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import os, glob, re, csv, time, shutil
from time import *
from datetime import *
#from ch.systemsx.cisd.openbis.dss.etl.dto.api.v2 import *
from ch.systemsx.cisd.openbis.dss.etl.dto.api.v2 import SimpleFeatureVectorDataConfig
#from java.util import Properties
from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchCriteria
from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchSubCriteria
from ch.systemsx.cisd.openbis.dss.etl.dto.api.v2 import FeatureListDataConfig
'''
Dropbox for importing a feature vector dataset and for creating feature lists datasets from there.
This dataset is set to be a child of the segmentation dataset produced by Fethallah.
'''
print '###################################'
tz=localtime()[3]-gmtime()[3]
d=datetime.now()
print d.strftime("%Y-%m-%d %H:%M:%S GMT"+"%+.2d" % tz+":00")
accuracyA1_sirna_list = []
accuracyA2_sirna_list =[]
KStestA2_sirna_list = []
KStestA1_sirna_list = []
KSdeltaA2_sirna_list = []
KSdeltaA1_sirna_list = []
KSpvalueA2_sirna_list = []
KSpvalueA1_sirna_list = []
feature_directionA2_sirna_list = []
feature_directionA1_sirna_list = []
accuracyA1_gene_list = []
accuracyA2_gene_list =[]
KStestA2_gene_list = []
KStestA1_gene_list = []
KSdeltaA2_gene_list = []
KSdeltaA1_gene_list = []
KSpvalueA2_gene_list = []
KSpvalueA1_gene_list = []
feature_directionA2_gene_list = []
feature_directionA1_gene_list = []
def process(transaction):
incoming = transaction.getIncoming()
# def copyTextFile(incoming):
# for textfile in glob.glob(os.path.join(incoming, 'OriginalDataDirectory.txt')):
# rawDataFile = incoming + '/RawDataDirectory.txt'
# shutil.copyfile(textfile, rawDataFile)
#
# copyTextFile(incoming.getPath())
#extract dataset code and plate of original image files from file OriginalDataDirectory.txt
def extractImageDataSetCode(incoming):
dataSetCode = ''
plateCode = ''
for textfile in glob.glob(os.path.join(incoming, 'OriginalDataDirectory.txt')):
text = open(textfile, "r")
lineIndex =0
for line in text:
lineIndex=lineIndex+1
if re.match('/raid', line):
# if re.match('/Users', line):
token_list = re.split(r"[/]",line)
token_list = [ item.strip() for item in token_list ]
token_list = filter(lambda x: len(x) > 0, token_list)
dataSetCode = token_list[8] #right position for raid is 8, for local use is 10
if re.match('PLATE',line):
plateCode = line
return dataSetCode, plateCode
extractImageDataSetCode(incoming.getPath())
# check if plate code extracted above is the same as one of those in file AnalysisFethallaExample_location.txt. If so, get the dataset code associated with that plate. This is the dataset
# that contains the analysis matlab files produced by Fethallah, which have been used by Riwal to perform his analysis, so the new dataset registered should be a child of Fethallah's dataset.
