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pcwg03_convert_df.py
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pcwg03_convert_df.py
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import numpy as np
import pandas as pd
import itertools
from pathlib import Path
import pickle
import pcwg03_initialize as p_init
import pcwg03_config as pc
import pcwg03_read_data as prd
def turn_submission_to_series(file):
submission_series = prd.load_PCWG03(file, 'Submission').read_xls_submission()
return submission_series
def turn_meta_to_series(file):
meta_series = prd.load_PCWG03(file, 'Meta Data').read_xls_metadata()
return meta_series
def turn_error_matrices_to_df(file, sheet, bt_choice):
error_df = prd.load_PCWG03(file, sheet, bin_or_total=bt_choice).read_xls_matrix()
return error_df
def loop_matrix_sheet(bt_list, count, error_dict, file_list, sheet_name, sheet_name_short):
"""Put error data frames into dictionary."""
for bt_count in range(len(bt_list)):
all_error = [turn_error_matrices_to_df(file, sheet_name[count], bt_list[bt_count])
for file in file_list]
all_error_df = pd.concat(all_error, axis=0)
all_error_df.reset_index(inplace=True, drop=True)
error_dict[sheet_name_short[count] + bt_list[bt_count]] = all_error_df
return error_dict
def search_pkl_existence(pkl_name):
pkl_file = p_init.py_file_path + '/data_pcwg03_' + pkl_name + '.pkl'
pkl_path = Path(pkl_file)
return pkl_path.exists()
def load_existing_pkl(pkl_name):
pkl_file = p_init.py_file_path + '/data_pcwg03_' + pkl_name + '.pkl'
print(pkl_name + ' pkl file exists; loading pkl file directly!')
with open(pkl_file, 'rb') as f:
out_df = pickle.load(f)
return out_df
def read_xls_write_pkl(pkl_name, turn_function, data_file):
print(pkl_name + ' pkl file does not exist; reading in excel files...')
data = [turn_function(file) for file in data_file]
out_df = pd.concat(data, axis=1).T
pkl_file = p_init.py_file_path + '/data_pcwg03_' + pkl_name + '.pkl'
with open(pkl_file, 'wb') as f:
pickle.dump(out_df, f)
return out_df
def get_submission_df(data_file):
if search_pkl_existence('submission') is True:
submission_df = load_existing_pkl('submission')
else:
submission_df = read_xls_write_pkl('submission', turn_submission_to_series, data_file)
submission_df = submission_df.rename_axis('file_name').reset_index()
return submission_df
def get_metadata_df(data_file):
if search_pkl_existence('metadata') is True:
meta_df = load_existing_pkl('metadata')
else:
meta_df = read_xls_write_pkl('metadata', turn_meta_to_series, data_file)
meta_df = meta_df.rename_axis('file_name').reset_index()
# calculate rated power
meta_df['turbi_rated_power'] = meta_df['turbi_spower']*(np.pi*((meta_df['turbi_dia']/2)**2))/1e3
# calculate rotor-diameter-to-hub-height ratio
meta_df['turbi_d_hh_ratio'] = meta_df['turbi_dia']/meta_df['turbi_hh']
return meta_df
def get_error_df_dict(data_file):
"""Load dictionary of error data frames from pickle file.
If pickle file does not exist, make one.
"""
if search_pkl_existence('error') is True:
error_df_dict = load_existing_pkl('error')
else:
print('error pkl file does not exist; reading in excel files...')
error_df_dict = {}
for ms_count in range(len(pc.matrix_sheet_name)):
print('working on ' + pc.matrix_sheet_name[ms_count])
error_df_dict = loop_matrix_sheet(bt_list=pc.bt_choice, count=ms_count, error_dict=error_df_dict,
file_list=data_file, sheet_name=pc.matrix_sheet_name,
sheet_name_short=pc.matrix_sheet_name_short)
error_pkl_file = p_init.py_file_path + '/data_pcwg03_error.pkl'
with open(error_pkl_file, 'wb') as f:
pickle.dump(error_df_dict, f)
return error_df_dict
def get_extra_error_df_dict(data_file):
data_file_path = str(Path(data_file[0]).parent)
if search_pkl_existence('extra_error') is True:
extra_error_df_dict = load_existing_pkl('extra_error')
else:
print('extra error pkl file does not exist; reading in excel files...')
extra_error_df_dict = {}
for correct_i in range(len(pc.correction_list)):
all_list = []
for file in data_file:
all_list.append(prd.load_PCWG03(file,
'Submission').read_xls_extra_matrix(pc.correction_list[correct_i]))
select_list = [x for x in all_list if x is not None]
extra_sheet_list = [data_file_path + '/' + x + '.xls' for x in select_list]
print('working on ' + pc.extra_matrix_sheet_name[correct_i])
extra_error_df_dict = loop_matrix_sheet(bt_list=pc.bt_choice, count=correct_i,
error_dict=extra_error_df_dict, file_list=extra_sheet_list,
sheet_name=pc.extra_matrix_sheet_name,
sheet_name_short=pc.extra_matrix_sheet_name_short)
extra_error_pkl_file = p_init.py_file_path + '/data_pcwg03_extra_error.pkl'
with open(extra_error_pkl_file, 'wb') as f:
pickle.dump(extra_error_df_dict, f)
return extra_error_df_dict
def remove_error_entry(sheet_i, bt, df, df_filter, nme_filter_files):
"""Remove submissions that fail the NME filter."""
sheet = sheet_i + bt
df_filter[sheet] = df[sheet][(~df[sheet]['file_name'].isin(nme_filter_files))]
file_out = (len(df[sheet]) - len(df_filter[sheet])) / pc.error_entry
print('removing ' + str(round(file_out)) + ' files in ' + sheet)
def filter_base_bin_nme(error_df, extra_error_df):
"""Filter out a fraction of submissions based on Baseline NME.
Submissions with bin NME per bin energy (bin_e) not close to 0 are labelled as bad data.
"""
error_df_filter = dict.fromkeys(error_df) # copy keys
extra_error_df_filter = dict.fromkeys(extra_error_df)
nme_only = error_df['base_bin_e'].loc[(error_df['base_bin_e']['error_cat'] == 'by_range')
& (error_df['base_bin_e']['bin_name'] == 'Inner')
& (error_df['base_bin_e']['error_name'] == 'nme')]
nme_filter_files = nme_only.loc[(nme_only['error_value'].values * 100 < -pc.nme_filter_thres)
| (nme_only['error_value'].values * 100 > pc.nme_filter_thres)]['file_name']
print('data files with baseline bin error per bin energy exceed ' + str(pc.nme_filter_thres) + '% are:')
print(nme_filter_files)
print('a total of ' + str(len(nme_filter_files)) + ' files contain bad data')
for idx, (bt, sheet_i) in enumerate(itertools.product(pc.bt_choice, pc.matrix_sheet_name_short)):
remove_error_entry(sheet_i, bt, error_df, error_df_filter, nme_filter_files)
for idx, (bt, sheet_i) in enumerate(itertools.product(pc.bt_choice, pc.extra_matrix_sheet_name_short)):
remove_error_entry(sheet_i, bt, extra_error_df, extra_error_df_filter, nme_filter_files)
return error_df_filter, extra_error_df_filter