import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from pax import units
from pax.configuration import load_configuration
from hax.utils import flatten_dict
##
# Load the PMT data from the pax configuration
##
# Convert PMT/channel map to record array
pax_config = load_configuration('XENON1T') # TODO: depends on experiment, should do after init
pmt_data = pd.DataFrame([flatten_dict(info, separator=':')
for info in pax_config['DEFAULT']['pmts']
if 'array' in info])
pmt_numbering_start = pmt_data['pmt_position'].min()
##
# Plotting functions
##
def _pad_to_length_of(a, b):
"""Pads a with zeros until it has the length of b"""
lendiff = len(b) - len(a)
if lendiff < 0:
raise ValueError("Cannot pad a negative number of zeros!")
elif lendiff > 0:
def update_datasets(query=None):
"""Update hax.runs.datasets to contain latest datasets.
Currently just loads XENON100 run 10 runs from a csv file.
query: custom query, in case you only want to update partially??
"""
global datasets
experiment = hax.config['experiment']
version_policy = hax.config['pax_version_policy']
if not hax.config.get('use_runs_db', True):
hax.log.info("Not looking for datasets in runs, db since you put use_runs_db = False")
return
if experiment == 'XENON100':
# Fetch runs information from static csv files in runs info
for rundbfile in glob(os.path.join(hax.config['runs_info_dir'], '*.csv')):
tpc, run = os.path.splitext(os.path.basename(rundbfile))[0].split('_')
dsets = pd.read_csv(rundbfile)
dsets = pd.concat((dsets, pd.DataFrame([{'tpc': tpc, 'run': run}] * len(dsets))), axis=1)
if datasets is not None and len(datasets):
datasets = pd.concat((datasets, dsets), ignore_index=True)
else:
datasets = dsets
elif experiment == 'XENON1T':
collection = get_rundb_collection()
docs = []
if query is None:
query = {}
query['detector'] = hax.config.get('detector', hax.config['detector'])
log.debug("Updating datasets from runs database... ")
cursor = collection.find(query,
['name', 'number', 'start', 'end', 'source',
'reader.self_trigger', 'reader.ini.name',
'trigger.events_built', 'trigger.status',
'tags.name',
'data'])
for doc in cursor:
# Process and flatten the doc
# Convert tags to single string
doc['tags'] = ','.join([t['name'] for t in doc.get('tags', [])])
doc = flatten_dict(doc, separator='__')
del doc['_id'] # Remove the Mongo document ID
if 'data' in doc:
data_docs = doc['data']
del doc['data']
else:
data_docs = []
doc = flatten_dict(doc, separator='__')
if version_policy != 'loose':
# Does the run db know where to find the processed data at this host?
processed_data_docs = [d for d in data_docs
if (d['type'] == 'processed'
and hax.config['cax_key'] in d['host']
and d['status'] == 'transferred')]
if version_policy != 'latest':
# Filter out versions not consistent with the version policy.
# We will take the latest of the remaining ones later
processed_data_docs = [
d for d in processed_data_docs if version_is_consistent_with_policy(d['pax_version'])]
# If there is a processed data consistent with the version
# policy, set its location
doc['location'] = ''
doc['pax_version'] = ''
if len(processed_data_docs):
# Take the data doc with the most recent policy-consistent
# pax version
data_we_take = max(processed_data_docs, key=lambda x: LooseVersion(x['pax_version']))
doc['location'] = data_we_take['location']
doc['pax_version'] = data_we_take['pax_version'][1:]
docs.append(doc)
datasets = pd.DataFrame(docs)
log.debug("... done.")
# These may or may not have been set already:
if 'pax_version' not in datasets:
datasets['pax_version'] = [''] * len(datasets)
if 'location' not in datasets:
datasets['location'] = [''] * len(datasets)
if 'raw_data_subfolder' not in datasets:
datasets['raw_data_subfolder'] = [''] * len(datasets)
if 'raw_data_found' not in datasets:
datasets['raw_data_found'] = [False] * len(datasets)
if 'raw_data_used_local_path' not in datasets:
datasets['raw_data_used_local_path'] = [''] * len(datasets)
dataset_names = datasets['name'].values
if version_policy == 'loose':
# Walk through main_data_paths, looking for root files
# Reversed, since if we find a dataset again, we overwrite, and
# usually people put first priority stuff at the front.
for data_dir in reversed(hax.config.get('main_data_paths', [])):
for candidate in glob(os.path.join(data_dir, '*.root')):
# What dataset is this file for?
dsetname = os.path.splitext(os.path.basename(candidate))[0]
bla = np.where(dataset_names == dsetname)[0]
if len(bla):
# Dataset was found, index is in bla[0]
datasets.loc[bla[0], 'location'] = candidate
# For the raw data, we may need to look in subfolders ('run_10' etc)
# don't do os.path.exist for each dataset, it will take minutes, at least
# over sshfs
if hax.config['raw_data_access_mode'] == 'local':
for raw_data_path in hax.config['raw_data_local_path']:
for subfolder, dsets_in_subfolder in datasets.groupby(
'raw_data_subfolder'):
subfolder_path = os.path.join(raw_data_path, subfolder)
if not os.path.exists(subfolder_path):
log.debug(
"Folder %s not found when looking for raw data" %
subfolder_path)
continue
for candidate in os.listdir(subfolder_path):
bla = np.where(dataset_names == candidate)[0]
if len(bla):
if not datasets.loc[bla[0], 'raw_data_found']:
datasets.loc[bla[0],
'raw_data_used_local_path'] = raw_data_path
datasets.loc[bla[0], 'raw_data_found'] = True
def update_datasets(query=None):
"""Update hax.runs.datasets to contain latest datasets.
