def setup(self):
import tensorflow as tf
keras = tf.keras
nn_conf = get_resource(self.config['nn_architecture'], fmt='json')
# badPMTList was inserted by a very clever person into the keras json
# file. Let's delete it to prevent future keras versions from crashing.
# Do NOT try `del nn_conf['badPMTList']`! See get_resource docstring
# for the gruesome details.
bad_pmts = nn_conf['badPMTList']
nn = keras.models.model_from_json(
json.dumps({k: v
for k, v in nn_conf.items() if k != 'badPMTList'}))
self.pmt_mask = ~np.in1d(np.arange(self.config['n_top_pmts']),
bad_pmts)
# Keras needs a file to load its weights. We can't put the load
# inside the context, then it would break on Windows,
# because there temporary files cannot be opened again.
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(get_resource(self.config['nn_weights'], fmt='binary'))
fname = f.name
nn.load_weights(fname)
os.remove(fname)
self.nn = nn
def setup(self):
is_CMT = isinstance(self.config['s1_xyz_correction_map'], tuple)
if is_CMT:
cmt, cmt_conf, is_nt = self.config['s1_xyz_correction_map']
cmt_conf = (
f'{cmt_conf[0]}_{self.config["default_reconstruction_algorithm"]}',
cmt_conf[1])
map_algo = cmt, cmt_conf, is_nt
self.s1_map = InterpolatingMap(
get_resource(get_config_from_cmt(self.run_id, map_algo)))
else:
self.s1_map = InterpolatingMap(
get_resource(self.config['s1_xyz_correction_map']))
self.s2_map = InterpolatingMap(
get_resource(
get_config_from_cmt(self.run_id,
self.config['s2_xy_correction_map'])))
self.elife = get_correction_from_cmt(self.run_id,
self.config['elife_conf'])
if isinstance(self.elife, str):
# Legacy 1T support
self.elife = get_elife(self.run_id, self.elife)
def setup(self):
self.electron_drift_velocity = get_correction_from_cmt(
self.run_id, self.config['electron_drift_velocity'])
self.electron_drift_time_gate = get_correction_from_cmt(
self.run_id, self.config['electron_drift_time_gate'])
if isinstance(self.config['fdc_map'], str):
self.map = InterpolatingMap(
get_resource(self.config['fdc_map'], fmt='binary'))
elif is_cmt_option(self.config['fdc_map']):
self.map = InterpolatingMap(
get_cmt_resource(
self.run_id,
tuple([
'suffix',
self.config['default_reconstruction_algorithm'],
*self.config['fdc_map']
]),
fmt='binary'))
self.map.scale_coordinates([1., 1., -self.electron_drift_velocity])
else:
raise NotImplementedError('FDC map format not understood.')
def setup(self):
c = self.config
c.update(get_resource(c['fax_config'], fmt='json'))
# Update gains to the nT defaults
self.to_pe = get_to_pe(self.run_id, c['gain_model'],
c['channel_map']['tpc'][1] + 1)
c['gains'] = 1 / self.to_pe * (1e-8 * 2.25 / 2**14) / (1.6e-19 * 10 *
50)
c['gains'][self.to_pe == 0] = 0
if c['seed'] != False:
np.random.seed(c['seed'])
overrides = self.config['fax_config_override']
if overrides is not None:
c.update(overrides)
#We hash the config to load resources. Channel map is immutable and cannot be hashed
self.config['channel_map'] = dict(self.config['channel_map'])
self.config['channel_map']['sum_signal'] = 800
self.config['channels_bottom'] = np.arange(self.config['n_top_pmts'],
self.config['n_tpc_pmts'])
self.get_instructions()
self.check_instructions()
self._setup()
def setup(self):
import keras
import tensorflow as tf
self.pmt_mask = to_pe[:self.n_top_pmts] > 0
nn = keras.models.model_from_json(
get_resource(self.config['nn_architecture']))
temp_f = '_temp.h5'
with open(temp_f, mode='wb') as f:
f.write(get_resource(self.config['nn_weights'], binary=True))
nn.load_weights(temp_f)
self.nn = nn
# Workaround for using keras/tensorflow in a threaded environment. See:
# https://github.com/keras-team/keras/issues/5640#issuecomment-345613052
self.nn._make_predict_function()
self.graph = tf.get_default_graph()
def setup(self):
import keras
import tensorflow as tf
import tempfile
self.to_pe = get_to_pe(self.run_id, self.config['to_pe_file'])
nn_json = get_resource(self.config['nn_architecture'])
nn = keras.models.model_from_json(nn_json)
bad_pmts = json.loads(nn_json)['badPMTList']
self.pmt_mask = ~np.in1d(np.arange(self.n_top_pmts), bad_pmts)
with tempfile.NamedTemporaryFile() as f:
f.write(get_resource(self.config['nn_weights'], fmt='binary'))
nn.load_weights(f.name)
self.nn = nn
# Workaround for using keras/tensorflow in a threaded environment. See:
# https://github.com/keras-team/keras/issues/5640#issuecomment-345613052
self.nn._make_predict_function()
self.graph = tf.get_default_graph()
def setup(self):
is_CMT = isinstance(self.config['fdc_map'], tuple)
if is_CMT:
cmt, cmt_conf, is_nt = self.config['fdc_map']
cmt_conf = (
f'{cmt_conf[0]}_{self.config["default_reconstruction_algorithm"]}',
cmt_conf[1])
map_algo = cmt, cmt_conf, is_nt
self.map = InterpolatingMap(
get_resource(get_config_from_cmt(self.run_id, map_algo),
fmt='binary'))
self.map.scale_coordinates(
[1., 1., -self.config['electron_drift_velocity']])
elif isinstance(self.config['fdc_map'], str):
self.map = InterpolatingMap(
get_resource(self.config['fdc_map'], fmt='binary'))
else:
raise NotImplementedError('FDC map format not understood.')
