continue
for j in range(i + 1, n):
d = a[j] - a[j - 1]
print(a[i], a[j], d)
if d < hmin or d > hmax:
i = j - 1
invalid = 1
break
if a[j] - a[i] > m:
invalid = 1
break
if a[j] - a[i] == m:
found = 1
invalid = 0
break
if invalid == 1:
i = i + 1
continue
if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:
print(a[i])
break
i = i + 1
if n == 1:
print(a[0] + hmax - m)
<|reserved_special_token_1|>
import sys
n=int(input().strip())
a=list(input().strip().split(' '))
H=list(input().strip().split(' '))
a = [int(i) for i in a]
m=int(H[0])
hmin=int(H[1])
hmax=int(H[2])
pos=0
found = 0
d=a[-1]-a[0]
if(d==m):
print(a[0])
elif(0<d<m):
for i in range(hmin, hmax+1):
fin1 = a[0]-i+m
if(hmin<=fin1-a[-1]<=hmax or fin1==a[-1]):
print(a[0]-i)
found = 1
break
if(found == 0):
i = 0
while(i<(n-1)):
found = 0
invalid = 0
d = a[i+1]-a[i]
print(a[i], a[i+1], d)
if(d<hmin or d>hmax):
i=i+1
continue
for j in range(i+1, n):
d = a[j]-a[j-1]
print(a[i], a[j], d)
if(d<hmin or d>hmax):
i = j-1
invalid = 1
break
if(a[j]-a[i]>m):
invalid = 1
break
if(a[j]-a[i]==m):
found = 1
invalid = 0
break
if(invalid == 1):
i = i+1
continue
if(found == 1 or (a[-1]-a[i]+hmin<=m and a[-1]-a[i]+hmax>=m)):
print(a[i])
break
i = i+1
if(n == 1):
print(a[0]+hmax-m)
|
flexible
|
{
"blob_id": "3da82bcff0a4f91c1245892bc01e9f743ea354a8",
"index": 4484,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif d == m:\n print(a[0])\nelif 0 < d < m:\n for i in range(hmin, hmax + 1):\n fin1 = a[0] - i + m\n if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:\n print(a[0] - i)\n found = 1\n break\nif found == 0:\n i = 0\n while i < n - 1:\n found = 0\n invalid = 0\n d = a[i + 1] - a[i]\n print(a[i], a[i + 1], d)\n if d < hmin or d > hmax:\n i = i + 1\n continue\n for j in range(i + 1, n):\n d = a[j] - a[j - 1]\n print(a[i], a[j], d)\n if d < hmin or d > hmax:\n i = j - 1\n invalid = 1\n break\n if a[j] - a[i] > m:\n invalid = 1\n break\n if a[j] - a[i] == m:\n found = 1\n invalid = 0\n break\n if invalid == 1:\n i = i + 1\n continue\n if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:\n print(a[i])\n break\n i = i + 1\nif n == 1:\n print(a[0] + hmax - m)\n",
"step-3": "<mask token>\nn = int(input().strip())\na = list(input().strip().split(' '))\nH = list(input().strip().split(' '))\na = [int(i) for i in a]\nm = int(H[0])\nhmin = int(H[1])\nhmax = int(H[2])\npos = 0\nfound = 0\nd = a[-1] - a[0]\nif d == m:\n print(a[0])\nelif 0 < d < m:\n for i in range(hmin, hmax + 1):\n fin1 = a[0] - i + m\n if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:\n print(a[0] - i)\n found = 1\n break\nif found == 0:\n i = 0\n while i < n - 1:\n found = 0\n invalid = 0\n d = a[i + 1] - a[i]\n print(a[i], a[i + 1], d)\n if d < hmin or d > hmax:\n i = i + 1\n continue\n for j in range(i + 1, n):\n d = a[j] - a[j - 1]\n print(a[i], a[j], d)\n if d < hmin or d > hmax:\n i = j - 1\n invalid = 1\n break\n if a[j] - a[i] > m:\n invalid = 1\n break\n if a[j] - a[i] == m:\n found = 1\n invalid = 0\n break\n if invalid == 1:\n i = i + 1\n continue\n if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:\n print(a[i])\n break\n i = i + 1\nif n == 1:\n print(a[0] + hmax - m)\n",
"step-4": "import sys\nn = int(input().strip())\na = list(input().strip().split(' '))\nH = list(input().strip().split(' '))\na = [int(i) for i in a]\nm = int(H[0])\nhmin = int(H[1])\nhmax = int(H[2])\npos = 0\nfound = 0\nd = a[-1] - a[0]\nif d == m:\n print(a[0])\nelif 0 < d < m:\n for i in range(hmin, hmax + 1):\n fin1 = a[0] - i + m\n if hmin <= fin1 - a[-1] <= hmax or fin1 == a[-1]:\n print(a[0] - i)\n found = 1\n break\nif found == 0:\n i = 0\n while i < n - 1:\n found = 0\n invalid = 0\n d = a[i + 1] - a[i]\n print(a[i], a[i + 1], d)\n if d < hmin or d > hmax:\n i = i + 1\n continue\n for j in range(i + 1, n):\n d = a[j] - a[j - 1]\n print(a[i], a[j], d)\n if d < hmin or d > hmax:\n i = j - 1\n invalid = 1\n break\n if a[j] - a[i] > m:\n invalid = 1\n break\n if a[j] - a[i] == m:\n found = 1\n invalid = 0\n break\n if invalid == 1:\n i = i + 1\n continue\n if found == 1 or a[-1] - a[i] + hmin <= m and a[-1] - a[i] + hmax >= m:\n print(a[i])\n break\n i = i + 1\nif n == 1:\n print(a[0] + hmax - m)\n",
"step-5": "import sys\n\nn=int(input().strip())\na=list(input().strip().split(' '))\nH=list(input().strip().split(' '))\na = [int(i) for i in a]\nm=int(H[0])\nhmin=int(H[1])\nhmax=int(H[2])\npos=0\nfound = 0\nd=a[-1]-a[0]\nif(d==m):\n print(a[0])\nelif(0<d<m):\n for i in range(hmin, hmax+1):\n fin1 = a[0]-i+m\n if(hmin<=fin1-a[-1]<=hmax or fin1==a[-1]):\n print(a[0]-i)\n found = 1\n break\nif(found == 0):\n i = 0 \n while(i<(n-1)):\n found = 0\n invalid = 0\n d = a[i+1]-a[i]\n print(a[i], a[i+1], d)\n if(d<hmin or d>hmax):\n i=i+1\n continue\n for j in range(i+1, n):\n d = a[j]-a[j-1]\n print(a[i], a[j], d)\n if(d<hmin or d>hmax):\n i = j-1\n invalid = 1\n break\n if(a[j]-a[i]>m):\n invalid = 1\n break\n if(a[j]-a[i]==m):\n found = 1\n invalid = 0\n break\n if(invalid == 1):\n i = i+1\n continue\n if(found == 1 or (a[-1]-a[i]+hmin<=m and a[-1]-a[i]+hmax>=m)): \n print(a[i])\n break\n i = i+1\nif(n == 1):\n print(a[0]+hmax-m)\n",
"step-ids": [
0,
1,
2,
3,
4
]
}
|
[
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@transaction.atomic
def computers(request):
ctx = {}
computer = Computer.objects.all()
ctx['brand'] = Brand.objects.all()
if request.method == 'POST':
if request.POST['computer_id'] != '':
computer = computer.filter(computer_id__icontains=request.POST[
'computer_id'])
if request.POST['cpu'] != '':
computer = computer.filter(cpu__icontains=request.POST['cpu'])
if request.POST['graphics_card'] != '':
computer = computer.filter(graphics_card__icontains=request.
POST['graphics_card'])
try:
if request.POST['minMemory'] != '':
computer = computer.filter(memory__gte=int(request.POST[
'minMemory']))
if request.POST['maxMemory'] != '':
computer = computer.exclude(memory__gte=int(request.POST[
'maxMemory']))
if request.POST['minssd'] != '':
computer = computer.filter(ssd_capacity__gte=int(request.
POST['minssd']))
if request.POST['maxssd'] != '':
computer = computer.exclude(ssd_capacity__gte=int(request.
POST['maxssd']))
if request.POST['minDisk'] != '':
computer = computer.filter(disk_capacity__gte=int(request.
POST['minDisk']))
if request.POST['maxDisk'] != '':
computer = computer.exclude(disk_capacity__gte=int(request.
POST['maxDisk']))
except ValueError:
return render(request, 'Dashio/error.html', {'error': '请输入整数'})
if request.POST.get('brand', '') != '':
print(request.POST['brand'])
computer = computer.filter(brand__name__icontains=request.POST[
'brand'])
if request.POST['sort'] != '':
sortKey = request.POST['sortType'] + request.POST['sort']
computer = computer.order_by(sortKey)
ctx['computer'] = computer
return render(request, 'Dashio/computers.html', ctx)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@transaction.atomic
def computers(request):
ctx = {}
computer = Computer.objects.all()
ctx['brand'] = Brand.objects.all()
if request.method == 'POST':
if request.POST['computer_id'] != '':
computer = computer.filter(computer_id__icontains=request.POST[
'computer_id'])
if request.POST['cpu'] != '':
computer = computer.filter(cpu__icontains=request.POST['cpu'])
if request.POST['graphics_card'] != '':
computer = computer.filter(graphics_card__icontains=request.
