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findtime/utils.py | MattCCS/FindTime | 0 | 27445 | <filename>findtime/utils.py<gh_stars>0
def day_of_year(dt):
return dt.timetuple().tm_yday
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CoV19/base/urls.py | just-ary27/CovBot-revamp | 1 | 166941 | <filename>CoV19/base/urls.py
from django.urls import path,include
from . import views
urlpatterns = [
path('',views.index),
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groot.py | adit-chandra/project-groot | 0 | 67727 | import sys
import gzip
import bz2
import os
import time
import subprocess
import math
import multiprocessing as mp
from optparse import OptionParser
from utils import *
import AAF
def main():
parser = OptionParser()
parser.add_option("-k", dest="kLen", type=int, default=25,
help="k for reconstruction, default = 25")
parser.add_option("--ks", dest="ksLen", type=int, default=25,
help="k for reads selection, default = 25")
parser.add_option("-n", dest="filter", type=int, default=1,
help="k-mer filtering threshold, default = 1")
parser.add_option("-d", dest="data_dir", default='data',
help="directory containing the data, default = data/")
parser.add_option("--mem", dest="mem_size", type=int, default=4,
help="total memory limit (in GB), default = 4")
parser.add_option("-t", dest="n_threads", type=int, default=1,
help="number of threads to use, default = 1")
parser.add_option("-l", dest="long", action='store_true',
help="use fitch_kmerX_long instead of fitch_kmerX")
(options, args) = parser.parse_args()
n = options.filter
mem_size = options.mem_size
n_threads = options.n_threads
kl = options.kLen
ks = options.ksLen
mem_per_thread = int(options.mem_size / float(n_threads))
data_dir = options.data_dir
if not mem_per_thread:
print('Not enough memory, decrease n_threads or increase mem_size')
sys.exit()
if not os.path.isdir(data_dir):
print('Cannot find data directory {}'.format(data_dir))
sys.exit(2)
# kmer_count
if kl > 25:
if os.system('which kmer_countx > /dev/null'):
kmer_count_bin = './kmer_countx'
if not is_exe(kmer_count_bin):
print('kmer_countx not found!')
sys.exit(1)
else:
kmer_count_bin = 'kmer_countx'
else:
if os.system('which kmer_count > /dev/null'):
kmer_count_bin = './kmer_count'
if not is_exe(kmer_count_bin):
print('kmer_count not found!')
sys.exit(1)
else:
kmer_count_bin = 'kmer_count'
# kmer_merge
if os.system('which kmer_merge > /dev/null'):
filt_bin = './kmer_merge'
if not is_exe(filt_bin):
print('kmer_merge not found!')
sys.exit(1)
else:
filt_bin = 'kmer_merge'
# ReadsSelector
if os.system('which ReadsSelector > /dev/null'):
reads_select_bin = './ReadsSelector'
if not is_exe(filt_bin):
print('ReadsSelector not found!')
sys.exit(1)
else:
reads_select_bin = 'ReadsSelector'
# fitch
if os.system('which fitch_kmerX > /dev/null'):
if options.long:
filtch_bin = './fitch_kmerX_long'
else:
filtch_bin = './fitch_kmerX'
if not is_exe(filtch_bin):
print(filtch_bin + ' not found')
sys.exit()
else:
if options.long:
filtch_bin = 'fitch_kmerX_long'
else:
filtch_bin = 'fitch_kmerX'
selection_dir = '{}_ks{}_pairwise'.format(
os.path.basename(data_dir.rstrip('/')), ks)
if os.path.exists('./' + selection_dir):
command = 'rm -r {}'.format(selection_dir)
os.system(command)
command = 'mkdir {}'.format(selection_dir)
os.system(command)
samples = AAF.aaf_kmer_count(data_dir, ks, n, n_threads, mem_per_thread)
# build distance matrix
sn = len(samples)
dist = [[0] * sn for i in range(sn)]
for i in range(sn):
for j in range(i + 1, sn):
command = []
command.append(
'mkdir {}/{}_{}'.format(selection_dir, samples[i], samples[j]))
command.append(
'{} -k s -c -d 0 -A A -B A {}.pkdat.gz {}.pkdat.gz | cut -f 1 > test.kmer'.format(filt_bin, samples[i], samples[j]))
command.append('{} -k test.kmer -fa 1 -o {}/{}_{}/{} -s {}/{}/*'
.format(reads_select_bin, selection_dir, samples[i], samples[j],
samples[i], data_dir, samples[i]))
command.append('{} -k test.kmer -fa 1 -o {}/{}_{}/{} -s {}/{}/*'
.format(reads_select_bin, selection_dir, samples[i], samples[j],
samples[j], data_dir, samples[j]))
for comm in command:
print(comm)
os.system(comm)
# kmer_count
ntotal = []
command = '{} -l {} -n {} -G {} -o {}_temp.pkdat -f FA -i {}/{}_{}/{}.*'.format(
kmer_count_bin, kl, n, mem_size / 2, samples[i], selection_dir, samples[i], samples[j], samples[i])
output = subprocess.check_output(
command, shell=True, stderr=subprocess.STDOUT)
ntotal.append(float(output.decode('ascii').split()[1]))
command = '{} -l {} -n {} -G {} -o {}_temp.pkdat -f FA -i {}/{}_{}/{}.*'.format(
kmer_count_bin, kl, n, mem_size / 2, samples[j], selection_dir, samples[i], samples[j], samples[j])
output = subprocess.check_output(
command, shell=True, stderr=subprocess.STDOUT)
ntotal.append(float(output.decode('ascii').split()[1]))
# kmer_merge
command = "{} -k s -c -d '0' -a 'T,M,F' {}_temp.pkdat {}_temp.pkdat | wc -l".format(
filt_bin, samples[i], samples[j])
output = subprocess.check_output(
command, shell=True, stderr=subprocess.STDOUT)
nshared = int(output.decode('ascii').split()[0])
if nshared == 0:
distance = 1
else:
distance = (-1.0 / kl) * math.log(nshared / min(ntotal))
dist[j][i] = dist[i][j] = distance
os.system('rm *.pkdat*')
# construct the tree
try:
infile = open('infile', 'w')
except IOError:
print('Cannot open infile for writing')
sys.exit()
infile.write('{} {}'.format(sn, sn))
namedic = {}
for i in range(sn):
lsl = len(samples[i])
if lsl >= 10:
ssl = samples[i][:10]
appendix = 1
while ssl in namedic:
if appendix < 10:
ssl = samples[i][:9] + str(appendix)
elif appendix > 9:
ssl = samples[i][:8] + str(appendix)
appendix += 1
else:
ssl = samples[i] + ' ' * (10 - lsl)
namedic[ssl] = samples[i]
infile.write('\n{}'.format(ssl))
for j in range(sn):
infile.write('\t{}'.format(dist[i][j]))
infile.close()
# fitch_kmer
print('{} building tree'.format(time.strftime("%c")))
if os.path.exists("./outfile"):
os.system("rm -f outfile outtree")
command = 'printf "K\n{}\nY" | {} > /dev/null'.format(int(kl), filtch_bin)
os.system(command)
fh1 = open('outtree', 'rt')
fh2 = open(selection_dir + '.tre', 'wt')
for line in fh1:
for key in namedic:
key_new = key.rstrip() + ":"
if key_new in line:
newline = line.replace(key_new, namedic[key].rstrip() + ":", 1)
line = newline
fh2.write(line)
fh1.close()
fh2.close()
command = 'mv infile {}.dist'.format(selection_dir)
os.system(command)
os.system('rm -f outfile outtree')
print('{} end'.format(time.strftime("%c")))
if __name__ == '__main__':
main()
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] |
Scripts/GeMS_MetadataCSDGM2_Arc10.py | mahunterUSGS/GeMS_Tools | 0 | 66485 | """
script to ease construction of CSDGM2-style metadata for an GeMS-style geodatabase.
To use,
Run ValidateDatabase to make sure that the database is complete and there are
no missing DMU, Glossary, or DataSources entries
In ArcCatalog, go to Customize>Options>Metadata and set Metadata Style to
"FGDC CSDGM Metadata". OK and exit.
In ArcCatalog, use the ArcGIS metadata editor to complete the record for the
GeologicMap feature dataset. Save. NOTE THAT whatever errors or you create
in this master metadata record will be faithfully propagated to metadata
records for all parts of the geodatabase!
Run script GeMS_MetadataCSDGM2_Arc10.1.py. This script will:
Export the GeologicMap metadata record in CSDGM2 format
Polish this metadata slightly for use as a master record
For the geodatabase as a whole and for each entity (table, feature dataset,
feature class) in the geodatabase:
Copies the master record.
Adds supplemental information (ArcGIS reports this in Resouce:Details)
about the the GeMS standard and continents of the geodatabase.
Adds a description of the entity taken from the GeMS documentation.
Adds entity-attribute information taken from the GeMS documentation
and the DMU, Glossary, and DataSources tables of the geodatabase.
Writes this XML to a file in the directory that contains the geodatabase.
Imports this XML into the geodatabase as metadata for the appropriate entity.
Look at file <geodatabasename>-metadataLog.txt to see what parts of which metadata
records need to be completed by hand. This will occur wherever you extend the
database schema beyond the schema outlined in the GeMS documentation.
***Note that this script provides for a file that automates description of your
extensions to the GeMS schema so that you need not edit metadata by hand--see
file my_GeMSDefinitions.py in the GeMS Scripts directory.***
Inspect metadata records in ArcCatalog (the Description tab) to see that they are
complete.
Open saved XML files in browser to see that they are appropriate. Scan for duplicate
entries.
You want ISO metadata? Change your Metadata Style and fix records using the
ArcCatalog metadata editor. Export as ISO of your flavor, insofar as ArcCatalog allows.
Let us know how this works.
Usage: prompt>GeMS_MetadataCSDGM2_Arc10.1.py <geodatabase>
<NAME> and <NAME>, US Geological Survey
<EMAIL>, <EMAIL>
"""
# 17 March 2017 Changed NCGMP09 to GeMS, etc.
# 18 April 2017 Added utility functions, local definition-extension file
# 12 August 2017 Modified to recognize GeoMaterial, GeoMaterialConfidence, and GeoMaterialDict.
# Added number of rows in each table to gdb description in SupplementalInfo
#Metadata conversion (ImportMetadata_conversion) is not supported in Pro as of 180926, but is on the roadmap.
import arcpy, sys, os.path, copy, imp, glob
from GeMS_Definition import enumeratedValueDomainFieldList, rangeDomainDict, unrepresentableDomainDict, attribDict, entityDict, GeoMatConfDict
from GeMS_utilityFunctions import *
from xml.dom.minidom import *
versionString = 'GeMS_MetadataCSDGM2_Arc10.py, version of 10 December 2017'
translator = arcpy.GetInstallInfo("desktop")["InstallDir"]+'Metadata/Translator/ARCGIS2FGDC.xml'
debug = False
ncgmp = 'GeMS'
ncgmpFullRef = '"GeMS (Geologic Map Schema)--a standard format for digital publication of geologic maps, version 2.0", available at http://ngmdb.usgs.gov/Info/standards/GeMS/'
eaoverviewCitation = 'Detailed descriptions of entities, attributes, and attribute values are given in metadata for constituent elements of this composite dataset. See also '+ncgmpFullRef+'.'
gdbDesc0a = ' is a composite geodataset that conforms to '+ncgmpFullRef+'. '
gdbDesc0b = ' is part of a composite geodataset that conforms to '+ncgmpFullRef+'. '
gdbDesc2 = 'Metadata records associated with each element within the geodataset contain more detailed descriptions of their purposes, constituent entities, and attributes. '
gdbDesc3 = ('Two shapefile versions of the dataset are also available. The OPEN shapefile version consists '+
'of shapefiles, DBF files, and delimited text files and retains all information in the native '+
'geodatabase, but some programming will likely be necessary to assemble these components into '+
'usable formats. The SIMPLE shapefile version consists only of shapefiles and is easily used, but '+
'lacks some information present in the native geodatabase.')
def __appendOrReplace(rootNode,newNode,nodeTag):
if len(rootNode.getElementsByTagName(nodeTag)) == 0:
rootNode.appendChild(newNode)
else:
rootNode.replaceChild(newNode,rootNode.getElementsByTagName(nodeTag)[0])
def __fieldNameList(fc):
#Returns a list of field names from Field.name in arcpy.ListFields
fldList = arcpy.ListFields(fc)
nameList = []
for fld in fldList:
if not fld.name in ('OBJECTID', 'SHAPE','Shape', 'Shape_Length', 'Shape_Area'):
nameList.append(fld.name)
return nameList
def __findInlineRef(sourceID):
# finds the Inline reference for each DataSource_ID
query = '"DataSources_ID" = \'' + sourceID + '\''
rows = arcpy.SearchCursor(dataSources, query)
row = next(rows)
if not row is None:
#return row.Inline
return row.Source
else:
return ""
def __newElement(dom,tag,text):
nd = dom.createElement(tag)
ndText = dom.createTextNode(text)
nd.appendChild(ndText)
return nd
def __updateAttrDef(fld,dom):
##element tag names are
## attr = Attribute
## attrlabl = Attribute_Label
## attrdef = Attribute_Definition
## attrdefs = Attribute_Definition_Source
labelNodes = dom.getElementsByTagName('attrlabl')
for attrlabl in labelNodes:
if attrlabl.firstChild.data == fld:
attr = attrlabl.parentNode
if fld.find('_ID') > -1:
# substitute generic _ID field for specific
attrdefText = attribDict['_ID']
else:
attrdefText = attribDict[fld]
attrdef = __newElement(dom,'attrdef',attrdefText)
__appendOrReplace(attr,attrdef,'attrdef')
attrdefs = __newElement(dom,'attrdefs',ncgmp)
__appendOrReplace(attr,attrdefs,'attrdefs')
return dom
def __updateEdom(fld, defs, dom):
##element tag names are
## attr = Attribute
## attrdomv = Attribute_Domain_Values
## edom = Enumerated_Domain
## edomv = Enumerated_Domain_Value
## edomd = Enumerated_Domain_Definition
## edomvds = Enumerated_Domain_Value_Definition_Source
labelNodes = dom.getElementsByTagName('attrlabl')
for attrlabl in labelNodes:
if attrlabl.firstChild.data == fld:
attr = attrlabl.parentNode
attrdomv = dom.createElement('attrdomv')
for k in defs.items():
edom = dom.createElement('edom')
edomv = __newElement(dom,'edomv',k[0])
edomvd = __newElement(dom,'edomvd',k[1][0])
edom.appendChild(edomv)
edom.appendChild(edomvd)
if len(k[1][1]) > 0:
edomvds = __newElement(dom,'edomvds',k[1][1])
edom.appendChild(edomvds)
attrdomv.appendChild(edom)
__appendOrReplace(attr,attrdomv,'attrdomv')
return dom
def __updateEntityAttributes(fc, fldList, dom, logFile):
"""For each attribute (field) in fldList,
adds attribute definition and definition source,
classifies as range domain, unrepresentable-value domain or enumerated-value domain, and
for range domains, adds rangemin, rangemax, and units;
for unrepresentable value domains, adds unrepresentable value statement;
for enumerated value domains:
1) Finds all controlled-vocabulary fields in the table sent to it
2) Builds a set of unique terms in each field, ie, the domain
3) Matches each domain value to an entry in the glossary
4) Builds a dictionary of term:(definition, source) items
5) Takes the dictionary items and put them into the metadata
document as Attribute_Domain_Values
Field MapUnit in table DescriptionOfMapUnits is treated as a special case.
"""
cantfindTerm = []
cantfindValue = []
for fld in fldList:
addMsgAndPrint( ' Field: '+ fld)
# if is _ID field or if field definition is available, update definition
if fld.find('_ID') > -1 or fld in attribDict:
dom = __updateAttrDef(fld,dom)
else:
cantfindTerm.append(fld)
#if this is an _ID field
if fld.find('_ID') > -1:
dom = __updateUdom(fld,dom,unrepresentableDomainDict['_ID'])
#if this is another unrepresentable-domain field
if fld in unrepresentableDomainDict:
dom = __updateUdom(fld,dom,unrepresentableDomainDict[fld])
#if this is a defined range-domain field
elif fld in rangeDomainDict:
dom = __updateRdom(fld,dom)
#if this is MapUnit in DMU
elif fld == 'MapUnit' and fc == 'DescriptionOfMapUnits':
dom = __updateUdom(fld,dom,unrepresentableDomainDict['default'])
#if this is a defined Enumerated Value Domain field
elif fld in enumeratedValueDomainFieldList:
valList = []
#create a search cursor on the field
rows = arcpy.SearchCursor(fc,'','', fld)
row = next(rows)
#collect all values/terms in that field
while row:
if not row.getValue(fld) is None:
valList.append(row.getValue(fld))
row = next(rows)
#uniquify the list by converting it to a set object
valList = set(valList)
#create an empty dictionary object to hold the matches between the unique terms
#and their definitions (grabbed from the glossary)
defs = {}
#for each unique term, try to create a search cursor of just one record where the term
#matchs a Term field value from the glossary
if fld == 'MapUnit' and fc != 'DescriptionOfMapUnits':
for t in valList:
query = '"MapUnit" = \'' + t + '\''
rows = arcpy.SearchCursor(DMU, query)
row = next(rows)
#if the searchcursor contains a row
if row:
#create an entry in the dictionary of term:[definition, source] key:value pairs
#this is how we will enumerate through the enumerated_domain section
defs[t] = []
if row.FullName != None:
defs[t].append(row.FullName.encode('utf_8'))
defs[t].append('this report, table DescriptionOfMapUnits')
else:
addMsgAndPrint('MapUnit = '+t+', FullName not defined')
defs[t].append(row.Name.encode('utf_8'))
defs[t].append('this report, table DescriptionOfMapUnits')
else:
if not t in ('',' '): cantfindValue.append([fld,t])
elif fld == 'GeoMaterialConfidence' and fc == 'DescriptionOfMapUnits':
if debug:
addMsgAndPrint('DMU / GeoMaterialsConfidence')
defs = GeoMatConfDict
elif fld == 'GeoMaterial' and fc == 'DescriptionOfMapUnits':
if debug:
addMsgAndPrint('DMU / GeoMaterials!')
for t in valList:
query = '"GeoMaterial" = \'' + t + '\''
if debug:
addMsgAndPrint('query='+query)
rows = arcpy.SearchCursor(gmDict, query)
row = next(rows)
#if the searchcursor contains a row
if row:
if debug:
addMsgAndPrint(row.GeoMaterial+' : '+row.Definition.encode('utf_8'))
#create an entry in the dictionary of term:[definition, source] key:value pairs
#this is how we will enumerate through the enumerated_domain section
defs[t] = []
defs[t].append(row.Definition.encode('utf_8'))
defs[t].append(' GeMS documentation')
else:
addMsgAndPrint('GeoMaterial = '+t+': not defined in GeoMaterialDict')
cantfindValue.append([fld,t])
elif fld.find('SourceID') > -1: # is a source field
for t in valList:
query = '"DataSources_ID" = \'' + t + '\''
rows = arcpy.SearchCursor(dataSources, query)
row = next(rows)
#if the searchcursor contains a row
if row:
#create an entry in the dictionary of term:[definition, source] key:value pairs
#this is how we will enumerate through the enumerated_domain section
defs[t] = []
defs[t].append(row.Source.encode('utf_8'))
defs[t].append('this report, table DataSources')
else:
cantfindValue.append([fld,t])
else:
for t in valList:
query = '"Term" = '+"'"+ t + "'"
if debug:
addMsgAndPrint('query='+query)
rows = arcpy.SearchCursor(gloss, query)
row = next(rows)
#if the searchcursor contains a row
if row:
#create an entry in the dictionary of term:[definition, source] key:value pairs
#this is how we will enumerate through the enumerated_domain section
defs[t] = []
defs[t].append(row.Definition.encode('utf_8'))
defs[t].append(__findInlineRef(row.DefinitionSourceID).encode('utf_8'))
else:
if fld != 'GeoMaterial' and fc != 'GeoMaterialDict':
cantfindValue.append([fld,t])
dom = __updateEdom(fld, defs, dom)
else: #presumed to be an unrepresentable domain
dom = __updateUdom(fld,dom,unrepresentableDomainDict['default'])
if len(cantfindValue) > 0:
logFile.write('Missing enumerated-domain values\n')
logFile.write(' ENTITY TERM VALUE\n')
for term in cantfindValue:
logFile.write(' '+fc+' '+term[0]+' **'+term[1]+'**\n')
if len(cantfindTerm) > 0:
logFile.write('Missing terms\n')
logFile.write(' ENTITY TERM\n')
for term in cantfindTerm:
logFile.write(' '+fc + ' '+term+'\n')
return dom
def __updateRdom(fld,dom):
labelNodes = dom.getElementsByTagName('attrlabl')
for attrlabl in labelNodes:
if attrlabl.firstChild.data == fld:
attr = attrlabl.parentNode
attrdomv = dom.createElement('attrdomv')
rdom = dom.createElement('rdom')
rdommin = __newElement(dom,'rdommin',rangeDomainDict[fld][0])
rdom.appendChild(rdommin)
rdommax = __newElement(dom,'rdommax',rangeDomainDict[fld][1])
rdom.appendChild(rdommax)
attrunit = __newElement(dom,'attrunit',rangeDomainDict[fld][2])
rdom.appendChild(attrunit)
attrdomv.appendChild(rdom)
__appendOrReplace(attr,attrdomv,'attrdomv')
return dom
def __updateUdom(fld,dom,udomTextString):
labelNodes = dom.getElementsByTagName('attrlabl')
for attrlabl in labelNodes:
if attrlabl.firstChild.data == fld:
attr = attrlabl.parentNode
attrdomv = dom.createElement('attrdomv')
udom = __newElement(dom,'udom',udomTextString)
attrdomv.appendChild(udom)
__appendOrReplace(attr,attrdomv,'attrdomv')
return dom
def addSupplinf(dom,supplementaryInfo):
rtNode = dom.getElementsByTagName('descript')[0]
siNode = __newElement(dom,'supplinf',supplementaryInfo)
__appendOrReplace(rtNode,siNode,'supplinf')
return dom
def cleanTitle(dom):
# trims all ": table...", ": feature..." from title
title = dom.getElementsByTagName('title')[0]
titleText = title.firstChild.data
#if debug: addMsgAndPrint(titleText)
for txt in (': feature',': table'):
cn = titleText.find(txt)
if cn > 0:
titleText = titleText[0:cn]
title.firstChild.data = titleText
return dom
def eaoverviewDom(dom,eainfo,eaoverText,edcTxt):
overview = dom.createElement('overview')
eaover = __newElement(dom,'eaover',eaoverText)
overview.appendChild(eaover)
eadetcit = __newElement(dom,'eadetcit',edcTxt)
overview.appendChild(eadetcit)
eainfo.appendChild(overview)
return dom
def purgeChildren(dom,nodeTag):
nodes = dom.getElementsByTagName(nodeTag)
for aNode in nodes:
while len(aNode.childNodes) > 0:
aNode.removeChild(aNode.lastChild)
return dom
def purgeIdenticalSiblings(dom,ndTag,ndTxt):
nodes = dom.getElementsByTagName(ndTag)
parentNodes = []
n = 0
for nd in nodes:
if nd.firstChild.data == ndTxt:
parentNodes.append(nd.parentNode)
n = n+1
for i in range(1,n):
grandparent = parentNodes[i].parentNode
grandparent.removeChild(parentNodes[i])
return dom
def titleSuffix(dom,suffix):
# adds suffix to title text
title = dom.getElementsByTagName('title')[0]
titleText = title.firstChild.data
if titleText.find(suffix) == -1: # titleSuffix isn't already present
title.firstChild.data = titleText+suffix
return dom
def updateTableDom(dom,fc,logFile):
#def __updateTable(domMR,fc,gdbFolder,titleSuffix,logFile,isAnno):
#try to export metadata from fc
desc = arcpy.Describe(fc)
if desc.datasetType == 'FeatureClass' and desc.FeatureType == 'Annotation':
isAnno = True
else: isAnno = False
if fc in entityDict:
hasDesc = True
descText = entityDict[fc]
descSourceText = ncgmp
else:
hasDesc = False
if not isAnno:
descText = '**Need Description of '+fc+'**'
descSourceText = '**Need Description Source**'
logFile.write('No description for entity '+fc+'\n')
logFile.write('No description source for entity '+fc+'\n')
eainfo = dom.getElementsByTagName('eainfo')[0]
# DELETE EXISTING CHILD NODES
while len(eainfo.childNodes) > 0:
eainfo.removeChild(eainfo.lastChild)
if isAnno:
if hasDesc: eaoverText = descText
else: eaoverText = 'annotation feature class'
if hasDesc: edcTxt = descSourceText
else: edcTxt = 'See ESRI documentation for structure of annotation feature classes.'
# add overview to dom
dom = eaoverviewDom(dom,eainfo,eaoverText,edcTxt)
#overview = dom.createElement('overview')
#eaover = __newElement(dom,'eaover',eaoverText)
#overview.appendChild(eaover)
#eadetcit = __newElement(dom,'eadetcit',edcTxt)
#overview.appendChild(eadetcit)
#eainfo.appendChild(overview)
else: # is table or non-Anno feature class
# check for e-a detailed node, add if necessary
if len(eainfo.getElementsByTagName('detailed')) == 0:
#add detailed/enttyp/enttypl nodes
detailed = dom.createElement('detailed')
enttyp = dom.createElement('enttyp')
enttypl = __newElement(dom,'enttypl',fc)
enttypd = __newElement(dom,'enttypd',descText)
enttypds = __newElement(dom,'enttypds',descSourceText)
for nd in enttypl,enttypd,enttypds:
enttyp.appendChild(nd)
detailed.appendChild(enttyp)
eainfo.appendChild(detailed)
##check that each field has a corresponding attr node
#get a list of the field names in the fc
fldNameList = __fieldNameList(fc)
#get list of attributes in this metadata record
# we assume there eainfoNode has only one 'detailed' child
attrlablNodes = eainfo.getElementsByTagName('attrlabl')
attribs = []
detailed = dom.getElementsByTagName('detailed')[0]
for nd in attrlablNodes:
attribs.append(nd.firstChild.data)
for fieldName in fldNameList:
if not fieldName in attribs:
attr = dom.createElement('attr')
attrlabl = __newElement(dom,'attrlabl',fieldName)
attr.appendChild(attrlabl)
detailed.appendChild(attr)
#update the entity description and entity description source
if fc in entityDict or ( fc[0:2] == 'CS' and fc[2:] in entityDict):
enttypl = dom.getElementsByTagName('enttypl')
if len(enttypl) > 0:
enttyp = enttypl[0].parentNode
# entity description node
if fc[0:2] == 'CS':
descriptionText = entityDict[fc[2:]]
else:
descriptionText = entityDict[fc]
newEnttypd = __newElement(dom,'enttypd',descriptionText)
__appendOrReplace(enttyp,newEnttypd,'enttypd')
# entity description source node
newEnttypds = __newElement(dom,'enttypds',ncgmp)
__appendOrReplace(enttyp,newEnttypds,'enttypds')
#update attribute descriptions and value domains
dom = __updateEntityAttributes(fc, fldNameList, dom, logFile)
return dom
def writeDomToFile(workDir,dom,fileName):
if debug:
addMsgAndPrint(arcpy.env.workspace)
addMsgAndPrint('fileName='+fileName)
outf = open(os.path.join(workDir,fileName),'w')
dom.writexml(outf)
outf.close()
def writeGdbDesc(gdb):
desc = 'The geodatabase contains the following elements: '
arcpy.env.workspace = gdb
for aTable in arcpy.ListTables():
desc = desc+'non-spatial table '+ aTable+' ('+str(numberOfRows(aTable))+' rows); '
for anFds in arcpy.ListDatasets():
desc = desc + 'feature dataset '+anFds+' which contains '
fcs = arcpy.ListFeatureClasses('','All',anFds)
if len(fcs) == 1:
desc = desc + 'feature class '+fcs[0]+' ('+str(numberOfRows(fcs[0]))+' features); '
else:
for n in range(0,len(fcs)-1):
desc = desc+'feature class '+fcs[n]+' ('+str(numberOfRows(fcs[n]))+' features), '
lastn = len(fcs)-1
desc = desc+'and feature class '+fcs[lastn]+' ('+str(numberOfRows(fcs[lastn]))+' features); '
desc = desc[:-2]+'. '
return desc
def writeFdsDesc(gdb,fdsName):
if fdsName in entityDict:
desc = entityDict[fdsName] +' It contains the following elements: '
else:
desc = 'Feature dataset '+fdsName+' contains the following elements: '
arcpy.env.workspace = gdb+'/'+fdsName
fcs = arcpy.ListFeatureClasses('','All')
if len(fcs) == 1:
desc = desc + 'feature class '+fcs[0]+' ('+str(numberOfRows(fcs[0]))+' features); '
else:
for n in range(0,len(fcs)-2):
desc = desc+'feature class '+fcs[n]+' ('+str(numberOfRows(fcs[n]))+' features), '
lastn = len(fcs)-1
desc = desc+'and feature class '+fcs[lastn]+' ('+str(numberOfRows(fcs[lastn]))+' features). '
desc = desc[:-2]+'. '
return desc
##############################################################################
inGdb = sys.argv[1]
inGdb = os.path.abspath(inGdb)
workDir = os.path.dirname(inGdb)
gdb = os.path.basename(inGdb)
## supplement entity and field dictionaries from GeMS_Definition
if sys.argv[2] != '#':
if os.path.exists(sys.argv[2]):
myDefs = imp.load_source('module1',sys.argv[2])
myDefs.addDefs()
#forceExit()
######
gloss = os.path.join(inGdb, 'Glossary')
dataSources = os.path.join(inGdb, 'DataSources')
DMU = os.path.join(inGdb, 'DescriptionOfMapUnits')
gmDict = os.path.join(inGdb, 'GeoMaterialDict')
logFileName = inGdb+'-metadataLog.txt'
xmlFileMR = gdb+'-MR.xml'
xmlFileGdb = gdb+'.xml'
# export master record
fXML = workDir+'/'+gdb+ '.xml'
addMsgAndPrint('fXML = '+fXML)
if os.path.exists(fXML):
os.remove(fXML)
gdbObj = inGdb+'/GeologicMap'
if debug:
addMsgAndPrint(' gdbObj = '+gdbObj)
addMsgAndPrint(' translator = '+translator)
addMsgAndPrint(' fXML = '+fXML)
arcpy.ExportMetadata_conversion(gdbObj,translator,fXML)
addMsgAndPrint(' Metadata for GeologicMap exported to file ')
addMsgAndPrint(' '+fXML)
# parse xml to DOM
try:
domMR = xml.dom.minidom.parse(fXML)
addMsgAndPrint(' Master record parsed successfully')
# should then delete xml file
if not debug: os.remove(fXML)
except:
addMsgAndPrint(arcpy.GetMessages())
addMsgAndPrint('Failed to parse '+fXML)
raise arcpy.ExecuteError
sys.exit()
# clean up master record
## purge of eainfo and spdoinfo
for nodeTag in ('eainfo','spdoinfo'):
domMR = purgeChildren(domMR,nodeTag)
## get rid of extra <themekt>ISO 19115 Topic Categories entries
#domMR = purgeIdenticalSiblings(domMR,'themekt','ISO 19115 Topic Categories')
## fix title
domMR = cleanTitle(domMR)
## ensure that there is an eainfo node
try:
eanode = domMR.getElementsByTagName('eainfo')[0]
except:
rtNode = domMR.getElementsByTagName('metadata')[0]
eanode = domMR.createElement('eainfo')
rtNode.appendChild(eanode)
writeDomToFile(workDir,domMR,xmlFileMR)
addMsgAndPrint(' Running mp on master metadata record '+xmlFileMR+':')
if os.path.exists(logFileName):
os.remove(logFileName)
arcpy.USGSMPTranslator_conversion(os.path.join(workDir,xmlFileMR),'#','#','#',logFileName)
for aline in open(logFileName,'r').readlines():
addMsgAndPrint(aline[:-1])
addMsgAndPrint(' ')
logFile = open(logFileName,'a')
# import to geodatabase as whole
arcpy.env.workspace = workDir
supplementaryInfo = gdb+gdbDesc0a+gdbDesc2+gdbDesc3
dom = addSupplinf(domMR,supplementaryInfo)
eainfo = dom.getElementsByTagName('eainfo')[0]
gdbDesc = writeGdbDesc(inGdb) # listing of all tables, feature datasets, feature classes
dom = eaoverviewDom(dom,eainfo,gdbDesc,eaoverviewCitation)
addMsgAndPrint(' Importing XML to metadata for GDB as a whole')
writeDomToFile(workDir,dom,xmlFileGdb)
try:
arcpy.ImportMetadata_conversion(os.path.join(workDir,xmlFileGdb),'FROM_FGDC',inGdb,'ENABLED')
except:
addMsgAndPrint('Failed to import '+os.path.join(workDir,xmlFileGdb))
# import to tables
arcpy.env.workspace = inGdb
tables = arcpy.ListTables()
for aTable in tables:
revisedMetadata = gdb+'-'+aTable+'.xml'
addMsgAndPrint(' Creating XML for '+aTable)
dom = xml.dom.minidom.parse(os.path.join(workDir,xmlFileMR))
dom = titleSuffix(dom,': table '+aTable)
supplementaryInfo = 'Table '+aTable+gdbDesc0b+gdbDesc2
dom = addSupplinf(dom,supplementaryInfo)
dom = updateTableDom(dom,aTable,logFile)
addMsgAndPrint(' Importing XML to metadata for table '+aTable)
writeDomToFile(workDir,dom,revisedMetadata)
try:
arcpy.ImportMetadata_conversion(os.path.join(workDir,revisedMetadata),'FROM_FGDC',inGdb+'/'+aTable,'ENABLED')
except:
addMsgAndPrint('Failed to import '+os.path.join(workDir,revisedMetadata))
# import to feature datasets and constituent feature classes
arcpy.env.workspace = inGdb
fds = arcpy.ListDatasets('','Feature')
for anFds in fds:
revisedMetadata = gdb+'-'+anFds+'.xml'
addMsgAndPrint(' Creating XML for '+anFds)
dom = xml.dom.minidom.parse(os.path.join(workDir,xmlFileMR))
dom = titleSuffix(dom,': feature dataset '+anFds)
supplementaryInfo = 'Feature dataset '+anFds+gdbDesc0b+gdbDesc2
dom = addSupplinf(dom,supplementaryInfo)
if anFds in entityDict:
overText = entityDict[anFds]
overSrc = ncgmpFullRef
elif anFds.find('CrossSection') == 0:
overText = entityDict['CrossSection']
overSrc = ncgmpFullRef
else:
overText = '**Need Description of '+anFds+'**'
overSrc = '**Need Description Source**'
logFile.write('No description for entity '+anFds+'\n')
logFile.write('No description source for entity '+anFds+'\n')
eainfo = dom.getElementsByTagName('eainfo')[0]
dom = eaoverviewDom(dom,eainfo,overText,overSrc)
addMsgAndPrint(' Importing XML to metadata for '+anFds)
writeDomToFile(workDir,dom,revisedMetadata)
arcpy.ImportMetadata_conversion(os.path.join(workDir,revisedMetadata),'FROM_FGDC',inGdb+'/'+anFds,'ENABLED')
fcs = arcpy.ListFeatureClasses('','All',anFds)
del dom
for anFc in fcs:
revisedMetadata = inGdb + '-' + anFc + '.xml'
addMsgAndPrint(' Creating XML for '+anFc)
dom = xml.dom.minidom.parse(os.path.join(workDir,xmlFileMR))
dom = titleSuffix(dom,': feature class '+anFds+'/'+anFc)
supplementaryInfo = 'Feature class '+anFc+gdbDesc0b+gdbDesc2
dom = addSupplinf(dom,supplementaryInfo)
dom = updateTableDom(dom,anFc,logFile)
addMsgAndPrint(' Importing XML to metadata for '+anFc)
writeDomToFile(workDir,dom,revisedMetadata)
arcpy.ImportMetadata_conversion(revisedMetadata,'FROM_FGDC',inGdb+'/'+anFds+'/'+anFc,'ENABLED')
del dom
# clean up empty log files
addMsgAndPrint(' Deleting empty log files')
logfiles = glob.glob(workDir+'/*.log')
for lf in logfiles:
if os.path.getsize(lf) == 0:
addMsgAndPrint(' deleting '+os.path.basename(lf))
os.remove(lf)
addMsgAndPrint('\nBe sure to check file '+os.path.basename(logFileName)+' !')
logFile.close()
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sprite-animation-generator.py | eijiuema/sprite-animation-generator | 0 | 38285 | from collections import defaultdict
from PIL import Image
import operator
image = Image.open("images\\test.png")
pixel_map = image.load()
initial_coordinate = tuple(int(x.strip())
for x in input("Initial coordinates: ").split(','))
pixel_list = []
directions = [(1, 0), (0, -1), (1, -1), (-1, 0), (-1, 1), (0, 1)]
def store_pixel(current_pixel):
if(
current_pixel[0] == image.size[0] or
current_pixel[1] == image.size[1] or
current_pixel[0] < 0 or
current_pixel[1] < 0 or
current_pixel in pixel_list or
pixel_map[current_pixel][3] == 0
):
return
pixel_list.append(current_pixel)
for direction in directions:
store_pixel(tuple(map(operator.add, current_pixel, direction)))
store_pixel(initial_coordinate)
print(pixel_list)
object_image = Image.new('RGBA', image.size, (0, 0, 0, 0))
object_image_pixel_map = object_image.load()
line_image = Image.new('RGBA', (1, len(pixel_list)), (0, 0, 0, 0))
line_image_pixel_map = line_image.load()
for index, pixel in enumerate(pixel_list):
object_image_pixel_map[pixel] = pixel_map[pixel]
line_image_pixel_map[0, index] = pixel_map[pixel]
object_image.save(f"out/{index}.png")
line_image.save(f"out/line.png")
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] |
subsurface/geological_formats/segy_reader.py | andieie/subsurface | 55 | 30690 | from typing import Union
from scipy.spatial.qhull import Delaunay
from shapely.geometry import LineString
from subsurface.structs.base_structures import StructuredData
import numpy as np
try:
import segyio
segyio_imported = True
except ImportError:
segyio_imported = False
def read_in_segy(filepath: str, coords=None) -> StructuredData:
"""Reader for seismic data stored in sgy/segy files
Args:
filepath (str): the path of the sgy/segy file
coords (dict): If data is a numpy array coords provides the values for
the xarray dimension. These dimensions are 'x', 'y' and 'z'
Returns: a StructuredData object with data, the traces with samples written into an xr.Dataset, optionally with
labels defined by coords
"""
segyfile = segyio.open(filepath, ignore_geometry=True)
data = np.asarray([np.copy(tr) for tr in segyfile.trace[:]])
sd = StructuredData.from_numpy(data) # data holds traces * (samples per trace) values
segyfile.close()
return sd
def create_mesh_from_coords(coords: Union[dict, LineString],
zmin: Union[float, int], zmax: Union[float, int] = 0.0):
"""Creates a mesh for plotting StructuredData
Args:
coords (Union[dict, LineString]): the x and y, i.e. latitude and longitude, location of the traces of the seismic profile
zmax (float): the maximum elevation of the seismic profile, by default 0.0
zmin (float): the location in z where the lowest sample was taken
Returns: vertices and faces for creating an UnstructuredData object
"""
if type(coords) == LineString:
linestring = coords
n = len(list(linestring.coords))
coords = np.array([[x[0] for x in list(linestring.coords)],
[y[1] for y in list(linestring.coords)]]).T
else:
n = len(coords['x'])
coords = np.array([coords['x'],
coords['y']]).T
# duplicating the line, once with z=lower and another with z=upper values
vertices = np.zeros((2*n, 3))
vertices[:n, :2] = coords
vertices[:n, 2] = zmin
vertices[n:, :2] = coords
vertices[n:, 2] = zmax
# i+n --- i+n+1
# |\ |
# | \ |
# | \ |
# | \ |
# i --- i+1
tri = Delaunay(vertices[:, [0, 2]])
faces = tri.simplices
return vertices, faces
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] |
text_studio/pipeline.py | tevnpowers/thesis | 2 | 44015 | <filename>text_studio/pipeline.py
"""Sequence of text processing components.
A text processing pipeline is a sequence of text processing components,
namely text_studio.Annotator and text_studio.Action objects that are
executed in order to produce newly annotated data and/or insights.
"""
from collections import OrderedDict
from text_studio.action import Action
from text_studio.annotator import Annotator
class Pipeline(object):
"""Sequence of text processing components.
A text processing pipeline is a sequence of text processing components,
namely text_studio.Annotator and text_studio.Action objects that are
executed in order to produce newly annotated data and/or insights.
Parameters
-------
id : uuid
Unique identifier for a pipeline in a TextStudio project.
name : string
Display name of the pipeline in a TextStudio project.
components : OrderedDict of text_studio.Annotator and text_studio.Action objects
Ordered components to execute for a pipeline.
Methods
-------
add_component(self, component):
Provide new annotations for an individual data instance.
remove_component(self, id):
Provide new annotations for each data instance in a collection.
execute(self, data, output_path, verbose):
Execute the text processing pipeline on data.
"""
def __init__(self, id, name="", components=None):
self.id = id
self.name = name
self.components = components if components else OrderedDict()
def add_component(self, component):
"""Add a new component to the end of a pipeline.
Parameters
----------
component : text_studio.Action or text_studio.Annotator
The component to add to the pipeline.
"""
self.components[component.id] = component
def remove_component(self, id):
"""Remove a component from the pipeline.
Parameters
----------
id : uuid
The id of the component to remove.
"""
del self.components[id]
def execute(self, data, output_path, verbose=False):
"""Execute the text processing pipeline on data.
Parameters
----------
data : collection of data instances
Input data to be processed by the pipeline.
output_path : string
Location for any output from text_studio.Action objects
in the pipeline.
verbose : bool
True if progress statements should be written to the console.
Returns
----------
data : collection of data instances
The collection of input data instances, that is
potentially modified if there are any Annotator
components in the pipeline.
"""
for id, component in self.components.items():
if verbose:
print("Executing component {}...".format(component.name))
if isinstance(component, Annotator):
data = component.process_batch(data)
elif isinstance(component, Action):
component.process_batch(data, output_path)
return data
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] |
src/utils/summarize_data.py | bgerofi/mlperf-deepcam | 16 | 71972 | # The MIT License (MIT)
#
# Copyright (c) 2020 NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import os
import sys
import numpy as np
import h5py as h5
from mpi4py import MPI
#merge function helpers
def merge_all_token(token, comm):
#first, allreduce the counts
n = token[0]
nres = comm.allreduce(token[0])
weight = float(n)/float(nres)
dmeanres = comm.allreduce(weight*token[1], op = MPI.SUM)
dsqmeanres = comm.allreduce(weight*token[2], op = MPI.SUM)
#these guys require a custom reduction because there is no elemwise mean
#so lets just gather them
#min
token_all = comm.allgather(token[3])
dminres = token_all[0]
for tk in token_all[1:]:
dminres = np.minimum(dminres, tk)
#max
token_all = comm.allgather(token[4])
dmaxres = token_all[0]
for tk in token_all[1:]:
dmaxres = np.maximum(dmaxres, tk)
return (nres, dmeanres, dsqmeanres, dminres, dmaxres)
def merge_token(token1, token2):
#extract data
#first
n1 = token1[0]
dmean1 = token1[1]
dsqmean1 = token1[2]
dmin1 = token1[3]
dmax1 = token1[4]
#second
n2 = token2[0]
dmean2 = token2[1]
dsqmean2 = token2[2]
dmin2 = token2[3]
dmax2 = token2[4]
#create new token
nres = n1 + n2
dmeanres = float(n1)/float(nres)*dmean1 + float(n2)/float(nres)*dmean2
dsqmeanres = float(n1)/float(nres)*dsqmean1 + float(n2)/float(nres)*dsqmean2
dminres = np.minimum(dmin1, dmin2)
dmaxres = np.maximum(dmax1, dmax2)
return (nres, dmeanres, dsqmeanres, dminres, dmaxres)
#create data token
def create_token(filename, data_format="nchw", rank = 0):
try:
with h5.File(filename, "r") as f:
arr = f["climate/data"][...]
except:
raise IOError("Cannot open file {} on rank {}".format(filename, rank))
#prep axis for ops
axis = (1,2) if data_format == "nchw" else (0,1)
#how many samples do we have: just 1 here
n = 1
#compute stats
mean = np.mean(arr, axis=axis)
meansq = np.mean(np.square(arr), axis=axis)
minimum = np.amin(arr, axis=axis)
maximum = np.amax(arr, axis=axis)
#result
result = (n, mean, meansq, minimum, maximum)
return result
#global parameters
overwrite = False
data_format = "nhwc"
data_path_prefix = "/data"
#MPI
comm = MPI.COMM_WORLD.Dup()
comm_rank = comm.rank
comm_size = comm.size
#root path
root = os.path.join( data_path_prefix, "train" )
#get files
allfiles = [ os.path.join(root, x) for x in os.listdir(root) \
if x.endswith('.h5') and x.startswith('data-') ]
#split list
numfiles = len(allfiles)
chunksize = int(np.ceil(numfiles / comm_size))
start = chunksize * comm_rank
end = min([start + chunksize, numfiles])
files = allfiles[start:end]
#get first token and then merge recursively
token = create_token(files[0], data_format)
for filename in files[1:]:
token = merge_token(create_token(filename, data_format, comm_rank), token)
#communicate results
token = merge_all_token(token, comm)
#write file on rank 0
if comm_rank == 0:
#save the stuff
with h5.File(os.path.join(data_path_prefix, "stats.h5"), "w") as f:
f["climate/count"]=token[0]
f["climate/mean"]=token[1]
f["climate/sqmean"]=token[2]
f["climate/minval"]=token[3]
f["climate/maxval"]=token[4]
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app/__init__.py | ch0ppy35/pyTed | 0 | 154013 | from flask import Flask
import logging
import os
from logging.handlers import RotatingFileHandler, SMTPHandler
from config import Config
app = Flask(__name__)
app.config.from_object(Config)
if not os.path.exists('logs'):
os.mkdir('logs')
# Normal logging setup
file_handler = RotatingFileHandler('logs/pyted.log', maxBytes=10240, backupCount=10)
file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]'))
file_handler.setLevel(logging.INFO)
app.logger.addHandler(file_handler)
app.logger.setLevel(logging.INFO)
import setup
setup.dbCheck()
app.logger.info('~ pyTed is starting up ~')
from app import routes, errors
from app.chores import cronTasks, tasks, queries, scheduledTasks
from app.tools import database
| [
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] |
gym_pool/envs/utils.py | to314as/gym-pool | 1 | 162502 | <reponame>to314as/gym-pool<filename>gym_pool/envs/utils.py
import functools
import operator
def prod(l):
return functools.reduce(operator.mul, l, 1) | [
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backend/tests/test_search.py | Davidw1339/GroceryBuddy | 0 | 35458 | <filename>backend/tests/test_search.py
import json
import search
import test_data
import copy
from utils import Error
def test_no_args(client):
'''
Tests search without arguments.
'''
rv = client.get('/search')
response = json.loads(rv.data)
assert response == {'success': False,
'error': Error.MISSING_KEYWORD_UPC.value}
def test_extra_args(client):
'''
Tests search with wrong argument names.
'''
rv = client.get('/search', data=json.dumps({
'extra': 'peilun'
}))
response = json.loads(rv.data)
assert response == {'success': False,
'error': Error.MISSING_KEYWORD_UPC.value}
def test_search_by_upc(client, existing_item):
'''
Tests searching items by UPC.
'''
upc = str(existing_item.upc)
rv = client.get('/search?upc=' + upc)
response = json.loads(rv.data)
first = response[0]
assert first['upc'] == upc
def test_search_by_keyword_lower(client, existing_item):
'''
Tests searching items with a lowercase keyword.
'''
upc = str(existing_item.upc)
name = str(existing_item.name).lower()
rv = client.get('/search?keyword=' + name)
response = json.loads(rv.data)
first = response[0]
assert first['upc'] == upc
def test_search_by_keyword_upper(client, existing_item):
'''
Tests searching items with an uppercase keyword.
'''
upc = str(existing_item.upc)
name = str(existing_item.name).upper()
rv = client.get('/search?keyword=' + name)
response = json.loads(rv.data)
first = response[0]
assert first['upc'] == upc
def test_search_by_keyword_mixed(client, existing_item):
'''
Tests searching items with a mixed case keyword.
'''
upc = str(existing_item.upc)
name = str(existing_item.name).lower()
first = name[:int(len(name) / 2)].upper()
second = name[int(len(name) / 2):]
name = first + second
rv = client.get('/search?keyword=' + name)
response = json.loads(rv.data)
first = response[0]
assert first['upc'] == upc
def test_search_by_keyword_partial(client, existing_item):
'''
Tests searching items with a keyword partially matches.
'''
upc = str(existing_item.upc)
name = str(existing_item.name).lower()
name = name[:int(len(name) / 2)]
rv = client.get('/search?keyword=' + name)
response = json.loads(rv.data)
first = response[0]
assert first['upc'] == upc
def test_search_upc_over_keyword(client):
'''
Tests that UPC is prioritized over keyword
if both are given.
'''
first_item = copy.deepcopy(test_data.valid_items[0])
first_item.save()
first_upc = first_item.upc
first_name = first_item.name
second_item = copy.deepcopy(test_data.valid_items[1])
second_item.save()
second_upc = second_item.upc
second_name = second_item.name
assert first_upc != second_upc
assert not first_name in second_name and not second_name in first_name
rv = client.get(str.format(
'/search?upc={}&keyword={}', first_upc, second_name))
response = json.loads(rv.data)
first_result = response[0]
assert first_result['upc'] == first_upc
def test_invalid_limit(client, existing_item):
'''
Tests searching items with a max limit of 0 results.
'''
name = str(existing_item.name).lower()
rv = client.get('/search?keyword=' + name + '&limit=0')
response = json.loads(rv.data)
assert response == {'success': False,
'error': Error.INVALID_LIMIT.value}
def test_limit(client, db):
'''
Tests searching items with a max limit of results.
