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+"""There is a set of checkers for handling of correctness of the calibration database"""
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+
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+import logging
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+from datetime import datetime
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+import pandas as pd
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+import numpy as np
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+from compton_combiner import SEASONS
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+from compton_combiner import RunsDBHandler, Combiner
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+from compton_filter import SlowdbComptonHandler, CalibrdbHandler
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+
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+def check_season(season, parser):
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+ """Check correctness of the average calibrations.
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+ This function tests that all the points of the season are filled in the calibration database and
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+ this database has not duplicates.
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+
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+ Parameters
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+ ----------
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+ season : str
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+ a season for checking (can be from `compton_combiner.SEASONS` enum)
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+ parser : Configparser
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+ configparser of the toml file with credentials
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+ """
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+
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+ idx = SEASONS['name'].index(season)
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+ start_run, stop_run = SEASONS['start_run'][idx], SEASONS['start_run'][idx + 1]
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+ logging.info(f'Season {season}, runs from {start_run} to {stop_run}')
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+
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+ rdb = RunsDBHandler(**parser['cmdruns'])
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+ rdb_df = rdb.load_tables((start_run, stop_run))
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+ trng = (rdb_df[0][-1][1], rdb_df[0][0][2])
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+ logging.info(f'Runs table has been loaded. \n\
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+First row is {rdb_df[0][-1][0]} run (at {rdb_df[0][-1][1].strftime("%Y-%m-%d")}), \
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+last row is {rdb_df[0][0][0]} run (at {rdb_df[0][0][1].strftime("%Y-%m-%d")}). \n\
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+Total rows {len(rdb_df[0])}, unique energies {sum(map(lambda x: x[0][3]!=x[1][3], zip(rdb_df[0][1:], rdb_df[0][:-1]))) + 1}' )
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+
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+ rdb_df = pd.DataFrame(rdb_df[0], columns=rdb_df[1]).sort_values(by='run')
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+ rdb_df['eprev'] = rdb_df.energy.shift(1).fillna(-1)
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+ rdb_df['enext'] = rdb_df.energy.shift(-1).fillna(-1)
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+
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+ # start_runs_df = rdb_df.drop_duplicates(subset=['energy'], keep='first')
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+ # end_runs_df = rdb_df.drop_duplicates(subset=['energy'], keep='last')
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+
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+ good_mask = np.invert(np.isclose(rdb_df['energy'], rdb_df['eprev']) & np.isclose(rdb_df['energy'], rdb_df['enext']))
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+ rdb_df = rdb_df.loc[good_mask].drop(['eprev', 'enext'], axis=1)
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+ rdb_df = pd.concat([rdb_df.iloc[::2].reset_index(drop=True).rename({'run': 'firstrun'}, axis=1)[['energy', 'firstrun']],
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+ rdb_df.iloc[1::2].reset_index(drop=True).rename({'run': 'lastrun'}, axis=1)[['lastrun']]], axis=1).assign(goods=1)
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+
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+ clbrdb = CalibrdbHandler(**parser['clbrDB'])
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+ # trng = (rdb_df.starttime.min(), rdb_df.stoptime.max())
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+ logging.info(f'Time range from {trng[0]} to {trng[1]}')
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+ # print(trng)
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+ res_clbrdb = clbrdb.load_table('Misc', 'RunHeader', 'Compton_run_avg', num_last_rows = None, timerange = trng)
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+ clbr_df = pd.DataFrame(res_clbrdb[0], columns=res_clbrdb[1]).drop(['cid', 'sid', 'createdby', 'parameters'], axis=1).sort_values(by='time')
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+ clbr_df = clbr_df.apply(
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+ lambda x: pd.Series([x['comment'], x['time'], x['data'][0], x['data'][1], x['data'][2], round(x['data'][3], 3)],
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+ index=['pname', 'time', 'energy', 'firstrun', 'lastrun', 'emeas']),
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+ axis=1).reset_index(drop=True)
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+
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+ concat_df = pd.merge(clbr_df, rdb_df, on=['firstrun', 'lastrun'], how='outer')
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+ concat_df.loc[concat_df.isna().max(axis=1), 'goods'] = 0
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+ concat_df.goods = concat_df.goods.astype(int)
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+
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+ nbads = sum(concat_df.goods==0)
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+ if nbads > 0:
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+ logging.warning(f'{sum(concat_df.goods==0)} bad rows are found')
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+ else:
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+ logging.info(f'bad rows are not found')
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+
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+
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+ return concat_df
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+
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+if __name__=="__main__":
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+ import argparse
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+ import sys
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+ from configparser import ConfigParser
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+
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+ log_format = '[%(asctime)s] %(levelname)s: %(message)s'
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+ logging.basicConfig(stream=sys.stdout, format=log_format, level=logging.INFO) #"filename=compton_combiner.log"
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+ logging.info("compton_checker is started")
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+
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+ parser = argparse.ArgumentParser(description = 'Checker of the energy measurement calibrations')
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+ parser.add_argument('-s', '--season', help = 'Name of the season')
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+ parser.add_argument('-c', '--config', help = 'Config file containing information for access to databases')
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+
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+ args = parser.parse_args()
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+ logging.info(f"""Arguments: season {args.season}, config {args.config}""")
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+
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+ parser = ConfigParser()
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+ parser.read(args.config);
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+
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+ res_df = check_season(args.season, parser)
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+
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+ with pd.option_context('display.max_rows', None,):
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+ print(res_df)
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+
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