POST['graphics_card'])
try:
if request.POST['minMemory'] != '':
computer = computer.filter(memory__gte=int(request.POST[
'minMemory']))
if request.POST['maxMemory'] != '':
computer = computer.exclude(memory__gte=int(request.POST[
'maxMemory']))
if request.POST['minssd'] != '':
computer = computer.filter(ssd_capacity__gte=int(request.
POST['minssd']))
if request.POST['maxssd'] != '':
computer = computer.exclude(ssd_capacity__gte=int(request.
POST['maxssd']))
if request.POST['minDisk'] != '':
computer = computer.filter(disk_capacity__gte=int(request.
POST['minDisk']))
if request.POST['maxDisk'] != '':
computer = computer.exclude(disk_capacity__gte=int(request.
POST['maxDisk']))
except ValueError:
return render(request, 'Dashio/error.html', {'error': '请输入整数'})
if request.POST.get('brand', '') != '':
print(request.POST['brand'])
computer = computer.filter(brand__name__icontains=request.POST[
'brand'])
if request.POST['sort'] != '':
sortKey = request.POST['sortType'] + request.POST['sort']
computer = computer.order_by(sortKey)
ctx['computer'] = computer
return render(request, 'Dashio/computers.html', ctx)
<|reserved_special_token_0|>
@transaction.atomic
def post(request, user_id, computer_id):
if request.method == 'POST':
computer = Computer.objects.get(pk=computer_id)
user = User.objects.get(pk=user_id)
computer_comment(computer_id=computer, user_id=user, content=
request.POST['comment']).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
def makeMark(request, computer_id, user_id):
try:
m = mark.objects.get(computer_id__computer_id=computer_id,
user_id__user_id=user_id)
m.delete()
except ObjectDoesNotExist:
computer = get_object_or_404(Computer, pk=computer_id)
user = get_object_or_404(User, pk=user_id)
mark(computer_id=computer, user_id=user).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@transaction.atomic
def computers(request):
ctx = {}
computer = Computer.objects.all()
ctx['brand'] = Brand.objects.all()
if request.method == 'POST':
if request.POST['computer_id'] != '':
computer = computer.filter(computer_id__icontains=request.POST[
'computer_id'])
if request.POST['cpu'] != '':
computer = computer.filter(cpu__icontains=request.POST['cpu'])
if request.POST['graphics_card'] != '':
computer = computer.filter(graphics_card__icontains=request.
POST['graphics_card'])
try:
if request.POST['minMemory'] != '':
computer = computer.filter(memory__gte=int(request.POST[
'minMemory']))
if request.POST['maxMemory'] != '':
computer = computer.exclude(memory__gte=int(request.POST[
'maxMemory']))
if request.POST['minssd'] != '':
computer = computer.filter(ssd_capacity__gte=int(request.
POST['minssd']))
if request.POST['maxssd'] != '':
computer = computer.exclude(ssd_capacity__gte=int(request.
POST['maxssd']))
if request.POST['minDisk'] != '':
computer = computer.filter(disk_capacity__gte=int(request.
POST['minDisk']))
if request.POST['maxDisk'] != '':
computer = computer.exclude(disk_capacity__gte=int(request.
POST['maxDisk']))
except ValueError:
return render(request, 'Dashio/error.html', {'error': '请输入整数'})
if request.POST.get('brand', '') != '':
print(request.POST['brand'])
computer = computer.filter(brand__name__icontains=request.POST[
'brand'])
if request.POST['sort'] != '':
sortKey = request.POST['sortType'] + request.POST['sort']
computer = computer.order_by(sortKey)
ctx['computer'] = computer
return render(request, 'Dashio/computers.html', ctx)
@transaction.atomic
def details(request, computer_id):
rtx = {}
rtx['isUser'] = request.session['type'] == 'user'
rtx['computer'] = get_object_or_404(Computer, pk=computer_id)
rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=
computer_id).count()
rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)
rtx['user_id'] = request.session['id']
rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id
).count()
rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id
=computer_id).order_by('-comment_date')
rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id
).order_by('-buy_time')[:5]
if rtx['isUser']:
rtx['mark'] = '收藏' if mark.objects.filter(user_id__user_id=rtx[
'user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏'
return render(request, 'Dashio/computer_detail.html', rtx)
@transaction.atomic
def post(request, user_id, computer_id):
if request.method == 'POST':
computer = Computer.objects.get(pk=computer_id)
user = User.objects.get(pk=user_id)
computer_comment(computer_id=computer, user_id=user, content=
request.POST['comment']).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
def makeMark(request, computer_id, user_id):
try:
m = mark.objects.get(computer_id__computer_id=computer_id,
user_id__user_id=user_id)
m.delete()
except ObjectDoesNotExist:
computer = get_object_or_404(Computer, pk=computer_id)
user = get_object_or_404(User, pk=user_id)
mark(computer_id=computer, user_id=user).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
<|reserved_special_token_1|>
from django.shortcuts import *
from shop.models import *
from django.db import transaction
from django.core.exceptions import *
@transaction.atomic
def computers(request):
ctx = {}
computer = Computer.objects.all()
ctx['brand'] = Brand.objects.all()
if request.method == 'POST':
if request.POST['computer_id'] != '':
computer = computer.filter(computer_id__icontains=request.POST[
'computer_id'])
if request.POST['cpu'] != '':
computer = computer.filter(cpu__icontains=request.POST['cpu'])
if request.POST['graphics_card'] != '':
computer = computer.filter(graphics_card__icontains=request.
POST['graphics_card'])
try:
if request.POST['minMemory'] != '':
computer = computer.filter(memory__gte=int(request.POST[
'minMemory']))
if request.POST['maxMemory'] != '':
computer = computer.exclude(memory__gte=int(request.POST[
'maxMemory']))
if request.POST['minssd'] != '':
computer = computer.filter(ssd_capacity__gte=int(request.
POST['minssd']))
if request.POST['maxssd'] != '':
computer = computer.exclude(ssd_capacity__gte=int(request.
POST['maxssd']))
if request.POST['minDisk'] != '':
computer = computer.filter(disk_capacity__gte=int(request.
POST['minDisk']))
if request.POST['maxDisk'] != '':
computer = computer.exclude(disk_capacity__gte=int(request.