def setup(self):
import tensorflow as tf
self.has_tf2 = parse_version(tf.__version__) > parse_version('2.0.a')
if self.has_tf2:
keras = tf.keras
else:
import keras
nn_conf = get_resource(self.config['nn_architecture'], fmt='json')
# badPMTList was inserted by a very clever person into the keras json
# file. Let's delete it to prevent future keras versions from crashing.
# Do NOT try `del nn_conf['badPMTList']`! See get_resource docstring
# for the gruesome details.
bad_pmts = nn_conf['badPMTList']
nn = keras.models.model_from_json(
json.dumps({k: v
for k, v in nn_conf.items() if k != 'badPMTList'}))
self.pmt_mask = ~np.in1d(np.arange(self.config['n_top_pmts']),
bad_pmts)
# Keras needs a file to load its weights. We can't put the load
# inside the context, then it would break on windows
# because there temporary files cannot be opened again.
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(get_resource(self.config['nn_weights'], fmt='binary'))
fname = f.name
nn.load_weights(fname)
os.remove(fname)
self.nn = nn
if not self.has_tf2:
# Workaround for using keras/tensorflow in a threaded environment.
# See: https://github.com/keras-team/keras/issues/
# 5640#issuecomment-345613052
self.nn._make_predict_function()
self.graph = tf.get_default_graph()
def setup(self):
c = self.config
c.update(get_resource(c['fax_config'], fmt='json'))
# Update gains to the nT defaults
self.to_pe = get_to_pe(self.run_id, c['gain_model'],
len(c['channels_in_detector']['tpc']))
c['gains'] = 1 / self.to_pe * (1e-8 * 2.25 / 2**14) / (1.6e-19 * 10 *
50)
c['gains'][self.to_pe == 0] = 0
if c['seed'] != False:
np.random.seed(c['seed'])
overrides = self.config['fax_config_override']
if overrides is not None:
c.update(overrides)
if c['optical']:
self.instructions, self.channels, self.timings = read_optical(
c['fax_file'])
c['nevents'] = len(self.instructions['event_number'])
elif c['fax_file']:
assert c['fax_file'][
-5:] != '.root', 'None optical g4 input is deprecated use EPIX instead'
self.instructions = instruction_from_csv(c['fax_file'])
c['nevents'] = np.max(self.instructions['event_number'])
else:
self.instructions = rand_instructions(c)
# Let below cathode S1 instructions pass but remove S2 instructions
m = (self.instructions['z'] <
-c['tpc_length']) & (self.instructions['type'] == 2)
self.instructions = self.instructions[~m]
assert np.all(self.instructions['x']**2 + self.instructions['y']**2 < c['tpc_radius']**2), \
"Interation is outside the TPC"
assert np.all(self.instructions['z'] < 0.25), \
"Interation is outside the TPC"
assert np.all(self.instructions['amp'] > 0), \
"Interaction has zero size"
def setup(self):
c = self.config
c.update(get_resource(c['fax_config'], fmt='json'))
if c['fax_file']:
if c['fax_file'][-5:] == '.root':
self.instructions = read_g4(c['fax_file'])
c['nevents'] = np.max(self.instructions['event_number'])
else:
self.instructions = instruction_from_csv(c['fax_file'])
c['nevents'] = np.max(self.instructions['event_number'])
else:
self.instructions = rand_instructions(c)
assert np.all(
self.instructions['x']**2 +
self.instructions['y']**2 < 2500), "Interation is outside the TPC"
assert np.all(self.instructions['z'] < 0) & np.all(
self.instructions['z'] > -100), "Interation is outside the TPC"
assert np.all(
self.instructions['amp'] > 0), "Interaction has zero size"
def __init__(self, config={}):
self.config = default_config
self.config.update(get_resource(self.config['fax_config'], fmt='json'))
self.config.update(config)
if self.config['fax_file']:
if self.config['fax_file'][-5:] == '.root':
self.instructions = read_g4(self.config['fax_file'])
self.config['nevents'] = np.max(
self.instructions['event_number'])
else:
self.instructions = instruction_from_csv(
self.config['fax_file'])
self.config['nevents'] = np.max(
self.instructions['event_number'])
else:
self.instructions = rand_instructions(self.config)
self.pax_event = PaxEvents(self.config)
self.transfer_plugin = self.WriteZippedEncoder(self.config)
self.output_plugin = self.WriteZipped(self.config)
def setup(self):
self.s1_map = InterpolatingMap(
get_resource(self.config['s1_relative_lce_map']))
self.s2_map = InterpolatingMap(
get_resource(self.config['s2_relative_lce_map']))
self.elife = get_elife(self.run_id,self.config['elife_file'])
def setup(self):
self.map = InterpolatingMap(
get_resource(self.config['fdc_map'], fmt='binary'))
def setup(self):
self.s1_map = InterpolatingMap(
get_resource(self.config['s1_relative_lce_map']))
self.s2_map = InterpolatingMap(
get_resource(self.config['s2_relative_lce_map']))
def setup(self):
self.map = InterpolatingMap(
get_resource(self.config['fdc_map'], binary=True))
