# Copyright (c) 2019, MD2K Center of Excellence
# - Nasir Ali <nasir.ali08@gmail.com>
# All rights reserved.
#
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# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
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import pandas as pd
from cerebralcortex.data_importer.util.helper_methods import rename_column_name
[docs]def assign_column_names_types_strict(df: pd, metadata: dict = None) -> pd:
"""
Change column names to the names defined in metadata->data_descriptor block
Args:
df (pandas): pandas dataframe
metadata (dict): metadata of the data
Returns:
pandas dataframe
"""
metadata_columns = []
new_column_names = {0: "timestamp", 1: "localtime"}
if metadata is not None:
data_desciptor = metadata.get("data_descriptor", [])
if isinstance(data_desciptor, dict):
data_desciptor = [data_desciptor]
for dd in data_desciptor:
name = rename_column_name(dd.get("name", "", ))
metadata_columns.append({"name": name, "type": dd.get("data_type", "")})
if len(metadata_columns) > 0:
col_no = 2 # first two column numbers are timestamp and offset
for mc in metadata_columns:
new_column_names[col_no] = mc["name"]
col_no += 1
else:
for column in df:
if column != 0 and column != 1:
new_column_names[column] = "value_" + str(column - 1)
if len(df.columns)>0:
tmp = len(metadata_columns)+2
for df_col in df:
new_column_names[tmp] = "value_"+str(tmp)
tmp +=1
for column in df:
if column not in [0, 1]:
df[column] = pd.to_numeric(df[column], errors='ignore')
df.rename(columns=new_column_names, inplace=True)
return df
[docs]def assign_column_names_types(df: pd, metadata: dict = None) -> pd:
"""
Change column names to the names defined in metadata->data_descriptor block
Args:
df (pandas): pandas dataframe
metadata (dict): metadata of the data
Returns:
pandas dataframe
"""
metadata_columns = []
new_column_names = {0: "timestamp", 1: "localtime"}
if metadata is not None:
data_desciptor = metadata.data_descriptor
if isinstance(data_desciptor, dict):
data_desciptor = [data_desciptor]
for dd in data_desciptor:
name = rename_column_name(dd.name)
metadata_columns.append({"name": name, "type": dd.type})
if len(metadata_columns) > 0:
col_no = 2 # first two column numbers are timestamp and offset
for mc in metadata_columns:
new_column_names[col_no] = mc.get("name")
col_no += 1
else:
for column in df:
if column != 0 and column != 1:
new_column_names[column] = "value_" + str(column - 1)
for column in df:
if column not in [0, 1]:
df[column] = pd.to_numeric(df[column], errors='ignore')
df.rename(columns=new_column_names, inplace=True)
return df