POST['maxDisk']))
except ValueError:
return render(request, 'Dashio/error.html', {'error': '请输入整数'})
if request.POST.get('brand', '') != '':
print(request.POST['brand'])
computer = computer.filter(brand__name__icontains=request.POST[
'brand'])
if request.POST['sort'] != '':
sortKey = request.POST['sortType'] + request.POST['sort']
computer = computer.order_by(sortKey)
ctx['computer'] = computer
return render(request, 'Dashio/computers.html', ctx)
@transaction.atomic
def details(request, computer_id):
rtx = {}
rtx['isUser'] = request.session['type'] == 'user'
rtx['computer'] = get_object_or_404(Computer, pk=computer_id)
rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=
computer_id).count()
rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)
rtx['user_id'] = request.session['id']
rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id
).count()
rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id
=computer_id).order_by('-comment_date')
rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id
).order_by('-buy_time')[:5]
if rtx['isUser']:
rtx['mark'] = '收藏' if mark.objects.filter(user_id__user_id=rtx[
'user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏'
return render(request, 'Dashio/computer_detail.html', rtx)
@transaction.atomic
def post(request, user_id, computer_id):
if request.method == 'POST':
computer = Computer.objects.get(pk=computer_id)
user = User.objects.get(pk=user_id)
computer_comment(computer_id=computer, user_id=user, content=
request.POST['comment']).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
def makeMark(request, computer_id, user_id):
try:
m = mark.objects.get(computer_id__computer_id=computer_id,
user_id__user_id=user_id)
m.delete()
except ObjectDoesNotExist:
computer = get_object_or_404(Computer, pk=computer_id)
user = get_object_or_404(User, pk=user_id)
mark(computer_id=computer, user_id=user).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(
computer_id,)))
<|reserved_special_token_1|>
from django.shortcuts import *
from shop.models import *
from django.db import transaction
from django.core.exceptions import *
@transaction.atomic
def computers(request):
ctx = {}
computer = Computer.objects.all()
ctx['brand'] = Brand.objects.all()
if request.method == 'POST':
if request.POST['computer_id'] != '':
computer = computer.filter(computer_id__icontains=request.POST['computer_id'])
if request.POST['cpu'] != '':
computer = computer.filter(cpu__icontains=request.POST['cpu'])
if request.POST['graphics_card'] != '':
computer = computer.filter(graphics_card__icontains=request.POST['graphics_card'])
try:
if request.POST['minMemory'] != '':
computer = computer.filter(memory__gte=int(request.POST['minMemory']))
if request.POST['maxMemory'] != '':
computer = computer.exclude(memory__gte=int(request.POST['maxMemory']))
if request.POST['minssd'] != '':
computer = computer.filter(ssd_capacity__gte=int(request.POST['minssd']))
if request.POST['maxssd'] != '':
computer = computer.exclude(ssd_capacity__gte=int(request.POST['maxssd']))
if request.POST['minDisk'] != '':
computer = computer.filter(disk_capacity__gte=int(request.POST['minDisk']))
if request.POST['maxDisk'] != '':
computer = computer.exclude(disk_capacity__gte=int(request.POST['maxDisk']))
except ValueError:
return render(request, 'Dashio/error.html', {'error': "请输入整数"})
if request.POST.get('brand', '') != '':
print(request.POST['brand'])
computer = computer.filter(brand__name__icontains=request.POST['brand'])
if request.POST['sort'] != '':
sortKey = request.POST['sortType'] + request.POST['sort']
computer = computer.order_by(sortKey)
ctx['computer'] = computer
return render(request, "Dashio/computers.html", ctx)
@transaction.atomic
def details(request, computer_id):
rtx = {}
rtx['isUser'] = request.session['type'] == 'user'
rtx['computer'] = get_object_or_404(Computer, pk=computer_id)
rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=computer_id).count()
rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)
rtx['user_id'] = request.session['id']
rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id).count()
rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id=computer_id).order_by('-comment_date')
rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id).order_by('-buy_time')[:5]
if rtx['isUser']:
rtx['mark'] = ('收藏' if mark.objects.filter(user_id__user_id=rtx['user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏')
return render(request, 'Dashio/computer_detail.html', rtx)
@transaction.atomic
def post(request, user_id, computer_id):
if request.method == 'POST':
computer = Computer.objects.get(pk=computer_id)
user = User.objects.get(pk=user_id)
computer_comment(computer_id=computer, user_id=user, content=request.POST['comment']).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(computer_id, )))
def makeMark(request, computer_id, user_id):
try:
m = mark.objects.get(computer_id__computer_id=computer_id, user_id__user_id=user_id)
m.delete()
except ObjectDoesNotExist:
computer = get_object_or_404(Computer, pk=computer_id)
user = get_object_or_404(User, pk=user_id)
mark(computer_id=computer, user_id=user).save()
return HttpResponseRedirect(reverse('shop:computerDetail', args=(computer_id, )))
|
flexible
|
{
"blob_id": "18689741a33e6d17e694ee0619a1f36d8d178cbb",
"index": 3223,
"step-1": "<mask token>\n\n\[email protected]\ndef computers(request):\n ctx = {}\n computer = Computer.objects.all()\n ctx['brand'] = Brand.objects.all()\n if request.method == 'POST':\n if request.POST['computer_id'] != '':\n computer = computer.filter(computer_id__icontains=request.POST[\n 'computer_id'])\n if request.POST['cpu'] != '':\n computer = computer.filter(cpu__icontains=request.POST['cpu'])\n if request.POST['graphics_card'] != '':\n computer = computer.filter(graphics_card__icontains=request.\n POST['graphics_card'])\n try:\n if request.POST['minMemory'] != '':\n computer = computer.filter(memory__gte=int(request.POST[\n 'minMemory']))\n if request.POST['maxMemory'] != '':\n computer = computer.exclude(memory__gte=int(request.POST[\n 'maxMemory']))\n if request.POST['minssd'] != '':\n computer = computer.filter(ssd_capacity__gte=int(request.\n POST['minssd']))\n if request.POST['maxssd'] != '':\n computer = computer.exclude(ssd_capacity__gte=int(request.\n POST['maxssd']))\n if request.POST['minDisk'] != '':\n computer = computer.filter(disk_capacity__gte=int(request.\n POST['minDisk']))\n if request.POST['maxDisk'] != '':\n computer = computer.exclude(disk_capacity__gte=int(request.\n POST['maxDisk']))\n except ValueError:\n return render(request, 'Dashio/error.html', {'error': '请输入整数'})\n if request.POST.get('brand', '') != '':\n print(request.POST['brand'])\n computer = computer.filter(brand__name__icontains=request.POST[\n 'brand'])\n if request.POST['sort'] != '':\n sortKey = request.POST['sortType'] + request.POST['sort']\n computer = computer.order_by(sortKey)\n ctx['computer'] = computer\n return render(request, 'Dashio/computers.html', ctx)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\[email protected]\ndef computers(request):\n ctx = {}\n computer = Computer.objects.all()\n ctx['brand'] = Brand.objects.all()\n if request.method == 'POST':\n if request.POST['computer_id'] != '':\n computer = computer.filter(computer_id__icontains=request.POST[\n 'computer_id'])\n if request.POST['cpu'] != '':\n computer = computer.filter(cpu__icontains=request.POST['cpu'])\n if request.POST['graphics_card'] != '':\n computer = computer.filter(graphics_card__icontains=request.\n POST['graphics_card'])\n try:\n if request.POST['minMemory'] != '':\n computer = computer.filter(memory__gte=int(request.POST[\n 'minMemory']))\n if request.POST['maxMemory'] != '':\n computer = computer.exclude(memory__gte=int(request.POST[\n 'maxMemory']))\n if request.POST['minssd'] != '':\n computer = computer.filter(ssd_capacity__gte=int(request.\n POST['minssd']))\n if request.POST['maxssd'] != '':\n computer = computer.exclude(ssd_capacity__gte=int(request.\n POST['maxssd']))\n if request.POST['minDisk'] != '':\n computer = computer.filter(disk_capacity__gte=int(request.\n POST['minDisk']))\n if request.POST['maxDisk'] != '':\n computer = computer.exclude(disk_capacity__gte=int(request.\n POST['maxDisk']))\n except ValueError:\n return render(request, 'Dashio/error.html', {'error': '请输入整数'})\n if request.POST.get('brand', '') != '':\n print(request.POST['brand'])\n computer = computer.filter(brand__name__icontains=request.POST[\n 'brand'])\n if request.POST['sort'] != '':\n sortKey = request.POST['sortType'] + request.POST['sort']\n computer = computer.order_by(sortKey)\n ctx['computer'] = computer\n return render(request, 'Dashio/computers.html', ctx)\n\n\n<mask token>\n\n\[email protected]\ndef post(request, user_id, computer_id):\n if request.method == 'POST':\n computer = Computer.objects.get(pk=computer_id)\n user = User.objects.get(pk=user_id)\n computer_comment(computer_id=computer, user_id=user, content=\n request.POST['comment']).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n\n\ndef makeMark(request, computer_id, user_id):\n try:\n m = mark.objects.get(computer_id__computer_id=computer_id,\n user_id__user_id=user_id)\n m.delete()\n except ObjectDoesNotExist:\n computer = get_object_or_404(Computer, pk=computer_id)\n user = get_object_or_404(User, pk=user_id)\n mark(computer_id=computer, user_id=user).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n",