def extractSegmentationDataSetCode(incoming):
segmentationDataSetCode = ''
segmentationPlateCode = ''
for textfile in glob.glob(os.path.join(incoming, 'FethallahAnalysisOBLocation.txt')):
text = open(textfile, "r")
lineIndex =0
for line in text:
lineIndex=lineIndex+1
token_list = re.split(r"[\t]",line)
token_list = [ item.strip() for item in token_list ]
token_list = filter(lambda x: len(x) > 0, token_list)
segmentationPlateCode = token_list[1]
if (segmentationPlateCode == extractImageDataSetCode(incoming)[1].strip()):
segmentationDataSetCode = token_list[0]
return segmentationDataSetCode
extractSegmentationDataSetCode(incoming.getPath())
def parse_gene_csv(incoming):
for csv_file in glob.glob(os.path.join(incoming, 'A*gene.csv')):
(dirName2, fileName2) = os.path.split(csv_file)
(basename2, extension2) = os.path.splitext(fileName2)
well_list = re.split(r"[_]",basename2)
control = well_list[0]
measure = well_list[1]
csvfile = open(csv_file, "rb")
test = csv.reader(csvfile, delimiter=',', quotechar='"')
for i, row in enumerate(test):
if i !=0:
fnv = row[0]
accuracy_value = row[1]
KStest_value = row[2]
KSdelta_value = row[3]
KSpvalue_value = row[4]
feature_direction_value = row[5]
accuracyA2 = (fnv +"_G_ac_A2").upper()
KStestA2 = (fnv+"_G_KSt_A2").upper()
KSdeltaA2 = (fnv+"_G_KSd_A2").upper()
KSpvalueA2 = (fnv+"_G_KSp_A2").upper()
feature_directionA2 = (fnv+"_G_dir_A2").upper()
accuracyA1 = (fnv +"_G_ac_A1").upper()
KStestA1 = (fnv+"_G_KSt_A1").upper()
KSdeltaA1 = (fnv+"_G_KSd_A1").upper()
KSpvalueA1 = (fnv+"_G_KSp_A1").upper()
feature_directionA1 = (fnv+"_G_dir_A1").upper()
accuracyA2_gene_list.append(accuracyA2)
accuracyA1_gene_list.append(accuracyA1)
KStestA2_gene_list.append(KStestA2)
KStestA1_gene_list.append(KStestA1)
KSdeltaA2_gene_list.append(KSdeltaA2)
KSdeltaA1_gene_list.append(KSdeltaA1)
KSpvalueA2_gene_list.append(KSpvalueA2)
KSpvalueA1_gene_list.append(KSpvalueA1)
feature_directionA2_gene_list.append(feature_directionA2)
feature_directionA1_gene_list.append(feature_directionA1)
return accuracyA2_gene_list, accuracyA1_gene_list, KStestA2_gene_list, KStestA1_gene_list, KSdeltaA2_gene_list, KSdeltaA1_gene_list, KSpvalueA2_gene_list, KSpvalueA1_gene_list, feature_directionA2_gene_list, feature_directionA1_gene_list
parse_gene_csv(incoming.getPath())
def parse_sirna_csv(incoming):
for csv_file in glob.glob(os.path.join(incoming, 'A*sirna.csv')):
(dirName2, fileName2) = os.path.split(csv_file)
(basename2, extension2) = os.path.splitext(fileName2)
if re.search("-", basename2):
continue
else:
well_list = re.split(r"[_]",basename2)
control = well_list[0]
measure = well_list[1]
csvfile = open(csv_file, "rb")
test = csv.reader(csvfile, delimiter=',', quotechar='"')
for i, row in enumerate(test):
if i !=0:
fnv = row[0]
accuracy_value = row[1]
KStest_value = row[2]
KSdelta_value = row[3]
KSpvalue_value = row[4]
feature_direction_value = row[5]
accuracyA2 = (fnv +"_S_ac_A2").upper()
KStestA2 = (fnv+"_S_KSt_A2").upper()
KSdeltaA2 = (fnv+"_S_KSd_A2").upper()
KSpvalueA2 = (fnv+"_S_KSp_A2").upper()
feature_directionA2 = (fnv+"_S_dir_A2").upper()
accuracyA1 = (fnv +"_S_ac_A1").upper()
KStestA1 = (fnv+"_S_KSt_A1").upper()
KSdeltaA1 = (fnv+"_S_KSd_A1").upper()
KSpvalueA1 = (fnv+"_S_KSp_A1").upper()
feature_directionA1 = (fnv+"_S_dir_A1").upper()
accuracyA2_sirna_list.append(accuracyA2)
accuracyA1_sirna_list.append(accuracyA1)
KStestA2_sirna_list.append(KStestA2)
KStestA1_sirna_list.append(KStestA1)
KSdeltaA2_sirna_list.append(KSdeltaA2)
KSdeltaA1_sirna_list.append(KSdeltaA1)
KSpvalueA2_sirna_list.append(KSpvalueA2)
KSpvalueA1_sirna_list.append(KSpvalueA1)
feature_directionA2_sirna_list.append(feature_directionA2)
feature_directionA1_sirna_list.append(feature_directionA1)
return accuracyA2_sirna_list, accuracyA1_sirna_list, KStestA2_sirna_list, KStestA1_sirna_list, KSdeltaA2_sirna_list, KSdeltaA1_sirna_list, KSpvalueA2_sirna_list, KSpvalueA1_sirna_list, feature_directionA2_sirna_list, feature_directionA1_sirna_list
