def compute_scores( data, demographics ):
# Get rid of all records that don't have YR2
data.dropna( axis=1, subset=['youth_report_2_complete'] )
data = data[ data['youth_report_2_complete'] > 0 ]
data = data[ ~(data['youthreport2_missing'] > 0) ]
# If no records to score, return empty DF
if len( data ) == 0:
return pandas.DataFrame()
# Replace all column labels with the original LimeSurvey names
data.columns = RwrapperNew.map_labels( data.columns, rc2lime )
# Call the scoring function for all table rows
scores = data.apply( RwrapperNew.runscript, axis=1, Rscript='pgd/PGD.R' )
# Replace all score columns with REDCap field names
scores.columns = RwrapperNew.map_labels( scores.columns, R2rc )
# Simply copy completion status from the input surveys
scores['pgd_complete'] = data['youth_report_2_complete'].map( int )
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ['study_id', 'redcap_event_name']
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = [ 'pgd_complete' ] + R2rc.values()
return scores[ outfield_list ]
def compute_scores(data, demographics):
# Get rid of all records that don't have YR2
data.dropna(axis=1, subset=['youth_report_2_complete'])
data = data[data['youth_report_2_complete'] > 0]
data = data[~(data['youthreport2_missing'] > 0)]
# If no records to score, return empty DF
if len(data) == 0:
return pandas.DataFrame()
# Replace all column labels with the original LimeSurvey names
data.columns = RwrapperNew.map_labels(data.columns, rc2lime)
# Call the scoring function for all table rows
scores = data.apply(RwrapperNew.runscript, axis=1, Rscript='aeq/AEQ.R')
# Replace all score columns with REDCap field names
scores.columns = RwrapperNew.map_labels(scores.columns, R2rc)
# Simply copy completion status from the input surveys
scores['aeq_complete'] = data['youth_report_2_complete'].map(int)
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ['study_id', 'redcap_event_name']
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = ['aeq_complete'] + R2rc.values()
return scores[outfield_list]
def compute_scores( data, demographics ):
# Get rid of all records that don't have YR2
data.dropna( axis=0, subset=['youth_report_2_complete'] )
data = data[ data['youth_report_2_complete'] > 0 ]
data = data[ ~(data['youthreport2_missing'] > 0) ]
# If no records to score, return empty DF
if len( data ) == 0:
return pandas.DataFrame()
# Replace all column labels with the original LimeSurvey names
data.columns = RwrapperNew.map_labels( data.columns, rc2lime )
# Call the scoring function for all table rows
scores = data.apply( RwrapperNew.runscript, axis=1, Rscript='psqi/PSQI.R' )
# Replace all score columns with REDCap field names
scores.columns = RwrapperNew.map_labels( scores.columns, R2rc )
# Simply copy completion status from the input surveys
scores['psqi_complete'] = data['youth_report_2_complete'].map( int )
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ['study_id', 'redcap_event_name']
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = [ 'psqi_complete' ] + R2rc.values()
returnDF= scores[ outfield_list ]
# remove nan entries as they corrupt data ingest (REDCAP cannot handle it correctly) and superfluous zeros as these should all be integer scores
return returnDF.applymap(lambda x: '' if math.isnan(x) else '{0:g}'.format(float(x)))
def compute_scores(data, demographics):
# Get rid of all records that don't have YR2
data.dropna(axis=1, subset=["youth_report_2_complete"])
data = data[data["youth_report_2_complete"] > 0]
data = data[~(data["youthreport2_missing"] > 0)]
# If no records to score, return empty DF
if len(data) == 0:
return pandas.DataFrame()
# Set "ydi2" ("sex" field in LimeSurvey) based on what is in REDCap - this is chacked against subject ID and should avoid mis-entered data
data["ydi2"] = data.index.map(lambda key: demographics["sex"][key[0]])
# Replace all column labels with the original LimeSurvey names
data.columns = RwrapperNew.map_labels(data.columns, rc2lime)
# Call the scoring function for all table rows
scores = data.apply(RwrapperNew.runscript, axis=1, Rscript="pds/PDS.R")
# Replace all score columns with REDCap field names
scores.columns = RwrapperNew.map_labels(scores.columns, R2rc)
# Simply copy completion status from the input surveys
scores["pds_complete"] = data["youth_report_2_complete"].map(int)
# Make a proper multi-index for the scores table
scores.index = pandas.MultiIndex.from_tuples(scores.index)
scores.index.names = ["study_id", "redcap_event_name"]
# Return the computed scores - this is what will be imported back into REDCap
outfield_list = ["pds_complete"] + R2rc.values()
return scores[outfield_list]
import pandas
import RwrapperNew
#
# Variables from surveys needed for PGD
#
# LimeSurvey field names
lime_fields = [ "PGD_sec1 [pgd1]", "PGD_sec1 [pgd2]", "PGD_sec1 [pgd3]", "PGD_sec1 [pgd4]", "PGD_sec1 [pgd5]", "PGD_sec1 [pgd6]", "PGD_sec2 [pgd7]",
"PGD_sec2 [pgd8]", "PGD_sec2 [pgd9]", "PGD_sec2 [pgd10]", "PGD_sec2 [pgd11]", "PGD_sec2 [pgd12]" ]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[RwrapperNew.label_to_sri( 'youthreport2', field )] = field
# REDCap fields names
input_fields = { 'mrireport' : [ 'youth_report_2_complete', 'youthreport2_missing' ] + rc2lime.keys() }
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# PGD field names mapping from R to REDCap
#
R2rc = { 'PGD.SUM' : 'pgd_score' }
#
# LimeSurvey field names
lime_fields = [
"AEQ_sec1 [aeq1]", "AEQ_sec1 [aeq2]", "AEQ_sec1 [aeq3]", "AEQ_sec1 [aeq4]",
"AEQ_sec1 [aeq5]", "AEQ_sec1 [aeq6]", "AEQ_sec1 [aeq7]", "AEQ_sec2 [aeq8]",
"AEQ_sec2 [aeq9]", "AEQ_sec2 [aeq10]", "AEQ_sec2 [aeq11]",
"AEQ_sec2 [aeq12]", "AEQ_sec2 [aeq13]", "AEQ_sec2 [aeq14]",
"AEQ_sec3 [aeq15]", "AEQ_sec3 [aeq16]", "AEQ_sec3 [aeq17]",
"AEQ_sec3 [aeq18]", "AEQ_sec3 [aeq19]", "AEQ_sec3 [aeq20]",
"AEQ_sec3 [aeq21]"
]
# Dictionary to recover LimeSurvey field names from REDCap names
rc2lime = dict()
for field in lime_fields:
rc2lime[RwrapperNew.label_to_sri('youthreport2', field)] = field
# REDCap fields names
input_fields = {
'youthreport2':
['youth_report_2_complete', 'youthreport2_missing'] + rc2lime.keys()
}
#
# This determines the name of the form in REDCap where the results are posted.
#
output_form = 'clinical'
#
# AEQ field names mapping from R to REDCap
#