"step-3": "<mask token>\n\n\[email protected]\ndef computers(request):\n ctx = {}\n computer = Computer.objects.all()\n ctx['brand'] = Brand.objects.all()\n if request.method == 'POST':\n if request.POST['computer_id'] != '':\n computer = computer.filter(computer_id__icontains=request.POST[\n 'computer_id'])\n if request.POST['cpu'] != '':\n computer = computer.filter(cpu__icontains=request.POST['cpu'])\n if request.POST['graphics_card'] != '':\n computer = computer.filter(graphics_card__icontains=request.\n POST['graphics_card'])\n try:\n if request.POST['minMemory'] != '':\n computer = computer.filter(memory__gte=int(request.POST[\n 'minMemory']))\n if request.POST['maxMemory'] != '':\n computer = computer.exclude(memory__gte=int(request.POST[\n 'maxMemory']))\n if request.POST['minssd'] != '':\n computer = computer.filter(ssd_capacity__gte=int(request.\n POST['minssd']))\n if request.POST['maxssd'] != '':\n computer = computer.exclude(ssd_capacity__gte=int(request.\n POST['maxssd']))\n if request.POST['minDisk'] != '':\n computer = computer.filter(disk_capacity__gte=int(request.\n POST['minDisk']))\n if request.POST['maxDisk'] != '':\n computer = computer.exclude(disk_capacity__gte=int(request.\n POST['maxDisk']))\n except ValueError:\n return render(request, 'Dashio/error.html', {'error': '请输入整数'})\n if request.POST.get('brand', '') != '':\n print(request.POST['brand'])\n computer = computer.filter(brand__name__icontains=request.POST[\n 'brand'])\n if request.POST['sort'] != '':\n sortKey = request.POST['sortType'] + request.POST['sort']\n computer = computer.order_by(sortKey)\n ctx['computer'] = computer\n return render(request, 'Dashio/computers.html', ctx)\n\n\[email protected]\ndef details(request, computer_id):\n rtx = {}\n rtx['isUser'] = request.session['type'] == 'user'\n rtx['computer'] = get_object_or_404(Computer, pk=computer_id)\n rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=\n computer_id).count()\n rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)\n rtx['user_id'] = request.session['id']\n rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id\n ).count()\n rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id\n =computer_id).order_by('-comment_date')\n rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id\n ).order_by('-buy_time')[:5]\n if rtx['isUser']:\n rtx['mark'] = '收藏' if mark.objects.filter(user_id__user_id=rtx[\n 'user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏'\n return render(request, 'Dashio/computer_detail.html', rtx)\n\n\[email protected]\ndef post(request, user_id, computer_id):\n if request.method == 'POST':\n computer = Computer.objects.get(pk=computer_id)\n user = User.objects.get(pk=user_id)\n computer_comment(computer_id=computer, user_id=user, content=\n request.POST['comment']).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n\n\ndef makeMark(request, computer_id, user_id):\n try:\n m = mark.objects.get(computer_id__computer_id=computer_id,\n user_id__user_id=user_id)\n m.delete()\n except ObjectDoesNotExist:\n computer = get_object_or_404(Computer, pk=computer_id)\n user = get_object_or_404(User, pk=user_id)\n mark(computer_id=computer, user_id=user).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n",
"step-4": "from django.shortcuts import *\nfrom shop.models import *\nfrom django.db import transaction\nfrom django.core.exceptions import *\n\n\[email protected]\ndef computers(request):\n ctx = {}\n computer = Computer.objects.all()\n ctx['brand'] = Brand.objects.all()\n if request.method == 'POST':\n if request.POST['computer_id'] != '':\n computer = computer.filter(computer_id__icontains=request.POST[\n 'computer_id'])\n if request.POST['cpu'] != '':\n computer = computer.filter(cpu__icontains=request.POST['cpu'])\n if request.POST['graphics_card'] != '':\n computer = computer.filter(graphics_card__icontains=request.\n POST['graphics_card'])\n try:\n if request.POST['minMemory'] != '':\n computer = computer.filter(memory__gte=int(request.POST[\n 'minMemory']))\n if request.POST['maxMemory'] != '':\n computer = computer.exclude(memory__gte=int(request.POST[\n 'maxMemory']))\n if request.POST['minssd'] != '':\n computer = computer.filter(ssd_capacity__gte=int(request.\n POST['minssd']))\n if request.POST['maxssd'] != '':\n computer = computer.exclude(ssd_capacity__gte=int(request.\n POST['maxssd']))\n if request.POST['minDisk'] != '':\n computer = computer.filter(disk_capacity__gte=int(request.\n POST['minDisk']))\n if request.POST['maxDisk'] != '':\n computer = computer.exclude(disk_capacity__gte=int(request.\n POST['maxDisk']))\n except ValueError:\n return render(request, 'Dashio/error.html', {'error': '请输入整数'})\n if request.POST.get('brand', '') != '':\n print(request.POST['brand'])\n computer = computer.filter(brand__name__icontains=request.POST[\n 'brand'])\n if request.POST['sort'] != '':\n sortKey = request.POST['sortType'] + request.POST['sort']\n computer = computer.order_by(sortKey)\n ctx['computer'] = computer\n return render(request, 'Dashio/computers.html', ctx)\n\n\[email protected]\ndef details(request, computer_id):\n rtx = {}\n rtx['isUser'] = request.session['type'] == 'user'\n rtx['computer'] = get_object_or_404(Computer, pk=computer_id)\n rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=\n computer_id).count()\n rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)\n rtx['user_id'] = request.session['id']\n rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id\n ).count()\n rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id\n =computer_id).order_by('-comment_date')\n rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id\n ).order_by('-buy_time')[:5]\n if rtx['isUser']:\n rtx['mark'] = '收藏' if mark.objects.filter(user_id__user_id=rtx[\n 'user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏'\n return render(request, 'Dashio/computer_detail.html', rtx)\n\n\[email protected]\ndef post(request, user_id, computer_id):\n if request.method == 'POST':\n computer = Computer.objects.get(pk=computer_id)\n user = User.objects.get(pk=user_id)\n computer_comment(computer_id=computer, user_id=user, content=\n request.POST['comment']).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n\n\ndef makeMark(request, computer_id, user_id):\n try:\n m = mark.objects.get(computer_id__computer_id=computer_id,\n user_id__user_id=user_id)\n m.delete()\n except ObjectDoesNotExist:\n computer = get_object_or_404(Computer, pk=computer_id)\n user = get_object_or_404(User, pk=user_id)\n mark(computer_id=computer, user_id=user).save()\n return HttpResponseRedirect(reverse('shop:computerDetail', args=(\n computer_id,)))\n",
"step-5": "from django.shortcuts import *\nfrom shop.models import *\nfrom django.db import transaction\nfrom django.core.exceptions import *\n\[email protected]\ndef computers(request):\n ctx = {}\n computer = Computer.objects.all()\n ctx['brand'] = Brand.objects.all()\n\n if request.method == 'POST':\n if request.POST['computer_id'] != '':\n computer = computer.filter(computer_id__icontains=request.POST['computer_id'])\n if request.POST['cpu'] != '':\n computer = computer.filter(cpu__icontains=request.POST['cpu'])\n if request.POST['graphics_card'] != '':\n computer = computer.filter(graphics_card__icontains=request.POST['graphics_card'])\n \n try:\n if request.POST['minMemory'] != '':\n computer = computer.filter(memory__gte=int(request.POST['minMemory']))\n if request.POST['maxMemory'] != '':\n computer = computer.exclude(memory__gte=int(request.POST['maxMemory']))\n\n if request.POST['minssd'] != '':\n computer = computer.filter(ssd_capacity__gte=int(request.POST['minssd']))\n if request.POST['maxssd'] != '':\n computer = computer.exclude(ssd_capacity__gte=int(request.POST['maxssd']))\n\n if request.POST['minDisk'] != '':\n computer = computer.filter(disk_capacity__gte=int(request.POST['minDisk']))\n if request.POST['maxDisk'] != '':\n computer = computer.exclude(disk_capacity__gte=int(request.POST['maxDisk']))\n\n except ValueError:\n return render(request, 'Dashio/error.html', {'error': \"请输入整数\"})\n \n if request.POST.get('brand', '') != '':\n print(request.POST['brand'])\n computer = computer.filter(brand__name__icontains=request.POST['brand'])\n\n if request.POST['sort'] != '':\n sortKey = request.POST['sortType'] + request.POST['sort']\n computer = computer.order_by(sortKey)\n\n ctx['computer'] = computer\n return render(request, \"Dashio/computers.html\", ctx)\n\[email protected]\ndef details(request, computer_id):\n rtx = {}\n rtx['isUser'] = request.session['type'] == 'user'\n rtx['computer'] = get_object_or_404(Computer, pk=computer_id)\n rtx['markAmount'] = mark.objects.filter(computer_id__computer_id=computer_id).count()\n rtx['sell'] = Sell.objects.filter(computer_id__computer_id=computer_id)\n rtx['user_id'] = request.session['id']\n rtx['sellAmount'] = Buy.objects.filter(computer_id__computer_id=computer_id).count()\n rtx['comments'] = computer_comment.objects.filter(computer_id__computer_id=computer_id).order_by('-comment_date')\n rtx['buys'] = Buy.objects.filter(computer_id__computer_id=computer_id).order_by('-buy_time')[:5]\n \n if rtx['isUser']:\n rtx['mark'] = ('收藏' if mark.objects.filter(user_id__user_id=rtx['user_id'], computer_id=rtx['computer']).count() == 0 else '取消收藏')\n\n return render(request, 'Dashio/computer_detail.html', rtx)\n\[email protected]\ndef post(request, user_id, computer_id):\n if request.method == 'POST':\n computer = Computer.objects.get(pk=computer_id)\n user = User.objects.get(pk=user_id)\n computer_comment(computer_id=computer, user_id=user, content=request.POST['comment']).save()\n \n return HttpResponseRedirect(reverse('shop:computerDetail', args=(computer_id, )))\n\ndef makeMark(request, computer_id, user_id):\n try:\n m = mark.objects.get(computer_id__computer_id=computer_id, user_id__user_id=user_id)\n m.delete()\n except ObjectDoesNotExist:\n computer = get_object_or_404(Computer, pk=computer_id)\n user = get_object_or_404(User, pk=user_id)\n mark(computer_id=computer, user_id=user).save()\n \n return HttpResponseRedirect(reverse('shop:computerDetail', args=(computer_id, )))",
"step-ids": [
1,
3,
4,
5,
6
]
}
|
[
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while t:
t -= 1
y = []
z = []
x = str(input())
for i in range(len(x)):
if not int(i) % 2:
y.append(x[i])
else:
z.append(x[i])
print(''.join(y) + ' ' + ''.join(z))
<|reserved_special_token_1|>
t = eval(input())
while t:
t -= 1
y = []
z = []
x = str(input())
for i in range(len(x)):
if not int(i) % 2:
y.append(x[i])
else:
z.append(x[i])
print(''.join(y) + ' ' + ''.join(z))
<|reserved_special_token_1|>
t = eval(input())
while t:
t -= 1
y = []
z = []
x = str(input())
for i in range(len(x)):
if (not int(i)%2):
y.append(x[i])
else:
z.append(x[i])
print("".join(y) + " " + "".join(z))
|
flexible
|
{
"blob_id": "ac32fb5fcd71790f9dbf0794992a9dc92a202c9b",
"index": 7972,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile t:\n t -= 1\n y = []\n z = []\n x = str(input())\n for i in range(len(x)):\n if not int(i) % 2:\n y.append(x[i])\n else:\n z.append(x[i])\n print(''.join(y) + ' ' + ''.join(z))\n",
"step-3": "t = eval(input())\nwhile t:\n t -= 1\n y = []\n z = []\n x = str(input())\n for i in range(len(x)):\n if not int(i) % 2:\n y.append(x[i])\n else:\n z.append(x[i])\n print(''.join(y) + ' ' + ''.join(z))\n",
"step-4": "t = eval(input())\nwhile t:\n t -= 1\n y = []\n z = []\n x = str(input())\n for i in range(len(x)):\n if (not int(i)%2):\n y.append(x[i])\n else:\n z.append(x[i])\n print(\"\".join(y) + \" \" + \"\".join(z))\n",
"step-5": null,
"step-ids": [
0,
1,
2,
3
]
}
|
[
0,
1,
2,
3
] |
#!usr/bin/env python
#-*- coding:utf-8 -*-
# this model is for decision tree
# objective: To cluster different service
# JialongLi 2017/03/18
import re
import os
import sys
import pickle
import copy
import random
import pydotplus
USER_NUM = 1000
reload(sys)
sys.setdefaultencoding( "utf-8" )
from sklearn import tree
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.cluster import KMeans
# 0 represent Sunday, 1: Monday, 6: Saturday, 0: Sunday
day_index = {'0507': 1, '0508': 2, '0509': 3, '0510': 4, '0511': 5, '0512': 6, '0513': 0,
'0604': 1, '0605': 2, '0606': 3, '0607': 4, '0608': 5, '0609': 6, '0610': 0,
'0702': 1, '0703': 2, '0704': 3, '0705': 4, '0706': 5, '0707': 6, '0708': 0,
'0806': 1, '0807': 2, '0808': 3, '0809': 4, '0810': 5, '0811': 6, '0812': 0}
service_type = ['I', 'F', 'W', 'G', 'S', 'V']
# get activity_dict
# user's activity: default value is 'F'
# format: {id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}
def get_activity_dict(activity_dict_path):
pkl_file = open(activity_dict_path, 'rb')
activity_dict = pickle.load(pkl_file)
pkl_file.close()
return activity_dict
# data are divided into train data and test data
# first three weeks: train data; last week: test data
# train_dict and test_dict are subset of activity_dict, id format is different
# activity_dict format: {real id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}
# user_id_index: key = number, value = real id
def data_segement(activity_dict, train_dict_path, test_dict_path, user_id_index_path):
train_dict = {}
test_dict = {}
user_count = 0
user_id_index = {}
for key_0, value_0 in activity_dict.items(): # key_0: real user_id
train_dict[user_count] = {}
test_dict[user_count] = {}
user_id_index[user_count] = key_0
for key, value in value_0.items():
if key[1] == '8': # data of August, test set
test_dict[user_count][key] = value
else:
train_dict[user_count][key] = value # train set
user_count += 1
output_1 = open(train_dict_path, 'wb')
pickle.dump(train_dict, output_1)
output_2 = open(test_dict_path, 'wb')
pickle.dump(test_dict, output_2)
output_3 = open(user_id_index_path, 'wb')
pickle.dump(user_id_index, output_3)
output_1.close()
output_2.close()
output_3.close()
# get train data and test data
# train_dict, test_dict format: {number id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}
def get_data(train_dict_path, test_dict_path, user_id_index_path):
pkl_file_1 = open(train_dict_path, 'rb')
pkl_file_2 = open(test_dict_path, 'rb')
pkl_file_3 = open(user_id_index_path, 'rb')
train_dict = pickle.load(pkl_file_1)
test_dict = pickle.load(pkl_file_2)
user_id_index = pickle.load(pkl_file_3)
pkl_file_1.close()
pkl_file_2.close()
pkl_file_3.close()
return train_dict, test_dict, user_id_index
# get profile
def get_profile(profile_path):
pkl_file = open(profile_path, 'rb')
profile = pickle.load(pkl_file)
return profile
# select different features
# feature format: [user_id, gender, age, edu, job, hour, date], 7 features
# profile: dict, {real user_id: [gender, age, edu, job]}
# feature format: double list, outer list element is a sample: [number user_id, gender, age, edu, job, hour, date]
# category format: list, element is service type, length = feature
def feature_select(data_dict, profile, user_id_index, is_over_sampling):
feature = []
category = []
over_sampling_num = 0
for user_id, all_dates in data_dict.items():
real_user_id = user_id_index[user_id]
one_user_profile = copy.deepcopy(profile[real_user_id]) # gender, age, edu, job
one_user_profile.insert(0, user_id) # insert user_id
for date, activity in all_dates.items():
for i in range(len(activity)):
if 1: #activity[i] != 'F': # do not add 'F'
sample = copy.deepcopy(one_user_profile)
#del(sample[1:4])
sample.append(i) #(int(i/6)) # i represents hour
sample.append(day_index[date]) # day_index: 7 days in one week
feature.append(sample)
#category.append(activity[i])
if activity[i] == 'F':
category.append('F')
else:
category.append('O')
if is_over_sampling and len(sample) > 5: # make sure that features are completed
if activity[i] != 'F':
sample_over = [[] for k in range(over_sampling_num)]
for j in range(over_sampling_num):
sample_over[j] = copy.deepcopy(sample)
sample_over[j][-3] = random.randint(0, 8) # random disturbance in job feature
feature.append(sample_over[j])