parse_sirna_csv(incoming.getPath())
def defineGeneFeatures(featuresBuilder, incoming):
for csv_file in glob.glob(os.path.join(incoming, 'global_gene.csv')):
csvf = open(csv_file,'r')
globcsv = csv.reader(csvf, delimiter=',')
globcsv.next()
result_accuracy = {} # accuracy_label => {measure_well => accuracy_value}
result_kstest ={} # kstest => {measure_well => kstest_value}
result_ksdelta = {} # ksdelta => {measure_well => ksdelta_value}
result_kspvalue ={} # kspvalue => {measure_well => kspvalue_value}
result_feature_direction ={}# feature_direction => {measure_well => feature_direction_value}
for row in globcsv:
measure_well = row[0]
group_well = re.split(r"[-]",measure_well)
group_well1 = group_well[0]
control_well = row[1]
feature_name = row[3]
accuracy_label = feature_name + "_G_ac"
accuracy_value = row[4]
kstest = feature_name + "_G_KSt"
kstest_value = row[5]
ksdelta = feature_name + "_G_KSd"
ksdelta_value = row[6]
kspvalue = feature_name + "_G_KSp"
kspvalue_value = row[7]
feature_direction = feature_name + "_G_dir"
feature_direction_value = row[8]
accuracy_key = "%s:%s" %(accuracy_label, control_well)
kstest_key = "%s:%s" %(kstest, control_well)
ksdelta_key = "%s:%s" %(ksdelta, control_well)
kspvalue_key ="%s:%s" %(kspvalue, control_well)
feature_direction_key = "%s:%s" %(feature_direction, control_well)
if not accuracy_key in result_accuracy:
result_accuracy[accuracy_key] = {}
result_accuracy[accuracy_key][group_well1] = accuracy_value
# if not kstest_key in result_kstest:
# result_kstest[kstest_key] = {}
#
# result_kstest[kstest_key][measure_well] = kstest_value
#
#
# if not ksdelta_key in result_ksdelta:
# result_ksdelta[ksdelta_key] = {}
#
# result_ksdelta[ksdelta_key][measure_well] = ksdelta_value
#
# if not kspvalue_key in result_kspvalue:
# result_kspvalue[kspvalue_key] = {}
#
# result_kspvalue[kspvalue_key][measure_well] = kspvalue_value
#
#
# if not feature_direction_key in result_feature_direction:
# result_feature_direction[feature_direction_key] = {}
#
# result_feature_direction[feature_direction_key][measure_well] = feature_direction_value
for feature in result_accuracy:
feature_accuracy = featuresBuilder.defineFeature(feature)
for well in result_accuracy[feature]:
value = result_accuracy[feature][well]
feature_accuracy.addValue(well, value)
# for feature_kst in result_kstest:
# feature_kstest = featuresBuilder.defineFeature(feature_kst)
# for well2 in result_kstest[feature_kst]:
# value2 = result_kstest[feature_kst][well2]
# feature_kstest.addValue(well2, value2)
#
#
# for feature_ksd in result_ksdelta:
# feature_ksdelta = featuresBuilder.defineFeature(feature_ksd)
# for well1 in result_ksdelta[feature_ksd]:
# value1 = result_ksdelta[feature_ksd][well1]
# feature_ksdelta.addValue(well1, value1)
#
# for feature_ksp in result_kspvalue:
# feature_kspvalue = featuresBuilder.defineFeature(feature_ksp)
# for well3 in result_kspvalue[feature_ksp]:
# value3 = result_kspvalue[feature_ksp][well3]
# feature_kspvalue.addValue(well3, value3)
#
# for feature_fd in result_feature_direction:
# feature_feature_direction= featuresBuilder.defineFeature(feature_fd)
# for well4 in result_feature_direction[feature_fd]:
# value4 = result_feature_direction[feature_fd][well4]
# feature_feature_direction.addValue(well4, value4)
for csv_file2 in glob.glob(os.path.join(incoming, 'global_siRNA.csv')):
csvf2 = open(csv_file2,'r')
globcsv2 = csv.reader(csvf2, delimiter=',')
globcsv2.next()
result_accuracy_sirna = {} # accuracy_label => {measure_well => accuracy_value}
result_kstest_sirna ={} # kstest => {measure_well => kstest_value}
result_ksdelta_sirna = {} # ksdelta => {measure_well => ksdelta_value}
result_kspvalue_sirna ={} # kspvalue => {measure_well => kspvalue_value}
result_feature_direction_sirna ={}# feature_direction => {measure_well => feature_direction_value}
for row in globcsv2:
measure_well_sirna = row[0]
control_well_sirna = row[1]
feature_name_sirna = row[4]
accuracy_label_sirna = feature_name_sirna + "_S_ac"
accuracy_value_sirna = row[5]
kstest_sirna = feature_name_sirna + "_S_KSt"
kstest_value_sirna = row[6]
ksdelta_sirna = feature_name_sirna + "_S_KSd"
ksdelta_value_sirna = row[7]
kspvalue_sirna = feature_name_sirna + "_S_KSp"
kspvalue_value_sirna = row[8]
feature_direction_sirna = feature_name_sirna + "_S_dir"
feature_direction_value_sirna = row[9]
accuracy_key_sirna = "%s:%s" %(accuracy_label_sirna, control_well_sirna)
kstest_key_sirna = "%s:%s" %(kstest_sirna, control_well_sirna)
ksdelta_key_sirna = "%s:%s" %(ksdelta_sirna, control_well_sirna)
kspvalue_key_sirna ="%s:%s" %(kspvalue_sirna, control_well_sirna)
feature_direction_key_sirna = "%s:%s" %(feature_direction_sirna, control_well_sirna)
if not accuracy_key_sirna in result_accuracy_sirna:
result_accuracy_sirna[accuracy_key_sirna] = {}
result_accuracy_sirna[accuracy_key_sirna][measure_well_sirna] = accuracy_value_sirna
# if not kstest_key in result_kstest:
# result_kstest[kstest_key] = {}
#
# result_kstest[kstest_key][measure_well] = kstest_value
#
#
# if not ksdelta_key in result_ksdelta:
# result_ksdelta[ksdelta_key] = {}
#
# result_ksdelta[ksdelta_key][measure_well] = ksdelta_value
#
# if not kspvalue_key in result_kspvalue:
# result_kspvalue[kspvalue_key] = {}
#
# result_kspvalue[kspvalue_key][measure_well] = kspvalue_value
#
#
# if not feature_direction_key in result_feature_direction:
# result_feature_direction[feature_direction_key] = {}
#
# result_feature_direction[feature_direction_key][measure_well] = feature_direction_value
for feature_sirna in result_accuracy_sirna:
feature_accuracy_sirna = featuresBuilder.defineFeature(feature_sirna)
for well_sirna in result_accuracy_sirna[feature_sirna]:
value_sirna = result_accuracy_sirna[feature_sirna][well_sirna]
feature_accuracy_sirna.addValue(well_sirna, value_sirna)
# for feature_kst in result_kstest:
# feature_kstest = featuresBuilder.defineFeature(feature_kst)
# for well2 in result_kstest[feature_kst]:
# value2 = result_kstest[feature_kst][well2]
# feature_kstest.addValue(well2, value2)
#
#
# for feature_ksd in result_ksdelta:
# feature_ksdelta = featuresBuilder.defineFeature(feature_ksd)
# for well1 in result_ksdelta[feature_ksd]:
# value1 = result_ksdelta[feature_ksd][well1]
# feature_ksdelta.addValue(well1, value1)
#
# for feature_ksp in result_kspvalue:
# feature_kspvalue = featuresBuilder.defineFeature(feature_ksp)
# for well3 in result_kspvalue[feature_ksp]:
# value3 = result_kspvalue[feature_ksp][well3]
# feature_kspvalue.addValue(well3, value3)
#
# for feature_fd in result_feature_direction:
# feature_feature_direction= featuresBuilder.defineFeature(feature_fd)
# for well4 in result_feature_direction[feature_fd]:
# value4 = result_feature_direction[feature_fd][well4]
# feature_feature_direction.addValue(well4, value4)
config = SimpleFeatureVectorDataConfig()
featuresBuilder = config.featuresBuilder
defineGeneFeatures(featuresBuilder, incoming.getPath())
analysisDataset = transaction.createNewFeatureVectorDataSet(config, incoming)
rawImagesDataSetSample1 = transaction.getDataSet(extractSegmentationDataSetCode(incoming.getPath())).getSample()
rawImagesDataSetSample = transaction.getSample('/SINERGIA/' + rawImagesDataSetSample1.getCode())
# plateIdentifier = "/SINERGIA/PLATE1-G1-10X"
# test = transaction.getSample("/SINERGIA/PLATE1-G1-10X")
# analysisDataset.setSample(test)
analysisDataset.setSample(rawImagesDataSetSample)
search_service = transaction.getSearchService()
sc = SearchCriteria()
sc.addMatchClause(SearchCriteria.MatchClause.createAttributeMatch(SearchCriteria.MatchClauseAttribute.CODE, extractSegmentationDataSetCode(incoming.getPath()) ));
foundDataSets = search_service.searchForDataSets(sc)
if foundDataSets.size() > 0:
analysisDataset.setParentDatasets([ds.getDataSetCode() for ds in foundDataSets])
# store the original file in the dataset.
transaction.moveFile(incoming.getPath(), analysisDataset)
######################## Create Feature lists Datasets ###########################################
config_accA2 = FeatureListDataConfig()
config_accA2.setName("siRNA-based accuracy (reference well: A2)");
config_accA2.setFeatureList(accuracyA2_sirna_list)
config_accA2.setContainerDataSet(analysisDataset)
transaction.createNewFeatureListDataSet(config_accA2)
# config_accA1 = FeatureListDataConfig()
# config_accA1.setName("siRNA-based accuracy (reference well: A1)");
# config_accA1.setFeatureList(accuracyA1_sirna_list)
# config_accA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_accA1)
#
# config_gene_accA2 = FeatureListDataConfig()
# config_gene_accA2.setName("gene-based accuracy (reference well: A2)");
# config_gene_accA2.setFeatureList(accuracyA2_gene_list)
# config_gene_accA2.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_gene_accA2)
#
# config_gene_accA1 = FeatureListDataConfig()
# config_gene_accA1.setName("gene-based accuracy (reference well: A1)");
# config_gene_accA1.setFeatureList(accuracyA1_gene_list)
# config_gene_accA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_gene_accA1)
#
#
#
# config_KStestA2 = FeatureListDataConfig()
# config_KStestA2.setName("KStest (reference well: A2)");
# config_KStestA2.setFeatureList(KStestA2_sirna_list)
# config_KStestA2.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KStestA2)
#
# config_KStestA1 = FeatureListDataConfig()
# config_KStestA1.setName("KStest (reference well: A1)");
# config_KStestA1.setFeatureList(KStestA1_sirna_list)
# config_KStestA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KStestA1)
#
#
# config_KSdeltaA2 = FeatureListDataConfig()
# config_KSdeltaA2.setName("KSdelta (reference well: A2)");
# config_KSdeltaA2.setFeatureList(KSdeltaA2_sirna_list)
# config_KSdeltaA2.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KSdeltaA2)
#
# config_KSdeltaA1 = FeatureListDataConfig()
# config_KSdeltaA1.setName("KSdelta (reference well: A1)");
# config_KSdeltaA1.setFeatureList(accuracyA1_sirna_list)
# config_KSdeltaA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KSdeltaA1)
#
#
# config_KSpvalueA2 = FeatureListDataConfig()
# config_KSpvalueA2.setName("KSpvalue (reference well: A2)");
# config_KSpvalueA2.setFeatureList(KSpvalueA2_sirna_list)
# config_KSpvalueA2.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KSpvalueA2)
#
# config_KSpvalueA1 = FeatureListDataConfig()
# config_KSpvalueA1.setName("KSpvalue (reference well: A1)");
# config_KSpvalueA1.setFeatureList(KSpvalueA1_sirna_list)
# config_KSpvalueA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_KSpvalueA1)
#
# config_feature_directionA2 = FeatureListDataConfig()
# config_feature_directionA2.setName("Direction (reference well: A2)");
# config_feature_directionA2.setFeatureList(feature_directionA2_sirna_list)
# config_feature_directionA2.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_feature_directionA2)
#
# config_feature_directionA1 = FeatureListDataConfig()
# config_feature_directionA1.setName("Direction (reference well: A1)");
# config_feature_directionA1.setFeatureList(feature_directionA1_sirna_list)
# config_feature_directionA1.setContainerDataSet(analysisDataset)
# transaction.createNewFeatureListDataSet(config_feature_directionA1)