From 1705196a51c0232f28f83faa8c92c81fd01dc075 Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Fri, 13 Dec 2024 10:42:47 -0500 Subject: [PATCH 001/137] + neutronics materials file --- libra_toolbox/neutronics/materials.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py new file mode 100644 index 0000000..aa4b4b0 --- /dev/null +++ b/libra_toolbox/neutronics/materials.py @@ -0,0 +1 @@ +import openmc \ No newline at end of file From 1a73ee83cbe369c8e955eafa3834befd8401d4dd Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 19 Dec 2024 13:51:02 -0500 Subject: [PATCH 002/137] - neutronics materials file --- libra_toolbox/neutronics/materials.py | 1 - 1 file changed, 1 deletion(-) delete mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py deleted file mode 100644 index aa4b4b0..0000000 --- a/libra_toolbox/neutronics/materials.py +++ /dev/null @@ -1 +0,0 @@ -import openmc \ No newline at end of file From 96ca43eb384d60d3b4c7c1a6ad1153fda6b9ceca Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 19 Dec 2024 16:46:00 -0500 Subject: [PATCH 003/137] + neutronics mat file & examples --- libra_toolbox/neutronics/materials.py | 142 ++++++++++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py new file mode 100644 index 0000000..27a63b6 --- /dev/null +++ b/libra_toolbox/neutronics/materials.py @@ -0,0 +1,142 @@ +import openmc + + +def get_exp_cllif_density(temp, LiCl_frac=0.695): + """ Calculates density of ClLiF [g/cc] from temperature in Celsius + and molar concentration of LiCl. Valid for 660 C - 1000 C. + Source: + G. J. Janz, R. P. T. Tomkins, C. B. Allen; + Molten Salts: Volume 4, Part 4 + Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data. + J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302. + https://doi.org/10.1063/1.555590 + """ + temp = temp + 273.15 # Convert temperature from Celsius to Kelvin + C = LiCl_frac * 100 # Convert molar concentration to molar percent + + a = 2.25621 + b = -8.20475e-3 + c = -4.09235e-4 + d = 6.37250e-5 + e = -2.52846e-7 + f = 8.73570e-9 + g = -5.11184e-10 + + rho = a + b * C + c * temp + d * C**2 \ + + e * C**3 + f * temp * C**2 + g * C * temp**2 + + + return rho + + +# Define Materials +# Source: PNNL Materials Compendium April 2021 +# PNNL-15870, Rev. 2 +inconel625 = openmc.Material(name='Inconel 625') +inconel625.set_density('g/cm3', 8.44) +inconel625.add_element('C', 0.000990, 'wo') +inconel625.add_element('Al', 0.003960, 'wo') +inconel625.add_element('Si', 0.004950, 'wo') +inconel625.add_element('P', 0.000148, 'wo') +inconel625.add_element('S', 0.000148, 'wo') +inconel625.add_element('Ti', 0.003960, 'wo') +inconel625.add_element('Cr', 0.215000, 'wo') +inconel625.add_element('Mn', 0.004950, 'wo') +inconel625.add_element('Fe', 0.049495, 'wo') +inconel625.add_element('Co', 0.009899, 'wo') +inconel625.add_element('Ni', 0.580000, 'wo') +inconel625.add_element('Nb', 0.036500, 'wo') +inconel625.add_element('Mo', 0.090000, 'wo') + +# Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2) +SS304 = openmc.Material(name="Stainless Steel 304") +# SS304.temperature = 700 + 273 +SS304.add_element('C', 0.000800, "wo") +SS304.add_element('Mn', 0.020000, "wo") +SS304.add_element('P', 0.000450 , "wo") +SS304.add_element('S', 0.000300, "wo") +SS304.add_element('Si', 0.010000, "wo") +SS304.add_element('Cr', 0.190000, "wo") +SS304.add_element('Ni', 0.095000, "wo") +SS304.add_element('Fe', 0.683450, "wo") +SS304.set_density("g/cm3", 8.00) + +# Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 +# https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ +firebrick = openmc.Material(name="Firebrick") +# Estimate average temperature of Firebrick to be around 300 C +# Firebrick.temperature = 273 + 300 +firebrick.add_element('Al', 0.004, 'ao') +firebrick.add_element('O', 0.666, 'ao') +firebrick.add_element('Si', 0.240, 'ao') +firebrick.add_element('Zr', 0.090, 'ao') +firebrick.set_density('g/cm3', 0.30) + +# alumina insulation +# data from https://precision-ceramics.com/materials/alumina/ +alumina = openmc.Material(name='Alumina insulation') +alumina.add_element('O', 0.6, 'ao') +alumina.add_element('Al', 0.4, 'ao') +alumina.set_density('g/cm3', 3.98) + +# air +air = openmc.Material(name="Air") +air.add_element("C", 0.00012399 , 'wo') +air.add_element('N', 0.75527, 'wo') +air.add_element('O', 0.23178, 'wo') +air.add_element('Ar', 0.012827, 'wo') +air.set_density('g/cm3', 0.0012) + +# epoxy +epoxy = openmc.Material(name='Epoxy') +epoxy.add_element('C', 0.70, 'wo') +epoxy.add_element('H', 0.08, 'wo') +epoxy.add_element('O', 0.15, 'wo') +epoxy.add_element('N', 0.07, 'wo') +epoxy.set_density('g/cm3', 1.2) + +# helium @5psig +pressure = 