def load_data_target_min_classification_2(input_file, max_size=2679):
just_file_name = os.path.splitext(os.path.split(input_file)[1])[0] + '_'
file_path = input_file
location = 'B4'
branches = ['rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz', 'rechit_energy', 'rechit_layer']
types = ['float64', 'float64', 'float64', 'float64', 'float64', 'float64', 'float64']
max_size = [max_size for i in range(len(branches))]
data, sizes = sparse_hgcal.read_np_array(file_path, location, branches, types, max_size)
num_samples = len(data[0])
data_joined = np.concatenate([x[..., np.newaxis] for x in data], axis=2).astype(np.float32)
pairs = find_unique_pairs(num_samples)
print((data_joined).shape)
showers_1 = data_joined[pairs[:, 0]]
showers_2 = data_joined[pairs[:, 1]]
# TODO: Order the elements if you want to!
shower_combined = np.concatenate((showers_1, showers_2), axis=2)
shower_combined = shower_combined[:, :, [5, 12, 0, 1, 2, 3, 4, 6]] # [Energy 1, Energy 2, x, y, z, vxy, vz, layer]
return shower_combined
def run_conversion_multi_threaded(input_file):
global jobs_queue, max_gpu_events
just_file_name = os.path.splitext(os.path.split(input_file)[1])[0] + '_'
file_path = input_file
location = 'B4'
branches = [
'rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz',
'rechit_energy', 'rechit_layer', 'isElectron', 'isMuon',
'isPionCharged', 'isPionNeutral', 'isK0Long', 'isK0Short'
]
types = [
'float64', 'float64', 'float64', 'float64', 'float64', 'float64',
'float64', 'int32', 'int32', 'int32', 'int32', 'int32', 'int32'
]
max_size = [3000, 3000, 3000, 3000, 3000, 3000, 3000, 1, 1, 1, 1, 1, 1]
data, sizes = sparse_hgcal.read_np_array(file_path, location, branches,
types, max_size)
ex_data = [
np.expand_dims(group, axis=2) for group in
[data[0], data[1], data[2], data[3], data[4], data[5], data[6]]
]
ex_data_lables = [
np.expand_dims(group, axis=2)
for group in [data[7], data[8], data[9], data[10], data[11], data[12]]
]
all_features = np.concatenate(
(ex_data[0], ex_data[1], ex_data[2], ex_data[5], ex_data[6]), axis=2)
spatial = np.concatenate((ex_data[0], ex_data[1], ex_data[2]), axis=2)
spatial_local = np.concatenate((ex_data[3], ex_data[4]), axis=2)
labels_one_hot = np.concatenate(tuple(ex_data_lables), axis=1)
num_entries = sizes[0]
assert int(np.mean(np.sum(labels_one_hot, axis=1))) == 1
total_events = len(sizes[0])
assert np.array_equal(np.shape(all_features), [total_events, 3000, 5])
assert np.array_equal(np.shape(spatial), [total_events, 3000, 3])
assert np.array_equal(np.shape(spatial_local), [total_events, 3000, 2])
events_per_jobs = int(total_events / jobs)
for i in range(jobs):
start = i * events_per_jobs
stop = (i + 1) * events_per_jobs
A = all_features[start:stop]
B = spatial[start:stop]
C = spatial_local[start:stop]
D = labels_one_hot[start:stop]
E = num_entries[start:stop]
output_file_prefix = os.path.join(args.output,
just_file_name + "_" + str(i) + "_")
data_packed = A, B, C, D, E, i, output_file_prefix
jobs_queue.put(data_packed)
jobs_queue.join()
def run_conversion_simple(input_file):
np.set_printoptions(threshold=np.nan)
location = 'B4'
branches = [
'rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz',
'rechit_energy', 'rechit_layer', 'true_x', 'true_y', 'true_r',
'true_energy', 'isGamma'
]
types = [
'float64', 'float64', 'float64', 'float64', 'float64', 'float64',
'float64', 'float64', 'float64', 'float64', 'float64', 'int32'
]
max_size = [2679 for _ in range(7)] + [1, 1, 1, 1, 1]
nparray, sizes = sparse_hgcal.read_np_array(input_file, location, branches,
types, max_size)
if args.isGamma:
isGamma = np.where(nparray[11] == 1)
nparray = [x[isGamma] for x in nparray]