Currently just loads XENON100 run 10 runs from a csv file.
query: custom query, in case you only want to update partially??
"""
global datasets
experiment = hax.config['experiment']
version_policy = hax.config['pax_version_policy']
if experiment == 'XENON100':
# Fetch runs information from static csv files in runs info
for rundbfile in glob(os.path.join(hax.config['runs_info_dir'], '*.csv')):
tpc, run = os.path.splitext(os.path.basename(rundbfile))[0].split('_')
dsets = pd.read_csv(rundbfile)
dsets = pd.concat((dsets, pd.DataFrame([{'tpc': tpc, 'run': run}] * len(dsets))),
axis=1)
if datasets is not None and len(datasets):
datasets = dsets
else:
datasets = pd.concat((datasets, dsets))
elif experiment == 'XENON1T':
collection = get_rundb_collection()
docs = []
if query is None:
query = {}
query['detector'] = hax.config.get('detector', hax.config['detector'])
log.debug("Updating datasets from runs database... ")
cursor = collection.find(query,
['name', 'number', 'start', 'end', 'source',
'reader.self_trigger',
'trigger.events_built', 'trigger.status',
'tags.name',
'data'])
for doc in cursor:
# Process and flatten the doc
doc['tags'] = ','.join([t['name'] for t in doc.get('tags', [])]) # Convert tags to single string
doc = flatten_dict(doc, separator='__')
del doc['_id'] # Remove the Mongo document ID
if 'data' in doc:
data_docs = doc['data']
del doc['data']
else:
data_docs = []
doc = flatten_dict(doc, separator='__')
if version_policy != 'loose':
# Does the run db know where to find the processed data at this host?
processed_data_docs = [d for d in data_docs
if (d['type'] == 'processed'
and hax.config['cax_key'] in d['host']
and d['status'] == 'transferred')]
# Choose whether to use this data / which data to use, based on the version policy
doc['location'] = ''
if processed_data_docs:
if version_policy == 'latest':
doc['location'] = max(processed_data_docs,
key=lambda x: LooseVersion(x['pax_version']))['location']
else:
for dd in processed_data_docs:
if dd['pax_version'][1:] == hax.config['pax_version_policy']:
doc['location'] = dd['location']
docs.append(doc)
datasets = pd.DataFrame(docs)
log.debug("... done.")
# These may or may not have been set already:
if not 'location' in datasets:
datasets['location'] = [''] * len(datasets)
if not 'raw_data_subfolder' in datasets:
datasets['raw_data_subfolder'] = [''] * len(datasets)
if not 'raw_data_found' in datasets:
datasets['raw_data_found'] = [False] * len(datasets)
dataset_names = datasets['name'].values
if version_policy == 'loose':
# Walk through main_data_paths, looking for root files
# Reversed, since if we find a dataset again, we overwrite, and
# usually people put first priority stuff at the front.
for data_dir in reversed(hax.config.get('main_data_paths', [])):
for candidate in glob(os.path.join(data_dir, '*.root')):
# What dataset is this file for?
dsetname = os.path.splitext(os.path.basename(candidate))[0]
bla = np.where(dataset_names == dsetname)[0]
if len(bla):
# Dataset was found, index is in bla[0]
datasets.loc[bla[0], 'location'] = candidate
# For the raw data, we may need to look in subfolders ('run_10' etc)
# don't do os.path.exist for each dataset, it will take minutes, at least over sshfs
if hax.config['raw_data_access_mode'] == 'local':
for subfolder, dsets_in_subfolder in datasets.groupby('raw_data_subfolder'):
subfolder_path = os.path.join(hax.config['raw_data_local_path'], subfolder)
if not os.path.exists(subfolder_path):
log.debug("Folder %s not found when looking for raw data" % subfolder_path)
continue
for candidate in os.listdir(subfolder_path):
bla = np.where(dataset_names == candidate)[0]
if len(bla):
datasets.loc[bla[0], 'raw_data_found'] = True
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from pax.configuration import load_configuration
from hax.utils import flatten_dict
##
# Load the PMT data from the pax configuration
##
# Convert PMT/channel map to record array
# TODO: depends on experiment, should do after init
pax_config = load_configuration('XENON1T')
pmt_data = pd.DataFrame([
flatten_dict(info, separator=':') for info in pax_config['DEFAULT']['pmts']
if 'array' in info
])
pmt_numbering_start = pmt_data['pmt_position'].min()
##
# Plotting functions
##
def _pad_to_length_of(a, b):
"""Pads a with zeros until it has the length of b"""
lendiff = len(b) - len(a)
if lendiff < 0:
raise ValueError("Cannot pad a negative number of zeros!")
elif lendiff > 0:
