def beta_fits_overall(input_file, output_file):
"""Get data, do beta fits on data, write all back to a file.
Args:
input_file: Filename to get data from
output_file: Filename to write data and fits to.
"""
data = jmutils.get_csv_dict(input_file)
meta = np.array([[float(p['BTS_Lower']), float(p['BTS_Avg']), float(p['BTS_Upper'])] for p in data])
fits = np.apply_along_axis(lambda m: beta_fitting_python(m, True), 1, meta)
combined = combine_fits(data, fits)
jmutils.write_csv_dict(output_file, combined, [])
def split_ifp(input_dir, output_dir, ifp_fname):
"""Split ifp file into individual date files - this is overall func that calls other ones.
Args:
input_dir: dir containing the ifp file to be split.
ifp_prefix: ifp filename (must be a .csv) to split up w/o the .csv.
output_dir: dir to write split up files in.
"""
ifp_file = os.path.join(input_dir, ifp_fname)
data = jmutils.get_csv_dict(ifp_file)
add_pythondate(data)
aggregation_dates = all_aggregation_dates(data)
split, dates = split_by_dates(data, aggregation_dates) #use dates for filenames later.
newest = [only_retain_newest(s) for s in split]
omit_writting = ['python_date']
for d, to_write in zip(dates, newest):
ifp_prefix = ifp_fname.partition('.')[0]
ifp_date = os.path.join(output_dir, ifp_prefix + '_date_' + d + ".csv")
jmutils.write_csv_dict(ifp_date, to_write, omit_writting)
