def count_delta_psi(json_file_path,
min_npe=None,
max_npe=None,
notebook=False,
delta_angle_degrees=0.05,
border=False):
# FETCH SCORE AND COMPUTE HISTOGRAM #######################################
json_dict = common.parse_json_file(json_file_path)
if min_npe is not None:
json_dict = common.image_filter_range(json_dict, "img_cleaned_sum_pe", min_value=min_npe)
if max_npe is not None:
json_dict = common.image_filter_range(json_dict, "img_cleaned_sum_pe", max_value=max_npe)
if border:
json_dict = common.image_filter_range(json_dict, "img_cleaned_signal_to_border_distance", min_value=1)
score_array = common.extract_score_array(json_dict, "hillas2_delta_psi_norm2")
score_array = np.abs(score_array)
count = len(score_array[score_array < delta_angle_degrees])
return count
def count_delta_psi(json_file_path,
min_npe=None,
max_npe=None,
notebook=False,
delta_angle_degrees=0.05,
border=False):
# FETCH SCORE AND COMPUTE HISTOGRAM #######################################
json_dict = common.parse_json_file(json_file_path)
if min_npe is not None:
json_dict = common.image_filter_range(json_dict,
"img_cleaned_sum_pe",
min_value=min_npe)
if max_npe is not None:
json_dict = common.image_filter_range(json_dict,
"img_cleaned_sum_pe",
max_value=max_npe)
if border:
json_dict = common.image_filter_range(
json_dict, "img_cleaned_signal_to_border_distance", min_value=1)
score_array = common.extract_score_array(json_dict,
"hillas2_delta_psi_norm2")
score_array = np.abs(score_array)
count = len(score_array[score_array < delta_angle_degrees])
return count
def main():
# PARSE OPTIONS ###########################################################
parser = argparse.ArgumentParser(
description="Denoise FITS images with the tailcut algorithm.")
parser.add_argument("--output",
"-o",
required=True,
metavar="FILE",
help="The output file path")
parser.add_argument("fileargs",
nargs="+",
metavar="FILE",
help="The input (JSON) files to convert.")
args = parser.parse_args()
output_file_path = args.output
input_file_list = args.fileargs
# CONVERT FILES ###########################################################
global_output_array = None
for input_file_path in input_file_list:
print(input_file_path)
json_dict = common.parse_json_file(input_file_path)
common_lines = [input_file_path]
# Make the file output array
io_list = json_dict["io"]
file_output_list = [
common_lines + extract_columns(image_dict)
for image_dict in io_list if "score" in image_dict
]
file_output_array = np.array(file_output_list)
# Append the file output array to the global ouput array
if global_output_array is None:
global_output_array = file_output_array
else:
global_output_array = np.vstack(
[global_output_array, file_output_array])
# SAVE FILE ###############################################################
save(output_file_path, global_output_array,
OUTPUT_HEADER_LIST + score_name_list, OUTPUT_DTYPE_LIST + [
"f8",
] * len(score_name_list))
def main():
# PARSE OPTIONS ###########################################################
parser = argparse.ArgumentParser(description="Denoise FITS images with the tailcut algorithm.")
parser.add_argument("--output", "-o", required=True, metavar="FILE",
help="The output file path")
parser.add_argument("fileargs", nargs="+", metavar="FILE",
help="The input (JSON) files to convert.")
