# Path: mimic3benchmark/util.py
# def dataframe_from_csv(path, header=0, index_col=0):
#     return pd.read_csv(path, header=header, index_col=index_col)


import numpy as np
import re
            import traceback
from pandas import DataFrame, Series
from mimic3benchmark.util import dataframe_from_csv
        if l not in labels:
            labels[l] = 0
    labels = labels[diagnosis_labels]
    return labels.rename(dict(zip(diagnosis_labels, ['Diagnosis ' + d for d in diagnosis_labels])), axis=1)


def add_hcup_ccs_2015_groups(diagnoses, definitions):
    def_map = {}
    for dx in definitions:
        for code in definitions[dx]['codes']:
            def_map[code] = (dx, definitions[dx]['use_in_benchmark'])
    diagnoses['HCUP_CCS_2015'] = diagnoses.ICD9_CODE.apply(lambda c: def_map[c][0] if c in def_map else None)
    diagnoses['USE_IN_BENCHMARK'] = diagnoses.ICD9_CODE.apply(lambda c: int(def_map[c][1]) if c in def_map else None)
    return diagnoses


def make_phenotype_label_matrix(phenotypes, stays=None):
    phenotypes = phenotypes[['ICUSTAY_ID', 'HCUP_CCS_2015']].loc[phenotypes.USE_IN_BENCHMARK > 0].drop_duplicates()
    phenotypes['VALUE'] = 1
    phenotypes = phenotypes.pivot(index='ICUSTAY_ID', columns='HCUP_CCS_2015', values='VALUE')
    if stays is not None:
        phenotypes = phenotypes.reindex(stays.ICUSTAY_ID.sort_values())
    return phenotypes.fillna(0).astype(int).sort_index(axis=0).sort_index(axis=1)


###################################
# Time series preprocessing
###################################

def read_itemid_to_variable_map(fn, variable_column='LEVEL2'):

# Path: mimic3benchmark/util.py
# def dataframe_from_csv(path, header=0, index_col=0):
#     return pd.read_csv(path, header=header, index_col=index_col)


# Path: mimic3benchmark/preprocessing.py
import numpy as np
import re
            import traceback
from pandas import DataFrame, Series
from mimic3benchmark.util import dataframe_from_csv

        if l not in labels:
            labels[l] = 0
    labels = labels[diagnosis_labels]
    return labels.rename(dict(zip(diagnosis_labels, ['Diagnosis ' + d for d in diagnosis_labels])), axis=1)


def add_hcup_ccs_2015_groups(diagnoses, definitions):
    def_map = {}
    for dx in definitions:
        for code in definitions[dx]['codes']:
            def_map[code] = (dx, definitions[dx]['use_in_benchmark'])
    diagnoses['HCUP_CCS_2015'] = diagnoses.ICD9_CODE.apply(lambda c: def_map[c][0] if c in def_map else None)
    diagnoses['USE_IN_BENCHMARK'] = diagnoses.ICD9_CODE.apply(lambda c: int(def_map[c][1]) if c in def_map else None)
    return diagnoses


def make_phenotype_label_matrix(phenotypes, stays=None):
    phenotypes = phenotypes[['ICUSTAY_ID', 'HCUP_CCS_2015']].loc[phenotypes.USE_IN_BENCHMARK > 0].drop_duplicates()
    phenotypes['VALUE'] = 1
    phenotypes = phenotypes.pivot(index='ICUSTAY_ID', columns='HCUP_CCS_2015', values='VALUE')
    if stays is not None:
        phenotypes = phenotypes.reindex(stays.ICUSTAY_ID.sort_values())
    return phenotypes.fillna(0).astype(int).sort_index(axis=0).sort_index(axis=1)


###################################
# Time series preprocessing
###################################

def read_itemid_to_variable_map(fn, variable_column='LEVEL2'):

    var_map = dataframe_from_csv(fn, index_col=None).fillna('').astype(str)