'''
keyword = 'test'
limit = 1
matches = []
for i in test_data.valid_items:
if keyword in i.name:
copy.deepcopy(i).save()
matches.append(i)
matches = sorted(matches, key=lambda i: i.upc)
rv = client.get('/search?keyword=' + keyword + '&limit=' + str(limit))
response = json.loads(rv.data)
assert len(response) == limit
assert response[0]['upc'] == matches[0].upc
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] |
udacity/take_a_break.py | JuanBalceda/python-basics | 0 | 167015 | import webbrowser
import time
# Take a break every hours
num = 0
while num < 3:
print('Begin at: ' + time.ctime())
time.sleep(2*60*60)
webbrowser.open("https://www.youtube.com/watch?v=dlFA0Zq1k2A&list=RDdlFA0Zq1k2A")
num += 1
print('End at: ' + time.ctime())
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1,
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] |
src/preprocessor.py | aaditkamat/dr-ranking-metric | 18 | 75221 | <gh_stars>10-100
import codecs
import yaml
from typing import Tuple, Dict
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.python.framework import ops
from model import MF
def sigmoid(x: np.ndarray) -> np.ndarray:
"""Calculate sigmoid."""
return 1 / (1 + np.exp(-x))
def transform_rating(ratings: np.ndarray, eps: float = 0.1) -> np.ndarray:
"""Transform ratings into graded relevance information."""
ratings -= 1
return eps + (1.0 - eps) * (2 ** ratings - 1) / (2 ** np.max(ratings) - 1)
def preprocess_movielens(
power: float = 1.0, seed: int = 12345
) -> Dict[str, np.ndarray]:
"""Load and preprocess ML 100K."""
np.random.seed(seed)
with open("../config.yaml", "rb") as f:
config = yaml.safe_load(f)
val_size = config["val_size"]
hyperparams = config["mf_hyperparams"]
with codecs.open(
f"../data/ml-100k/ml-100k.data", "r", "utf-8", errors="ignore"
) as f:
data = pd.read_csv(f, delimiter="\t", header=None).loc[:, :2]
data.rename(columns={0: "user", 1: "item", 2: "rate"}, inplace=True)
data.user, data.item = data.user - 1, data.item - 1
data = data.values
num_users, num_items = data[:, 0].max() + 1, data[:, 1].max() + 1
user_item_ = (
pd.DataFrame(np.zeros((num_users, num_items)))
.stack()
.reset_index()
.values[:, :2]
)
# generate CVR by MF.
ops.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(seed)
model = MF(
num_users=num_users,
num_items=num_items,
dim=hyperparams["dim"],
eta=hyperparams["eta"],
lam=hyperparams["lam"],
)
# initialise all the TF variables
init_op = tf.global_variables_initializer()
sess.run(init_op)
for _ in np.arange(hyperparams["iters"]):
idx = np.random.choice(np.arange(data.shape[0]), size=hyperparams["batch_size"])
_ = sess.run(
model.apply_grads_mse,
feed_dict={
model.users: data[idx, 0],
model.items: data[idx, 1],
model.labels: np.expand_dims(data[idx, 2], 1),
model.pscore: np.ones((hyperparams["batch_size"], 1)),
},
)
cvr = sess.run(
model.preds,
feed_dict={model.users: user_item_[:, 0], model.items: user_item_[:, 1]},
)
cvr = np.clip(cvr.flatten(), 1, 5)
cvr = transform_rating(cvr, eps=0.1)
cv = np.random.binomial(n=1, p=cvr)
# generate CTR by logistic MF.
all_data = (
pd.DataFrame(np.zeros((num_users, num_items)))
.stack()
.reset_index()
.values[:, :2]
)
pos_data = data[:, :2]
unlabeled_data = np.array(
list(set(map(tuple, all_data)) - set(map(tuple, pos_data))), dtype=int
)
data = np.r_[
np.c_[pos_data, np.ones(pos_data.shape[0])],
np.c_[unlabeled_data, np.zeros(unlabeled_data.shape[0])],
]
ops.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(seed)
model = MF(
num_users=num_users,
num_items=num_items,
dim=hyperparams["dim"],
eta=hyperparams["eta"],
lam=hyperparams["lam"],
)
# initialise all the TF variables
init_op = tf.global_variables_initializer()
sess.run(init_op)
for _ in np.arange(hyperparams["iters"]):
idx = np.random.choice(np.arange(data.shape[0]), size=hyperparams["batch_size"])
_ = sess.run(
model.apply_grads_ce,
feed_dict={
model.users: data[idx, 0],
model.items: data[idx, 1],
model.labels: np.expand_dims(data[idx, 2], 1),
model.pscore: np.ones((hyperparams["batch_size"], 1)),
},
)
ctr = sess.run(
model.preds,
feed_dict={model.users: user_item_[:, 0], model.items: user_item_[:, 1]},
)
ctr = sigmoid(ctr.flatten()) ** power
ct = np.random.binomial(n=1, p=ctr)
train_indicator = np.random.binomial(n=1, p=(1.0 - val_size), size=ct.shape[0])
ct_train, ct_val = ct * train_indicator, ct * (1 - train_indicator)
train = np.c_[user_item_, ct_train * cv]
val = np.c_[user_item_, ct_val * cv, ct_val, cv, ctr * val_size, cvr]
test = np.c_[user_item_, ct * cv, ct, cv, ctr, cvr]
return train, val, test
def preprocess_yahoo_coat(
data: str, val_ratio: float = 0.3, seed: int = 12345
) -> Tuple:
"""Load and preprocess Yahoo! R3 and Coat datasets."""
np.random.seed(seed)
with open("../config.yaml", "rb") as f:
hyperparams = yaml.safe_load(f)["mf_hyperparams"]
if data == "yahoo":
cols = {0: "user", 1: "item", 2: "rate"}
with codecs.open(
f"../data/yahoo/train.txt", "r", "utf-8", errors="ignore"
) as f:
train_ = pd.read_csv(f, delimiter="\t", header=None)
train_.rename(columns=cols, inplace=True)
with codecs.open(f"../data/yahoo/test.txt", "r", "utf-8", errors="ignore") as f:
test_ = pd.read_csv(f, delimiter="\t", header=None)
test_.rename(columns=cols, inplace=True)
for data_ in [train_, test_]:
data_.user, data_.item = data_.user - 1, data_.item - 1
elif data == "coat":
cols = {"level_0": "user", "level_1": "item", 2: "rate", 0: "rate"}
with codecs.open(
f"../data/coat/train.ascii", "r", "utf-8", errors="ignore"
) as f:
train_ = pd.read_csv(f, delimiter=" ", header=None)
train_ = train_.stack().reset_index().rename(columns=cols)
train_ = train_[train_.rate != 0].reset_index(drop=True)
with codecs.open(
f"../data/coat/test.ascii", "r", "utf-8", errors="ignore"
) as f:
test_ = pd.read_csv(f, delimiter=" ", header=None)
test_ = test_.stack().reset_index().rename(columns=cols)
test_ = test_[test_.rate != 0].reset_index(drop=True)
# binarize ratings
for data_ in [train_, test_]:
data_.rate = np.array(data_.rate >= 4, dtype=int)
# estimate propensity score by MF
train, test = train_.values, test_.values
pos_train = train_[train_.rate == 1].values
pos_test = test_[test_.rate == 1].values
# preprocess datasets
unique_user_train, user_counts_train = np.unique(
pos_train[:, 0], return_counts=True
)
unique_user_train = unique_user_train[user_counts_train >= 2]
unique_user_test, user_counts_test = np.unique(pos_test[:, 0], return_counts=True)
unique_user_test = unique_user_test[user_counts_test <= 9]
valid_users = np.intersect1d(unique_user_train, unique_user_test)
train = train[np.array([u in valid_users for u in train[:, 0]])]
test = test[np.array([u in valid_users for u in test[:, 0]])]
train[:, 0] = stats.rankdata(train[:, 0], method="dense") - 1
test[:, 0] = stats.rankdata(test[:, 0], method="dense") - 1
num_users, num_items = train[:, 0].max() + 1, train[:, 1].max() + 1
all_data = (
pd.DataFrame(np.zeros((num_users, num_items)))
.stack()
.reset_index()
.values[:, :2]
)
unobs_data = np.array(
list(set(map(tuple, all_data)) - set(map(tuple, train[:, :2])))
)
train = np.r_[
np.c_[train, np.ones(train.shape[0])],
np.c_[unobs_data, np.zeros((unobs_data.shape[0], 2))],
]
train, val = train_test_split(train, test_size=val_ratio, random_state=seed)
unobs_data = np.array(list(set(map(tuple, all_data)) - set(map(tuple, val[:, :2]))))
val = np.r_[val, np.c_[unobs_data, np.zeros((unobs_data.shape[0], 2))]]
# define the matrix factorization model
ops.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(seed)
model = MF(
num_users=num_users,
num_items=num_items,
dim=hyperparams["dim"],
eta=hyperparams["eta"],
lam=hyperparams["lam"],
)
# initialise all the TF variables
init_op = tf.global_variables_initializer()
sess.run(init_op)
for _ in np.arange(hyperparams["iters"]):
idx = np.random.choice(np.arange(val.shape[0]), size=hyperparams["batch_size"])
_ = sess.run(
model.apply_grads_ce,
feed_dict={
model.users: val[idx, 0],
model.items: val[idx, 1],
model.labels: np.expand_dims(val[idx, 3], 1),
model.pscore: np.ones((hyperparams["batch_size"], 1)),
},
)
# obtain dense user-item matrix
ctr_hat = sess.run(
model.preds,
feed_dict={
model.users: val[:, 0].astype(int),
model.items: val[:, 1].astype(int),
},
)
val = np.c_[val, sigmoid(ctr_hat)]
# estimate relevance parameter (gamma) by MF.
ops.reset_default_graph()
sess = tf.Session()
tf.set_random_seed(seed)
model = MF(
num_users=num_users,
num_items=num_items,
dim=hyperparams["dim"],
eta=hyperparams["eta"],
lam=hyperparams["lam"],
)
# observed data
val_obs = val[val[:, 3] == 1]
# initialise all the TF variables
init_op = tf.global_variables_initializer()
sess.run(init_op)
for _ in np.arange(hyperparams["iters"]):
idx = np.random.choice(
np.arange(val_obs.shape[0]), size=hyperparams["batch_size"]
)
_ = sess.run(
model.apply_grads_ce,
feed_dict={
model.users: val_obs[idx, 0],
model.items: val_obs[idx, 1],
model.labels: np.expand_dims(val_obs[idx, 2], 1),
model.pscore: np.expand_dims(val_obs[idx, 4], 1),
},
)
# obtain dense user-item matrix
gamma_hat = sess.run(
model.preds,
feed_dict={
model.users: val[:, 0].astype(int),
model.items: val[:, 1].astype(int),
},
)
val = np.c_[val, sigmoid(gamma_hat)]
# create test data containing all items
all_data = (
pd.DataFrame(np.zeros((num_users, num_items)))
.stack()
.reset_index()
.values[:, :2]
)
unobs_data = np.array(
list(set(map(tuple, all_data)) - set(map(tuple, test[:, :2])))
)
test = np.r_[
np.c_[test, np.ones(test.shape[0])],
np.c_[unobs_data, np.zeros((unobs_data.shape[0], 2))],
]
avg_test_pscore = test[:, -1].mean()
test = np.c_[test, np.ones(test.shape[0]) * avg_test_pscore]
return train, val, test
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scripts/trials.py | brychanrobot/orrt-star-ros | 3 | 90403 | import subprocess
import time
for obstacles in [5, 10, 15, 20]:
scores = []
for i in range(30):
procs = []
for j in range(4):
time.sleep(0.1) # allow for different random number generation
procs.append(subprocess.Popen(['build/main', '-w', '15' , '-l', '180', '-o', str(obstacles), '--fmt'], stdout=subprocess.PIPE))
for proc in procs:
proc.wait()
try:
line = proc.stdout.readline()
scores.append(float(line))
except ValueError:
print("invalid value: %s" % line)
print('%d completed' % (i * 4))
print("obstacles: %d" % obstacles)
print(scores)
print('finished') | [
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tests/integration/test_integration_article.py | pwitab/visma | 5 | 35871 | <reponame>pwitab/visma<filename>tests/integration/test_integration_article.py
import pytest
from visma.api import VismaClientException
from visma.models import Article, ArticleAccountCoding, Unit
class TestCRUDArticle:
@pytest.fixture()
def article(self):
article = Article.objects.all()[0]
yield article
@pytest.fixture()
def coding(self):
coding = ArticleAccountCoding.objects.all()[0]
yield coding
@pytest.fixture()
def unit(self):
unit = Unit.objects.all()[0]
yield unit
def test_list_articles(self):
articles = Article.objects.all()
assert len(articles) is not 0
def test_create_article(self, coding, unit):
# article = Article(number=100, name='test article', coding_id=coding.id, unit_id=unit.id)
# article.save()
# assert article.id is not None
# Since we cannot delete articles we don't want to keep on creating new ones.
pass
def test_read_article(self, article):
read_article = Article.objects.get(article.id)
assert read_article.id == article.id
def test_update_article(self, article):
article.net_price = 50
article.save()
updated_article = Article.objects.get(article.id)
assert updated_article.net_price == 50
updated_article.net_price = 10
updated_article.save()
def test_delete_article(self, article):
# Not allowed
# TODO: raise more explaining exception
with pytest.raises(VismaClientException):
article.delete()
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] |
test/test_brute_force_2.py | MikeJongen/nonogram-solver | 0 | 167208 | <gh_stars>0
import sys
import os
import unittest
import nonogram_solver.basic.brute_force_2 as brute_force
class TestBasic(unittest.TestCase):
def test_number_of_solutions(self):
row = brute_force.BruteForceRowSolver2([2, 1], [0] * 5)
no_solutions = row._get_number_of_solutions()
self.assertEqual(no_solutions, 3)
def test_number_of_solutions_large(self):
row = brute_force.BruteForceRowSolver2([1, 1, 1], [0] * 10)
no_solutions = row._get_number_of_solutions()
self.assertEqual(no_solutions, 56)
def test_number_of_solutions_large_2(self):
row = brute_force.BruteForceRowSolver2([2, 1, 1], [0] * 10)
no_solutions = row._get_number_of_solutions()
self.assertEqual(no_solutions, 35)
def test_number_of_solutions_one_clue(self):
row = brute_force.BruteForceRowSolver2([2], [0] * 5)
no_solutions = row._get_number_of_solutions()
self.assertEqual(no_solutions, 4)
def test_number_of_solutions_empty(self):
row = brute_force.BruteForceRowSolver2([], [0] * 5)
no_solutions = row._get_number_of_solutions()
self.assertEqual(no_solutions, 1)
def test_get_all_solutions(self):
row = brute_force.BruteForceRowSolver2([2, 1], [0] * 5)
expected_solutions = [
[1, 1, -1, 1, -1],
[1, 1, -1, -1, 1],
[-1, 1, 1, -1, 1],
]
solutions = row._get_all_solutions()
self.assertEqual(solutions, expected_solutions)
def test_get_all_solutions_one_clue(self):
row = brute_force.BruteForceRowSolver2([3], [0] * 5)
expected_solutions = [
[1, 1, 1, -1, -1],
[-1, 1, 1, 1, -1],
[-1, -1, 1, 1, 1],
]
solutions = row._get_all_solutions()
self.assertEqual(solutions, expected_solutions)
def test_get_all_solutions_two_clues(self):
row = brute_force.BruteForceRowSolver2([3, 4], [0] * 10)
solution = row._get_all_solutions()
expected_solution = [[1, 1, 1, -1, 1, 1, 1, 1, -1, -1],
[1, 1, 1, -1, -1, 1, 1, 1, 1, -1],
[1, 1, 1, -1, -1, -1, 1, 1, 1, 1],
[-1, 1, 1, 1, -1, 1, 1, 1, 1, -1],
[-1, 1, 1, 1, -1, -1, 1, 1, 1, 1],
[-1, -1, 1, 1, 1, -1, 1, 1, 1, 1]]
self.assertEqual(expected_solution, solution)
def test_get_all_solutions_three_clues(self):
row = brute_force.BruteForceRowSolver2([2, 2, 3], [0] * 10)
solution = row._get_all_solutions()
expected_solution = [[1, 1, -1, 1, 1, -1, 1, 1, 1, -1],
[1, 1, -1, 1, 1, -1, -1, 1, 1, 1],
[1, 1, -1, -1, 1, 1, -1, 1, 1, 1],
[-1, 1, 1, -1, 1, 1, -1, 1, 1, 1]]
self.assertEqual(expected_solution, solution)
def test_get_all_solutions_empty(self):
row = brute_force.BruteForceRowSolver2([], [0] * 5)
expected_solutions = [
[-1, -1, -1, -1, -1],
]
solutions = row._get_all_solutions()
self.assertEqual(solutions, expected_solutions)
def test_check_solutions(self):
row = brute_force.BruteForceRowSolver2([2, 1], [1, 0, 0, 0, 0])
valid = row._check_solution([1, 1, 0, 1, 0])
self.assertTrue(valid)
valid = row._check_solution([0, 1, 1, 0, 1])
self.assertFalse(valid)
def test_matching_solutions(self):
row = brute_force.BruteForceRowSolver2([2, 1], [0] * 5)
solution1 = [-1, 1, 1, -1, 1]
solution2 = [1, 1, -1, -1, 1]
expected_solution = [0, 1, 0, -1, 1]
solution = row._get_matching_solution(solution1, solution2)
self.assertEqual(expected_solution, solution)
def test_row_brute_force_empty_values(self):
row = brute_force.BruteForceRowSolver2([2, 1], [0] * 5)
changed = row.solve_brute_force_save_intermediate()
self.assertTrue(changed)
self.assertEqual(row.values, [0, 1, 0, 0, 0])
def test_row_brute_force(self):
row = brute_force.BruteForceRowSolver2([2, 1], [1, 0, 0, 0, 0])
changed = row.solve_brute_force_save_intermediate()
self.assertTrue(changed)
self.assertEqual(row.values, [1, 1, -1, 0, 0])
def test_brute_force(self):
puzzle = brute_force.BruteForceSolver2(file="test/puzzles/stairs.json")
puzzle.solve()
puzzle.update_row_solvers()
puzzle.solve()
puzzle.update_row_solvers()
puzzle.solve()
self.assertTrue(puzzle.is_complete())
self.assertTrue(puzzle.is_correct())
def test_reset(self):
puzzle = brute_force.BruteForceSolver2(file="test/puzzles/stairs.json")
puzzle.solve()
puzzle.update_row_solvers()
puzzle.solve()
puzzle.update_row_solvers()
puzzle.solve()
for axis in puzzle.row_solver:
for row_solver in puzzle.row_solver[axis]:
self.assertTrue(hasattr(row_solver, 'number_of_solutions'))
self.assertTrue(hasattr(row_solver, 'all_solutions'))
puzzle.reset_solution()
for axis in puzzle.row_solver:
for row_solver in puzzle.row_solver[axis]:
self.assertFalse(hasattr(row_solver, 'number_of_solutions'))
self.assertFalse(hasattr(row_solver, 'all_solutions'))
if __name__ == '__main__':
unittest.main()
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tls_parser/change_cipher_spec_protocol.py | nabla-c0d3/tls_parser | 6 | 198391 | <reponame>nabla-c0d3/tls_parser
from tls_parser.record_protocol import TlsRecord, TlsRecordHeader, TlsSubprotocolMessage, TlsRecordTypeByte
from tls_parser.tls_version import TlsVersionEnum
from typing import Tuple
class TlsChangeCipherSpecRecord(TlsRecord):
@classmethod
def from_parameters(cls, tls_version: TlsVersionEnum) -> "TlsChangeCipherSpecRecord":
ccs_message = TlsSubprotocolMessage(b"\x01")
record_header = TlsRecordHeader(TlsRecordTypeByte.CHANGE_CIPHER_SPEC, tls_version, ccs_message.size)
return TlsChangeCipherSpecRecord(record_header, [ccs_message])
@classmethod
def from_bytes(cls, raw_byte: bytes) -> Tuple["TlsChangeCipherSpecRecord", int]:
raise NotImplementedError()
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cpp/msvc/parse_sln_internal.py | btc-ag/revengtools | 2 | 134968 | <reponame>btc-ag/revengtools
'''
Created on 01.04.2011
@author: SIGIESEC
'''
import logging
import re
import sys
import ntpath
import posixpath
class InternalSolutionFileParser(object):
"""
A parser for Visual Studio 2008 Solution files (.SLN). It parses the dependencies between native
C++ projects only. For C# projects, the dependencies are not contained in the solution file.
"""
def __init__(self, solution_file):
"""
Creates an instance
@param solution_file: An iterator of lines of a VS2008 solution file, typically
a .SLN file opened for reading.
"""
self.__vcprojs = []
self.__project_ids_to_name = dict()
self.__project_dependencies = dict()
self.__logger = logging.getLogger(self.__class__.__module__)
self.__parse(solution_file)
def __parse(self, solution_file):
in_deps = 0
current_project_id = None
for line in solution_file:
line = line.strip()
if line.startswith("Project("):
#print("starts with Project(")
m = re.match(r"Project\(\"{[-A-F0-9]*}\"\) = \"([A-Za-z0-9_.]*)\", \"([^\"].*)\", \"{([-A-F0-9]*)}\"", line)
if m != None:
current_project_name = m.group(1)
current_project_vcproj = m.group(2)
current_project_id = m.group(3)
if current_project_vcproj.endswith('proj'):
#self.__logger.debug("project %s with id %s" % (current_project_name, current_project_id))
self.__project_ids_to_name[current_project_id] = current_project_name
self.__project_dependencies[current_project_id] = set()
self.__vcprojs.append(current_project_vcproj.replace(ntpath.sep, posixpath.sep))
else:
self.__logger.warning("unparsable line %s" % (line,))
elif line.startswith("EndProject"):
current_project_id = None
in_deps = 0
elif line.startswith("ProjectSection(ProjectDependencies)"):
in_deps = 1
elif line.startswith("EndProjectSection"):
in_deps = 0
elif in_deps:
m = re.match(r"{([-A-F0-9]*)} = {[-A-F0-9]*}", line)
if m != None:
depend_project_id = m.group(1)
self.__logger.debug("project %s depends on %s" % (current_project_id,
depend_project_id))
if current_project_id in self.__project_dependencies:
self.__project_dependencies[current_project_id].add(depend_project_id)
else:
self.__logger.warning("unparsable line %s" % (line,))
def vcprojs(self):
return self.__vcprojs
def project_ids_to_name(self):
return self.__project_ids_to_name
def project_id_dependencies(self):
return self.__project_dependencies
@staticmethod
def __is_empty(iterator):
try:
iterator.next()
except StopIteration:
return True
return False
def get_dependencies_iter(self):
ids_sorted_by_name = sorted(self.__project_ids_to_name.keys(), #
lambda x,y: cmp(self.__project_ids_to_name[x], self.__project_ids_to_name[y]))
self.__logger.debug("ids_sorted_by_name = %s", ids_sorted_by_name)
for current_project_id in ids_sorted_by_name:
current_project_name = self.__project_ids_to_name[current_project_id]
self.__logger.debug("project_id = %s, project_name = %s", current_project_id, current_project_name)
if len(self.__project_dependencies[current_project_id]) != 0:
for depend_project_id in sorted(self.__project_dependencies[current_project_id]):
if depend_project_id in self.__project_ids_to_name:
yield (current_project_name,
self.__project_ids_to_name[depend_project_id])
else:
self.__logger.debug("No project with id %s (in project %s)" % (depend_project_id, current_project_name))
else:
self.__logger.debug("Project %s has no outgoing dependencies within the solution file." % (current_project_name))
incoming_dependencies = (project_id for project_id in self.__project_dependencies if current_project_id in self.__project_dependencies[project_id])
if self.__is_empty(incoming_dependencies):
self.__logger.info("Project %s has no dependencies within the solution file. It will not be shown in any further output." % (current_project_name))
if __name__ == "__main__":
if len(sys.argv) == 1:
import doctest
doctest.testmod()
else:
logging.basicConfig(level=logging.DEBUG)
parser = InternalSolutionFileParser(open(sys.argv[1], "r"))
for (source, target) in parser.get_dependencies_iter():
print "%s,%s" % (source, target)
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] |
sdk/webpubsub/azure-messaging-webpubsubservice/azure/messaging/webpubsubservice/aio.py | rsdoherty/azure-sdk-for-python | 207 | 120774 | <gh_stars>100-1000
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
__all__ = ["WebPubSubServiceClient"]
from typing import TYPE_CHECKING
from copy import deepcopy
import azure.core.pipeline as corepipeline
import azure.core.pipeline.policies as corepolicies
import azure.core.pipeline.transport as coretransport
# Temporary location for types that eventually graduate to Azure Core
from .core import rest as corerest
from ._policies import JwtCredentialPolicy
if TYPE_CHECKING:
import azure.core.credentials as corecredentials
from azure.core.pipeline.policies import HTTPPolicy, SansIOHTTPPolicy
from typing import Any, List, cast # pylint: disable=ungrouped-imports
class WebPubSubServiceClient(object):
def __init__(self, endpoint, credential, **kwargs):
# type: (str, corecredentials.AzureKeyCredential, Any) -> None
"""Create a new WebPubSubServiceClient instance
:param endpoint: Endpoint to connect to.
:type endpoint: ~str
:param credential: Credentials to use to connect to endpoint.
:type credential: ~azure.core.credentials.AzureKeyCredential
:keyword api_version: Api version to use when communicating with the service.
:type api_version: str
:keyword user: User to connect as. Optional.
:type user: ~str
"""
self.endpoint = endpoint.rstrip("/")
transport = kwargs.pop("transport", None) or coretransport.RequestsTransport(
**kwargs
)
policies = [
corepolicies.HeadersPolicy(**kwargs),
corepolicies.UserAgentPolicy(**kwargs),
corepolicies.AsyncRetryPolicy(**kwargs),
corepolicies.ProxyPolicy(**kwargs),
corepolicies.CustomHookPolicy(**kwargs),
corepolicies.AsyncRedirectPolicy(**kwargs),
JwtCredentialPolicy(credential, kwargs.get("user", None)),
corepolicies.NetworkTraceLoggingPolicy(**kwargs),
] # type: Any
self._pipeline = corepipeline.AsyncPipeline(
transport,
policies,
) # type: corepipeline.AsyncPipeline
def _format_url(self, url):
# type: (str) -> str
assert self.endpoint[-1] != "/", "My endpoint should not have a trailing slash"
return "/".join([self.endpoint, url.lstrip("/")])
async def send_request(
self, http_request: corerest.HttpRequest, **kwargs: "Any"
) -> corerest.AsyncHttpResponse:
"""Runs the network request through the client's chained policies.
We have helper methods to create requests specific to this service in `azure.messaging.webpubsub.rest`.
Use these helper methods to create the request you pass to this method. See our example below:
>>> from azure.messaging.webpubsub.rest import build_healthapi_get_health_status_request
>>> request = build_healthapi_get_health_status_request(api_version)
<HttpRequest [HEAD], url: '/api/health'>
>>> response = await client.send_request(request)
<AsyncHttpResponse: 200 OK>
For more information on this code flow, see https://aka.ms/azsdk/python/llcwiki
For advanced cases, you can also create your own :class:`~azure.messaging.webpubsub.core.rest.HttpRequest`
and pass it in.
:param http_request: The network request you want to make. Required.
:type http_request: ~azure.messaging.webpubsub.core.rest.HttpRequest
:keyword bool stream_response: Whether the response payload will be streamed. Defaults to False.
:return: The response of your network call. Does not do error handling on your response.
:rtype: ~azure.messaging.webpubsub.core.rest.AsyncHttpResponse
"""
request_copy = deepcopy(http_request)
request_copy.url = self._format_url(request_copy.url)
# can't do AsyncStreamContextManager yet. This client doesn't have a pipeline client,
# AsyncStreamContextManager requires a pipeline client. WIll look more into it
# if kwargs.pop("stream_response", False):
# return corerest._AsyncStreamContextManager(
# client=self._client,
# request=request_copy,
# )
pipeline_response = await self._pipeline.run(
request_copy._internal_request, **kwargs # pylint: disable=protected-access
)
response = corerest.AsyncHttpResponse(
status_code=pipeline_response.http_response.status_code,
request=request_copy,
_internal_response=pipeline_response.http_response,
)
await response.read()
return response
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949,
580,
13,
4706,
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2933,
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2
] |
foobar2020/solutionProblem19.py | franklinvp/foobar | 7 | 186702 | <gh_stars>1-10
This one I had done before
https://github.com/franklinvp/foobar/blob/master/foobar2018/extra_challenge_solar_panels.py
but hadn't tested it against the secret testcases. It passed.
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pybond/bond/bond_helpers/observe_files.py | necula01/bond | 8 | 35448 | <reponame>necula01/bond
# Helper functions to observe files and directories
import os
import re
def collect_directory_contents(directory,
file_filter=None,
collect_file_contents=False):
"""
Collect an object reflecting the contents of a directory
:param directory: the directory where to start the traversal
:param file_filter: either a string representing a regular expression on the name of the files
and directories to be included, or a function that given the directory and the filename
returns true or false, whether the directory or file should be included.
:param collect_file_contents: indicates whether to collect the contents of files.
True means to include contents of all files,
:return: a dictionary with keys corresponding to basename of files and subdirectories.
Only files that are allowed by the file_filter are included.
If the file contents is collected then the dictionary contains a list of lines.
"""
# TODO: figure out a more general form for this, perhaps using
# a configurable visitor to define how to visit each file
result = { } # map from file name to file data.
# file data is either None (if the contents is not spied),
# or an array of lines
# Prepare the file filter
file_filter_func = None
if file_filter:
if isinstance(file_filter, basestring):
file_filter_regexp = re.compile(file_filter)
file_filter_func = lambda rel_file: file_filter_regexp.match(rel_file)
else:
# TODO: assert that it is a function
file_filter_func = file_filter
collect_file_contents_func = None
if collect_file_contents:
if isinstance(collect_file_contents, bool):
if collect_file_contents:
collect_file_contents_func = lambda rel_file: True
elif isinstance(collect_file_contents, basestring):
include_file_contents_regexp = re.compile(collect_file_contents)
collect_file_contents_func = lambda rel_file: include_file_contents_regexp.match(rel_file)
else:
# TODO: assert that it is a function
collect_file_contents_func = collect_file_contents
def recurse(rel_subdir, result_data):
name_subdir = os.path.join(directory, rel_subdir)
for basename in os.listdir(name_subdir):
rel_file = os.path.join(rel_subdir, basename)
file = os.path.join(directory, rel_file)
if file_filter_func and not file_filter_func(rel_file):
continue
if os.path.isdir(file):
subresult_data = {}
result_data[basename] = subresult_data
recurse(rel_file, subresult_data)
else:
if collect_file_contents_func and collect_file_contents_func(rel_file):
with open(file, 'r') as f:
lines = f.readlines ()
result_data[basename] = [l.rstrip() for l in lines ]
else:
result_data[basename] = None
recurse('', result)
return result
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mathgenerator/funcs/fibonacciSeriesFunc.py | furins/mathgenerator | 0 | 53188 | <filename>mathgenerator/funcs/fibonacciSeriesFunc.py
from .__init__ import *
def fibonacciSeriesFunc(minNo=1):
n = random.randint(minNo,20)
def createFibList(n):
l=[]
for i in range(n):
if i<2:
l.append(i)
else:
val = l[i-1]+l[i-2]
l.append(val)
return l
fibList=createFibList(n)
problem = "The Fibonacci Series of the first "+str(n)+" numbers is ?"
solution = fibList
return problem,solution
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tests/test_gists.py | github3py/github3py | 2 | 129885 | import github3
from github3 import gists
from tests.utils import (BaseCase, load)
class TestGist(BaseCase):
def __init__(self, methodName='runTest'):
super(TestGist, self).__init__(methodName)
self.gist = gists.Gist(load('gist'))
self.api = 'https://api.github.com/gists/3813862'
def setUp(self):
super(TestGist, self).setUp()
self.gist = gists.Gist(self.gist.to_json(), self.g)
def test_str(self):
assert str(self.gist) == str(self.gist.id)
def test_repr(self):
assert repr(self.gist) == '<Gist [{0}]>'.format(self.gist)
def test_create_comment(self):
self.response('gist_comment', 201)
self.post(self.api + '/comments')
self.conf = {'data': {'body': 'bar'}}
self.assertRaises(github3.GitHubError, self.gist.create_comment)
self.login()
assert self.gist.create_comment(None) is None
assert self.gist.create_comment('') is None
self.not_called()
assert isinstance(self.gist.create_comment('bar'),
gists.comment.GistComment)
self.mock_assertions()
def test_delete(self):
self.response('', 204)
self.delete(self.api)
self.conf = {}
self.assertRaises(github3.GitHubError, self.gist.delete)
self.not_called()
self.login()
assert self.gist.delete()
self.mock_assertions()
def test_edit(self):
self.response('gist', 200)
self.patch(self.api)
self.conf = {
'data': {
'description': 'desc',
'files': {'file1': {'content': 'foo bar'}}
}
}
self.assertRaises(github3.GitHubError, self.gist.edit)
self.login()
assert self.gist.edit() is False
self.not_called()
assert self.gist.edit(**self.conf['data'])
self.mock_assertions()
def test_fork(self):
self.response('gist', 201)
self.post(self.api + '/forks')
self.conf = {}
self.assertRaises(github3.GitHubError, self.gist.fork)
self.not_called()
self.login()
assert isinstance(self.gist.fork(), gists.Gist)
self.mock_assertions()
def test_is_public(self):
assert self.gist.is_public() == self.gist.public
def test_is_starred(self):
self.response('', 204)
self.get(self.api + '/star')
self.assertRaises(github3.GitHubError, self.gist.is_starred)
self.not_called()
self.login()
assert self.gist.is_starred()
self.mock_assertions()
def test_iter_comments(self):
self.response('gist_comment', _iter=True)
self.get(self.api + '/comments')
self.conf = {'params': {'per_page': 100}}
c = next(self.gist.iter_comments())
assert isinstance(c, gists.comment.GistComment)
self.mock_assertions()
def test_iter_commits(self):
self.response('gist_history', _iter=True)
self.get(self.api + '/commits')
self.conf = {'params': {'per_page': 100}}
h = next(self.gist.iter_commits())
assert isinstance(h, gists.history.GistHistory)
self.mock_assertions()
def test_iter_files(self):
gist_file = next(self.gist.iter_files())
assert gist_file == self.gist._files[0]
assert isinstance(gist_file, gists.file.GistFile)
assert repr(gist_file).startswith('<Gist File')
def test_iter_forks(self):
self.assertRaises(StopIteration, next, self.gist.iter_forks())
def test_refresh(self):
self.response('gist', 200)
self.get(self.api)
assert self.gist.refresh() is self.gist
self.mock_assertions()
def test_star(self):
self.response('', 204)
self.put(self.api + '/star')
self.conf = {}
self.assertRaises(github3.GitHubError, self.gist.star)
self.not_called()
self.login()
assert self.gist.star()
self.mock_assertions()
def test_unstar(self):
self.response('', 204)
self.delete(self.api + '/star')
self.conf = {}
self.assertRaises(github3.GitHubError, self.gist.unstar)
self.not_called()
self.login()
assert self.gist.unstar()
self.mock_assertions()
# As opposed to creating an all new class for this
def test_history(self):
hist = self.gist.history[0]
self.response('gist', 200)
self.get(hist._api)
assert isinstance(hist, gists.history.GistHistory)
assert isinstance(hist.get_gist(), gists.Gist)
self.mock_assertions()
assert repr(hist).startswith('<Gist History')
def test_equality(self):
g = gists.Gist(load('gist'))
assert self.gist == g
g._uniq = 1
assert self.gist != g
class TestGistComment(BaseCase):
def __init__(self, methodName='runTest'):
super(TestGistComment, self).__init__(methodName)
self.comment = gists.comment.GistComment(load('gist_comment'))
self.api = "https://api.github.com/gists/4321394/comments/655725"
def setUp(self):
super(TestGistComment, self).setUp()
self.comment = gists.comment.GistComment(self.comment.to_json(),
self.g)
def test_equality(self):
c = gists.comment.GistComment(load('gist_comment'))
assert self.comment == c
c._uniq = 1
assert self.comment != c
def test_repr(self):
assert repr(self.comment) != ''
def test_edit(self):
self.response('gist_comment', 200)
self.patch(self.api)
self.conf = {'data': {'body': 'body'}}
self.assertRaises(github3.GitHubError, self.comment.edit)
self.login()
assert self.comment.edit(None) is False
self.not_called()
assert self.comment.edit('body')
self.mock_assertions()
class TestGistHistory(BaseCase):
def __init__(self, methodName='runTest'):
super(TestGistHistory, self).__init__(methodName)
self.hist = gists.history.GistHistory(load('gist_history'))
def test_equality(self):
h = gists.history.GistHistory(load('gist_history'))
assert self.hist == h
h._uniq = 'foo'
assert self.hist != h
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] |
src/lib/__init__.py | gfjiangly/RCNet | 0 | 14424 | # -*- encoding:utf-8 -*-
# @Time : 2019/10/23 15:45
# @Author : gfjiang
# @Site :
# @File : __init__.py
# @Software: PyCharm
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worker/config.py | projectweekend/3spot | 0 | 74614 | <reponame>projectweekend/3spot
import os
SQS_WAIT_TIME = os.getenv('SQS_WAIT_TIME', 20)
SLEEP_TIME = os.getenv('SLEEP_TIME', 60)
PROD_LOGGER = os.getenv('PROD_LOGGER', False)
SQS_QUEUE_NAME = os.getenv('SQS_QUEUE_NAME', 'song-feed-playlists-to-process')
SQS_REGION = os.getenv('SQS_REGION', 'us-east-1')
SPOTIFY_CLIENT_ID = os.getenv('SPOTIFY_CLIENT_ID')
assert SPOTIFY_CLIENT_ID
SPOTIFY_CLIENT_SECRET = os.getenv('SPOTIFY_CLIENT_SECRET')
assert SPOTIFY_CLIENT_SECRET
ECHO_NEST_API_KEY = os.getenv('ECHO_NEST_API_KEY')
assert ECHO_NEST_API_KEY
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Algorithms/Easy/1475. Final Prices With a Special Discount in a Shop/answer.py | KenWoo/Algorithm | 0 | 127365 | <reponame>KenWoo/Algorithm
from typing import List
class Solution:
def finalPrices(self, prices: List[int]) -> List[int]:
res = []
N = len(prices)
for i in range(N-1):
j = i + 1
while j < N and prices[i] < prices[j]:
j += 1
res.append(prices[i] - (0 if j == N else prices[j]))
res.append(prices[-1])
return res
if __name__ == "__main__":
s = Solution()
result = s.finalPrices([8, 4, 6, 2, 3])
print(result)
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.env/lib/python3.8/site-packages/aws_cdk/aws_codebuild/_jsii/__init__.py | careck/begariver-cdk | 0 | 1611363 | import abc
import builtins
import datetime
import enum
import typing
import jsii
import publication
import typing_extensions
import aws_cdk.assets._jsii
import aws_cdk.aws_cloudwatch._jsii
import aws_cdk.aws_codecommit._jsii
import aws_cdk.aws_ec2._jsii
import aws_cdk.aws_ecr._jsii
import aws_cdk.aws_ecr_assets._jsii
import aws_cdk.aws_events._jsii
import aws_cdk.aws_iam._jsii
import aws_cdk.aws_kms._jsii
import aws_cdk.aws_logs._jsii
import aws_cdk.aws_s3._jsii
import aws_cdk.aws_s3_assets._jsii
import aws_cdk.aws_secretsmanager._jsii
import aws_cdk.core._jsii
import aws_cdk.region_info._jsii
import constructs._jsii
__jsii_assembly__ = jsii.JSIIAssembly.load(
"@aws-cdk/aws-codebuild",
"1.108.1",
__name__[0:-6],
"[email protected]",
)
__all__ = [
"__jsii_assembly__",
]
publication.publish()
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main.py | Spain-AI/dark_helper | 0 | 33789 | <filename>main.py
from capture_monitor import CaptureMonitor
from face_lib import FaceSystem
from visualizer import Visualizer #MainWindow
#from PyQt5 import QtCore, QtGui, QtWidgets
import os
monitor = CaptureMonitor(bb=(0, 0, 600, 480))
face_system = FaceSystem(os.path.abspath("./bio/bio.json"))
def thread_process():
frame = monitor.get_frame()
faces = []
for face in face_system(frame):
faces.append(face)
return frame, faces
if __name__ == '__main__':
# app = QtWidgets.QApplication([])
# window = MainWindow()
# window.process(thread_process)
# window.show()
# app.exec_()
app = Visualizer(monitor, thread_process)
app.run() | [
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] |
tests/unit/app/test_session.py | bernease/whylogs-python | 0 | 8533 | <reponame>bernease/whylogs-python
import pytest
from whylogs.app.session import get_or_create_session, get_session, get_logger, reset_default_session, session_from_config
from whylogs.app.config import SessionConfig
from whylogs.app.session import Session
from pandas import util
def test_get_global_session():
_session = None
session = get_or_create_session()
global_session = get_session()
assert session == global_session
def test_reset():
session = get_or_create_session()
reset_default_session()
global_session = get_session()
assert global_session.project is not None
def test_session_log_dataframe():
_session = None
session = session_from_config(SessionConfig(
"default-project", "default-pipeline", [], False
))
df = util.testing.makeDataFrame()
profile = session.log_dataframe(df)
assert session.logger() is not None
assert session.logger("default-project").dataset_name == "default-project"
def test_session_profile():
session = session_from_config(SessionConfig(
"default-project", "default-pipeline", [], False
))
df = util.testing.makeDataFrame()
profile = session.log_dataframe(df)
assert profile is not None
summary = profile.flat_summary()
flat_summary = summary['summary']
assert len(flat_summary) == 4
def test_profile_df():
session = get_or_create_session()
df = util.testing.makeDataFrame()
log_profile = session.log_dataframe(df)
profile = session.profile_dataframe(df)
assert log_profile.name == profile.name
assert log_profile.dataset_timestamp == profile.dataset_timestamp
assert log_profile.session_timestamp == profile.session_timestamp
assert len(profile.columns) == 4
assert len(log_profile.tags) == 1
assert len(profile.tags) == 2
def test_close_session():
session = get_or_create_session()
session.close()
assert session.is_active() == False
df = util.testing.makeDataFrame()
log_profile = session.log_dataframe(df)
assert log_profile == None
profile = session.profile_dataframe(df)
assert profile == None
profile = session.new_profile(df)
assert profile == None
with pytest.raises(RuntimeError):
session.logger()
def test_logger_cache():
_session = None
session = get_or_create_session()
with session.logger("cache-test", with_rotation_time="s") as logger:
logger.log({"name": 1})
session.close()
def test_remove_logger():
session = get_or_create_session()
session.logger("default-project")
with pytest.raises(KeyError):
session.remove_logger("test")
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pred_to_eval/test_ct_engine.py | FluteXu/ms-project | 0 | 68013 | # Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
"""Test a Detectron network on an imdb (image database)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from collections import defaultdict
import cv2
import datetime
import logging
import numpy as np
import os
import yaml
import shutil
import torch
from mitok.detect.bound3d import Bound3D, combine_bounds_3d_direct
from core.config import cfg
# from core.rpn_generator import generate_rpn_on_dataset #TODO: for rpn only case
# from core.rpn_generator import generate_rpn_on_range
from core.test import ct_detect_all
from datasets.ct_json_dataset import JsonDataset
from modeling.model_factory import GetRCNNModel
import nn as mynn
from utils.detectron_weight_helper import load_detectron_weight
import utils.env as envu
import utils.boxes as box_utils
import utils.net as net_utils
import utils.subprocess as subprocess_utils
import utils.vis as vis_utils
import utils.vis_gt as vis_gt_utils
from utils.ImageIO import windowing
from utils.io import save_object
from utils.timer import Timer
import os.path as osp
from sync_batchnorm.replicate import patch_replication_callback
logger = logging.getLogger(__name__)
from datasets.dataset_catalog import ANN_FN
from datasets.dataset_catalog import DATASETS
from datasets.dataset_catalog import CT_DIR
from datasets.dataset_catalog import CT_SUB_DIRS
from datasets.dataset_catalog import IM_PREFIX
# For CT
from datasets.dataset_catalog import MASK_DIR
def get_eval_functions():
# Determine which parent or child function should handle inference
if cfg.MODEL.RPN_ONLY:
raise NotImplementedError
# child_func = generate_rpn_on_range
# parent_func = generate_rpn_on_dataset
else:
# Generic case that handles all network types other than RPN-only nets
# and RetinaNet
child_func = test_net
parent_func = test_net_on_dataset
return parent_func, child_func
def get_inference_dataset(index, is_parent=True):
assert is_parent or len(cfg.TEST.DATASETS) == 1, \
'The child inference process can only work on a single dataset'
dataset_name = cfg.TEST.DATASETS[index]
if cfg.TEST.PRECOMPUTED_PROPOSALS:
assert is_parent or len(cfg.TEST.PROPOSAL_FILES) == 1, \
'The child inference process can only work on a single proposal file'
assert len(cfg.TEST.PROPOSAL_FILES) == len(cfg.TEST.DATASETS), \
'If proposals are used, one proposal file must be specified for ' \
'each dataset'
proposal_file = cfg.TEST.PROPOSAL_FILES[index]
else:
proposal_file = None
return dataset_name, proposal_file
def run_inference(
args, ind_range=None,
multi_gpu_testing=False, gpu_id=0,
check_expected_results=False):
parent_func, child_func = get_eval_functions()
is_parent = ind_range is None
def result_getter():
if is_parent:
# Parent case:
# In this case we're either running inference on the entire dataset in a
# single process or (if multi_gpu_testing is True) using this process to
# launch subprocesses that each run inference on a range of the dataset
all_results = {}
for i in range(len(cfg.TEST.DATASETS)):
dataset_name, proposal_file = get_inference_dataset(i)
output_dir = args.output_dir
results = parent_func(
args,
dataset_name,
proposal_file,
output_dir,
multi_gpu=multi_gpu_testing
)
return
else:
# Subprocess child case:
# In this case test_net was called via subprocess.Popen to execute on a
# range of inputs on a single dataset
dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
output_dir = args.output_dir
return child_func(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=ind_range,
gpu_id=gpu_id
)
result_getter()
return None
def test_net_on_dataset(
args,
dataset_name,
proposal_file,
output_dir,
multi_gpu=False,
gpu_id=0):
"""Run inference on a dataset."""
if 'testA' in dataset_name or 'testB' in dataset_name:
dataset = None
sub_dirs = DATASETS[dataset_name][CT_SUB_DIRS]
image_dir = DATASETS[dataset_name][CT_DIR]
mask_dir = DATASETS[dataset_name][MASK_DIR]
with open(sub_dirs) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
roidb = []
for line in lines:
entry = {}
entry['image'] = osp.join(image_dir, line, 'norm_image.npz')
entry['mask_image'] = osp.join(mask_dir, line, 'mask_image.npz')
#entry[] = osp.join(mask_dir, line)
roidb.append(entry)
elif cfg.DATA_SOURCE == 'coco':
dataset = JsonDataset(dataset_name)
elif cfg.DATA_SOURCE == 'mammo':
dataset = MammoDataset(dataset_name)
#elif cfg.DATA_SOURCE == 'lesion':
# dataset = LesionDataset(dataset_name)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
if 'testA' in dataset_name or 'testB' in dataset_name:
num_images = len(roidb)
else:
num_images = len(dataset.get_roidb())
multi_gpu_test_net_on_dataset(
args, dataset_name, proposal_file, num_images, output_dir
)
else:
test_net(
args, dataset_name, proposal_file, output_dir, gpu_id=gpu_id
)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
return None
def multi_gpu_test_net_on_dataset(
args, dataset_name, proposal_file, num_images, output_dir):
"""Multi-gpu inference on a dataset."""
binary_dir = envu.get_runtime_dir()
binary_ext = envu.get_py_bin_ext()
binary = os.path.join(binary_dir, args.test_net_file + binary_ext)
assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)
# Pass the target dataset and proposal file (if any) via the command line
opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
if proposal_file:
opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]
# Run inference in parallel in subprocesses
# Outputs will be a list of outputs from each subprocess, where the output
# of each subprocess is the dictionary saved by test_net().
outputs = subprocess_utils.process_in_parallel(
'CT_detection', num_images, binary, output_dir,
args.load_ckpt, args.load_detectron, opts
)
# Collate the results from each subprocess
all_results_dicts = {}
for det_data in outputs:
all_results_dicts.update(det_data)
CT_det_file = os.path.join(output_dir, 'CT_detections.pkl')
save_object(all_results_dicts,CT_det_file)
logger.info('Wrote CT level detections to: {}'.format(os.path.abspath(CT_det_file)))
return None
def test_net(
args,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
# for debug
#roidb=roidb[0:1]
#import pdb
#pdb.set_trace()
model = initialize_model_from_cfg(args, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_results_dicts = {}
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
image_tensor = np.load(open(entry['image'], 'rb'))['data']
mask_tensor = None
#mask_tensor = np.load(open(entry['mask_image'], 'rb'))['data']
results_dict, num_slice = ct_detect_all(model, image_tensor, mask_tensor, slice_expand=10, box_proposals=None)
if cfg.VIS:
visualize_pred_gt(dataset, image_tensor, results_dict, entry, output_dir, folder_name='vis_ct', vis_pred_only=True)
bound_groups, bound3ds = combine_slice_level_pred(results_dict, cfg.CT_INFERENCE.COMBINE_THRESH)
all_results_dicts[entry['image']] = results_dict
#for bound3d in bound3ds:
# print(bound3d.cube, image_tensor.shape)
#combined_results_dict = bound3d_to_return_dict(bound3ds, results_dict)
#print("Merged bbox num %d" % len(bound3ds))
#if cfg.VIS:
#visualize_pred_gt(dataset, image_tensor, combined_results_dict, entry, output_dir, folder_name='vis_combined_ct')
if i % 100 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
))
cfg_yaml = yaml.dump(cfg)
# save CT level det results(image_path-return_dict map)
if ind_range is not None:
CT_det_name = 'CT_detection_range_%s_%s.pkl' % tuple(ind_range)
else:
CT_det_name = 'CT_detections.pkl'
CT_det_file = os.path.join(output_dir, CT_det_name)
save_object(all_results_dicts,CT_det_file)
logger.info('Wrote CT level detections to: {}'.format(os.path.abspath(CT_det_file)))
return None
def bound3d_to_dict(bound3d):
"""Convert instance of class Bound to python dict.
"""
bound3d_dict = {'cube':bound3d.cube, 'pathch_number':bound3d.get_patch_num(),\
'score':bound3d.score, 'label':bound3d.label}
return bound3d_dict
def bound3ds_to_list(bound3ds):
"""Convert instance of class Bound to python list.
"""
bound3ds_list = []
for bound3d in bound3ds:
bound_dict = {'cube':bound3d.cube, 'pathch_number':bound3d.get_patch_num(),\
'score':bound3d.score, 'label':bound3d.label}
bound3ds_list.append(bound_dict)
return bound3ds_list
def initialize_model_from_cfg(args, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
set to evaluation mode.
"""
model = GetRCNNModel()
model.eval()
if args.cuda:
model.cuda()
if args.load_ckpt:
load_name = args.load_ckpt
logger.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(model, checkpoint['model'])
if args.load_detectron:
logger.info("loading detectron weights %s", args.load_detectron)
load_detectron_weight(model, args.load_detectron)
model = mynn.DataParallel(model, cpu_keywords=['im_info', 'roidb'], minibatch=True)
if cfg.TRAIN_SYNC_BN:
# Shu:For synchorinized BN
patch_replication_callback(model)
return model
def get_roidb_and_dataset(dataset_name, proposal_file, ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
if 'testA' in dataset_name or 'testB' in dataset_name:
print('#'*20)
print('tianchi testA/B')
print('='*20)
dataset = None
sub_dirs = DATASETS[dataset_name][CT_SUB_DIRS]
image_dir = DATASETS[dataset_name][CT_DIR]
mask_dir = DATASETS[dataset_name][MASK_DIR]
with open(sub_dirs) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
roidb = []
for line in lines:
entry = {}
entry['image'] = osp.join(image_dir, line, 'norm_image.npz')
entry['mask_image'] = osp.join(mask_dir, line, 'mask_image.npz')
#entry[] = osp.join(mask_dir, line)
roidb.append(entry)
else:
dataset = JsonDataset(dataset_name)
if cfg.TEST.PRECOMPUTED_PROPOSALS:
assert proposal_file, 'No proposal file given'
roidb = dataset.get_roidb(
proposal_file=proposal_file,
proposal_limit=cfg.TEST.PROPOSAL_LIMIT
)
else:
roidb = dataset.get_roidb(gt=True)
print('len roidb: ',len(roidb))
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, dataset, start, end, total_num_images
def empty_results(num_classes, num_images):
"""Return empty results lists for boxes, masks, and keypoints.
Box detections are collected into:
all_boxes[cls][image] = N x 5 array with columns (x1, y1, x2, y2, score)
Instance mask predictions are collected into:
all_segms[cls][image] = [...] list of COCO RLE encoded masks that are in
1:1 correspondence with the boxes in all_boxes[cls][image]
Keypoint predictions are collected into:
all_keyps[cls][image] = [...] list of keypoints results, each encoded as
a 3D array (#rois, 4, #keypoints) with the 4 rows corresponding to
[x, y, logit, prob] (See: utils.keypoints.heatmaps_to_keypoints).
Keypoints are recorded for person (cls = 1); they are in 1:1
correspondence with the boxes in all_boxes[cls][image].
"""
# Note: do not be tempted to use [[] * N], which gives N references to the
# *same* empty list.
all_bound3ds = [[[] for _ in range(num_images)] for _ in range(num_classes)]
return all_bound3ds
def extend_results(index, all_res, bound3ds):
"""Add results for an image to the set of all results at the specified
index.
"""
# Skip cls_idx 0 (__background__)
#for cls_idx in range(1, len(im_res)):
for bound3d in bound3ds:
cls_idx = bound3d.label
all_res[cls_idx][index].append(bound3d_to_dict(bound3d))
def combine_slice_level_pred(results_dict, thresh, cls_num=15, max_slices_stride=5, iom_thresh=0.7):
""" Group slice level bounds (x,y,w,h,slice_idx,label,score) into bound3d. """
combine_opt = {}
# is per-class-threshold difined
if isinstance(thresh, list):
thresh_list = thresh
else:
thresh_list = [thresh for _ in range(cls_num)]
label_combine_matrix = np.ones((cls_num, cls_num), dtype='int32') * -1
label_combine_matrix[range(1,cls_num), range(1,cls_num)] = range(1,cls_num)
least_inter_ratio_matrix = np.zeros((cls_num, cls_num), dtype='float32')
least_inter_ratio_matrix[range(1,cls_num), range(1,cls_num)] = np.ones((cls_num-1), dtype='float32') * iom_thresh
combine_opt['label_combine_matrix'] = label_combine_matrix
combine_opt['least_inter_ratio_matrix'] = least_inter_ratio_matrix
combine_opt['max_slices_stride'] = max_slices_stride
bound2d_list = []
for slice_idx, slice_pred in results_dict.items():
scores, boxes, cls_boxes = slice_pred
_,_,_, classes = vis_gt_utils.convert_from_cls_format(
cls_boxes, None, None)
#print(len(cls_boxes), cls_boxes[0])
for idx, score in enumerate(scores):
if score >= thresh_list[classes[idx]]:
x, y, w, h = box_utils.xyxy_to_xywh(boxes[idx, :].tolist())
bound2d_list.append([x, y, w, h, slice_idx, classes[idx], score])
#print([x, y, w, h, slice_idx, classes[idx], score])
bound_groups = combine_bounds_3d_direct(bound2d_list, combine_opt)
bound3ds = []
direct = 0
for bound_group in bound_groups:
bound3d = Bound3D()
for bound2d in bound_group:
x, y, w, h, slice_id, label, score = bound2d
cube = [x, y, slice_id, w, h, 1]
bound3d.append(cube, direct, label, score, combine_opt['label_combine_matrix'])
if not bound3d.is_valid:
return None
bound3ds.append(bound3d)
#print(bound3d)
return bound_groups, bound3ds
def bound3d_to_return_dict(bound3ds, results_dict):
"""convert from bound3d to results_dict.
bound3d.cube (x,y,z,w,h,d)
cls_boxes is a list of #classes elements, each element has shape (#detection, 5).
[x1, y1, x2, y2, score]
return_dict: dict with {slice: cls_boxes}
"""
slice_pred = bound3d_to_slice_pred(bound3ds)
class_num = int(np.max(slice_pred[:, 6]))
print(class_num)
total_slice = np.unique(slice_pred[:, 5]).tolist()
return_dict = {}
for key in results_dict.keys():
return_dict[key] = ([], [], [[] for i in range(class_num + 1)])
for cur_slice in total_slice:
if cur_slice not in results_dict.keys():
#print("cur_slice %d is not in image_tensor"%cur_slice)
continue
cur_index = np.where(slice_pred[:, 5] == cur_slice)[0]
cur_bboxes = slice_pred[cur_index, :]
#print('cur_boxes all classes score, slice, label: ', cur_bboxes)
for cur_class in range(class_num + 1):
class_index = np.where(cur_bboxes[:, 6] == cur_class)[0]
return_dict[int(cur_slice)][2][cur_class] = cur_bboxes[class_index, 0:5]
return return_dict
def bound3d_to_slice_pred(bound3ds):
"""convert from bound3d to slice pred.
slice pred in the form of N*6 array, with each row represents [x1, y1, x2, y2, score, slice, label]
"""
slice_pred = np.empty((0, 7), dtype=np.float32)
for bound3d in bound3ds:
x, y, z, w, h, d = bound3d.cube
label = bound3d.label
score = bound3d.score
x2 = x + max(0, w - 1)
y2 = y + max(0, h - 1)
z2 = z + max(0, d - 1)
for cur_slice in range(int(z), int(z2) + 1):
tmp_pred = np.zeros((1, 7), dtype=np.float32)
tmp_pred[0,:] = [x, y, x2, y2, score, cur_slice, label]
slice_pred = np.append(slice_pred, tmp_pred, axis=0)
return slice_pred
def visualize_pred_gt(dataset, image_tensor, results_dict, entry, output_dir, folder_name='vis_ct', vis_pred_only=False):
im_dir =os.sep.join(entry['image'].split(os.sep)[-4:-1])
if cfg.LESION.LESION_ENABLED:
image_tensor = image_tensor.astype(np.float32)
image_tensor -= 32768
print(image_tensor.max(), image_tensor.min())
image_tensor = windowing(image_tensor, [-1400,200])
image_tensor = image_tensor.astype(np.uint8)
for center_idx in range(image_tensor.shape[0]):
other_im_show = cv2.merge([image_tensor[center_idx, :, :], image_tensor[center_idx, :, :], image_tensor[center_idx, :, :]])
if vis_pred_only:
gt_idx = []
gt_boxes_show = [[], []]
gt_classes = [[], []]
else:
gt_idx = np.where(entry['boxes'][:, 4] == center_idx)[0]
gt_boxes_show = [entry['boxes'][gt_idx, :4].tolist(), []]
gt_classes = [entry['gt_classes'][gt_idx].tolist(), []]
im_show = cv2.merge([image_tensor[center_idx, :, :], image_tensor[center_idx, :, :], image_tensor[center_idx, :, :]])
if len(gt_boxes_show[0]) == 0:
tmp_name = '%03d' % center_idx
else:
tmp_name = '%03d_gt' % center_idx
vis_gt_utils.vis_one_image(
im_show,
other_im_show,
tmp_name,
os.path.join(output_dir, folder_name, im_dir),
results_dict[center_idx][2],
gt_boxes_show,
segms=None,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True,
gt_classes = gt_classes
)
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2
] |
prune_main.py | Sallyeen/DeepCompression-PyTorch-master | 7 | 1605124 | <reponame>Sallyeen/DeepCompression-PyTorch-master
if __name__ == '__main__':
import pruning.main
| [
1,
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276,
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544,
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29889,
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2
] |
models.py | shepherd-dc/flask-blog | 0 | 85983 | <reponame>shepherd-dc/flask-blog
from exts import db
from datetime import datetime
from werkzeug.security import generate_password_hash, check_password_hash
class User(db.Model):
__tablename__ = 'user'
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
email = db.Column(db.String(50), nullable=False)
username = db.Column(db.String(50), nullable=False)
password = db.Column(db.String(100), nullable=False)
def __init__(self, *args, **kwargs):
email = kwargs.get('email')
username = kwargs.get('username')
password = kwargs.get('password')
self.email = email
self.username = username
self.password = generate_password_hash(password)
def check_password(self, raw_password):
result = check_password_hash(self.password, raw_password)
return result
class Question(db.Model):
__tablename__ = 'question'
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
title = db.Column(db.String(100), nullable=False)
content = db.Column(db.Text, nullable=False)
create_time = db.Column(db.DateTime, default=datetime.now, nullable=False)
author_id = db.Column(db.Integer, db.ForeignKey('user.id'))
author = db.relationship('User', backref=db.backref('questions'))
class Answer(db.Model):
__tablename__ = 'answer'
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
content = db.Column(db.Text, nullable=False)
create_time = db.Column(db.DateTime, default=datetime.now, nullable=False)
question_id = db.Column(db.Integer, db.ForeignKey('question.id'))
author_id = db.Column(db.Integer, db.ForeignKey('user.id'))
question = db.relationship('Question', backref=db.backref('answers', order_by=create_time.desc()))
author = db.relationship('User', backref=db.backref('answers'))
| [
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] |
src/dflow/analysis.py | slawler/SI_2019_Coastal | 1 | 82685 | <reponame>slawler/SI_2019_Coastal<filename>src/dflow/analysis.py
def tide_constituents(dates, elevation):
from tappy import tappy
quiet = True
debug = False
outputts = False
outputxml = False
ephemeris = False
rayleigh = 1.0
print_vau_table = False
missing_data = 'ignore'
linear_trend = False
remove_extreme = False
zero_ts = None
filter = None
pad_filters = None
include_inferred = True
if rayleigh:
ray = float(rayleigh)
x = tappy.tappy(
outputts=outputts,
outputxml=outputxml,
quiet=quiet,
debug=debug,
ephemeris=ephemeris,
rayleigh=rayleigh,
print_vau_table=print_vau_table,
missing_data=missing_data,
linear_trend=linear_trend,
remove_extreme=remove_extreme,
zero_ts=zero_ts,
filter=filter,
pad_filters=pad_filters,
include_inferred=include_inferred,
)
x.dates = dates
x.elevation = elevation
package = x.astronomic(x.dates)
(x.zeta, x.nu, x.nup, x.nupp, x.kap_p, x.ii, x.R, x.Q, x.T, x.jd, x.s, x.h,
x.N, x.p, x.p1) = package
(x.speed_dict, x.key_list) = x.which_constituents(len(x.dates),
package,
rayleigh_comp=ray)
# the analysis
x.constituents()
return x
def _decompose(dates, elvs_list):
from tqdm import tqdm
amps_list, phases_list = [], []
for elvs in tqdm(elvs_list):
con = tide_constituents(dates, elvs)
amps_list.append((con.r['M2'], con.r['M4']))
phases_list.append((con.phase['M2'], con.phase['M4']))
return [amps_list, phases_list]
def decompose(dates, elvs_list):
import multiprocessing
from functools import partial
import numpy as np
pool = multiprocessing.Pool()
print('Computing the tidal constituents in parallel with ' +
f'{pool._processes} processors ...')
decompose_el = partial(_decompose, dates)
tide_list = pool.map(decompose_el, elvs_list)
pool.close()
amps_list = [np.column_stack(tide[0]) for tide in tide_list]
phases_list = [np.column_stack(tide[1]) for tide in tide_list]
return amps_list, phases_list
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teuthology/orchestra/test/util.py | varshar16/teuthology | 117 | 135471 | def assert_raises(excClass, callableObj, *args, **kwargs):
"""
Like unittest.TestCase.assertRaises, but returns the exception.
"""
try:
callableObj(*args, **kwargs)
except excClass as e:
return e
else:
if hasattr(excClass,'__name__'): excName = excClass.__name__
else: excName = str(excClass)
raise AssertionError("%s not raised" % excName)
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Stage_3/Task11_Graph/depth_first_search.py | Pyabecedarian/Algorithms-and-Data-Structures-using-Python | 0 | 7271 | """
The Depth First Search (DFS)
The goal of a dfs is to search as deeply as possible, connecting as many nodes in the graph as possible and
branching where necessary. Think of the BFS that builds a search tree one level at a time, whereas the DFS
creates a search tree by exploring one branch of the tree as deeply as possible.
As with bfs the dfs makes use of `predecessor` links to construct the tree. In
addition, the dfs will make use of two additional instance variables in the Vertex class, `discovery` and
`finish_time`.
predecessor : same as bfs
discovery : tracks the number of steps in the algorithm before a vertex is first encountered;
finish_time : is the number of steps before a vertex is colored black
"""
from datastruct.graph import Vertex, Graph
class DFSGraph(Graph):
def __init__(self):
super(DFSGraph, self).__init__()
self.time = 0
def reset(self):
self.time = 0
for v in self:
v.color = 'white'
v.predecessor = None
def dfs(self):
self.reset()
for v in self:
if v.color == 'white':
self._dfs_visit(v)
def _dfs_visit(self, vert: Vertex):
vert.color = 'gray'
self.time += 1
vert.discovery = self.time
for nextv in vert.get_connections():
if nextv.color == 'white':
nextv.predecessor = vert
self._dfs_visit(nextv)
vert.color = 'black'
self.time += 1
vert.finish_time = self.time
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] |
src/prefpanels/PrintingPanel.py | vadmium/grailbrowser | 9 | 81325 | """General Grail preferences panel."""