true_values_1 = np.concatenate(
[nparray[i][..., np.newaxis] for i in [7, 8, 9, 10]], axis=1)
# common part:
common = concat_all_branches(nparray, [0, 1, 2, 3, 4, 6])
positions = concat_all_branches(nparray, [0, 1, 2])[0]
sizes_1 = make_fixed_array(nparray[3], expand=False)[0]
colors = make_fixed_array(nparray[6], expand=False)[0]
sizes_2 = make_fixed_array(nparray[4], expand=False)[0]
plot_calo(positions, sizes_1, colors)
def run_conversion_simple(input_file, firstrun=False):
np.set_printoptions(threshold=np.nan)
location = 'B4'
branches = [
'rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz',
'rechit_energy', 'rechit_layer', 'true_x', 'true_y', 'true_r',
'true_energy', 'isGamma'
]
types = [
'float64', 'float64', 'float64', 'float64', 'float64', 'float64',
'float64', 'float64', 'float64', 'float64', 'float64', 'int32'
]
max_size = [2679 for _ in range(7)] + [1, 1, 1, 1, 1]
nparray, sizes = sparse_hgcal.read_np_array(input_file, location, branches,
types, max_size)
print(len(nparray))
if args.isGamma:
isGamma = np.where(nparray[11] == 1)
nparray = [x[isGamma] for x in nparray]
true_values_1 = np.concatenate(
[nparray[i][..., np.newaxis] for i in [7, 8, 9, 10]], axis=1)
# common part:
common = concat_all_branches(nparray, [0, 1, 2, 3, 4, 6])
positions = concat_all_branches(nparray, [0, 1, 2])
energy1 = make_fixed_array(nparray[5], expand=False)
shuffleindices = np.array(range(1, energy1.shape[0]))
shuffleindices = np.concatenate([shuffleindices, np.array([0])])
energy2 = energy1[shuffleindices]
true_values_2 = true_values_1[shuffleindices]
print(energy1.shape)
maxenergyids1 = energy1.argmax(axis=1)
maxenergyids2 = energy2.argmax(axis=1)
positions1 = positions[range(energy1.shape[0]), maxenergyids1]
positions2 = positions[range(energy2.shape[0]), maxenergyids2]
# np.logical_or(a, b)
diff = positions1 - positions2
diff[:, 0] *= 1e6
diff[:, 1] *= 1e3
totdiff = diff[:, 0] + diff[:, 1] + diff[:, 2]
# print(totdiff)
# common = common[totdiff != 0]
esum = energy2 + energy1
print(esum.shape)
fraction1 = energy1 / esum # np.ma.masked_array(esum, mask=esum==0)
fraction1[esum == 0] = 0
fraction2 = energy2 / esum
fraction2[esum == 0] = 0
fraction_temp = np.array(fraction1)
fraction1[totdiff > 0] = fraction2[totdiff > 0]
fraction2[totdiff > 0] = fraction_temp[totdiff > 0]
true_values_temp = np.array(true_values_1)
true_values_1[totdiff > 0] = true_values_2[totdiff > 0]
true_values_2[totdiff > 0] = true_values_temp[totdiff > 0]
# prepare additional information about the seeds
# BX1 maxenergy1, make same ordering
maxenergyids1_temp = np.array(maxenergyids1)
maxenergyids1[totdiff > 0] = maxenergyids2[totdiff > 0]
maxenergyids2[totdiff > 0] = maxenergyids1_temp[totdiff > 0]
maxenergyids1 = np.expand_dims(maxenergyids1, axis=1)
maxenergyids2 = np.expand_dims(maxenergyids2, axis=1)
moreinfo = np.concatenate([maxenergyids1, maxenergyids2], axis=-1)
esum = np.expand_dims(esum, axis=2)
fraction1 = np.expand_dims(fraction1, axis=2)
fraction2 = np.expand_dims(fraction2, axis=2)
allout = np.concatenate([esum, common, fraction1, fraction2], axis=-1)
zeropad = np.zeros(shape=(moreinfo.shape[0],
allout.shape[2] - moreinfo.shape[1]))
moreinfo = np.concatenate([moreinfo, zeropad], axis=-1)
moreinfo = np.expand_dims(moreinfo, axis=1)
allout = np.concatenate([allout, moreinfo], axis=1)
# allout = allout[totdiff!=0] #remove same seeded showers
output_data = []
for i in range(len(allout)):
if totdiff[i] != 0:
output_data.append((allout[i], true_values_1[i], true_values_2[i]))
if firstrun:
print('output shape ', allout.shape)
print('last entry in axis 1 is: [idx seed0, idx seed1, 0, ...]')