args = parser.parse_args()
output_file_path = args.output
input_file_list = args.fileargs
# CONVERT FILES ###########################################################
global_output_array = None
for input_file_path in input_file_list:
print(input_file_path)
json_dict = common.parse_json_file(input_file_path)
common_lines = [input_file_path]
# Make the file output array
io_list = json_dict["io"]
file_output_list = [common_lines + extract_columns(image_dict) for image_dict in io_list if "score" in image_dict]
file_output_array = np.array(file_output_list)
# Append the file output array to the global ouput array
if global_output_array is None:
global_output_array = file_output_array
else:
global_output_array = np.vstack([global_output_array, file_output_array])
# SAVE FILE ###############################################################
save(output_file_path,
global_output_array,
OUTPUT_HEADER_LIST + score_name_list,
OUTPUT_DTYPE_LIST + ["f8",] * len(score_name_list))
json_file_path_list = args.fileargs
if args.output is None:
prefix1 = "_o" if overlaid else ""
prefix2 = "_" + str(max_abscissa) if max_abscissa is not None else ""
output_file_path = "execution_time{}{}.pdf".format(prefix1, prefix2)
else:
output_file_path = args.output
# FETCH SCORE #############################################################
data_list = []
label_list = []
for json_file_path in json_file_path_list:
json_dict = common.parse_json_file(json_file_path)
execution_time_list = extract_execution_time_list(json_dict)
if max_abscissa is not None:
execution_time_list = [val for val in execution_time_list if val <= max_abscissa]
data_list.append(np.array(execution_time_list))
label_list.append(json_dict["label"])
# PLOT STATISTICS #########################################################
fig, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))
plot_hist(ax1, data_list, label_list, logx, logy, overlaid)
def hist_ratio(json_file_path_list,
metric,
min_npe=None,
max_npe=None,
exclude_aborted=False,
aborted_only=False,
tel_id=None,
notebook=False,
delta_angle_degrees=0.2):
if len(json_file_path_list) != 2:
raise Exception('"json_file_path_list" should have exactly 2 elements')
if exclude_aborted and aborted_only:
raise Exception('"exclude-aborted" and "aborted-only" options are not compatible"')
# FETCH SCORE AND COMPUTE HISTOGRAM #######################################
data_list = []
label_list = []
hist_list = []
bins_list = []
for json_file_path in json_file_path_list:
if not notebook:
print("Parsing {}...".format(json_file_path))
json_dict = common.parse_json_file(json_file_path)
if tel_id is not None:
json_dict = common.image_filter_equals(json_dict, "tel_id", tel_id)
if min_npe is not None:
#json_dict = common.image_filter_range(json_dict, "npe", min_value=min_npe)
json_dict = common.image_filter_range(json_dict, "img_cleaned_sum_pe", min_value=min_npe)
if max_npe is not None:
#json_dict = common.image_filter_range(json_dict, "npe", max_value=max_npe)
json_dict = common.image_filter_range(json_dict, "img_cleaned_sum_pe", max_value=max_npe)
if not notebook:
print(len(json_dict["io"]), "images")
score_array = common.extract_score_array(json_dict, metric)
data_list.append(score_array)
label_list.append(json_dict["label"])
min_range, max_range = 0., math.sin(math.radians(delta_angle_degrees))
hist, bin_edges = np.histogram(score_array,
range=(min_range, max_range),
bins=1)
#bins=[0., math.sin(math.radians(0.2)), math.sin(math.radians(0.4)), math.sin(math.radians(0.6))] )
hist_list.append(hist)
bins_list.append(bin_edges)
# COMPUTE RATIO ###########################################################
#assert bins_list[0] == bins_list[1]
edges_of_bins = bins_list[0]
val_of_bins_data_1 = hist_list[0]
val_of_bins_data_2 = hist_list[1]
print(val_of_bins_data_1)
print(val_of_bins_data_2)
# Set ratio where val_of_bins_data_2 is not zero
ratio = np.divide(val_of_bins_data_1,
val_of_bins_data_2,
where=(val_of_bins_data_2 != 0))
# Compute error on ratio (null if cannot be computed)
error = np.divide(val_of_bins_data_1 * np.sqrt(val_of_bins_data_2) + val_of_bins_data_2 * np.sqrt(val_of_bins_data_1),
np.power(val_of_bins_data_2, 2),
where=(val_of_bins_data_2 != 0))
return ratio, error, edges_of_bins
if __name__ == '__main__':
# PARSE OPTIONS ###########################################################
parser = argparse.ArgumentParser(description="Make statistics on score files (JSON files).")
parser.add_argument("fileargs", nargs=1, metavar="FILE",
help="The JSON file to process")
args = parser.parse_args()
json_file_path = args.fileargs[0]
# FETCH SCORE #############################################################
json_data = common.parse_json_file(json_file_path)
success_list, aborted_list = extract_data_list(json_data)
print("{} images".format(len(success_list) + len(aborted_list)))
print("{} succeeded".format(len(success_list)))
print("{} failed".format(len(aborted_list)))
if len(aborted_list) > 0:
error_message_dict = {}
for image_dict in aborted_list:
error_message = image_dict["error"]["message"]
if error_message in error_message_dict:
error_message_dict[error_message] += 1
else:
error_message_dict[error_message] = 1