__version__ = "$Revision: 1.12 $"
import grailutil
import PrefsPanels
import string
import Tkinter
GROUP = "printing"
LABEL_WIDTH = 16
class FontSizeVar(Tkinter.StringVar):
_default = "10.0 / 10.7"
def get(self):
sizes = grailutil.conv_fontsize(Tkinter.StringVar.get(self))
return "%s / %s" % sizes
def set(self, value):
sizes = grailutil.conv_fontsize(value)
return Tkinter.StringVar.set(self, "%s / %s" % sizes)
class StringSetVar(Tkinter.StringVar):
def get(self):
return string.lower(Tkinter.StringVar.get(self))
def set(self, value):
value = string.capitalize(value)
return Tkinter.StringVar.set(self, value)
class PrintingPanel(PrefsPanels.Framework):
"""Printing preferences."""
# Class var for help button - relative to grail-help-root.
HELP_URL = "help/prefs/printing.html"
def CreateLayout(self, name, frame):
# Printer configs are simple enough to use the convenience functions
self.PrefsEntry(frame, 'Print command: ',
GROUP, 'command',
entry_width=20, label_width=LABEL_WIDTH)
self.PrefsCheckButton(frame, "Images: ", "Print images ",
GROUP, 'images',
label_width=LABEL_WIDTH)
self.PrefsCheckButton(frame, " ", "Reduce images to greyscale",
GROUP, 'greyscale',
label_width=LABEL_WIDTH)
self.PrefsCheckButton(frame, "Anchors: ", "Footnotes for anchors",
GROUP, 'footnote-anchors',
label_width=LABEL_WIDTH)
self.PrefsCheckButton(frame, " ", "Underline anchors",
GROUP, 'underline-anchors',
label_width=LABEL_WIDTH)
# paper size:
var = StringSetVar()
import printing.paper
sizes = printing.paper.paper_sizes.keys()
sizes.sort()
sizes = map(string.capitalize, sizes)
self.PrefsOptionMenu(frame, "Paper size: ", GROUP, 'paper-size',
sizes, label_width=LABEL_WIDTH,
variable=StringSetVar())
# page orientation:
var = StringSetVar()
opts = printing.paper.paper_rotations.keys()
opts.sort()
opts = map(string.capitalize, opts)
self.PrefsOptionMenu(frame, "Orientation: ", GROUP, 'orientation',
opts, label_width=LABEL_WIDTH,
variable=StringSetVar())
# font size and leading:
self.PrefsEntry(frame, "Font size: ",
GROUP, 'font-size',
typename='string', entry_width=12,
label_width=LABEL_WIDTH, variable=FontSizeVar())
# paragraph treatment:
f = Tkinter.Frame(frame)
self.PrefsWidgetLabel(f, "Paragraphs:", label_width=LABEL_WIDTH)
# Pack some preferences entries together in a frame - we use the
# PrefsEntry 'composite' feature here, to put them together on the
# right-hand side of the label:
tempfr = Tkinter.Frame(f, borderwidth=1)
tempfr.pack(side=Tkinter.LEFT)
entries_frame = Tkinter.Frame(
tempfr, relief=Tkinter.SUNKEN, borderwidth=1)
self.PrefsEntry(entries_frame,
"Indentation:",
GROUP, 'paragraph-indent', 'float',
label_width=10, entry_width=5, composite=1)
self.PrefsEntry(entries_frame,
"Vertical separation:",
GROUP, 'paragraph-skip', 'float',
label_width=16, entry_width=5, composite=1)
f.pack(fill=Tkinter.X, side=Tkinter.TOP, pady='1m')
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tests/components/sonos/test_sensor.py | MrDelik/core | 3 | 129171 | """Tests for the Sonos battery sensor platform."""
from unittest.mock import PropertyMock
from soco.exceptions import NotSupportedException
from homeassistant.components.sensor import SCAN_INTERVAL
from homeassistant.components.sonos.binary_sensor import ATTR_BATTERY_POWER_SOURCE
from homeassistant.const import STATE_OFF, STATE_ON
from homeassistant.helpers import entity_registry as ent_reg
from homeassistant.util import dt as dt_util
from tests.common import async_fire_time_changed
async def test_entity_registry_unsupported(hass, async_setup_sonos, soco):
"""Test sonos device without battery registered in the device registry."""
soco.get_battery_info.side_effect = NotSupportedException
await async_setup_sonos()
entity_registry = ent_reg.async_get(hass)
assert "media_player.zone_a" in entity_registry.entities
assert "sensor.zone_a_battery" not in entity_registry.entities
assert "binary_sensor.zone_a_power" not in entity_registry.entities
async def test_entity_registry_supported(hass, async_autosetup_sonos, soco):
"""Test sonos device with battery registered in the device registry."""
entity_registry = ent_reg.async_get(hass)
assert "media_player.zone_a" in entity_registry.entities
assert "sensor.zone_a_battery" in entity_registry.entities
assert "binary_sensor.zone_a_power" in entity_registry.entities
async def test_battery_attributes(hass, async_autosetup_sonos, soco):
"""Test sonos device with battery state."""
entity_registry = ent_reg.async_get(hass)
battery = entity_registry.entities["sensor.zone_a_battery"]
battery_state = hass.states.get(battery.entity_id)
assert battery_state.state == "100"
assert battery_state.attributes.get("unit_of_measurement") == "%"
power = entity_registry.entities["binary_sensor.zone_a_power"]
power_state = hass.states.get(power.entity_id)
assert power_state.state == STATE_ON
assert (
power_state.attributes.get(ATTR_BATTERY_POWER_SOURCE) == "SONOS_CHARGING_RING"
)
async def test_battery_on_s1(hass, async_setup_sonos, soco, device_properties_event):
"""Test battery state updates on a Sonos S1 device."""
soco.get_battery_info.return_value = {}
await async_setup_sonos()
subscription = soco.deviceProperties.subscribe.return_value
sub_callback = subscription.callback
entity_registry = ent_reg.async_get(hass)
assert "sensor.zone_a_battery" not in entity_registry.entities
assert "binary_sensor.zone_a_power" not in entity_registry.entities
# Update the speaker with a callback event
sub_callback(device_properties_event)
await hass.async_block_till_done()
battery = entity_registry.entities["sensor.zone_a_battery"]
battery_state = hass.states.get(battery.entity_id)
assert battery_state.state == "100"
power = entity_registry.entities["binary_sensor.zone_a_power"]
power_state = hass.states.get(power.entity_id)
assert power_state.state == STATE_OFF
assert power_state.attributes.get(ATTR_BATTERY_POWER_SOURCE) == "BATTERY"
async def test_device_payload_without_battery(
hass, async_setup_sonos, soco, device_properties_event, caplog
):
"""Test device properties event update without battery info."""
soco.get_battery_info.return_value = None
await async_setup_sonos()
subscription = soco.deviceProperties.subscribe.return_value
sub_callback = subscription.callback
bad_payload = "BadKey:BadValue"
device_properties_event.variables["more_info"] = bad_payload
sub_callback(device_properties_event)
await hass.async_block_till_done()
assert bad_payload in caplog.text
async def test_device_payload_without_battery_and_ignored_keys(
hass, async_setup_sonos, soco, device_properties_event, caplog
):
"""Test device properties event update without battery info and ignored keys."""
soco.get_battery_info.return_value = None
await async_setup_sonos()
subscription = soco.deviceProperties.subscribe.return_value
sub_callback = subscription.callback
ignored_payload = "SPID:InCeiling,TargetRoomName:Bouncy House"
device_properties_event.variables["more_info"] = ignored_payload
sub_callback(device_properties_event)
await hass.async_block_till_done()
assert ignored_payload not in caplog.text
async def test_audio_input_sensor(
hass, async_autosetup_sonos, soco, tv_event, no_media_event
):
"""Test audio input sensor."""
entity_registry = ent_reg.async_get(hass)
subscription = soco.avTransport.subscribe.return_value
sub_callback = subscription.callback
sub_callback(tv_event)
await hass.async_block_till_done()
audio_input_sensor = entity_registry.entities["sensor.zone_a_audio_input_format"]
audio_input_state = hass.states.get(audio_input_sensor.entity_id)
assert audio_input_state.state == "Dolby 5.1"
# Set mocked input format to new value and ensure poll success
no_input_mock = PropertyMock(return_value="No input")
type(soco).soundbar_audio_input_format = no_input_mock
async_fire_time_changed(hass, dt_util.utcnow() + SCAN_INTERVAL)
await hass.async_block_till_done()
no_input_mock.assert_called_once()
audio_input_state = hass.states.get(audio_input_sensor.entity_id)
assert audio_input_state.state == "No input"
# Ensure state is not polled when source is not TV and state is already "No input"
sub_callback(no_media_event)
await hass.async_block_till_done()
unpolled_mock = PropertyMock(return_value="Will not be polled")
type(soco).soundbar_audio_input_format = unpolled_mock
async_fire_time_changed(hass, dt_util.utcnow() + SCAN_INTERVAL)
await hass.async_block_till_done()
unpolled_mock.assert_not_called()
audio_input_state = hass.states.get(audio_input_sensor.entity_id)
assert audio_input_state.state == "No input"
async def test_microphone_binary_sensor(
hass, async_autosetup_sonos, soco, device_properties_event
):
"""Test microphone binary sensor."""
entity_registry = ent_reg.async_get(hass)
assert "binary_sensor.zone_a_microphone" in entity_registry.entities
mic_binary_sensor = entity_registry.entities["binary_sensor.zone_a_microphone"]
mic_binary_sensor_state = hass.states.get(mic_binary_sensor.entity_id)
assert mic_binary_sensor_state.state == STATE_OFF
# Update the speaker with a callback event
subscription = soco.deviceProperties.subscribe.return_value
subscription.callback(device_properties_event)
await hass.async_block_till_done()
mic_binary_sensor_state = hass.states.get(mic_binary_sensor.entity_id)
assert mic_binary_sensor_state.state == STATE_ON
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] |
asteval/asteval.py | sthagen/asteval | 125 | 198352 | #!/usr/bin/env python
"""Safe(ish) evaluation of mathematical expression using Python's ast
module.
This module provides an Interpreter class that compiles a restricted set of
Python expressions and statements to Python's AST representation, and then
executes that representation using values held in a symbol table.
The symbol table is a simple dictionary, giving a simple, flat namespace.
This comes pre-loaded with many functions from Python's builtin and math
module. If numpy is installed, many numpy functions are also included.
Additional symbols can be added when an Interpreter is created, but the
user of that interpreter will not be able to import additional modules.
Expressions, including loops, conditionals, and function definitions can be
compiled into ast node and then evaluated later, using the current values
in the symbol table.
The result is a restricted, simplified version of Python meant for
numerical calculations that is somewhat safer than 'eval' because many
unsafe operations (such as 'import' and 'eval') are simply not allowed.
Many parts of Python syntax are supported, including:
for loops, while loops, if-then-elif-else conditionals
try-except (including 'finally')
function definitions with def
advanced slicing: a[::-1], array[-3:, :, ::2]
if-expressions: out = one_thing if TEST else other
list comprehension out = [sqrt(i) for i in values]
The following Python syntax elements are not supported:
Import, Exec, Lambda, Class, Global, Generators,
Yield, Decorators
In addition, while many builtin functions are supported, several builtin
functions that are considered unsafe are missing ('eval', 'exec', and
'getattr' for example)
"""
import ast
import inspect
import time
from sys import exc_info, stderr, stdout
from .astutils import (HAS_NUMPY, UNSAFE_ATTRS, ExceptionHolder, ReturnedNone,
make_symbol_table, numpy, op2func, valid_symbol_name)
ALL_NODES = ['arg', 'assert', 'assign', 'attribute', 'augassign', 'binop',
'boolop', 'break', 'call', 'compare', 'continue', 'delete',
'dict', 'ellipsis', 'excepthandler', 'expr', 'extslice',
'for', 'functiondef', 'if', 'ifexp', 'index', 'interrupt',
'list', 'listcomp', 'module', 'name', 'nameconstant', 'num',
'pass', 'raise', 'repr', 'return', 'slice', 'str',
'subscript', 'try', 'tuple', 'unaryop', 'while', 'constant']
class Interpreter:
"""create an asteval Interpreter: a restricted, simplified interpreter
of mathematical expressions using Python syntax.
Parameters
----------
symtable : dict or `None`
dictionary to use as symbol table (if `None`, one will be created).
usersyms : dict or `None`
dictionary of user-defined symbols to add to symbol table.
writer : file-like or `None`
callable file-like object where standard output will be sent.
err_writer : file-like or `None`
callable file-like object where standard error will be sent.
use_numpy : bool
whether to use functions from numpy.
minimal : bool
create a minimal interpreter: disable all options (see Note 1).
no_if : bool
whether to support `if` blocks
no_for : bool
whether to support `for` blocks.
no_while : bool
whether to support `while` blocks.
no_try : bool
whether to support `try` blocks.
no_functiondef : bool
whether to support user-defined functions.
no_ifexp : bool
whether to support if expressions.
no_listcomp : bool
whether to support list comprehension.
no_augassign : bool
whether to support augemented assignments (`a += 1`, etc).
no_assert : bool
whether to support `assert`.
no_delete : bool
whether to support `del`.
no_raise : bool
whether to support `raise`.
no_print : bool
whether to support `print`.
readonly_symbols : iterable or `None`
symbols that the user can not assign to
builtins_readonly : bool
whether to blacklist all symbols that are in the initial symtable
Notes
-----
1. setting `minimal=True` is equivalent to setting all
`no_***` options to `True`.
"""
def __init__(self, symtable=None, usersyms=None, writer=None,
err_writer=None, use_numpy=True, minimal=False,
no_if=False, no_for=False, no_while=False, no_try=False,
no_functiondef=False, no_ifexp=False, no_listcomp=False,
no_augassign=False, no_assert=False, no_delete=False,
no_raise=False, no_print=False, max_time=None,
readonly_symbols=None, builtins_readonly=False):
self.writer = writer or stdout
self.err_writer = err_writer or stderr
if symtable is None:
if usersyms is None:
usersyms = {}
symtable = make_symbol_table(use_numpy=use_numpy, **usersyms)
self.symtable = symtable
self._interrupt = None
self.error = []
self.error_msg = None
self.expr = None
self.retval = None
self._calldepth = 0
self.lineno = 0
self.start_time = time.time()
self.use_numpy = HAS_NUMPY and use_numpy
symtable['print'] = self._printer
self.no_print = no_print or minimal
nodes = ALL_NODES[:]
if minimal or no_if:
nodes.remove('if')
if minimal or no_for:
nodes.remove('for')
if minimal or no_while:
nodes.remove('while')
if minimal or no_try:
nodes.remove('try')
if minimal or no_functiondef:
nodes.remove('functiondef')
if minimal or no_ifexp:
nodes.remove('ifexp')
if minimal or no_assert:
nodes.remove('assert')
if minimal or no_delete:
nodes.remove('delete')
if minimal or no_raise:
nodes.remove('raise')
if minimal or no_listcomp:
nodes.remove('listcomp')
if minimal or no_augassign:
nodes.remove('augassign')
self.node_handlers = {}
for node in nodes:
self.node_handlers[node] = getattr(self, "on_%s" % node)
# to rationalize try/except try/finally for Python2.6 through Python3.3
if 'try' in self.node_handlers:
self.node_handlers['tryexcept'] = self.node_handlers['try']
self.node_handlers['tryfinally'] = self.node_handlers['try']
if readonly_symbols is None:
self.readonly_symbols = set()
else:
self.readonly_symbols = set(readonly_symbols)
if builtins_readonly:
self.readonly_symbols |= set(self.symtable)
self.no_deepcopy = [key for key, val in symtable.items()
if (callable(val)
or 'numpy.lib.index_tricks' in repr(val)
or inspect.ismodule(val))]
def remove_nodehandler(self, node):
"""remove support for a node
returns current node handler, so that it
might be re-added with add_nodehandler()
"""
out = None
if node in self.node_handlers:
out = self.node_handlers.pop(node)
return out
def set_nodehandler(self, node, handler):
"""set node handler"""
self.node_handlers[node] = handler
def user_defined_symbols(self):
"""Return a set of symbols that have been added to symtable after
construction.
I.e., the symbols from self.symtable that are not in
self.no_deepcopy.
Returns
-------
unique_symbols : set
symbols in symtable that are not in self.no_deepcopy
"""
sym_in_current = set(self.symtable.keys())
sym_from_construction = set(self.no_deepcopy)
unique_symbols = sym_in_current.difference(sym_from_construction)
return unique_symbols
def unimplemented(self, node):
"""Unimplemented nodes."""
self.raise_exception(node, exc=NotImplementedError,
msg="'%s' not supported" %
(node.__class__.__name__))
def raise_exception(self, node, exc=None, msg='', expr=None,
lineno=None):
"""Add an exception."""
if self.error is None:
self.error = []
if expr is None:
expr = self.expr
if len(self.error) > 0 and not isinstance(node, ast.Module):
msg = '%s' % msg
err = ExceptionHolder(node, exc=exc, msg=msg, expr=expr, lineno=lineno)
self._interrupt = ast.Raise()
self.error.append(err)
if self.error_msg is None:
self.error_msg = "at expr='%s'" % (self.expr)
elif len(msg) > 0:
self.error_msg = msg
if exc is None:
try:
exc = self.error[0].exc
except:
exc = RuntimeError
raise exc(self.error_msg)
# main entry point for Ast node evaluation
# parse: text of statements -> ast
# run: ast -> result
# eval: string statement -> result = run(parse(statement))
def parse(self, text):
"""Parse statement/expression to Ast representation."""
self.expr = text
try:
out = ast.parse(text)
except SyntaxError:
self.raise_exception(None, msg='Syntax Error', expr=text)
except:
self.raise_exception(None, msg='Runtime Error', expr=text)
return out
def run(self, node, expr=None, lineno=None, with_raise=True):
"""Execute parsed Ast representation for an expression."""
# Note: keep the 'node is None' test: internal code here may run
# run(None) and expect a None in return.
out = None
if len(self.error) > 0:
return out
if self.retval is not None:
return self.retval
if isinstance(self._interrupt, (ast.Break, ast.Continue)):
return self._interrupt
if node is None:
return out
if isinstance(node, str):
node = self.parse(node)
if lineno is not None:
self.lineno = lineno
if expr is not None:
self.expr = expr
# get handler for this node:
# on_xxx with handle nodes of type 'xxx', etc
try:
handler = self.node_handlers[node.__class__.__name__.lower()]
except KeyError:
return self.unimplemented(node)
# run the handler: this will likely generate
# recursive calls into this run method.
try:
ret = handler(node)
if isinstance(ret, enumerate):
ret = list(ret)
return ret
except:
if with_raise:
if len(self.error) == 0:
# Unhandled exception that didn't use raise_exception
self.raise_exception(node, expr=expr)
raise
def __call__(self, expr, **kw):
"""Call class instance as function."""
return self.eval(expr, **kw)
def eval(self, expr, lineno=0, show_errors=True, raise_errors=False):
"""Evaluate a single statement."""
self.lineno = lineno
self.error = []
self.start_time = time.time()
try:
node = self.parse(expr)
except:
errmsg = exc_info()[1]
if len(self.error) > 0:
errmsg = "\n".join(self.error[0].get_error())
if raise_errors:
try:
exc = self.error[0].exc
except:
exc = RuntimeError
raise exc(errmsg)
if show_errors:
print(errmsg, file=self.err_writer)
return
try:
return self.run(node, expr=expr, lineno=lineno)
except:
errmsg = exc_info()[1]
if len(self.error) > 0:
errmsg = "\n".join(self.error[0].get_error())
if raise_errors:
try:
exc = self.error[0].exc
except:
exc = RuntimeError
raise exc(errmsg)
if show_errors:
print(errmsg, file=self.err_writer)
return
@staticmethod
def dump(node, **kw):
"""Simple ast dumper."""
return ast.dump(node, **kw)
# handlers for ast components
def on_expr(self, node):
"""Expression."""
return self.run(node.value) # ('value',)
def on_index(self, node):
"""Index."""
return self.run(node.value) # ('value',)
def on_return(self, node): # ('value',)
"""Return statement: look for None, return special sentinel."""
if self._calldepth == 0:
raise SyntaxError('cannot return at top level')
self.retval = self.run(node.value)
if self.retval is None:
self.retval = ReturnedNone
return
def on_repr(self, node):
"""Repr."""
return repr(self.run(node.value)) # ('value',)
def on_module(self, node): # ():('body',)
"""Module def."""
out = None
for tnode in node.body:
out = self.run(tnode)
return out
def on_expression(self, node):
"basic expression"
return self.on_module(node) # ():('body',)
def on_pass(self, node):
"""Pass statement."""
return None # ()
def on_ellipsis(self, node):
"""Ellipses."""
return Ellipsis
# for break and continue: set the instance variable _interrupt
def on_interrupt(self, node): # ()
"""Interrupt handler."""
self._interrupt = node
return node
def on_break(self, node):
"""Break."""
return self.on_interrupt(node)
def on_continue(self, node):
"""Continue."""
return self.on_interrupt(node)
def on_assert(self, node): # ('test', 'msg')
"""Assert statement."""
if not self.run(node.test):
msg = node.msg.s if node.msg else ""
self.raise_exception(node, exc=AssertionError, msg=msg)
return True
def on_list(self, node): # ('elt', 'ctx')
"""List."""
return [self.run(e) for e in node.elts]
def on_tuple(self, node): # ('elts', 'ctx')
"""Tuple."""
return tuple(self.on_list(node))
def on_dict(self, node): # ('keys', 'values')
"""Dictionary."""
return {self.run(k): self.run(v) for k, v in
zip(node.keys, node.values)}
def on_constant(self, node): # ('value', 'kind')
"""Return constant value."""
return node.value
def on_num(self, node): # ('n',)
"""Return number."""
return node.n
def on_str(self, node): # ('s',)
"""Return string."""
return node.s
def on_name(self, node): # ('id', 'ctx')
"""Name node."""
ctx = node.ctx.__class__
if ctx in (ast.Param, ast.Del):
return str(node.id)
else:
if node.id in self.symtable:
return self.symtable[node.id]
else:
msg = "name '%s' is not defined" % node.id
self.raise_exception(node, exc=NameError, msg=msg)
def on_nameconstant(self, node):
"""True, False, or None"""
return node.value
def node_assign(self, node, val):
"""Assign a value (not the node.value object) to a node.
This is used by on_assign, but also by for, list comprehension,
etc.
"""
if node.__class__ == ast.Name:
if (not valid_symbol_name(node.id) or
node.id in self.readonly_symbols):
errmsg = "invalid symbol name (reserved word?) %s" % node.id
self.raise_exception(node, exc=NameError, msg=errmsg)
self.symtable[node.id] = val
if node.id in self.no_deepcopy:
self.no_deepcopy.remove(node.id)
elif node.__class__ == ast.Attribute:
if node.ctx.__class__ == ast.Load:
msg = "cannot assign to attribute %s" % node.attr
self.raise_exception(node, exc=AttributeError, msg=msg)
setattr(self.run(node.value), node.attr, val)
elif node.__class__ == ast.Subscript:
self.run(node.value)[self.run(node.slice)] = val
elif node.__class__ in (ast.Tuple, ast.List):
if len(val) == len(node.elts):
for telem, tval in zip(node.elts, val):
self.node_assign(telem, tval)
else:
raise ValueError('too many values to unpack')
def on_attribute(self, node): # ('value', 'attr', 'ctx')
"""Extract attribute."""
ctx = node.ctx.__class__
if ctx == ast.Store:
msg = "attribute for storage: shouldn't be here!"
self.raise_exception(node, exc=RuntimeError, msg=msg)
sym = self.run(node.value)
if ctx == ast.Del:
return delattr(sym, node.attr)
# ctx is ast.Load
if not (node.attr in UNSAFE_ATTRS or
(node.attr.startswith('__') and
node.attr.endswith('__'))):
try:
return getattr(sym, node.attr)
except AttributeError:
pass
# AttributeError or accessed unsafe attribute
msg = "no attribute '%s' for %s" % (node.attr, self.run(node.value))
self.raise_exception(node, exc=AttributeError, msg=msg)
def on_assign(self, node): # ('targets', 'value')
"""Simple assignment."""
val = self.run(node.value)
for tnode in node.targets:
self.node_assign(tnode, val)
return
def on_augassign(self, node): # ('target', 'op', 'value')
"""Augmented assign."""
return self.on_assign(ast.Assign(targets=[node.target],
value=ast.BinOp(left=node.target,
op=node.op,
right=node.value)))
def on_slice(self, node): # ():('lower', 'upper', 'step')
"""Simple slice."""
return slice(self.run(node.lower),
self.run(node.upper),
self.run(node.step))
def on_extslice(self, node): # ():('dims',)
"""Extended slice."""
return tuple([self.run(tnode) for tnode in node.dims])
def on_subscript(self, node): # ('value', 'slice', 'ctx')
"""Subscript handling -- one of the tricky parts."""
val = self.run(node.value)
nslice = self.run(node.slice)
ctx = node.ctx.__class__
if ctx in (ast.Load, ast.Store):
return val[nslice]
else:
msg = "subscript with unknown context"
self.raise_exception(node, msg=msg)
def on_delete(self, node): # ('targets',)
"""Delete statement."""
for tnode in node.targets:
if tnode.ctx.__class__ != ast.Del:
break
children = []
while tnode.__class__ == ast.Attribute:
children.append(tnode.attr)
tnode = tnode.value
if (tnode.__class__ == ast.Name and
tnode.id not in self.readonly_symbols):
children.append(tnode.id)
children.reverse()
self.symtable.pop('.'.join(children))
else:
msg = "could not delete symbol"
self.raise_exception(node, msg=msg)
def on_unaryop(self, node): # ('op', 'operand')
"""Unary operator."""
return op2func(node.op)(self.run(node.operand))
def on_binop(self, node): # ('left', 'op', 'right')
"""Binary operator."""
return op2func(node.op)(self.run(node.left),
self.run(node.right))
def on_boolop(self, node): # ('op', 'values')
"""Boolean operator."""
val = self.run(node.values[0])
is_and = ast.And == node.op.__class__
if (is_and and val) or (not is_and and not val):
for n in node.values[1:]:
val = op2func(node.op)(val, self.run(n))
if (is_and and not val) or (not is_and and val):
break
return val
def on_compare(self, node): # ('left', 'ops', 'comparators')
"""comparison operators, including chained comparisons (a<b<c)"""
lval = self.run(node.left)
results = []
for op, rnode in zip(node.ops, node.comparators):
rval = self.run(rnode)
ret = op2func(op)(lval, rval)
results.append(ret)
if ((self.use_numpy and not isinstance(ret, numpy.ndarray)) and
not ret):
break
lval = rval
if len(results) == 1:
return results[0]
else:
out = True
for r in results:
out = out and r
return out
def _printer(self, *out, **kws):
"""Generic print function."""
if self.no_print:
return
flush = kws.pop('flush', True)
fileh = kws.pop('file', self.writer)
sep = kws.pop('sep', ' ')
end = kws.pop('sep', '\n')
print(*out, file=fileh, sep=sep, end=end)
if flush:
fileh.flush()
def on_if(self, node): # ('test', 'body', 'orelse')
"""Regular if-then-else statement."""
block = node.body
if not self.run(node.test):
block = node.orelse
for tnode in block:
self.run(tnode)
def on_ifexp(self, node): # ('test', 'body', 'orelse')
"""If expressions."""
expr = node.orelse
if self.run(node.test):
expr = node.body
return self.run(expr)
def on_while(self, node): # ('test', 'body', 'orelse')
"""While blocks."""
while self.run(node.test):
self._interrupt = None
for tnode in node.body:
self.run(tnode)
if self._interrupt is not None:
break
if isinstance(self._interrupt, ast.Break):
break
else:
for tnode in node.orelse:
self.run(tnode)
self._interrupt = None
def on_for(self, node): # ('target', 'iter', 'body', 'orelse')
"""For blocks."""
for val in self.run(node.iter):
self.node_assign(node.target, val)
self._interrupt = None
for tnode in node.body:
self.run(tnode)
if self._interrupt is not None:
break
if isinstance(self._interrupt, ast.Break):
break
else:
for tnode in node.orelse:
self.run(tnode)
self._interrupt = None
def on_listcomp(self, node): # ('elt', 'generators')
"""List comprehension."""
out = []
for tnode in node.generators:
if tnode.__class__ == ast.comprehension:
for val in self.run(tnode.iter):
self.node_assign(tnode.target, val)
add = True
for cond in tnode.ifs:
add = add and self.run(cond)
if add:
out.append(self.run(node.elt))
return out
def on_excepthandler(self, node): # ('type', 'name', 'body')
"""Exception handler..."""
return (self.run(node.type), node.name, node.body)
def on_try(self, node): # ('body', 'handlers', 'orelse', 'finalbody')
"""Try/except/else/finally blocks."""
no_errors = True
for tnode in node.body:
self.run(tnode, with_raise=False)
no_errors = no_errors and len(self.error) == 0
if len(self.error) > 0:
e_type, e_value, e_tback = self.error[-1].exc_info
for hnd in node.handlers:
htype = None
if hnd.type is not None:
htype = __builtins__.get(hnd.type.id, None)
if htype is None or isinstance(e_type(), htype):
self.error = []
if hnd.name is not None:
self.node_assign(hnd.name, e_value)
for tline in hnd.body:
self.run(tline)
break
break
if no_errors and hasattr(node, 'orelse'):
for tnode in node.orelse:
self.run(tnode)
if hasattr(node, 'finalbody'):
for tnode in node.finalbody:
self.run(tnode)
def on_raise(self, node): # ('type', 'inst', 'tback')
"""Raise statement: note difference for python 2 and 3."""
excnode = node.exc
msgnode = node.cause
out = self.run(excnode)
msg = ' '.join(out.args)
msg2 = self.run(msgnode)
if msg2 not in (None, 'None'):
msg = "%s: %s" % (msg, msg2)
self.raise_exception(None, exc=out.__class__, msg=msg, expr='')
def on_call(self, node):
"""Function execution."""
# ('func', 'args', 'keywords'. Py<3.5 has 'starargs' and 'kwargs' too)
func = self.run(node.func)
if not hasattr(func, '__call__') and not isinstance(func, type):
msg = "'%s' is not callable!!" % (func)
self.raise_exception(node, exc=TypeError, msg=msg)
args = [self.run(targ) for targ in node.args]
starargs = getattr(node, 'starargs', None)
if starargs is not None:
args = args + self.run(starargs)
keywords = {}
if func == print:
keywords['file'] = self.writer
for key in node.keywords:
if not isinstance(key, ast.keyword):
msg = "keyword error in function call '%s'" % (func)
self.raise_exception(node, msg=msg)
if key.arg is None:
keywords.update(self.run(key.value))
elif key.arg in keywords:
self.raise_exception(node,
msg="keyword argument repeated: %s" % key.arg,
exc=SyntaxError)
else:
keywords[key.arg] = self.run(key.value)
kwargs = getattr(node, 'kwargs', None)
if kwargs is not None:
keywords.update(self.run(kwargs))
if isinstance(func, Procedure):
self._calldepth += 1
try:
out = func(*args, **keywords)
except Exception as ex:
out = None
func_name = getattr(func, '__name__', str(func))
self.raise_exception(
node, msg="Error running function call '%s' with args %s and "
"kwargs %s: %s" % (func_name, args, keywords, ex))
finally:
if isinstance(func, Procedure):
self._calldepth -= 1
return out
def on_arg(self, node): # ('test', 'msg')
"""Arg for function definitions."""
return node.arg
def on_functiondef(self, node):
"""Define procedures."""
# ('name', 'args', 'body', 'decorator_list')
if node.decorator_list:
raise Warning("decorated procedures not supported!")
kwargs = []
if (not valid_symbol_name(node.name) or
node.name in self.readonly_symbols):
errmsg = "invalid function name (reserved word?) %s" % node.name
self.raise_exception(node, exc=NameError, msg=errmsg)
offset = len(node.args.args) - len(node.args.defaults)
for idef, defnode in enumerate(node.args.defaults):
defval = self.run(defnode)
keyval = self.run(node.args.args[idef+offset])
kwargs.append((keyval, defval))
args = [tnode.arg for tnode in node.args.args[:offset]]
doc = None
nb0 = node.body[0]
if isinstance(nb0, ast.Expr) and isinstance(nb0.value, ast.Str):
doc = nb0.value.s
varkws = node.args.kwarg
vararg = node.args.vararg
if isinstance(vararg, ast.arg):
vararg = vararg.arg
if isinstance(varkws, ast.arg):
varkws = varkws.arg
self.symtable[node.name] = Procedure(node.name, self, doc=doc,
lineno=self.lineno,
body=node.body,
args=args, kwargs=kwargs,
vararg=vararg, varkws=varkws)
if node.name in self.no_deepcopy:
self.no_deepcopy.remove(node.name)
class Procedure:
"""Procedure: user-defined function for asteval.
This stores the parsed ast nodes as from the 'functiondef' ast node
for later evaluation.
"""
def __init__(self, name, interp, doc=None, lineno=0,
body=None, args=None, kwargs=None,
vararg=None, varkws=None):
"""TODO: docstring in public method."""
self.__ininit__ = True
self.name = name
self.__name__ = self.name
self.__asteval__ = interp
self.raise_exc = self.__asteval__.raise_exception
self.__doc__ = doc
self.body = body
self.argnames = args
self.kwargs = kwargs
self.vararg = vararg
self.varkws = varkws
self.lineno = lineno
self.__ininit__ = False
def __setattr__(self, attr, val):
if not getattr(self, '__ininit__', True):
self.raise_exc(None, exc=TypeError,
msg="procedure is read-only")
self.__dict__[attr] = val
def __dir__(self):
return ['name']
def __repr__(self):
"""TODO: docstring in magic method."""
sig = ""
if len(self.argnames) > 0:
sig = "%s%s" % (sig, ', '.join(self.argnames))
if self.vararg is not None:
sig = "%s, *%s" % (sig, self.vararg)
if len(self.kwargs) > 0:
if len(sig) > 0:
sig = "%s, " % sig
_kw = ["%s=%s" % (k, v) for k, v in self.kwargs]
sig = "%s%s" % (sig, ', '.join(_kw))
if self.varkws is not None:
sig = "%s, **%s" % (sig, self.varkws)
sig = "<Procedure %s(%s)>" % (self.name, sig)
if self.__doc__ is not None:
sig = "%s\n %s" % (sig, self.__doc__)
return sig
def __call__(self, *args, **kwargs):
"""TODO: docstring in public method."""
symlocals = {}
args = list(args)
nargs = len(args)
nkws = len(kwargs)
nargs_expected = len(self.argnames)
# check for too few arguments, but the correct keyword given
if (nargs < nargs_expected) and nkws > 0:
for name in self.argnames[nargs:]:
if name in kwargs:
args.append(kwargs.pop(name))
nargs = len(args)
nargs_expected = len(self.argnames)
nkws = len(kwargs)
if nargs < nargs_expected:
msg = "%s() takes at least %i arguments, got %i"
self.raise_exc(None, exc=TypeError,
msg=msg % (self.name, nargs_expected, nargs))
# check for multiple values for named argument
if len(self.argnames) > 0 and kwargs is not None:
msg = "multiple values for keyword argument '%s' in Procedure %s"
for targ in self.argnames:
if targ in kwargs:
self.raise_exc(None, exc=TypeError,
msg=msg % (targ, self.name),
lineno=self.lineno)
# check more args given than expected, varargs not given
if nargs != nargs_expected:
msg = None
if nargs < nargs_expected:
msg = 'not enough arguments for Procedure %s()' % self.name
msg = '%s (expected %i, got %i)' % (msg, nargs_expected, nargs)
self.raise_exc(None, exc=TypeError, msg=msg)
if nargs > nargs_expected and self.vararg is None:
if nargs - nargs_expected > len(self.kwargs):
msg = 'too many arguments for %s() expected at most %i, got %i'
msg = msg % (self.name, len(self.kwargs)+nargs_expected, nargs)
self.raise_exc(None, exc=TypeError, msg=msg)
for i, xarg in enumerate(args[nargs_expected:]):
kw_name = self.kwargs[i][0]
if kw_name not in kwargs:
kwargs[kw_name] = xarg
for argname in self.argnames:
symlocals[argname] = args.pop(0)
try:
if self.vararg is not None:
symlocals[self.vararg] = tuple(args)
for key, val in self.kwargs:
if key in kwargs:
val = kwargs.pop(key)
symlocals[key] = val
if self.varkws is not None:
symlocals[self.varkws] = kwargs
elif len(kwargs) > 0:
msg = 'extra keyword arguments for Procedure %s (%s)'
msg = msg % (self.name, ','.join(list(kwargs.keys())))
self.raise_exc(None, msg=msg, exc=TypeError,
lineno=self.lineno)
except (ValueError, LookupError, TypeError,
NameError, AttributeError):
msg = 'incorrect arguments for Procedure %s' % self.name
self.raise_exc(None, msg=msg, lineno=self.lineno)
save_symtable = self.__asteval__.symtable.copy()
self.__asteval__.symtable.update(symlocals)
self.__asteval__.retval = None
self.__asteval__._calldepth += 1
retval = None
# evaluate script of function
for node in self.body:
self.__asteval__.run(node, expr='<>', lineno=self.lineno)
if len(self.__asteval__.error) > 0:
break
if self.__asteval__.retval is not None:
retval = self.__asteval__.retval
self.__asteval__.retval = None
if retval is ReturnedNone:
retval = None
break
self.__asteval__.symtable = save_symtable
self.__asteval__._calldepth -= 1
symlocals = None
return retval
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1990,
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1275,
8717,
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hw0/code/hw0.py | kandluis/cs224w | 6 | 119200 | <reponame>kandluis/cs224w
# coding: utf-8
# ## 1. Analyzing the Wikipedia voters network [9 points]
# Download the Wikipedia voting network wiki-Vote.txt.gz: http://snap.stanford.edu/
# data/wiki-Vote.html.
#
# Using one of the network analysis tools above, load the Wikipedia voting network.
#
# Note that Wikipedia is a directed network. Formally, we consider the Wikipedia network as a directed graph $G = (V, E)$, with node set $V$ and edge set $E ⊂ V × V$ where (edges are ordered pairs of nodes). An edge $(a, b) ∈ E$ means that user $a$ voted on user $b$.
#
# To make our questions clearer, we will use the following small graph as a running example:
# $$
# Gsmall = (Vsmall, Esmall)
# $$
# where
# $$
# Vsmall = \{1, 2, 3\}
# $$ and
# $$
# Esmall = \{(1, 2),(2, 1),(1, 3),(1, 1)\}
# $$
# In[56]:
import snap
# In[57]:
# Load the network
SOURCE_FILE = './data/wiki-Vote.txt'
wikiGraph = snap.LoadEdgeList(snap.PNGraph, SOURCE_FILE, 0, 1)
assert 103689 == wikiGraph.GetEdges()
assert 7115 == wikiGraph.GetNodes()
# In[58]:
# 1.1
print("The number of nodes in the network is %s." % (
wikiGraph.GetNodes()))
# In[59]:
# 1.2
print("The number of nodes with a self-edge is %s." % (
snap.CntSelfEdges(wikiGraph)))
# In[60]:
# 1.3
print("The number of directed edges %s." % (
snap.CntUniqDirEdges(wikiGraph)))
# In[61]:
# 1.4
print("The number of undirected edges is %s." % (
snap.CntUniqUndirEdges(wikiGraph)))
# In[62]:
# 1.5
print("The number of reciprocated edges is %s." % (
snap.CntUniqDirEdges(wikiGraph) - snap.CntUniqUndirEdges(wikiGraph)))
# In[63]:
# 1.6
print("The number of nodes of zero out-degree is %s." % (
snap.CntOutDegNodes(wikiGraph, 0)))
# In[64]:
# 1.7
print("The number of nodes of zero in-degree is %s." % (
snap.CntInDegNodes(wikiGraph, 0)))
# In[65]:
# 1.8
DEGREE_BOUNDARY = 10
outDegreeToCount = snap.TIntPrV()
snap.GetOutDegCnt(wikiGraph, outDegreeToCount)
numNodesLargeOutDegree = sum([item.GetVal2()
for item in outDegreeToCount
if item.GetVal1() > DEGREE_BOUNDARY])
print("The number of nodes with more than %s outgoing edges is %s." % (
DEGREE_BOUNDARY, numNodesLargeOutDegree))
# In[66]:
# 1.9
inDegreeCount = snap.TIntPrV()
snap.GetOutDegCnt(wikiGraph, inDegreeCount)
numNodesSmallInDegree = sum([item.GetVal2()
for item in inDegreeCount
if item.GetVal1() < DEGREE_BOUNDARY])
print("The number of nodes with less than %s incoming edges is %s." % (
DEGREE_BOUNDARY, numNodesSmallInDegree))
# ## 2 Further Analyzing the Wikiepedia voters network
# In[67]:
import snap
import pandas as pd
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
# In[68]:
# 2.1: Histogram of out-degree distribution.
## Calculate degree and construct data frame.
outDegreeToCount = snap.TIntPrV()
snap.GetOutDegCnt(wikiGraph, outDegreeToCount)
data = pd.DataFrame([[item.GetVal1(), item.GetVal2()]
for item in outDegreeToCount
if item.GetVal1() > 0 and item.GetVal2() > 0])
data.columns = ['degree', 'count']
## Plot.
get_ipython().magic(u'matplotlib inline')
fig = plt.plot(data['degree'], data['count'], 'bo--', markersize=2)[0]
fig.axes.set_xscale('log')
fig.axes.set_yscale('log')
fig.axes.set_xlim(data['degree'].min(), data['degree'].max())
fig.axes.set_ylim(data['count'].min(), data['count'].max())
fig.axes.set_title("Log-Log Degree Distribution Plot for WikiGraph")
fig.axes.set_xlabel("Node Degree")
fig.axes.set_ylabel("Node Count")
## Save image.
plt.savefig("WikiGraphOutDegreeDistribution", format='svg', dpi=600)
plt.savefig("WikiGraphOutDegreeDistribution", dpi=600)
# In[69]:
# Alternative 2.1.
snap.PlotOutDegDistr(wikiGraph, "WikiGraph",
"WikiGraph - Out Degree Distribution")
# In[70]:
# 2.2: Compute and plot the least-square regression line.
## Calculate the best fit line on the log data.
DEGREE = 1
slope, intercept = np.polyfit(
np.log10(data['degree']),np.log10(data['count']), DEGREE)
predict = lambda x: 10**(intercept)*x**slope
## Plot.
get_ipython().magic(u'matplotlib inline')
fig = plt.plot(data['degree'], data['count'], 'bo--',
data['degree'], predict(data['degree']), 'g',
markersize=2)[0]
fig.axes.set_xscale('log')
fig.axes.set_yscale('log')
fig.axes.set_xlim(data['degree'].min(), data['degree'].max())
fig.axes.set_ylim(data['count'].min(), data['count'].max())
fig.axes.set_title("Log-Log Degree Distribution Plot And Fit for WikiGraph")
fig.axes.set_xlabel("Node Degree")
fig.axes.set_ylabel("Node Count")
## Save image.
plt.savefig("WikiGraphOutDegreeDistributionFit", format='svg', dpi=600)
plt.savefig("WikiGraphOutDegreeDistributionFit", dpi=600)
# In[71]:
## Print the results.
print("The best fit line is given by equation "
"log(y) = %s * log(x) + %s" % (slope, intercept))
# In[72]:
# Alternative 2.1.
snap.PlotOutDegDistr(wikiGraph, "WikiGraphFit",
"WikiGraph - Out Degree Distribution with Fit",
False, True)
print("The best fit line is given by equation "
"log(y) = -1.411 * log(x) + 3.3583 with R^2=0.91")
# ## 3 Finding Experts on the Java Programming Language on StackOverflow [4 points]
#
# Download the StackOverflow network stackoverflow-Java.txt.gz: http://snap.stanford.edu/class/cs224w-data/hw0/stackoverflow-Java.txt.gz.
#
# An edge (a, b) in the network means that person a endorsed an answer from person b on a Java-related question.
# In[73]:
import snap
# In[74]:
# Load the network
SOURCE_FILE = './data/stackoverflow-Java.txt'
SOGraph = snap.LoadEdgeList(snap.PNGraph, SOURCE_FILE, 0, 1)
assert 146874 == SOGraph.GetNodes()
assert 333606 == SOGraph.GetEdges()
# In[75]:
# 3.1
components = snap.TCnComV()
snap.GetWccs(SOGraph, components)
print("The number of weakly connected components in the SO network"
"is %s." % (len(components)))
# In[76]:
# 3.2
maxWeaklyConnectedComponent = snap.GetMxWcc(SOGraph)
print("The largest weakly connected component in the SO network"
"has %s nodes and %s edges." % (
maxWeaklyConnectedComponent.GetNodes(),
maxWeaklyConnectedComponent.GetEdges()))
# In[77]:
def sortTIntFltH(mapping, desc = True):
return sorted([(nodeId, mapping[nodeId])
for nodeId in mapping
], reverse=desc,
key = lambda x: x[1])
# In[78]:
# 3.3
TOPN = 3
SOPageRanks = snap.TIntFltH()
snap.GetPageRank(SOGraph, SOPageRanks, 0.85, 1e-4, 1000)
sortedSOPageRanks = sortTIntFltH(SOPageRanks)
print("The node IDs of the top %s most central nodes in the network "
"by PageRank scores are %s with scores %s respectively." % (
TOPN,
tuple(t[0] for t in sortedSOPageRanks[:TOPN]),
tuple(t[1] for t in sortedSOPageRanks[:TOPN])))
# In[79]:
# 3.4
TOPN = 3
hubsScores = snap.TIntFltH()
authScores = snap.TIntFltH()
snap.GetHits(SOGraph, hubsScores, authScores, 100)
sortedHubScores = sortTIntFltH(hubsScores)
sortedAuthScores = sortTIntFltH(authScores)
print("The node IDs of the top %s hubs in the network by HITS scores "
"are %s with scores %s respectively." % (
TOPN,
tuple(t[0] for t in sortedHubScores[:TOPN]),
tuple(t[1] for t in sortedHubScores[:TOPN])))
print
print("The node IDs of the top %s authorities in the network by HITS "
"scores are %s with score %s respectively." % (
TOPN,
tuple(t[0] for t in sortedAuthScores[:TOPN]),
tuple(t[1] for t in sortedAuthScores[:TOPN])))
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] |
deepthinking/utils/prefix_sums_data.py | aks2203/deep-thinking | 19 | 101752 | """ prefix_sums_data.py
Prefix sum related dataloaders
Collaboratively developed
by <NAME>, <NAME>,
<NAME>, and <NAME>.
Developed for DeepThinking project
October 2021
"""
import torch
from torch.utils import data
from easy_to_hard_data import PrefixSumDataset
# Ignore statemenst for pylint:
# Too many branches (R0912), Too many statements (R0915), No member (E1101),
# Not callable (E1102), Invalid name (C0103), No exception (W0702),
# Too many local variables (R0914), Missing docstring (C0116, C0115),
# Unused import (W0611).
# pylint: disable=R0912, R0915, E1101, E1102, C0103, W0702, R0914, C0116, C0115, W0611
def prepare_prefix_loader(train_batch_size, test_batch_size, train_data, test_data,
train_split=0.8, shuffle=True):
dataset = PrefixSumDataset("../../../data", num_bits=train_data)
testset = PrefixSumDataset("../../../data", num_bits=test_data)
train_split = int(train_split * len(dataset))
trainset, valset = torch.utils.data.random_split(dataset,
[train_split,
int(len(dataset) - train_split)],
generator=torch.Generator().manual_seed(42))
trainloader = data.DataLoader(trainset, num_workers=0, batch_size=train_batch_size,
shuffle=shuffle, drop_last=True)
testloader = data.DataLoader(testset, num_workers=0, batch_size=test_batch_size,
shuffle=False, drop_last=False)
valloader = data.DataLoader(valset, num_workers=0, batch_size=test_batch_size,
shuffle=False, drop_last=False)
loaders = {"train": trainloader, "test": testloader, "val": valloader}
return loaders
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tqsdk/demo/example/stgy4zd.py | boyscout2008/tqsdk-python | 0 | 176617 | <filename>tqsdk/demo/example/stgy4zd.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'Golden'
'''
震荡品种策略函数：两波多后的大空，一波两浪多后的空
实测和效果见策略执行文件
'''
import talib
#两波多后的空策略
def dual_bo_duo(quote, klines, logger):
df = klines.to_dataframe()
if len(df) < 20:
return False
#logger.info("klines.high: %f, klines.close: %f" % (klines.high[-1], klines.close[-1]))
ma5 = talib.MA(df.close, timeperiod=5)
ma10 = talib.MA(df.close, timeperiod=10)
#STEP1： 明确当前一波收新高多中 --- 收阳收新高
if klines.close[-1] > max(klines.high[-9:-1]) and klines.close[-1] > klines.open[-1]:
#STEP2：找出这波多之前是否存在另一波多 --- 至少前1(主多)+4（调整）站稳在10日之上 + 5日线高于10日线 + 之前有背离5日线的阳线
if (klines.close[14:20] > ma10[14:20]).all() and (ma10[16:20] < ma5[16:20]).all() and (klines.close[0:15] > ma5[0:15]*1.02).any():
return True
return False
#一波两浪多后的空策略
def yi_bo_duo(quote, klines, logger):
df = klines.to_dataframe()
if len(df) < 20:
return False
#logger.info("klines.high: %f, klines.close: %f" % (klines.high[-1], klines.close[-1]))
ma5 = talib.MA(df.close, timeperiod=5)
ma10 = talib.MA(df.close, timeperiod=10)
#STEP1：当日背离中大阳或背离小阳 + 5日线高于10日线
if klines.close[-1] > klines.open[-1] and ma5[19] > ma10[19] \
and klines.close[-1] > ma5[19]*1.025:
#STEP2：近4日还有背离中大阳
if (klines.close[15:17] > ma5[15:17]*1.02).any():
return True
return False | [
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dvc/remote/gdrive/utils.py | Baranowski/dvc | 0 | 41650 | <reponame>Baranowski/dvc<gh_stars>0
import os
from dvc.progress import Tqdm
FOLDER_MIME_TYPE = "application/vnd.google-apps.folder"
class TrackFileReadProgress(object):
def __init__(self, progress_name, fobj):
self.progress_name = progress_name
self.fobj = fobj
file_size = os.fstat(fobj.fileno()).st_size
self.tqdm = Tqdm(desc=self.progress_name, total=file_size)
def read(self, size):
self.tqdm.update(size)
return self.fobj.read(size)
def close(self):
self.fobj.close()
self.tqdm.close()
def __getattr__(self, attr):
return getattr(self.fobj, attr)
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build_tools/typhoon-blade/src/blade/blade_platform.py | algo-data-platform/PredictorService | 2 | 145245 | # Copyright (c) 2011 Tencent Inc.
# All rights reserved.
#
# Author: <NAME> <<EMAIL>>
# Date: October 20, 2011
"""
This is the blade_platform module which deals with the environment
variable.
"""
import os
import subprocess
import configparse
import console
from blade_util import var_to_list
class BuildArchitecture(object):
"""
The BuildArchitecture class manages architecture/bits configuration
across various platforms/compilers combined with the input from
command line.
"""
_build_architecture = {
'i386' : {
'alias' : ['x86'],
'bits' : '32',
},
'x86_64' : {
'alias' : ['amd64'],
'bits' : '64',
'models' : {
'32' : 'i386',
}
},
'ppc' : {
'alias' : ['powerpc'],
'bits' : '32',
},
'ppc64' : {
'alias' : ['powerpc64'],
'bits' : '64',
'models' : {
'32' : 'ppc',
}
},
'ppc64le' : {
'alias' : ['powerpc64le'],
'bits' : '64',
'models' : {
'32' : 'ppcle',
}
},
}
@staticmethod
def get_canonical_architecture(arch):
"""Get the canonical architecture from the specified arch. """
canonical_arch = None
for k, v in BuildArchitecture._build_architecture.iteritems():
if arch == k or arch in v['alias']:
canonical_arch = k
break
return canonical_arch
@staticmethod
def get_architecture_bits(arch):
"""Get the architecture bits. """
arch = BuildArchitecture.get_canonical_architecture(arch)
if arch:
return BuildArchitecture._build_architecture[arch]['bits']
return None
@staticmethod
def get_model_architecture(arch, bits):
"""
Get the model architecture from the specified arch and bits,
such as, if arch is x86_64 and bits is '32', then the resulting
model architecture is i386 which effectively means building
32 bit target in a 64 bit environment.
"""
arch = BuildArchitecture.get_canonical_architecture(arch)
if arch:
if bits == BuildArchitecture._build_architecture[arch]['bits']:
return arch
models = BuildArchitecture._build_architecture[arch].get('models')
if models and bits in models:
return models[bits]
return None
class BuildPlatform(object):
"""The build platform class which handles and gets the platform info. """
def __init__(self):
"""Init. """
self.gcc_version = self._get_gcc_version()
self.python_inc = self._get_python_include()
self.php_inc_list = self._get_php_include()
self.java_inc_list = self._get_java_include()
self.nvcc_version = self._get_nvcc_version()
self.cuda_inc_list = self._get_cuda_include()
@staticmethod
def _get_gcc_version():
"""Get the gcc version. """
gcc = os.path.join(os.environ.get('TOOLCHAIN_DIR', ''),
os.environ.get('CC', 'gcc'))
returncode, stdout, stderr = BuildPlatform._execute(gcc + ' -dumpversion')
if returncode == 0:
return stdout.strip()
return ''
@staticmethod
def _get_cc_target_arch():
"""Get the cc target architecture. """
gcc = os.path.join(os.environ.get('TOOLCHAIN_DIR', ''),
os.environ.get('CC', 'gcc'))
returncode, stdout, stderr = BuildPlatform._execute(gcc + ' -dumpmachine')
if returncode == 0:
return stdout.strip()
return ''
@staticmethod
def _get_nvcc_version():
"""Get the nvcc version. """
nvcc = os.environ.get('NVCC', 'nvcc')
returncode, stdout, stderr = BuildPlatform._execute(nvcc + ' --version')
if returncode == 0:
version_line = stdout.splitlines(True)[-1]
version = version_line.split()[5]
return version
return ''
@staticmethod
def _get_python_include():
"""Get the python include dir. """
returncode, stdout, stderr = BuildPlatform._execute('python-config --includes')
if returncode == 0:
include_line = stdout.splitlines(True)[0]
header = include_line.split()[0][2:]
return header
return ''
@staticmethod
def _get_php_include():
returncode, stdout, stderr = BuildPlatform._execute('php-config --includes')
if returncode == 0:
include_line = stdout.splitlines(True)[0]
headers = include_line.split()
header_list = ["'%s'" % s[2:] for s in headers]
return header_list
return []
@staticmethod
def _get_java_include():
include_list = []
java_home = os.environ.get('JAVA_HOME', '')
if java_home:
include_list.append('%s/include' % java_home)
include_list.append('%s/include/linux' % java_home)
return include_list
returncode, stdout, stderr = BuildPlatform._execute(
'java -version', redirect_stderr_to_stdout = True)
if returncode == 0:
version_line = stdout.splitlines(True)[0]
version = version_line.split()[2]
version = version.replace('"', '')
include_list.append('/usr/java/jdk%s/include' % version)
include_list.append('/usr/java/jdk%s/include/linux' % version)
return include_list
return []
@staticmethod
def _get_cuda_include():
include_list = []
cuda_path = os.environ.get('CUDA_PATH')
if cuda_path:
include_list.append('%s/include' % cuda_path)
include_list.append('%s/samples/common/inc' % cuda_path)
return include_list
returncode, stdout, stderr = BuildPlatform._execute('nvcc --version')
if returncode == 0:
version_line = stdout.splitlines(True)[-1]
version = version_line.split()[4]
version = version.replace(',', '')
if os.path.isdir('/usr/local/cuda-%s' % version):
include_list.append('/usr/local/cuda-%s/include' % version)
include_list.append('/usr/local/cuda-%s/samples/common/inc' % version)
return include_list
return []
@staticmethod
def _execute(cmd, redirect_stderr_to_stdout = False):
redirect_stderr = subprocess.PIPE
if redirect_stderr_to_stdout:
redirect_stderr = subprocess.STDOUT
p = subprocess.Popen(cmd,
env=os.environ,
stderr=redirect_stderr,
stdout=subprocess.PIPE,
shell=True,
universal_newlines=True)
stdout, stderr = p.communicate()
return p.returncode, stdout, stderr
def get_gcc_version(self):
"""Returns gcc version. """
return self.gcc_version
def get_python_include(self):
"""Returns python include. """
return self.python_inc
def get_php_include(self):
"""Returns a list of php include. """
return self.php_inc_list
def get_java_include(self):
"""Returns a list of java include. """
return self.java_inc_list
def get_nvcc_version(self):
"""Returns nvcc version. """
return self.nvcc_version
def get_cuda_include(self):
"""Returns a list of cuda include. """
return self.cuda_inc_list
class CcFlagsManager(object):
"""The CcFlagsManager class.
This class manages the compile warning flags.