print('other are: [esum,rechit_x, rechit_y, rechit_z, rechit_vxy, '
'rechit_vz, rechit_layer, fraction1, fraction2]')
print('ordering of seed0 and seed1 is done in order by: '
'x,y,z. Events with same positioned seeds are removed')
just_file_name = os.path.splitext(os.path.split(input_file)[1])[0] + '_'
output_file_prefix = os.path.join(args.output, just_file_name)
write_to_tf_records(output_data, 0, output_file_prefix)
def load_data_target_min_classification(input_file):
just_file_name = os.path.splitext(os.path.split(input_file)[1])[0] + '_'
file_path = input_file
location = 'B4'
branches = [
'rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz',
'rechit_energy', 'rechit_layer', 'rechit_detid', 'isElectron',
'isMuon', 'isPionCharged', 'isPionNeutral', 'isK0Long', 'isK0Short'
]
types = [
'float64', 'float64', 'float64', 'float64', 'float64', 'float64',
'float64', 'int32', 'int32', 'int32', 'int32', 'int32', 'int32',
'int32'
]
max_size = [
3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 1, 1, 1, 1, 1, 1
]
data, sizes = sparse_hgcal.read_np_array(file_path, location, branches,
types, max_size)
ex_ids = data[7]
ex_data = [
np.expand_dims(group, axis=2) for group in
[data[0], data[1], data[2], data[3], data[4], data[5], data[6]]
]
ex_data_lables = [
np.expand_dims(group, axis=2)
for group in [data[7], data[8], data[9], data[10], data[11], data[12]]
]
ex_sizes = sizes[0]
other_features = np.concatenate((ex_data[5], ex_data[6]), axis=2)
spatial = np.concatenate((ex_data[0], ex_data[1], ex_data[2]), axis=2)
spatial_local = np.concatenate((ex_data[3], ex_data[4]), axis=2)
num_entries = sizes[0]
indices_1 = np.arange(len(data[0]))
np.random.shuffle(indices_1)
indices_2 = np.arange(len(data[0]))
np.random.shuffle(indices_2)
pairs = np.concatenate(
(np.expand_dims(indices_1, axis=1), np.expand_dims(indices_2, axis=1)),
axis=1)
output_data_all = list()
for i, j in pairs:
if i == j:
continue
ids_1 = ex_ids[i].astype(np.int32) # [3000]
features_others_1 = other_features[i]
energy_1 = features_others_1[:, 0]
features_spatial_local_1 = spatial_local[i]
features_spatial_1 = spatial[i] # VxF
features_combined_1 = np.concatenate(
(features_others_1, features_spatial_1, features_spatial_local_1),
axis=1).astype(np.float32)
sizes_1 = ex_sizes[i].astype(np.int32)
location_1 = np.sum(features_spatial_1 * energy_1[..., np.newaxis],
axis=0) / np.sum(energy_1)
ids_2 = ex_ids[j].astype(np.int32)
features_others_2 = other_features[j]
energy_2 = (other_features[j])[:, 0]
features_spatial_local_2 = spatial_local[j]
features_spatial_2 = spatial[j]
features_combined_2 = np.concatenate(
(features_others_2, features_spatial_2, features_spatial_local_2),
axis=1).astype(np.float32)
sizes_2 = ex_sizes[j].astype(np.int32)
location_2 = np.sum(features_spatial_2 * energy_2[..., np.newaxis],
axis=0) / np.sum(energy_2)
comparison = location_1[1] < location_1[1] if abs(
location_1[0] -
location_1[0]) < 1 else location_1[0] < location_1[0]
if comparison:
output_data_all.append(
(features_combined_1, features_combined_2, ids_1, ids_2,
sizes_1, sizes_2, location_1, location_2))
else:
output_data_all.append(
