def execute_analyzeReplicates(self,experiment_id_I,sample_name_abbreviations_I=[],sample_names_I=[],component_names_I=[]):
'''calculate the replicates by subtracting out the filtrate
NOTE: data_stage01_quantification_normalized must be populated
Input:
experiment_id
Output:
sample_name_short
component_group_name
component_name
concentration
concentration units
'''
print('execute_analyzeReplicates...')
data_O = [];
#SPLIT 1:
#1 query unique calculated_concentration_units/sample_name_abbreviations/component_names/component_group_names/time_points/sample_names/sample_ids/sample_description
uniqueRows_all = self.getQueryResult_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
experiment_id_I
);
#2 filter in broth samples
uniqueRows = self.filter_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
uniqueRows_all,
calculated_concentration_units_I=[],
component_names_I=component_names_I,
component_group_names_I=[],
sample_names_I=sample_names_I,
sample_name_abbreviations_I=sample_name_abbreviations_I,
time_points_I=[],
);
if type(uniqueRows)==type(listDict()):
uniqueRows.convert_dataFrame2ListDict()
uniqueRows = uniqueRows.get_listDict();
data_tmp = {};#reorganize the data into a dictionary for quick traversal of the replicates
for uniqueRow_cnt,uniqueRow in enumerate(uniqueRows):
unique = (uniqueRow['sample_name_abbreviation'],
uniqueRow['experiment_id'],
uniqueRow['time_point'],
uniqueRow['component_name'],
uniqueRow['calculated_concentration_units'])
if not unique in data_tmp.keys():
data_tmp[unique] = [];
data_tmp[unique].append(uniqueRow);
for unique,replicates in data_tmp.items():
print('analyzing replicates for sample_name_abbreviation ' + replicates[0]['sample_name_abbreviation'] + ' and component_name ' + replicates[0]['component_name']);
# extract filtrate data
concs = [d['calculated_concentration'] for d in replicates if d['sample_desc']=='Filtrate'
and not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
conc_units = [d['calculated_concentration_units'] for d in replicates if d['sample_desc']=='Filtrate'
and not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
if conc_units: conc_units = conc_units[0];
else: conc_units = None;
n_replicates = len(concs);
conc_average_filtrate = 0.0;
conc_var_filtrate = 0.0;
# calculate average and CV of concentrations
if (not(concs)): conc_average_filtrate = 0;
elif n_replicates<2: conc_average_filtrate = concs[0];
else:
conc_average_filtrate = numpy.mean(numpy.array(concs));
conc_var_filtrate = numpy.var(numpy.array(concs));
# extract broth data
for rep in replicates:
if rep['sample_desc']!='Broth': continue;
conc = None;
conc_unit = None;
if rep['calculated_concentration'] is None or rep['calculated_concentration']==0: continue;
else: conc=rep['calculated_concentration'];
# record all replicate broth samples whether they were measured or not
# needed for MI2 later on
if rep['calculated_concentration_units']: conc_units = rep['calculated_concentration_units'];
# subract out filtrate average from each broth
conc_broth = 0.0;
if conc:
conc_broth = conc-conc_average_filtrate;
if (conc_broth < 0 ): conc_broth = None;
else: conc_broth = None;
# add data to the DB
row = {};
row = {'experiment_id':experiment_id_I,
'sample_name_short':rep['sample_name_short'],
'time_point':rep['time_point'],
'component_group_name':rep['component_group_name'],
'component_name':rep['component_name'],
'calculated_concentration':conc_broth,
'calculated_concentration_units':conc_units,
'used_':True,
'comment_':None,};
data_O.append(row);
##SPLIT 2:
## get sample_name_abbreviations
#if sample_name_abbreviations_I:
# sample_name_abbreviations = sample_name_abbreviations_I
#else:
# sample_name_abbreviations = [];
# sample_name_abbreviations = self.get_sampleNameAbbreviations_experimentID_dataStage01Normalized(experiment_id_I);
## create database table
#for sna in sample_name_abbreviations:
# print('analyzing replicates for sample_name_abbreviation ' + sna);
# # get component names
# if component_names_I:
# component_names = component_names_I
# else:
# component_names = [];
# component_names = self.get_componentsNames_experimentIDAndSampleNameAbbreviation_dataStage01Normalized(experiment_id_I,sna);
# for cn in component_names:
# print('analyzing replicates for component_name ' + cn);
# component_group_name = self.get_componentGroupName_experimentIDAndComponentName_dataStage01Normalized(experiment_id_I,cn);
# # get time points
# time_points = self.get_timePoint_experimentIDAndSampleNameAbbreviation_dataStage01Normalized(experiment_id_I,sna);
# for tp in time_points:
# print('analyzing replicates for time_point ' + tp);
# # get filtrate sample names
# sample_names = [];
# sample_description = 'Filtrate';
# sample_names = self.get_sampleNames_experimentIDAndSampleNameAbbreviationAndSampleDescriptionAndComponentNameAndTimePoint_dataStage01Normalized(experiment_id_I,sna,sample_description,cn,tp);
# if sample_names_I: # screen out sample names that are not in the input
# sample_names = [x for x in sample_names if x in sample_names_I];
# concs = [];
# conc_units = None;
# for sn in sample_names:
# # concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(sn,cn);
# if not(conc): continue
# if (conc_unit): conc_units = conc_unit;
# concs.append(conc);
# n_replicates = len(concs);
# conc_average_filtrate = 0.0;
# conc_var_filtrate = 0.0;
# # calculate average and CV of concentrations
# if (not(concs)): conc_average_filtrate = 0;
# elif n_replicates<2: conc_average_filtrate = concs[0];
# else:
# conc_average_filtrate = numpy.mean(numpy.array(concs));
# conc_var_filtrate = numpy.var(numpy.array(concs));
# # get filtrate sample names
# sample_names = [];
# sample_description = 'Broth';
# sample_names = self.get_sampleNames_experimentIDAndSampleNameAbbreviationAndSampleDescriptionAndComponentNameAndTimePoint_dataStage01Normalized(experiment_id_I,sna,sample_description,cn,tp);
# if sample_names_I: # screen out sample names that are not in the input
# sample_names = [x for x in sample_names if x in sample_names_I];
# for sn in sample_names:
# print('analyzing replicates for sample_name ' + sn);
# # query concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(sn,cn);
# if (not(conc) or conc==0): continue # record all replicate broth samples whether they were measured or not
# # needed for MI2 later on
# if (conc_unit): conc_units = conc_unit;
# # subract out filtrate average from each broth
# conc_broth = 0.0;
# if conc:
# conc_broth = conc-conc_average_filtrate;
# if (conc_broth < 0 ): conc_broth = None;
# else: conc_broth = None;
# # get sample name short
# sample_name_short = self.get_sampleNameShort_experimentIDAndSampleName_dataStage01Normalized(experiment_id_I, sn);
# # add data to the session
# # add data to the DB
# row = {'experiment_id':experiment_id_I,
# 'sample_name_short':sample_name_short,
# 'time_point':tp,
# 'component_group_name':component_group_name,
# 'component_name':cn,
# 'calculated_concentration':conc_broth,
# 'calculated_concentration_units':conc_units,
# 'used_':True,
# 'comment_':None,};
# data_O.append(row);
self.add_rows_table('data_stage01_quantification_replicates',data_O);
def export_dataStage01NormalizedAndAverages_js(self,
analysis_id_I,
sample_name_abbreviations_I=[],
sample_names_I=[],
component_names_I=[],
cv_threshold_I=40,
extracellular_threshold_I=80,
data_dir_I='tmp'):
'''export data_stage01_quantification_normalized and averages for visualization with ddt'''
calc = calculate_interface();
print('export_dataStage01Normalized_js...')
