def analyze_hades():
"""
"""
dg = DataGroup('HADES.json')
dg.lh5_dir_setup()
# dg.lh5_dir_setup(create=True)
dg.scan_daq_dir()
# -- experiment-specific stuff --
dg.file_keys['runtype'] = dg.file_keys['detSN']
# add a sortable timestamp column
def get_ts(row):
ts = f"{row['YYmmdd']} {row['hhmmss']}"
row['date'] = pd.to_datetime(ts, format='%y%m%d %H%M%S')
return row
dg.file_keys = dg.file_keys.apply(get_ts, axis=1)
dg.file_keys.sort_values('date', inplace=True)
dg.get_lh5_cols()
print(dg.file_keys['raw_file'].values)
dg.save_df('HADES_fileDB.h5')
def analyze_lpgta():
dg = DataGroup('LPGTA.json')
dg.lh5_dir_setup()
dg.scan_daq_dir()
# -- experiment-specific choices --
# run 1 & 2 files don't match template
dg.file_keys.query('run > 2', inplace=True)
dg.file_keys.sort_values(['run', 'YYYYmmdd', 'hhmmss'], inplace=True)
dg.file_keys.reset_index(drop=True, inplace=True)
def get_cmap(row):
row['cmap'] = dg.runDB[f"{row['run']:0>4d}"]["cmap"]
return row
dg.file_keys = dg.file_keys.apply(get_cmap, axis=1)
dg.file_keys['runtype'] = dg.file_keys['rtp']
dg.get_lh5_cols()
dg.save_df('./LPGTA_fileDB.h5')
print(dg.file_keys)
def analyze_cage():
dg = DataGroup('CAGE.json')
dg.lh5_dir_setup()
dg.scan_daq_dir()
# -- experiment-specific choices --
dg.file_keys.sort_values(['cycle'], inplace=True)
dg.file_keys.reset_index(drop=True, inplace=True)
def get_cyc_info(row):
"""
map cycle numbers to physics runs, and identify detector
"""
cyc = row['cycle']
for run, cycles in dg.runDB.items():
tmp = cycles[0].split(',')
for rng in tmp:
if '-' in rng:
clo, chi = [int(x) for x in rng.split('-')]
if clo <= cyc <= chi:
row['run'] = run
break
else:
clo = int(rng)
if cyc == clo:
row['run'] = run
break
# label the detector ('runtype' matches 'run_types' in config file)
if cyc < 126:
row['runtype'] = 'oppi'
else:
row['runtype'] = 'icpc'
return row
dg.file_keys = dg.file_keys.apply(get_cyc_info, axis=1)
dg.get_lh5_cols()
for col in ['run']:
dg.file_keys[col] = pd.to_numeric(dg.file_keys[col])
print(dg.file_keys)
dg.save_df('CAGE_fileDB.h5')
def main():
"""
Requires LPGTA.json config file.
Save an hdf5 file with pygama daq, raw, and dsp names + paths.
"""
dg = DataGroup('LPGTA.json')
# dg.lh5_dir_setup() # <-- run this once with create=True
dg.scan_daq_dir()
# -- organize and augment the dg.file_keys DataFrame --
# run 1 & 2 files don't match template
dg.file_keys.query('run > 2', inplace=True)
dg.file_keys.sort_values(['run', 'YYYYmmdd', 'hhmmss'], inplace=True)
dg.file_keys.reset_index(drop=True, inplace=True)
def get_cmap(row):
run_str = f"{row['run']:0>4d}"
if run_str not in dg.runDB:
print("Warning: no runDB entry for run", run_str)
row['cmap'] = ''
return row
row['cmap'] = dg.runDB[f"{row['run']:0>4d}"]["cmap"]
return row
dg.file_keys = dg.file_keys.apply(get_cmap, axis=1)
dg.file_keys['runtype'] = dg.file_keys['rtp']
dg.get_lh5_cols()
# save to file used by processing.py
dg.save_df('./LPGTA_fileDB.h5')
print(dg.file_keys)
def analyze_surf():
"""
"""
dg = DataGroup('SURFCHAR.json')
dg.lh5_dir_setup()
# dg.lh5_dir_setup(create=True)
dg.scan_daq_dir()
# -- experiment-specific choices --
dg.file_keys.sort_values(['cycle'], inplace=True)
dg.file_keys.reset_index(drop=True, inplace=True)
# TODO: adapt "get_cyc_info" function from analyze_cage to
# fill in serial numbers for each detector
dg.file_keys['runtype'] = "P9999A"
dg.get_lh5_cols()
# print(dg.file_keys.query('cycle < 10'))
dg.save_df('SURFCHAR_fileDB.h5')