(features_combined_2, features_combined_1, ids_2, ids_1,
sizes_2, sizes_1, location_2, location_1))
features_1 = np.concatenate([(x[0])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.float32)
features_2 = np.concatenate([(x[1])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.float32)
ids_1 = np.concatenate([(x[2])[np.newaxis, ...] for x in output_data_all],
axis=0).astype(np.int32)
ids_2 = np.concatenate([(x[3])[np.newaxis, ...] for x in output_data_all],
axis=0).astype(np.int32)
sizes_1 = np.concatenate([(x[4])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.int32)
sizes_2 = np.concatenate([(x[5])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.int32)
locations_1 = np.concatenate([(x[6])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.float32)
locations_2 = np.concatenate([(x[7])[np.newaxis, ...]
for x in output_data_all],
axis=0).astype(np.float32)
merged_features, _, num_entries_result = merge_two_arrays_separate(
features_1,
features_2,
ids_1,
ids_2,
sizes_1,
sizes_2,
)
energies_1 = merged_features[:, :, 0]
energies_2 = merged_features[:, :, 1]
e = energies_1 + energies_2
fractions = energies_1 / np.ma.masked_array(e, mask=e == 0) # 10000x6000
target_0 = np.array(fractions)[:, :, np.newaxis]
target_1 = np.array(1 - fractions)[:, :, np.newaxis]
unmerged_energies = np.copy(merged_features)
merged_features[:, :, 1] += merged_features[:, :, 0]
merged_features = merged_features[:, :, 1:]
assert np.array_equal(np.shape(np.array(target_0[0])), [6000, 1])
data_output = np.concatenate((merged_features, target_0, target_1),
axis=2).astype(np.float32)
total_events = len(target_0)
assert np.array_equal(np.shape(merged_features[0]), [6000, 7])
assert len(target_0) == len(num_entries_result) and len(target_0) == len(
merged_features)
return_dict = {}
return_dict['num_entries_result'] = num_entries_result
return_dict['data_output_merged'] = data_output
return_dict['data_output_unmerged_energies'] = unmerged_energies
return_dict['fractions_random'] = unmerged_energies
print("Returning min loss dataset")
return return_dict
def load_data_target_center(input_file):
file_path = input_file
location = 'B4'
branches = ['rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz', 'rechit_energy', 'rechit_layer',
'rechit_detid', 'isElectron', 'isMuon', 'isPionCharged', 'isPionNeutral', 'isK0Long', 'isK0Short']
types = ['float64', 'float64', 'float64', 'float64', 'float64', 'float64', 'float64', 'int32',
'int32', 'int32', 'int32', 'int32', 'int32', 'int32']
# Maximum number of elements in the column? But why are the final sizes '1'
max_size = [3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 1, 1, 1, 1, 1, 1]
data, sizes = sparse_hgcal.read_np_array(file_path, location, branches, types, max_size)
ex_ids = data[7]
ex_data = [np.expand_dims(group, axis=2) for group in
[data[0], data[1], data[2], data[3], data[4], data[5], data[6]]]
ex_data_lables = [np.expand_dims(group, axis=2) for group in
[data[7], data[8], data[9], data[10], data[11], data[12]]]
ex_sizes = sizes[0]
other_features = np.concatenate((ex_data[5], ex_data[6]), axis=2)
spatial = np.concatenate((ex_data[0], ex_data[1], ex_data[2]), axis=2)