data_norm_broth = [];
data_norm_filtrate = [];
data_norm_combined = [];
data_ave = [];
#SPLIT 1:
#1 query unique calculated_concentration_units/sample_name_abbreviations/component_names/component_group_names/time_points/sample_names/sample_ids/sample_description
uniqueRows_all = self.getQueryResult_groupNormalizedAveragesSamples_analysisID_dataStage01QuantificationNormalizedAndAverages(
analysis_id_I
);
#2 filter in broth samples
uniqueRows = self.filter_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
uniqueRows_all,
calculated_concentration_units_I=[],
component_names_I=component_names_I,
component_group_names_I=[],
sample_names_I=sample_names_I,
sample_name_abbreviations_I=sample_name_abbreviations_I,
time_points_I=[],
);
if type(uniqueRows)==type(listDict()):
uniqueRows.convert_dataFrame2ListDict()
uniqueRows = uniqueRows.get_listDict();
replicates_tmp = {};#reorganize the data into a dictionary for quick traversal of the replicates
for uniqueRow_cnt,uniqueRow in enumerate(uniqueRows):
unique = (
uniqueRow['sample_name_abbreviation'],
uniqueRow['experiment_id'],
uniqueRow['time_point'],
uniqueRow['component_name'],
uniqueRow['calculated_concentration_units'])
if not unique in replicates_tmp.keys():
replicates_tmp[unique] = [];
replicates_tmp[unique].append(uniqueRow);
for unique,replicates in replicates_tmp.items():
#get data from averages once per sample_name_abbreviation/component_name
#print('exporting sample_name_abbreviation ' + replicates[0]['sample_name_abbreviation'] + " and component_name " + replicates[0]['component_name']);
# get the averages and %CV samples
row_ave = {};
row_ave = self.get_row_experimentIDAndSampleNameAbbreviationAndTimePointAndComponentNameAndCalculatedConcentrationCVAndExtracellularPercent_dataStage01Averages(
replicates[0]['experiment_id'],
replicates[0]['sample_name_abbreviation'],
replicates[0]['time_point'],
replicates[0]['component_name'],
cv_threshold_I=cv_threshold_I,
extracellular_threshold_I=extracellular_threshold_I);
if row_ave:
stdev = calc.convert_cv2StDev(row_ave['calculated_concentration_filtrate_average'],row_ave['calculated_concentration_filtrate_cv']);
row_ave['calculated_concentration_filtrate_lb'] = row_ave['calculated_concentration_filtrate_average']-stdev;
row_ave['calculated_concentration_filtrate_ub'] = row_ave['calculated_concentration_filtrate_average']+stdev;
stdev = calc.convert_cv2StDev(row_ave['calculated_concentration_broth_average'],row_ave['calculated_concentration_broth_cv']);
row_ave['calculated_concentration_broth_lb'] = row_ave['calculated_concentration_broth_average']-stdev;
row_ave['calculated_concentration_broth_ub'] = row_ave['calculated_concentration_broth_average']+stdev;
stdev = calc.convert_cv2StDev(row_ave['calculated_concentration_average'],row_ave['calculated_concentration_cv']);
row_ave['calculated_concentration_lb'] = row_ave['calculated_concentration_average']-stdev;
row_ave['calculated_concentration_ub'] = row_ave['calculated_concentration_average']+stdev;
row_ave['analysis_id'] = analysis_id_I;
# get data from normalized
filtrate_conc = [];
broth_conc = [];
for rep in replicates:
row = {};
row['analysis_id'] = analysis_id_I;
row['extracellular_percent'] = row_ave['extracellular_percent']
row['calculated_concentration_cv'] = row_ave['calculated_concentration_cv']
row.update(rep)
if rep['sample_desc'] == 'Filtrate':
data_norm_filtrate.append(row);
filtrate_conc.append(rep['calculated_concentration'])
if rep['sample_desc'] == 'Broth':
data_norm_broth.append(row);
broth_conc.append(rep['calculated_concentration'])
data_norm_combined.append(row);
#add data to aggregate and sample_name_abbreviations_all
if not broth_conc: broth_conc = [0];
if not filtrate_conc: filtrate_conc = [0];
row_ave['calculated_concentration_min']=min(broth_conc+filtrate_conc)
row_ave['calculated_concentration_max']=max(broth_conc+filtrate_conc)
row_ave['calculated_concentration_broth_min']=min(broth_conc)
row_ave['calculated_concentration_broth_max']=max(broth_conc)
row_ave['calculated_concentration_filtrate_min']=min(filtrate_conc)
row_ave['calculated_concentration_filtrate_max']=max(filtrate_conc)
data_ave.append(row_ave);
# dump chart parameters to a js files
data1_keys = ['analysis_id',
'experiment_id',
'sample_name',
'sample_id',
'sample_name_abbreviation',
'component_group_name',
'component_name',
'calculated_concentration_units',
'extracellular_percent',
'calculated_concentration_cv'
];
data1_nestkeys = ['component_name'];
data1_keymap = {'xdata':'component_name',
'ydata':'calculated_concentration',
#'ydatalb':'peakInfo_lb',
#'ydataub':'peakInfo_ub',
#'ydatamin':None,
#'ydatamax':None,
#'ydataiq1':None,
#'ydataiq3':None,
#'ydatamedian':None,
'serieslabel':'sample_name_abbreviation',
'featureslabel':'sample_name'};
data2_keys = ['analysis_id',
'experiment_id',
'sample_name_abbreviation',
'time_point',
'component_group_name',
'component_name',
'calculated_concentration_units',
'extracellular_percent',
'calculated_concentration_broth_cv'
];
data2_nestkeys = ['component_name'];
data2_keymap = {'xdata':'component_name',
'ydatamean':'calculated_concentration_broth_average',
'ydatalb':'calculated_concentration_broth_lb',
'ydataub':'calculated_concentration_broth_ub',
'ydatamin':'calculated_concentration_broth_min',
'ydatamax':'calculated_concentration_broth_max',
#'ydataiq1':None,
#'ydataiq3':None,
#'ydatamedian':None,
'serieslabel':'sample_name_abbreviation',
'featureslabel':'component_name'};
data3_keys = ['analysis_id',
'experiment_id',
'sample_name_abbreviation',
'time_point',
'component_group_name',
'component_name',
