2020-11-25 22:34:05 +01:00
|
|
|
import pandas as pd
|
|
|
|
|
import numpy as np
|
|
|
|
|
from importlib import import_module, util
|
|
|
|
|
from pathlib import Path
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# import filter_data_by_segment from src/features/utils/utils.py
|
|
|
|
|
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent.parent / "features" / "utils" / "utils.py"))
|
|
|
|
|
mod = util.module_from_spec(spec)
|
|
|
|
|
spec.loader.exec_module(mod)
|
|
|
|
|
filter_data_by_segment = getattr(mod, "filter_data_by_segment")
|
|
|
|
|
|
|
|
|
|
targets = pd.read_csv(snakemake.input["targets"])
|
2020-12-03 00:41:03 +01:00
|
|
|
time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0)
|
2020-11-25 22:34:05 +01:00
|
|
|
|
|
|
|
|
all_targets = pd.DataFrame(columns=["local_segment"])
|
2020-12-03 00:41:03 +01:00
|
|
|
for time_segment in time_segments_labels["label"]:
|
|
|
|
|
filtered_targets = filter_data_by_segment(targets, time_segment)
|
2020-11-25 22:34:05 +01:00
|
|
|
all_targets = all_targets.merge(filtered_targets, how="outer")
|
|
|
|
|
|
|
|
|
|
segment_colums = pd.DataFrame()
|
|
|
|
|
split_segemnt_columns = all_targets["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
|
|
|
|
new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"])
|
|
|
|
|
segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns
|
|
|
|
|
for i in range(segment_colums.shape[1]):
|
|
|
|
|
all_targets.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
|
|
|
|
|
|
|
|
|
all_targets.to_csv(snakemake.output[0], index=False)
|