"""
def __init__(self, options, build_dir, gcc_version):
self.cc = ''
self.options = options
self.build_dir = build_dir
self.gcc_version = gcc_version
def _filter_out_invalid_flags(self, flag_list, language='c'):
"""Filter the unsupported compilation flags. """
supported_flags, unsupported_flags = [], []
# Put compilation output into test.o instead of /dev/null
# because the command line with '--coverage' below exit
# with status 1 which makes '--coverage' unsupported
# echo "int main() { return 0; }" | gcc -o /dev/null -c -x c --coverage - > /dev/null 2>&1
obj = os.path.join(self.build_dir, 'test.o')
for flag in var_to_list(flag_list):
cmd = ('echo "int main() { return 0; }" | '
'%s -o %s -c -x %s %s - > /dev/null 2>&1 && rm -f %s' % (
self.cc, obj, language, flag, obj))
if subprocess.call(cmd, shell=True) == 0:
supported_flags.append(flag)
else:
unsupported_flags.append(flag)
if unsupported_flags:
console.warning('Unsupported C/C++ flags: %s' %
', '.join(unsupported_flags))
return supported_flags
def set_cc(self, cc):
"""set up the compiler. """
self.cc = cc
def get_flags_except_warning(self):
"""Get the flags that are not warning flags. """
global_config = configparse.blade_config.get_config('global_config')
cc_config = configparse.blade_config.get_config('cc_config')
flags_except_warning = ['-m%s' % self.options.m, '-mcx16', '-pipe']
linkflags = ['-m%s' % self.options.m]
# Debugging information setting
debug_info_level = global_config['debug_info_level']
debug_info_options = cc_config['debug_info_levels'][debug_info_level]
flags_except_warning += debug_info_options
# Option debugging flags
if self.options.profile == 'debug':
flags_except_warning.append('-fstack-protector')
elif self.options.profile == 'release':
flags_except_warning.append('-DNDEBUG')
flags_except_warning += [
'-D_FILE_OFFSET_BITS=64',
'-D__STDC_CONSTANT_MACROS',
'-D__STDC_FORMAT_MACROS',
'-D__STDC_LIMIT_MACROS',
]
if getattr(self.options, 'gprof', False):
flags_except_warning.append('-pg')
linkflags.append('-pg')
if getattr(self.options, 'coverage', False):
if self.gcc_version > '4.1':
flags_except_warning.append('--coverage')
linkflags.append('--coverage')
else:
flags_except_warning.append('-fprofile-arcs')
flags_except_warning.append('-ftest-coverage')
linkflags += ['-Wl,--whole-archive', '-lgcov',
'-Wl,--no-whole-archive']
flags_except_warning = self._filter_out_invalid_flags(
flags_except_warning)
return (flags_except_warning, linkflags)
def get_warning_flags(self):
"""Get the warning flags. """
cc_config = configparse.blade_config.get_config('cc_config')
cppflags = cc_config['warnings']
cxxflags = cc_config['cxx_warnings']
cflags = cc_config['c_warnings']
filtered_cppflags = self._filter_out_invalid_flags(cppflags)
filtered_cxxflags = self._filter_out_invalid_flags(cxxflags, 'c++')
filtered_cflags = self._filter_out_invalid_flags(cflags, 'c')
return (filtered_cppflags, filtered_cxxflags, filtered_cflags)
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] |
tests/test_dynamic_growths.py | zerorock1312/lt-maker-master | 0 | 146062 | import random
"""
With 50% growth, 38 level-ups
random level-up variance of 2.5 stats off average
dynamic level-up variance of 0.8 stats off average
With 30% growth, 38 level-ups
random level-up variance of 2.3 stats off average
dynamic level-up variance of 0.7 stats off average
With 10% growth, 38 level-ups
random level-up variance of 1.6 stats off average
dyanmic level-up variance of 0.5 stats off average
With 90% growth, 38 level-ups
random level-up variance of 2.25 stats off average
dynamic level-up variane of 0.7 stats off average
"""
growth = 25
constant = 10.
num_trials = 1000
dynamic_variance = 0
random_variance = 0
ddirectionality = 0
rdirectionality = 0
for _ in range(num_trials):
num_levels = 19
avg_stat = num_levels * (growth / 100.)
growth_points = 0
dynamic_stat = 0
for num in range(num_levels):
start_growth = growth + growth_points
if num_trials == 1:
print(start_growth)
if start_growth <= 0:
growth_points += growth/5.
else:
num_tries = growth // 100
free_levels = num_tries
dynamic_stat += free_levels
new_growth = growth % 100
start_growth = new_growth + growth_points
val = random.randint(0, 99)
if num_trials == 1:
print("Roll: %d" % val)
if val < int(start_growth):
dynamic_stat += 1
growth_points -= (100 - new_growth)/constant
else:
growth_points += new_growth/constant
random_stat = 0
for num in range(num_levels):
start_growth = growth
while start_growth > 0:
val = random.randint(0, 99)
if val < int(start_growth):
random_stat += 1
start_growth -= 100
if num_trials == 1:
print("Average Stat: %f" % avg_stat)
print("Dynamic Stat: %f" % dynamic_stat)
print("Random Stat: %f" % random_stat)
dynamic_variance += abs(dynamic_stat - avg_stat)
ddirectionality += dynamic_stat - avg_stat
random_variance += abs(random_stat - avg_stat)
rdirectionality += random_stat - avg_stat
if abs(dynamic_stat - avg_stat) > 5:
print(dynamic_stat, avg_stat)
print("Dynamic Variance: %f" % (dynamic_variance/num_trials))
print("Random Variance: %f" % (random_variance/num_trials))
print("Dynamic Directionality: %f" % (ddirectionality/num_trials))
print("Random Directionality: %f" % (rdirectionality/num_trials))
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sug-blog/unit4/account.py | SolangeUG/blog-application | 1 | 101515 | <filename>sug-blog/unit4/account.py
import handler.handler as handler
import util.security as security
import util.validator as validator
from google.appengine.ext import db
class AccountHandler(handler.TemplateHandler):
"""
AccountHandler inherits from the hander.TemplateHandler class.
It gives users the possibility to signup for, or log into, an account.
"""
def get(self):
self.render("accounts.html")
class SignupHandler(handler.TemplateHandler):
"""
SignupHandler inherits from the hander.TemplateHandler class.
It aggregates methods that offer users the possibility to create and persist an account.
"""
def get(self):
self.render("signup.html")
def post(self):
username = self.request.get('username')
password = self.request.get('password')
cfpassword = self.request.get('verify')
user_email = self.request.get('email')
comments = self.request.get('text')
if validator.validate_user(username, password, cfpassword, user_email):
# check if the input username is unique
query = db.Query(Account)
query.filter('username =', username)
known_account = query.get()
if known_account:
# ask the user to choose a different username
username_error = "Username already exists"
self.render("signup.html", username=username, username_error=username_error)
else:
# create and persist an account for the user
hashed_password = security.hash_password(password)
new_account = Account(username=username, password=<PASSWORD>, email=user_email, comments=comments)
key = new_account.put()
self.response.headers['Content-Type'] = 'text/plain'
# set a cookie with the username
user_cookie = security.make_secure_val(username)
self.response.set_cookie('user', str(user_cookie), max_age=3600, path='/')
self.response.set_cookie('account_key', str(key), max_age=360, path='/')
self.redirect('/account_created')
else:
username_error = ""
password_error = ""
cfpassword_error = ""
email_error = ""
if not validator.is_username_valid(username):
username_error = "Invalid username!"
if not validator.is_password_valid(password):
password_error = "Invalid password!"
if not validator.is_cfpassword_valid(password, cfpassword):
cfpassword_error = "Your passwords don't match!"
if not validator.is_email_valid(user_email):
email_error = "Invalid email!"
self.render("signup.html",
username=username,
username_error=username_error,
password_error=password_error,
cfpassword_error=cfpassword_error,
email=user_email,
email_error=email_error,
comments=comments)
class WelcomeHandler(handler.TemplateHandler):
"""
WelcomeHandler inherits from the handler.TemplateHandler class.
Its sole purpose is to confirm user account signup, or redirect
them to the signup page if the input information isn't valid.
"""
def get(self):
user_cookie = self.request.cookies.get('user')
account_key = self.request.cookies.get('account_key')
# retrieve username from cookie
if user_cookie:
username = security.check_secure_val(user_cookie)
# retrieve user account by their username from the datastore
query = db.Query(Account)
query.filter('username =', username)
account = query.get()
# if the above attempt doesn't work, try retrieving by the account key
if not account:
if account_key:
account_key = db.Key(account_key)
account = db.get(account_key)
# make sure we have a valid account before proceeding
if account:
self.render("account.html", username=account.username,
email=account.email, creation_date=account.created, comments=account.comments)
else:
if user_cookie:
# in case the user couldn't be found but a cookie with a username had been set
self.response.unset_cookie('user')
if account_key:
# in case the user couldn't be found but a cookie with an account key had been set
self.response.unset_cookie('account_key')
username_error = "Unknown username: " + username
self.render("login.html", username_error=username_error)
else:
self.redirect('/account_signup')
def post(self):
# when the logout button is clicked, redirect to the logout page
self.redirect('/account_logout')
class LoginHandler(handler.TemplateHandler):
"""
LoginHandler inherits from the handler.TemplateHandler class.
It aggregates methods that let users sign into and view their account information.
"""
def get(self):
self.render("login.html")
def post(self):
username = self.request.get('username')
password = self.request.get('password')
if username and password:
# retrieve user from the datastore
query = db.Query(Account)
query.filter('username =', username)
account = query.get()
if account:
# verify input password
if security.check_password(password, account.password):
self.response.headers['Content-Type'] = 'text/plain'
# set a cookie with the username
user_cookie = security.make_secure_val(account.username)
self.response.set_cookie('user', str(user_cookie), max_age=3600, path='/')
self.redirect("/account_created")
else:
# the input password is not valid
message = "Invalid password!"
self.render("login.html", password_error=message)
else:
# the input username is unknown
message = "Invalid username!"
self.render("login.html", username_error=message)
else:
# no username or password were input
self.render("login.html", username_error="Please input valid usename!",
password_error="<PASSWORD>!")
class LogoutHandler(handler.TemplateHandler):
"""
LoginHandler inherits from the handler.TemplateHandler class.
It allows users to sign out of their accounts.
"""
def get(self):
# clear out any account cookie that might have been set
self.response.headers.add_header('Set-Cookie', 'user=%s; Path=/' % str())
self.response.headers.add_header('Set-Cookie', 'account_key=%s; Path=/' % str())
# all we ever do from here is go back to the general accounts page
self.redirect('/account')
class Account(db.Model):
"""
This class inherits from the GAE db.Model (entity) class, and represents a user account.
A user account is made of the following properties:
- username : the username chosen by the user
- password : the <PASSWORD> of the password chosen by the user
- email : the user's email address
- comments : the user's comments upon account creation
- created : creation date and time of user account
"""
username = db.StringProperty(required=True, indexed=True)
password = db.StringProperty(required=True)
email = db.StringProperty()
comments = db.TextProperty()
created = db.DateTimeProperty(auto_now_add=True)
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tst/test_features_extraction.py | mitchellkrogza/Malicious-Web-Content-Detection-Using-Machine-Learning | 0 | 67966 | <gh_stars>0
# This file will test all the functionality of features_extraction.py
import unittest
from features_extraction import *
class TestFeaturesExtraction(unittest.TestCase):
def test_having_ip_address(self):
ipv4_address = "172.16.31.10"
ipv6_address_1 = "fe80:0:0:0:204:61ff:fe9d:f156"
ipv6_address_2 = "fe80::204:61ff:fe9d:f156"
ipv6_address_3 = "fe80:0000:0000:0000:0204:61ff:fe9d:f156"
ipv6_address_4 = "fe80:0:0:0:0204:61ff:254.157.241.86"
url_1 = "www.google.com"
url_2 = "888.com"
# IP address cases
self.assertEqual(having_ip_address(ipv4_address), -1, "Given input URL has an IP address.")
self.assertEqual(having_ip_address(ipv6_address_1), -1, "Given input URL has an IP address.")
self.assertEqual(having_ip_address(ipv6_address_2), -1, "Given input URL has an IP address.")
self.assertEqual(having_ip_address(ipv6_address_3), -1, "Given input URL has an IP address.")
self.assertEqual(having_ip_address(ipv6_address_4), -1, "Given input URL has an IP address.")
# Non IP address cases
self.assertEqual(having_ip_address(url_1), 1, "Given input URL does not have an IP address.")
self.assertEqual(having_ip_address(url_2), 1, "Given input URL does not have an IP address.")
def test_shortening_services(self):
url_1 = "bit.ly/akhd9a9"
url_2 = "http://goo.gl/shan78a"
url_3 = "https://github.com/philomathic-guy"
# Shortening services links
self.assertEqual(shortening_service(url_1), -1, "Given input URL is a shortening service URL.")
self.assertEqual(shortening_service(url_2), -1, "Given input URL is a shortening service URL.")
# Non-shortening services links
self.assertEqual(shortening_service(url_3), 1, "Given input URL is a non-shortening service URL.")
def test_url_length(self):
# Short URL.
url_1 = "https://docs.python.org/2/library/re.html"
self.assertEqual(url_length(url_1), 1, "The URL length is not suspicious.")
# Long URL
url_2 = "https://myfunds.000webhostapp.com/new_now/8f66d5a47bf3ec8e6c1ag6s3dc770001a4bd/"
self.assertEqual(url_length(url_1), -1, "The URL length is suspicious.")
if __name__ == "__main__":
unittest.main()
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examples/producer_consumer.py | ctismer/goless | 1 | 133829 | <gh_stars>1-10
"""
This file demonstrates a simple producer/consumer setup.
The produce goroutine generates some data and sends on a channel,
and the consume goroutine receives on the channel and processes
the data.
This is one of the simplest models of concurrent programming
and very easy with goless.
Code modeled on http://www.golangpatterns.info/concurrency/producer-consumer.
"""
import sys
import goless
def main():
done = goless.chan()
msgs = goless.chan()
out = goless.chan()
def produce():
for i in xrange(10):
msgs.send(i)
done.send()
def consume(name):
for msg in msgs:
out.send('%s:%s ' % (name,msg))
def logger():
for msg in out:
sys.stdout.write(msg)
sys.stdout.write('\n')
goless.go(produce)
goless.go(consume, "one")
goless.go(consume, "two")
goless.go(logger)
done.recv()
if __name__ == '__main__':
main()
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2396,
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580,
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server.py | BoziDaniel/textAdv | 0 | 1607255 | <gh_stars>0
from flask import Flask, render_template, url_for
app = Flask(__name__)
@app.route('/')
@app.route('/main_menu')
def route_main():
return render_template("main.html")
@app.route('/game')
def route_play_game():
return render_template('game.html')
if __name__ == '__main__':
app.run(
port=5000,
debug=True,
)
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src/genie/libs/parser/iosxr/tests/ShowIpInterfaceBrief/cli/equal/golden_output4_expected.py | balmasea/genieparser | 204 | 188709 | <gh_stars>100-1000
expected_output = {
"interface": {
"Loopback0": {
"interface_status": "Up",
"ip_address": "192.168.3.11",
"protocol_status": "Up"
},
"MgmtEth0/RSP0/CPU0/0": {
"interface_status": "Up",
"ip_address": "172.16.17.32",
"protocol_status": "Up"
},
"MgmtEth0/RSP0/CPU0/1": {
"interface_status": "Shutdown",
"ip_address": "unassigned",
"protocol_status": "Down"
},
"MgmtEth0/RSP1/CPU0/0": {
"interface_status": "Up",
"ip_address": "172.16.58.3",
"protocol_status": "Up"
},
"MgmtEth0/RSP1/CPU0/1": {
"interface_status": "Shutdown",
"ip_address": "unassigned",
"protocol_status": "Down"
}
}
}
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NN_autograd_bias.py | JaPhi/NN_autograd_bias | 1 | 125676 | import autograd.numpy as np
from matplotlib import pyplot, cm
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from numba import njit, prange
import numba
import time
from autograd import grad, elementwise_grad, jacobian
def feed_forward(X, Y, W0, W1, W2, W3, W4, return_loss=True):
''' simple 4-layer feed forward neural net. Gradients are caluclated with
the autograd package that performs automatic differentiation.
Layers are hardcoded but can easily be modified.
Parameters
----------
X : is a 2-D array with shape (m, n) where m is the number of input variables
and n the number of dimensions. Here for a 2-D problem we have n = 2 + 1
because of the bias. Last row of the input array has to be all "1".
Y : Target vector with shape (m, n) where m is the number of corresponding
output values and n is only one)
W0...W4 : pre-created weight arrays. Used dimensions need to allow for matrix multiplications
return_loss : flag to switch between the loss and the actual result
Returns
-------
loss: l2 error metric loss
l5 : value of the very last layer for evaluation
'''
def ReLU(x, deriv=False):
if(deriv == True):
return 1 * (x > 0)
return x * (x > 0)
def lin(x, deriv=False):
if(deriv == True):
return 1
return x
def sigmoid(x, deriv=False):
if(deriv == True):
return x*(1-x)
return 1/(1+np.exp(-x))
def leakyReLU(x, deriv=False):
if(deriv == True):
return np.where(x > 0, 1, 0.01)
return x * (x > 0) + 0.01 * x * (x < 0)
l0 = X
l1 = ReLU(np.dot(l0, W0))
l2 = ReLU(np.dot(l1, W1))
l3 = ReLU(np.dot(l2, W2))
l4 = ReLU(np.dot(l3, W3))
l5 = lin(np.dot(l4, W4))
if return_loss == True:
loss = (Y-l5)**2 # simple quadratic loss
return loss
else:
return l5
# define input array
X = np.array([[-1, -1], [1, -1], [1, 1], [-1, 1], [0.5, 0.5],
[0.5, -0.5], [-0.5, 0.5], [-0.5, -0.5], [0, 0], [0, 0]])
X = np.hstack((X, np.ones((np.shape(X)[0], 1)))) # add bias
# define output array
Y = np.array([[0.3], [0.3], [0.5], [0.6], [0.5],
[0.5], [0.5], [0.7], [0.8], [0.85]])
# create weight arrays with random initialization. Zero centered
W0 = (2*np.random.random((2+1, 128)) - 1)*0.2
W1 = (2*np.random.random((128, 64)) - 1)*0.2
W2 = (2*np.random.random((64, 32)) - 1)*0.2
W3 = (2*np.random.random((32, 16)) - 1)*0.2
W4 = (2*np.random.random((16, 1)) - 1)*0.2
# optimizers like adam and rmp-prob need more array space to save values
W0_momentum = np.zeros_like(W0)
W1_momentum = np.zeros_like(W1)
W2_momentum = np.zeros_like(W2)
W3_momentum = np.zeros_like(W3)
W4_momentum = np.zeros_like(W4)
W0_history = np.zeros_like(W0)
W1_history = np.zeros_like(W1)
W2_history = np.zeros_like(W2)
W3_history = np.zeros_like(W3)
W4_history = np.zeros_like(W4)
# set up figure for 3-D plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# start main iteration loop
wframe = None
for i in range(0, 10000):
print("Iteration {}".format(i))
if wframe:
ax.collections.remove(wframe)
ax.collections.remove(scatter)
a = 0.001
eps = 1*10**-8
beta1 = 0.9
beta2 = 0.95
optimizer = "adam"
W0_grad = elementwise_grad(feed_forward, 2)(X, Y, W0, W1, W2, W3, W4)
W1_grad = elementwise_grad(feed_forward, 3)(X, Y, W0, W1, W2, W3, W4)
W2_grad = elementwise_grad(feed_forward, 4)(X, Y, W0, W1, W2, W3, W4)
W3_grad = elementwise_grad(feed_forward, 5)(X, Y, W0, W1, W2, W3, W4)
W4_grad = elementwise_grad(feed_forward, 6)(X, Y, W0, W1, W2, W3, W4)
if optimizer == "rmsprop":
W0_history = W0_history * beta1 + (1 - beta1) * W0_grad**2
W1_history = W1_history * beta1 + (1 - beta1) * W1_grad**2
W2_history = W2_history * beta1 + (1 - beta1) * W2_grad**2
W3_history = W3_history * beta1 + (1 - beta1) * W3_grad**2
W0 = W0 - (a/np.sqrt(W0_history + eps)) * W0_grad
W1 = W1 - (a/np.sqrt(W1_history + eps)) * W1_grad
W2 = W2 - (a/np.sqrt(W2_history + eps)) * W2_grad
W3 = W3 - (a/np.sqrt(W3_history + eps)) * W3_grad
if optimizer == "adam":
W0_momentum = beta1 * W0_momentum + (1 - beta1) * W0_grad
W1_momentum = beta1 * W1_momentum + (1 - beta1) * W1_grad
W2_momentum = beta1 * W2_momentum + (1 - beta1) * W2_grad
W3_momentum = beta1 * W3_momentum + (1 - beta1) * W3_grad
W4_momentum = beta1 * W4_momentum + (1 - beta1) * W4_grad
W0_history = W0_history * beta2 + (1 - beta2) * W0_grad**2
W1_history = W1_history * beta2 + (1 - beta2) * W1_grad**2
W2_history = W2_history * beta2 + (1 - beta2) * W2_grad**2
W3_history = W3_history * beta2 + (1 - beta2) * W3_grad**2
W4_history = W4_history * beta2 + (1 - beta2) * W4_grad**2
W0 = W0 - (a / np.sqrt(W0_history + eps)) * W0_momentum
W1 = W1 - (a / np.sqrt(W1_history + eps)) * W1_momentum
W2 = W2 - (a / np.sqrt(W2_history + eps)) * W2_momentum
W3 = W3 - (a / np.sqrt(W3_history + eps)) * W3_momentum
W4 = W4 - (a / np.sqrt(W4_history + eps)) * W4_momentum
if optimizer == "sgd":
W0 = W0 - a * W0_grad
W1 = W1 - a * W1_grad
W2 = W2 - a * W2_grad
W3 = W3 - a * W3_grad
# set up mesh grid
xs = np.linspace(-1, 1, 40)
ys = np.linspace(-1, 1, 40)
Xs, Ys = np.meshgrid(xs, ys)
# convert grid to neural net input dimensions
Xnet = np.hstack((Xs.reshape((40*40, 1)), Ys.reshape((40*40, 1))))
Xnet = np.hstack((Xnet, np.ones((np.shape(Xnet)[0], 1)))) # add bias
# evaluate net for current results. Return shape to grid-like structure
Znet = feed_forward(Xnet, Y, W0, W1, W2, W3, W4, return_loss=False)
Znet = Znet.reshape((40, 40))
# plot results
ax.set_xlabel('$X$')
ax.set_ylabel('$Y$')
ax.set_zlabel('$Z$')
ax.set_zlim3d(0, 1)
wframe = ax.plot_surface(Xs, Ys, Znet, cmap="CMRmap")
scatter = ax.scatter(X[:, 0], X[:, 1], Y, c="b", marker="o")
plt.pause(.00001)
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src/analysis.py | luca-bottero/Parallel_n_body | 2 | 191905 | <gh_stars>1-10
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
class DataAnalyzer():
def __init__(self, ResultsPath = ''):
try:
self.PosHistory = np.load(ResultsPath + '/PosHistory.npy')
self.Mass = np.load(ResultsPath + '/Masses.npy')
#load yaml with configurations
except Exception as e:
print(e)
self.NBodies = self.Mass.shape[0]
self.dt = 1
self.Vel = (self.PosHistory[self.NBodies:] - self.PosHistory[:-self.NBodies])/self.dt
def CalcVelFromPos(self):
self.Vel = (self.PosHistory[self.NBodies:] - self.PosHistory[:-self.NBodies])/self.dt
pass
def PlotTotalKineticEnergy(self):
self.TotalKineticEnergy = 0.5 * self.Mass * np.split(np.sum(self.Vel ** 2, axis = -1), self.Vel.shape[0]/self.NBodies)
plt.plot(np.ediff1d(self.TotalKineticEnergy))
plt.show()
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localshop/urls.py | rcoup/localshop | 0 | 26942 | import re
from django.conf import settings
from django.conf.urls import patterns, include, url
from django.contrib import admin
from django.views.generic.base import RedirectView
from localshop.apps.packages.xmlrpc import handle_request
admin.autodiscover()
static_prefix = re.escape(settings.STATIC_URL.lstrip('/'))
urlpatterns = patterns('',
url(r'^$', 'localshop.views.index', name='index'),
# Default path for xmlrpc calls
url(r'^RPC2$', handle_request),
url(r'^packages/',
include('localshop.apps.packages.urls', namespace='packages')),
url(r'^simple/', include('localshop.apps.packages.urls_simple',
namespace='packages-simple')),
# We add a separate route for simple without the trailing slash so that
# POST requests to /simple/ and /simple both work
url(r'^simple$', 'localshop.apps.packages.views.simple_index'),
url(r'^permissions/',
include('localshop.apps.permissions.urls', namespace='permissions')),
url(r'^accounts/signup/', RedirectView.as_view(url="/")),
url(r'^accounts/', include('userena.urls')),
url(r'^admin/', include(admin.site.urls)),
url(r'^%s(?P<path>.*)$' % static_prefix,
'django.contrib.staticfiles.views.serve', {'insecure': True}),
)
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gecko/fan.py | mmillmor/home_assistant-components | 0 | 1613793 | """Fan platform for Gecko."""
from .geckolib import GeckoPump
from homeassistant.components.fan import SUPPORT_PRESET_MODE, FanEntity
from .const import DOMAIN, ICON
from .entity import GeckoEntity
async def async_setup_entry(hass, entry, async_add_entities):
"""Setup sensor platform."""
facade = hass.data[DOMAIN][entry.entry_id].facade
entities = [GeckoFan(entry, pump) for pump in facade.pumps]
async_add_entities(entities, True)
class GeckoFan(GeckoEntity, FanEntity):
"""gecko fan class."""
def __init__(self, config_entry, automation_entity):
super().__init__(config_entry, automation_entity)
async def async_turn_on(self, **kwargs): # pylint: disable=unused-argument
"""Turn on the switch."""
self._automation_entity.set_mode(
next(mode for mode in self.preset_modes if mode != "OFF")
)
async def async_turn_off(self, **kwargs): # pylint: disable=unused-argument
"""Turn off the switch."""
self._automation_entity.set_mode("OFF")
async def async_set_preset_mode(self, preset_mode):
self._automation_entity.set_mode(preset_mode)
@property
def is_on(self):
return self.preset_mode != "OFF"
@property
def supported_features(self):
return SUPPORT_PRESET_MODE
@property
def icon(self):
"""Return the icon of this switch."""
return "mdi:pump"
@property
def preset_modes(self):
return ["OFF","HI"]
@property
def preset_mode(self):
mode = self._automation_entity._state_sensor.state
if mode != "OFF":
return mode[0:2]
return mode | [
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pybricks/libraries.py | emthomas/pybricks | 2 | 81195 | import json
import os
import requests
class LibrariesApi(object):
"""The Libraries API"""
def __init__(self, hostname, token):
self.hostname = "%s/api/" % hostname
self.__headers = {'authorization': "Bearer %s" % token, "content-type": "application/scim+json",
"accept": "application/scim+json"}
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vindauga/widgets/desktop.py | gabbpuy/vindauga | 5 | 156143 | <reponame>gabbpuy/vindauga
# -*- coding: utf-8 -*-
import logging
from vindauga.constants.command_codes import cmNext, cmPrev, cmReleasedFocus
from vindauga.constants.grow_flags import gfGrowHiX, gfGrowHiY
from vindauga.constants.event_codes import evCommand
from vindauga.constants.option_flags import ofTileable
from vindauga.constants.state_flags import sfVisible
from vindauga.types.group import Group
from vindauga.types.point import Point
from vindauga.types.rect import Rect
from .background import Background
logger = logging.getLogger(__name__)
class Desktop(Group):
"""
The desktop of the application.
`Desktop` inherits from `Group` `Desktop` is a simple group that owns the
`Background` view upon which the application's windows and other views appear.
`Desktop` represents the desk top area of the screen between the top menu
bar and bottom status line (but only when the bar and line exist). By
default, `Desktop` has a `Background` object inside which paints its
background.
"""
DEFAULT_BACKGROUND = '▒'
name = 'Desktop'
def __init__(self, bounds):
super().__init__(bounds)
self.cascadeNum = 0
self.lastView = None
self.numCols = 0
self.numRows = 0
self.numTileable = 0
self.leftOver = 0
self.tileNum = 0
self.growMode = gfGrowHiX | gfGrowHiY
self.tileColumnsFirst = False
background = self.initBackground(self.getExtent())
if background:
self._background = background
self.insert(self._background)
else:
self._background = None
@staticmethod
def isTileable(window):
return window.options & ofTileable and window.state & sfVisible
def mostEqualDivisors(self, n):
"""
Find a 'square' to tile the windows.
:param n: Number of windows
:return: Width and height
"""
i = int(n ** .5)
if n % i:
if not n % (i + 1):
i += 1
i = max(i, n // i)
a, b = n // i, i
if self.tileColumnsFirst:
x, y = b, a
else:
x, y = a, b
return x, y
def calcTileRect(self, pos, bounds):
def dividerLoc(lo, hi, num, dPos):
return int((hi - lo) * dPos // num + lo)
d = (self.numCols - self.leftOver) * self.numRows
if pos < d:
x, y = divmod(pos, self.numRows)
else:
x = (pos - d) // (self.numRows + 1) + (self.numCols - self.leftOver)
y = (pos - d) % (self.numRows + 1)
nRect = Rect(0, 0, 0, 0)
nRect.topLeft.x = dividerLoc(bounds.topLeft.x, bounds.bottomRight.x, self.numCols, x)
nRect.bottomRight.x = dividerLoc(bounds.topLeft.x, bounds.bottomRight.x, self.numCols, x + 1)
if pos >= d:
nRect.topLeft.y = dividerLoc(bounds.topLeft.y, bounds.bottomRight.y, self.numRows + 1, y)
nRect.bottomRight.y = dividerLoc(bounds.topLeft.y, bounds.bottomRight.y, self.numRows + 1, y + 1)
else:
nRect.topLeft.y = dividerLoc(bounds.topLeft.y, bounds.bottomRight.y, self.numRows, y)
nRect.bottomRight.y = dividerLoc(bounds.topLeft.y, bounds.bottomRight.y, self.numRows, y + 1)
return nRect
def doCountTileable(self, window, *_args):
if self.isTileable(window):
self.numTileable += 1
def doCascade(self, window, bounds):
if self.isTileable(window) and self.cascadeNum >= 0:
newRect = bounds.copy()
newRect.topLeft.x += self.cascadeNum
newRect.topLeft.y += self.cascadeNum
window.locate(newRect)
self.cascadeNum -= 1
def doCount(self, window, *_args):
if self.isTileable(window):
self.cascadeNum += 1
self.lastView = window
def doTile(self, window, bounds):
if self.isTileable(window):
r = self.calcTileRect(self.tileNum, bounds)
window.locate(r)
self.tileNum -= 1
def shutdown(self):
self._background = None
super().shutdown()
def cascade(self, r):
"""
Moves all the windows in a cascade-like fashion.
Redisplays all tileable windows owned by the desk top in cascaded
format. The first tileable window in Z-order (the window "in back") is
zoomed to fill the desk top, and each succeeding window fills a region
beginning one line lower and one space further to the right than the
one before. The active window appears "on top" as the smallest window.
:param r: Rectangle to cascade into
"""
minSize = Point()
maxSize = Point()
self.cascadeNum = 0
self.forEach(self.doCount, None)
if self.cascadeNum > 0:
self.lastView.sizeLimits(minSize, maxSize)
if ((minSize.x > r.bottomRight.x - r.topLeft.x - self.cascadeNum) or
(minSize.y > r.bottomRight.y - r.topLeft.y - self.cascadeNum)):
self.tileError()
else:
self.cascadeNum -= 1
self.lock()
try:
self.forEach(self.doCascade, r)
finally:
self.unlock()
def handleEvent(self, event):
"""
Calls `super().handleEvent()` and takes care of the commands `cmNext`
(usually the hot key F6) and `cmPrev` by cycling through the windows
owned by the desktop, starting with the currently selected view.
:param event: The event to handle
"""
super().handleEvent(event)
if event.what == evCommand:
c = event.message.command
if c == cmNext:
if self.valid(cmReleasedFocus):
self.selectNext(False)
elif c == cmPrev:
if self.valid(cmReleasedFocus):
self.current.putInFrontOf(self._background)
else:
return
self.clearEvent(event)
def initBackground(self, bounds):
"""
Creates a new background.
Returns a newly-created `Background` object.
The `background` data member is set to point at the new `Background` object.
Override this method if you want a custom background.
:param bounds: Bounds of the desktop
:return: A `Background` object
"""
return Background(bounds, self.DEFAULT_BACKGROUND)
def tile(self, bounds):
"""
Moves all the windows in a tile-like fashion.
:param bounds: Bounds of the desktop
"""
self.numTileable = 0
self.forEach(self.doCountTileable, None)
if self.numTileable > 0:
self.numCols, self.numRows = self.mostEqualDivisors(self.numTileable)
if (((bounds.bottomRight.x - bounds.topLeft.x) // self.numCols == 0) or
((bounds.bottomRight.y - bounds.topLeft.y) // self.numRows == 0)):
self.tileError()
else:
self.leftOver = self.numTileable % self.numCols
self.tileNum = self.numTileable - 1
self.lock()
try:
self.forEach(self.doTile, bounds)
finally:
self.unlock()
def tileError(self):
"""
Called on tiling error.
This method is called whenever `cascade()` or `tile()` run into
troubles in moving the windows. You can override it if you want to
give an error message to the user. By default, it does nothing.
"""
logger.error('tile error.')
pass
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] |
examples/processors/other.py | calibre12/zipreport | 0 | 85087 | from zipreport import ZipReportCli
from zipreport.processors.zipreport import ZipReportClient, ZipReportProcessor
from zipreport.report import ReportFileLoader, ReportJob
from zipreport.template import JinjaRender
def generate_pdf_server(report:str, data: dict, output_file:str) -> bool:
zpt = ReportFileLoader.load(report) # load zpt file
JinjaRender(zpt).render(data) # render jinja template with parameters
job = ReportJob(zpt) # create new rendering job
job.set_jsevent(True) # report uses event hook
job.set_jsevent_timeout(500)
job.set_process_timeout(600)
job.set_render_timeout(600)
client = ZipReportClient('http://127.0.0.1:6543', "") # zipreport-server client
result = ZipReportProcessor(client).process(job) # render
if result.success:
with open(output_file, 'wb') as rpt:
rpt.write(result.report.read())
return True
return False
def generate_pdf_cli(zipreport_cli:str, report: str, data: dict, output_file: str) -> bool:
zpt = ReportFileLoader.load(report) # load zpt file
JinjaRender(zpt).render(data) # render jinja template with parameters
job = ReportJob(zpt) # create new rendering job
job.set_jsevent(True) # report uses event hook
job.set_jsevent_timeout(500)
job.set_process_timeout(600)
job.set_render_timeout(600)
result = ZipReportCli(zipreport_cli).render(job, data)
if result.success:
with open(output_file, 'wb') as rpt:
rpt.write(result.report.read())
return True
return False
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scripts/NameParser.py | lauriholmas/harn-tools | 5 | 128060 | import csv, json
def writeNames():
given_names = { 'male': [], 'female': [] }
surnames = []
with open('ScandinavianNames.data.json', 'w', encoding='utf-8') as jsonFile:
given_names['male'] = parseNames('ScandinavianMaleNames.csv')
given_names['female'] = parseNames('ScandinavianFemaleNames.csv')
# surnames = praseNames()
json.dump({'given_names': given_names, 'surnames': surnames}, jsonFile)
def parseNames(filename):
names = []
with open(filename, encoding='utf-8') as nameFile:
nameReader = csv.reader(nameFile)
for row in nameReader:
for item in row:
if item:
words = item.split(',')
for word in words:
names.append(word.replace('?', '').strip())
return names
if __name__ == '__main__':
writeNames()
print("Done")
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test/unit/jobs/test_statuses.py | NHSDigital/list-reconciliation | 4 | 51513 | import pytest
from jobs.statuses import JobStatus
@pytest.mark.parametrize(
"code,value",
[
(JobStatus.SENT_TO_DPS, "SENT_TO_DPS"),
(JobStatus.PROCESSED_BY_DPS, "PROCESSED_BY_DPS"),
(JobStatus.COMPLETE, "COMPLETE"),
(JobStatus.NOTIFIED_VALIDATION_FAILED, "NOTIFIED_VALIDATION_FAILED"),
(JobStatus.CLEANED_UP, "CLEANED_UP"),
],
)
def test_statuses_have_correct_value(code, value):
assert code.value == value
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itdtool/tasks.py | nikoskal/itd_tool | 0 | 106816 | # from __future__ import absolute_import
#
# from itdtool.celeryapp import app
# # from lxml import etree
# # import xmltodict
# import sys
# # from passlib.hash import md5_crypt
# from django.conf import settings
#
#
#
# # @app.task
# def get_query_params_id(id):
#
# from itdtool.models import QueryParameters
# query_parameters = QueryParameters.objects.filter(id=id)
# # print "get_query_params_id "+ str(id)
# # print "result " + str(query_parameters)
# for query_parameter in query_parameters:
# result = {
# "id": query_parameter.id,
# "description": query_parameter.description,
# "sources": query_parameter.sources,
# "location": query_parameter.location,
# "start_date": query_parameter.start_date,
# "end_date": query_parameter.end_date,
# "inference": query_parameter.inference,
# "questions": query_parameter.questions
# }
#
#
# # print("retrieve query_parameter by id" + str(result))
#
# return result
#
#
# # @app.task
# def delete_query_params_id(id):
#
# query_deleted = QueryParameters.objects.filter(id=id)[0]
# res = query_deleted.delete()
#
# return res
#
#
# # @app.task
# def add_query_params(description, sources, location, start_date, end_date, inference, questions):
#
# # description = models.TextField()
# # sources = models.CharField(max_length=30) #["Twitter", "Google", "Youtube"]
# # location = models.CharField(max_length=30) # ["Athens", "Rome", "Madrid"]
# # start_date = models.DateTimeField() # starting time period to search
# # end_date = models.DateTimeField() # ending time period to search
# # inference = models.BooleanField()
# # questions = models.BooleanField()
#
# query_parameters = QueryParameters(description=description, sources=sources,location=location,
# start_date=start_date, end_date=end_date, inference=inference,
# questions=questions)
# query_parameters.save()
# print "saved:" + str(query_parameters)
#
# return query_parameters
#
#
# # @app.task
# def get_all_query_params():
#
# query_parameters_all = QueryParameters.objects.all()
# print query_parameters_all
# results = []
#
# for query_parameter in query_parameters_all:
# print("retrieve all query_parameter " + str(query_parameter))
# d = {"id": query_parameter.id,
# "description": query_parameter.description,
# "sources": query_parameter.sources,
# "location": query_parameter.location,
# "start_date": query_parameter.start_date,
# "end_date": query_parameter.end_date,
# "inference": query_parameter.inference,
# "questions": query_parameter.questions}
# results.append(d)
# return results
#
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src/didery/controllers/history.py | reputage/didery | 8 | 1600574 | <reponame>reputage/didery
import falcon
import arrow
import time
try:
import simplejson as json
except ImportError:
import json
from ..help import helping
from ..did.didering import Did, getDIDModel
from ..db import dbing as db
from ..controllers.validation import factory
from ..models.models import BasicHistoryModel
def validate(req, resp, resource, params):
"""
Validate incoming requests and prepare
body of request for processing.
:param req: falcon.Request object
:param resp: falcon.Response object
:param resource: History controller object
:param params: (dict) URI Template field names
"""
validator = factory.historyFactory(resource.mode, req, params)
validator.validate()
class History:
def __init__(self, store=None, mode=None):
"""
:param store: Store
store is reference to ioflo data store
"""
self.store = store
self.mode = mode
@falcon.before(helping.parseQString)
def on_get(self, req, resp, did=None):
"""
Handle and respond to incoming GET request.
:param req: Request object
:param resp: Response object
:param did: string
URL parameter specifying a rotation history
"""
offset = req.offset
limit = req.limit
count = db.historyDB.historyCount()
if did is not None:
body = db.historyDB.getHistory(did)
if body is None:
raise falcon.HTTPError(falcon.HTTP_404)
body = body.data
else:
if offset >= count:
resp.body = json.dumps({}, ensure_ascii=False)
return
body = db.historyDB.getAllHistories(offset, limit)
resp.append_header('X-Total-Count', count)
resp.body = json.dumps(body, ensure_ascii=False)
@falcon.before(validate)
def on_post(self, req, resp):
"""
Handle and respond to incoming POST request.
:param req: Request object
:param resp: Response object
"""
request_json = BasicHistoryModel(req.body)
sigs = req.signatures
did = request_json.id
# TODO: review signature validation for any holes
response_json = db.historyDB.saveHistory(did, request_json.data, sigs)
db.eventsDB.saveEvent(did, request_json.data, sigs)
resp.body = json.dumps(response_json, ensure_ascii=False)
resp.status = falcon.HTTP_201
@falcon.before(validate)
def on_put(self, req, resp, did):
"""
Handle and respond to incoming PUT request.
:param req: Request object
:param resp: Response object
:param did: decentralized identifier
"""
request_data = BasicHistoryModel(req.body)
sigs = req.signatures
# TODO: review signature validation for any holes
response_json = db.historyDB.saveHistory(did, request_data.data, sigs)
db.eventsDB.saveEvent(did, request_data.data, sigs)
resp.body = json.dumps(response_json, ensure_ascii=False)
@falcon.before(validate)
def on_delete(self, req, resp, did):
"""
Handle and respond to incoming PUT request.
:param req: Request object
:param resp: Response object
:param did: decentralized identifier
"""
vk = req.body["vk"]
if self.mode == "method":
did_class = getDIDModel(did)
if did_class is not None: # Test id-string Resolver exists
did_obj = did_class(did)
if did_obj.match_vk(vk):
vk = None # Delete all data
success = db.historyDB.deleteHistory(did, vk)
db.eventsDB.deleteEvent(did, vk)
if success is None:
raise falcon.HTTPError(falcon.HTTP_409,
'Deletion Error',
'Error while attempting to delete the resource.')
success = success if isinstance(success, list) else [success]
resp.body = json.dumps({"deleted": success}, ensure_ascii=False)
class HistoryStream:
def __init__(self, store=None):
"""
:param store: Store
store is reference to ioflo data store
"""
self.store = store
self.id = 0
self.history = None
def historyGenerator(self, did=None):
if did is None:
count = db.historyDB.historyCount()
if self.history == count:
time.sleep(2)
else:
self.history = count
history = db.historyDB.getAllHistories(limit=self.history)
temp = []
for entry in history['data']:
temp.append(json.loads(entry.decode('utf-8')))
history = {"data": temp}
yield bytes("\nid:" + str(self.id) + "\nevent:message\ndata:" + str(history) + "\n\n", "ascii")
self.id += 1
time.sleep(2)
else:
value = db.historyDB.getHistory(did)
if value is None:
raise falcon.HTTPError(falcon.HTTP_404)
if self.history == value:
time.sleep(2)
else:
self.history = value
print(self.history)
yield bytes("\nid:" + str(self.id) + "\nevent:message\ndata:" + str(self.history) + "\n\n", "ascii")
self.id += 1
time.sleep(2)
def on_get(self, req, resp, did=None):
resp.status = falcon.HTTP_200
resp.content_type = "text/event-stream"
resp.stream = self.historyGenerator(did)
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] |
src/Prototype.pyw | SilverousBlack/Prototypes | 0 | 106933 | """
Prototype Algorithms Library
(c) <NAME>, 2020
License(s): MIT Licence
Notice: The distribution, ownership, reproduction and use of this library is subject to the above stated license.
Contains all prototype algorithms from:
> Prototype Algorithms Core [Prototype.pyw]
> Searching Algorithms [SearchingAlgorithms.pyw]
> Sorting Algorithms [SortingAlgorithms.pyw]
"""
from PrototypeCore import *
from SearchingAlgorithms import *
from SortingAlgorithms import *
import visualized as visual | [
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Week3/exercise2.py | tb2010/pynet | 0 | 74921 | #!/usr/bin/env python
"""
Exercise 2
Using SNMPv3 create two SVG image files.
The first image file should graph the input and output octets on interface FA4 on pynet-rtr1 every five minutes
for an hour. Use the pygal library to create the SVG graph file. Note, you should be doing a subtraction here
(i.e. the input/output octets transmitted during this five minute interval).
The second SVG graph file should be the same as the first except graph the unicast packets received and transmitted.
"""
import time
from getpass import getpass
from snmp_helper import snmp_get_oid_v3, snmp_extract
import line_graph
def get_interface_stats(snmp_device, snmp_user, stat_type, if_index):
"""
statue-type can be 'in_octets, out_octets, in_ucast_pkts or out_ucast_pkts'
returns the counter value as an integer
"""
oid_dict = {
'in_octets': '1.3.6.1.2.1.2.2.1.10',
'out_octets': '1.3.6.1.2.1.2.2.1.16',
'in_ucast_pkts': '1.3.6.1.2.1.2.2.1.11',
'out_ucast_pkts': '1.3.6.1.2.1.2.2.1.17',
}
if stat_type not in oid_dict.keys():
raise ValueError("Invalid stat_type selected: {}" % stat_type)
if_index = int(if_index)
oid = oid_dict[stat_type]
oid = oid + '.' + str(if_index)
snmp_data = snmp_get_oid_v3(snmp_device, snmp_user, oid)
return int(snmp_extract(snmp_data))
def main():
# SNMPv3 Connection Parameters
rtr1_ip_addr = raw_input("Enter device IP: ")
auth_enc_key = getpass(prompt="Auth & Encryption Key: ")
v3_user = 'pysnmp'
auth_key = auth_enc_key
encr_key = auth_enc_key
snmp_user = (v3_user, auth_key, encr_key)
snmp_device = (rtr1_ip_addr, 161)
# Fa4 Interface Index is number 5 in the lab routers
if_index = 5
graph_stats = {
"in_octets": [],
"out_octets": [],
"in_ucast_pkts": [],
"out_ucast_pkts": [],
}
base_count_dict = {}
# for 0 - 65 stepping by 5
for time_track in range(0, 65, 5):
print "%20s %-60s" % ("time", time_track)
for entry in ("in_octets", "out_octets", "in_ucast_pkts", "out_ucast_pkts"):
snmp_count = get_interface_stats(snmp_device, snmp_user, entry, if_index)
base_count = base_count_dict.get(entry)
if base_count:
graph_stats[entry].append(snmp_count - base_count)
print "%20s %-60s" % (entry, graph_stats[entry][-1])
base_count_dict[entry] = snmp_count
time.sleep(10)
x_labels = []
for x_label in range(5, 65, 5):
x_labels.append(str(x_label))
if line_graph.twoline("rtr1-octets.svg", "rtr1 Fa4 Input/Output Bytes",
graph_stats["in_octets"], "In Octets",
graph_stats["out_octets"], "Out Octets", x_labels):
print "In/Out Octet graph created"
if line_graph.twoline('rtr1-packets.svg', 'rtr1 Fa4 Input/Output Packets',
graph_stats['in_ucast_pkts'], 'In Packets',
graph_stats['out_ucast_pkts'], 'Out Packets', x_labels):
print "In/Out Packet graph created"
if __name__ == "__main__":
main()
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] |
flask_response_builder/response_builder.py | cs91chris/flask_response_builder | 4 | 45392 | from functools import wraps
import flask
from flask import current_app as cap
from .builders.builder import Builder
from .config import DEFAULT_BUILDERS, set_default_config
class ResponseBuilder:
def __init__(self, app=None, builders=None):
"""
:param app:
:param builders:
"""
self._builders = {}
if app is not None:
self.init_app(app, builders)
def init_app(self, app, builders=None):
"""
:param app:
:param builders:
"""
set_default_config(app)
if not hasattr(app, 'extensions'):
app.extensions = dict()
app.extensions['response_builder'] = self
for name, builder in {**DEFAULT_BUILDERS, **(builders or {})}.items():
self.register_builder(name, builder, **app.config)
def register_builder(self, name, builder, **kwargs):
"""
:param name:
:param builder:
"""
if not issubclass(builder.__class__, Builder):
raise NameError(
"Invalid Builder: '{}'. "
"You must extend class: '{}'".format(builder, Builder.__name__)
)
if not builder.conf:
builder.conf = kwargs
else:
builder.conf.update(kwargs)
self._builders.update({name: builder})
def _builder_attr(**params):
def _wrapper(func=None, data=None):
"""
func and data are mutual exclusive:
if func is present means a decorator builder used
if data is provided means decorator used as attribute
:param func:
:param data:
:return:
"""
if func is not None:
@wraps(func)
def wrapped():
return self.build_response(name, func(), **params)
return wrapped
return self.build_response(name, data, **params)
return _wrapper
setattr(self, name, _builder_attr)
@staticmethod
def _empty_response(status, headers):
"""
:param status:
:param headers:
:return:
"""
resp = flask.make_response(b'', status, headers)
resp.headers.pop('Content-Type', None)
return resp
def build_response(self, builder=None, data=None, **kwargs):
"""
:param builder:
:param data:
:return:
"""
if isinstance(builder, str):
builder = self._builders.get(builder)
data, status, headers = self.normalize_response_data(data)
if data is None:
return self._empty_response(status, headers)
if not builder:
m = headers.get('Content-Type') or cap.config.get('RB_DEFAULT_RESPONSE_FORMAT')
for value in self._builders.values():
if value.mimetype == m:
builder = value
break
else:
raise NameError(
"Builder not found: using one of: '{}'".format(", ".join(self._builders.keys()))
)
elif not issubclass(builder.__class__, Builder):
raise NameError(
"Invalid Builder: '{}'. You must extend class: '{}'".format(builder, Builder.__name__)
)
builder.build(data, **kwargs)
return builder.response(status=status, headers=headers)
def get_mimetype_accept(self, default=None, acceptable=None, strict=True):
"""
:param default:
:param acceptable:
:param strict:
:return:
"""
def find_builder(a):
for b in self._builders.values():
if a == b.mimetype:
return b
mimetypes = flask.request.accept_mimetypes
default = default or cap.config['RB_DEFAULT_RESPONSE_FORMAT']
acceptable = acceptable or cap.config['RB_DEFAULT_ACCEPTABLE_MIMETYPES']
if not mimetypes or str(mimetypes) == '*/*':
builder = find_builder(default)
if builder:
return default, builder
for m in mimetypes:
m = m[0].split(';')[0] # in order to remove encoding param
accept = m if m in acceptable else None
builder = find_builder(accept)
if builder:
return accept, builder
if strict is True:
flask.abort(406, "Not Acceptable: {}".format(flask.request.accept_mimetypes))
return default, find_builder(default)
@staticmethod
def normalize_response_data(data):
"""
:param data:
:return:
"""
if isinstance(data, tuple):
v = data + (None,) * (3 - len(data))
if isinstance(v[1], int):
return v[0], v[1], v[2] or {}
return v[0], v[2], v[1] or {}
if isinstance(data, int):
return None, data, {}
return data, None, {}
def no_content(self, func):
"""
:param func:
:return:
"""
@wraps(func)
def wrapped(*args, **kwargs):
resp = func(*args, **kwargs)
data, status, headers = self.normalize_response_data(resp)
if data:
resp = self.build_response(data=resp)
else:
status = 204 if status in (None, 204) else status
resp = self._empty_response(status, headers)
return resp
return wrapped
def on_format(self, default=None, acceptable=None):
"""
:param default:
:param acceptable:
:return:
"""
def response(fun):
@wraps(fun)
def wrapper(*args, **kwargs):
builder = flask.request.args.get(cap.config.get('RB_FORMAT_KEY')) or default
if builder not in (acceptable or self._builders.keys()):
for k, v in self._builders.items():
if v.mimetype == cap.config.get('RB_DEFAULT_RESPONSE_FORMAT'):
builder = k
break
return self.build_response(builder, fun(*args, **kwargs))
return wrapper
return response
def on_accept(self, default=None, acceptable=None, strict=True):
"""
:param default:
:param acceptable:
:param strict:
:return:
"""
def response(fun):
@wraps(fun)
def wrapper(*args, **kwargs):
mimetype, builder = self.get_mimetype_accept(default, acceptable, strict)
return self.build_response(builder, fun(*args, **kwargs))
return wrapper
return response
def response(self, builder, **kwargs):
"""
:param builder:
:return:
"""
def _response(f):
@wraps(f)
def wrapper(*args, **kw):
return self.build_response(builder, f(*args, **kw), **kwargs)
return wrapper
return _response
def template_or_json(self, template: str, as_table=False, to_dict=None):
"""
:param template:
:param as_table:
:param to_dict:
:return:
"""
def response(fun):
@wraps(fun)
def wrapper(*args, **kwargs):
varargs = {}
builder = self._builders.get('json')
# check if request is XHR
if flask.request.headers.get('X-Requested-With', '').lower() == "xmlhttprequest":
builder = self._builders.get('html')
varargs.update(dict(
template=template,
as_table=as_table,
to_dict=to_dict
))
resp = fun(*args, **kwargs)
return self.build_response(builder, resp, **varargs)
return wrapper
return response
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] |
rosny/signal.py | lRomul/rosny | 6 | 161990 | <gh_stars>1-10
import signal
_signals = [signal.SIGINT, signal.SIGTERM]
_default_handlers = {sig: signal.getsignal(signal.SIGINT) for sig in _signals}
class SignalException(BaseException):
def __init__(self, signum: signal.Signals, frame):
message = f"Handle signal: {signal.Signals(signum).name}"
super().__init__(message)
self.signum = signum
self.frame = frame
def start_signals(stream):
def signal_handler(signum: signal.Signals, frame):
exception = SignalException(signum, frame)
stream.logger.error(exception)
stream.stop()
raise exception
for sig in _signals:
signal.signal(sig, signal_handler)
stream.logger.info("Start handling signals")
def stop_signals(stream):
for sig in _signals:
signal.signal(sig, _default_handlers[sig])
stream.logger.info("Stop handling signals")
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2
] |
complete_workflow/image_process.py | totoyoyo/deformable-template-capstone | 1 | 170079 | import cv2
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from skimage.restoration import (denoise_tv_chambolle, denoise_bilateral,
denoise_wavelet, estimate_sigma)
from pathlib import Path
def process_img_and_save(img_path: Path, denoise_h=20,
sobel=True, kernel_size=3):
img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (350, 350),
interpolation=cv2.INTER_AREA)
denoised = cv2.fastNlMeansDenoising(src=img, dst=None, h=denoise_h)
if sobel:
filtered = apply_sobel(denoised, kernel_size)
else:
filtered = apply_laplacian(denoised, kernel_size)
masked = apply_circle_mask(filtered)
resized_to_100 = cv2.resize(masked, (100, 100),
interpolation=cv2.INTER_AREA)
# resized_to_100 = masked
rescaled = ((resized_to_100 / resized_to_100.max()) * 255).astype("uint8")
im = Image.fromarray(rescaled)
img_orig_name = img_path.stem
img_folder = img_path.parent
im.save(img_folder / (img_orig_name + "_p_l" + str(kernel_size) + ".png"))
return rescaled
def process_after_cropped(img_path: Path, denoise_h=20, filter=False,
sobel=True, kernel_size=3):
img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (270, 270),
interpolation=cv2.INTER_AREA)
denoised = cv2.fastNlMeansDenoising(src=img, dst=None, h=denoise_h)
if filter:
if sobel:
filtered = apply_sobel(denoised, kernel_size)
else:
filtered = apply_laplacian(denoised, kernel_size)
else:
filtered = denoised
masked = apply_circle_mask(filtered, radius=135)
normalized = cv2.equalizeHist(masked)
resized_to_100 = cv2.resize(normalized, (100, 100),
interpolation=cv2.INTER_AREA)
# resized_to_100 = masked
rescaled = ((resized_to_100 / resized_to_100.max()) * 255).astype("uint8")
im = Image.fromarray(rescaled)
img_orig_name = img_path.stem
img_folder = img_path.parent
im.save(img_folder / (img_orig_name + "no_filter" + ".png"))
return rescaled
def apply_sobel(img, kernel_size):
sobel_x = cv2.Sobel(img, dx=1, dy=0, ddepth=cv2.CV_64F,
ksize=kernel_size, borderType=cv2.BORDER_REFLECT)
sobel_y = cv2.Sobel(img, dx=0, dy=1, ddepth=cv2.CV_64F,
ksize=kernel_size, borderType=cv2.BORDER_REFLECT)
sobel_both = np.sqrt((sobel_x ** 2 + sobel_y ** 2))
return sobel_both
def apply_laplacian(img, kernel_size):
laplacian = np.abs(cv2.Laplacian(img, ddepth=cv2.CV_64F,
ksize=kernel_size, borderType=cv2.BORDER_REFLECT))
return laplacian
def apply_circle_mask(img, radius=150):
hh, ww = img.shape
ycen = hh // 2
xcen = ww // 2
mask = np.zeros_like(img)
mask = cv2.circle(mask, center=(ycen, xcen), radius=radius,
color=1, thickness=-1)
masked = np.where(mask == 1, img, 0)
return masked
def canny_process_img_and_save(img_path, l_t=150, h_t=300,
kernel_size=3):
img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (350, 350),
interpolation=cv2.INTER_AREA)
icanny_out = cv2.Canny(img, threshold1=l_t,
threshold2=h_t, L2gradient=True, apertureSize=kernel_size)
masked = apply_circle_mask(icanny_out)
resized_to_100 = cv2.resize(masked, (100, 100),
interpolation=cv2.INTER_AREA)
rescaled = ((resized_to_100 / resized_to_100.max()) * 255).astype("uint8")
im = Image.fromarray(rescaled)
img_orig_name = img_path.stem
img_folder = img_path.parent
im.save(img_folder / (img_orig_name + "_p_can" + ".png"))
return rescaled
# main_path = Path(__file__).resolve().parent
# image_folder = main_path / "orig_coin_5_classes"
# for image_path in image_folder.glob("**/*.jpg"):
# process_img_and_save(image_path,sobel=False,kernel_size=5)
main_path = Path(__file__).resolve().parent
image_folder = main_path / "input_coins"
for image_path in image_folder.glob("**/*.jpg"):
process_after_cropped(image_path)
# Sobel noisy
# Canny is very sensitive to thresholds
# Laplacian h=5 is best
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_scripts/parse.py | KaiL4eK/keras_traffic_signs_localization | 4 | 160113 | from __future__ import print_function
import os
import cv2
import argparse
parser = argparse.ArgumentParser(description='Process video with ANN')
parser.add_argument('filepath', action='store', help='Path to video file to process')
args = parser.parse_args()
def parse_file():
img_ref = cv2.imread(args.filepath)
pic_path = os.path.splitext(args.filepath)[0]+".jpg"
print(pic_path)
img = cv2.imread(pic_path)
cv2.imshow('1', img)
cv2.imshow('2', img_ref)
cv2.waitKey(0)
if __name__ == '__main__':
parse_file()
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test/test_words.py | dubmix3105/pysh | 7 | 27471 | import sys
import pytest
from pysh import shwords, shwords_f
def test_conversions():
with pytest.raises(ValueError):
shwords('{:{}}', 1, 2)
assert '{:{}}'.format(1, 2) == ' 1' # by contrast
def test_multiword():
assert shwords('touch {!@}', ['a', 'b']) \
== ['touch', 'a', 'b']
with pytest.raises(ValueError):
shwords('a b{!@}', ['x'])
with pytest.raises(ValueError):
shwords('a {!@}c', ['x'])
with pytest.raises(ValueError):
shwords('a {!@}{}', ['b'], 'c')
assert shwords('touch {!@} c', ['a', 'b']) \
== ['touch', 'a', 'b', 'c']
def test_splitting():
assert shwords('git grep {}', 'hello world') \
== ['git', 'grep', 'hello world']
assert shwords('{} {} {}', 'a', 'b c', 'd') \
== ['a', 'b c', 'd']
assert shwords(' a {} c ', 'b') \
== ['a', 'b', 'c']
assert shwords('tar -C {outdir} -xzf {tarball}',
outdir='/path/with/spaces in it',
tarball='2019 Planning (final) (v2) (final final).tgz') \
== ['tar', '-C', '/path/with/spaces in it', '-xzf', '2019 Planning (final) (v2) (final final).tgz']
def test_within_word():
assert shwords('git log --format={}', '%aN') \
== ['git', 'log', '--format=%aN']
assert shwords('{basedir}/deployments/{deploy_id}/bin/start',
basedir='/srv/app', deploy_id='0f1e2d3c') \
== ['/srv/app/deployments/0f1e2d3c/bin/start']
def test_locals():
import pytest
l = ['a', 'b']
assert shwords_f('touch {l!@}') \
== ['touch', 'a', 'b']
assert shwords_f('touch {l[1]}') \
== ['touch', 'b']
assert shwords_f('echo {pytest.__name__}') \
== ['echo', 'pytest']