34473.8 # Pa ~ 5 psig +temperature = 300 # K +R_he = 2077 # J/(kg*K) +density = pressure / (R_he * temperature) # in kg/cm^3 +density *= 1 / 1000 # in g/cm^3 +he = openmc.Material(name="Helium") +he.add_element('He', 1.0, 'ao') +he.set_density('g/cm3', density) + +# PbLi - natural - pure +pbli = openmc.Material(name="pbli") +pbli.add_element("Pb", 84.2, "ao") +pbli.add_element("Li", 15.2, "ao") +pbli.set_density("g/cm3", 11) + +flibe = openmc.Material(name="flibe") +flibe.add_element("Li", 2.0, "ao") +flibe.add_element("Be", 1.0, "ao") +flibe.add_element("F", 4.0, "ao") +flibe.set_density("g/cm3", 1.94) + +# lif-licl - natural - pure +cllif_nat = openmc.Material(name='ClLiF natural') +LiCl_frac = 0.695 # at.fr. + +cllif_nat.add_element('F', .5*(1 - LiCl_frac), 'ao') +cllif_nat.add_element('Li', 1.0, 'ao') +cllif_nat.add_element('Cl', .5*LiCl_frac, 'ao') +cllif_nat.set_density('g/cm3', get_exp_cllif_density(650)) # 69.5 at. % LiCL at 650 C + +# lif-licl - natural - EuF3 spiced +spicyclif = openmc.Material(name="spicyclif") +spicyclif.add_element("F", .15935, "wo") +spicyclif.add_element("Li", .17857, "wo") +spicyclif.add_element("Cl", .6340, "wo") +spicyclif.add_element("Eu", .0279, "wo") + +# FLiNaK - natural - pure +flinak = openmc.Material(name="flinak") +flinak.add_element("F", 50, "ao") +flinak.add_element("Li", 23.25, "ao") +flinak.add_element("Na", 5.75, "ao") +flinak.add_element("K", 21, "ao") +flinak.set_density("g/cm3", 2.020) From d511a43d632662cf65d3ae82c0cfd85b6b56e245 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 10:37:27 -0400 Subject: [PATCH 004/137] initial commit --- .../activation_foils/compass.py | 60 +++++++++++++++++-- 1 file changed, 54 insertions(+), 6 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b16f37b..f96752a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -1,5 +1,6 @@ import numpy as np import os +import pandas as pd def get_channel(filename): @@ -22,24 +23,24 @@ def get_channel(filename): >>> get_channel("Data_CH4@V1725_292_Background_250322.CSV") 4 """ - return int(filename.split('@')[0][7:]) + return int(filename.split("@")[0][7:]) def sort_compass_files(directory: str) -> dict: - """ Gets Compass csv data filenames + """Gets Compass csv data filenames and sorts them according to channel and ending number. The filenames need to be sorted by ending number because only the first csv file for each channel contains a header. - - Example of sorted filenames in directory: + + Example of sorted filenames in directory: 1st file: Data_CH4@...22.CSV 2nd file: Data_CH4@...22_1.CSV - 3rd file: Data_CH4@...22_2.CSV """ + 3rd file: Data_CH4@...22_2.CSV""" filenames = os.listdir(directory) data_filenames = {} for filename in filenames: - if filename.lower().endswith('.csv'): + if filename.lower().endswith(".csv"): ch = get_channel(filename) # initialize filenames for each channel if ch not in data_filenames.keys(): @@ -53,5 +54,52 @@ def sort_compass_files(directory: str) -> dict: return data_filenames +def get_events(directory): + """ + From a directory with unprocessed Compass data CSV files, + this returns dictionaries of detector pulse times and energies + with digitizer channels as the keys to the dictionaries. + + This function is also built to be able to read-in problematic + Compass CSV files that have been incorrectly post-processed to + reduce waveform data.""" + + time_values = {} + energy_values = {} + + data_filenames = sort_compass_files(directory) + + for ch in data_filenames.keys(): + # Initialize time_values and energy_values for each channel + time_values[ch] = [] + energy_values[ch] = [] + for i, filename in enumerate(data_filenames[ch]): + # First file has a header, so skip the first row + # Eventually, we can use the header to index the values + # but since some csv datafiles have been changed without header info, + # this is the code that will work + if i == 0: + skiprows = 1 + else: + skiprows = 0 + + csv_file_path = os.path.join(directory, filename) + + try: + df = pd.read_csv( + csv_file_path, delimiter=";", header=None, skiprows=skiprows + ) + except: + raise Exception(f"Could not read in file: {csv_file_path}") + time_column = 2 + energy_column = 3 + time_data = df[time_column].to_numpy() + # print(time_data.shape) + energy_data = df[energy_column].to_numpy() + # Extract and append the energy data to the list + time_values[ch].extend(time_data) + # print(len(time_values[source])) + energy_values[ch].extend(energy_data) + return time_values, energy_values From 334074256577f10f8d5e973989173a31798b7384 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 10:49:46 -0400 Subject: [PATCH 005/137] refactoring + header --- .../activation_foils/compass.py | 53 ++++++++++--------- 1 file changed, 29 insertions(+), 24 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index f96752a..8af0ebd 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -1,6 +1,7 @@ import numpy as np import os import pandas as pd +from typing import Tuple, Dict def get_channel(filename): @@ -54,7 +55,7 @@ def sort_compass_files(directory: str) -> dict: return data_filenames -def get_events(directory): +def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndarray]]: """ From a directory with unprocessed Compass data CSV files, this returns dictionaries of detector pulse times and energies @@ -62,7 +63,14 @@ def get_events(directory): This function is also built to be able to read-in problematic Compass CSV files that have been incorrectly post-processed to - reduce waveform data.""" + reduce waveform data. + + Args: + directory: directory containing CSV files with Compass data + + Returns: + time values and energy values for each channel + """ time_values = {} energy_values = {} @@ -71,35 +79,32 @@ def get_events(directory): for ch in data_filenames.keys(): # Initialize time_values and energy_values for each channel - time_values[ch] = [] - energy_values[ch] = [] + time_values[ch] = np.empty(0) + energy_values[ch] = np.empty(0) for i, filename in enumerate(data_filenames[ch]): - # First file has a header, so skip the first row - # Eventually, we can use the header to index the values - # but since some csv datafiles have been changed without header info, - # this is the code that will work + + # only the first file has a header if i == 0: - skiprows = 1 + header = 0 else: - skiprows = 0 + header = None csv_file_path = os.path.join(directory, filename) - try: - df = pd.read_csv( - csv_file_path, delimiter=";", header=None, skiprows=skiprows - ) - except: - raise Exception(f"Could not read in file: {csv_file_path}") + df = pd.read_csv(csv_file_path, delimiter=";", header=header) - time_column = 2 - energy_column = 3 - time_data = df[time_column].to_numpy() - # print(time_data.shape) - energy_data = df[energy_column].to_numpy() + # read the header and store in names + if i == 0: + names = df.columns.values + else: + # apply the column names if not the first file + df.columns = names + + time_data = df["TIMETAG"].to_numpy() + energy_data = df["ENERGY"].to_numpy() # Extract and append the energy data to the list - time_values[ch].extend(time_data) - # print(len(time_values[source])) - energy_values[ch].extend(energy_data) + time_values[ch] = np.concatenate([time_values[ch], time_data]) + energy_values[ch] = np.concatenate([energy_values[ch], energy_data]) + return time_values, energy_values From 9dcf53b84a0f68e049f9a3e03d98f2d5c6a8c593 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 11:00:14 -0400 Subject: [PATCH 006/137] added test --- test/neutron_detection/test_compass.py | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 422eb88..329f586 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -2,6 +2,7 @@ import numpy as np import os from libra_toolbox.neutron_detection.activation_foils import compass +from pathlib import Path @pytest.mark.parametrize( @@ -85,3 +86,27 @@ def test_sort_compass_files(tmpdir, base_name: str, expected_filenames: dict): assert np.array_equal(a, b) else: assert a == b + + +@pytest.mark.parametrize( + "expected_time, expected_energy, expected_idx", + [ + (6685836624, 515, 5), + (11116032249, 568, 6), + (1623550122, 589, -1), + (535148093, 1237, -2), + ], +) +def test_get_events(expected_time, expected_energy, expected_idx): + """ + Test the get_events function from the compass module. + Checks that specific time and energy values are returned for a given channel + """ + test_directory = Path(__file__).parent / "compass_test_data" + times, energies = compass.get_events(test_directory) + assert isinstance(times, dict) + assert isinstance(energies, dict) + + ch = 5 + assert times[ch][expected_idx] == expected_time + assert energies[ch][expected_idx] == expected_energy From 468deb8040e7d5afdef9eab5ecde1841bebc900d Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 11:03:36 -0400 Subject: [PATCH 007/137] improved tests --- test/neutron_detection/test_compass.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 329f586..818583f 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -107,6 +107,11 @@ def test_get_events(expected_time, expected_energy, expected_idx): assert isinstance(times, dict) assert isinstance(energies, dict) + expected_keys = [5, 15] + for key in expected_keys: + assert key in times + assert key in energies + ch = 5 assert times[ch][expected_idx] == expected_time assert energies[ch][expected_idx] == expected_energy From 1de8d0664a7ef4b7670af46d39738adbd3d94941 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 11:04:34 -0400 Subject: [PATCH 008/137] added data files --- ...