'calculated_concentration_units',
'extracellular_percent',
'calculated_concentration_filtrate_cv',
];
data3_nestkeys = ['component_name'];
data3_keymap = {'xdata':'component_name',
'ydatamean':'calculated_concentration_filtrate_average',
'ydatalb':'calculated_concentration_filtrate_lb',
'ydataub':'calculated_concentration_filtrate_ub',
'ydatamin':'calculated_concentration_filtrate_min',
'ydatamax':'calculated_concentration_filtrate_max',
#'ydataiq1':None,
#'ydataiq3':None,
#'ydatamedian':None,
'serieslabel':'sample_name_abbreviation',
'featureslabel':'component_name'};
data4_keys = ['analysis_id',
'experiment_id',
'sample_name_abbreviation',
'time_point',
'component_group_name',
'component_name',
'calculated_concentration_units',
'extracellular_percent',
'calculated_concentration_cv'
];
data4_nestkeys = ['component_name'];
data4_keymap = {'xdata':'component_name',
'ydata':'calculated_concentration_average',
'ydatamean':'calculated_concentration_average',
'ydatalb':'calculated_concentration_lb',
'ydataub':'calculated_concentration_ub',
#'ydatamin':'calculated_concentration_min',
#'ydatamax':'calculated_concentration_max',
#'ydataiq1':None,
#'ydataiq3':None,
#'ydatamedian':None,
'serieslabel':'sample_name_abbreviation',
'featureslabel':'component_name'};
# make the data object
dataobject_O = [{"data":data_norm_broth,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},
{"data":data_norm_filtrate,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},
{"data":data_norm_combined,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},
{"data":data_ave,"datakeys":data2_keys,"datanestkeys":data2_nestkeys},
{"data":data_ave,"datakeys":data3_keys,"datanestkeys":data3_nestkeys},
{"data":data_ave,"datakeys":data4_keys,"datanestkeys":data4_nestkeys}];
# make the tile parameter objects for the normalized and averages
formtileparameters_averages_O = {'tileheader':'Filter menu averages','tiletype':'html','tileid':"filtermenu2",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-6"};
formparameters_averages_O = {'htmlid':'filtermenuform2',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit2','text':'submit'},"formresetbuttonidtext":{'id':'reset2','text':'reset'},"formupdatebuttonidtext":{'id':'update2','text':'update'}};
formtileparameters_averages_O.update(formparameters_averages_O);
# make the svg objects for the averages data
svgparameters_averages_broth_O = {"svgtype":'boxandwhiskersplot2d_02',"svgkeymap":[data2_keymap,data1_keymap],
'svgid':'svg4',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":250,"svgheight":250,
"svgx1axislabel":"component_name","svgy1axislabel":"concentration",
'svgformtileid':'filtermenu2','svgresetbuttonid':'reset2','svgsubmitbuttonid':'submit2'};
svgtileparameters_averages_broth_O = {'tileheader':'Broth data','tiletype':'svg','tileid':"tile4",'rowid':"row2",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
svgtileparameters_averages_broth_O.update(svgparameters_averages_broth_O);
if data_norm_filtrate:
svgparameters_averages_filtrate_O = {"svgtype":'boxandwhiskersplot2d_02',"svgkeymap":[data3_keymap,data1_keymap],
'svgid':'svg5',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":250,"svgheight":250,
"svgx1axislabel":"component_name","svgy1axislabel":"concentration",
'svgformtileid':'filtermenu2','svgresetbuttonid':'reset2','svgsubmitbuttonid':'submit2'};
svgtileparameters_averages_filtrate_O = {'tileheader':'Filtrate data','tiletype':'svg','tileid':"tile5",'rowid':"row2",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
svgtileparameters_averages_filtrate_O.update(svgparameters_averages_filtrate_O);
else:
svgparameters_averages_filtrate_O = {"svgtype":'boxandwhiskersplot2d_01',"svgkeymap":[data3_keymap],
'svgid':'svg5',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":250,"svgheight":250,
"svgx1axislabel":"component_name","svgy1axislabel":"concentration",
'svgformtileid':'filtermenu2','svgresetbuttonid':'reset2','svgsubmitbuttonid':'submit2'};
svgtileparameters_averages_filtrate_O = {'tileheader':'Filtrate data','tiletype':'svg','tileid':"tile5",'rowid':"row2",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
svgtileparameters_averages_filtrate_O.update(svgparameters_averages_filtrate_O);
svgparameters_averages_combined_O = {
#"svgtype":'boxandwhiskersplot2d_02',
"svgtype":'boxandwhiskersplot2d_01',
#"svgkeymap":[data4_keymap,data1_keymap],
"svgkeymap":[data4_keymap],
'svgid':'svg6',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":250,"svgheight":250,
"svgx1axislabel":"component_name","svgy1axislabel":"concentration",
'svgformtileid':'filtermenu2','svgresetbuttonid':'reset2','svgsubmitbuttonid':'submit2'};
svgtileparameters_averages_combined_O = {'tileheader':'Broth-Filtrate data','tiletype':'svg','tileid':"tile6",'rowid':"row2",'colid':"col3",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
svgtileparameters_averages_combined_O.update(svgparameters_averages_combined_O);
# make the tables for the normalized and averages data
tableparameters_normalized_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_normalized_O = {'tileheader':'normalized data','tiletype':'table','tileid':"tile7",'rowid':"row4",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_normalized_O.update(tableparameters_normalized_O);
tableparameters_averages_O = {"tabletype":'responsivetable_01',
'tableid':'table2',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu2','tableresetbuttonid':'reset2','tablesubmitbuttonid':'submit2'};