# Known limitation: locals only, no globals...
with pytest.raises(KeyError, match='sys'):
shwords_f('echo {sys}')
# (unlike real, compiler-assisted f-strings)
assert f'{sys}' \
== "<module 'sys' (built-in)>"
# ... and enclosing scopes' locals are complicated.
def inner1():
with pytest.raises(KeyError):
return shwords_f('touch {l!@}')
inner1()
def inner2():
l
assert shwords_f('touch {l!@}') \
== ['touch', 'a', 'b']
inner2()
def inner3():
nonlocal l
assert shwords_f('touch {l!@}') \
== ['touch', 'a', 'b']
inner3()
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] |
server.py | swyngaard/sensor-logger | 0 | 136918 | from flask_socketio import SocketIO
from flask import Flask, render_template, request
from time import sleep
from threading import Thread, Event
from flask_autoindex import AutoIndex
import logging
import os
import getpass
import sys
from shongololo import start_up as SU
from shongololo import sys_admin as SA
from shongololo import K30_serial as KS
from shongololo import Imet_serial as IS
# TODO enable following with pkg_resources module later
user = getpass.getuser()
data_dir = '/home/' + user + '/DATA/'
datafile = 'data.csv'
logfile = data_dir + 'Shongololo_log.log'
i_head = ",IMET_ID, Latitude, Longitude, Altitude, Air Speed (m/s), Mode, Fixed Satellites, Available Satellites," \
"voltage,current,level,id "
k_head = ",K30_ID, CO2 ppm"
period = 0.5
app = Flask(__name__)
# turn the flask app into a socketio app
socketio = SocketIO(app)
filesindex = AutoIndex(app, os.path.join(app.root_path, data_dir), add_url_rules=False)
#sthread = Thread()
sthread_stop_event = Event()
mthread = Thread()
mthread_stop_event = Event()
sensors = []
class FlaskHandler(logging.Handler):
def __init__(self, a_socket, level=logging.NOTSET):
super().__init__(level=level)
self.socketio = a_socket
def emit(self, record):
socketio.emit('newmsg', {'lmsg': self.format(record)}, namespace='/test')
class shongololo_thread(Thread):
def __init__(self):
self.delay = 0.5
super(shongololo_thread, self).__init__()
self.device_dict = {}
self.imet_sockets = []
self.k30_sockets = []
self.fd = None # File handle
def capture_data(self):
"""
Start capturing data from sensore and writing it to file
"""
sthread_stop_event.clear()
# Access devices
status, self.device_dict = SA.find_devices()
# Connect to imets
self.imet_sockets = IS.open_imets(self.device_dict["imets"])
# Connect to CO2 meters
self.k30_sockets = KS.open_k30s(self.device_dict["k30s"])
# Start data log file
status, numbered_nd = SA.mk_numbered_nd(data_dir)
if status != 0:
error = "Failed to create directory for data logging, data will not be saved to file, try restarting the " \
"application "
socketio.emit('newnumber', {'number': error}, namespace='/test')
sys.exit()
else:
header = ""
for c in range(len(self.device_dict["k30s"])):
header = header + str(k_head)
for i in range(len(self.device_dict["imets"])):
header = header + str(i_head)
self.fd = SA.ini_datafile(str(numbered_nd) + datafile, header)
socketio.emit('newnumber', {'number': "Starting log in {}".format(datafile)}, namespace='/test')
socketio.emit('newnumber', {'number': header}, namespace='/test')
self.fd.write(header)
# Sample data until told to stop
while not sthread_stop_event.isSet():
pack = []
dataline = ""
latest_idata, latest_kdata = SA.read_data(self.imet_sockets, self.k30_sockets)
# pack data
for count, k in zip(range(len(self.device_dict["k30s"])), self.device_dict["k30s"]):
pack.append(k[1] + "," + latest_kdata[count])
for count, i in zip(range(len(self.device_dict["imets"])), self.device_dict["imets"]):
pack.append(i[1] + "," + latest_idata[count])
for x in pack:
dataline = dataline + "," + x
socketio.emit('newnumber', {'number': dataline}, namespace='/test')
self.fd.write("\n" + dataline)
sleep(self.delay)
def stop_capture(self):
SA.close_sensors(self.imet_sockets + self.k30_sockets)
if self.fd != None:
self.fd.close()
def run(self):
self.capture_data()
class monitoring_thread(Thread):
"""Prints application log to webpage and carries out initial setup work"""
def __init__(self):
self.delay = 0.5
self.imet_sockets = []
self.k30_sockets = []
self.device_dict = {}
self.datafile = ""
super(monitoring_thread, self).__init__()
def setup_shongololo(self):
"""
Capture the apps log stream and output it to webpage along with performing initial setup work
"""
mthread_stop_event.clear()
flask_handler = FlaskHandler(socketio)
# Do startup sequence
SA.if_mk_dir(data_dir)
SU.start_logging(logfile, flask_handler, 1)
self.imet_sockets, self.k30_sockets, self.device_dict = SU.start_up()
# Test sensors
if SU.test_sensors(self.imet_sockets, self.k30_sockets) == 0:
# Successful test, close sensor sockets and move on
SA.close_sensors(mthread.imet_sockets + mthread.k30_sockets)
sys.stdout.flush()
else:
mthread_stop_event.set()
sys.stdout.flush()
def run(self):
self.setup_shongololo()
sthread = shongololo_thread()
@app.route('/')
def index():
# only by sending this page first will the client be connected to the socketio instance
my_list = ['./one.csv', './two.csv', './three.csv']
return render_template('index.html', option_list=my_list)
@app.route('/files')
@app.route('/files/<path:path>')
def autoindex(path='.'):
return filesindex.render_autoindex(path)
@socketio.on('connect', namespace='/test')
def test_connect():
print('Client connected')
@socketio.on('disconnect', namespace='/test')
def test_disconnect():
print('Client disconnected')
# Functions for controlling sensors and data capture
@socketio.on('start capture', namespace='/test') # 'my start' is referenced in java script
def start_capture():
"""Start a data capture session"""
global sthread
# Start the random number generator thread only if the thread has not been started before.
if not sthread.isAlive():
print("Starting Thread")
sthread = shongololo_thread()
sthread.start()
@socketio.on('stop capture', namespace='/test')
def stop_capture():
"""Stop a data capture session"""
sthread.stop_capture()
sthread_stop_event.set()
# Functions controlling whole system
@socketio.on('do setup', namespace='/test')
def do_setuplogging():
"""Do pre data capture setup and initialise application logging"""
global mthread
if not mthread.isAlive():
mthread = monitoring_thread()
mthread.start()
@socketio.on('Shutdown Pi', namespace='/test')
def Shutdown_Pi():
"""Shutdown Pi gracefully"""
# Stop data capture thread if running
if sthread.isAlive:
sthread.stop_capture()
sthread_stop_event.set()
# Close monitoring thread if still alive
SA.shutdown_monitor()
if mthread.isAlive:
mthread_stop_event.set()
SA.shutdown_computer()
@socketio.on('shutdown app', namespace='/test')
def shutdown_app():
"""Shutdown whole application gracefully"""
# Stop data capture thread if running
if sthread.isAlive:
sthread.stop_capture()
sthread_stop_event.set()
# Close monitoring thread
SA.shutdown_monitor()
if mthread.isAlive:
mthread_stop_event.set()
if __name__ == "__main__":
socketio.run(app, host='0.0.0.0')
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test/ResultsAndPrizes/top-3/test_top_3_winning_numbers_of_the_last_draw.py | FearFactor1/SPA | 1 | 52867 | # Топ-3 + Выигрышные номера последнего тиража
def test_top_3_winning_numbers_last_draw(app):
app.ResultAndPrizes.open_page_results_and_prizes()
app.ResultAndPrizes.click_game_top_3()
app.ResultAndPrizes.button_get_report_winners()
assert "ВЫИГРЫШНЫЕ НОМЕРА" in app.ResultAndPrizes.parser_report_text_winners()
app.ResultAndPrizes.message_id_33_top_3_last_draw()
app.ResultAndPrizes.message_id_33_top_3_winning_numbers_last_draw()
app.ResultAndPrizes.comeback_main_page() | [
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] |
scraper/storage_spiders/anhducdigitalvn.py | chongiadung/choinho | 0 | 83965 | <reponame>chongiadung/choinho
# Auto generated by generator.py. Delete this line if you make modification.
from scrapy.spiders import Rule
from scrapy.linkextractors import LinkExtractor
XPATH = {
'name' : "//form[@id='product_addtocart_form']/div[@class='product-shop']/div[@class='product-name']/h1",
'price' : "//div[@class='product-img-box']/div[@class='price-box']/span/span[@class='price']",
'category' : "//div[@class='breadcrumbs']/ul/li",
'description' : "//div[@class='tabs_wrapper_details collateral_wrapper ui-tabs ui-widget ui-widget-content ui-corner-all ui-tabs-nav ui-helper-reset ui-helper-clearfix ui-widget-header']/div[@class='product-collateral tab_content']/div[@id='box-review']/div[@class='std']",
'images' : "//div[@class='product-img-box']/p[@class='product-image']//a/@href",
'canonical' : "",
'base_url' : "",
'brand' : ""
}
name = 'anhducdigital.vn'
allowed_domains = ['anhducdigital.vn']
start_urls = ['http://anhducdigital.vn']
tracking_url = ''
sitemap_urls = ['']
sitemap_rules = [('', 'parse_item')]
sitemap_follow = []
rules = [
Rule(LinkExtractor(allow=['']), 'parse_item'),
Rule(LinkExtractor(allow=['']), 'parse'),
#Rule(LinkExtractor(), 'parse_item_and_links'),
]
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bootloader/waflib/Tools/perl.py | BA7JCM/pyinstaller | 0 | 1612383 | <reponame>BA7JCM/pyinstaller
#! /usr/bin/env python
# encoding: utf-8
# WARNING! Do not edit! https://waf.io/book/index.html#_obtaining_the_waf_file
import os
from waflib import Task, Options, Utils, Errors
from waflib.Configure import conf
from waflib.TaskGen import extension, feature, before_method
@before_method('apply_incpaths', 'apply_link', 'propagate_uselib_vars')
@feature('perlext')
def init_perlext(self):
self.uselib = self.to_list(getattr(self, 'uselib', []))
if not 'PERLEXT' in self.uselib:
self.uselib.append('PERLEXT')
self.env.cshlib_PATTERN = self.env.cxxshlib_PATTERN = self.env.perlext_PATTERN
@extension('.xs')
def xsubpp_file(self, node):
outnode = node.change_ext('.c')
self.create_task('xsubpp', node, outnode)
self.source.append(outnode)
class xsubpp(Task.Task):
run_str = '${PERL} ${XSUBPP} -noprototypes -typemap ${EXTUTILS_TYPEMAP} ${SRC} > ${TGT}'
color = 'BLUE'
ext_out = ['.h']
@conf
def check_perl_version(self, minver=None):
res = True
if minver:
cver = '.'.join(map(str, minver))
else:
cver = ''
self.start_msg('Checking for minimum perl version %s' % cver)
perl = self.find_program('perl', var='PERL', value=getattr(Options.options, 'perlbinary', None))
version = self.cmd_and_log(perl + ["-e", 'printf \"%vd\", $^V'])
if not version:
res = False
version = "Unknown"
elif not minver is None:
ver = tuple(map(int, version.split(".")))
if ver < minver:
res = False
self.end_msg(version, color=res and 'GREEN' or 'YELLOW')
return res
@conf
def check_perl_module(self, module):
cmd = self.env.PERL + ['-e', 'use %s' % module]
self.start_msg('perl module %s' % module)
try:
r = self.cmd_and_log(cmd)
except Errors.WafError:
self.end_msg(False)
return None
self.end_msg(r or True)
return r
@conf
def check_perl_ext_devel(self):
env = self.env
perl = env.PERL
if not perl:
self.fatal('find perl first')
def cmd_perl_config(s):
return perl + ['-MConfig', '-e', 'print \"%s\"' % s]
def cfg_str(cfg):
return self.cmd_and_log(cmd_perl_config(cfg))
def cfg_lst(cfg):
return Utils.to_list(cfg_str(cfg))
def find_xsubpp():
for var in ('privlib', 'vendorlib'):
xsubpp = cfg_lst('$Config{%s}/ExtUtils/xsubpp$Config{exe_ext}' % var)
if xsubpp and os.path.isfile(xsubpp[0]):
return xsubpp
return self.find_program('xsubpp')
env.LINKFLAGS_PERLEXT = cfg_lst('$Config{lddlflags}')
env.INCLUDES_PERLEXT = cfg_lst('$Config{archlib}/CORE')
env.CFLAGS_PERLEXT = cfg_lst('$Config{ccflags} $Config{cccdlflags}')
env.EXTUTILS_TYPEMAP = cfg_lst('$Config{privlib}/ExtUtils/typemap')
env.XSUBPP = find_xsubpp()
if not getattr(Options.options, 'perlarchdir', None):
env.ARCHDIR_PERL = cfg_str('$Config{sitearch}')
else:
env.ARCHDIR_PERL = getattr(Options.options, 'perlarchdir')
env.perlext_PATTERN = '%s.' + cfg_str('$Config{dlext}')
def options(opt):
opt.add_option(
'--with-perl-binary', type='string', dest='perlbinary', help='Specify alternate perl binary', default=None
)
opt.add_option(
'--with-perl-archdir',
type='string',
dest='perlarchdir',
help='Specify directory where to install arch specific files',
default=None
)
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] |
structural/proxy/udemy-1.py | rafaelcassau/design-patterns | 13 | 170132 | """
A proxy provides a surrogate or place holder to provide
access to an object.
Ex1:
Use an extra level of indirection to support distributed,
controlled, or conditional access.
"""
class SubjectInterface:
"""
Define the common interface for RealSubject and Proxy so that a
Proxy can be used anywhere a RealSubject is expected.
"""
def request(self):
raise NotImplementedError()
class Proxy(SubjectInterface):
"""
Maintain a reference that lets the proxy access the real subject.
Provide an interface identical to Subject's.
"""
def __init__(self, real_subject):
self.real_subject = real_subject
def request(self):
print('Proxy may be doing something, like controlling request access.')
self.real_subject.request()
class RealSubject(SubjectInterface):
"""
Define the real object that the proxy represents.
"""
def request(self):
print('The real thing is dealing with the request')
real_subject = RealSubject()
real_subject.request()
proxy = Proxy(real_subject)
proxy.request() | [
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scripts_v1/22_Save_Proximity_model.py | onebacha/recipe_rec | 0 | 76724 | # %% Packages
import os
os.chdir('/Users/czarrar/Dropbox/Circle/Jerb/recipe_rec/scripts')
import recipe_rec
import importlib
recipe_rec = importlib.reload(recipe_rec)
# %% Read in ingredients
recipe_file = '../data/30_ingredients+ave_ratings.csv'
recs = recipe_rec.RecipeRec()
recs.load_from_csv(recipe_file, index_col=0)
# %% Apply TFIDF
recs.fit_model()
# %% Save
#!rm z_model.p
recs.save_model("z_model.p")
# %% Test loading it back
import numpy as np
recs2 = recipe_rec.RecipeRec.load_model('z_model.p')
np.all(recs.model.features == recs2.model.features)
# %% Test Recipes + Try Out a Simple Proximity Model
test_recipes = [
'caramels water chopped pecans Rice Krispies milk chocolate chips shortening',
'peanut butter sugar large egg room temperature vanilla extract milk chocolate kisses',
'semisweet chocolate chips, water, large egg yolk lightly beaten, teaspoons vanilla extract, heavy whipping cream, sugar, Whipped cream, raspberries',
'dried minced onion, salt, chili powder, cornstarch, ground cumin, red pepper flakes, cayenne pepper, dried minced garlic, dried oregano, ground beef, water',
'reduced-sodium soy sauce, rice wine vinegar, cornstarch, sesame oil, divided, pork tenderloin, cut into strips, red chile pepper, chopped, cloves garlic, minced, onion, chopped, green bell pepper, chopped, head bok choy, leaves and stalks separated, chopped, crowns broccoli, chopped, ground ginger',
'Ingredient Checklist, hoisin sauce, brown sugar, soy sauce, applesauce, pork loin, sliced, cut into thin strips, cornstarch, peanut oil, sesame oil, chopped fresh ginger root, broccoli florets'
]
tmp = recs.proximity_model(test_recipes[0], to_clean=False)
tmp
d = tmp.iloc[0,:].to_dict()
d
"{name}: {ingredients}".format(**d)
recs.recipes.recipe_id.min()
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errores.py | fbzavaleta/DS_Software_Stack | 0 | 14911 | #
E_LEN = "No es posible operar vectores de diferente módulo"
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ADT7310_CTR.py | pit-ray/ADT7310-CTR | 0 | 119067 | <reponame>pit-ray/ADT7310-CTR
#!/usr/bin/python3
# coding: utf-8
import spidev
import time
class ADT7310_CTR:
def __init__(self, spi_port, device, max_hz=100000):
self.port = spi_port
self.device = device
self.max_hz = max_hz
self.initialize()
def initialize(self):
# create spi object
self.spi = spidev.SpiDev()
# open spi port, device
self.spi.open(self.port, self.device)
# reset register data
self.clear_register()
# set frequency
self.spi.max_speed_hz = self.max_hz
self.enable_16bit_mode()
self.enable_c_read_mode()
def enable_16bit_mode(self):
self.spi.xfer2([0x0C, 0x80])
time.sleep(0.1)
def clear_register(self):
self.spi.xfer2([0xFF, 0xFF, 0xFF, 0xFF])
time.sleep(0.005)
def enable_c_read_mode(self):
self.spi.xfer2([0x54])
time.sleep(0.240)
def disable_c_read_mode(self):
self.spi.xfer2([0x50])
def terminate(self):
self.disable_c_read_mode()
self.spi.close()
def reinitialize(self):
self.terminate()
self.initialize()
def temp(self):
# return is [8bit, 8bit] of temp
upper, lower = self.spi.xfer2([0x00, 0x00])
# convert from [8bit,8bit] to 16bit
temp = ((upper << 8) | lower)
# is this negative?
if temp & 0x8000:
temp = temp - 65536
# convert to celsius
temp /= 128.0
# If value is NULL, failed.
if temp == 0:
raise RuntimeError('Failed to get value')
return temp
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] |
sample_server.py | sandu-alexandru/asn_diameter_python | 0 | 188838 | <reponame>sandu-alexandru/asn_diameter_python
"""
Sample Diameter Server, which can be used for testing purposes.
Generates a Diameter Server with the default(local) config,
and then starts the server, making it available
for incoming connections and requests handling.
"""
from diameter_server import *
if __name__ == "__main__":
"""
Main method for the Sample Diameter Server.
Instantiates a Diameter Server, and then starts it.
"""
# Generating a Diameter server and starting it
server = DiameterServer()
server.start()
| [
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] |
misc/hangman.py | maryletteroa/practice-space | 0 | 164020 | <gh_stars>0
# Hangman Game
#
# Credit to <NAME>'s "Python Programming for the Absolute Beginner 3rd Edition"
#
# The classic game of Hangman. The computer picks a random word
# and the player wrong to guess it, one letter at a time. If the player
# can't guess the word in time, the little stick figure gets hanged.
# imports
from sys import exit
from random import randint
# constants
HANGMAN = (
"""
------
| |
|
|
|
|
|
|
|
----------
""",
"""
------
| |
| O
|
|
|
|
|
|
----------
""",
"""
------
| |
| O
| -+-
|
|
|
|
|
----------
""",
"""
------
| |
| O
| /-+-
|
|
|
|
|
----------
""",
"""
------
| |
| O
| /-+-/
|
|
|
|
|
----------
""",
"""
------
| |
| O
| /-+-/
| |
|
|
|
|
----------
""",
"""
------
| |
| O
| /-+-/
| |
| |
| |
| |
|
----------
""",
"""
------
| |
| O
| /-+-/
| |
| |
| | |
| | |
|
----------
""")
MAX_WRONG = len(HANGMAN)-1
WORDS = ("OCTOPUS", "NEMO", "STARFISH", "ORCA") # Change these as you please
# initialize variables
word = WORDS[randint(0,len(WORDS)-1)] # pick random word to be guessed
so_far = "-" * len(word) # one dash for each letter in word to be guessed
wrong = 0 # number of wrong guesses player has made
used = [] # letters already guessed
print("Welcome to Hangman. Good luck!")
print(HANGMAN[wrong])
# Ask for a guess while the game is not yet over
while wrong != MAX_WRONG and '-' in so_far:
guess = input("\n\nEnter your guess: ").upper()
# only accept one character guesses, no symbols and numbers
if len(guess) == 1 and guess.isalpha() == True:
if guess in used:
print(f"You have guessed '{guess}' before.")
else:
used.append(guess)
if guess in word:
# find all positions that match guess and
# substitute that .. but convert
# string to list first to do this
lword = list(word)
l_so_far = list(so_far)
matched_indices = [i for i, j in enumerate(lword) if j == guess]
for index in matched_indices:
l_so_far[index] = guess
so_far = "".join(l_so_far)
else:
wrong +=1
print(HANGMAN[wrong])
print(f"You have {MAX_WRONG-wrong} guesses left!")
print("\nYou've used the following letters:\n", used)
print("\nSo far, the word is:\n", so_far)
else:
print("Guess ONE (1) LETTER only, and no symbols or numbers")
# Inform if the user won or lose
print("\nThe word was", word)
if "-" in so_far:
print("You lost!")
else:
print("You won!")
input("\n\nPress the enter key to exit.")
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] |
tools/try_test1.py | csuzhuzhuxia/GlassDetect | 6 | 119834 | import sys
from mmdet.apis import inference_detector, init_detector
import json
import os
import numpy as np
import argparse
from tqdm import tqdm
import json
class MyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
return super(MyEncoder, self).default(obj)
#generate result
def result_from_dir():
# build the model from a config file and a checkpoint file
model = init_detector(config2make_json, model2make_json, device='cuda:0')
pics = os.listdir(pic_path)
meta = {}
images = []
annotations = []
num = 0
for im in tqdm(pics):
num += 1
img = os.path.join(pic_path,im)
result_ = inference_detector(model, img)
images_anno = {}
images_anno['file_name'] = im
images_anno['id'] = num
images.append(images_anno)
for i ,boxes in enumerate(result_,1):
if len(boxes):
defect_label = i
for box in boxes:
anno = {}
anno['image_id'] = num
anno['category_id'] = defect_label
anno['bbox'] = [round(float(i), 2) for i in box[0:4]]
anno['bbox'][2] = anno['bbox'][2]-anno['bbox'][0]
anno['bbox'][3] = anno['bbox'][3]-anno['bbox'][1]
anno['score'] = float(box[4])
annotations.append(anno)
meta['images'] = images
meta['annotations'] = annotations
with open(json_out_path, 'w') as fp:
json.dump(meta, fp, cls=MyEncoder, indent=4, separators=(',', ': '))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate result")
parser.add_argument("-m", "--model",help="Model path",type=str,)
parser.add_argument("-c", "--config",help="Config path",type=str,)
parser.add_argument("-im", "--im_dir",help="Image path",type=str,)
parser.add_argument('-o', "--out",help="Save path", type=str,)
args = parser.parse_args()
model2make_json = args.model
config2make_json = args.config
json_out_path = args.out
pic_path = args.im_dir
result_from_dir()
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] |
Calibration/IsolatedParticles/python/isolatedTracksCone_cfi.py | pasmuss/cmssw | 0 | 65286 | <gh_stars>0
import FWCore.ParameterSet.Config as cms
isolatedTracksCone= cms.EDAnalyzer("IsolatedTracksCone",
doMC = cms.untracked.bool(False),
Verbosity = cms.untracked.int32( 1 ),
useJetTrigger = cms.untracked.bool(False),
drLeadJetVeto = cms.untracked.double(1.2),
ptMinLeadJet = cms.untracked.double(15.0),
DebugTracks = cms.untracked.int32(0),
PrintTrkHitPattern=cms.untracked.bool(True),
minTrackP = cms.untracked.double(1.0),
maxTrackEta = cms.untracked.double(2.6),
maxNearTrackP = cms.untracked.double(1.0),
DebugEcalSimInfo= cms.untracked.bool(False),
ApplyEcalIsolation=cms.untracked.bool(True),
DebugL1Info = cms.untracked.bool(False),
L1extraTauJetSource = cms.InputTag("l1extraParticles", "Tau"),
L1extraCenJetSource = cms.InputTag("l1extraParticles", "Central"),
L1extraFwdJetSource = cms.InputTag("l1extraParticles", "Forward"),
TrackAssociatorParameters = cms.PSet(
muonMaxDistanceSigmaX = cms.double(0.0),
muonMaxDistanceSigmaY = cms.double(0.0),
CSCSegmentCollectionLabel = cms.InputTag("cscSegments"),
dRHcal = cms.double(9999.0),
dREcal = cms.double(9999.0),
CaloTowerCollectionLabel = cms.InputTag("towerMaker"),
useEcal = cms.bool(True),
dREcalPreselection = cms.double(0.05),
HORecHitCollectionLabel = cms.InputTag("horeco"),
dRMuon = cms.double(9999.0),
crossedEnergyType = cms.string('SinglePointAlongTrajectory'),
muonMaxDistanceX = cms.double(5.0),
muonMaxDistanceY = cms.double(5.0),
useHO = cms.bool(False),
accountForTrajectoryChangeCalo = cms.bool(False),
DTRecSegment4DCollectionLabel = cms.InputTag("dt4DSegments"),
EERecHitCollectionLabel = cms.InputTag("ecalRecHit","EcalRecHitsEE"),
dRHcalPreselection = cms.double(0.2),
useMuon = cms.bool(False),
useCalo = cms.bool(True),
EBRecHitCollectionLabel = cms.InputTag("ecalRecHit","EcalRecHitsEB"),
dRMuonPreselection = cms.double(0.2),
truthMatch = cms.bool(False),
HBHERecHitCollectionLabel = cms.InputTag("hbhereco"),
useHcal = cms.bool(True),
usePreshower = cms.bool(False),
dRPreshowerPreselection = cms.double(0.2),
trajectoryUncertaintyTolerance = cms.double(-1.0)
)
)
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compute_extrinsic.py | Ericcsr/ClothFromDepth | 0 | 87712 | import numpy as np
import open3d as o3d
from argparse import ArgumentParser
import os
parser = ArgumentParser()
parser.add_argument("--red",type=float, default= 0.5)
parser.add_argument("--blue", type=float, default = 0.4)
parser.add_argument("--green", type=float, default = 0.4)
parser.add_argument("--scan_folder",type=str, default = "scatters")
parser.add_argument("--extrinsic_name", type = str, default = "extrinsic")
args = parser.parse_args()
def computeTF(pointcloud_registers):
top = np.zeros(3,dtype=np.float)
bottom = np.zeros(3,dtype=np.float)
left = np.zeros(3,dtype=np.float)
right = np.zeros(3,dtype=np.float)
for pc in pointcloud_registers:
pc.remove_statistical_outlier(nb_neighbors=20, std_ratio=0.04)
points = np.asarray(pc.points)
top += points[np.argmax(points[:,1])]
bottom += points[np.argmin(points[:,1])]
left += points[np.argmin(points[:,0])]
right += points[np.argmax(points[:,0])]
top /= len(pointcloud_registers)
bottom /= len(pointcloud_registers)
left /= len(pointcloud_registers)
right /= len(pointcloud_registers)
R = np.zeros((3,3))
R[:,0] = (right - left)/np.linalg.norm(right-left,2)
R[:,1] = (top - bottom)/np.linalg.norm(top - bottom,2)
R[:,2] = np.cross(R[:,0],R[:,1])
R = R.T
# set the point with minimum x then minimum y as origin
T = -(top + bottom+left+right)/4
return R, T
def process_point_cloud(pcd):
color = np.asarray(pcd.colors)
color[:,[0,2]] = color[:,[2,0]]
pcd.colors = o3d.utility.Vector3dVector(color)
mask = (color[:,0] > args.red) * (color[:,1] < args.green) * (color[:,2] < args.blue)
points = np.asarray(pcd.points)
truncated_pcd = o3d.geometry.PointCloud()
truncated_pcd.points = o3d.utility.Vector3dVector(points[mask])
truncated_pcd.colors = o3d.utility.Vector3dVector(color[mask])
return truncated_pcd
filelist = os.listdir(f"./pointcloud_raw/{args.scan_folder}/")
truncated_pcds = []
for file in filelist:
filename = f"./pointcloud_raw/{args.scan_folder}/{file}"
pcd = o3d.io.read_point_cloud(filename)
truncated_pcd = process_point_cloud(pcd)
truncated_pcds.append(truncated_pcd)
R, T = computeTF(truncated_pcds)
np.savez(f"./extrinsic/{args.extrinsic_name}.npz", R = R, T = T)
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sdk/python/tests/integration/feature_repos/universal/data_source_creator.py | marsishandsome/feast | 1 | 16920 | from abc import ABC, abstractmethod
from typing import Dict
import pandas as pd
from feast.data_source import DataSource
from feast.repo_config import FeastConfigBaseModel
class DataSourceCreator(ABC):
@abstractmethod
def create_data_source(
self,
destination: str,
df: pd.DataFrame,
event_timestamp_column="ts",
created_timestamp_column="created_ts",
field_mapping: Dict[str, str] = None,
) -> DataSource:
...
@abstractmethod
def create_offline_store_config(self) -> FeastConfigBaseModel:
...
@abstractmethod
def teardown(self):
...
@abstractmethod
def get_prefixed_table_name(self, name: str, suffix: str) -> str:
...
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kyk/kyk.py | atrautsch/kyk | 0 | 33118 | #!/usr/bin/env python3
import os
import time
import shutil
import signal
import yaml
import sass
import pyinotify
from colorama import init, Fore, Style
from jsmin import jsmin
from csscompressor import compress
VERSION = '1.4.7'
class Kyk(object):
"""Kyk
Build JS
- if min: minify in place, append _minifed
- concat to destfile
Build SASS:
- compile SASS file
- concat to destfile
Build partial js not yet implemented completely
"""
def __init__(self, folder, debug):
self._folder = folder
self._debug = debug
init()
self._load_config()
signal.signal(signal.SIGINT, self.teardown)
def teardown(self, signal, frame):
if type(self.notifier) == pyinotify.ThreadedNotifier:
print('stopping watching')
self.notifier.stop()
def _load_config(self, reloading=False):
cfgfile = os.path.normpath(os.path.join(self._folder, 'kyk.yaml'))
if not os.path.isfile(cfgfile) and reloading:
time.sleep(1) # wait for it to appear (ftp program maybe?)
if not os.path.isfile(cfgfile):
raise Exception('no config file "{}" found!'.format(cfgfile))
with open(cfgfile, 'r', encoding='utf-8') as f:
dat = f.read()
self._cfg = yaml.load(dat)
self._js = {}
self._css = {}
self._jswatchlist = []
self._listen_events = []
self._timestamp_file = None
if 'watch_path' in self._cfg.keys():
self._folder = self._cfg['watch_path']
# no overwriting of command line debug, if config file does not hold debug: True we do not want to disable --debug
if not self._debug:
self._debug = 'debug' in self._cfg.keys() and self._cfg['debug']
self._version = self._cfg['version']
self._listen_events = self._cfg['events']
if 'timestamp_file' in self._cfg.keys():
self._timestamp_file = self._cfg['timestamp_file']
for minfile in self._cfg.keys():
if minfile.endswith('.js'):
jsfile = self._cfg[minfile]
minify = False
for jsfile in self._cfg[minfile]:
if jsfile.startswith('min:'):
minify = True
jsfile = jsfile.split('min:')[1].strip()
if minfile not in self._js.keys():
self._js[minfile] = []
self._js[minfile].append({'file': os.path.abspath(jsfile), 'minify': minify})
self._jswatchlist.append(os.path.abspath(jsfile))
elif minfile.endswith('.css'):
self._css[minfile] = self._cfg[minfile]
print('Kyk version: {}'.format(VERSION))
print('config version: {}'.format(self._version))
def oneshot(self):
print('oneshot')
self.build_js()
self.build_sass()
def watch_forever(self):
print('listening on:')
print(self._listen_events)
# first run, build everything
self.build_js()
self.build_sass()
# now only changed files
self.wm = pyinotify.WatchManager()
self.notifier = pyinotify.ThreadedNotifier(self.wm, default_proc_fun=self.handler)
self.wm.add_watch(self._folder, pyinotify.ALL_EVENTS, rec=True, auto_add=True)
# self.notifier.loop()
self.notifier.start()
signal.pause()
def reload(self):
self.notifier.stop()
self._load_config(True)
self.watch_forever()
def handler(self, event):
# catch every scss file change, we can do this here because we are limited by the watchpath
if getattr(event, 'pathname'):
if event.pathname.endswith('.scss'):
if event.maskname in self._listen_events:
print('{} changed!'.format(event.pathname))
self.build_sass()
# catch only changes to our configured jsfiles
elif event.pathname in self._jswatchlist:
if event.maskname in self._listen_events:
print('{} changed!'.format(event.pathname))
self.build_js()
elif event.pathname.endswith('kyk.yaml'):
print('kyk config changed, reloading')
self.reload()
def build_js(self):
"""minify everything, then concat everything
"""
print('building js...')
for minfile in self._js.keys():
with open(minfile, 'w', encoding='utf-8') as f:
for jsfile in self._js[minfile]:
if jsfile['minify'] and not self._debug:
self.minify_js(jsfile['file'])
out = self._load_js(jsfile['file'], load_minified=jsfile['minify'])
f.write(out + "\n")
print('finished')
self._update_timestamp()
def concat_js(self, destfile):
print('building {}...'.format(destfile))
with open(destfile, 'w', encoding='utf-8') as f:
for jsfile in self._js[destfile]:
if self._debug:
f.write('{}\n'.format(self._load_js(jsfile['file'])))
f.write(self._load_js(jsfile['file']) + ';')
print('finished')
def minify_js(self, jsfile=None):
"""Minify JS in place, append _minified
"""
out = jsmin(self._load_js(jsfile, load_minified=False))
with open('{}_minified'.format(jsfile), 'w', encoding='utf-8') as f:
f.write(out)
def build_partial_js(self, changed):
print('building partial js...')
for minfile in self._js:
for jsfile in self._js[minfile]:
if changed == jsfile['file']:
if jsfile['minify'] and not self._debug:
self.minify_js(jsfile['file'])
self.concat_js(minfile)
print('finished')
def _load_js(self, jsfile, load_minified=True):
"""Load js from file, load _minifed if exists and we want to have it (we do not want it if we minify anew)
"""
if load_minified and os.path.isfile('{}_minified'.format(jsfile)):
jsfile = '{}_minified'.format(jsfile)
if not os.path.isfile(jsfile):
print(Fore.RED + 'File {} not found!'.format(jsfile))
print(Style.RESET_ALL)
return ''
else:
with open(jsfile, 'r', encoding='utf-8') as f:
out = f.read()
return out
def _update_timestamp(self):
try:
if self._timestamp_file:
with open(self._timestamp_file, 'w') as f:
f.write('{}'.format(int(time.time())))
print('timestamp file updated')
except Exception as e:
print(Fore.RED + 'Error updating timestamp file: {}'.format(e))
print(Style.RESET_ALL)
def build_sass(self):
print('building sass...')
for minfile in self._css.keys():
try:
# tmp minfile name for writing
minfile_name = os.path.basename(minfile)
tmp_minfile_name = 'kyk_{}'.format(minfile_name)
tmp_minfile = minfile.replace(minfile_name, tmp_minfile_name)
# only scss source map file
mapfile = tmp_minfile.replace('.css', '.css.map')
smapfile = tmp_minfile.replace('.css', '.smap.css') # this holds the css and the source comments
with open(tmp_minfile, 'w', encoding='utf-8') as f, open(mapfile, 'w', encoding='utf-8') as smf, open(smapfile, 'w', encoding='utf-8') as sma:
for sassfile in self._css[minfile]:
if sassfile.endswith('.scss'):
ost = 'compressed'
if self._debug:
ost = 'expanded'
sc, sm = sass.compile(filename=sassfile, source_comments=True, source_map_filename=mapfile, output_style=ost)
sc_clean = sass.compile(filename=sassfile, source_comments=False, output_style=ost) # without source comments
f.write(sc_clean)
smf.write(sm)
sma.write(sc)
else:
sc = open(sassfile, 'r', encoding='utf-8').read()
if not self._debug:
sc = compress(sc)
f.write(sc)
shutil.copy(tmp_minfile, minfile)
shutil.copy(mapfile, minfile.replace('.css', '.css.map'))
shutil.copy(smapfile, minfile.replace('.css', '.smap.css'))
except sass.CompileError as e:
print(Fore.RED + 'SASS Error: {}'.format(e))
print(Style.RESET_ALL)
# in debug mode we break things on purpose here
if self._debug:
shutil.copy(tmp_minfile, minfile)
shutil.copy(mapfile, minfile.replace('.css', '.css.map'))
shutil.copy(smapfile, minfile.replace('.css', '.smap.css'))
print('finished')
self._update_timestamp()
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src/dataset/config.py | Digital-Pathology/CustomDataset | 0 | 157130 |
"""
Configuration for development - change these with caution!
"""
REGION_DIMS = (512, 512)
DEFAULT_FILTRATION_STATUS = None
DEFAULT_FILTRATION_CACHE_FILEPATH = "filtration_cache.h5"
DEFULAT_FILTRATION_CACHE_TITLE = "filtration_cache"
DATASET_FILTRATION_PREPROCESSING_MULTIPROCESSING = False
FILTRATION_CACHE_APPLY_FILTRATION_MULTIPROCESSING = True
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networkx/classes/tests/test_digraph_historical.py | KyleBenson/networkx | 0 | 22636 | <filename>networkx/classes/tests/test_digraph_historical.py
#!/usr/bin/env python
"""Original NetworkX graph tests"""
from nose.tools import *
import networkx
import networkx as nx
from networkx.testing.utils import *
from historical_tests import HistoricalTests
class TestDiGraphHistorical(HistoricalTests):
def setUp(self):
HistoricalTests.setUp(self)
self.G=nx.DiGraph
def test_in_degree(self):
G=self.G()
G.add_nodes_from('GJK')
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
('B', 'C'), ('C', 'D')])
assert_equal(sorted(d for n, d in G.in_degree()),[0, 0, 0, 0, 1, 2, 2])
assert_equal(dict(G.in_degree()),
{'A': 0, 'C': 2, 'B': 1, 'D': 2, 'G': 0, 'K': 0, 'J': 0})
def test_out_degree(self):
G=self.G()
G.add_nodes_from('GJK')
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
('B', 'C'), ('C', 'D')])
assert_equal(sorted([v for k,v in G.in_degree()]),
[0, 0, 0, 0, 1, 2, 2])
assert_equal(dict(G.out_degree()),
{'A': 2, 'C': 1, 'B': 2, 'D': 0, 'G': 0, 'K': 0, 'J': 0})
def test_degree_digraph(self):
H=nx.DiGraph()
H.add_edges_from([(1,24),(1,2)])
assert_equal(sorted(d for n, d in H.in_degree([1,24])), [0, 1])
assert_equal(sorted(d for n, d in H.out_degree([1,24])), [0, 2])
assert_equal(sorted(d for n, d in H.degree([1,24])), [1, 2])
def test_neighbors(self):
G=self.G()
G.add_nodes_from('GJK')
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
('B', 'C'), ('C', 'D')])
assert_equal(sorted(G.neighbors('C')),['D'])
assert_equal(sorted(G['C']),['D'])
assert_equal(sorted(G.neighbors('A')),['B', 'C'])
assert_raises(nx.NetworkXError,G.neighbors,'j')
assert_raises(nx.NetworkXError,G.neighbors,'j')
def test_successors(self):
G=self.G()
G.add_nodes_from('GJK')
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
('B', 'C'), ('C', 'D')])
assert_equal(sorted(G.successors('A')),['B', 'C'])
assert_equal(sorted(G.successors('A')),['B', 'C'])
assert_equal(sorted(G.successors('G')),[])
assert_equal(sorted(G.successors('D')),[])
assert_equal(sorted(G.successors('G')),[])
assert_raises(nx.NetworkXError,G.successors,'j')
assert_raises(nx.NetworkXError,G.successors,'j')
def test_predecessors(self):
G=self.G()
G.add_nodes_from('GJK')
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'),
('B', 'C'), ('C', 'D')])
assert_equal(sorted(G.predecessors('C')),['A', 'B'])
assert_equal(sorted(G.predecessors('C')),['A', 'B'])
assert_equal(sorted(G.predecessors('G')),[])
assert_equal(sorted(G.predecessors('A')),[])
assert_equal(sorted(G.predecessors('G')),[])
assert_equal(sorted(G.predecessors('A')),[])
assert_equal(sorted(G.successors('D')),[])
assert_raises(nx.NetworkXError,G.predecessors,'j')
assert_raises(nx.NetworkXError,G.predecessors,'j')
def test_reverse(self):
G=nx.complete_graph(10)
H=G.to_directed()
HR=H.reverse()
assert_true(nx.is_isomorphic(H,HR))
assert_equal(sorted(H.edges()),sorted(HR.edges()))
def test_reverse2(self):
H=nx.DiGraph()
foo=[H.add_edge(u,u+1) for u in range(0,5)]
HR=H.reverse()
for u in range(0,5):
assert_true(HR.has_edge(u+1,u))
def test_reverse3(self):
H=nx.DiGraph()
H.add_nodes_from([1,2,3,4])
HR=H.reverse()
assert_equal(sorted(HR.nodes()),[1, 2, 3, 4])
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flashcards/views.py | YellowAndGreen/Anki2 | 0 | 137349 | import collections
import json
import random
import re
from datetime import timedelta, datetime
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.core.cache import cache
from django.db.models import Q
from django.http import JsonResponse, FileResponse
from django.shortcuts import render, get_object_or_404, redirect
from django.utils import timezone
from django.views.decorators.http import require_POST
from taggit.models import Tag
from flashcards.utils import find_example_from_database
from .crontab import update_due_list
from .forms import *
from .models import *
from .utils import to_chinese
from .web_query import find_synonym
# 启动时执行的函数
def onstart():
coca = {}
# 将coca存入内存中
with open('flashcards/static/dict/coca.txt', 'r', encoding='utf-8') as f:
coca = json.loads(f.read())
cache.set('coca', coca, timeout=None)
onstart()
# 首页
@login_required
def index(request):
card_num = Card.objects.count()
# 获取当前用户的单词列表
wordlists = request.user.owner.all()
# 获取过去七天的背诵数据
now = timezone.now()
# 七天前作为开始时间
start = now - timedelta(days=7, hours=now.hour, minutes=now.minute, seconds=now.second)
# 找到过去七天的背诵数据
recitedata = Recitedata.objects.filter(date__gt=start)
rank_count_by_day = []
for i in range(1, 8):
all_data = recitedata.filter(date__gt=start + timedelta(days=i), date__lt=start + timedelta(days=i + 1))
counter = collections.Counter([data.rank for data in all_data])
rank_count_by_day.append([counter[1], counter[2], counter[3], counter[4]])
rank_count_by_rank = []
for i in range(0, 4):
rank_count_by_rank.append([rank_day[i] for rank_day in rank_count_by_day])
# 仅返回有单词的tag
tags = filter(lambda tag: len(Card.objects.filter(tags__in=[tag])), Tag.objects.all())
return render(request, 'flashcards/anki.html',
{'len': card_num,
'lenlist': len(wordlists),
'wordlists': wordlists,
'tags': tags,
'recitedata': rank_count_by_rank,
})
# 单张卡片浏览
def card_detailview(request, card_id):
# 从缓存中获取当前设置，若无则从数据库读取并存入缓存
random_example = request.user.settings.all()[0] if cache.get('random_example') is None else cache.get(
'random_example')
# 获取card实例
card = get_object_or_404(Card, id=card_id)
# 若当前设置为随机例句且是该卡片属于词汇分组
if request.user.settings.all()[0].random_example and card.group[0:2] == "词汇":
result = find_example_from_database(card.question)
# 有可能查不到
if len(result[0]) > 0:
rand_index = random.randint(0, len(result[0]) - 1)
card.example = result[0][rand_index]
card.translation = result[1][rand_index]
# 将例句中的此词标红
if len(re.findall(r"[a-zA-Z]+", card.question)) > 0:
reple = re.findall(r"[a-zA-Z]+", card.question)[0]
card.example = card.example.replace(reple, "<span id='red'><b>" + reple + "</b></span>")
return render(request,
'flashcards/card_detail.html',
{'object': card,
'tags': card.tags.all(),
'random_example': random_example
})
# 下一张卡片
@login_required
def nextcardview(request):
# 有待改进，随机卡片操作十分耗时
card = Card.objects.all()[random.randrange(1, Card.objects.count(), 1)]
return redirect(card.get_absolute_url())
# 背诵
@login_required
def cardreciteview(request, card_id, rank):
# 获取card实例
card = get_object_or_404(Card, id=card_id)
# 创建记忆数据并保存
recitedata = Recitedata(rank=rank, card=card)
recitedata.save()
# 生成随机数并跳转至下一卡片
card = Card.objects.all()[random.randrange(1, Card.objects.count(), 1)]
return redirect(card.get_absolute_url())
# 展示背诵数据
@login_required
def recitedatadisplay(request):
# 按rank排序，后续改进
recitedata = Recitedata.objects.order_by("rank")
# 使用set以防止card重复
cards = set([data.card for data in recitedata])
datas = [{'card': card, 'recitedata': card.recitedata.all()} for card in cards]
return render(request,
'flashcards/recitedata.html',
{'datas': datas}
)
# 单张卡片背诵数据
def card_recitedata_view(request, card_id):
card = get_object_or_404(Card, id=card_id)
all_recitedata = card.recitedata.all()
return render(request,
'flashcards/card_recitedata_detail.html',
{'all_recitedata': all_recitedata
})
# 修改到此
@login_required
def search(request):
cards = []
cd = {"query": ''}
form = SearchForm(request.GET)
if form.is_valid():
cd = form.cleaned_data
if cd['query'][0:2] == 'T:':
tag = get_object_or_404(Tag, slug=cd['query'][2:])
cards = Card.objects.filter(tags__in=[tag])
elif cd['query'][0:2] == 'G:':
cards = Card.objects.filter(group__startswith=cd['query'][2:])
else:
cards = Card.objects.filter(
Q(question__icontains=cd['query']) | Q(example__icontains=cd['query']))
# 将搜索字段替换为红色
for card in cards:
card.question = card.question.replace(cd['query'], "<span id='red'><b>" + cd['query'] + "</b></span>")
card.example = card.example.replace(cd['query'], "<span id='red'><b>" + cd['query'] + "</b></span>")
if cd['query'] == '':
form = SearchForm()
return render(request, 'flashcards/search.html', {'cards': cards, 'searchvalue': cd['query'], 'form': form})
# 撤回：删除背诵记录
@login_required
def undo(request, card_id):
# 需要撤回的卡片和需要删除背诵记录的卡是一张卡
card = get_object_or_404(Card, id=card_id)
card.recitedata.latest('date').delete()
return redirect(card.get_absolute_url())
# 列表的撤回：删除背诵记录
@login_required
def undo_list(request, list_id, progress):
wordlist = get_object_or_404(WordList, id=list_id)
id_list = list(map(lambda dic: dic['id'], json.loads(wordlist.wordlist)))
card_id = id_list[progress - 1]
# 需要撤回到的卡片和需要删除背诵记录的卡是一张卡
card = Card.objects.filter(id=card_id)
latest_recitedata = card[0].recitedata.latest('date')
recitedata_params = json.loads(latest_recitedata.params)
card.due = datetime.strptime(recitedata_params['due'], "%Y-%m-%d %H:%M:%S.%f")
card.review_num = recitedata_params['review_num']
card.forget_num = recitedata_params['forget_num']
card.ratio = recitedata_params['ratio']
card.interval = recitedata_params['interval']
card.save()
latest_recitedata.delete()
# 修改进度
return redirect('flashcards:recite_wordlist', list_id, progress - 1, 0)
# 网络搜索
@login_required
def websearch(request):
cd = {"query": ''}
form = SearchForm(request.GET)
if form.is_valid():
cd = form.cleaned_data
words = find_synonym(cd['query'])
else:
form = SearchForm()
words = []
return render(request, 'flashcards/webquery.html',
{"words": words, 'searchvalue': cd['query'], 'form': form}, )
@login_required
def dict_search(request):
cd = {"query": ''}
form = SearchForm(request.GET)
if form.is_valid():
cd = form.cleaned_data
# 查词，返回单词和html文件
html_result = ''
queryword = cd['query']
dict_query = Dict.objects.filter(headword=queryword)
if len(dict_query) == 0:
dict_query = Dict.objects.filter(headword=queryword.lower())
if len(dict_query) == 0:
html_result = 'No Match!'
html_result = dict_query[0].item
else:
form = SearchForm()
html_result = ''
return render(request, 'flashcards/dict.html',
{'html_result': html_result, 'form': form, 'searchvalue': cd['query'],
})
@login_required
def create_wordlist_by_diff(request):
cd = {"query": 'list'}
form = SearchForm(request.GET)
if form.is_valid():
cd = form.cleaned_data
# 找到最近的三个复习data并累计，生成一个字典
rank_sum_dict = map(lambda card: {'id': card.id, 'rank_sum': sum(
sorted([Recitedata.rank for Recitedata in card.recitedata.order_by('-date')], reverse=True)[0:3])
if len(card.recitedata.all()) > 2
else sum([Recitedata.rank for Recitedata in card.recitedata.order_by('-date')])}
, Card.objects.all())
# 按rank排序，取前五十个值
# 不知道为什么id列表总为空
# id_list = [dic['id'] for dic in sorted(list(rank_sum_dict), key=lambda dic: dic['rank_sum'], reverse=True)[0:50]]
# id_list = list(
# map(lambda dic: dic['id'], sorted(list(rank_sum_dict), key=lambda dic: dic['rank_sum'], reverse=True)[0:50]))
# print(id_list)
sort_list = sorted(list(rank_sum_dict), key=lambda dic: dic['rank_sum'], reverse=True)[0:50]
wordlist = WordList(owner=request.user, name=cd['query'], wordlist=json.dumps(sort_list)
, len_list=len(sort_list))
wordlist.save()
messages.success(request, '单词列表创建成功')
return redirect('flashcards:dashboard')
@login_required
def create_wordlist_by_tag(request):
cd = {"query": 'list', "tag": 'marked'}
form = SearchForm(request.GET)
if form.is_valid():
cd = form.cleaned_data
tag = get_object_or_404(Tag, slug=request.GET['tag'])
cards = Card.objects.filter(tags__in=[tag])
id_list = [{'id': card.id} for card in cards]
wordlist = WordList(owner=request.user, name=cd['query'], wordlist=json.dumps(id_list)
, len_list=len(id_list))
wordlist.save()
messages.success(request, '单词列表创建成功')
return redirect('flashcards:dashboard')
@login_required
def recite_wordlist(request, wordlist_id, progress, rank):
wordlist = WordList.objects.filter(id=wordlist_id)[0]
# 判断请求progress和数据库progress是否相等，若不同，则将后者覆盖（在重新复习的时候可能出现的问题）
if progress == 0 and wordlist.progress != 0:
wordlist.progress = progress
# json解析单词列表id
id_list = list(map(lambda dic: dic['id'], json.loads(wordlist.wordlist)))
# 判断是刚进入还是进入后第一次提交卡片
# 若提交，则更新进度
# 若刚进入，则不更新进度
if rank > 0:
# 获取当前card实例
current_card = get_object_or_404(Card, id=id_list[progress])
# 获取当前时间
current_time = datetime.datetime.now()
# 创建并保存记忆数据
recitedata = Recitedata(rank=rank, card=current_card)
recitedata.save()
# 计算完成度
wordlist.progress = progress + 1
percentage = int((wordlist.progress / wordlist.len_list) * 100)
# 更新进度
wordlist.save()
# 若为due列表则还需要更新due参数
if wordlist.name == 'due':
# 将参数保存进recitedata
recitedata_params = {'due': str(current_card.due), 'review_num': current_card.review_num,
'forget_num': current_card.forget_num,
'ratio': current_card.ratio, 'interval': current_card.interval}
recitedata.params = json.dumps(recitedata_params)
recitedata.save()
if rank == 4:
print(
f"{current_card.question}:更新前due为{current_card.due}，interval为{current_card.interval}，简易度为{current_card.ratio}。")
# 卡片进入重新学习队列，简易度减少 20 个百分点
current_card.due = current_time
# 间隔设置为0，以便于区分
current_card.interval = 0
# 简易度最低为 130%
if current_card.ratio >= 150:
current_card.ratio -= 20
print(
f"{current_card.question}:更新后due为{current_card.due}，interval为{current_card.interval}，简易度为{current_card.ratio}。")
elif rank == 3:
# 卡片的简易度减少 15% ，当前的间隔乘以 1.2。
current_card.interval = int(1.2 * current_card.interval)
current_card.due = current_time + timedelta(days=current_card.interval)
if current_card.ratio >= 145:
current_card.ratio -= 15
# 困难惩罚，防止一直选3和2时的间隔增长
if current_card.ratio <= 180:
current_card.interval = int(0.8 * current_card.interval)
current_card.due = current_time + timedelta(days=current_card.interval)
elif rank == 2:
if current_card.interval == 0:
current_card.interval = 3
# 卡片的简易度减少 5% ，当前的间隔乘以 1.1。
else:
current_card.interval = int(1.1 * current_card.interval)
current_card.due = current_time + timedelta(days=current_card.interval)
if current_card.ratio >= 135:
current_card.ratio -= 5
elif rank == 1:
print(
f"{current_card.question}:更新前due为{current_card.due}，interval为{current_card.interval}，简易度为{current_card.ratio}。")
if current_card.interval == 0:
current_card.interval = 4
# 当前间隔乘以当前简易度再乘以简单奖励，简易度增加 15 个百分点。
else:
current_card.interval = int(0.01 * current_card.interval * current_card.ratio)
current_card.due = current_time + timedelta(days=current_card.interval)
if current_card.ratio >= 135:
current_card.ratio += 15
print(
f"{current_card.question}:更新后due为{current_card.due}，interval为{current_card.interval}，简易度为{current_card.ratio}。")
current_card.save()
else:
wordlist.progress = progress
percentage = int((wordlist.progress / wordlist.len_list) * 100)
# 进度更新后判断是否完成单词列表，若完成，则返回主页
if wordlist.progress == wordlist.len_list:
messages.success(request, '您已背完该单词列表！')
# 若为due列表，则刷新此列表
if wordlist.name == 'due':
update_due_list()
return redirect('flashcards:dashboard')
# 获取下一个单词
# 必须使用数据库中的wordlist.progress，否则使用progress会导致第一个不更新
next_word = get_object_or_404(Card, id=id_list[wordlist.progress])
if request.user.settings.all()[0].random_example and next_word.group[0:2] == "词汇":
result = find_example_from_database(next_word.question)
# 有可能查不到
if len(result[0]) > 0:
rand_index = random.randint(0, len(result[0]) - 1)
next_word.example = result[0][rand_index]
next_word.translation = result[1][rand_index]
# 将例句中的此词标红
reple = re.findall(r"[a-zA-Z]+", next_word.question)[0]
next_word.example = next_word.example.replace(reple, "<span id='red'><b>" + reple + "</b></span>")
return render(request, 'flashcards/card_list.html',
{
"percentage": percentage,
'object': next_word,
'progress': wordlist.progress,
'wordlist_id': wordlist_id,
'tags': next_word.tags.all(),
'section': 'list',
})
@login_required
def delete_wordlist(request, wordlist_id):
get_object_or_404(WordList, id=wordlist_id).delete()
messages.success(request, '单词列表删除成功')
return redirect('flashcards:dashboard')
@login_required
def word_add_tags(request, word_id, tag, section):
word = get_object_or_404(Card, id=word_id)
word.tags.add(tag)
messages.success(request, '标签添加成功')
if section == 0:
return redirect('flashcards:card_detail', card_id=word_id)
else:
return redirect('flashcards:recite_wordlist', wordlist_id=section,
progress=get_object_or_404(WordList, id=section).progress, rank=0)
@login_required
def word_delete_tags(request, word_id, tag_id, section):
word = get_object_or_404(Card, id=word_id)
word.tags.remove(Tag.objects.filter(id=tag_id)[0])
messages.success(request, '标签删除成功')
# 判断是否为单词列表
if section == 0:
return redirect('flashcards:card_detail', card_id=word_id)
else:
return redirect('flashcards:recite_wordlist', wordlist_id=section,
progress=get_object_or_404(WordList, id=section).progress, rank=0)
@login_required
def word_edit(request, word_id, section):
word = get_object_or_404(Card, id=word_id)
cd = request.POST
word.group = cd['group']
word.question = cd['question']
word.answer = cd['answer']
word.example = cd['example']
word.translation = cd['translation']
word.extra = cd['extra']
if cd['tag'] != '':
word.tags.add(cd['tag'])
word.save()
messages.success(request, '词汇编辑成功！')
if section == 0:
return redirect('flashcards:card_detail', card_id=word_id)
else:
return redirect('flashcards:recite_wordlist', wordlist_id=section,
progress=get_object_or_404(WordList, id=section).progress, rank=0)
@login_required
def word_add(request):
set = request.user.settings.all()[0]
set_group = json.loads(set.current_group)
if request.POST:
word = Card()
cd = request.POST
word.group = cd['group']
word.question = cd['question']
word.answer = cd['answer'].replace('；', '，')
word.example = cd['example'].replace('\n', ' ').replace('"', '')
word.translation = cd['translation'].replace('\n', ' ')
word.extra = cd['extra']
if cd['tag'] != '':
word.tags.add(cd['tag'])
# 仅在前两字为词汇或者整个为空的时候进行检查
if word.group == '' or word.group[0:2] == '词汇':
# 检查setting中数值是否大于等于50，若大于50，则group加1,数量初始化为1，最后保存set
if set_group['max_word_num'] >= 50:
set_group['max_word_group'] += 1
set_group['max_word_num'] = 0
# 转化为最大group并转为中文
word.group = ''.join(['词汇-第', to_chinese(set_group['max_word_group']), '组'])
# 最后num数加一
set_group['max_word_num'] += 1
set.current_group = json.dumps(set_group)
set.save()
word.save()
return JsonResponse({'status': 'ok'})
else:
return render(request, 'flashcards/card_create.html')
@login_required
def word_delete(request, word_id):
get_object_or_404(Card, id=word_id).delete()
messages.success(request, '词汇删除成功！')
return redirect('flashcards:next')
@login_required
def settings_view(request):
settings = request.user.settings.all()[0]
# 根据有无path来判断是GET还是POST
if not ("path" in request.POST):
return render(request, 'flashcards/settings.html', {'random_example': settings.random_example})
# if not (bool(request.GET) and bool(request.POST)):
# return render(request, 'flashcards/settings.html', {'random_example': settings.random_example})
if request.POST:
if "random_example" in request.POST and request.POST['random_example'] == 'on':
settings.random_example = True
# 将其存入内存
cache.set('random_example', True, timeout=600)
settings.save()
else:
settings.random_example = False
cache.set('random_example', False, timeout=600)
settings.save()
if request.POST['path'] == "/flashcards/settings/":
# 防止多次跳转出错
return render(request, 'flashcards/settings.html', {'random_example': settings.random_example})
return redirect(request.POST['path'])
@login_required
@require_POST
def dict_query(request):
re_filter = re.findall(r"[a-zA-Z]+", request.POST.get('query_word'))
if len(re_filter) == 0:
return JsonResponse({"query_word": request.POST.get('query_word'), 'html_result': 'No Match!'})
query_word = re_filter[0]
# print(query_word)
# 备选列表
query_list = [query_word, query_word.lower(), query_word[0:-1], query_word[0:-2], query_word[0:-3]]
for query_list_word in query_list:
query_result = Dict.objects.filter(headword__startswith=query_list_word)
if len(query_result) != 0:
# 查询缓存中的COCA
rank = cache.get('coca', default=-1).get(query_result[0].headword, -1)
return JsonResponse(
{"query_word": query_result[0].headword, 'html_result': query_result[0].item, 'rank': rank})
return JsonResponse({"query_word": query_word, 'html_result': 'No Result!'})
# get方式的字典请求
def dict_query_get(request, query_word):
re_filter = re.findall(r"[a-zA-Z]+", query_word)
if len(re_filter) == 0:
return JsonResponse({"query_word": request.POST.get('query_word'), 'html_result': 'No Match!'})
query_word = re_filter[0]
# print(query_word)
# 备选列表
query_list = [query_word, query_word.lower(), query_word[0:-1], query_word[0:-2], query_word[0:-3]]
for query_list_word in query_list:
query_result = Dict.objects.filter(headword__startswith=query_list_word)
if len(query_result) != 0:
return JsonResponse({"query_word": query_list_word, 'html_result': query_result[0].item})
return JsonResponse({"query_word": query_word, 'html_result': 'No Result!'})
@require_POST
def get_example(request):
re_filter = re.findall(r"[a-zA-Z]+", request.POST.get('query_word'))
if len(re_filter) == 0:
return JsonResponse({"query_word": request.POST.get('query_word'), 'html_result': 'No Match!'})
query_word = re_filter[0]
# print(query_word)
# 备选列表
query_list = [query_word, query_word.lower(), query_word[0:-1], query_word[0:-2], query_word[0:-3]]
for query_list_word in query_list:
query_result = Dict.objects.filter(headword__startswith=query_list_word)
if len(query_result) != 0:
result = find_example_from_database(query_list_word)
return JsonResponse({"example": json.dumps(list(zip(result[0], result[1]))),
"meanings": json.dumps(result[2])
})
return JsonResponse({"example": json.dumps([[], []])})
@require_POST
def export_card_as_txt(request):
settings = request.user.settings.all()[0]
num = int(request.POST["num"])
cards = Card.objects.all()[len(Card.objects.all()) - num:]
try:
with open("export.txt", 'w', encoding="utf-8") as f:
for card in cards:
card.answer = card.answer.replace('\n', '<br>')
f.write(
f"{card.group}\t{card.question}\t{card.answer}\t{card.example}\t{card.translation}\t{card.extra}\n")
messages.success(request, f'导出成功，共导出{len(cards)}张卡片')
return FileResponse(open('export.txt', 'rb'), as_attachment=True)
except Exception as e:
messages.error(request, '导出失败')
return redirect('flashcards:settings_view')
def export_db(request):
return FileResponse(open('db.sqlite3', 'rb'), as_attachment=True)
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] |
crawler_mongo.py | 9527A/practice | 0 | 148966 | import pymongo
def data_seve(mydict):
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
mydb = myclient["cralwer"]
mycol = mydb["data"]
for m in mydict:
if m in mycol.find():
pass
else:
x = mycol.insert_one(m)
def data_get():
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
mydb = myclient["cralwer"]
mycol = mydb["data"]
m = mycol.find()
return m
def delete():
myclient = pymongo.MongoClient("mongodb://localhost:27017/")
mydb = myclient["cralwer"]
mycol = mydb["data"]
x = mycol.delete_many({})
| [
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2
] |
src/toast/tests/cache.py | doicbek/toast | 29 | 194624 | <reponame>doicbek/toast
# Copyright (c) 2015-2020 by the parties listed in the AUTHORS file.
# All rights reserved. Use of this source code is governed by
# a BSD-style license that can be found in the LICENSE file.
import sys
import numpy as np
import ctypes
from .mpi import MPITestCase
from ..cache import Cache
from ..utils import AlignedF64, memreport
class CacheTest(MPITestCase):
def setUp(self):
self.nsamp = 1000
self.membytes = 100000000
self.memnbuf = 100
self.cache = Cache(pymem=False)
self.pycache = Cache(pymem=True)
self.types = {
"f64": np.float64,
"f32": np.float32,
"i64": np.int64,
"u64": np.uint64,
"i32": np.int32,
"u32": np.uint32,
"i16": np.int16,
"u16": np.uint16,
"i8": np.int8,
"u8": np.uint8,
}
def tearDown(self):
del self.cache
del self.pycache
def test_dict_interface(self):
cache = Cache()
cache["aaa"] = np.arange(10)
cache["ccc"] = np.arange(10) + 10
self.assertTrue("aaa" in cache)
self.assertFalse("bbb" in cache)
self.assertEqual(len(cache), 2)
n = 0
for x in cache:
n += 1
self.assertEqual(len(cache), n)
del cache["aaa"]
self.assertEqual(len(cache), n - 1)
return
def test_refcount(self):
buf = AlignedF64.zeros(self.nsamp)
# print("REFCNT: ", sys.getrefcount(buf), flush=True)
# wbuf1 = buf.weak()
# print("REFCNT 1: ", sys.getrefcount(buf), flush=True)
# wbuf2 = buf.weak()
# print("REFCNT 2: ", sys.getrefcount(buf), flush=True)
return
def test_create(self):
for k, v in self.types.items():
ref = self.cache.create("test-{}".format(k), v, (self.nsamp, 4))
del ref
for k, v in self.types.items():
data = self.cache.reference("test-{}".format(k))
data[:] += np.repeat(np.arange(self.nsamp, dtype=v), 4).reshape(-1, 4)
del data
for k, v in self.types.items():
ex = self.cache.exists("test-{}".format(k))
self.assertTrue(ex)
for k, v in self.types.items():
self.cache.destroy("test-{}".format(k))
self.cache.clear()
for k, v in self.types.items():
ref = self.pycache.create("test-{}".format(k), v, (self.nsamp, 4))
del ref
for k, v in self.types.items():
data = self.pycache.reference("test-{}".format(k))
data[:] += np.repeat(np.arange(self.nsamp, dtype=v), 4).reshape(-1, 4)
del data
for k, v in self.types.items():
ex = self.pycache.exists("test-{}".format(k))
self.assertTrue(ex)
for k, v in self.types.items():
self.pycache.destroy("test-{}".format(k))
self.pycache.clear()
return
def test_put(self):
# Populate some buffers
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 4), v)
ref = self.cache.put(pname, pdata)
del ref
# Test putting the same buffer- it should just return a new array
# wrapping the same underlying memory.
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 4), v)
ref = self.cache.reference(pname)
copyref = self.cache.put(pname, ref)
ref_pnt = ref.ctypes.data_as(ctypes.c_void_p).value
copyref_pnt = copyref.ctypes.data_as(ctypes.c_void_p).value
self.assertTrue(ref_pnt == copyref_pnt)
del ref
del copyref
# Test replacement of buffers with the same name
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 8), v)
try:
# This should raise- same name as existing buffer, but
# replace == False
ref = self.cache.put(pname, pdata)
del ref
self.assertTrue(False)
except RuntimeError:
# Success!
pass
# This should work
ref = self.cache.put(pname, pdata, replace=True)
del ref
self.cache.clear()
# Populate some buffers
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 4), v)
ref = self.pycache.put(pname, pdata)
del ref
# Test putting the same buffer- it should just return a new array
# wrapping the same underlying memory.
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 4), v)
ref = self.pycache.reference(pname)
copyref = self.pycache.put(pname, ref)
ref_pnt = ref.ctypes.data_as(ctypes.c_void_p).value
copyref_pnt = copyref.ctypes.data_as(ctypes.c_void_p).value
self.assertTrue(ref_pnt == copyref_pnt)
del ref
del copyref
# Test replacement of buffers with the same name
for k, v in self.types.items():
pname = "test-{}".format(k)
pdata = np.ones((self.nsamp, 8), v)
try:
# This should raise- same name as existing buffer, but
# replace == False
ref = self.pycache.put(pname, pdata)
del ref
self.assertTrue(False)
except RuntimeError:
# Success!
pass
# This should work
ref = self.pycache.put(pname, pdata, replace=True)
del ref
self.pycache.clear()
return
def test_create_none(self):
try:
ref = self.cache.create(None, np.float, (1, 10))
del ref
ref = self.pycache.create(None, np.float, (1, 10))
del ref
raise RuntimeError("Creating object with None key succeeded")
except ValueError:
pass
self.cache.clear()
self.pycache.clear()
return
def test_put_none(self):
try:
ref = self.cache.put(None, np.float, np.arange(10))
del ref
ref = self.pycache.put(None, np.float, np.arange(10))
del ref
raise RuntimeError("Putting an object with None key succeeded")
except ValueError:
pass
self.cache.clear()
self.pycache.clear()
return
def test_clear(self):
for k, v in self.types.items():
ref = self.cache.create("test-{}".format(k), v, (self.nsamp, 4))
del ref
ref = self.pycache.create("test-{}".format(k), v, (self.nsamp, 4))
del ref
self.cache.clear()
self.pycache.clear()
return
def test_alias(self):
ref = self.cache.put("test", np.arange(10))
del ref
self.cache.add_alias("test-alias", "test")
self.cache.add_alias("test-alias-2", "test")
data = self.cache.reference("test-alias")
del data
self.cache.destroy("test-alias")
data = self.cache.reference("test-alias-2")
del data
self.cache.destroy("test")
ex = self.cache.exists("test-alias-2")
self.assertFalse(ex)
self.cache.clear()
ref = self.pycache.put("test", np.arange(10))
del ref
self.pycache.add_alias("test-alias", "test")
self.pycache.add_alias("test-alias-2", "test")
data = self.pycache.reference("test-alias")
del data
self.pycache.destroy("test-alias")
data = self.pycache.reference("test-alias-2")
del data
self.pycache.destroy("test")
ex = self.pycache.exists("test-alias-2")
self.assertFalse(ex)
self.pycache.clear()
return
def test_memfree(self):
memreport(comm=self.comm, msg="Before large buffer creation")
memcache = Cache(pymem=False)
ref = memcache.create("test-big", np.uint8, (self.membytes,))
del ref
memreport(comm=self.comm, msg="After large buffer creation")
mem = memcache.report(silent=True)
self.assertEqual(mem, self.membytes)
print("Cache now has {} bytes".format(mem), flush=True)
memcache.clear()
memreport(comm=self.comm, msg="After cache clear")
mem = memcache.report(silent=True)
self.assertEqual(mem, 0)
print("Cache now has {} bytes".format(mem), flush=True)
smallbytes = self.membytes // self.memnbuf
for i in range(self.memnbuf):
name = "test-small_{}".format(i)
ref = memcache.create(name, np.uint8, (smallbytes,))
del ref
memreport(
comm=self.comm,
msg="After creation of {} small buffers".format(self.memnbuf),
)
mem = memcache.report(silent=True)
self.assertEqual(mem, self.memnbuf * smallbytes)
print("Cache now has {} bytes".format(mem), flush=True)
memcache.clear()
memreport(comm=self.comm, msg="After cache clear")
mem = memcache.report(silent=True)
self.assertEqual(mem, 0)
print("Cache now has {} bytes".format(mem), flush=True)
| [
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] |
Others/Source/02/2.2/print_test.py | silence0201/Learn-Python | 1 | 161098 | # coding: utf-8
#########################################################################
# 网站: <a href="http://www.crazyit.org">疯狂Java联盟</a> #
# author yeeku.H.lee <EMAIL> #
# #
# version 1.0 #
# #
# Copyright (C), 2001-2018, yeeku.H.Lee #
# #
# This program is protected by copyright laws. #
# #
# Program Name: #
# #
# <br>Date: #
#########################################################################
user_name = 'Charlie'
user_age = 8
# 同时输出多个变量和字符串
print("读者名:" , user_name, "年龄:", user_age)
# 同时输出多个变量和字符串，指定分隔符
print("读者名:" , user_name, "年龄:", user_age, sep='|')
# 指定end参数，指定输出之后不再换行
print(40, '\t', end="")
print(50, '\t', end="")
print(60, '\t', end="")
f = open("poem.txt", "w") # 打开文件以便写入
print('沧海月明珠有泪', file=f)
print('蓝田日暖玉生烟', file=f)
f.close() | [
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src/tag_spy/http_client.py | DD-DeCaF/tag-spy | 0 | 129776 | # Copyright 2020 Novo Nordisk Foundation Center for Biosustainability,
# Technical University of Denmark.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Provide an HTTP client for making requests."""
import gzip
import json
import logging
import zlib
from http.client import HTTPResponse
from typing import Dict, List, Optional, Union
from urllib.parse import urlencode, urljoin, urlsplit, urlunsplit
from urllib.request import OpenerDirector, Request
logger = logging.getLogger(__name__)
class Response:
"""
Define an HTTP response class.
This class should not have to be instantiated directly.
Attributes:
status (int): The numeric HTTP status code of the response.
body (bytes): The raw body of the response as bytes.
Methods:
json: Deserialize the response's body as JSON and return the object.
"""
def __init__(self, *, response: HTTPResponse, **kwargs) -> None:
"""
Initialize an HTTP response object.
Args:
response (http.client.HTTPResponse): A standard library response that is
wrapped by this object.
**kwargs: Passed to parent classes.
"""
super().__init__(**kwargs) # type: ignore
self._response = response
# Immediately read the body while the response context is still available.
self._body = self._content()
def _content(self) -> bytes:
"""Return the response body and decompress it if required."""
if self._response.getheader("Content-Encoding", "").lower() == "gzip":
return gzip.decompress(self._response.read())
elif self._response.getheader("Content-Encoding", "").lower() == "deflate":
return zlib.decompress(self._response.read())
else:
return self._response.read()
@property
def status(self) -> int:
"""Get the response's HTTP status code."""
return self._response.status
@property
def body(self) -> bytes:
"""Get the response's raw body content as bytes."""
return self._body
def json(self) -> Union[Dict, List, str]:
"""Deserialize the response's body as JSON and return the object."""
if len(self.body) == 0:
return ""
try:
return json.loads(self.body) # type: ignore
except json.JSONDecodeError:
logger.debug(self.body)
raise
class Client:
"""
Define an HTTP client for making requests.
Methods:
get: Make an HTTP GET request and return the response.
"""
def __init__(
self,
*,
opener: OpenerDirector,
base_url: str,
token: Optional[str] = None,
**kwargs,
) -> None:
"""
Initialize an HTTP client with a base URL.
Args:
opener (urllib.request.OpenerDirector): The opener with attached handlers
and headers for calling the URL.
base_url (str): The base URL for all future requests.
token (str, optional): A bearer access token.
**kwargs: Passed to parent constructors.
"""
super().__init__(**kwargs) # type: ignore
self._opener = opener
self._parts = urlsplit(base_url)
self.token = token
def _build_request(
self, path: Optional[str] = None, params: Optional[Dict[str, str]] = None,
) -> Request:
"""Construct a correctly encoded URL from its parts."""
url = self._parts
if path is not None:
url = url._replace(path=urljoin(url.path, path))
if params is not None:
url = url._replace(query=urlencode(params))
request = Request(urlunsplit(url))
if self.token:
request.add_unredirected_header("Authorization", f"Bearer {self.token}")
return request
def get(
self,
path: Optional[str] = None,
params: Optional[Dict[str, str]] = None,
**kwargs,
) -> Response:
"""
Make an HTTP GET request and return the response.
Args:
path (str, optional): An optional relative or absolute URL path to modify
the base URL.
params (dict, optional): An optional mapping from query parameter names to
their values.
Returns:
Response: An HTTP response instance.
"""
logger.debug("Making GET request to %r.", self._build_request(path, params))
with self._opener.open(self._build_request(path, params), **kwargs) as response:
logger.debug("%r", response.geturl())
logger.debug("%d %s", response.status, response.reason)
logger.debug("HTTP Headers\n%s", response.info())
# Must be returned within the context such that the response body is
# available for reading.
return Response(response=response)
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] |
Week 01/problems/arrays/equilibrium_point.py | imran-rashid/11-weeks-dsa-workshop | 2 | 152880 | # Given an array A of N positive numbers. The task is to find the first Equilibrium Point in the array.
# https://www.geeksforgeeks.org/equilibrium-index-of-an-array/
# https://practice.geeksforgeeks.org/problems/equilibrium-point-1587115620/1/
import math
# time is O(n) | Space is O(1)
def equilibriumPoint(A, N):
if (N == 1): return 1
left_sum = 0
total_sum = sum(A)
for i, num in enumerate(A):
total_sum -= num
if total_sum == left_sum:
return i+1
left_sum += num
return -1
def main():
T = int(input())
while(T > 0):
N = int(input())
A = [int(x) for x in input().strip().split()]
print(equilibriumPoint(A, N))
T -= 1
if __name__ == "__main__":
main()
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] |
scripts/fine_tuning.py | pedbrgs/Pruning-Darknet | 3 | 83579 | import os
import argparse
import subprocess
import numpy as np
import pandas as pd
def training_model(filename, technique, pruning_rate, layer):
""" Training the pruned model """
# Opens the temporary file
f = open('../eval.txt', 'a+')
# Training with pre-trained weights
if technique.upper() != 'FROM-SCRATCH':
weights = filename + str(pruning_rate) + '.conv.' + str(layer)
command = './darknet detector train ' + filename + '.data ' + filename + '.cfg ' + weights + ' -dont_show -map'
# Training from scratch
else:
command = './darknet detector train ' + filename + '.data ' + filename + '.cfg -dont_show -map'
# Running training algorithm and saving results to temporary file
subprocess.call(command, shell = True, stdout = f)
# Closing file
f.close()
print('\n[DONE] Fine-tuning of the model pruned by ' + str(pruning_rate) + '%\n')
def pre_weights(filename, pruning_rate, layer):
""" Generates a pre-weight from a trained weight """
# Opens the temporary file
f = open('../eval.txt', 'a+')
# Running freezing algorithm and saving results to temporary file
weights = filename + str(pruning_rate) + '.conv.' + str(layer)
command = './darknet partial ' + filename + '.cfg ' + filename + '.weights ' + weights + ' ' + str(layer)
subprocess.call(command, shell = True, stdout = f)
# Closing file
f.close()
print('\n[DONE] Pre-weights of the model pruned by ' + str(pruning_rate) + '%\n')
def valid_set(filename, set):
""" Changes validation set in the .data file """
# Opens the file in read-only mode
f = open(filename + '.data', 'r')
# Reads lines until EOF
lines = f.readlines()
# Loop over the lines
for i, line in enumerate(lines):
if 'valid' in line:
lines[i] = 'valid = data/' + filename + '_' + set + '.txt\n'
# Opens the file in write-only mode
f = open(filename + '.data', 'w')
# Changing validation set in the data file
f.writelines(lines)
# Closing file
f.close()
def hyperparams(filename, img_size, iter, lr, steps):
""" Changes hyperparameters of the .cfg file """
# Opens the file in read-only mode
f = open(filename + '.cfg', 'r')
# Read lines until EOF
lines = f.readlines()
# Loop over the lines
for i, line in enumerate(lines):
if 'width' in line:
lines[i] = 'width = ' + str(img_size) + '\n'
elif 'height' in line:
lines[i] = 'height = ' + str(img_size) + '\n'
elif 'steps = ' in line:
lines[i] = 'steps = ' + steps + '\n'
elif 'learning_rate' in line:
lines[i] = 'learning_rate = ' + str(lr) + '\n'
elif 'max_batches' in line:
lines[i] = 'max_batches = ' + str(iter) + '\n'
elif '^batch =' in line:
lines[i] = 'batch = 64\n'
elif '^subdivisions' in line:
lines[i] = 'subdivisions = 16\n'
# Opens the file in write-only mode
f = open(filename + '.cfg', 'w')
# Changing image size in the config file
f.writelines(lines)
# Closing file
f.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--method', type = str, help = 'Pruning method')
parser.add_argument('--imgsize', type = int, default = 416, help = 'Image size')
parser.add_argument('--dataset', type = str, default = 'dfire', help = 'Dataset name')
parser.add_argument('--lr', type = float, default = 0.01, help = 'Learning rate')
parser.add_argument('--tuning-iter', type = int, default = 30000, help = 'Number of fine-tuning iterations')
parser.add_argument('--layer', type = int, default = 161, help = 'Weights frozen up to this layer for fine-tuning')
parser.add_argument('--steps', type = str, default = '24000,27000', help = 'At these iterations the learning rate will be multiplied by scales factor (0.1 by default)')
opt = parser.parse_args()
# Open root folder
root = 'Fine-Tuning/' + opt.method + os.sep
os.chdir(root)
# Pruned models with pruning rate from 10% to 90%
folders = np.arange(start = 10, stop = 100, step = 10)
# Partial weights
weights = np.arange(start = 1000, stop = opt.tuning_iter + 1000, step = 1000)
try:
# Remove current eval.txt file
os.remove('eval.txt')
except:
pass
for folder in folders:
# Open current folder
subdir = str(folder) + os.sep
os.chdir(subdir)
# Change hyperparameters
hyperparams(opt.dataset, opt.imgsize, opt.tuning_iter, opt.lr, opt.steps)
# Change validation set
valid_set(opt.dataset, 'valid')
# Pre-trained weights
if opt.method.upper() != 'FROM-SCRATCH':
# Freezing layers to generate pre-weights
pre_weights(opt.dataset, folder, opt.layer)
# Training pruned model
training_model(opt.dataset, opt.method, folder, opt.layer)
# Remove partial weights
os.chdir('weights/')
for w in weights:
try:
os.remove(opt.dataset + '_' + str(w) + '.weights')
except:
pass
os.remove(opt.dataset + '_last.weights')
os.remove(opt.dataset + '_final.weights')
# Returns to root folder
os.chdir('../../') | [
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305,
3972,
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21546,
1495,
2
] |
qharv/reel/test/test_config_h5.py | Paul-St-Young/harvest_qmcpack | 2 | 165738 | <reponame>Paul-St-Young/harvest_qmcpack
import h5py
import numpy as np
from qharv.reel import config_h5
def test_saveh5():
mat = np.arange(9).reshape(3, 3)
config_h5.saveh5('mat.h5', mat)
config_h5.saveh5('mat.h5', mat, name='mat')
fp = h5py.File('mat.h5', 'r')
mat1 = fp['mat'][()]
fp.close()
assert np.allclose(mat, mat1)
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Max/Max_0387_20200213.py | Morek999/OMSCS_Taiwan_Leetcode | 1 | 138453 | <reponame>Morek999/OMSCS_Taiwan_Leetcode
"""
387. First Unique Character in a String
https://leetcode.com/problems/first-unique-character-in-a-string/
Time complexity: O()
Space complexity: O()
"""
from typing import List
class Solution:
def firstUniqChar(self, s: str) -> int:
hodling = {}
for i in s:
if i not in hodling:
hodling[i] = 0
hodling[i] += 1
for n, i in enumerate(s):
if hodling[i] == 1:
return n
return -1
ans = [
"leetcode", # 0
"loveleetcode", # 2
"",
"cc"
]
for trails in ans:
print(Solution().firstUniqChar(trails))
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2. Existing Conversion Efficiency (EC), Appendix A/DL_FP_EC.py | rioarya/Land-Conversion_Woody-Biomass-Utilization-Scenarios | 0 | 67079 | <filename>2. Existing Conversion Efficiency (EC), Appendix A/DL_FP_EC.py<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 13 15:21:55 2019
@author: raryapratama
"""
#%%
#Step (1): Import Python libraries, set land conversion scenarios general parameters
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import quad
import seaborn as sns
import pandas as pd
#DL_FP_S1 Scenario
##Set parameters
#Parameters for primary forest
initAGB = 233 #source: van Beijma et al. (2018)
initAGB_min = 233-72
initAGB_max = 233 + 72
#parameters for timber plantation. Source: Khasanah et al. (2015)
tf = 201
a = 0.082
b = 2.53
#%%
#Step (2_1): C loss from the harvesting/clear cut
df1_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
df1_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
df1_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
df1_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
dfE_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
t = range(0,tf,1)
c_firewood_energy_S1_Ac7 = df1_Ac7['Firewood_other_energy_use'].values
c_firewood_energy_S1_Ac18 = df1_Ac18['Firewood_other_energy_use'].values
c_firewood_energy_S1_Tgr40 = df1_Tgr40['Firewood_other_energy_use'].values
c_firewood_energy_S1_Tgr60 = df1_Tgr60['Firewood_other_energy_use'].values
c_firewood_energy_E_Hbr40 = dfE_Hbr40['Firewood_other_energy_use'].values
#%%
#Step (2_2): C loss from the harvesting/clear cut as wood pellets
dfE = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
c_pellets_Hbr_40y = dfE['Wood_pellets'].values
#%%
#Step (3): Aboveground biomass (AGB) decomposition
#Ac_7y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
tf = 201
t = np.arange(tf)
def decomp_S1_Ac_7y(t,remainAGB_S1_Ac_7y):
return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Ac_7y
#set zero matrix
output_decomp_S1_Ac_7y = np.zeros((len(t),len(df['C_remainAGB'].values)))
for i,remain_part_S1_Ac_7y in enumerate(df['C_remainAGB'].values):
#print(i,remain_part)
output_decomp_S1_Ac_7y[i:,i] = decomp_S1_Ac_7y(t[:len(t)-i],remain_part_S1_Ac_7y)
print(output_decomp_S1_Ac_7y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Ac_7y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Ac_7y[:,i] = np.diff(output_decomp_S1_Ac_7y[:,i])
i = i + 1
print(subs_matrix_S1_Ac_7y[:,:4])
print(len(subs_matrix_S1_Ac_7y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Ac_7y = subs_matrix_S1_Ac_7y.clip(max=0)
print(subs_matrix_S1_Ac_7y[:,:4])
#make the results as absolute values
subs_matrix_S1_Ac_7y = abs(subs_matrix_S1_Ac_7y)
print(subs_matrix_S1_Ac_7y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Ac_7y = np.zeros((len(t)-200,len(df['C_remainAGB'].values)))
print(zero_matrix_S1_Ac_7y)
subs_matrix_S1_Ac_7y = np.vstack((zero_matrix_S1_Ac_7y, subs_matrix_S1_Ac_7y))
print(subs_matrix_S1_Ac_7y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Ac_7y = (tf,1)
decomp_tot_S1_Ac_7y = np.zeros(matrix_tot_S1_Ac_7y)
i = 0
while i < tf:
decomp_tot_S1_Ac_7y[:,0] = decomp_tot_S1_Ac_7y[:,0] + subs_matrix_S1_Ac_7y[:,i]
i = i + 1
print(decomp_tot_S1_Ac_7y[:,0])
#S1_Ac_18y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
tf = 201
t = np.arange(tf)
def decomp_S1_Ac_18y(t,remainAGB_S1_Ac_18y):
return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Ac_18y
#set zero matrix
output_decomp_S1_Ac_18y = np.zeros((len(t),len(df['C_remainAGB'].values)))
for i,remain_part_S1_Ac_18y in enumerate(df['C_remainAGB'].values):
#print(i,remain_part)
output_decomp_S1_Ac_18y[i:,i] = decomp_S1_Ac_18y(t[:len(t)-i],remain_part_S1_Ac_18y)
print(output_decomp_S1_Ac_18y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Ac_18y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Ac_18y[:,i] = np.diff(output_decomp_S1_Ac_18y[:,i])
i = i + 1
print(subs_matrix_S1_Ac_18y[:,:4])
print(len(subs_matrix_S1_Ac_18y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Ac_18y = subs_matrix_S1_Ac_18y.clip(max=0)
print(subs_matrix_S1_Ac_18y[:,:4])
#make the results as absolute values
subs_matrix_S1_Ac_18y = abs(subs_matrix_S1_Ac_18y)
print(subs_matrix_S1_Ac_18y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Ac_18y = np.zeros((len(t)-200,len(df['C_remainAGB'].values)))
print(zero_matrix_S1_Ac_18y)
subs_matrix_S1_Ac_18y = np.vstack((zero_matrix_S1_Ac_18y, subs_matrix_S1_Ac_18y))
print(subs_matrix_S1_Ac_18y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Ac_18y = (tf,1)
decomp_tot_S1_Ac_18y = np.zeros(matrix_tot_S1_Ac_18y)
i = 0
while i < tf:
decomp_tot_S1_Ac_18y[:,0] = decomp_tot_S1_Ac_18y[:,0] + subs_matrix_S1_Ac_18y[:,i]
i = i + 1
print(decomp_tot_S1_Ac_18y[:,0])
#S1_Tgr_40y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
tf = 201
t = np.arange(tf)
def decomp_S1_Tgr_40y(t,remainAGB_S1_Tgr_40y):
return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Tgr_40y
#set zero matrix
output_decomp_S1_Tgr_40y = np.zeros((len(t),len(df['C_remainAGB'].values)))
for i,remain_part_S1_Tgr_40y in enumerate(df['C_remainAGB'].values):
#print(i,remain_part)
output_decomp_S1_Tgr_40y[i:,i] = decomp_S1_Tgr_40y(t[:len(t)-i],remain_part_S1_Tgr_40y)
print(output_decomp_S1_Tgr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Tgr_40y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Tgr_40y[:,i] = np.diff(output_decomp_S1_Tgr_40y[:,i])
i = i + 1
print(subs_matrix_S1_Tgr_40y[:,:4])
print(len(subs_matrix_S1_Tgr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Tgr_40y = subs_matrix_S1_Tgr_40y.clip(max=0)
print(subs_matrix_S1_Tgr_40y[:,:4])
#make the results as absolute values
subs_matrix_S1_Tgr_40y = abs(subs_matrix_S1_Tgr_40y)
print(subs_matrix_S1_Tgr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Tgr_40y = np.zeros((len(t)-200,len(df['C_remainAGB'].values)))
print(zero_matrix_S1_Tgr_40y)
subs_matrix_S1_Tgr_40y = np.vstack((zero_matrix_S1_Tgr_40y, subs_matrix_S1_Tgr_40y))
print(subs_matrix_S1_Tgr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Tgr_40y = (tf,1)
decomp_tot_S1_Tgr_40y = np.zeros(matrix_tot_S1_Tgr_40y)
i = 0
while i < tf:
decomp_tot_S1_Tgr_40y[:,0] = decomp_tot_S1_Tgr_40y[:,0] + subs_matrix_S1_Tgr_40y[:,i]
i = i + 1
print(decomp_tot_S1_Tgr_40y[:,0])
#S1_Tgr_60y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
tf = 201
t = np.arange(tf)
def decomp_S1_Tgr_60y(t,remainAGB_S1_Tgr_60y):
return (1-(1-np.exp(-a*t))**b)*remainAGB_S1_Tgr_60y
#set zero matrix
output_decomp_S1_Tgr_60y = np.zeros((len(t),len(df['C_remainAGB'].values)))
for i,remain_part_S1_Tgr_60y in enumerate(df['C_remainAGB'].values):
#print(i,remain_part)
output_decomp_S1_Tgr_60y[i:,i] = decomp_S1_Tgr_60y(t[:len(t)-i],remain_part_S1_Tgr_60y)
print(output_decomp_S1_Tgr_60y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Tgr_60y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Tgr_60y[:,i] = np.diff(output_decomp_S1_Tgr_60y[:,i])
i = i + 1
print(subs_matrix_S1_Tgr_60y[:,:4])
print(len(subs_matrix_S1_Tgr_60y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Tgr_60y = subs_matrix_S1_Tgr_60y.clip(max=0)
print(subs_matrix_S1_Tgr_60y[:,:4])
#make the results as absolute values
subs_matrix_S1_Tgr_60y = abs(subs_matrix_S1_Tgr_60y)
print(subs_matrix_S1_Tgr_60y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Tgr_60y = np.zeros((len(t)-200,len(df['C_remainAGB'].values)))
print(zero_matrix_S1_Tgr_60y)
subs_matrix_S1_Tgr_60y = np.vstack((zero_matrix_S1_Tgr_60y, subs_matrix_S1_Tgr_60y))
print(subs_matrix_S1_Tgr_60y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Tgr_60y = (tf,1)
decomp_tot_S1_Tgr_60y = np.zeros(matrix_tot_S1_Tgr_60y)
i = 0
while i < tf:
decomp_tot_S1_Tgr_60y[:,0] = decomp_tot_S1_Tgr_60y[:,0] + subs_matrix_S1_Tgr_60y[:,i]
i = i + 1
print(decomp_tot_S1_Tgr_60y[:,0])
#E
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
tf = 201
t = np.arange(tf)
def decomp_E_Hbr_40y(t,remainAGB_E_Hbr_40y):
return (1-(1-np.exp(-a*t))**b)*remainAGB_E_Hbr_40y
#set zero matrix
output_decomp_E_Hbr_40y = np.zeros((len(t),len(df['C_remainAGB'].values)))
for i,remain_part_E_Hbr_40y in enumerate(df['C_remainAGB'].values):
#print(i,remain_part)
output_decomp_E_Hbr_40y[i:,i] = decomp_E_Hbr_40y(t[:len(t)-i],remain_part_E_Hbr_40y)
print(output_decomp_E_Hbr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_E_Hbr_40y = np.zeros((len(t)-1,len(df['C_remainAGB'].values-1)))
i = 0
while i < tf:
subs_matrix_E_Hbr_40y[:,i] = np.diff(output_decomp_E_Hbr_40y[:,i])
i = i + 1
print(subs_matrix_E_Hbr_40y[:,:4])
print(len(subs_matrix_E_Hbr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_E_Hbr_40y = subs_matrix_E_Hbr_40y.clip(max=0)
print(subs_matrix_E_Hbr_40y[:,:4])
#make the results as absolute values
subs_matrix_E_Hbr_40y = abs(subs_matrix_E_Hbr_40y)
print(subs_matrix_E_Hbr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_E_Hbr_40y = np.zeros((len(t)-200,len(df['C_remainAGB'].values)))
print(zero_matrix_E_Hbr_40y)
subs_matrix_E_Hbr_40y = np.vstack((zero_matrix_E_Hbr_40y, subs_matrix_E_Hbr_40y))
print(subs_matrix_E_Hbr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_E_Hbr_40y = (tf,1)
decomp_tot_E_Hbr_40y = np.zeros(matrix_tot_E_Hbr_40y)
i = 0
while i < tf:
decomp_tot_E_Hbr_40y[:,0] = decomp_tot_E_Hbr_40y[:,0] + subs_matrix_E_Hbr_40y[:,i]
i = i + 1
print(decomp_tot_E_Hbr_40y[:,0])
#plotting
t = np.arange(0,tf)
plt.plot(t,decomp_tot_S1_Ac_7y,label='Ac_7y')
plt.plot(t,decomp_tot_S1_Ac_18y,label='Ac_18y')
plt.plot(t,decomp_tot_S1_Tgr_40y,label='Tgr_40y')
plt.plot(t,decomp_tot_S1_Tgr_60y,label='Tgr_60y')
plt.plot(t,decomp_tot_E_Hbr_40y,label='E_Hbr_40y')
plt.xlim(0,200)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
plt.show()
#%%
#Step (4): Dynamic stock model of in-use wood materials
from dynamic_stock_model import DynamicStockModel
df1_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
df1_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
df1_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
df1_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
dfE_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
#product lifetime
#paper
P = 4
#furniture
F = 20
#building materials
B = 35
TestDSM1_Ac7 = DynamicStockModel(t = df1_Ac7['Year'].values, i = df1_Ac7['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([P]), 'StdDev': np.array([0.3*P])})
TestDSM1_Ac18 = DynamicStockModel(t = df1_Ac18['Year'].values, i = df1_Ac18['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([F]), 'StdDev': np.array([0.3*F])})
TestDSM1_Tgr40 = DynamicStockModel(t = df1_Tgr40['Year'].values, i = df1_Tgr40['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])})
TestDSM1_Tgr60 = DynamicStockModel(t = df1_Tgr60['Year'].values, i = df1_Tgr60['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])})
TestDSME_Hbr40 = DynamicStockModel(t = dfE_Hbr40['Year'].values, i = dfE_Hbr40['Input_PF'].values, lt = {'Type': 'Normal', 'Mean': np.array([B]), 'StdDev': np.array([0.3*B])})
CheckStr1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.dimension_check()
CheckStr1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.dimension_check()
CheckStr1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.dimension_check()
CheckStr1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.dimension_check()
CheckStrE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.dimension_check()
Stock_by_cohort1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_s_c_inflow_driven()
Stock_by_cohort1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_s_c_inflow_driven()
Stock_by_cohort1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_s_c_inflow_driven()
Stock_by_cohort1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_s_c_inflow_driven()
Stock_by_cohortE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_s_c_inflow_driven()
S1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_stock_total()
S1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_stock_total()
S1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_stock_total()
S1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_stock_total()
SE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_stock_total()
O_C1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_o_c_from_s_c()
O_C1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_o_c_from_s_c()
O_C1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_o_c_from_s_c()
O_C1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_o_c_from_s_c()
O_CE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_o_c_from_s_c()
O1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_outflow_total()
O1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_outflow_total()
O1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_outflow_total()
O1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_outflow_total()
OE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_outflow_total()
DS1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.compute_stock_change()
DS1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.compute_stock_change()
DS1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.compute_stock_change()
DS1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.compute_stock_change()
DSE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.compute_stock_change()
Bal1_Ac7, ExitFlag1_Ac7 = TestDSM1_Ac7.check_stock_balance()
Bal1_Ac18, ExitFlag1_Ac18 = TestDSM1_Ac18.check_stock_balance()
Bal1_Tgr40, ExitFlag1_Tgr40 = TestDSM1_Tgr40.check_stock_balance()
Bal1_Tgr60, ExitFlag1_Tgr60 = TestDSM1_Tgr60.check_stock_balance()
BalE_Hbr40, ExitFlagE_Hbr40 = TestDSME_Hbr40.check_stock_balance()
#print output flow
print(TestDSM1_Ac7.o)
print(TestDSM1_Ac18.o)
print(TestDSM1_Tgr40.o)
print(TestDSM1_Tgr60.o)
print(TestDSME_Hbr40.o)
#%%
#Step (5): Biomass growth
## one-year gap between rotation cycle
# <NAME> (Source: Anitha et al., 2015; Adiriono, 2009). Code: Ac
tf_Ac_7y = 8
tf_Ac_18y = 19
A1 = range(1,tf_Ac_7y,1)
A2 = range(1,tf_Ac_18y,1)
#calculate the biomass and carbon content of A. crassicarpa over time (7y)
def Y_Ac_7y(A1):
return 44/12*1000*np.exp(4.503-(2.559/A1))
output_Y_Ac_7y = np.array([Y_Ac_7y(A1i) for A1i in A1])
print(output_Y_Ac_7y)
#insert 0 value to the first element of the output result
output_Y_Ac_7y = np.insert(output_Y_Ac_7y,0,0)
print(output_Y_Ac_7y)
#calculate the biomass and carbon content of A. crassicarpa over time (18y)
def Y_Ac_18y(A2):
return 44/12*1000*np.exp(4.503-(2.559/A2))
output_Y_Ac_18y = np.array([Y_Ac_18y(A2i) for A2i in A2])
print(output_Y_Ac_18y)
#insert 0 value to the first element of the output result
output_Y_Ac_18y = np.insert(output_Y_Ac_18y,0,0)
print(output_Y_Ac_18y)
##26 times 8-year cycle (+1 year gap after the FP harvest)of new AGB of A. crassicarpa (7y), zero year gap between the cycle
counter_7y = range(0,26,1)
y_Ac_7y = []
for i in counter_7y:
y_Ac_7y.append(output_Y_Ac_7y)
flat_list_Ac_7y = []
for sublist in y_Ac_7y:
for item in sublist:
flat_list_Ac_7y.append(item)
#the length of the list is now 208, so we remove the last 7 elements of the list to make the len=tf
flat_list_Ac_7y = flat_list_Ac_7y[:len(flat_list_Ac_7y)-7]
print(len(flat_list_Ac_7y))
##11 times 19-year cycle (+1 year gap after the FP harvest) of new AGB of A. crassicarpa (18y), zero year gap between the cycle
counter_18y = range(0,11,1)
y_Ac_18y = []
for i in counter_18y:
y_Ac_18y.append(output_Y_Ac_18y)
flat_list_Ac_18y = []
for sublist in y_Ac_18y:
for item in sublist:
flat_list_Ac_18y.append(item)
#the length of the list is now 209, so we remove the last 8 elements of the list to make the len=tf
flat_list_Ac_18y = flat_list_Ac_18y[:len(flat_list_Ac_18y)-8]
#####Check the flat list length for Hbr
## T. grandis (Source: Anitha et al., 2015; Adiriono, 2009). Code: Tgr
tf_Tgr_40y = 41
tf_Tgr_60y = 61
T1 = range(0,tf_Tgr_40y,1)
T2 = range(0,tf_Tgr_60y,1)
#calculate the biomass and carbon content of T. grandis over time (40y)
def Y_Tgr_40y(T1):
