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import os
import re
import sys
import numpy as np
import pandas as pd
from lib.utilities import codebook
from lib.utilities import serialize
from lib.experiment_specs import study_config
""" Class that contains functions that
- labels the values of certain categorical variables specified in lib.{experiment_name}_specs.value_labels.yaml
- labels the variables of an expanded dataframe right before it becomes a stata file """
class Labeler:
def __init__(self, codebook_dict: dict, sheet: str, is_wide: bool):
self.label_codes = ["[PreviousSurvey]", "[Survey]", "[NextSurvey]", "[OldPhase]", "[NewPhase]"]
self.is_wide = is_wide
self.label_code_dic = Labeler._create_encode_dic()
self.codebook = codebook_dict
names = [x for x in study_config.surveys.keys()]
codes = [study_config.surveys[x]["Code"] for x in study_config.surveys.keys()]
self.code_survey_dic = dict(zip(codes, names))
self.manual_var_df = pd.read_excel(codebook.manual_specs_path)
""" creates a new first row in the dataframe which will contain the variable labels, as specified in the data\intermediate\mastercodebook"""
def add_labels_to_df(self,df):
df = df.reset_index(drop=True)
df.index = df.index + 1
df.loc[0] = ""
df = df.sort_index()
for col in df.columns:
# don't expand label if col is a main column or if we don't want the codebook to have expanded labels
if (col in study_config.main_cols+study_config.embedded_main_cols) or (self.is_wide == False):
if col in self.codebook:
new_label = self.codebook[col]["VariableLabel"]
else:
print(f"{col} not in codebook (main column) !!!")
new_label = col
else:
new_label = self._expand_label(col)
df.loc[0, col] = new_label
return df
""" Expands the label for a given variable specified in the master codebook. Specifically:
- if none of the label_codes are in the label, then a default expander is used
- if a label_code is in the label, then they are substituted using the dictionary created in _create_encode_dic, also visualized in
lib.tempation_specs.label_code_dic.json """
def _expand_label(self, variable):
try:
code, var = variable.split("_",1)
except:
print(f"Error extracting {variable} prefix")
return variable
survey = self.code_survey_dic[code]
if var not in self.codebook:
#if it's an intermediate variable or a text message survey variable, don't bother printing
if ("_str" in var) or ("T" == var[0]):
return variable
else:
print(f"{var} not in codebook!")
return variable
label = str(self.codebook[var]["VariableLabel"])
prefix = str(self.codebook[var]["PrefixEncoding"])
# if no manual specs, then no fancy labelling
if var not in self.manual_var_df["VariableName"]:
# if a survey variable
if prefix == "Survey":
new_label = survey + ": " + label
# if a phase variable; i.e. the encoding is "NewPhase" or "OldPhase"
else:
try:
phase = study_config.surveys[survey][prefix]
phase_label = study_config.phases[phase]["Label"]
new_label = phase_label + ": " + label
except:
print(
f"survey ({survey}) or phase label ({label}) not correctly specified in study config")
new_label = "BLAH"
# fancy labelling
else:
for label_code in self.label_codes:
if label_code in label:
label = label.replace(label_code,self.label_code_dic[survey][label_code])
new_label = label
return new_label
"""Creates a dictionary that will map macros in the compressed codebook to actual values in the expanded codebook. For example,
if a label in the compressed codebook contains '[PreviousSurvey]', the label for M_MobileUseMinutes well end up containing 'Baseline' """
@staticmethod
def _create_encode_dic():
label_code_dic = {}
for phase, specs in study_config.phases.items():
start_survey = specs["StartSurvey"]["Name"]
old_phase = study_config.surveys[start_survey]["OldPhase"]
new_phase = study_config.surveys[start_survey]["NewPhase"]
survey = start_survey
try:
old_survey = study_config.phases[old_phase]["StartSurvey"]["Name"]
except:
old_survey = "Doesn't Exist"
try:
new_survey = study_config.phases[new_phase]["EndSurvey"]["Name"]
except:
new_survey = "Doesn't Exist"
label_code_dic[survey] = {
"[PreviousSurvey]": old_survey,
"[Survey]": survey,
"[NextSurvey]": new_survey,
"[OldPhase]": old_phase,
"[NewPhase]": new_phase
}
import json
with open(os.path.join('lib','experiment_specs','label_code_dic.json'), 'w') as fp:
json.dump(label_code_dic, fp)
return label_code_dic
"""Encodes all values for variables listed in lib.experiment_specs.value_labels, and adds these value labels to codebook"""
@staticmethod
def label_values(df):
label_dic = serialize.open_yaml(codebook.value_label_path)
for encoding, chars in label_dic.items():
if encoding == "Scale":
for var_suffix in chars["VariableList"]:
for prefix in [x["Code"] for x in study_config.surveys.values()]:
var = prefix + "_" + var_suffix
if var in df.columns:
df[var] = df[var].astype(str).replace("nan", "")
df[f"{var}_num"] = df[var].apply(lambda x: re.sub("[^0-9]", "", x)).replace(" ", "")
df = df.rename(columns={var: var + "_str", var + "_num": var})
else:
for var_suffix in chars["VariableList"]:
for prefix in set([study_config.surveys[x]["Code"] for x in study_config.surveys.keys()]):
var = prefix + "_" + var_suffix
if var in df.columns:
try:
df[var] = df[var].astype(str).replace("nan", np.nan).replace("", np.nan)
df.loc[df[var].notnull(), var + "_num"] = df.loc[df[var].notnull(),
var].apply(
lambda x: chars["ValueLabels"][x]).astype(float)
df = df.rename(columns={var: var + "_str", var + "_num": var})
except:
print(f"{var} was not properly encoded!")
codebook.add_labels_to_codebook()
return df
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