tabletileparameters_averages_O = {'tileheader':'averages data','tiletype':'table','tileid':"tile8",'rowid':"row5",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_averages_O.update(tableparameters_averages_O);
parametersobject_O = [
formtileparameters_averages_O,
svgtileparameters_averages_broth_O,
svgtileparameters_averages_filtrate_O,
svgtileparameters_averages_combined_O,
tabletileparameters_normalized_O,
tabletileparameters_averages_O];
tile2datamap_O = {
"filtermenu2":[5],
"tile4":[3,0],
"tile5":[4,1],
#"tile6":[5,2],
"tile6":[5],
"tile7":[2],
"tile8":[5]
};
#if data_norm_filtrate: tile2datamap_O.update({"tile5":[4,1]})
#else: tile2datamap_O.update({"tile5":[4]})
filtermenuobject_O = [
#{"filtermenuid":"filtermenu1","filtermenuhtmlid":"filtermenuform1",
#"filtermenusubmitbuttonid":"submit1","filtermenuresetbuttonid":"reset1",
#"filtermenuupdatebuttonid":"update1"},
{"filtermenuid":"filtermenu2","filtermenuhtmlid":"filtermenuform2",
"filtermenusubmitbuttonid":"submit2","filtermenuresetbuttonid":"reset2",
"filtermenuupdatebuttonid":"update2"}
];
#
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = filtermenuobject_O);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
def execute_analyzeAverages_blanks(self,experiment_id_I,
sample_name_abbreviations_I=[],
sample_names_I=[],
component_names_I=[],
blank_sample_names_I=[],
blank_sample_name_abbreviations_I=[],
):
'''calculate the averages using the ave(broth),i - ave(blank,broth)
NOTE: data_stage01_quantification_normalized must be populated
Input:
experiment_id_I
sample_name_abbreviations_I
sample_names_I
component_names_I
blank_sample_names_I = []; if specified, specific blank samples will be used as the filtrate instead of filtrate samples
Output:
sample_name_abbreviation
component_group_name
component_name
concentration average
concentration CV
concentration units
% extracellular
'''
data_O=[];
calc = calculate_interface();
print('execute_analyzeAverages...')
#SPLIT 1:
#1 query unique calculated_concentration_units/sample_name_abbreviations/component_names/component_group_names/time_points/sample_names/sample_ids/sample_description
uniqueRows_all = self.getQueryResult_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
experiment_id_I
);
#2 filter in broth samples
uniqueRows = self.filter_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
uniqueRows_all,
calculated_concentration_units_I=[],
component_names_I=component_names_I,
component_group_names_I=[],
sample_names_I=sample_names_I,
sample_name_abbreviations_I=sample_name_abbreviations_I,
time_points_I=[],
);
if type(uniqueRows)==type(listDict()):
uniqueRows.convert_dataFrame2ListDict()
uniqueRows = uniqueRows.get_listDict();
data_tmp = {};#reorganize the data into a dictionary for quick traversal of the replicates
for uniqueRow_cnt,uniqueRow in enumerate(uniqueRows):
unique = (uniqueRow['sample_name_abbreviation'],
uniqueRow['experiment_id'],
uniqueRow['time_point'],
uniqueRow['component_name'],
uniqueRow['calculated_concentration_units'])
if not unique in data_tmp.keys():
data_tmp[unique] = [];
data_tmp[unique].append(uniqueRow);
#3 filter in blank samples
uniqueBlanks=[];
if blank_sample_names_I or blank_sample_name_abbreviations_I:
uniqueBlanks = self.filter_groupNormalizedAveragesSamples_experimentID_dataStage01QuantificationNormalizedAndAverages_limsSampleAndSampleID(
uniqueRows_all,
calculated_concentration_units_I=[],
component_names_I=component_names_I,
component_group_names_I=[],
sample_names_I=blank_sample_names_I,
sample_name_abbreviations_I=blank_sample_name_abbreviations_I,
time_points_I=[],
);
if type(uniqueBlanks)==type(listDict()):
uniqueBlanks.convert_dataFrame2ListDict()
uniqueBlanks = uniqueBlanks.get_listDict();
data_blanks_tmp = {}; #reorganize the data for a quick traversal of the components
for uniqueBlanks_cnt,uniqueBlank in enumerate(uniqueBlanks):
unique = uniqueBlank['component_name']
if not unique in data_tmp.keys():
data_blanks_tmp[unique] = [];
data_blanks_tmp[unique].append(uniqueBlank);
#4 iterate through each unique unique calculated_concentration_units/sample_name_abbreviations/component_names/component_group_names/time_points
# and determine the ave, cv, etc., after subtracting out the blanks
for unique,replicates in data_tmp.items():
print('analyzing averages for sample_name_abbreviation ' + replicates[0]['sample_name_abbreviation'] + ' and component_name ' + replicates[0]['component_name']);
# get blank concentrations
if data_blanks_tmp and replicates[0]['component_name'] in data_blanks_tmp.keys():
concs = [d['calculated_concentration'] for d in data_blanks_tmp[replicates[0]['component_name']]
if not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
conc_units = [d['calculated_concentration_units'] for d in data_blanks_tmp[replicates[0]['component_name']]
if not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
if conc_units: conc_units = conc_units[0];
else: conc_units = None;
else:
concs = [];
conc_units = None;
#if blank_sample_names_I:
# sample_names = blank_sample_names_I;
#else:
# sample_names = [];
#concs = [];
#conc_units = None;
#for sn in sample_names:
# # concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(
# sn,
# replicates[0]['component_name']);
# if (not(conc) or conc==0): continue
# if (conc_unit): conc_units = conc_unit;
# concs.append(conc);
n_replicates_filtrate = len(concs);
conc_average_filtrate = 0.0;
conc_var_filtrate = 0.0;
conc_cv_filtrate = 0.0;
# calculate average and CV of concentrations
if (not(concs)):
conc_average_filtrate = 0;
conc_var_filtrate = 0;
elif n_replicates_filtrate<2:
conc_average_filtrate = concs[0];
conc_var_filtrate = 0;
else:
#conc_average_filtrate, conc_var_filtrate = calc.calculate_ave_var_R(concs);
conc_average_filtrate = numpy.mean(numpy.array(concs));
conc_var_filtrate = numpy.var(numpy.array(concs));
if (conc_average_filtrate <= 0): conc_cv_filtrate = 0;
else: conc_cv_filtrate = sqrt(conc_var_filtrate)/conc_average_filtrate*100;
# get broth sample names
concs = [d['calculated_concentration'] for d in replicates if d['sample_desc']=='Broth'
and not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
conc_units = [d['calculated_concentration_units'] for d in replicates if d['sample_desc']=='Broth'
and not d['calculated_concentration'] is None and d['calculated_concentration']!=0];
if conc_units: conc_units = conc_units[0];
else: conc_units = None;
#concs = [];
#conc_units = None;
#sample_names = [d['sample_name'] for d in replicates if d['sample_desc']=='Broth'];
#for sn in sample_names:
# # query concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(
# sn,
# replicates[0]['component_name']);
# if (not(conc) or conc==0): continue
# if (conc_unit): conc_units = conc_unit;
# concs.append(conc);
n_replicates = len(concs);
conc_average_broth = 0.0;
conc_var_broth = 0.0;
conc_cv_broth = 0.0;
# calculate average and CV of concentrations
if (not(concs)):
continue
elif n_replicates<2:
continue
else:
#conc_average_broth, conc_var_broth = calc.calculate_ave_var_R(concs);
conc_average_broth = numpy.mean(numpy.array(concs));
conc_var_broth = numpy.var(numpy.array(concs));
if (conc_average_broth <= 0): conc_cv_broth = 0;
else: conc_cv_broth = sqrt(conc_var_broth)/conc_average_broth*100;
# calculate average and CV
conc_average = 0.0;
conc_var = 0.0;
conc_cv = 0.0;
conc_average = conc_average_broth-conc_average_filtrate;
if (conc_average < 0): conc_average = 0;
conc_var = conc_var_broth + conc_var_filtrate;
if (conc_average <= 0): conc_cv = 0;
else: conc_cv = sqrt(conc_var)/conc_average*100;
# calculate the % extracellular
extracellular_percent = conc_average_filtrate/conc_average_broth*100;
# add data to the session
row = {};
row = {'experiment_id':experiment_id_I,
'sample_name_abbreviation':replicates[0]['sample_name_abbreviation'],
'time_point':replicates[0]['time_point'],
'component_group_name':replicates[0]['component_group_name'],
'component_name':replicates[0]['component_name'],
'n_replicates_broth':n_replicates,
'calculated_concentration_broth_average':conc_average_broth,
'calculated_concentration_broth_cv':conc_cv_broth,
'n_replicates_filtrate':n_replicates_filtrate,
'calculated_concentration_filtrate_average':conc_average_filtrate,
'calculated_concentration_filtrate_cv':conc_cv_filtrate,
'n_replicates':n_replicates,
'calculated_concentration_average':conc_average,
'calculated_concentration_cv':conc_cv,
'calculated_concentration_units':conc_units,
'extracellular_percent':extracellular_percent,
'used_':True,};
data_O.append(row);
##SPLIT2
## get sample_name_abbreviations
#if sample_name_abbreviations_I:
# sample_name_abbreviations = sample_name_abbreviations_I
#else:
# sample_name_abbreviations = [];
# sample_name_abbreviations = self.get_sampleNameAbbreviations_experimentID_dataStage01Normalized(experiment_id_I);
#for sna in sample_name_abbreviations:
# print('analyzing averages for sample_name_abbreviation ' + sna);
# # get component names
# if component_names_I:
# component_names = component_names_I
# else:
# component_names = [];
# component_names = self.get_componentsNames_experimentIDAndSampleNameAbbreviation_dataStage01Normalized(experiment_id_I,sna);
# for cn in component_names:
# print('analyzing averages for component_name ' + cn);
# component_group_name = self.get_componentGroupName_experimentIDAndComponentName_dataStage01Normalized(experiment_id_I,cn);
# # get time points
# time_points = self.get_timePoint_experimentIDAndSampleNameAbbreviation_dataStage01Normalized(experiment_id_I,sna);
# if not time_points: continue;
# for tp in time_points:
# print('analyzing averages for time_point ' + tp);
# # get blank concentrations
# if blank_sample_names_I:
# sample_names = blank_sample_names_I;
# else:
# sample_names = [];
# concs = [];
# conc_units = None;
# for sn in sample_names:
# # concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(sn,cn);
# if (not(conc) or conc==0): continue
# if (conc_unit): conc_units = conc_unit;
# concs.append(conc);
# n_replicates_filtrate = len(concs);
# conc_average_filtrate = 0.0;
# conc_var_filtrate = 0.0;
# conc_cv_filtrate = 0.0;
# # calculate average and CV of concentrations
# if (not(concs)):
# conc_average_filtrate = 0;
# conc_var_filtrate = 0;
# elif n_replicates_filtrate<2:
# conc_average_filtrate = concs[0];
# conc_var_filtrate = 0;
# else:
# #conc_average_filtrate, conc_var_filtrate = calc.calculate_ave_var_R(concs);
# conc_average_filtrate = numpy.mean(numpy.array(concs));
# conc_var_filtrate = numpy.var(numpy.array(concs));
# if (conc_average_filtrate <= 0): conc_cv_filtrate = 0;
# else: conc_cv_filtrate = sqrt(conc_var_filtrate)/conc_average_filtrate*100;
# # get broth sample names
# sample_names = [];
# sample_description = 'Broth';
# sample_names = self.get_sampleNames_experimentIDAndSampleNameAbbreviationAndSampleDescriptionAndComponentNameAndTimePoint_dataStage01Normalized(experiment_id_I,sna,sample_description,cn,tp);
# if sample_names_I: # screen out sample names that are not in the input
# sample_names = [x for x in sample_names if x in sample_names_I];
# concs = [];
# conc_units = None;
# for sn in sample_names:
# print('analyzing averages for sample_name ' + sn);
# # query concentrations and units
# conc = None;
# conc_unit = None;
# conc, conc_unit = self.get_concAndConcUnits_sampleNameAndComponentName_dataStage01Normalized(sn,cn);
# if (not(conc) or conc==0): continue
# if (conc_unit): conc_units = conc_unit;
# concs.append(conc);
# n_replicates = len(concs);
# conc_average_broth = 0.0;
# conc_var_broth = 0.0;
# conc_cv_broth = 0.0;
# # calculate average and CV of concentrations
# if (not(concs)):
# continue
# elif n_replicates<2:
# continue
# else:
# #conc_average_broth, conc_var_broth = calc.calculate_ave_var_R(concs);
# conc_average_broth = numpy.mean(numpy.array(concs));
# conc_var_broth = numpy.var(numpy.array(concs));
# if (conc_average_broth <= 0): conc_cv_broth = 0;
# else: conc_cv_broth = sqrt(conc_var_broth)/conc_average_broth*100;
# # calculate average and CV
# conc_average = 0.0;
# conc_var = 0.0;
# conc_cv = 0.0;
# conc_average = conc_average_broth-conc_average_filtrate;
# if (conc_average < 0): conc_average = 0;
# conc_var = conc_var_broth + conc_var_filtrate;
# if (conc_average <= 0): conc_cv = 0;
# else: conc_cv = sqrt(conc_var)/conc_average*100;
# # calculate the % extracellular
# extracellular_percent = conc_average_filtrate/conc_average_broth*100;
# # add data to the session
# row = {};
# row = {'experiment_id':experiment_id_I,
# 'sample_name_abbreviation':sna,
# 'time_point':tp,
# 'component_group_name':component_group_name,
# 'component_name':cn,
# 'n_replicates_broth':n_replicates,
# 'calculated_concentration_broth_average':conc_average_broth,
# 'calculated_concentration_broth_cv':conc_cv_broth,
# 'n_replicates_filtrate':n_replicates_filtrate,
# 'calculated_concentration_filtrate_average':conc_average_filtrate,
# 'calculated_concentration_filtrate_cv':conc_cv_filtrate,
# 'n_replicates':n_replicates,
# 'calculated_concentration_average':conc_average,
# 'calculated_concentration_cv':conc_cv,
# 'calculated_concentration_units':conc_units,
# 'extracellular_percent':extracellular_percent,
# 'used_':True,};
# data_O.append(row);
self.add_rows_table('data_stage01_quantification_averages',data_O);
def export_dataStage01RNASequencingGenesFpkmTracking_pairWisePlot_js(self,analysis_id_I,log2normalization_I=True,data_dir_I='tmp'):
'''Export data for a pairwise scatter plot
INPUT:
analysis_id = String, analysis_id
log2normalization_I = Boolean, apply a log2 normalization the FPKM values (default: True)
data_dir_I = string, data directory
OUTPUT:
'''
# get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
# query fpkm data:
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesFpkmTracking(experiment_ids[sample_name_cnt],sample_name);
if log2normalization_I:
for f in fpkms:
if f['FPKM'] == 0.0: f['FPKM'] = 0.0;
else: f['FPKM'] = log2(f['FPKM']);
data_O.extend(fpkms);
# reorganize the data
listdict = listDict(data_O);
data_O,columnValueHeader_O = listdict.convert_listDict2ColumnGroupListDict(
#value_labels_I = ['FPKM','FPKM_conf_lo','FPKM_conf_hi'],
value_labels_I = ['FPKM',],
column_labels_I = ['experiment_id','sample_name'],
feature_labels_I = ['gene_id','gene_short_name'],
na_str_I=0.0,
columnValueConnector_str_I='_',
);
# make the tile object
#data1 = filtermenu/table
data1_keymap_table = {
'xdata':'svd_method',
'ydata':'singular_value_index',
'zdata':'d_vector',
'rowslabel':'svd_method',
'columnslabel':'singular_value_index',
};
#data2 = svg
#if single plot, data2 = filter menu, data2, and table
data1_keys = ['gene_id','gene_short_name'
];
data1_nestkeys = ['gene_short_name'];
data1_keymap_svg = [];
svgtype = [];
svgtile2datamap = [];
data_svg_keymap = [];
for cnt1,column1 in enumerate(columnValueHeader_O):
for cnt2,column2 in enumerate(columnValueHeader_O[cnt1+1:]):
keymap = {
'xdata':column1,
'ydata':column2,
'serieslabel':'',
'featureslabel':'gene_short_name',
'tooltipdata':'gene_short_name',
};
data1_keymap_svg.append([keymap]);
data_svg_keymap.append(keymap);
svgtype.append('pcaplot2d_scores_01');
svgtile2datamap.append([0]);
nsvgtable = ddt_container_filterMenuAndChart2dAndTable();
nsvgtable.make_filterMenuAndChart2dAndTable(
data_filtermenu=data_O,
data_filtermenu_keys=data1_keys,
data_filtermenu_nestkeys=data1_nestkeys,
data_filtermenu_keymap=data1_keymap_table,
data_svg_keys=data1_keys,
data_svg_nestkeys=data1_nestkeys,
data_svg_keymap=data_svg_keymap,
data_table_keys=data1_keys,
data_table_nestkeys=data1_nestkeys,
data_table_keymap=data1_keymap_table,
data_svg=None,
data_table=None,
svgtype=svgtype,
tabletype='responsivetable_01',
svgx1axislabel='',
svgy1axislabel='',
tablekeymap = [data1_keymap_table],
svgkeymap = data1_keymap_svg,
formtile2datamap=[0],
tabletile2datamap=[0],
svgtile2datamap=svgtile2datamap,
svgfilters=None,
svgtileheader='Pair-wise scatter plot',
tablefilters=None,
tableheaders=None
);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = nsvgtable.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(nsvgtable.get_allObjects());
def execute_normalizeSamples2Biomass(self,experiment_id_I,biological_material_I=None,conversion_name_I=None,sample_names_I=[],component_names_I=[],use_height_I=False,sample_types_I=['Unknown']):
'''Normalize calculated concentrations to measured biomass
Input:
experiment_id_I
biological_material_I = biological material (if None, no normalization is done)
conversion_name_I = biomass conversion name (if None, no normalization is done)
use_height_I = if True, use the ion count for peak height instead of the calculated_concentration or height/area ratio
Output:
sample_name
sample_id
component_group_name
component_name
calculated_concentration
calculated_concentration_units
used_
'''
data_O=[];
calc = calculate_interface();
print('execute_normalizeSamples2Biomass...')
