import tensorflow as tf
import numpy as np
import py_datatools.datatools as dt
import py_ML.custom_models as cmod
import py_ML.custom_metrics as cmet
from py_datatools.plotting_tools import *
train_tracks, infosets = dt.load_whole_named_dataset(
'6_tracklets_large_calib_deconvoluted_train')
train_tracklets = train_tracks[:100000].reshape((-1, 17, 24, 1))
train_labels = np.repeat(infosets[:100000][:, 0], 6)
# test_tracks, infosets = dt.load_whole_named_dataset('6_tracklets_large_calib_test')
# test_tracklets = test_tracks.reshape((-1, 17, 24, 1))
# test_labels = np.repeat(infosets[:, 0], 6)
pid_model = cmod.SimpleSingleTrackletConvPID(2,
kernel_size=(17, 24),
use_bias=True)
pid_model.compile(
optimizer=tf.train.AdamOptimizer(learning_rate=0.0001),
loss='binary_crossentropy',
metrics=['accuracy'], #, cmet.PionEfficiencyAtElectronEfficiency(0.9)],
)
pid_model.fit(train_tracklets,
train_labels,
batch_size=32,
epochs=100,
validation_split=0.2)
import py_datatools.datatools as dt
import numpy as np
tracks, info_set = dt.load_whole_named_dataset('6_tracklets_large_calib')
e_tracks = tracks[info_set[:, 0] == 1]
e_info_set = info_set[info_set[:, 0] == 1]
p_tracks = tracks[info_set[:, 0] == 0]
p_info_set = info_set[info_set[:, 0] == 0]
num_electrons = len(e_tracks)
num_train_electrons = int(num_electrons)
num_test_electrons = num_electrons - num_train_electrons
num_train_pions = num_train_electrons
num_test_pions = num_train_pions
e_train_tracks = e_tracks[:num_train_electrons]
e_train_info_set = e_info_set[:num_train_electrons]
p_train_tracks = p_tracks[:num_train_pions]
p_train_info_set = p_info_set[:num_train_pions]
train_tracks = np.concatenate([e_train_tracks, p_train_tracks])
train_info_set = np.concatenate([e_train_info_set, p_train_info_set])
randomize = np.arange(len(train_tracks))
np.random.shuffle(randomize)
train_tracks = train_tracks[randomize]
train_info_set = train_info_set[randomize]
import tensorflow as tf
import numpy as np
import py_datatools.datatools as dt
import py_ML.custom_models as cmod
import py_ML.custom_metrics as cmet
train_tracks, train_infosets = dt.load_whole_named_dataset(
'6_tracklets_large_train')
train_tracks = np.expand_dims(train_tracks, axis=-1)
train_labels = train_infosets[:, 0]
test_tracks, test_infosets = dt.load_whole_named_dataset(
'6_tracklets_large_test')
test_tracks = np.expand_dims(test_tracks, axis=-1)
test_labels = test_infosets[:, 0]
tracklet_pid_model = cmod.ComplexConvTrackletPID()
track_pid_model = cmod.TrackletModelMultiplexer(tracklet_pid_model)
track_pid_model.compile(
optimizer=tf.train.AdamOptimizer(learning_rate=0.00001),
loss='binary_crossentropy',
metrics=['accuracy',
cmet.PionEfficiencyAtElectronEfficiency(0.9)],
)
history = track_pid_model.fit(
train_tracks,
train_labels,
batch_size=512,
epochs=10000,
import tensorflow as tf
import numpy as np
import py_datatools.datatools as dt
import py_ML.custom_models as cmod
import py_ML.custom_metrics as cmet
from py_datatools.plotting_tools import *
train_tracks, train_infosets = dt.load_whole_named_dataset(
'6_tracklets_large_calib_train')
train_tracks = np.expand_dims(train_tracks, axis=-1)
train_labels = train_infosets[:, 0]
tracklet_pid_model = cmod.ComplexConvTrackletPID()
track_pid_model = cmod.TrackletModelMultiplexer(tracklet_pid_model)
track_pid_model.compile(
optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
loss='binary_crossentropy',
metrics=['accuracy',
cmet.PionEfficiencyAtElectronEfficiency(0.9)],
)
history = track_pid_model.fit(
train_tracks,
train_labels,
batch_size=512,
epochs=100,
validation_split=0.2,
)
# validation_data=(test_tracks, test_labels),)
import tensorflow as tf
import numpy as np
import py_datatools.datatools as dt
import py_ML.custom_models as cmod
import py_ML.custom_metrics as cmet
train_tracks, infosets = dt.load_whole_named_dataset('medium_train')
train_tracklets = train_tracks.reshape((-1, 17, 24, 1))
train_labels = np.repeat(infosets[:, 0], 6)
test_tracks, infosets = dt.load_whole_named_dataset('medium_test')
test_tracklets = test_tracks.reshape((-1, 17, 24, 1))
test_labels = np.repeat(infosets[:, 0], 6)
pid_model = cmod.SimpleSingleTrackletConvPID(10, kernel_size=(3, 2), use_bias=True)
pid_model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.0001),
loss='binary_crossentropy',
metrics=['accuracy', cmet.PionEfficiencyAtElectronEfficiency(0.9)],
shuffle=True
)
pid_model.fit(train_tracklets,
train_labels,
batch_size=32,
epochs=100,
validation_data=(test_tracklets, test_labels), )
import numpy as np
import os
import glob
calib_params = {}
dataset_name = 'all_tracks_6_tracklets_valid_run_numbers'
new_dataset_name = dataset_name + '_calib'
for fil in glob.glob(
os.path.dirname(__file__) + '/calib_files/combined_local_gains*.txt'):
print('Loading calib file:', fil)
run_no = fil.split('_')[-1].split('.')[0]
calib_params[run_no] = np.genfromtxt(fil, delimiter=', ')[:, 2:].reshape(
(540, 16, 144))
tracks, infosets = dt.load_whole_named_dataset(dataset_name)
for i in range(len(tracks)):
run_no = infosets[i, 13]
run_gains = calib_params[str(int(run_no))]
dets = infosets[i, 14:20].astype(int)
rows = infosets[i, 21:27].astype(int)
cols = infosets[i, 28:34].astype(int)
track_gains = np.expand_dims(run_gains[dets, rows,
[cols - 8 + i
for i in range(17)]].T,
axis=-1)
tracks[i] *= track_gains