category.append('O')
return feature, category
# build features, all features
# False means test data do not need over sampling
def feature_build(train_dict, test_dict, profile, user_id_index):
feature_train, category_train = feature_select(train_dict, profile, user_id_index, True)
feature_test, category_test = feature_select(test_dict, profile, user_id_index, False)
return feature_train, feature_test, category_train, category_test
# calculating the hit rate
def cal_hit_rate(category_predict, category_test):
hit_count = 0
sample_test_count = len(category_predict)
for i in range(sample_test_count):
if category_predict[i] == category_test[i]:
hit_count += 1
hit_rate = float(hit_count) / float(sample_test_count)
print 'hit rate: ' + str(round(hit_rate, 4) * 100) + '%'
# calculating F value
def calculating_F_value(category_predict, category_test):
n_predict = 0
n_origin = 0
hit_count = 0
for item in category_predict:
if item != 'F':
n_predict += 1
for item in category_test:
if item != 'F':
n_origin += 1
for i in range(len(category_predict)):
if category_predict[i] != 'F' and category_predict[i] == category_test[i]:
hit_count += 1
precision = float(hit_count) / float(n_predict)
recall = float(hit_count) / float(n_origin)
F_value = 2 * precision * recall / (precision + recall)
print 'n_predict: ' + str(n_predict)
print 'n_origin: ' + str(n_origin)
print 'precision: ' + str(round(precision, 3))
print 'recall: ' + str(round(recall, 3))
print 'F_value: ' + str(round(F_value, 3))
# 1. select the service type using most in that period in past days
# 2. if user did not use service in that period before, select the service type using most in past days
# 3. if user did not use service before, select service randomly
# service_count_hour: key = (user_id, hour, service_type) value = count
# service_count_past: key = (user_id, service_type) value = count
# service_hour: key = (user_id, hour), value = [service_type, count]
# service_past: key = user_id, value = [service_type, count]
def conventional_method_Mused(feature_train, feature_test, category_train):
if len(feature_train[0]) != 7:
print 'feature wrong'
service_count_hour = {}
service_count_past = {}
for i in range(len(feature_train)):
key_hour = (feature_train[i][0], feature_train[i][5], category_train[i])
if key_hour not in service_count_hour:
service_count_hour[key_hour] = 1
else:
service_count_hour[key_hour] += 1
key_past = (feature_train[i][0], category_train[i])
if key_past not in service_count_past:
service_count_past[key_past] = 1
else:
service_count_past[key_past] += 1
service_hour = {}
service_past = {}
for key, value in service_count_hour.items():
key_hour = (key[0], key[1])
if key_hour not in service_hour:
service_hour[key_hour] = [key[2], value]
else:
if value > service_hour[key_hour][1]:
service_hour[key_hour] = [key[2], value]
else:
pass
for key, value in service_count_past.items():
key_past = key[0]
if key_past not in service_past:
service_past[key_past] = [key[1], value]
else:
if value > service_past[key_past][1]:
service_past[key_past] = [key[1], value]
else:
pass
category_predict = []
for i in range(len(feature_test)):
key_0 = (feature_test[i][0], feature_test[i][5])
key_1 = feature_test[i][0]
if key_0 in service_hour:
value_0 = service_hour[key_0]
category_predict.append(value_0[0])
elif key_1 in service_past:
value_1 = service_past[key_1]
category_predict.append(value_1[0])
else:
random_num = random.randint(0, len(service_type)-1)
category_predict.append(service_type[random_num])
return category_predict
# method 2: service in last week
def conventional_method_Lweek(feature_train, feature_test, category_train):
if len(feature_train[0]) != 7:
print 'feature wrong'
category_predict = ['FFF' for i in range(len(feature_test))]
for i in range(len(feature_train)):
sample = feature_train[i]
user_id = sample[0]
hour = sample[-2]
date = sample[-1]
if date == 0: # 0 means it is Sunday and should be the last
date = 7
else:
pass
service_position = user_id * 168 + (date - 1) * 24 + hour
category_predict[service_position] = category_train[i]
return category_predict
# decision tree
def decision_tree(feature_train, feature_test, category_train):
clf = tree.DecisionTreeClassifier()
clf = clf.fit(feature_train, category_train)
category_predict = clf.predict(feature_test) # the format of category_predict is weird
category_Dtree = []
for item in category_predict:
if item == 'F':
category_Dtree.append('F')
else:
category_Dtree.append('O')
return category_Dtree
# random forests
def random_forests(feature_train, feature_test, category_train):
clf = RandomForestClassifier(n_estimators = 80)
clf = clf.fit(feature_train, category_train)
category_predict = clf.predict(feature_test)
category_RF = []
for item in category_predict:
if item == 'F':
category_RF.append('F')
else:
category_RF.append('O')
return category_RF
# save user_activity as pkl file for migration.py
def user_activity_save(user_activity, user_activity_path):
output = open(user_activity_path, 'wb')
pickle.dump(user_activity, output)
output.close()
# user_activity is for migration.py
# key = user_id, range(1000), value = ['F', 'G'...], length is 7 * 24 = 168
def activity_restore(feature, category):
if len(feature[0]) != 7:
print 'feature wrong'
user_activity = {}
for i in range(USER_NUM):
user_activity[i] = ['FFF' for j in range(168)]
for i in range(len(feature)):
sample = feature[i]
user_id = sample[0]
hour = sample[5]
date = sample[-1]
if date == 0: # 0 means it is Sunday and should be the last
date = 7
else:
pass
position = (date - 1) * 24 + hour
user_activity[user_id][position] = category[i]
return user_activity
def counting_accuate_rate(category_Dtree, category_test):
on_on = 0
on_off = 0
off_on = 0
off_off = 0
print len(category_test)
print len(category_Dtree)
for i in range(21504): #(len(category_Dtree)):
if category_Dtree[i] == 'O' and category_test[i] == 'O':
on_on += 1
elif category_Dtree[i] == 'O' and category_test[i] == 'F':
on_off += 1
elif category_Dtree[i] == 'F' and category_test[i] == 'O':
off_on += 1
else:
off_off += 1
print 'on_on' + '\t' + str(on_on)
print 'on_off' + '\t' + str(on_off)
print 'off_on' + '\t' + str(off_on)
print 'off_off' + '\t' + str(off_off)
# save file for sleep.py
def save_file_for_sleep(category_predict, category_test):
category_predict_path = '../data/category_predict_Dtree.pkl'
category_test_path = '../data/category_test.pkl'
output_1 = open(category_predict_path, 'wb')
pickle.dump(category_predict, output_1)
output_2 = open(category_test_path, 'wb')
pickle.dump(category_test, output_2)
output_1.close()
output_2.close()
if __name__ == '__main__':
'''
activity_dict_path = '../data/activity_dict.pkl'
activity_dict = get_activity_dict(activity_dict_path)
train_dict_path = '../data/train_dict.pkl'
test_dict_path = '../data/test_dict.pkl'
user_id_index_path = '../data/user_id_index.pkl'
data_segement(activity_dict, train_dict_path, test_dict_path, user_id_index_path)
'''
train_dict_path = '../data/train_dict.pkl'
test_dict_path = '../data/test_dict.pkl'
user_id_index_path = '../data/user_id_index.pkl'
train_dict, test_dict, user_id_index = get_data(train_dict_path, test_dict_path, user_id_index_path)
profile_path = '../data/profile.pkl'
profile = get_profile(profile_path)
feature_train, feature_test, category_train, category_test = feature_build(train_dict, test_dict, profile, user_id_index)
print 'feature_train sample: ' + str(feature_train[1000])
print 'feature_test sample: ' + str(feature_test[1000])
# decision tree
category_Dtree = decision_tree(feature_train, feature_test, category_train)
# random_forests
#category_RF = random_forests(feature_train, feature_test, category_train)
# conventional method: most-used service
#category_Mused = conventional_method_Mused(feature_train, feature_test, category_train)
# conventional method: last-week service
#category_Lweek = conventional_method_Lweek(feature_train, feature_test, category_train)
#cal_hit_rate(category_Dtree, category_test)
#calculating_F_value(category_Dtree, category_test)
#counting_accuate_rate(category_Dtree, category_test)
#save_file_for_sleep(category_Dtree, category_test)
# this part is for migration.py
'''
# origin data, user_activity_origin is users' real behavior
user_activity_origin_path = '../data/user_activity_test/user_activity_origin.pkl'
user_activity_origin = activity_restore(feature_test, category_test)
user_activity_save(user_activity_origin, user_activity_origin_path)
'''
'''
# predition data using decision_tree
user_activity_Dtree_path = '../data/user_activity_test/user_activity_Dtree.pkl'
user_activity_Dtree = activity_restore(feature_test, category_Dtree)
user_activity_save(user_activity_Dtree, user_activity_Dtree_path)
'''
'''
# predition data according to users' most-used service
user_activity_Mused_path = '../data/user_activity_test/user_activity_Mused.pkl'
user_activity_Mused = activity_restore(feature_test, category_Mused)
user_activity_save(user_activity_Mused, user_activity_Mused_path)
'''
'''
# predition data according to users' last-week service
user_activity_Lweek_path = '../data/user_activity_test/user_activity_Lweek.pkl'
user_activity_Lweek = activity_restore(feature_test, category_Lweek)
user_activity_save(user_activity_Lweek, user_activity_Lweek_path)
'''
|
normal
|
{
"blob_id": "65c0d940bacc2d016121812c435cc60f3fc1ba90",
"index": 7233,
"step-1": "#!usr/bin/env python\r\n#-*- coding:utf-8 -*-\r\n\r\n# this model is for decision tree\r\n# objective: To cluster different service\r\n# JialongLi 2017/03/18\r\n\r\nimport re\r\nimport os\r\nimport sys\r\nimport pickle\r\nimport copy\r\nimport random\r\nimport pydotplus\r\n\r\n\r\nUSER_NUM = 1000\r\nreload(sys)\r\nsys.setdefaultencoding( \"utf-8\" )\r\nfrom sklearn import tree\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.cluster import KMeans\r\n\r\n# 0 represent Sunday, 1: Monday, 6: Saturday, 0: Sunday\r\nday_index = {'0507': 1, '0508': 2, '0509': 3, '0510': 4, '0511': 5, '0512': 6, '0513': 0, \r\n\t\t\t '0604': 1, '0605': 2, '0606': 3, '0607': 4, '0608': 5, '0609': 6, '0610': 0, \r\n\t\t\t '0702': 1, '0703': 2, '0704': 3, '0705': 4, '0706': 5, '0707': 6, '0708': 0, \r\n\t\t\t '0806': 1, '0807': 2, '0808': 3, '0809': 4, '0810': 5, '0811': 6, '0812': 0}\r\n\r\nservice_type = ['I', 'F', 'W', 'G', 'S', 'V']\r\n\r\n# get activity_dict\r\n# user's activity: default value is 'F'\r\n# format: {id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}\r\ndef get_activity_dict(activity_dict_path):\r\n\tpkl_file = open(activity_dict_path, 'rb')\r\n\tactivity_dict = pickle.load(pkl_file)\r\n\tpkl_file.close()\r\n\treturn activity_dict\r\n\r\n# data are divided into train data and test data\r\n# first three weeks: train data; last week: test data\r\n# train_dict and test_dict are subset of activity_dict, id format is different\r\n# activity_dict format: {real id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}\r\n# user_id_index: key = number, value = real id\r\ndef data_segement(activity_dict, train_dict_path, test_dict_path, user_id_index_path):\r\n\ttrain_dict = {}\r\n\ttest_dict = {}\r\n\tuser_count = 0\r\n\tuser_id_index = {}\r\n\tfor key_0, value_0 in activity_dict.items(): # key_0: real user_id\r\n\t\ttrain_dict[user_count] = {}\r\n\t\ttest_dict[user_count] = {}\r\n\t\tuser_id_index[user_count] = key_0\r\n\t\tfor key, value in value_0.items():\r\n\t\t\tif key[1] == '8': # data of August, test set\r\n\t\t\t\ttest_dict[user_count][key] = value\r\n\t\t\telse:\r\n\t\t\t\ttrain_dict[user_count][key] = value # train set\r\n\t\tuser_count += 1\r\n\r\n\toutput_1 = open(train_dict_path, 'wb')\r\n\tpickle.dump(train_dict, output_1)\r\n\toutput_2 = open(test_dict_path, 'wb')\r\n\tpickle.dump(test_dict, output_2)\r\n\toutput_3 = open(user_id_index_path, 'wb')\r\n\tpickle.dump(user_id_index, output_3)\r\n\toutput_1.close()\r\n\toutput_2.close()\r\n\toutput_3.close()\r\n\r\n# get train data and test data\r\n# train_dict, test_dict format: {number id_1:{'0507': [24/PERIOD], '0508': ['I', 'W', 'G']}, id_2}\r\ndef get_data(train_dict_path, test_dict_path, user_id_index_path):\r\n\tpkl_file_1 = open(train_dict_path, 'rb')\r\n\tpkl_file_2 = open(test_dict_path, 'rb')\r\n\tpkl_file_3 = open(user_id_index_path, 'rb')\r\n\ttrain_dict = pickle.load(pkl_file_1)\r\n\ttest_dict = pickle.load(pkl_file_2)\r\n\tuser_id_index = pickle.load(pkl_file_3)\r\n\tpkl_file_1.close()\r\n\tpkl_file_2.close()\r\n\tpkl_file_3.close()\r\n\treturn train_dict, test_dict, user_id_index\r\n\r\n# get profile\r\ndef get_profile(profile_path):\r\n\tpkl_file = open(profile_path, 'rb')\r\n\tprofile = pickle.load(pkl_file)\r\n\treturn profile\r\n\r\n# select different features\r\n# feature format: [user_id, gender, age, edu, job, hour, date], 7 features\r\n# profile: dict, {real user_id: [gender, age, edu, job]}\r\n# feature format: double list, outer list element is a sample: [number user_id, gender, age, edu, job, hour, date]\r\n# category format: list, element is service type, length = feature\r\ndef feature_select(data_dict, profile, user_id_index, is_over_sampling):\r\n\tfeature = []\r\n\tcategory = []\r\n\tover_sampling_num = 0\r\n\tfor user_id, all_dates in data_dict.items():\r\n\t\treal_user_id = user_id_index[user_id]\r\n\t\tone_user_profile = copy.deepcopy(profile[real_user_id]) # gender, age, edu, job\r\n\t\tone_user_profile.insert(0, user_id) # insert user_id\r\n\t\tfor date, activity in all_dates.items():\r\n\t\t\tfor i in range(len(activity)):\r\n\t\t\t\tif 1: #activity[i] != 'F': # do not add 'F'\r\n\t\t\t\t\tsample = copy.deepcopy(one_user_profile)\r\n\t\t\t\t\t#del(sample[1:4])\r\n\t\t\t\t\tsample.append(i) #(int(i/6)) # i represents hour\r\n\t\t\t\t\tsample.append(day_index[date]) # day_index: 7 days in one week\r\n\t\t\t\t\tfeature.append(sample)\r\n\t\t\t\t\t#category.append(activity[i])\r\n\t\t\t\t\tif activity[i] == 'F':\r\n\t\t\t\t\t\tcategory.append('F')\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tcategory.append('O')\r\n\t\t\t\t\tif is_over_sampling and len(sample) > 5: # make sure that features are completed\r\n\t\t\t\t\t\tif activity[i] != 'F':\r\n\t\t\t\t\t\t\tsample_over = [[] for k in range(over_sampling_num)]\r\n\t\t\t\t\t\t\tfor j in range(over_sampling_num):\r\n\t\t\t\t\t\t\t\tsample_over[j] = copy.deepcopy(sample)\r\n\t\t\t\t\t\t\t\tsample_over[j][-3] = random.randint(0, 8) # random disturbance in job feature\r\n\t\t\t\t\t\t\t\tfeature.append(sample_over[j])\r\n\t\t\t\t\t\t\t\tcategory.append('O')\r\n\treturn feature, category\r\n\r\n# build features, all features\r\n# False means test data do not need over sampling\r\ndef feature_build(train_dict, test_dict, profile, user_id_index):\r\n\tfeature_train, category_train = feature_select(train_dict, profile, user_id_index, True)\r\n\tfeature_test, category_test = feature_select(test_dict, profile, user_id_index, False)\r\n\treturn feature_train, feature_test, category_train, category_test\r\n\r\n# calculating the hit rate\r\ndef cal_hit_rate(category_predict, category_test):\r\n\thit_count = 0\r\n\tsample_test_count = len(category_predict)\r\n\tfor i in range(sample_test_count):\r\n\t\tif category_predict[i] == category_test[i]:\r\n\t\t\thit_count += 1\r\n\thit_rate = float(hit_count) / float(sample_test_count)\r\n\tprint 'hit rate: ' + str(round(hit_rate, 4) * 100) + '%'\r\n\r\n# calculating F value\r\ndef calculating_F_value(category_predict, category_test):\r\n\tn_predict = 0\r\n\tn_origin = 0\r\n\thit_count = 0\r\n\tfor item in category_predict:\r\n\t\tif item != 'F':\r\n\t\t\tn_predict += 1\r\n\tfor item in category_test:\r\n\t\tif item != 'F':\r\n\t\t\tn_origin += 1\r\n\tfor i in range(len(category_predict)):\r\n\t\tif category_predict[i] != 'F' and category_predict[i] == category_test[i]:\r\n\t\t\thit_count += 1\r\n\tprecision = float(hit_count) / float(n_predict)\r\n\trecall = float(hit_count) / float(n_origin)\r\n\tF_value = 2 * precision * recall / (precision + recall)\r\n\tprint 'n_predict: ' + str(n_predict)\r\n\tprint 'n_origin: ' + str(n_origin)\r\n\tprint 'precision: ' + str(round(precision, 3))\r\n\tprint 'recall: ' + str(round(recall, 3))\r\n\tprint 'F_value: ' + str(round(F_value, 3))\r\n\r\n# 1. select the service type using most in that period in past days\r\n# 2. if user did not use service in that period before, select the service type using most in past days\r\n# 3. if user did not use service before, select service randomly \r\n# service_count_hour: key = (user_id, hour, service_type) value = count\r\n# service_count_past: key = (user_id, service_type) value = count\r\n# service_hour: key = (user_id, hour), value = [service_type, count]\r\n# service_past: key = user_id, value = [service_type, count]\r\ndef conventional_method_Mused(feature_train, feature_test, category_train):\r\n\tif len(feature_train[0]) != 7:\r\n\t\tprint 'feature wrong'\r\n\tservice_count_hour = {}\r\n\tservice_count_past = {}\r\n\tfor i in range(len(feature_train)):\r\n\t\tkey_hour = (feature_train[i][0], feature_train[i][5], category_train[i])\r\n\t\tif key_hour not in service_count_hour:\r\n\t\t\tservice_count_hour[key_hour] = 1\r\n\t\telse:\r\n\t\t\tservice_count_hour[key_hour] += 1\r\n\r\n\t\tkey_past = (feature_train[i][0], category_train[i])\r\n\t\tif key_past not in service_count_past:\r\n\t\t\tservice_count_past[key_past] = 1\r\n\t\telse:\r\n\t\t\tservice_count_past[key_past] += 1\r\n\r\n\tservice_hour = {}\r\n\tservice_past = {}\r\n\tfor key, value in service_count_hour.items():\r\n\t\tkey_hour = (key[0], key[1])\r\n\t\tif key_hour not in service_hour:\r\n\t\t\tservice_hour[key_hour] = [key[2], value]\r\n\t\telse:\r\n\t\t\tif value > service_hour[key_hour][1]:\r\n\t\t\t\tservice_hour[key_hour] = [key[2], value]\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\r\n\tfor key, value in service_count_past.items():\r\n\t\tkey_past = key[0]\r\n\t\tif key_past not in service_past:\r\n\t\t\tservice_past[key_past] = [key[1], value]\r\n\t\telse:\r\n\t\t\tif value > service_past[key_past][1]:\r\n\t\t\t\tservice_past[key_past] = [key[1], value]\r\n\t\t\telse:\r\n\t\t\t\tpass\r\n\r\n\tcategory_predict = []\r\n\tfor i in range(len(feature_test)):\r\n\t\tkey_0 = (feature_test[i][0], feature_test[i][5])\r\n\t\tkey_1 = feature_test[i][0]\r\n\t\tif key_0 in service_hour:\r\n\t\t\tvalue_0 = service_hour[key_0]\r\n\t\t\tcategory_predict.append(value_0[0])\r\n\t\telif key_1 in service_past:\r\n\t\t\tvalue_1 = service_past[key_1]\r\n\t\t\tcategory_predict.append(value_1[0])\r\n\t\telse:\r\n\t\t\trandom_num = random.randint(0, len(service_type)-1)\r\n\t\t\tcategory_predict.append(service_type[random_num])\r\n\r\n\treturn category_predict\r\n# method 2: service in last week\r\ndef conventional_method_Lweek(feature_train, feature_test, category_train):\r\n\tif len(feature_train[0]) != 7:\r\n\t\tprint 'feature wrong'\r\n\tcategory_predict = ['FFF' for i in range(len(feature_test))]\r\n\tfor i in range(len(feature_train)):\r\n\t\tsample = feature_train[i]\r\n\t\tuser_id = sample[0]\r\n\t\thour = sample[-2]\r\n\t\tdate = sample[-1]\r\n\t\tif date == 0: # 0 means it is Sunday and should be the last\r\n\t\t\tdate = 7\r\n\t\telse:\r\n\t\t\tpass\r\n\t\tservice_position = user_id * 168 + (date - 1) * 24 + hour\r\n\t\tcategory_predict[service_position] = category_train[i]\r\n\treturn category_predict\r\n\r\n# decision tree\r\ndef decision_tree(feature_train, feature_test, category_train):\r\n\tclf = tree.DecisionTreeClassifier()\r\n\tclf = clf.fit(feature_train, category_train)\r\n\tcategory_predict = clf.predict(feature_test) # the format of category_predict is weird\r\n\tcategory_Dtree = []\r\n\tfor item in category_predict:\r\n\t\tif item == 'F':\r\n\t\t\tcategory_Dtree.append('F')\r\n\t\telse:\r\n\t\t\tcategory_Dtree.append('O')\r\n\treturn category_Dtree \r\n\r\n# random forests\r\ndef random_forests(feature_train, feature_test, category_train):\r\n\tclf = RandomForestClassifier(n_estimators = 80)\r\n\tclf = clf.fit(feature_train, category_train)\r\n\tcategory_predict = clf.predict(feature_test)\r\n\tcategory_RF = []\r\n\tfor item in category_predict:\r\n\t\tif item == 'F':\r\n\t\t\tcategory_RF.append('F')\r\n\t\telse:\r\n\t\t\tcategory_RF.append('O')\r\n\treturn category_RF\r\n\r\n# save user_activity as pkl file for migration.py\r\ndef user_activity_save(user_activity, user_activity_path):\r\n\toutput = open(user_activity_path, 'wb')\r\n\tpickle.dump(user_activity, output)\r\n\toutput.close()\r\n\r\n# user_activity is for migration.py\r\n# key = user_id, range(1000), value = ['F', 'G'...], length is 7 * 24 = 168\r\ndef activity_restore(feature, category):\r\n\tif len(feature[0]) != 7:\r\n\t\tprint 'feature wrong'\r\n\tuser_activity = {}\r\n\tfor i in range(USER_NUM):\r\n\t\tuser_activity[i] = ['FFF' for j in range(168)]\r\n\tfor i in range(len(feature)):\r\n\t\tsample = feature[i]\r\n\t\tuser_id = sample[0]\r\n\t\thour = sample[5]\r\n\t\tdate = sample[-1]\r\n\t\tif date == 0: # 0 means it is Sunday and should be the last\r\n\t\t\tdate = 7\r\n\t\telse:\r\n\t\t\tpass\r\n\t\tposition = (date - 1) * 24 + hour\r\n\t\tuser_activity[user_id][position] = category[i]\r\n\treturn user_activity\r\n\r\ndef counting_accuate_rate(category_Dtree, category_test):\r\n\ton_on = 0\r\n\ton_off = 0\r\n\toff_on = 0\r\n\toff_off = 0\r\n\tprint len(category_test)\r\n\tprint len(category_Dtree)\r\n\tfor i in range(21504): #(len(category_Dtree)):\r\n\t\tif category_Dtree[i] == 'O' and category_test[i] == 'O':\r\n\t\t\ton_on += 1\r\n\t\telif category_Dtree[i] == 'O' and category_test[i] == 'F':\r\n\t\t\ton_off += 1\r\n\t\telif category_Dtree[i] == 'F' and category_test[i] == 'O':\r\n\t\t\toff_on += 1\r\n\t\telse:\r\n\t\t\toff_off += 1\r\n\tprint 'on_on' + '\\t' + str(on_on)\r\n\tprint 'on_off' + '\\t' + str(on_off)\r\n\tprint 'off_on' + '\\t' + str(off_on)\r\n\tprint 'off_off' + '\\t' + str(off_off)\r\n\r\n# save file for sleep.py\r\ndef save_file_for_sleep(category_predict, category_test):\r\n\tcategory_predict_path = '../data/category_predict_Dtree.pkl'\r\n\tcategory_test_path = '../data/category_test.pkl'\r\n\toutput_1 = open(category_predict_path, 'wb')\r\n\tpickle.dump(category_predict, output_1)\r\n\toutput_2 = open(category_test_path, 'wb')\r\n\tpickle.dump(category_test, output_2)\r\n\toutput_1.close()\r\n\toutput_2.close()\r\n\r\nif __name__ == '__main__':\r\n\t'''\r\n\tactivity_dict_path = '../data/activity_dict.pkl'\r\n\tactivity_dict = get_activity_dict(activity_dict_path)\r\n\ttrain_dict_path = '../data/train_dict.pkl'\r\n\ttest_dict_path = '../data/test_dict.pkl'\r\n\tuser_id_index_path = '../data/user_id_index.pkl'\r\n\tdata_segement(activity_dict, train_dict_path, test_dict_path, user_id_index_path)\r\n\t'''\r\n\r\n\ttrain_dict_path = '../data/train_dict.pkl'\r\n\ttest_dict_path = '../data/test_dict.pkl'\r\n\tuser_id_index_path = '../data/user_id_index.pkl'\r\n\ttrain_dict, test_dict, user_id_index = get_data(train_dict_path, test_dict_path, user_id_index_path)\r\n\tprofile_path = '../data/profile.pkl'\r\n\tprofile = get_profile(profile_path)\r\n\r\n\tfeature_train, feature_test, category_train, category_test = feature_build(train_dict, test_dict, profile, user_id_index)\r\n\tprint 'feature_train sample: ' + str(feature_train[1000])\r\n\tprint 'feature_test sample: ' + str(feature_test[1000])\r\n\r\n\t# decision tree\r\n\tcategory_Dtree = decision_tree(feature_train, feature_test, category_train)\r\n\r\n\t# random_forests\r\n\t#category_RF = random_forests(feature_train, feature_test, category_train)\r\n\r\n\t# conventional method: most-used service\r\n\t#category_Mused = conventional_method_Mused(feature_train, feature_test, category_train)\r\n\r\n\t# conventional method: last-week service\r\n\t#category_Lweek = conventional_method_Lweek(feature_train, feature_test, category_train)\r\n\r\n\r\n\t#cal_hit_rate(category_Dtree, category_test)\r\n\t#calculating_F_value(category_Dtree, category_test)\r\n\t\r\n\t#counting_accuate_rate(category_Dtree, category_test)\r\n\r\n\t#save_file_for_sleep(category_Dtree, category_test)\r\n\r\n\t# this part is for migration.py\r\n\t'''\r\n\t# origin data, user_activity_origin is users' real behavior\r\n\tuser_activity_origin_path = '../data/user_activity_test/user_activity_origin.pkl'\r\n\tuser_activity_origin = activity_restore(feature_test, category_test)\r\n\tuser_activity_save(user_activity_origin, user_activity_origin_path)\r\n\t'''\r\n\t'''\r\n\t# predition data using decision_tree\r\n\tuser_activity_Dtree_path = '../data/user_activity_test/user_activity_Dtree.pkl'\r\n\tuser_activity_Dtree = activity_restore(feature_test, category_Dtree)\r\n\tuser_activity_save(user_activity_Dtree, user_activity_Dtree_path)\r\n\t'''\r\n\t'''\r\n\t# predition data according to users' most-used service\r\n\tuser_activity_Mused_path = '../data/user_activity_test/user_activity_Mused.pkl'\r\n\tuser_activity_Mused = activity_restore(feature_test, category_Mused)\r\n\tuser_activity_save(user_activity_Mused, user_activity_Mused_path)\r\n\t'''\r\n\t'''\r\n\t# predition data according to users' last-week service\r\n\tuser_activity_Lweek_path = '../data/user_activity_test/user_activity_Lweek.pkl'\r\n\tuser_activity_Lweek = activity_restore(feature_test, category_Lweek)\r\n\tuser_activity_save(user_activity_Lweek, user_activity_Lweek_path)\r\n\t'''",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
}
|
[
0
] |
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