return 44/12*1000*2.114*(T1**0.941)
output_Y_Tgr_40y = np.array([Y_Tgr_40y(T1i) for T1i in T1])
print(output_Y_Tgr_40y)
#calculate the biomass and carbon content of T. grandis over time (60y)
def Y_Tgr_60y(T2):
return 44/12*1000*2.114*(T2**0.941)
output_Y_Tgr_60y = np.array([Y_Tgr_60y(T2i) for T2i in T2])
print(output_Y_Tgr_60y)
##5 times 41-year cycle of new AGB of T. grandis (40y), zero year gap between the cycle
counter_40y = range(0,5,1)
y_Tgr_40y = []
for i in counter_40y:
y_Tgr_40y.append(output_Y_Tgr_40y)
flat_list_Tgr_40y = []
for sublist in y_Tgr_40y:
for item in sublist:
flat_list_Tgr_40y.append(item)
#the length of the list is now 205, so we remove the last 4 elements of the list to make the len=tf
flat_list_Tgr_40y = flat_list_Tgr_40y[:len(flat_list_Tgr_40y)-4]
##4 times 60-year cycle of new AGB of T. grandis (60y), zero year gap between the cycle
counter_60y = range(0,4,1)
y_Tgr_60y = []
for i in counter_60y:
y_Tgr_60y.append(output_Y_Tgr_60y)
flat_list_Tgr_60y = []
for sublist in y_Tgr_60y:
for item in sublist:
flat_list_Tgr_60y.append(item)
#the length of the list is now 244, so we remove the last 43 elements of the list to make the len=tf
flat_list_Tgr_60y = flat_list_Tgr_60y[:len(flat_list_Tgr_60y)-43]
## H. brasiliensis (Source: Guillaume et al., 2018). Code: Hbr
tf_Hbr_40y = 41
H1 = range(0,tf_Hbr_40y,1)
#calculate the biomass and carbon content of H. brasiliensis over time (40y)
def Y_Hbr_40y(H1):
return 44/12*1000*1.55*H1
output_Y_Hbr_40y = np.array([Y_Hbr_40y(H1i) for H1i in H1])
print(output_Y_Hbr_40y)
##5 times 40-year cycle of new AGB of H. brasiliensis (40y), zero year gap between the cycle
counter_40y = range(0,5,1)
y_Hbr_40y = []
for i in counter_40y:
y_Hbr_40y.append(output_Y_Hbr_40y)
flat_list_Hbr_40y = []
for sublist in y_Hbr_40y:
for item in sublist:
flat_list_Hbr_40y.append(item)
#the length of the list is now 205, so we remove the last 4 elements of the list to make the len=tf
flat_list_Hbr_40y = flat_list_Hbr_40y[:len(flat_list_Hbr_40y)-4]
#plotting
t = range (0,tf,1)
plt.xlim([0, 200])
plt.plot(t, flat_list_Ac_7y, color='lightcoral')
plt.plot(t, flat_list_Ac_18y, color='deeppink')
plt.plot(t, flat_list_Hbr_40y, color='darkviolet')
plt.plot(t, flat_list_Tgr_40y)
plt.plot(t, flat_list_Tgr_60y, color='seagreen')
#plt.fill_between(t, flat_list_nucleus, flat_list_plasma, color='darkseagreen', alpha='0.4')
plt.xlabel('Time (year)')
plt.ylabel('AGB (tC/ha)')
plt.show()
##Yearly sequestration
## <NAME> (7y)
#find the yearly sequestration by calculating the differences between elements in list 'flat_list_Ac_7y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
flat_list_Ac_7y = [p - q for q, p in zip(flat_list_Ac_7y, flat_list_Ac_7y[1:])]
#since there is no sequestration between the replanting year (e.g., year 7 to 8), we have to replace negative numbers in 'flat_list_Ac_7y' with 0 values
flat_list_Ac_7y = [0 if i < 0 else i for i in flat_list_Ac_7y]
#insert 0 value to the list as the first element, because there is no sequestration in year 0
var = 0
flat_list_Ac_7y.insert(0,var)
#make 'flat_list_Ac_7y' elements negative numbers to denote sequestration
flat_list_Ac_7y = [ -x for x in flat_list_Ac_7y]
print(flat_list_Ac_7y)
##<NAME> (18y)
#find the yearly sequestration by calculating the differences between elements in list 'flat_list_Ac_18y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
flat_list_Ac_18y = [t - u for u, t in zip(flat_list_Ac_18y, flat_list_Ac_18y[1:])]
#since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Ac_18y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
flat_list_Ac_18y = [0 if i < 0 else i for i in flat_list_Ac_18y]
#insert 0 value to the list as the first element, because there is no sequestration in year 0
var = 0
flat_list_Ac_18y.insert(0,var)
#make 'flat_list_plasma' elements negative numbers to denote sequestration
flat_list_Ac_18y = [ -x for x in flat_list_Ac_18y]
print(flat_list_Ac_18y)
##<NAME> (40y)
#find the yearly sequestration by calculating the differences between elements in list 'flat_list_Tgr_40y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
flat_list_Tgr_40y = [b - c for c, b in zip(flat_list_Tgr_40y, flat_list_Tgr_40y[1:])]
#since there is no sequestration between the replanting year (e.g., year 40 to 41), we have to replace negative numbers in 'flat_list_Tgr_40y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
flat_list_Tgr_40y = [0 if i < 0 else i for i in flat_list_Tgr_40y]
#insert 0 value to the list as the first element, because there is no sequestration in year 0
var = 0
flat_list_Tgr_40y.insert(0,var)
#make 'flat_list_plasma' elements negative numbers to denote sequestration
flat_list_Tgr_40y = [-x for x in flat_list_Tgr_40y]
print(flat_list_Tgr_40y)
##<NAME> (60y)
#find the yearly sequestration by calculating the differences between elements in list 'flat_list_Tgr_60y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
flat_list_Tgr_60y = [k - l for l, k in zip(flat_list_Tgr_60y, flat_list_Tgr_60y[1:])]
#since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Tgr_60y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
flat_list_Tgr_60y = [0 if i < 0 else i for i in flat_list_Tgr_60y]
#insert 0 value to the list as the first element, because there is no sequestration in year 0
var = 0
flat_list_Tgr_60y.insert(0,var)
#make 'flat_list_plasma' elements negative numbers to denote sequestration
flat_list_Tgr_60y = [ -x for x in flat_list_Tgr_60y]
print(flat_list_Tgr_60y)
##<NAME> (40y)
#find the yearly sequestration by calculating the differences between elements in list 'flat_list_Hbr_40y(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
flat_list_Hbr_40y = [c - d for d, c in zip(flat_list_Hbr_40y, flat_list_Hbr_40y[1:])]
#since there is no sequestration between the replanting year (e.g., year 25 to 26), we have to replace negative numbers in 'flat_list_Hbr_40y' with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
flat_list_Hbr_40y = [0 if i < 0 else i for i in flat_list_Hbr_40y]
#insert 0 value to the list as the first element, because there is no sequestration in year 0
var = 0
flat_list_Hbr_40y.insert(0,var)
#make 'flat_list_plasma' elements negative numbers to denote sequestration
flat_list_Hbr_40y = [ -x for x in flat_list_Hbr_40y]
print(flat_list_Hbr_40y)
#%%
#Step (6): post-harvest processing of wood
#post-harvest wood processing
df1_Ac_7y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
df1_Ac_18y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
df1_Tgr_40y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
dfl_Tgr_60y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
dfE_Hbr_40y = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
t = range(0,tf,1)
PH_Emissions_HWP1_Ac_7y = df1_Ac_7y['PH_Emissions_HWP'].values
PH_Emissions_HWP1_Ac_18y = df1_Ac_18y['PH_Emissions_HWP'].values
PH_Emissions_HWP1_Tgr_40y = df1_Tgr_40y['PH_Emissions_HWP'].values
PH_Emissions_HWP1_Tgr_60y = dfl_Tgr_60y['PH_Emissions_HWP'].values
PH_Emissions_HWPE_Hbr_40y = dfE_Hbr_40y ['PH_Emissions_HWP'].values
#%%
#Step (7_1): landfill gas decomposition (CH4)
#CH4 decomposition
hl = 20 #half-live
k = (np.log(2))/hl
#S1_Ac_7y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
tf = 201
t = np.arange(tf)
def decomp_CH4_S1_Ac_7y(t,remainAGB_CH4_S1_Ac_7y):
return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Ac_7y
#set zero matrix
output_decomp_CH4_S1_Ac_7y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values)))
for i,remain_part_CH4_S1_Ac_7y in enumerate(df['Landfill_decomp_CH4'].values):
#print(i,remain_part)
output_decomp_CH4_S1_Ac_7y[i:,i] = decomp_CH4_S1_Ac_7y(t[:len(t)-i],remain_part_CH4_S1_Ac_7y)
print(output_decomp_CH4_S1_Ac_7y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_CH4_S1_Ac_7y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1)))
i = 0
while i < tf:
subs_matrix_CH4_S1_Ac_7y[:,i] = np.diff(output_decomp_CH4_S1_Ac_7y[:,i])
i = i + 1
print(subs_matrix_CH4_S1_Ac_7y[:,:4])
print(len(subs_matrix_CH4_S1_Ac_7y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_CH4_S1_Ac_7y = subs_matrix_CH4_S1_Ac_7y.clip(max=0)
print(subs_matrix_CH4_S1_Ac_7y[:,:4])
#make the results as absolute values
subs_matrix_CH4_S1_Ac_7y = abs(subs_matrix_CH4_S1_Ac_7y)
print(subs_matrix_CH4_S1_Ac_7y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_CH4_S1_Ac_7y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values)))
print(zero_matrix_CH4_S1_Ac_7y)
subs_matrix_CH4_S1_Ac_7y = np.vstack((zero_matrix_CH4_S1_Ac_7y, subs_matrix_CH4_S1_Ac_7y))
print(subs_matrix_CH4_S1_Ac_7y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_CH4_S1_Ac_7y = (tf,1)
decomp_tot_CH4_S1_Ac_7y = np.zeros(matrix_tot_CH4_S1_Ac_7y)
i = 0
while i < tf:
decomp_tot_CH4_S1_Ac_7y[:,0] = decomp_tot_CH4_S1_Ac_7y[:,0] + subs_matrix_CH4_S1_Ac_7y[:,i]
i = i + 1
print(decomp_tot_CH4_S1_Ac_7y[:,0])
#S1_Ac_18y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
tf = 201
t = np.arange(tf)
def decomp_CH4_S1_Ac_18y(t,remainAGB_CH4_S1_Ac_18y):
return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Ac_18y
#set zero matrix
output_decomp_CH4_S1_Ac_18y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values)))
for i,remain_part_CH4_S1_Ac_18y in enumerate(df['Landfill_decomp_CH4'].values):
#print(i,remain_part)
output_decomp_CH4_S1_Ac_18y[i:,i] = decomp_CH4_S1_Ac_18y(t[:len(t)-i],remain_part_CH4_S1_Ac_18y)
print(output_decomp_CH4_S1_Ac_18y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_CH4_S1_Ac_18y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1)))
i = 0
while i < tf:
subs_matrix_CH4_S1_Ac_18y[:,i] = np.diff(output_decomp_CH4_S1_Ac_18y[:,i])
i = i + 1
print(subs_matrix_CH4_S1_Ac_18y[:,:4])
print(len(subs_matrix_CH4_S1_Ac_18y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_CH4_S1_Ac_18y = subs_matrix_CH4_S1_Ac_18y.clip(max=0)
print(subs_matrix_CH4_S1_Ac_18y[:,:4])
#make the results as absolute values
subs_matrix_CH4_S1_Ac_18y = abs(subs_matrix_CH4_S1_Ac_18y)
print(subs_matrix_CH4_S1_Ac_18y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_CH4_S1_Ac_18y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values)))
print(zero_matrix_CH4_S1_Ac_18y)
subs_matrix_CH4_S1_Ac_18y = np.vstack((zero_matrix_CH4_S1_Ac_18y, subs_matrix_CH4_S1_Ac_18y))
print(subs_matrix_CH4_S1_Ac_18y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_CH4_S1_Ac_18y = (tf,1)
decomp_tot_CH4_S1_Ac_18y = np.zeros(matrix_tot_CH4_S1_Ac_18y)
i = 0
while i < tf:
decomp_tot_CH4_S1_Ac_18y[:,0] = decomp_tot_CH4_S1_Ac_18y[:,0] + subs_matrix_CH4_S1_Ac_18y[:,i]
i = i + 1
print(decomp_tot_CH4_S1_Ac_18y[:,0])
#S1_Tgr_40y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
tf = 201
t = np.arange(tf)
def decomp_CH4_S1_Tgr_40y(t,remainAGB_CH4_S1_Tgr_40y):
return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Tgr_40y
#set zero matrix
output_decomp_CH4_S1_Tgr_40y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values)))
for i,remain_part_CH4_S1_Tgr_40y in enumerate(df['Landfill_decomp_CH4'].values):
#print(i,remain_part)
output_decomp_CH4_S1_Tgr_40y[i:,i] = decomp_CH4_S1_Tgr_40y(t[:len(t)-i],remain_part_CH4_S1_Tgr_40y)
print(output_decomp_CH4_S1_Tgr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_CH4_S1_Tgr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1)))
i = 0
while i < tf:
subs_matrix_CH4_S1_Tgr_40y[:,i] = np.diff(output_decomp_CH4_S1_Tgr_40y[:,i])
i = i + 1
print(subs_matrix_CH4_S1_Tgr_40y[:,:4])
print(len(subs_matrix_CH4_S1_Tgr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_CH4_S1_Tgr_40y = subs_matrix_CH4_S1_Tgr_40y.clip(max=0)
print(subs_matrix_CH4_S1_Tgr_40y[:,:4])
#make the results as absolute values
subs_matrix_CH4_S1_Tgr_40y = abs(subs_matrix_CH4_S1_Tgr_40y)
print(subs_matrix_CH4_S1_Tgr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_CH4_S1_Tgr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values)))
print(zero_matrix_CH4_S1_Tgr_40y)
subs_matrix_CH4_S1_Tgr_40y = np.vstack((zero_matrix_CH4_S1_Tgr_40y, subs_matrix_CH4_S1_Tgr_40y))
print(subs_matrix_CH4_S1_Tgr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_CH4_S1_Tgr_40y = (tf,1)
decomp_tot_CH4_S1_Tgr_40y = np.zeros(matrix_tot_CH4_S1_Tgr_40y)
i = 0
while i < tf:
decomp_tot_CH4_S1_Tgr_40y[:,0] = decomp_tot_CH4_S1_Tgr_40y[:,0] + subs_matrix_CH4_S1_Tgr_40y[:,i]
i = i + 1
print(decomp_tot_CH4_S1_Tgr_40y[:,0])
#S1_Tgr_60y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
tf = 201
t = np.arange(tf)
def decomp_CH4_S1_Tgr_60y(t,remainAGB_CH4_S1_Tgr_60y):
return (1-(1-np.exp(-k*t)))*remainAGB_CH4_S1_Tgr_60y
#set zero matrix
output_decomp_CH4_S1_Tgr_60y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values)))
for i,remain_part_CH4_S1_Tgr_60y in enumerate(df['Landfill_decomp_CH4'].values):
#print(i,remain_part)
output_decomp_CH4_S1_Tgr_60y[i:,i] = decomp_CH4_S1_Tgr_60y(t[:len(t)-i],remain_part_CH4_S1_Tgr_60y)
print(output_decomp_CH4_S1_Tgr_60y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_CH4_S1_Tgr_60y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1)))
i = 0
while i < tf:
subs_matrix_CH4_S1_Tgr_60y[:,i] = np.diff(output_decomp_CH4_S1_Tgr_60y[:,i])
i = i + 1
print(subs_matrix_CH4_S1_Tgr_60y[:,:4])
print(len(subs_matrix_CH4_S1_Tgr_60y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_CH4_S1_Tgr_60y = subs_matrix_CH4_S1_Tgr_60y.clip(max=0)
print(subs_matrix_CH4_S1_Tgr_60y[:,:4])
#make the results as absolute values
subs_matrix_CH4_S1_Tgr_60y = abs(subs_matrix_CH4_S1_Tgr_60y)
print(subs_matrix_CH4_S1_Tgr_60y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_CH4_S1_Tgr_60y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values)))
print(zero_matrix_CH4_S1_Tgr_60y)
subs_matrix_CH4_S1_Tgr_60y = np.vstack((zero_matrix_CH4_S1_Tgr_60y, subs_matrix_CH4_S1_Tgr_60y))
print(subs_matrix_CH4_S1_Tgr_60y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_CH4_S1_Tgr_60y = (tf,1)
decomp_tot_CH4_S1_Tgr_60y = np.zeros(matrix_tot_CH4_S1_Tgr_60y)
i = 0
while i < tf:
decomp_tot_CH4_S1_Tgr_60y[:,0] = decomp_tot_CH4_S1_Tgr_60y[:,0] + subs_matrix_CH4_S1_Tgr_60y[:,i]
i = i + 1
print(decomp_tot_CH4_S1_Tgr_60y[:,0])
#E
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
tf = 201
t = np.arange(tf)
def decomp_CH4_E_Hbr_40y(t,remainAGB_CH4_E_Hbr_40y):
return (1-(1-np.exp(-k*t)))*remainAGB_CH4_E_Hbr_40y
#set zero matrix
output_decomp_CH4_E_Hbr_40y = np.zeros((len(t),len(df['Landfill_decomp_CH4'].values)))
for i,remain_part_CH4_E_Hbr_40y in enumerate(df['Landfill_decomp_CH4'].values):
#print(i,remain_part)
output_decomp_CH4_E_Hbr_40y[i:,i] = decomp_CH4_E_Hbr_40y(t[:len(t)-i],remain_part_CH4_E_Hbr_40y)
print(output_decomp_CH4_E_Hbr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_CH4_E_Hbr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CH4'].values-1)))
i = 0
while i < tf:
subs_matrix_CH4_E_Hbr_40y[:,i] = np.diff(output_decomp_CH4_E_Hbr_40y[:,i])
i = i + 1
print(subs_matrix_CH4_E_Hbr_40y[:,:4])
print(len(subs_matrix_CH4_E_Hbr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_CH4_E_Hbr_40y = subs_matrix_CH4_E_Hbr_40y.clip(max=0)
print(subs_matrix_CH4_E_Hbr_40y[:,:4])
#make the results as absolute values
subs_matrix_CH4_E_Hbr_40y = abs(subs_matrix_CH4_E_Hbr_40y)
print(subs_matrix_CH4_E_Hbr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_CH4_E_Hbr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CH4'].values)))
print(zero_matrix_CH4_E_Hbr_40y)
subs_matrix_CH4_E_Hbr_40y = np.vstack((zero_matrix_CH4_E_Hbr_40y, subs_matrix_CH4_E_Hbr_40y))
print(subs_matrix_CH4_E_Hbr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_CH4_E_Hbr_40y = (tf,1)
decomp_tot_CH4_E_Hbr_40y = np.zeros(matrix_tot_CH4_E_Hbr_40y)
i = 0
while i < tf:
decomp_tot_CH4_E_Hbr_40y[:,0] = decomp_tot_CH4_E_Hbr_40y[:,0] + subs_matrix_CH4_E_Hbr_40y[:,i]
i = i + 1
print(decomp_tot_CH4_E_Hbr_40y[:,0])
#plotting
t = np.arange(0,tf)
plt.plot(t,decomp_tot_CH4_S1_Ac_7y,label='Ac_7y')
plt.plot(t,decomp_tot_CH4_S1_Ac_18y,label='Ac_18y')
plt.plot(t,decomp_tot_CH4_S1_Tgr_40y,label='Tgr_40y')
plt.plot(t,decomp_tot_CH4_S1_Tgr_60y,label='Tgr_60y')
plt.plot(t,decomp_tot_CH4_E_Hbr_40y,label='E_Hbr_40y')
plt.xlim(0,200)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
plt.show()
#%%
#Step (7_2): landfill gas decomposition (CO2)
#CO2 decomposition
hl = 20 #half-live
k = (np.log(2))/hl
#S1_Ac_7y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
tf = 201
t = np.arange(tf)
def decomp_S1_Ac_7y(t,remainAGB_S1_Ac_7y):
return (1-(1-np.exp(-k*t)))*remainAGB_S1_Ac_7y
#set zero matrix
output_decomp_S1_Ac_7y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values)))
for i,remain_part_S1_Ac_7y in enumerate(df['Landfill_decomp_CO2'].values):
#print(i,remain_part)
output_decomp_S1_Ac_7y[i:,i] = decomp_S1_Ac_7y(t[:len(t)-i],remain_part_S1_Ac_7y)
print(output_decomp_S1_Ac_7y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Ac_7y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Ac_7y[:,i] = np.diff(output_decomp_S1_Ac_7y[:,i])
i = i + 1
print(subs_matrix_S1_Ac_7y[:,:4])
print(len(subs_matrix_S1_Ac_7y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Ac_7y = subs_matrix_S1_Ac_7y.clip(max=0)
print(subs_matrix_S1_Ac_7y[:,:4])
#make the results as absolute values
subs_matrix_S1_Ac_7y = abs(subs_matrix_S1_Ac_7y)
print(subs_matrix_S1_Ac_7y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Ac_7y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values)))
print(zero_matrix_S1_Ac_7y)
subs_matrix_S1_Ac_7y = np.vstack((zero_matrix_S1_Ac_7y, subs_matrix_S1_Ac_7y))
print(subs_matrix_S1_Ac_7y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Ac_7y = (tf,1)
decomp_tot_CO2_S1_Ac_7y = np.zeros(matrix_tot_S1_Ac_7y)
i = 0
while i < tf:
decomp_tot_CO2_S1_Ac_7y[:,0] = decomp_tot_CO2_S1_Ac_7y[:,0] + subs_matrix_S1_Ac_7y[:,i]
i = i + 1
print(decomp_tot_CO2_S1_Ac_7y[:,0])
#S1_Ac_18y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
tf = 201
t = np.arange(tf)
def decomp_S1_Ac_18y(t,remainAGB_S1_Ac_18y):
return (1-(1-np.exp(-k*t)))*remainAGB_S1_Ac_18y
#set zero matrix
output_decomp_S1_Ac_18y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values)))
for i,remain_part_S1_Ac_18y in enumerate(df['Landfill_decomp_CO2'].values):
#print(i,remain_part)
output_decomp_S1_Ac_18y[i:,i] = decomp_S1_Ac_18y(t[:len(t)-i],remain_part_S1_Ac_18y)
print(output_decomp_S1_Ac_18y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Ac_18y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Ac_18y[:,i] = np.diff(output_decomp_S1_Ac_18y[:,i])
i = i + 1
print(subs_matrix_S1_Ac_18y[:,:4])
print(len(subs_matrix_S1_Ac_18y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Ac_18y = subs_matrix_S1_Ac_18y.clip(max=0)
print(subs_matrix_S1_Ac_18y[:,:4])
#make the results as absolute values
subs_matrix_S1_Ac_18y = abs(subs_matrix_S1_Ac_18y)
print(subs_matrix_S1_Ac_18y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Ac_18y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values)))
print(zero_matrix_S1_Ac_18y)
subs_matrix_S1_Ac_18y = np.vstack((zero_matrix_S1_Ac_18y, subs_matrix_S1_Ac_18y))
print(subs_matrix_S1_Ac_18y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Ac_18y = (tf,1)
decomp_tot_CO2_S1_Ac_18y = np.zeros(matrix_tot_S1_Ac_18y)
i = 0
while i < tf:
decomp_tot_CO2_S1_Ac_18y[:,0] = decomp_tot_CO2_S1_Ac_18y[:,0] + subs_matrix_S1_Ac_18y[:,i]
i = i + 1
print(decomp_tot_CO2_S1_Ac_18y[:,0])
#S1_Tgr_40y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
tf = 201
t = np.arange(tf)
def decomp_S1_Tgr_40y(t,remainAGB_S1_Tgr_40y):
return (1-(1-np.exp(-k*t)))*remainAGB_S1_Tgr_40y
#set zero matrix
output_decomp_S1_Tgr_40y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values)))
for i,remain_part_S1_Tgr_40y in enumerate(df['Landfill_decomp_CO2'].values):
#print(i,remain_part)
output_decomp_S1_Tgr_40y[i:,i] = decomp_S1_Tgr_40y(t[:len(t)-i],remain_part_S1_Tgr_40y)
print(output_decomp_S1_Tgr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Tgr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Tgr_40y[:,i] = np.diff(output_decomp_S1_Tgr_40y[:,i])
i = i + 1
print(subs_matrix_S1_Tgr_40y[:,:4])
print(len(subs_matrix_S1_Tgr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Tgr_40y = subs_matrix_S1_Tgr_40y.clip(max=0)
print(subs_matrix_S1_Tgr_40y[:,:4])
#make the results as absolute values
subs_matrix_S1_Tgr_40y = abs(subs_matrix_S1_Tgr_40y)
print(subs_matrix_S1_Tgr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Tgr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values)))
print(zero_matrix_S1_Tgr_40y)
subs_matrix_S1_Tgr_40y = np.vstack((zero_matrix_S1_Tgr_40y, subs_matrix_S1_Tgr_40y))
print(subs_matrix_S1_Tgr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Tgr_40y = (tf,1)
decomp_tot_CO2_S1_Tgr_40y = np.zeros(matrix_tot_S1_Tgr_40y)
i = 0
while i < tf:
decomp_tot_CO2_S1_Tgr_40y[:,0] = decomp_tot_CO2_S1_Tgr_40y[:,0] + subs_matrix_S1_Tgr_40y[:,i]
i = i + 1
print(decomp_tot_CO2_S1_Tgr_40y[:,0])
#S2_Tgr_60y
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
tf = 201
t = np.arange(tf)
def decomp_S1_Tgr_60y(t,remainAGB_S1_Tgr_60y):
return (1-(1-np.exp(-k*t)))*remainAGB_S1_Tgr_60y
#set zero matrix
output_decomp_S1_Tgr_60y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values)))
for i,remain_part_S1_Tgr_60y in enumerate(df['Landfill_decomp_CO2'].values):
#print(i,remain_part)
output_decomp_S1_Tgr_60y[i:,i] = decomp_S1_Tgr_60y(t[:len(t)-i],remain_part_S1_Tgr_60y)
print(output_decomp_S1_Tgr_60y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_S1_Tgr_60y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1)))
i = 0
while i < tf:
subs_matrix_S1_Tgr_60y[:,i] = np.diff(output_decomp_S1_Tgr_60y[:,i])
i = i + 1
print(subs_matrix_S1_Tgr_60y[:,:4])
print(len(subs_matrix_S1_Tgr_60y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_S1_Tgr_60y = subs_matrix_S1_Tgr_60y.clip(max=0)
print(subs_matrix_S1_Tgr_60y[:,:4])
#make the results as absolute values
subs_matrix_S1_Tgr_60y = abs(subs_matrix_S1_Tgr_60y)
print(subs_matrix_S1_Tgr_60y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_S1_Tgr_60y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values)))
print(zero_matrix_S1_Tgr_60y)
subs_matrix_S1_Tgr_60y = np.vstack((zero_matrix_S1_Tgr_60y, subs_matrix_S1_Tgr_60y))
print(subs_matrix_S1_Tgr_60y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_S1_Tgr_60y = (tf,1)
decomp_tot_CO2_S1_Tgr_60y = np.zeros(matrix_tot_S1_Tgr_60y)
i = 0
while i < tf:
decomp_tot_CO2_S1_Tgr_60y[:,0] = decomp_tot_CO2_S1_Tgr_60y[:,0] + subs_matrix_S1_Tgr_60y[:,i]
i = i + 1
print(decomp_tot_CO2_S1_Tgr_60y[:,0])
#E
df = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
tf = 201
t = np.arange(tf)
def decomp_E_Hbr_40y(t,remainAGB_E_Hbr_40y):
return (1-(1-np.exp(-k*t)))*remainAGB_E_Hbr_40y
#set zero matrix
output_decomp_E_Hbr_40y = np.zeros((len(t),len(df['Landfill_decomp_CO2'].values)))
for i,remain_part_E_Hbr_40y in enumerate(df['Landfill_decomp_CO2'].values):
#print(i,remain_part)
output_decomp_E_Hbr_40y[i:,i] = decomp_E_Hbr_40y(t[:len(t)-i],remain_part_E_Hbr_40y)
print(output_decomp_E_Hbr_40y[:,:4])
#find the yearly emissions from decomposition by calculating the differences between elements in list 'decomp_tot_S1'
#(https://stackoverflow.com/questions/5314241/difference-between-consecutive-elements-in-list)
# https://stackoverflow.com/questions/11095892/numpy-difference-between-neighboring-elements
#difference between element,
subs_matrix_E_Hbr_40y = np.zeros((len(t)-1,len(df['Landfill_decomp_CO2'].values-1)))
i = 0
while i < tf:
subs_matrix_E_Hbr_40y[:,i] = np.diff(output_decomp_E_Hbr_40y[:,i])
i = i + 1
print(subs_matrix_E_Hbr_40y[:,:4])
print(len(subs_matrix_E_Hbr_40y))
#since there is no carbon emission from decomposition at the beginning of the year (esp. from 'year 1' onward),
#we have to replace the positive numbers with 0 values (https://stackoverflow.com/questions/36310897/how-do-i-change-all-negative-numbers-to-zero-in-python/36310913)
subs_matrix_E_Hbr_40y = subs_matrix_E_Hbr_40y.clip(max=0)
print(subs_matrix_E_Hbr_40y[:,:4])
#make the results as absolute values
subs_matrix_E_Hbr_40y = abs(subs_matrix_E_Hbr_40y)
print(subs_matrix_E_Hbr_40y[:,:4])
#insert row of zeros into the first row of the subs_matrix
zero_matrix_E_Hbr_40y = np.zeros((len(t)-200,len(df['Landfill_decomp_CO2'].values)))
print(zero_matrix_E_Hbr_40y)
subs_matrix_E_Hbr_40y = np.vstack((zero_matrix_E_Hbr_40y, subs_matrix_E_Hbr_40y))
print(subs_matrix_E_Hbr_40y[:,:4])
#sum every column of the subs_matrix into one vector matrix
matrix_tot_E_Hbr_40y = (tf,1)
decomp_tot_CO2_E_Hbr_40y = np.zeros(matrix_tot_E_Hbr_40y)
i = 0
while i < tf:
decomp_tot_CO2_E_Hbr_40y[:,0] = decomp_tot_CO2_E_Hbr_40y[:,0] + subs_matrix_E_Hbr_40y[:,i]
i = i + 1
print(decomp_tot_CO2_E_Hbr_40y[:,0])
#plotting
t = np.arange(0,tf)
plt.plot(t,decomp_tot_CO2_S1_Ac_7y,label='Ac_7y')
plt.plot(t,decomp_tot_CO2_S1_Ac_18y,label='Ac_18y')
plt.plot(t,decomp_tot_CO2_S1_Tgr_40y,label='Tgr_40y')
plt.plot(t,decomp_tot_CO2_S1_Tgr_60y,label='Tgr_60y')
plt.plot(t,decomp_tot_CO2_E_Hbr_40y,label='E_Hbr_40y')
plt.xlim(0,200)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
plt.show()
#%%
#Step (8): Sum the emissions and sequestration (net carbon balance), CO2 and CH4 are separated
#https://stackoverflow.com/questions/52703442/python-sum-values-from-multiple-lists-more-than-two
#C_loss + C_remainAGB + C_remainHWP + PH_Emissions_PO
Emissions_S1_Ac_7y = [c_firewood_energy_S1_Ac7, decomp_tot_S1_Ac_7y[:,0], TestDSM1_Ac7.o, PH_Emissions_HWP1_Ac_7y, decomp_tot_CO2_S1_Ac_7y[:,0]]
Emissions_S1_Ac_18y = [c_firewood_energy_S1_Ac18, decomp_tot_S1_Ac_18y[:,0], TestDSM1_Ac18.o, PH_Emissions_HWP1_Ac_18y, decomp_tot_CO2_S1_Ac_18y[:,0]]
Emissions_S1_Tgr_40y = [c_firewood_energy_S1_Tgr40, decomp_tot_S1_Tgr_40y[:,0], TestDSM1_Tgr40.o, PH_Emissions_HWP1_Tgr_40y, decomp_tot_CO2_S1_Tgr_40y[:,0]]
Emissions_S1_Tgr_60y = [c_firewood_energy_S1_Tgr60, decomp_tot_S1_Tgr_60y[:,0], TestDSM1_Tgr60.o, PH_Emissions_HWP1_Tgr_60y, decomp_tot_CO2_S1_Tgr_60y[:,0]]
Emissions_E_Hbr_40y = [c_firewood_energy_E_Hbr40, c_pellets_Hbr_40y, decomp_tot_E_Hbr_40y[:,0], TestDSME_Hbr40.o, PH_Emissions_HWPE_Hbr_40y, decomp_tot_CO2_E_Hbr_40y[:,0]]
Emissions_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Emissions_S1_Ac_7y)]
Emissions_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Emissions_S1_Ac_18y)]
Emissions_DL_FP_S1_Tgr_40y = [sum(x) for x in zip(*Emissions_S1_Tgr_40y)]
Emissions_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Emissions_S1_Tgr_60y)]
Emissions_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Emissions_E_Hbr_40y)]
#CH4_S1_Ac_7y
Emissions_CH4_DL_FP_S1_Ac_7y = decomp_tot_CH4_S1_Ac_7y[:,0]
#CH4_S1_Ac_18y
Emissions_CH4_DL_FP_S1_Ac_18y = decomp_tot_CH4_S1_Ac_18y[:,0]
#CH4_S1_Tgr_40y
Emissions_CH4_DL_FP_S1_Tgr_40y = decomp_tot_CH4_S1_Tgr_40y[:,0]
#CH4_S1_Tgr_60y
Emissions_CH4_DL_FP_S1_Tgr_60y = decomp_tot_CH4_S1_Tgr_60y[:,0]
#CH4_E_Hbr_40y
Emissions_CH4_DL_FP_E_Hbr_40y = decomp_tot_CH4_E_Hbr_40y[:,0]
#%%
#Step (9): Generate the excel file (emissions_seq_scenarios.xlsx) from Step (8) calculation
#print year column
year = []
for x in range (0, tf):
year.append(x)
print (year)
#print CH4 emission column
import itertools
lst = [0]
Emissions_CH4 = list(itertools.chain.from_iterable(itertools.repeat(x, tf) for x in lst))
print(Emissions_CH4)
#print emission ref
lst1 = [0]
Emission_ref = list(itertools.chain.from_iterable(itertools.repeat(x, tf) for x in lst1))
print(Emission_ref)
#replace the first element with 1 to denote the emission reference as year 0 (for dynGWP calculation)
Emission_ref[0] = 1
print(Emission_ref)
Col1 = year
Col2_S1_Ac_7y = Emissions_DL_FP_S1_Ac_7y
Col2_S1_Ac_18y = Emissions_DL_FP_S1_Ac_18y
Col2_S1_Tgr_40y = Emissions_DL_FP_S1_Tgr_40y
Col2_S1_Tgr_60y = Emissions_DL_FP_S1_Tgr_60y
Col2_E_Hbr_40y = Emissions_DL_FP_E_Hbr_40y
Col3_S1_Ac_7y = Emissions_CH4_DL_FP_S1_Ac_7y
Col3_S1_Ac_18y = Emissions_CH4_DL_FP_S1_Ac_18y
Col3_S1_Tgr_40y = Emissions_CH4_DL_FP_S1_Tgr_40y
Col3_S1_Tgr_60y = Emissions_CH4_DL_FP_S1_Tgr_60y
Col3_E_Hbr_40y = Emissions_CH4_DL_FP_E_Hbr_40y
Col4 = Emission_ref
Col5 = flat_list_Ac_7y
Col6 = flat_list_Ac_18y
Col7 = flat_list_Tgr_40y
Col8 = flat_list_Tgr_60y
Col9 = flat_list_Hbr_40y
#<NAME>
df1_Ac_7y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Ac_7y,'kg_CH4':Col3_S1_Ac_7y,'kg_CO2_seq':Col5,'emission_ref':Col4})
df1_Ac_18y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Ac_18y,'kg_CH4':Col3_S1_Ac_18y,'kg_CO2_seq':Col6,'emission_ref':Col4})
#<NAME>
df1_Tgr_40y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Tgr_40y,'kg_CH4':Col3_S1_Tgr_40y,'kg_CO2_seq':Col7,'emission_ref':Col4})
df1_Tgr_60y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_S1_Tgr_60y,'kg_CH4':Col3_S1_Tgr_60y,'kg_CO2_seq':Col8,'emission_ref':Col4})
#<NAME>
dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Col1,'kg_CO2':Col2_E_Hbr_40y,'kg_CH4':Col3_E_Hbr_40y,'kg_CO2_seq':Col9,'emission_ref':Col4})
writer = pd.ExcelWriter('emissions_seq_DL_FP_S1.xlsx', engine = 'xlsxwriter')
df1_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_S1_Ac_7y', header=True, index=False )
df1_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_S1_Ac_18y', header=True, index=False)
df1_Tgr_40y.to_excel(writer, sheet_name = 'DL_FP_S1_Tgr_40y', header=True, index=False)
df1_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_S1_Tgr_60y', header=True, index=False)
dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_Hbr_40y', header=True, index=False)
writer.save()
writer.close()
#df1.to_excel('test.xlsx', 'nuclues', header=True, index=False)
#df2.to_excel('test.xlsx', 'plasma', header=True, index=False)
#%%
## DYNAMIC LCA
# Step (10): Set General Parameters for Dynamic LCA calculation
aCH4 = 0.129957e-12; # methane - instantaneous radiative forcing per unit mass [W/m2 /kgCH4]
TauCH4 = 12; # methane - lifetime (years)
aCO2 = 0.0018088e-12; # CO2 - instantaneous radiative forcing per unit mass [W/m2 /kgCO2]
TauCO2 = [172.9, 18.51, 1.186]; # CO2 parameters according to Bern carbon cycle-climate model
aBern = [0.259, 0.338, 0.186]; # CO2 parameters according to Bern carbon cycle-climate model
a0Bern = 0.217; # CO2 parameters according to Bern carbon cycle-climate model
tf = 202 #until 202 because we want to get the DCF(t-i) until DCF(201) to determine the impact from the emission from the year 200 (There is no DCF(0))
#%%
#Step (11): Bern 2.5 CC Model, determine atmospheric load (C(t)) for GHG (CO2 and CH4)
t = range(0,tf,1)
## CO2 calculation formula
# time dependant atmospheric load for CO2, Bern model
def C_CO2(t):
return a0Bern + aBern[0]*np.exp(-t/TauCO2[0]) + aBern[1]*np.exp(-t/TauCO2[1]) + aBern[2]*np.exp(-t/TauCO2[2])
output_CO2 = np.array([C_CO2(ti) for ti in t])
print(output_CO2)
## CH4 calculation formula
# time dependant atmospheric load for non-CO2 GHGs (Methane)
def C_CH4(t):
return np.exp(-t/TauCH4)
output_CH4 = np.array([C_CH4(ti) for ti in t])
plt.xlim([0, 200])
plt.ylim([0,1.1])
plt.plot(t, output_CO2, output_CH4)
plt.xlabel('Time (year)')
plt.ylabel('Fraction of CO$_2$')
plt.show()
output_CH4.size
#%%
#determine the C(t) for CO2
s = []
t = np.arange(0,tf,1)
for i in t:
s.append(quad(C_CO2,i-1,i))
res_list_CO2 = [x[0] for x in s]
len(res_list_CO2)
#%%
#determine the C(t) for CH4
s = []
for i in t:
s.append(quad(C_CH4,i-1,i))
res_list_CH4 = [p[0] for p in s]
#plot
plt.xlim([0, 200])
plt.ylim([0,1.5])
plt.plot(t, res_list_CO2, res_list_CH4)
plt.show()
#%%
#Step (12): Determine dynamic characterization factors (DCF) for CO2 and CH4
DCF_inst_CO2 = aCO2 * np.array(res_list_CO2)
print(DCF_inst_CO2)
DCF_inst_CH4 = aCH4 * np.array(res_list_CH4)
plt.xlim([0, 200])
plt.ylim([0,4e-15])
plt.plot(t, DCF_inst_CO2, DCF_inst_CH4)
plt.xlabel('Time (year)')
plt.ylabel('DCF_inst (10$^{-15}$ W/m$^2$.kg CO$_2$)')
plt.show()
len(DCF_inst_CO2)
#%%
#Step (13): import emission data from emissions_seq_scenarios.xlsx (Step (9))
##wood-based
#read S1_Ac_7y
df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Ac_7y') # can also index sheet by name or fetch all sheets
emission_CO2_S1_Ac_7y = df['kg_CO2'].tolist()
emission_CH4_S1_Ac_7y = df['kg_CH4'].tolist()
emission_CO2_seq_S1_Ac_7y = df['kg_CO2_seq'].tolist()
emission_CO2_ref = df['emission_ref'].tolist()
#read S1_Ac_18y
df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Ac_18y')
emission_CO2_S1_Ac_18y = df['kg_CO2'].tolist()
emission_CH4_S1_Ac_18y = df['kg_CH4'].tolist()
emission_CO2_seq_S1_Ac_18y = df['kg_CO2_seq'].tolist()
#read S1_Tgr_40y
df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Tgr_40y') # can also index sheet by name or fetch all sheets
emission_CO2_S1_Tgr_40y = df['kg_CO2'].tolist()
emission_CH4_S1_Tgr_40y = df['kg_CH4'].tolist()
emission_CO2_seq_S1_Tgr_40y = df['kg_CO2_seq'].tolist()
#read S1_Tgr_60y
df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_S1_Tgr_60y')
emission_CO2_S1_Tgr_60y = df['kg_CO2'].tolist()
emission_CH4_S1_Tgr_60y = df['kg_CH4'].tolist()
emission_CO2_seq_S1_Tgr_60y = df['kg_CO2_seq'].tolist()
#read E_Hbr_40y
df = pd.read_excel('emissions_seq_DL_FP_S1.xlsx', 'DL_FP_E_Hbr_40y') # can also index sheet by name or fetch all sheets
emission_CO2_E_Hbr_40y = df['kg_CO2'].tolist()
emission_CH4_E_Hbr_40y = df['kg_CH4'].tolist()
emission_CO2_seq_E_Hbr_40y = df['kg_CO2_seq'].tolist()
#%%
#Step (14): import emission data from the counter-use of non-renewable materials/energy scenarios (NR)
#read S1_Ac_7y
df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
emissions_NonRW_S1_Ac_7y = df['NonRW_emissions'].tolist()
emissions_NonRW_S1_Ac_7y_seq = df['kg_CO2_seq'].tolist()
emission_CO2_ref = df['emission_ref'].tolist()
#read S1_Ac_18y
df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
emissions_NonRW_S1_Ac_18y = df['NonRW_emissions'].tolist()
emissions_NonRW_S1_Ac_18y_seq = df['kg_CO2_seq'].tolist()
#read S1_Tgr_40y
df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_40y') # can also index sheet by name or fetch all sheets
emissions_NonRW_S1_Tgr_40y = df['NonRW_emissions'].tolist()
emissions_NonRW_S1_Tgr_40y_seq = df['kg_CO2_seq'].tolist()
#read S1_Tgr_60y
df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
emissions_NonRW_S1_Tgr_60y = df['NonRW_emissions'].tolist()
emissions_NonRW_S1_Tgr_60y_seq = df['kg_CO2_seq'].tolist()
#read E_Hbr_40y
df = pd.read_excel('NonRW_DL_FP.xlsx', 'DL_FP_E_Hbr_40y') # can also index sheet by name or fetch all sheets
emissions_NonRW_E_Hbr_40y = df['NonRW_emissions'].tolist()
emissions_NonRW_E_Hbr_40y_seq = df['kg_CO2_seq'].tolist()
#%%
#Step (15): Determine the time elapsed dynamic characterization factors, DCF(t-ti), for CO2 and CH4
#DCF(t-i) CO2
matrix = (tf-1,tf-1)
DCF_CO2_ti = np.zeros(matrix)
for t in range(0,tf-1):
i = -1
while i < t:
DCF_CO2_ti[i+1,t] = DCF_inst_CO2[t-i]
i = i + 1
print(DCF_CO2_ti)
#sns.heatmap(DCF_CO2_ti)
DCF_CO2_ti.shape
#DCF(t-i) CH4
matrix = (tf-1,tf-1)
DCF_CH4_ti = np.zeros(matrix)
for t in range(0,tf-1):
i = -1
while i < t:
DCF_CH4_ti[i+1,t] = DCF_inst_CH4[t-i]
i = i + 1
print(DCF_CH4_ti)
#sns.heatmap(DCF_CH4_ti)
DCF_CH4_ti.shape
#%%
# Step (16): Calculate instantaneous global warming impact (GWI)
##Wood-based
#S1_Ac_7y
t = np.arange(0,tf-1,1)
matrix_GWI_S1_Ac_7y = (tf-1,3)
GWI_inst_S1_Ac_7y = np.zeros(matrix_GWI_S1_Ac_7y)
for t in range(0,tf-1):
GWI_inst_S1_Ac_7y[t,0] = np.sum(np.multiply(emission_CO2_S1_Ac_7y,DCF_CO2_ti[:,t]))
GWI_inst_S1_Ac_7y[t,1] = np.sum(np.multiply(emission_CH4_S1_Ac_7y,DCF_CH4_ti[:,t]))
GWI_inst_S1_Ac_7y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Ac_7y,DCF_CO2_ti[:,t]))
matrix_GWI_tot_S1_Ac_7y = (tf-1,1)
GWI_inst_tot_S1_Ac_7y = np.zeros(matrix_GWI_tot_S1_Ac_7y)
GWI_inst_tot_S1_Ac_7y[:,0] = np.array(GWI_inst_S1_Ac_7y[:,0] + GWI_inst_S1_Ac_7y[:,1] + GWI_inst_S1_Ac_7y[:,2])
print(GWI_inst_tot_S1_Ac_7y[:,0])
t = np.arange(0,tf-1,1)
#S1_Ac_18y
t = np.arange(0,tf-1,1)
matrix_GWI_S1_Ac_18y = (tf-1,3)
GWI_inst_S1_Ac_18y = np.zeros(matrix_GWI_S1_Ac_18y)
for t in range(0,tf-1):
GWI_inst_S1_Ac_18y[t,0] = np.sum(np.multiply(emission_CO2_S1_Ac_18y,DCF_CO2_ti[:,t]))
GWI_inst_S1_Ac_18y[t,1] = np.sum(np.multiply(emission_CH4_S1_Ac_18y,DCF_CH4_ti[:,t]))
GWI_inst_S1_Ac_18y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Ac_18y,DCF_CO2_ti[:,t]))
matrix_GWI_tot_S1_Ac_18y = (tf-1,1)
GWI_inst_tot_S1_Ac_18y = np.zeros(matrix_GWI_tot_S1_Ac_18y)
GWI_inst_tot_S1_Ac_18y[:,0] = np.array(GWI_inst_S1_Ac_18y[:,0] + GWI_inst_S1_Ac_18y[:,1] + GWI_inst_S1_Ac_18y[:,2])
print(GWI_inst_tot_S1_Ac_18y[:,0])
#S1_Tgr_40y
t = np.arange(0,tf-1,1)
matrix_GWI_S1_Tgr_40y = (tf-1,3)
GWI_inst_S1_Tgr_40y = np.zeros(matrix_GWI_S1_Tgr_40y)
for t in range(0,tf-1):
GWI_inst_S1_Tgr_40y[t,0] = np.sum(np.multiply(emission_CO2_S1_Tgr_40y,DCF_CO2_ti[:,t]))
GWI_inst_S1_Tgr_40y[t,1] = np.sum(np.multiply(emission_CH4_S1_Tgr_40y,DCF_CH4_ti[:,t]))
GWI_inst_S1_Tgr_40y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Tgr_40y,DCF_CO2_ti[:,t]))
matrix_GWI_tot_S1_Tgr_40y = (tf-1,1)
GWI_inst_tot_S1_Tgr_40y = np.zeros(matrix_GWI_tot_S1_Tgr_40y)
GWI_inst_tot_S1_Tgr_40y[:,0] = np.array(GWI_inst_S1_Tgr_40y[:,0] + GWI_inst_S1_Tgr_40y[:,1] + GWI_inst_S1_Tgr_40y[:,2])
print(GWI_inst_tot_S1_Tgr_40y[:,0])
#S1_Tgr_60y
t = np.arange(0,tf-1,1)
matrix_GWI_S1_Tgr_60y = (tf-1,3)
GWI_inst_S1_Tgr_60y = np.zeros(matrix_GWI_S1_Tgr_60y)
for t in range(0,tf-1):
GWI_inst_S1_Tgr_60y[t,0] = np.sum(np.multiply(emission_CO2_S1_Tgr_60y,DCF_CO2_ti[:,t]))
GWI_inst_S1_Tgr_60y[t,1] = np.sum(np.multiply(emission_CH4_S1_Tgr_60y,DCF_CH4_ti[:,t]))
GWI_inst_S1_Tgr_60y[t,2] = np.sum(np.multiply(emission_CO2_seq_S1_Tgr_60y,DCF_CO2_ti[:,t]))
matrix_GWI_tot_S1_Tgr_60y = (tf-1,1)
GWI_inst_tot_S1_Tgr_60y = np.zeros(matrix_GWI_tot_S1_Tgr_60y)
GWI_inst_tot_S1_Tgr_60y[:,0] = np.array(GWI_inst_S1_Tgr_60y[:,0] + GWI_inst_S1_Tgr_60y[:,1] + GWI_inst_S1_Tgr_60y[:,2])
print(GWI_inst_tot_S1_Tgr_60y[:,0])
#E_Hbr_40y
t = np.arange(0,tf-1,1)
matrix_GWI_E_Hbr_40y = (tf-1,3)
GWI_inst_E_Hbr_40y = np.zeros(matrix_GWI_E_Hbr_40y)
for t in range(0,tf-1):
GWI_inst_E_Hbr_40y[t,0] = np.sum(np.multiply(emission_CO2_E_Hbr_40y,DCF_CO2_ti[:,t]))
GWI_inst_E_Hbr_40y[t,1] = np.sum(np.multiply(emission_CH4_E_Hbr_40y,DCF_CH4_ti[:,t]))
GWI_inst_E_Hbr_40y[t,2] = np.sum(np.multiply(emission_CO2_seq_E_Hbr_40y,DCF_CO2_ti[:,t]))
matrix_GWI_tot_E_Hbr_40y = (tf-1,1)
GWI_inst_tot_E_Hbr_40y = np.zeros(matrix_GWI_tot_E_Hbr_40y)
GWI_inst_tot_E_Hbr_40y[:,0] = np.array(GWI_inst_E_Hbr_40y[:,0] + GWI_inst_E_Hbr_40y[:,1] + GWI_inst_E_Hbr_40y[:,2])
print(GWI_inst_tot_E_Hbr_40y[:,0])
##NonRW
#S1_Ac_7y
t = np.arange(0,tf-1,1)
matrix_GWI_NonRW_S1_Ac_7y = (tf-1,2)
GWI_inst_NonRW_S1_Ac_7y = np.zeros(matrix_GWI_NonRW_S1_Ac_7y)
for t in range(0,tf-1):
GWI_inst_NonRW_S1_Ac_7y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Ac_7y,DCF_CO2_ti[:,t]))
GWI_inst_NonRW_S1_Ac_7y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Ac_7y_seq,DCF_CO2_ti[:,t]))
matrix_GWI_tot_NonRW_S1_Ac_7y = (tf-1,1)
GWI_inst_tot_NonRW_S1_Ac_7y = np.zeros(matrix_GWI_tot_NonRW_S1_Ac_7y)
GWI_inst_tot_NonRW_S1_Ac_7y[:,0] = np.array(GWI_inst_NonRW_S1_Ac_7y[:,0] + GWI_inst_NonRW_S1_Ac_7y[:,1])
print(GWI_inst_tot_NonRW_S1_Ac_7y[:,0])
#S1_Ac_18y
t = np.arange(0,tf-1,1)
matrix_GWI_NonRW_S1_Ac_18y = (tf-1,2)
GWI_inst_NonRW_S1_Ac_18y = np.zeros(matrix_GWI_NonRW_S1_Ac_18y)
for t in range(0,tf-1):
GWI_inst_NonRW_S1_Ac_18y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Ac_18y,DCF_CO2_ti[:,t]))
GWI_inst_NonRW_S1_Ac_18y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Ac_18y_seq,DCF_CO2_ti[:,t]))
matrix_GWI_tot_NonRW_S1_Ac_18y = (tf-1,1)
GWI_inst_tot_NonRW_S1_Ac_18y = np.zeros(matrix_GWI_tot_NonRW_S1_Ac_18y)
GWI_inst_tot_NonRW_S1_Ac_18y[:,0] = np.array(GWI_inst_NonRW_S1_Ac_18y[:,0] + GWI_inst_NonRW_S1_Ac_18y[:,1])
print(GWI_inst_tot_NonRW_S1_Ac_18y[:,0])
#S1_Tgr_40y
t = np.arange(0,tf-1,1)
matrix_GWI_NonRW_S1_Tgr_40y = (tf-1,2)
GWI_inst_NonRW_S1_Tgr_40y = np.zeros(matrix_GWI_NonRW_S1_Tgr_40y)
for t in range(0,tf-1):
GWI_inst_NonRW_S1_Tgr_40y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_40y,DCF_CO2_ti[:,t]))
GWI_inst_NonRW_S1_Tgr_40y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_40y_seq,DCF_CO2_ti[:,t]))
matrix_GWI_tot_NonRW_S1_Tgr_40y = (tf-1,1)
GWI_inst_tot_NonRW_S1_Tgr_40y = np.zeros(matrix_GWI_tot_NonRW_S1_Tgr_40y)
GWI_inst_tot_NonRW_S1_Tgr_40y[:,0] = np.array(GWI_inst_NonRW_S1_Tgr_40y[:,0] + GWI_inst_NonRW_S1_Tgr_40y[:,1])
print(GWI_inst_tot_NonRW_S1_Tgr_40y[:,0])
#S1_Tgr_60y
t = np.arange(0,tf-1,1)
matrix_GWI_NonRW_S1_Tgr_60y = (tf-1,2)
GWI_inst_NonRW_S1_Tgr_60y = np.zeros(matrix_GWI_NonRW_S1_Tgr_60y)
for t in range(0,tf-1):
GWI_inst_NonRW_S1_Tgr_60y[t,0] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_60y,DCF_CO2_ti[:,t]))
GWI_inst_NonRW_S1_Tgr_60y[t,1] = np.sum(np.multiply(emissions_NonRW_S1_Tgr_60y_seq,DCF_CO2_ti[:,t]))
matrix_GWI_tot_NonRW_S1_Tgr_60y = (tf-1,1)
GWI_inst_tot_NonRW_S1_Tgr_60y = np.zeros(matrix_GWI_tot_NonRW_S1_Tgr_60y)
GWI_inst_tot_NonRW_S1_Tgr_60y[:,0] = np.array(GWI_inst_NonRW_S1_Tgr_60y[:,0] + GWI_inst_NonRW_S1_Tgr_60y[:,1])
print(GWI_inst_tot_NonRW_S1_Tgr_60y[:,0])
#E_Hbr_40y
t = np.arange(0,tf-1,1)
matrix_GWI_NonRW_E_Hbr_40y = (tf-1,2)
GWI_inst_NonRW_E_Hbr_40y = np.zeros(matrix_GWI_NonRW_E_Hbr_40y)
for t in range(0,tf-1):
GWI_inst_NonRW_E_Hbr_40y[t,0] = np.sum(np.multiply(emissions_NonRW_E_Hbr_40y,DCF_CO2_ti[:,t]))
GWI_inst_NonRW_E_Hbr_40y[t,1] = np.sum(np.multiply(emissions_NonRW_E_Hbr_40y_seq,DCF_CO2_ti[:,t]))
matrix_GWI_tot_NonRW_E_Hbr_40y = (tf-1,1)
GWI_inst_tot_NonRW_E_Hbr_40y = np.zeros(matrix_GWI_tot_NonRW_E_Hbr_40y)
GWI_inst_tot_NonRW_E_Hbr_40y[:,0] = np.array(GWI_inst_NonRW_E_Hbr_40y[:,0] + GWI_inst_NonRW_E_Hbr_40y[:,1])
print(GWI_inst_tot_NonRW_E_Hbr_40y[:,0])
t = np.arange(0,tf-1,1)
#create zero list to highlight the horizontal line for 0
def zerolistmaker(n):
listofzeros = [0] * (n)
return listofzeros
#convert to flat list
GWI_inst_tot_NonRW_S1_Ac_7y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Ac_7y for item in sublist])
GWI_inst_tot_NonRW_S1_Ac_18y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Ac_18y for item in sublist])
GWI_inst_tot_NonRW_S1_Tgr_60y = np.array([item for sublist in GWI_inst_tot_NonRW_S1_Tgr_60y for item in sublist])
GWI_inst_tot_NonRW_E_Hbr_40y = np.array([item for sublist in GWI_inst_tot_NonRW_E_Hbr_40y for item in sublist])
GWI_inst_tot_S1_Ac_7y = np.array([item for sublist in GWI_inst_tot_S1_Ac_7y for item in sublist])
GWI_inst_tot_S1_Ac_18y = np.array([item for sublist in GWI_inst_tot_S1_Ac_18y for item in sublist])
GWI_inst_tot_S1_Tgr_60y = np.array([item for sublist in GWI_inst_tot_S1_Tgr_60y for item in sublist])
GWI_inst_tot_E_Hbr_40y = np.array([item for sublist in GWI_inst_tot_E_Hbr_40y for item in sublist])
plt.plot(t, GWI_inst_tot_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55)
plt.plot(t, GWI_inst_tot_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55)
#plt.plot(t, GWI_inst_tot_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55)
plt.plot(t, GWI_inst_tot_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55)
plt.plot(t, GWI_inst_tot_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55)
plt.plot(t, GWI_inst_tot_S1_Ac_7y, color='olive', label='M_EC_Ac_7y')
plt.plot(t, GWI_inst_tot_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y')
#plt.plot(t, GWI_inst_tot_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y')
plt.plot(t, GWI_inst_tot_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y')
plt.plot(t, GWI_inst_tot_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y')
plt.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75)
#plt.fill_between(t, GWI_inst_tot_NonRW_E_Hbr_40y, GWI_inst_tot_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3)
#plt.fill_between(t, GWI_inst_tot_NonRW_S1_Ac_7y, GWI_inst_tot_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3)
plt.grid(True)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
plt.xlim(0,200)
plt.ylim(-1e-9,1.4e-9)
plt.title('Instantaneous GWI, DL_FP_EC')
plt.xlabel('Time (year)')
#plt.ylabel('GWI_inst (10$^{-12}$ W/m$^2$)')
plt.ylabel('GWI_inst (W/m$^2$)')#
plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWI_inst_NonRW_DL_FP_S1', dpi=300)
plt.show()
#%%
#Step (17): Calculate cumulative global warming impact (GWI)
##Wood-based
GWI_cum_S1_Ac_7y = np.cumsum(GWI_inst_tot_S1_Ac_7y)
GWI_cum_S1_Ac_18y = np.cumsum(GWI_inst_tot_S1_Ac_18y)
GWI_cum_S1_Tgr_40y = np.cumsum(GWI_inst_tot_S1_Tgr_40y)
GWI_cum_S1_Tgr_60y = np.cumsum(GWI_inst_tot_S1_Tgr_60y)
GWI_cum_E_Hbr_40y = np.cumsum(GWI_inst_tot_E_Hbr_40y)
##NonRW
GWI_cum_NonRW_S1_Ac_7y = np.cumsum(GWI_inst_tot_NonRW_S1_Ac_7y)
GWI_cum_NonRW_S1_Ac_18y = np.cumsum(GWI_inst_tot_NonRW_S1_Ac_18y)
GWI_cum_NonRW_S1_Tgr_40y = np.cumsum(GWI_inst_tot_NonRW_S1_Tgr_40y)
GWI_cum_NonRW_S1_Tgr_60y = np.cumsum(GWI_inst_tot_NonRW_S1_Tgr_60y)
GWI_cum_NonRW_E_Hbr_40y = np.cumsum(GWI_inst_tot_NonRW_E_Hbr_40y)
#print(GWI_cum_NonRW_S1_Ac_18y)
plt.xlabel('Time (year)')
#plt.ylabel('GWI_cum (10$^{-10}$ W/m$^2$)')
plt.ylabel('GWI_cum (W/m$^2$)')
plt.xlim(0,200)
plt.ylim(-1e-7,1.5e-7)
plt.title('Cumulative GWI, DL_FP_EC')
plt.plot(t, GWI_cum_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55)
plt.plot(t, GWI_cum_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55)
#plt.plot(t, GWI_cum_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55)
plt.plot(t, GWI_cum_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55)
plt.plot(t, GWI_cum_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55)
plt.plot(t, GWI_cum_S1_Ac_7y, color='olive', label='M_EC_Ac_7y')
plt.plot(t, GWI_cum_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y')
#plt.plot(t, GWI_cum_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y')
plt.plot(t, GWI_cum_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y')
plt.plot(t, GWI_cum_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y')
plt.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75)
plt.grid(True)
#plt.fill_between(t, GWI_cum_NonRW_S1_Tgr_60y, GWI_cum_NonRW_S1_Ac_7y, color='lightcoral', alpha=0.3)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWI_cum_NonRW_DL_FP_EC', dpi=300)
plt.show()
#%%
#Step (18): Determine the Instantenous and Cumulative GWI for the emission reference (1 kg CO2 emission at time zero) before performing dynamic GWP calculation
t = np.arange(0,tf-1,1)
matrix_GWI_ref = (tf-1,1)
GWI_inst_ref = np.zeros(matrix_GWI_ref)
for t in range(0,tf-1):
GWI_inst_ref[t,0] = np.sum(np.multiply(emission_CO2_ref,DCF_CO2_ti[:,t]))
#print(GWI_inst_ref[:,0])
len(GWI_inst_ref)
#determine the GWI cumulative for the emission reference
t = np.arange(0,tf-1,1)
GWI_cum_ref = np.cumsum(GWI_inst_ref[:,0])
#print(GWI_cum_ref)
plt.xlabel('Time (year)')
plt.ylabel('GWI_cum_ref (10$^{-13}$ W/m$^2$.kgCO$_2$)')
plt.plot(t, GWI_cum_ref)
len(GWI_cum_ref)
#%%
#Step (19): Calculate dynamic global warming potential (GWPdyn)
##Wood-based
GWP_dyn_cum_S1_Ac_7y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Ac_7y, GWI_cum_ref)]
GWP_dyn_cum_S1_Ac_18y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Ac_18y, GWI_cum_ref)]
GWP_dyn_cum_S1_Tgr_40y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Tgr_40y, GWI_cum_ref)]
GWP_dyn_cum_S1_Tgr_60y = [x/(y*1000) for x,y in zip(GWI_cum_S1_Tgr_60y, GWI_cum_ref)]
GWP_dyn_cum_E_Hbr_40y = [x/(y*1000) for x,y in zip(GWI_cum_E_Hbr_40y, GWI_cum_ref)]
##NonRW
GWP_dyn_cum_NonRW_S1_Ac_7y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Ac_7y, GWI_cum_ref)]
GWP_dyn_cum_NonRW_S1_Ac_18y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Ac_18y, GWI_cum_ref)]
GWP_dyn_cum_NonRW_S1_Tgr_40y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Tgr_40y, GWI_cum_ref)]
GWP_dyn_cum_NonRW_S1_Tgr_60y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_S1_Tgr_60y, GWI_cum_ref)]
GWP_dyn_cum_NonRW_E_Hbr_40y = [x/(y*1000) for x,y in zip(GWI_cum_NonRW_E_Hbr_40y, GWI_cum_ref)]
#print(GWP_dyn_cum_NonRW_S1_Ac_18y)
fig=plt.figure()
fig.show()
ax=fig.add_subplot(111)
ax.plot(t, GWP_dyn_cum_NonRW_S1_Ac_7y, color='olive', label='NR_M_EC_Ac_7y', ls='--', alpha=0.55)
ax.plot(t, GWP_dyn_cum_NonRW_S1_Ac_18y, color='forestgreen', label='NR_M_EC_Ac_18y', ls='--', alpha=0.55)