spatial_local = np.concatenate((ex_data[3], ex_data[4]), axis=2)
num_entries = sizes[0]
indices_1 = np.arange(len(data[0]))
np.random.shuffle(indices_1)
indices_2 = np.arange(len(data[0]))
np.random.shuffle(indices_2)
pairs = np.concatenate((np.expand_dims(indices_1, axis=1),
np.expand_dims(indices_2, axis=1)), axis=1)
output_data_all = list()
for i, j in pairs:
if i == j:
continue
ids_1 = ex_ids[i].astype(np.int32) # [3000]
features_others_1 = other_features[i]
energy_1 = features_others_1[:,0]
features_spatial_local_1 = spatial_local[i]
features_spatial_1 = spatial[i] # VxF
features_combined_1 = np.concatenate((features_others_1, features_spatial_1, features_spatial_local_1), axis=1).astype(np.float32)
sizes_1 = ex_sizes[i].astype(np.int32)
location_1 = np.sum(features_spatial_1 * energy_1[..., np.newaxis], axis=0) / np.sum(energy_1)
ids_2 = ex_ids[j].astype(np.int32)
features_others_2 = other_features[j]
energy_2 = (other_features[j])[:,0]
features_spatial_local_2 = spatial_local[j]
features_spatial_2 = spatial[j]
features_combined_2 = np.concatenate((features_others_2, features_spatial_2, features_spatial_local_2), axis=1).astype(np.float32)
sizes_2 = ex_sizes[j].astype(np.int32)
location_2 = np.sum(features_spatial_2 * energy_2[..., np.newaxis], axis=0) / np.sum(energy_2)
output_data_all.append((features_combined_1, features_combined_2, ids_1, ids_2, sizes_1, sizes_2, location_1, location_2))
features_1 = np.concatenate([(x[0])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.float32)
features_2 = np.concatenate([(x[1])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.float32)
ids_1 = np.concatenate([(x[2])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.int32)
ids_2 = np.concatenate([(x[3])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.int32)
sizes_1 = np.concatenate([(x[4])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.int32)
sizes_2 = np.concatenate([(x[5])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.int32)
locations_1 = np.concatenate([(x[6])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.float32)
locations_2 = np.concatenate([(x[7])[np.newaxis, ...] for x in output_data_all], axis=0).astype(np.float32)
merged_features, _, num_entries_result = merge_two_arrays_separate(
features_1,
features_2,
ids_1,
ids_2,
sizes_1,
sizes_2,
)
energies_1 = merged_features[:, :, 0]
energies_2 = merged_features[:, :, 1]
e = energies_1 + energies_2
fractions = energies_1/np.ma.masked_array(e, mask=e==0) # 10000x6000
target = fractions[..., np.newaxis] * locations_1[:, np.newaxis, :] + (1 - fractions[..., np.newaxis]) * locations_2[:, np.newaxis, :]
# target[e==0, :] = 0
target = np.array(target)
unmerged_energies = np.copy(merged_features)
merged_features[:, :, 1] += merged_features[:, :, 0]
merged_features = merged_features[:, :, 1:]
data_output = np.concatenate((merged_features, target), axis=2).astype(np.float32)
total_events = len(target)
assert np.array_equal(np.shape(merged_features[0]), [6000, 7])
assert np.array_equal(np.shape(target[0]), [6000, 3])
assert len(target) == len(num_entries_result) and len(target) == len(merged_features)
