""" Implementation in progress. """
import numpy as np
from hed.tools.remodeling.operations.base_op import BaseOp
# TODO: This class is under development
[docs]class NumberRowsOp(BaseOp):
""" Implementation in progress. """
PARAMS = {
"operation": "number_rows",
"required_parameters": {
"number_column_name": str
},
"optional_parameters": {"overwrite": bool, "match_value": dict}
}
[docs] def __init__(self, parameters):
super().__init__(self.PARAMS, parameters)
self.number_column_name = parameters['number_column_name']
self.overwrite = parameters.get('overwrite', False)
self.match_value = parameters.get('match_value', False)
if self.match_value:
self.match_value_params = {"column": str, "value": str}
required = set(self.match_value_params.keys())
required_missing = required.difference(set(self.match_value.keys()))
if required_missing:
raise KeyError("MissingRequiredParameters",
f"Specified match_value for number_rows requires parameters {list(required_missing)}")
for param_name, param_value in self.match_value.items():
if param_name in required:
param_type = self.match_value_params[param_name]
else:
raise KeyError("BadParameter",
f"{param_name} not a required or optional parameter for {self.operation}")
# TODO: this has a syntax error
# if not isinstance(param_value, param_type):
# raise TypeError("BadType" f"{param_value} has type {type(param_value)} not {param_type}")
[docs] def do_op(self, dispatcher, df, name, sidecar=None):
""" Add numbers events dataframe.
Parameters:
dispatcher (Dispatcher): Manages operation I/O.
df (DataFrame): - The DataFrame to be remodeled.
name (str): - Unique identifier for the dataframe -- often the original file path.
sidecar (Sidecar or file-like): Only needed for HED operations.
Returns:
Dataframe - a new dataframe after processing.
"""
if self.number_column_name in df.columns:
if self.overwrite is False:
raise ValueError("ExistingNumberColumn",
f"Column {self.number_column_name} already exists in event file.", "")
if self.match_value:
if self.match_value['column'] not in df.columns:
raise ValueError("MissingMatchColumn",
f"Column {self.match_value['column']} does not exist in event file.", "")
if self.match_value['value'] not in df[self.match_value['column']].tolist():
raise ValueError("MissingMatchValue",
f"Value {self.match_value['value']} does not exist in event file column"
f"{self.match_value['column']}.", "")
df_new = df.copy()
# df_new[self.number_column_name] = np.nan
# if self.match_value:
# filter = df[self.match_value['column']] == self.match_value['value']
# numbers = [*range(1, sum(filter)+1)]
# df_new.loc[filter, self.number_column_name] = numbers
# else:
# df_new[self.number_column_name] = df_new.index + 1
return df_new