#ax.plot(t, GWP_dyn_cum_NonRW_S1_Tgr_40y, color='lightcoral', label='NR_M_EC_Tgr_40y', ls='--', alpha=0.55)
ax.plot(t, GWP_dyn_cum_NonRW_S1_Tgr_60y, color='deeppink', label='NR_M_EC_Tgr_60y', ls='--', alpha=0.55)
ax.plot(t, GWP_dyn_cum_NonRW_E_Hbr_40y, color='royalblue', label='NR_E_EC_Hbr_40y', ls='--', alpha=0.55)
ax.plot(t, GWP_dyn_cum_S1_Ac_7y, color='olive', label='M_EC_Ac_7y')
ax.plot(t, GWP_dyn_cum_S1_Ac_18y, color='forestgreen', label='M_EC_Ac_18y')
#ax.plot(t, GWP_dyn_cum_S1_Tgr_40y, color='lightcoral', label='M_EC_Tgr_40y')
ax.plot(t, GWP_dyn_cum_S1_Tgr_60y, color='deeppink', label='M_EC_Tgr_60y')
ax.plot(t, GWP_dyn_cum_E_Hbr_40y, color='royalblue', label='E_EC_Hbr_40y')
ax.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75)
#plt.fill_between(t, GWP_dyn_cum_NonRW_S1_Ac_7y, GWP_dyn_cum_NonRW_S1_Tgr_60y, color='lightcoral', alpha=0.3)
plt.grid(True)
ax.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax.set_xlim(0,200)
ax.set_ylim(-750,1000)
#ax.set_ylim(-600,1500)
ax.set_xlabel('Time (year)')
ax.set_ylabel('GWP$_{dyn}$ (t-CO$_2$-eq)')
ax.set_title('Dynamic GWP, DL_FP_EC')
plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_cum_NonRW_DL_FP_S1', dpi=300)
plt.draw()
#%%
#Step (20): Exporting the data behind result graphs to Excel
year = []
for x in range (0, 201):
year.append(x)
### Create Column
Col1 = year
##GWI_Inst
#GWI_inst from wood-based scenarios
Col_GI_1 = GWI_inst_tot_S1_Ac_7y
Col_GI_2 = GWI_inst_tot_S1_Ac_18y
Col_GI_3 = GWI_inst_tot_S1_Tgr_60y
Col_GI_4 = GWI_inst_tot_E_Hbr_40y
#print(Col_GI_1)
#print(np.shape(Col_GI_1))
#GWI_inst from counter use scenarios
Col_GI_5 = GWI_inst_tot_NonRW_S1_Ac_7y
Col_GI_6 = GWI_inst_tot_NonRW_S1_Ac_18y
Col_GI_7 = GWI_inst_tot_NonRW_S1_Tgr_60y
Col_GI_8 = GWI_inst_tot_NonRW_E_Hbr_40y
#print(Col_GI_7)
#print(np.shape(Col_GI_7))
#create column results
##GWI_cumulative
#GWI_cumulative from wood-based scenarios
Col_GC_1 = GWI_cum_S1_Ac_7y
Col_GC_2 = GWI_cum_S1_Ac_18y
Col_GC_3 = GWI_cum_S1_Tgr_60y
Col_GC_4 = GWI_cum_E_Hbr_40y
#GWI_cumulative from counter use scenarios
Col_GC_5 = GWI_cum_NonRW_S1_Ac_7y
Col_GC_6 = GWI_cum_NonRW_S1_Ac_18y
Col_GC_7 = GWI_cum_NonRW_S1_Tgr_60y
Col_GC_8 = GWI_cum_NonRW_E_Hbr_40y
#create column results
##GWPdyn
#GWPdyn from wood-based scenarios
Col_GWP_1 = GWP_dyn_cum_S1_Ac_7y
Col_GWP_2 = GWP_dyn_cum_S1_Ac_18y
Col_GWP_3 = GWP_dyn_cum_S1_Tgr_60y
Col_GWP_4 = GWP_dyn_cum_E_Hbr_40y
#GWPdyn from counter use scenarios
Col_GWP_5 = GWP_dyn_cum_NonRW_S1_Ac_7y
Col_GWP_6 = GWP_dyn_cum_NonRW_S1_Ac_18y
Col_GWP_7 = GWP_dyn_cum_NonRW_S1_Tgr_60y
Col_GWP_8 = GWP_dyn_cum_NonRW_E_Hbr_40y
#Create colum results
dfM_EC_GI = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (W/m2)':Col_GI_1, 'M_EC_Ac_18y (W/m2)':Col_GI_2,
'M_EC_Tgr_60y (W/m2)':Col_GI_3, 'E_EC_Hbr_40y (W/m2)':Col_GI_4,
'NR_M_EC_Ac_7y (W/m2)':Col_GI_5, 'NR_M_EC_Ac_18y (W/m2)':Col_GI_6,
'NR_M_EC_Tgr_60y (W/m2)':Col_GI_7, 'NR_E_EC_Hbr_40y (W/m2)':Col_GI_8})
dfM_EC_GC = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (W/m2)':Col_GC_1, 'M_EC_Ac_18y (W/m2)':Col_GC_2,
'M_EC_Tgr_60y (W/m2)':Col_GC_3, 'E_EC_Hbr_40y (W/m2)':Col_GC_4,
'NR_M_EC_Ac_7y (W/m2)':Col_GC_5, 'NR_M_EC_Ac_18y (W/m2)':Col_GC_6,
'NR_M_EC_Tgr_60y (W/m2)':Col_GC_7, 'NR_E_EC_Hbr_40y (W/m2)':Col_GC_8})
dfM_EC_GWPdyn = pd.DataFrame.from_dict({'Year':Col1,'M_EC_Ac_7y (t-CO2-eq)':Col_GWP_1, 'M_EC_Ac_18y (t-CO2-eq)':Col_GWP_2,
'M_EC_Tgr_60y (t-CO2-eq)':Col_GWP_3, 'E_EC_Hbr_40y (t-CO2-eq)':Col_GWP_4,
'NR_M_EC_Ac_7y (t-CO2-eq)':Col_GWP_5, 'NR_M_EC_Ac_18y (t-CO2-eq)':Col_GWP_6,
'NR_M_EC_Tgr_60y (t-CO2-eq)':Col_GWP_7, 'NR_E_EC_Hbr_40y (t-CO2-eq)':Col_GWP_8})
#Export to excel
writer = pd.ExcelWriter('GraphResults_DL_FP_EC.xlsx', engine = 'xlsxwriter')
dfM_EC_GI.to_excel(writer, sheet_name = 'GWI_Inst_DL_FP_EC', header=True, index=False )
dfM_EC_GC.to_excel(writer, sheet_name = 'Cumulative GWI_DL_FP_EC', header=True, index=False )
dfM_EC_GWPdyn.to_excel(writer, sheet_name = 'GWPdyn_DL_FP_EC', header=True, index=False )
writer.save()
writer.close()
#%%
#Step (21): Generate the excel file for the individual carbon emission and sequestration flows
#print year column
year = []
for x in range (0, 201):
year.append(x)
print (year)
division = 1000*44/12
division_CH4 = 1000*16/12
#M_Ac_7y
c_firewood_energy_S1_Ac7 = [x/division for x in c_firewood_energy_S1_Ac7]
decomp_tot_S1_Ac_7y[:,0] = [x/division for x in decomp_tot_S1_Ac_7y[:,0]]
TestDSM1_Ac7.o = [x/division for x in TestDSM1_Ac7.o]
PH_Emissions_HWP1_Ac_7y = [x/division for x in PH_Emissions_HWP1_Ac_7y]
#OC_storage_S1_Ac7 = [x/division for x in OC_storage_S1_Ac7]
flat_list_Ac_7y = [x/division for x in flat_list_Ac_7y]
decomp_tot_CO2_S1_Ac_7y[:,0] = [x/division for x in decomp_tot_CO2_S1_Ac_7y[:,0]]
decomp_tot_CH4_S1_Ac_7y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Ac_7y[:,0]]
#M_Ac_18y
c_firewood_energy_S1_Ac18 = [x/division for x in c_firewood_energy_S1_Ac18]
decomp_tot_S1_Ac_18y[:,0] = [x/division for x in decomp_tot_S1_Ac_18y[:,0]]
TestDSM1_Ac18.o = [x/division for x in TestDSM1_Ac18.o]
PH_Emissions_HWP1_Ac_18y = [x/division for x in PH_Emissions_HWP1_Ac_18y]
#OC_storage_S1_Ac18 = [x/division for x in OC_storage_S1_Ac18]
flat_list_Ac_18y = [x/division for x in flat_list_Ac_18y]
decomp_tot_CO2_S1_Ac_18y[:,0] = [x/division for x in decomp_tot_CO2_S1_Ac_18y[:,0]]
decomp_tot_CH4_S1_Ac_18y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Ac_18y[:,0]]
#M_Tgr_60y
c_firewood_energy_S1_Tgr60 = [x/division for x in c_firewood_energy_S1_Tgr60]
decomp_tot_S1_Tgr_60y[:,0] = [x/division for x in decomp_tot_S1_Tgr_60y[:,0]]
TestDSM1_Tgr60.o = [x/division for x in TestDSM1_Tgr60.o]
PH_Emissions_HWP1_Tgr_60y = [x/division for x in PH_Emissions_HWP1_Tgr_60y]
#OC_storage_S1_Tgr60 = [x/division for x in OC_storage_S1_Tgr60]
flat_list_Tgr_60y = [x/division for x in flat_list_Tgr_60y]
decomp_tot_CO2_S1_Tgr_60y[:,0] = [x/division for x in decomp_tot_CO2_S1_Tgr_60y[:,0]]
decomp_tot_CH4_S1_Tgr_60y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_S1_Tgr_60y[:,0]]
#E_Hbr_40y
c_firewood_energy_E_Hbr40 = [x/division for x in c_firewood_energy_E_Hbr40]
c_pellets_Hbr_40y = [x/division for x in c_pellets_Hbr_40y]
decomp_tot_E_Hbr_40y[:,0] = [x/division for x in decomp_tot_E_Hbr_40y[:,0]]
TestDSME_Hbr40.o = [x/division for x in TestDSME_Hbr40.o]
PH_Emissions_HWPE_Hbr_40y = [x/division for x in PH_Emissions_HWPE_Hbr_40y]
##ColumnOC_storage_E_Hbr40 = [x/division for x in OC_storage_E_Hbr40]
flat_list_Hbr_40y = [x/division for x in flat_list_Hbr_40y]
decomp_tot_CO2_E_Hbr_40y[:,0] = [x/division for x in decomp_tot_CO2_E_Hbr_40y[:,0]]
decomp_tot_CH4_E_Hbr_40y[:,0] = [x/division_CH4 for x in decomp_tot_CH4_E_Hbr_40y[:,0]]
#landfill aggregate flows
Landfill_decomp_DL_FP_S1_Ac_7y = decomp_tot_CH4_S1_Ac_7y, decomp_tot_CO2_S1_Ac_7y
Landfill_decomp_DL_FP_S1_Ac_18y = decomp_tot_CH4_S1_Ac_18y, decomp_tot_CO2_S1_Ac_18y
Landfill_decomp_DL_FP_S1_Tgr_60y = decomp_tot_CH4_S1_Tgr_60y, decomp_tot_CO2_S1_Tgr_60y
Landfill_decomp_DL_FP_E_Hbr_40y = decomp_tot_CH4_E_Hbr_40y, decomp_tot_CO2_E_Hbr_40y
Landfill_decomp_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Ac_7y)]
Landfill_decomp_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Ac_18y)]
Landfill_decomp_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_S1_Tgr_60y)]
Landfill_decomp_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Landfill_decomp_DL_FP_E_Hbr_40y)]
Landfill_decomp_DL_FP_S1_Ac_7y = [item for sublist in Landfill_decomp_DL_FP_S1_Ac_7y for item in sublist]
Landfill_decomp_DL_FP_S1_Ac_18y = [item for sublist in Landfill_decomp_DL_FP_S1_Ac_18y for item in sublist]
Landfill_decomp_DL_FP_S1_Tgr_60y = [item for sublist in Landfill_decomp_DL_FP_S1_Tgr_60y for item in sublist]
Landfill_decomp_DL_FP_E_Hbr_40y = [item for sublist in Landfill_decomp_DL_FP_E_Hbr_40y for item in sublist]
#M_Ac_7y
Column1 = year
Column2 = c_firewood_energy_S1_Ac7
Column3 = decomp_tot_S1_Ac_7y[:,0]
Column4 = TestDSM1_Ac7.o
Column5 = PH_Emissions_HWP1_Ac_7y
#Column6_1 = OC_storage_S1_Ac7
Column6 = Landfill_decomp_DL_FP_S1_Ac_7y
Column7 = flat_list_Ac_7y
#M_Ac_18y
Column8 = c_firewood_energy_S1_Ac18
Column9 = decomp_tot_S1_Ac_18y[:,0]
Column10 = TestDSM1_Ac18.o
Column11 = PH_Emissions_HWP1_Ac_18y
#Column12_1 = OC_storage_S1_Ac18
Column12 = Landfill_decomp_DL_FP_S1_Ac_18y
Column13 = flat_list_Ac_18y
#M_Tgr_60y
Column14 = c_firewood_energy_S1_Tgr60
Column15 = decomp_tot_S1_Tgr_60y[:,0]
Column16 = TestDSM1_Tgr60.o
Column17 = PH_Emissions_HWP1_Tgr_60y
#Column18_1 = OC_storage_S1_Tgr60
Column18 = Landfill_decomp_DL_FP_S1_Tgr_60y
Column19 = flat_list_Tgr_60y
#E_Hbr_40y
Column20 = c_firewood_energy_E_Hbr40
Column20_1 = c_pellets_Hbr_40y
Column21 = decomp_tot_E_Hbr_40y[:,0]
Column22 = TestDSME_Hbr40.o
Column23 = PH_Emissions_HWPE_Hbr_40y
#Column24_1 = OC_storage_E_Hbr40
Column24 = Landfill_decomp_DL_FP_E_Hbr_40y
Column25 = flat_list_Hbr_40y
#create columns
dfM_Ac_7y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column7,
# '9: Landfill storage (t-C)':Column6_1,
'F1-0: Residue decomposition (t-C)':Column3,
'F6-0-1: Emissions from firewood/other energy use (t-C)':Column2,
'F8-0: Operational stage/processing emissions (t-C)':Column5,
'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column4,
'F7-0: Landfill gas decomposition (t-C)':Column6})
dfM_Ac_18y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column13,
# '9: Landfill storage (t-C)':Column12_1,
'F1-0: Residue decomposition (t-C)':Column9,
'F6-0-1: Emissions from firewood/other energy use (t-C)':Column8,
'F8-0: Operational stage/processing emissions (t-C)':Column11,
'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column10,
'F7-0: Landfill gas decomposition (t-C)':Column12})
dfE_Tgr_60y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column19,
# '9: Landfill storage (t-C)':Column18_1,
'F1-0: Residue decomposition (t-C)':Column15,
'F6-0-1: Emissions from firewood/other energy use (t-C)':Column14,
'F8-0: Operational stage/processing emissions (t-C)':Column17,
'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column16,
'F7-0: Landfill gas decomposition (t-C)':Column18})
dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Column1,'F0-1: Biomass C sequestration (t-C)':Column25,
# '9: Landfill storage (t-C)':Column24_1,
'F1-0: Residue decomposition (t-C)':Column21,
'F6-0-1: Emissions from firewood/other energy use (t-C)':Column20,
'F8-0: Operational stage/processing emissions (t-C)':Column23,
'F6-0-2: Energy use emissions from in-use stocks outflow (t-C)':Column22,
'F7-0: Landfill gas decomposition (t-C)':Column24,
'F4-0: Emissions from wood pellets use (t-C)':Column20_1})
writer = pd.ExcelWriter('C_flows_DL_FP_EC.xlsx', engine = 'xlsxwriter')
dfM_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_M_Ac_7y (EC)', header=True, index=False)
dfM_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_M_Ac_18y (EC)', header=True, index=False)
dfE_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_M_Tgr_60y (EC)', header=True, index=False)
dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_Hbr_40y (EC)', header=True, index=False)
writer.save()
writer.close()
#%%
#Step (22): Plot of the individual carbon emission and sequestration flows for normal and symlog-scale graphs
#DL_FP_M_EC_Ac_7y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax1_s=fig.add_subplot(111)
#plot
ax1_s.plot(t, flat_list_Ac_7y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax1_s.plot(t, OC_storage_S1_Ac7, color='darkturquoise', label='9: Landfill storage')
ax1_s.plot(t, decomp_tot_S1_Ac_7y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax1_s.plot(t, c_firewood_energy_S1_Ac7, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax1_s.plot(t, PH_Emissions_HWP1_Ac_7y, color='orange', label='F8-0: Operational stage/processing emissions')
ax1_s.plot(t, TestDSM1_Ac7.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax1_s.plot(t, Landfill_decomp_DL_FP_S1_Ac_7y, color='yellow', label='F7-0: Landfill gas decomposition')
ax1_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax1_s.set_xlim(-1,200)
ax1_s.set_yscale('symlog')
ax1_s.set_xlabel('Time (year)')
ax1_s.set_ylabel('C flows (t-C) (symlog)')
ax1_s.set_title('Carbon flow, DL_FP_M_EC_Ac_7y (EC) (symlog-scale)')
plt.show()
#%%
#plot for the individual carbon flows
#DL_FP_M_EC_Ac_7y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax1=fig.add_subplot(111)
ax1.plot(t, flat_list_Ac_7y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax1.plot(t, OC_storage_S1_Ac7, color='darkturquoise', label='9: Landfill storage')
ax1.plot(t, decomp_tot_S1_Ac_7y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax1.plot(t, c_firewood_energy_S1_Ac7, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax1.plot(t, PH_Emissions_HWP1_Ac_7y, color='orange', label='F8-0: Operational stage/processing emissions')
ax1.plot(t, TestDSM1_Ac7.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax1.plot(t, Landfill_decomp_DL_FP_S1_Ac_7y, color='yellow', label='F7-0: Landfill gas decomposition')
ax1.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax1.set_xlim(0,200)
ax1.set_xlabel('Time (year)')
ax1.set_ylabel('C flows(t-C)')
ax1.set_title('Carbon flow, DL_FP_M_Ac_7y (EC)')
#plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M')
plt.draw()
#%%
#plot for the individual carbon flows - test for symlog-scale graphs
#DL_FP_M_EC_Ac_18y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax2_s=fig.add_subplot(111)
#plot
ax2_s.plot(t, flat_list_Ac_18y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax2_s.plot(t, OC_storage_S1_Ac18, color='darkturquoise', label='9: Landfill storage')
ax2_s.plot(t, decomp_tot_S1_Ac_18y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax2_s.plot(t, c_firewood_energy_S1_Ac18, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax2_s.plot(t, PH_Emissions_HWP1_Ac_18y, color='orange', label='F8-0: Operational stage/processing emissions')
ax2_s.plot(t, TestDSM1_Ac18.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax2_s.plot(t, Landfill_decomp_DL_FP_S1_Ac_18y, color='yellow', label='F7-0: Landfill gas decomposition')
ax2_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax2_s.set_xlim(-1,200)
ax2_s.set_yscale('symlog')
ax2_s.set_xlabel('Time (year)')
ax2_s.set_ylabel('C flows (t-C) (symlog)')
ax2_s.set_title('Carbon flow, DL_FP_M_EC_Ac_18y (EC) (symlog-scale)')
plt.show()
#%%
#plot for the individual carbon flows
#DL_FP_M_EC_Ac_18y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax2=fig.add_subplot(111)
#plot
ax2.plot(t, flat_list_Ac_18y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax2.plot(t, OC_storage_S1_Ac18, color='darkturquoise', label='9: Landfill storage')
ax2.plot(t, decomp_tot_S1_Ac_18y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax2.plot(t, c_firewood_energy_S1_Ac18, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax2.plot(t, PH_Emissions_HWP1_Ac_18y, color='orange', label='F8-0: Operational stage/processing emissions')
ax2.plot(t, TestDSM1_Ac18.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax2.plot(t, Landfill_decomp_DL_FP_S1_Ac_18y, color='yellow', label='F7-0: Landfill gas decomposition')
ax2.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax2.set_xlim(0,200)
ax2.set_xlabel('Time (year)')
ax2.set_ylabel('C flows(t-C)')
ax2.set_title('Carbon flow, DL_FP_M_Ac_18y (EC)')
#plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M')
plt.draw()
#%%
#plot for the individual carbon flows - test for symlog-scale graphs
#DL_FP_M_EC_Tgr_60y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax3_s=fig.add_subplot(111)
#plot
ax3_s.plot(t, flat_list_Tgr_60y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax3_s.plot(t, OC_storage_S1_Tgr60, color='darkturquoise', label='9: Landfill storage')
ax3_s.plot(t, decomp_tot_S1_Tgr_60y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax3_s.plot(t, c_firewood_energy_S1_Tgr60, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax3_s.plot(t, PH_Emissions_HWP1_Tgr_60y, color='orange', label='F8-0: Operational stage/processing emissions')
ax3_s.plot(t, TestDSM1_Tgr60.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax3_s.plot(t, Landfill_decomp_DL_FP_S1_Tgr_60y, color='yellow', label='F7-0: Landfill gas decomposition')
ax3_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax3_s.set_xlim(-1,200)
ax3_s.set_yscale('symlog')
ax3_s.set_xlabel('Time (year)')
ax3_s.set_ylabel('C flows (t-C) (symlog)')
ax3_s.set_title('Carbon flow, DL_FP_M_EC_Tgr_60y (EC) (symlog-scale)')
plt.show()
#%%
#plot for the individual carbon flows
#DL_FP_M_EC_Tgr_60y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax3=fig.add_subplot(111)
#plot
ax3.plot(t, flat_list_Tgr_60y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax3.plot(t, OC_storage_S1_Tgr60, color='darkturquoise', label='9: Landfill storage')
ax3.plot(t, decomp_tot_S1_Tgr_60y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax3.plot(t, c_firewood_energy_S1_Tgr60, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax3.plot(t, PH_Emissions_HWP1_Tgr_60y, color='orange', label='F8-0: Operational stage/processing emissions')
ax3.plot(t, TestDSM1_Tgr60.o, color='royalblue', label='F6-0-2: Energy use emissions from in-use stocks outflow')
ax3.plot(t, Landfill_decomp_DL_FP_S1_Tgr_60y, color='yellow', label='F7-0: Landfill gas decomposition')
ax3.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax3.set_xlim(0,200)
ax3.set_xlabel('Time (year)')
ax3.set_ylabel('C flows(t-C)')
ax3.set_title('Carbon flow, DL_FP_M_Tgr_60y (EC)')
#plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M')
plt.draw()
#%%
#plot for the individual carbon flows - test for symlog-scale graphs
#DL_FP_E_EC_Hbr_40y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax4_s=fig.add_subplot(111)
#plot
ax4_s.plot(t, flat_list_Hbr_40y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax4_s.plot(t, OC_storage_E_Hbr40, color='darkturquoise', label='9: Landfill storage')
ax4_s.plot(t, decomp_tot_E_Hbr_40y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax4_s.plot(t, c_firewood_energy_E_Hbr40, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax4_s.plot(t, PH_Emissions_HWPE_Hbr_40y, color='orange', label='F8-0: Operational stage/processing emissions')
ax4_s.plot(t, Landfill_decomp_DL_FP_E_Hbr_40y, color='yellow', label='F7-0: Landfill gas decomposition')
ax4_s.plot(t, c_pellets_Hbr_40y, color='slategrey', label='F4-0: Emissions from wood pellets use')
#ax4_s.plot(t, TestDSME_Hbr40.o, label='in-use stock output')
ax4_s.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax4_s.set_xlim(-1,200)
ax4_s.set_yscale('symlog')
ax4_s.set_xlabel('Time (year)')
ax4_s.set_ylabel('C flows (t-C) (symlog)')
ax4_s.set_title('Carbon flow, DL_FP_E_EC_Hbr_40y (EC) (symlog-scale)')
plt.show()
#%%
#plot for the individual carbon flows
#DL_FP_E_Hbr_40y (Existing conversion efficiency)
fig=plt.figure()
fig.show()
ax4=fig.add_subplot(111)
#plot
ax4.plot(t, flat_list_Hbr_40y, color='darkkhaki', label='F0-1: Biomass C sequestration')
#ax4.plot(t, OC_storage_E_Hbr40, color='darkturquoise', label='9: Landfill storage')
ax4.plot(t, decomp_tot_E_Hbr_40y[:,0], color='lightcoral', label='F1-0: Residue decomposition')
ax4.plot(t, c_firewood_energy_E_Hbr40, color='mediumseagreen', label='F6-0-1: Emissions from firewood/other energy use')
ax4.plot(t, PH_Emissions_HWPE_Hbr_40y, color='orange', label='F8-0: Operational stage/processing emissions')
ax4.plot(t, Landfill_decomp_DL_FP_E_Hbr_40y, color='yellow', label='F7-0: Landfill gas decomposition')
ax4.plot(t, c_pellets_Hbr_40y, color='slategrey', label='F4-0: Emissions from wood pellets use')
#ax_g.plot(t, TestDSME_Hbr40.o, label='in-use stock output')
ax4.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax4.set_xlim(0,200)
ax4.set_xlabel('Time (year)')
ax4.set_ylabel('C flows(t-C)')
ax4.set_title('Carbon flow, DL_FP_E_Hbr_40y (EC)')
#plt.savefig('C:\Work\Data\ID Future Scenarios\Hectare-based\Fig\GWP_dyn_1_RIL_M')
#%%
#Step (23): Generate the excel file for the net carbon balance
Agg_Cflow_S1_Ac_7y = [c_firewood_energy_S1_Ac7, decomp_tot_S1_Ac_7y[:,0], TestDSM1_Ac7.o, PH_Emissions_HWP1_Ac_7y, Landfill_decomp_DL_FP_S1_Ac_7y, flat_list_Ac_7y]
Agg_Cflow_S1_Ac_18y = [c_firewood_energy_S1_Ac18, decomp_tot_S1_Ac_18y[:,0], TestDSM1_Ac18.o, PH_Emissions_HWP1_Ac_18y, Landfill_decomp_DL_FP_S1_Ac_18y, flat_list_Ac_18y]
Agg_Cflow_S1_Tgr_60y = [c_firewood_energy_S1_Tgr60, decomp_tot_S1_Tgr_60y[:,0], TestDSM1_Tgr60.o, PH_Emissions_HWP1_Tgr_60y, Landfill_decomp_DL_FP_S1_Tgr_60y, flat_list_Tgr_60y]
Agg_Cflow_E_Hbr_40y = [c_firewood_energy_E_Hbr40, c_pellets_Hbr_40y, decomp_tot_E_Hbr_40y[:,0], TestDSME_Hbr40.o, PH_Emissions_HWPE_Hbr_40y, Landfill_decomp_DL_FP_E_Hbr_40y, flat_list_Hbr_40y]
Agg_Cflow_DL_FP_S1_Ac_7y = [sum(x) for x in zip(*Agg_Cflow_S1_Ac_7y)]
Agg_Cflow_DL_FP_S1_Ac_18y = [sum(x) for x in zip(*Agg_Cflow_S1_Ac_18y)]
Agg_Cflow_DL_FP_S1_Tgr_60y = [sum(x) for x in zip(*Agg_Cflow_S1_Tgr_60y)]
Agg_Cflow_DL_FP_E_Hbr_40y = [sum(x) for x in zip(*Agg_Cflow_E_Hbr_40y)]
#create column year
year = []
for x in range (0, 201):
year.append(x)
print (year)
#Create colum results
dfM_DL_FP_EC = pd.DataFrame.from_dict({'Year':year,'M_EC_Ac_7y (t-C)':Agg_Cflow_DL_FP_S1_Ac_7y, 'M_EC_Ac_18y (t-C)':Agg_Cflow_DL_FP_S1_Ac_18y,
'M_EC_Tgr_60y (t-C)':Agg_Cflow_DL_FP_S1_Tgr_60y, 'E_EC_Hbr_40y (t-C)':Agg_Cflow_DL_FP_E_Hbr_40y})
#Export to excel
writer = pd.ExcelWriter('AggCFlow_DL_FP_EC.xlsx', engine = 'xlsxwriter')
dfM_DL_FP_EC.to_excel(writer, sheet_name = 'DL_FP_EC', header=True, index=False)
writer.save()
writer.close()
#%%
#Step (24): Plot the net carbon balance
fig=plt.figure()
fig.show()
ax5=fig.add_subplot(111)
# plot
ax5.plot(t, Agg_Cflow_DL_FP_S1_Ac_7y, color='orange', label='M_EC_Ac_7y')
ax5.plot(t, Agg_Cflow_DL_FP_S1_Ac_18y, color='darkturquoise', label='M_EC_Ac_18y')
ax5.plot(t, Agg_Cflow_DL_FP_S1_Tgr_60y, color='lightcoral', label='M_EC_Tgr_60y')
ax5.plot(t, Agg_Cflow_DL_FP_E_Hbr_40y, color='mediumseagreen', label='E_EC_Hbr_40y')
ax5.plot(t, zerolistmaker(tf-1), color='black', label='Zero line', ls='--', alpha=0.75)
ax5.legend(bbox_to_anchor=(1.04,1), loc="upper left", frameon=False)
ax5.set_xlim(-1,200)
ax5.set_ylim(-25,220)
#ax5.set_yscale('symlog')
ax5.set_xlabel('Time (year)')
ax5.set_ylabel('C flows (t-C)')
ax5.set_title('Net carbon balance, DL_FP_EC')
plt.show()
#%%
#Step (25): Generate the excel file for documentation of individual carbon flows in the system definition (Fig. 1)
#print year column
year = []
for x in range (0, 201):
year.append(x)
print (year)
df2_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
df2_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
df2_Tgr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_40y')
df2_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
dfE2_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
Column1 = year
division = 1000*44/12
division_CH4 = 1000*16/12
## S1_Ac_7y
## define the input flow for the landfill (F5-7)
OC_storage_S1_Ac7 = df1_Ac7['Other_C_storage'].values
OC_storage_S1_Ac7 = [x/division for x in OC_storage_S1_Ac7]
OC_storage_S1_Ac7 = [abs(number) for number in OC_storage_S1_Ac7]
C_LF_S1_Ac7 = [x*1/0.82 for x in OC_storage_S1_Ac7]
## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3)
HWP_S1_Ac7 = [x/division for x in df1_Ac7['Input_PF'].values]
HWP_S1_Ac7_energy = [x*1/3 for x in c_firewood_energy_S1_Ac7]
HWP_S1_Ac7_landfill = [x*1/0.82 for x in OC_storage_S1_Ac7]
HWP_S1_Ac7_sum = [HWP_S1_Ac7, HWP_S1_Ac7_energy, HWP_S1_Ac7_landfill]
HWP_S1_Ac7_sum = [sum(x) for x in zip(*HWP_S1_Ac7_sum )]
#in-use stocks (S-4)
TestDSM1_Ac7.s = [x/division for x in TestDSM1_Ac7.s]
#TestDSM1_Ac7.i = [x/division for x in TestDSM1_Ac7.i]
#calculate the F1-2
#In general, F1-2 = F2-3 + F2-6,
#To split the F1-2 to F1a-2 and F1c-2, we need to differentiate the flow for the initial land conversion (PF) and the subsequent land type (FP)
#create F1a-2
#tf = 201
#zero_PF_S2_Ac_7y = (tf,1)
#PF_S2_Ac_7y = np.zeros(zero_PF_S2_Ac_7y)
#PF_S2_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S2_Ac7_sum, [x*2/3 for x in c_firewood_energy_S2_Ac7])][0:8]
#create F1c-2
#zero_FP_S2_Ac_7y = (tf,1)
#FP_S2_Ac_7y = np.zeros(zero_FP_S2_Ac_7y)
#FP_S2_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S2_Ac7_sum, [x*2/3 for x in c_firewood_energy_S2_Ac7])][8:tf]
# calculate C stocks in landfill (S-7)
tf = 201
zero_matrix_stocks_S1_Ac_7y = (tf,1)
stocks_S1_Ac_7y = np.zeros(zero_matrix_stocks_S1_Ac_7y)
i = 0
stocks_S1_Ac_7y[0] = C_LF_S1_Ac7[0] - Landfill_decomp_DL_FP_S1_Ac_7y[0]
while i < tf-1:
stocks_S1_Ac_7y[i+1] = np.array(C_LF_S1_Ac7[i+1] - Landfill_decomp_DL_FP_S1_Ac_7y[i+1] + stocks_S1_Ac_7y[i])
i = i + 1
## calculate aggregate flow of logged wood (F1-2)
HWP_logged_S1_Ac_7y = [x1+x2 for (x1,x2) in zip(HWP_S1_Ac7_sum, [x*2/3 for x in c_firewood_energy_S1_Ac7])]
## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c)
tf = 201
zero_matrix_ForCstocks_S1_Ac_7y = (tf,1)
ForCstocks_S1_Ac_7y = np.zeros(zero_matrix_ForCstocks_S1_Ac_7y)
i = 0
ForCstocks_S1_Ac_7y[0] = initAGB - flat_list_Ac_7y[0] - decomp_tot_S1_Ac_7y[0] - HWP_logged_S1_Ac_7y[0]
while i < tf-1:
ForCstocks_S1_Ac_7y[i+1] = np.array(ForCstocks_S1_Ac_7y[i] - flat_list_Ac_7y[i+1] - decomp_tot_S1_Ac_7y[i+1] - HWP_logged_S1_Ac_7y[i+1])
i = i + 1
##NonRW materials/energy amount (F9-0-1)
df1_amount_Ac7 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_7y')
NonRW_amount_S1_Ac_7y = df1_amount_Ac7['NonRW_amount'].values
NonRW_amount_S1_Ac_7y = [x/1000 for x in NonRW_amount_S1_Ac_7y]
##NonRW emissions (F9-0-2)
emissions_NonRW_S1_Ac_7y = [x/division for x in emissions_NonRW_S1_Ac_7y]
#create columns
dfM_Ac_7y = pd.DataFrame.from_dict({'Year':Column1,
'F0-1 (t-C)': flat_list_Ac_7y,
'F1-0 (t-C)': decomp_tot_S1_Ac_7y[:,0],
#'F1a-2 (t-C)': PF_S1_Ac_7y,
#'F1c-2 (t-C)': FP_S1_Ac_7y,
'F1-2 (t-C)': HWP_logged_S1_Ac_7y,
'St-1 (t-C)':ForCstocks_S1_Ac_7y[:,0],
'F2-3 (t-C)': HWP_S1_Ac7_sum,
'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Ac7],
'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Ac7_sum, [x*1/0.82 for x in OC_storage_S1_Ac7], [x*1/3 for x in c_firewood_energy_S1_Ac7])],
'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Ac7],
'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Ac7],
# 'F4-0 (t-C)':,
'St-4 (t-C)': TestDSM1_Ac7.s,
#'S-4-i (t-C)': TestDSM1_Ac7.i,
'F4-5 (t-C)': TestDSM1_Ac7.o,
'F5-6 (t-C)': TestDSM1_Ac7.o,
'F5-7 (t-C)': C_LF_S1_Ac7,
'F6-0-1 (t-C)': c_firewood_energy_S1_Ac7,
'F6-0-2 (t-C)': TestDSM1_Ac7.o,
'St-7 (t-C)': stocks_S1_Ac_7y[:,0],
'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Ac_7y,
'F8-0 (t-C)': PH_Emissions_HWP1_Ac_7y,
'S9-0 (t)': NonRW_amount_S1_Ac_7y,
'F9-0 (t-C)': emissions_NonRW_S1_Ac_7y,
})
##S1_Ac_18y
## define the input flow for the landfill (F5-7)
OC_storage_S1_Ac18 = df1_Ac18['Other_C_storage'].values
OC_storage_S1_Ac18 = [x/division for x in OC_storage_S1_Ac18]
OC_storage_S1_Ac18 = [abs(number) for number in OC_storage_S1_Ac18]
C_LF_S1_Ac18 = [x*1/0.82 for x in OC_storage_S1_Ac18]
## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3)
HWP_S1_Ac18 = [x/division for x in df1_Ac18['Input_PF'].values]
HWP_S1_Ac18_energy = [x*1/3 for x in c_firewood_energy_S1_Ac18]
HWP_S1_Ac18_landfill = [x*1/0.82 for x in OC_storage_S1_Ac18]
HWP_S1_Ac18_sum = [HWP_S1_Ac18, HWP_S1_Ac18_energy, HWP_S1_Ac18_landfill]
HWP_S1_Ac18_sum = [sum(x) for x in zip(*HWP_S1_Ac18_sum )]
## in-use stocks (S-4)
TestDSM1_Ac18.s = [x/division for x in TestDSM1_Ac18.s]
#TestDSM1_Ac18.i = [x/division for x in TestDSM1_Ac18.i]
#calculate C stocks in landfill (S-7)
tf = 201
zero_matrix_stocks_S1_Ac_18y = (tf,1)
stocks_S1_Ac_18y = np.zeros(zero_matrix_stocks_S1_Ac_18y)
i = 0
stocks_S1_Ac_18y[0] = C_LF_S1_Ac18[0] - Landfill_decomp_DL_FP_S1_Ac_18y[0]
while i < tf-1:
stocks_S1_Ac_18y[i+1] = np.array(C_LF_S1_Ac18[i+1] - Landfill_decomp_DL_FP_S1_Ac_18y[i+1] + stocks_S1_Ac_18y[i])
i = i + 1
## calculate aggregate flow of logged wood (F1-2)
HWP_logged_S1_Ac_18y = [x1+x2 for (x1,x2) in zip(HWP_S1_Ac18_sum, [x*2/3 for x in c_firewood_energy_S1_Ac18])]
## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c)
tf = 201
zero_matrix_ForCstocks_S1_Ac_18y = (tf,1)
ForCstocks_S1_Ac_18y = np.zeros(zero_matrix_ForCstocks_S1_Ac_18y)
i = 0
ForCstocks_S1_Ac_18y[0] = initAGB - flat_list_Ac_18y[0] - decomp_tot_S1_Ac_18y[0] - HWP_logged_S1_Ac_18y[0]
while i < tf-1:
ForCstocks_S1_Ac_18y[i+1] = np.array(ForCstocks_S1_Ac_18y[i] - flat_list_Ac_18y[i+1] - decomp_tot_S1_Ac_18y[i+1] - HWP_logged_S1_Ac_18y[i+1])
i = i + 1
##NonRW materials/energy amount (F9-0-1)
df1_amount_Ac18 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Ac_18y')
NonRW_amount_S1_Ac_18y = df1_amount_Ac18['NonRW_amount'].values
NonRW_amount_S1_Ac_18y = [x/1000 for x in NonRW_amount_S1_Ac_18y]
##NonRW emissions (F9-0-2)
emissions_NonRW_S1_Ac_18y = [x/division for x in emissions_NonRW_S1_Ac_18y]
#create columns
dfM_Ac_18y = pd.DataFrame.from_dict({'Year':Column1,
'F0-1 (t-C)': flat_list_Ac_18y,
'F1-0 (t-C)': decomp_tot_S1_Ac_18y[:,0],
#'F1a-2 (t-C)': PF_S1_Ac_18y,
#'F1c-2 (t-C)': FP_S1_Ac_18y,
'F1-2 (t-C)': HWP_logged_S1_Ac_18y,
'St-1 (t-C)':ForCstocks_S1_Ac_18y[:,0],
'F2-3 (t-C)': HWP_S1_Ac18_sum,
'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Ac18],
'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Ac18_sum, [x*1/0.82 for x in OC_storage_S1_Ac18], [x*1/3 for x in c_firewood_energy_S1_Ac18])],
'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Ac18],
'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Ac18],
# 'F4-0 (t-C)':,
'St-4 (t-C)': TestDSM1_Ac18.s,
#'S-4-i (t-C)': TestDSM1_Ac7.i,
'F4-5 (t-C)': TestDSM1_Ac18.o,
'F5-6 (t-C)': TestDSM1_Ac18.o,
'F5-7 (t-C)': C_LF_S1_Ac18,
'F6-0-1 (t-C)': c_firewood_energy_S1_Ac18,
'F6-0-2 (t-C)': TestDSM1_Ac18.o,
'St-7 (t-C)': stocks_S1_Ac_18y[:,0],
'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Ac_18y,
'F8-0 (t-C)': PH_Emissions_HWP1_Ac_18y,
'S9-0 (t)': NonRW_amount_S1_Ac_18y,
'F9-0 (t-C)': emissions_NonRW_S1_Ac_18y,
})
##S1_Tgr_60y
## define the input flow for the landfill (F5-7)
OC_storage_S1_Tgr60 = df1_Tgr60['Other_C_storage'].values
OC_storage_S1_Tgr60 = [x/division for x in OC_storage_S1_Tgr60]
OC_storage_S1_Tgr60 = [abs(number) for number in OC_storage_S1_Tgr60]
C_LF_S1_Tgr60 = [x*1/0.82 for x in OC_storage_S1_Tgr60]
## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3)
HWP_S1_Tgr60 = [x/division for x in df1_Tgr60['Input_PF'].values]
HWP_S1_Tgr60_energy = [x*1/3 for x in c_firewood_energy_S1_Tgr60]
HWP_S1_Tgr60_landfill = [x*1/0.82 for x in OC_storage_S1_Tgr60]
HWP_S1_Tgr60_sum = [HWP_S1_Tgr60, HWP_S1_Tgr60_energy, HWP_S1_Tgr60_landfill]
HWP_S1_Tgr60_sum = [sum(x) for x in zip(*HWP_S1_Tgr60_sum )]
## in-use stocks (S-4)
TestDSM1_Tgr60.s = [x/division for x in TestDSM1_Tgr60.s]
#TestDSM1_Tgr60.i = [x/division for x in TestDSM1_Tgr60.i]
## calculate C stocks in landfill (S-7)
tf = 201
zero_matrix_stocks_S1_Tgr_60y = (tf,1)
stocks_S1_Tgr_60y = np.zeros(zero_matrix_stocks_S1_Tgr_60y)
i = 0
stocks_S1_Tgr_60y[0] = C_LF_S1_Tgr60[0] - Landfill_decomp_DL_FP_S1_Tgr_60y[0]
while i < tf-1:
stocks_S1_Tgr_60y[i+1] = np.array(C_LF_S1_Tgr60[i+1] - Landfill_decomp_DL_FP_S1_Tgr_60y[i+1] + stocks_S1_Tgr_60y[i])
i = i + 1
#print(stocks_S2_Ac_7y[:])
#print(type(stocks_S2_Ac_7y))
#print(type(C_LF_S2_Ac7))
#print(type(Landfill_decomp_PF_FP_S2_Ac_7y))
## calculate aggregate flow of logged wood (F1-2)
HWP_logged_S1_Tgr_60y = [x1+x2 for (x1,x2) in zip(HWP_S1_Tgr60_sum, [x*2/3 for x in c_firewood_energy_S1_Tgr60])]
## calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c)
tf = 201
zero_matrix_ForCstocks_S1_Tgr_60y = (tf,1)
ForCstocks_S1_Tgr_60y = np.zeros(zero_matrix_ForCstocks_S1_Tgr_60y)
i = 0
ForCstocks_S1_Tgr_60y[0] = initAGB - flat_list_Tgr_60y[0] - decomp_tot_S1_Tgr_60y[0] - HWP_logged_S1_Tgr_60y[0]
while i < tf-1:
ForCstocks_S1_Tgr_60y[i+1] = np.array(ForCstocks_S1_Tgr_60y[i] - flat_list_Tgr_60y[i+1] - decomp_tot_S1_Tgr_60y[i+1] - HWP_logged_S1_Tgr_60y[i+1])
i = i + 1
##NonRW materials/energy amount (F9-0-1)
df1_amount_Tgr60 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_S1_Tgr_60y')
NonRW_amount_S1_Tgr_60y = df1_amount_Tgr60['NonRW_amount'].values
NonRW_amount_S1_Tgr_60y = [x/1000 for x in NonRW_amount_S1_Tgr_60y]
##NonRW emissions (F9-0-2)
emissions_NonRW_S1_Tgr_60y = [x/division for x in emissions_NonRW_S1_Tgr_60y]
#create columns
dfM_Tgr_60y = pd.DataFrame.from_dict({'Year':Column1,
'F0-1 (t-C)': flat_list_Tgr_60y,
'F1-0 (t-C)': decomp_tot_S1_Tgr_60y[:,0],
#'F1a-2 (t-C)': PF_S1_Tgr_60y,
#'F1c-2 (t-C)': FP_S1_Tgr_60y,
'F1-2 (t-C)': HWP_logged_S1_Tgr_60y,
'St-1 (t-C)':ForCstocks_S1_Tgr_60y[:,0],
'F2-3 (t-C)': HWP_S1_Tgr60_sum,
'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_S1_Tgr60],
'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_S1_Tgr60_sum, [x*1/0.82 for x in OC_storage_S1_Tgr60], [x*1/3 for x in c_firewood_energy_S1_Tgr60])],
'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_S1_Tgr60],
'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_S1_Tgr60],
# 'F4-0 (t-C)':,
'St-4 (t-C)': TestDSM1_Tgr60.s,
#'S-4-i (t-C)': TestDSM1_Tgr60.i,
'F4-5 (t-C)': TestDSM1_Tgr60.o,
'F5-6 (t-C)': TestDSM1_Tgr60.o,
'F5-7 (t-C)': C_LF_S1_Tgr60,
'F6-0-1 (t-C)': c_firewood_energy_S1_Tgr60,
'F6-0-2 (t-C)': TestDSM1_Tgr60.o,
'St-7 (t-C)': stocks_S1_Tgr_60y[:,0],
'F7-0 (t-C)': Landfill_decomp_DL_FP_S1_Tgr_60y,
'F8-0 (t-C)': PH_Emissions_HWP1_Tgr_60y,
'S9-0 (t)': NonRW_amount_S1_Tgr_60y,
'F9-0 (t-C)': emissions_NonRW_S1_Tgr_60y,
})
##S1_E_Hbr_40y
## define the input flow for the landfill (F5-7)
OC_storage_E_Hbr40 = dfE_Hbr40['Other_C_storage'].values
OC_storage_E_Hbr40 = [x/division for x in OC_storage_E_Hbr40]
OC_storage_E_Hbr40 = [abs(number) for number in OC_storage_E_Hbr40]
C_LF_E_Hbr40 = [x*1/0.82 for x in OC_storage_E_Hbr40]
## define the input flow from the logging/harvesting to wood materials/pellets processing (F2-3)
HWP_E_Hbr40 = [x/division for x in dfE_Hbr40['Wood_pellets'].values]
HWP_E_Hbr40_energy = [x*1/3 for x in c_firewood_energy_E_Hbr40]
HWP_E_Hbr40_landfill = [x*1/0.82 for x in OC_storage_E_Hbr40]
HWP_E_Hbr40_sum = [HWP_E_Hbr40, HWP_E_Hbr40_energy, HWP_E_Hbr40_landfill]
HWP_E_Hbr40_sum = [sum(x) for x in zip(*HWP_E_Hbr40_sum )]
## in-use stocks (S-4)
TestDSME_Hbr40.s = [x/division for x in TestDSME_Hbr40.s]
## calculate C stocks in landfill (S-7)
tf = 201
zero_matrix_stocks_E_Hbr_40y = (tf,1)
stocks_E_Hbr_40y = np.zeros(zero_matrix_stocks_E_Hbr_40y)
i = 0
stocks_E_Hbr_40y[0] = C_LF_E_Hbr40[0] - Landfill_decomp_DL_FP_E_Hbr_40y[0]
while i < tf-1:
stocks_E_Hbr_40y[i+1] = np.array(C_LF_E_Hbr40[i+1] - Landfill_decomp_DL_FP_E_Hbr_40y[i+1] + stocks_E_Hbr_40y[i])
i = i + 1
## calculate aggregate flow of logged wood (F1-2)
HWP_logged_E_Hbr_40y = [x1+x2 for (x1,x2) in zip(HWP_E_Hbr40_sum, [x*2/3 for x in c_firewood_energy_E_Hbr40])]
#calculate the stocks in the forest (AGB + undecomposed residue) (S-1a+S-1c)
tf = 201
zero_matrix_ForCstocks_E_Hbr_40y = (tf,1)
ForCstocks_E_Hbr_40y = np.zeros(zero_matrix_ForCstocks_E_Hbr_40y)
i = 0
ForCstocks_E_Hbr_40y[0] = initAGB - flat_list_Hbr_40y[0] - decomp_tot_E_Hbr_40y[0] - HWP_logged_E_Hbr_40y[0]
while i < tf-1:
ForCstocks_E_Hbr_40y[i+1] = np.array(ForCstocks_E_Hbr_40y[i] - flat_list_Hbr_40y[i+1] - decomp_tot_E_Hbr_40y[i+1] - HWP_logged_E_Hbr_40y[i+1])
i = i + 1
##NonRW materials/energy amount (F9-0-1)
dfE_amount_Hbr40 = pd.read_excel('C:\\Work\\Programming\\Practice\\NonRW_DL_FP.xlsx', 'DL_FP_E_Hbr_40y')
NonRW_amount_E_Hbr_40y = dfE_amount_Hbr40['NonRW_amount'].values
NonRW_amount_E_Hbr_40y = [x/1000 for x in NonRW_amount_E_Hbr_40y]
##NonRW emissions (F9-0-2)
emissions_NonRW_E_Hbr_40y = [x/division for x in emissions_NonRW_E_Hbr_40y]
#create columns
dfE_Hbr_40y = pd.DataFrame.from_dict({'Year':Column1,
'F0-1 (t-C)': flat_list_Hbr_40y,
'F1-0 (t-C)': decomp_tot_E_Hbr_40y[:,0],
#'F1a-2 (t-C)': PF_S2_Tgr_60y,
#'F1c-2 (t-C)': FP_S2_Tgr_60y,
'F1-2 (t-C)': HWP_logged_E_Hbr_40y,
'St-1 (t-C)':ForCstocks_E_Hbr_40y[:,0],
'F2-3 (t-C)': HWP_E_Hbr40_sum,
'F2-6 (t-C)': [x*2/3 for x in c_firewood_energy_E_Hbr40],
'SM/E (t-C)': [x1-x2-x3 for (x1,x2,x3) in zip(HWP_E_Hbr40_sum, [x*1/0.82 for x in OC_storage_E_Hbr40], [x*1/3 for x in c_firewood_energy_E_Hbr40])],
'F3-5 (t-C)':[x*1/0.82 for x in OC_storage_E_Hbr40],
'F3-6 (t-C)': [x*1/3 for x in c_firewood_energy_E_Hbr40],
'F4-0 (t-C)': c_pellets_Hbr_40y,
'St-4 (t-C)': TestDSME_Hbr40.s,
#'S-4-i (t-C)': TestDSME_Hbr40.i,
'F4-5 (t-C)': TestDSME_Hbr40.o,
'F5-6 (t-C)': TestDSME_Hbr40.o,
'F5-7 (t-C)': C_LF_E_Hbr40,
'F6-0-1 (t-C)': c_firewood_energy_E_Hbr40,
'F6-0-2 (t-C)': TestDSME_Hbr40.o,
'St-7 (t-C)': stocks_E_Hbr_40y[:,0],
'F7-0 (t-C)': Landfill_decomp_DL_FP_E_Hbr_40y,
'F8-0 (t-C)': PH_Emissions_HWPE_Hbr_40y,
'S9-0 (t)': NonRW_amount_E_Hbr_40y,
'F9-0 (t-C)': emissions_NonRW_E_Hbr_40y,
})
writer = pd.ExcelWriter('C_flows_SysDef_DL_FP_EC.xlsx', engine = 'xlsxwriter')
dfM_Ac_7y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Ac_7y', header=True, index=False)
dfM_Ac_18y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Ac_18y', header=True, index=False)
dfM_Tgr_60y.to_excel(writer, sheet_name = 'DL_FP_M_EC_Tgr_60y', header=True, index=False)
dfE_Hbr_40y.to_excel(writer, sheet_name = 'DL_FP_E_EC_Hbr_40y', header=True, index=False)
writer.save()
writer.close()
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radiaTest-server/server/model/user.py | openeuler-mirror/radiaTest | 0 | 125144 | <filename>radiaTest-server/server/model/user.py
from server.model.base import BaseModel
from server.model.permission import Role
from server import db, redis_client
from server.utils.redis_util import RedisKey
class User(db.Model, BaseModel):
__tablename__ = "user"
gitee_id = db.Column(db.Integer(), primary_key=True)
gitee_login = db.Column(db.String(50), nullable=False)
gitee_name = db.Column(db.String(50), nullable=False)
phone = db.Column(db.String(20), nullable=True, default=None)
avatar_url = db.Column(db.String(512), nullable=True, default=None)
cla_email = db.Column(db.String(128), nullable=True, default=None)
re_user_role = db.relationship("ReUserRole", backref="user")
re_user_group = db.relationship("ReUserGroup", backref="user")
re_user_organization = db.relationship("ReUserOrganization", backref="user")
def _get_roles(self):
roles = []
for re in self.re_user_role:
role = Role.query.filter_by(id=re.role_id).first()
roles.append(role.to_json())
return roles
def _get_public_role(self):
from server.model.permission import Role, ReUserRole
_filter = [ReUserRole.user_id == self.gitee_id, Role.type == 'public']
_role = Role.query.join(ReUserRole).filter(*_filter).first()
return _role.to_json() if _role else None
def to_dict(self):
return {
"gitee_id": self.gitee_id,
"gitee_login": self.gitee_login,
"gitee_name": self.gitee_name,
"phone": self.phone,
"avatar_url": self.avatar_url,
"cla_email": self.cla_email,
"roles": self._get_roles()
}
def to_json(self):
return {
"gitee_id": self.gitee_id,
"gitee_login": self.gitee_login,
"gitee_name": self.gitee_name,
"phone": self.phone,
"avatar_url": self.avatar_url,
"cla_email": self.cla_email,
"roles": self._get_roles(),
"role": self._get_public_role()
}
@staticmethod
def synchronize_gitee_info(gitee_user, user=None):
user.gitee_login = gitee_user.get("login")
user.gitee_name = gitee_user.get("name")
user.avatar_url = gitee_user.get("avatar_url")
user.add_update()
return user
def save_redis(self, access_token, refresh_token, current_org_id=None):
redis_data = self.to_dict()
redis_data['gitee_access_token'] = access_token
redis_data['gitee_refresh_token'] = refresh_token
if current_org_id:
redis_data['current_org_id'] = current_org_id
else:
for item in self.re_user_organization:
if item.default is True:
redis_data['current_org_id'] = item.organization_id
redis_data['current_org_name'] = item.organization.name
redis_client.hmset(RedisKey.user(self.gitee_id), redis_data)
@staticmethod
def create_commit(gitee_user, cla_email=None):
new_user = User()
new_user.gitee_id = gitee_user.get("id")
new_user.gitee_login = gitee_user.get("login")
new_user.gitee_name = gitee_user.get("name")
new_user.avatar_url = gitee_user.get("avatar_url")
new_user.cla_email = cla_email
new_user.add_update()
return new_user
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] |
qbt_migrate/cli.py | Reverse256/qbt_migrate | 42 | 1603284 | <filename>qbt_migrate/cli.py
import logging
import argparse
from . import QBTBatchMove, discover_bt_backup_path
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--existing-path', help='Existing root of path to look for.')
parser.add_argument('-n', '--new-path', help='New root path to replace existing root path with.')
parser.add_argument('-t', '--target-os', help='Target OS (converts slashes). '
'Default will auto-detect if conversion is needed '
'based on existing vs new.',
choices=['Windows', 'Linux', 'Mac'])
parser.add_argument('-b', '--bt-backup-path', help='BT_Backup Path Override. Default is %s'
% discover_bt_backup_path())
parser.add_argument('-s', '--skip-bad-files', help='Skips bad .fastresume files instead of exiting. '
'Default behavior is to exit.',
action='store_true', default=False)
parser.add_argument('-l', '--log-level', help='Log Level, Default is INFO.',
choices=['DEBUG', 'INFO'], default='INFO')
return parser.parse_args()
def main():
args = parse_args()
logging.basicConfig()
logger.setLevel(args.log_level)
logging.getLogger('qbt_migrate').setLevel(args.log_level)
logging.getLogger('qbt_migrate').propagate = True
qbm = QBTBatchMove()
if args.bt_backup_path is not None:
qbm.bt_backup_path = args.bt_backup_path
else:
bt_backup_path = input('BT_Backup Path (%s): ' % qbm.bt_backup_path)
if bt_backup_path.strip():
qbm.bt_backup_path = bt_backup_path
if args.existing_path is None:
args.existing_path = input('Existing Path: ')
if args.new_path is None:
args.new_path = input('New Path: ')
if args.target_os is None:
args.target_os = input('Target OS (Windows, Linux, Mac, Blank for auto-detect): ')
if args.target_os.strip() and args.target_os.lower() not in ('windows', 'linux', 'mac'):
raise ValueError('Target OS is not valid. Must be Windows, Linux, or Mac. Received: %s' % args.target_os)
elif not args.target_os.strip():
if '/' in args.existing_path and '\\' in args.new_path:
logger.info('Auto detected target OS change. Will convert slashes to Windows.')
args.target_os = 'windows'
elif '\\' in args.existing_path and '/' in args.new_path:
logger.info('Auto detected target OS change. Will convert slashes to Linux/Mac.')
args.target_os = 'linux'
else:
args.target_os = None
qbm.run(args.existing_path, args.new_path, args.target_os, args.skip_bad_files)
if __name__ == '__main__':
main()
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] |
tf_metric_learning/utils/recall.py | Ximilar-com/tf-metric-learning | 28 | 99848 | import tensorflow as tf
import numpy as np
from tqdm import tqdm
from tf_metric_learning.utils.index import AnnoyDataIndex
class AnnoyEvaluatorCallback(AnnoyDataIndex):
"""
Callback, extracts embeddings, add them to AnnoyIndex and evaluate them as recall.
"""
def __init__(
self,
model,
data_store,
data_search,
save_dir=None,
eb_size=256,
metric="euclidean",
freq=1,
batch_size=None,
normalize_eb=True,
normalize_fn=None,
progress=True,
**kwargs
):
super().__init__(eb_size, data_store["labels"], metric=metric, save_dir=save_dir, progress=progress)
self.base_model = model
self.data_store = data_store
self.data_search = data_search
self.batch_size = batch_size
self.freq = int(freq)
self.normalize_eb = normalize_eb
self.normalize_fn = normalize_fn
self.results = {}
def on_epoch_begin(self, epoch, logs=None):
if self.freq and epoch % self.freq == 0:
self.compute_data()
def batch(self, iterable, n=1):
l = len(iterable)
for ndx in range(0, l, n):
yield iterable[ndx:min(ndx + n, l)]
def compute_data(self):
self.create_index()
i = 0
with tqdm(total=len(self.data_store["images"]), desc="Indexing ... ") as pbar:
for batch in self.batch(self.data_store["images"], n=self.batch_size*10):
store_images = self.normalize_fn(batch) if self.normalize_fn is not None else batch
embeddings_store = self.base_model.predict(store_images, batch_size=self.batch_size)
if self.normalize_eb:
embeddings_store = tf.nn.l2_normalize(embeddings_store, axis=1).numpy()
for embedding in embeddings_store:
self.add_to_index(i, embedding)
i += 1
pbar.update(len(batch))
self.build(k=5)
self.evaluate(self.data_search["images"])
def evaluate(self, images):
self.results = {"default": []}
i = 0
with tqdm(total=len(images), desc="Evaluating ... ") as pbar:
for batch in self.batch(images, n=self.batch_size*10):
search_images = self.normalize_fn(batch) if self.normalize_fn is not None else batch
embeddings_search = self.base_model.predict(search_images, batch_size=self.batch_size)
if self.normalize_eb:
embeddings_search = tf.nn.l2_normalize(embeddings_search, axis=1).numpy()
for embedding in embeddings_search:
annoy_results = self.search(embedding, n=20, include_distances=False)
annoy_results = [self.get_label(result) for result in annoy_results]
recalls = self.eval_recall(annoy_results, self.data_search["labels"][i], [1, 5, 10, 20])
self.results["default"].append(recalls)
i += 1
pbar.update(len(batch))
print("\nRecall@[1, 3, 5, 10, 20] Computed:", np.mean(np.asarray(self.results["default"]), axis=0), "\n")
def eval_recall(self, annoy_results, label, recalls):
return [1 if label in annoy_results[:recall_n] else 0 for recall_n in recalls]
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] |
examples/client/main.py | TheFarGG/Discode | 3 | 1039 | <filename>examples/client/main.py<gh_stars>1-10
import os
import discode
TOKEN = os.environ.get("TOKEN")
# The token from the developer portal.
client = discode.Client(token=TOKEN, intents=discode.Intents.default())
@client.on_event("ready")
async def on_ready():
print(client.user, "is ready!")
# The ready listener gets fired when the bot/client is completely ready for use.
@client.on_event("message_create")
async def on_message(message: discode.Message):
msg: str = msg.content
if msg.startswith("?hi"):
await message.channel.send("Hi!!!")
# The message_create listener is fired whenever a message is sent to any channel that the bot has access to.
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] |
laserAtomTrap/src/__init__.py | statisdisc/modellingAndSimulation | 0 | 151427 | <gh_stars>0
__all__ = ["objects", "plots", "utilities"] | [
1,
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] |
tests/test_eval_metrics.py | IntheGrass/citeomatic_learning | 162 | 169574 | <filename>tests/test_eval_metrics.py
import unittest
from citeomatic.eval_metrics import precision_recall_f1_at_ks, average_results
class TestEvalMetrics(unittest.TestCase):
def test_precision_recall_f1_at_ks(self):
gold_y = ['1', '2', '3']
pred_y = ['1', '4', '3']
scores_y = [1.0, 0.1, 0.5]
k = [1, 2, 3]
results = precision_recall_f1_at_ks(gold_y, pred_y, scores=None, k_list=k)
assert results['precision'] == [1.0, 0.5, 2/3]
assert results['recall'] == [1/3, 1/3, 2/3]
assert results['f1'] == [1/2, 2/5, 2/3]
assert results['mrr'] == 1.0
results_2 = precision_recall_f1_at_ks(gold_y, pred_y, scores_y, k)
assert results_2['precision'] == [1.0, 1.0, 2/3]
assert results_2['recall'] == [1/3, 2/3, 2/3]
assert results_2['f1'] == [1/2, 4/5, 2/3]
assert results_2['mrr'] == 1.0
def test_average_results(self):
r1 = {
'precision': [1.0, 0.5, 2/3],
'recall': [1.0, 0.5, 2/3],
'f1': [1.0, 0.5, 2/3],
'mrr': 1.0,
}
r2 = {
'precision': [3.0, 1.0, 4/3],
'recall': [3.0, 1.0, 4/3],
'f1': [3.0, 1.0, 4/3],
'mrr': 0.5,
}
averaged_results = average_results([r1, r2])
assert averaged_results['precision'] == [2.0, 0.75, 1.0]
assert averaged_results['mrr'] == 0.75
if __name__ == '__main__':
unittest.main() | [
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2
] |
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