return_dict = {}
return_dict['num_entries_result'] = num_entries_result
return_dict['data_output_merged'] = data_output
return_dict['data_output_unmerged_energies'] = unmerged_energies
return return_dict
args = parser.parse_args()
file = args.input
location = 'B4'
branches = [
'isElectron', 'isMuon', 'isPionCharged', 'isPionNeutral', 'isK0Long',
'isK0Short', 'rechit_energy', 'rechit_x', 'rechit_y', 'rechit_z'
]
types = [
'int32', 'int32', 'int32', 'int32', 'int32', 'int32', 'float64', 'float64',
'float64', 'float64'
]
max_sizes = [1, 1, 1, 1, 1, 1, 3000, 3000, 3000, 3000]
print("Loading data")
data, sizes = hg.read_np_array(file, location, branches, types, max_sizes)
print("Data loaded")
events = np.size(data[0])
E = data[6]
X = data[7]
Y = data[8]
Z = data[9]
T = np.concatenate((np.expand_dims(data[0], 1), np.expand_dims(
data[1], 1), np.expand_dims(data[2], 1), np.expand_dims(
data[3], 1), np.expand_dims(data[4], 1), np.expand_dims(data[5], 1)),
axis=1)
def run_conversion_multi_threaded(input_file):
global jobs_queuze, max_gpu_events
just_file_name = os.path.splitext(os.path.split(input_file)[1])[0] + '_'
file_path = input_file
location = 'B4'
branches = [
'rechit_x', 'rechit_y', 'rechit_z', 'rechit_vxy', 'rechit_vz',
'rechit_energy', 'rechit_layer', 'isElectron', 'isMuon',
'isPionCharged', 'isPionNeutral', 'isK0Long', 'isK0Short'
]
types = [
'float64', 'float64', 'float64', 'float64', 'float64', 'float64',
'float64', 'int32', 'int32', 'int32', 'int32', 'int32', 'int32'
]
max_size = [3000, 3000, 3000, 3000, 3000, 3000, 3000, 1, 1, 1, 1, 1, 1]
data, sizes = sparse_hgcal.read_np_array(file_path, location, branches,
types, max_size)
per_rechit_data = np.concatenate([
np.expand_dims(group, axis=2) for group in
[data[0], data[1], data[2], data[3], data[4], data[5], data[6]]
],
axis=2)
labels_one_hot = np.concatenate([
np.expand_dims(group, axis=2)
for group in [data[7], data[8], data[9], data[10], data[11], data[12]]
],
axis=1)
num_entries = sizes[0]
if args.pion_vs_electron:
labels_indexed = np.argmax(labels_one_hot, axis=1)
interesting_indices = np.where((labels_indexed == 0) +
(labels_indexed == 3))
labels_indexed = labels_indexed[interesting_indices]
labels_indexed[labels_indexed == 3] = 1
labels_one_hot = one_hot(labels_indexed, num_classes=2)
per_rechit_data = data[interesting_indices]
num_entries = num_entries[interesting_indices]
total_events = len(labels_one_hot)
assert int(np.mean(np.sum(labels_one_hot, axis=1))) == 1
assert np.array_equal(np.shape(per_rechit_data), [total_events, 3000, 7])
jobs = int(args.jobs)
processes = []
events_per_jobs = int(total_events / jobs)
for i in range(jobs):
start = i * events_per_jobs
stop = (i + 1) * events_per_jobs
per_rechit_data_job = per_rechit_data[start:stop]
labels_jobs = labels_one_hot[start:stop]
num_entries_jobs = num_entries[start:stop]
output_file_prefix = os.path.join(args.output,
just_file_name + "_" + str(i) + "_")
data_packed = per_rechit_data_job, labels_jobs, num_entries_jobs, output_file_prefix
processes.append(Process(target=worker, args=(data_packed, )))
for p in processes:
p.start()
for p in processes:
p.join()