##SPLIT 1:
# get the unique sample_names/sample_ids/sample_types/component_names/component_group_names/calculated_concentration_units
groupJoin = self.getGroupJoin_experimentAndQuantitationMethodAndMQResultsTable_experimentID_dataStage01QuantificationMQResultsTable(
experiment_id_I,
sample_types_I=sample_types_I,
sample_names_I=sample_names_I,
component_names_I=component_names_I,
sample_ids_I=[],
);
if type(groupJoin)==type(listDict()):
groupJoin.convert_dataFrame2ListDict()
groupJoin = groupJoin.get_listDict();
if (biological_material_I and conversion_name_I):
# get the conversion units once
conversion = None;
conversion_units = None;
conversion, conversion_units = self.get_conversionAndConversionUnits_biologicalMaterialAndConversionName(biological_material_I,conversion_name_I);
for row_cnt,row in enumerate(groupJoin):
print('normalizing samples2Biomass for component_name ' + row['component_name']);
# get physiological parameters
cvs = None;
cvs_units = None;
od600 = None;
dil = None;
dil_units = None;
cvs, cvs_units, od600, dil,dil_units = self.get_CVSAndCVSUnitsAndODAndDilAndDilUnits_sampleName(row['sample_name']);
if not(cvs and cvs_units and od600 and dil and dil_units):
print('cvs, cvs_units, or od600 are missing from physiological parameters');
print('or dil and dil_units are missing from sample descripton');
exit(-1);
elif not(conversion and conversion_units):
print('biological_material or conversion name is incorrect');
exit(-1);
else:
#calculate the cell volume or biomass depending on the conversion units
#cell_volume, cell_volume_units = calc.calculate_cellVolume_CVSAndCVSUnitsAndODAndConversionAndConversionUnits(cvs,cvs_units,od600,conversion,conversion_units);
cell_volume, cell_volume_units = calc.calculate_biomass_CVSAndCVSUnitsAndODAndConversionAndConversionUnits(cvs,cvs_units,od600,conversion,conversion_units);
# get the calculated concentration
calc_conc = None;
calc_conc_units = None;
#data_row = self.get_row_sampleNameAndComponentName(
# row['sample_name'],
# row['component_name']);
if use_height_I:
#calc_conc, calc_conc_units = data_row['height'],'height';
calc_conc, calc_conc_units = row['height'],'height';
elif row['use_calculated_concentration']:
#calc_conc, calc_conc_units = data_row['calculated_concentration'],data_row['conc_units'];
calc_conc, calc_conc_units = row['calculated_concentration'],row['conc_units'];
elif not row['use_calculated_concentration'] and row['use_area']:
#calc_conc, calc_conc_units = data_row['area_ratio'],'area_ratio';
calc_conc, calc_conc_units = row['area_ratio'],'area_ratio';
elif not row['use_calculated_concentration'] and not row['use_area']:
#calc_conc, calc_conc_units = data_row['height_ratio'],'height_ratio';
calc_conc, calc_conc_units = row['height_ratio'],'height_ratio';
# calculate the normalized concentration
norm_conc = None;
norm_conc_units = None;
if calc_conc:
norm_conc, norm_conc_units = calc.calculate_conc_concAndConcUnitsAndDilAndDilUnitsAndConversionAndConversionUnits(calc_conc,calc_conc_units,dil,dil_units,cell_volume, cell_volume_units);
# update data_stage01_quantification_normalized
if norm_conc:
row = {'experiment_id':experiment_id_I,
'sample_name':row['sample_name'],
'sample_id':row['sample_id'],
'component_group_name':row['component_group_name'],
'component_name':row['component_name'],
'calculated_concentration':norm_conc,
'calculated_concentration_units':norm_conc_units,
'used_':True,};
data_O.append(row);
else:
for row_cnt,row in enumerate(groupJoin):
print('normalizing samples2Biomass for sample_name ' + row['sample_name'] + ' and component_name ' + row['component_name']);
# get the calculated concentration
calc_conc = None;
calc_conc_units = None;
#data_row = self.get_row_sampleNameAndComponentName(
# row['sample_name'],
# row['component_name']);
if use_height_I:
#calc_conc, calc_conc_units = data_row['height'],'height';
calc_conc, calc_conc_units = row['height'],'height';
elif row['use_calculated_concentration']:
#calc_conc, calc_conc_units = data_row['calculated_concentration'],data_row['conc_units'];
calc_conc, calc_conc_units = row['calculated_concentration'],row['conc_units'];
elif not row['use_calculated_concentration'] and row['use_area']:
#calc_conc, calc_conc_units = data_row['area_ratio'],'area_ratio';
calc_conc, calc_conc_units = row['area_ratio'],'area_ratio';
elif not row['use_calculated_concentration'] and not row['use_area']:
#calc_conc, calc_conc_units = data_row['height_ratio'],'height_ratio';
calc_conc, calc_conc_units = row['height_ratio'],'height_ratio';
# add data to the DB
if calc_conc:
row = {'experiment_id':experiment_id_I,
'sample_name':row['sample_name'],
'sample_id':row['sample_id'],
'component_group_name':row['component_group_name'],
'component_name':row['component_name'],
'calculated_concentration':calc_conc,
'calculated_concentration_units':calc_conc_units,
'used_':True,};
data_O.append(row);
##SPLIT 2:
## get sample names
#sample_names = [];
#sample_ids = [];
#for st in sample_types_I:
# sample_names_tmp = [];
# sample_ids_tmp = [];
# #sample_names_tmp = self.get_sampleNames_experimentIDAndSampleType(experiment_id_I,st);
# sample_names_tmp,sample_ids_tmp = self.get_sampleNamesAndSampleIDs_experimentIDAndSampleType(experiment_id_I,st);
# sample_names.extend(sample_names_tmp);
# sample_ids.extend(sample_ids_tmp);
#if sample_names_I:
# sample_names_ind = [i for i,x in enumerate(sample_names) if x in sample_names_I];
# sample_names_cpy = copy.copy(sample_names);
# sample_ids = copy.copy(sample_ids);
# sample_names = [x for i,x in enumerate(sample_names) if i in sample_names_ind]
# sample_ids = [x for i,x in enumerate(sample_ids) if i in sample_names_ind]
## create database table
#for sn_cnt,sn in enumerate(sample_names):
# print('normalizing samples2Biomass for sample_name ' + sn);
# # get component names
# component_names = [];
# component_group_names = [];
# #component_names = self.get_componentsNames_experimentIDAndSampleName(experiment_id_I,sn);
# component_names,component_group_names = self.get_componentsNamesAndComponentGroupNames_experimentIDAndSampleName(experiment_id_I,sn);
# if component_names_I:
# component_names_ind = [i for i,x in enumerate(component_names) if x in component_names_I];
# component_names_cpy = copy.copy(component_names);
# component_group_names = copy.copy(component_group_names);
# component_names = [x for i,x in enumerate(component_names) if i in component_names_ind]
# component_group_names = [x for i,x in enumerate(component_group_names) if i in component_names_ind]
# ## get sample id
# #sample_id = self.get_sampleID_experimentIDAndSampleName(experiment_id_I,sn);
# if (biological_material_I and conversion_name_I):
# # get physiological parameters
# cvs = None;
# cvs_units = None;
# od600 = None;
# dil = None;
# dil_units = None;
# conversion = None;
# conversion_units = None;
# cvs, cvs_units, od600, dil,dil_units = self.get_CVSAndCVSUnitsAndODAndDilAndDilUnits_sampleName(sn);
# conversion, conversion_units = self.get_conversionAndConversionUnits_biologicalMaterialAndConversionName(biological_material_I,conversion_name_I);
# if not(cvs and cvs_units and od600 and dil and dil_units):
# print('cvs, cvs_units, or od600 are missing from physiological parameters');
# print('or dil and dil_units are missing from sample descripton');
# exit(-1);
# elif not(conversion and conversion_units):
# print('biological_material or conversion name is incorrect');
# exit(-1);
# else:
# #calculate the cell volume or biomass depending on the conversion units
# #cell_volume, cell_volume_units = calc.calculate_cellVolume_CVSAndCVSUnitsAndODAndConversionAndConversionUnits(cvs,cvs_units,od600,conversion,conversion_units);
# cell_volume, cell_volume_units = calc.calculate_biomass_CVSAndCVSUnitsAndODAndConversionAndConversionUnits(cvs,cvs_units,od600,conversion,conversion_units);
# for cn_cnt,cn in enumerate(component_names):
# print('normalizing samples2Biomass for component_name ' + cn);
# # get component group name
# #component_group_name = self.get_componentGroupName_experimentIDAndComponentName(experiment_id_I,cn);
# #component_group_name = self.get_msGroup_componentName_MSComponents(cn);
# # get the calculated concentration
# calc_conc = None;
# calc_conc_units = None;
# if use_height_I:
# calc_conc, calc_conc_units = self.get_peakHeight_sampleNameAndComponentName(sn,cn);
# else:
# calc_conc, calc_conc_units = self.get_concAndConcUnits_sampleNameAndComponentName(sn,cn);
# # calculate the normalized concentration
# norm_conc = None;
# norm_conc_units = None;
# if calc_conc:
# norm_conc, norm_conc_units = calc.calculate_conc_concAndConcUnitsAndDilAndDilUnitsAndConversionAndConversionUnits(calc_conc,calc_conc_units,dil,dil_units,cell_volume, cell_volume_units);
# # update data_stage01_quantification_normalized
# if norm_conc:
# row = {'experiment_id':experiment_id_I,
# 'sample_name':sn,
# 'sample_id':sample_ids[sn_cnt],
# 'component_group_name':component_group_names[cn_cnt],
# 'component_name':cn,
# 'calculated_concentration':norm_conc,
# 'calculated_concentration_units':norm_conc_units,
# 'used_':True,};
# data_O.append(row);
# else:
# for cn_cnt,cn in enumerate(component_names):
# print('normalizing samples2Biomass for component_name ' + cn);
# # get component group name
# #component_group_name = self.get_componentGroupName_experimentIDAndComponentName(experiment_id_I,cn);
# #component_group_name = self.get_msGroup_componentName_MSComponents(cn);
# # get the calculated concentration
# calc_conc = None;
# calc_conc_units = None;
# if use_height_I:
# calc_conc, calc_conc_units = self.get_peakHeight_sampleNameAndComponentName(sn,cn);
# else:
# calc_conc, calc_conc_units = self.get_concAndConcUnits_sampleNameAndComponentName(sn,cn);
# # add data to the DB
# row = {'experiment_id':experiment_id_I,
# 'sample_name':sn,
# 'sample_id':sample_ids[sn_cnt],
# 'component_group_name':component_group_names[cn_cnt],
# 'component_name':cn,
# 'calculated_concentration':calc_conc,
# 'calculated_concentration_units':calc_conc_units,
# 'used_':True,};
# data_O.append(row);
self.add_rows_table('data_stage01_quantification_normalized',data_O);