@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv | 11 +++++++++++ ...@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv | 11 +++++++++++ ...1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv | 4 ++++ 3 files changed, 26 insertions(+) create mode 100644 test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv create mode 100644 test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv create mode 100644 test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv diff --git a/test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv new file mode 100644 index 0000000..fadcce6 --- /dev/null +++ b/test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv @@ -0,0 +1,11 @@ +BOARD;CHANNEL;TIMETAG;ENERGY;ENERGYSHORT;FLAGS +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 +0;5;5997211248;375;251;0x4000 +0;5;6685836624;515;340;0x4000 +0;5;11116032249;568;380;0x4000 +0;5;11281099382;1;0;0x4000 +0;5;12783039350;5;0;0x4000 +0;5;18306299412;2;0;0x4000 diff --git a/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv new file mode 100644 index 0000000..fadcce6 --- /dev/null +++ b/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv @@ -0,0 +1,11 @@ +BOARD;CHANNEL;TIMETAG;ENERGY;ENERGYSHORT;FLAGS +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 +0;5;5997211248;375;251;0x4000 +0;5;6685836624;515;340;0x4000 +0;5;11116032249;568;380;0x4000 +0;5;11281099382;1;0;0x4000 +0;5;12783039350;5;0;0x4000 +0;5;18306299412;2;0;0x4000 diff --git a/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv b/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv new file mode 100644 index 0000000..7c2f11b --- /dev/null +++ b/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv @@ -0,0 +1,4 @@ +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 From 8f40b52330e243634df92802d49d1cf180d54003 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 13:11:54 -0400 Subject: [PATCH 009/137] test for COINC_2 --- test/neutron_detection/test_prt.py | 69 ++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 78d6cb6..b4a4a4d 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -113,3 +113,72 @@ def test_get_count_rate(bin_time: float, count_rate_real: float): # test assert np.allclose(count_rates, count_rate_real) + + +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected", + [ + # Test case 1: Simple match within time window + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [10, 20, 30], + [15, 25, 35], + 0.2, + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [10, 20, 30], + [15, 25, 35], + ), + ), + # Test case 2: No match due to time window + ( + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + [10, 20, 30], + [15, 25, 35], + 0.1, + ([], [], [], []), + ), + # Test case 3: Partial match + ( + [1.0, 2.0, 3.0], + [1.05, 5.0, 3.05], + [10, 20, 30], + [15, 25, 35], + 0.1, + ( + [1.0, 3.0], + [1.05, 3.05], + [10, 30], + [15, 35], + ), + ), + # Test case 4: Empty input + ( + [], + [], + [], + [], + 0.1, + ([], [], [], []), + ), + ], +) +def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): + """ + Test the COINC_2 function. + This function checks if the coincidence detection works correctly + for two channels within a given time window. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) + assert result == expected From 53fe9b02a9bde627ac3afde7ec551b8304f28040 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 13:24:08 -0400 Subject: [PATCH 010/137] fixed test --- test/neutron_detection/test_prt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index b4a4a4d..7900315 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -144,9 +144,9 @@ def test_get_count_rate(bin_time: float, count_rate_real: float): # Test case 3: Partial match ( [1.0, 2.0, 3.0], - [1.05, 5.0, 3.05], + [1.05, 3.05, 5.0], [10, 20, 30], - [15, 25, 35], + [15, 35, 25], 0.1, ( [1.0, 3.0], From df992bfeea1db83efa50329023b7c473760c3fc8 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 13:28:57 -0400 Subject: [PATCH 011/137] tests for coinc3 and 4 --- test/neutron_detection/test_prt.py | 205 +++++++++++++++++++++++++++++ 1 file changed, 205 insertions(+) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 7900315..cbeb68d 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -182,3 +182,208 @@ def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): """ result = prt.COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) assert result == expected + + +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", + [ + # Test case 1: All channels match within the time window + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [1.15, 2.15, 3.15], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + 0.2, + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [1.15, 2.15, 3.15], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + ), + ), + # Test case 2: No matches due to time window + ( + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + [7.0, 8.0, 9.0], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + 0.1, + ([], [], [], [], [], []), + ), + # Test case 3: Partial matches + ( + [1.0, 2.0, 3.0], + [1.05, 1.8, 3.05], + [1.1, 2.1, 3.1], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + 0.11, + ( + [1.0, 3.0], + [1.05, 3.05], + [1.1, 3.1], + [10, 30], + [15, 35], + [12, 32], + ), + ), + # Test case 4: Empty input + ( + [], + [], + [], + [], + [], + [], + 0.1, + ([], [], [], [], [], []), + ), + ], +) +def test_COINC_3( + ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected +): + """ + Test the COINC_3 function. + This function checks if the coincidence detection works correctly + for three channels within a given time window. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch3_time: List of timestamps for channel 3. + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + ch3_ampl: List of amplitudes for channel 3. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.COINC_3( + ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window + ) + assert result == expected + + +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + [ + # Test case 1: All channels match within the time window + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [1.15, 2.15, 3.15], + [1.2, 2.2, 3.2], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + [14, 24, 34], + 0.3, + ( + [1.0, 2.0, 3.0], + [1.1, 2.1, 3.1], + [1.15, 2.15, 3.15], + [1.2, 2.2, 3.2], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + [14, 24, 34], + ), + ), + # Test case 2: No matches due to time window + ( + [1.0, 2.0, 3.0], + [4.0, 5.0, 6.0], + [7.0, 8.0, 9.0], + [10.0, 11.0, 12.0], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + [14, 24, 34], + 0.1, + ([], [], [], [], [], [], [], []), + ), + # Test case 3: Partial matches + ( + [1.0, 2.0, 3.0], + [1.05, 2.05, 5.0], + [1.1, 2.1, 3.1], + [1.15, 2.15, 3.15], + [10, 20, 30], + [15, 25, 35], + [12, 22, 32], + [14, 24, 34], + 0.2, + ( + [1.0, 2.0], + [1.05, 2.05], + [1.1, 2.1], + [1.15, 2.15], + [10, 20], + [15, 25], + [12, 22], + [14, 24], + ), + ), + # Test case 4: Empty input + ( + [], + [], + [], + [], + [], + [], + [], + [], + 0.1, + ([], [], [], [], [], [], [], []), + ), + ], +) +def test_COINC_4( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + expected, +): + """ + Test the COINC_4 function. + This function checks if the coincidence detection works correctly + for four channels within a given time window. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch3_time: List of timestamps for channel 3. + ch4_time: List of timestamps for channel 4. + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + ch3_ampl: List of amplitudes for channel 3. + ch4_ampl: List of amplitudes for channel 4. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.COINC_4( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + ) + assert result == expected From b41b85ccc4e83589a6e24e20aa2a4d003243d3fd Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 13:32:07 -0400 Subject: [PATCH 012/137] vectorised functions --- .../neutron_detection/diamond/prt.py | 219 +++++++----------- test/neutron_detection/test_prt.py | 27 +++ 2 files changed, 107 insertions(+), 139 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index ed842a1..9dfaed0 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -104,88 +104,57 @@ def get_count_rate( def COINC_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): - pos_Ch1 = 0 - pos_Ch2 = 0 + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) - length_Ch1 = len(Ch1_AMPL) - length_Ch2 = len(Ch2_AMPL) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - while pos_Ch1 < length_Ch1 and pos_Ch2 < length_Ch2: - diff = Ch1_TIME[pos_Ch1] - Ch2_TIME[pos_Ch2] - if abs(diff) <= t_window: - - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) - - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) + # For each Ch1 time, find window in Ch2 where match is possible + idx_start = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") + idx_end = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") - pos_Ch1 += 1 - pos_Ch2 += 1 + # Keep only those with at least one match + has_match = idx_start < idx_end - elif diff < 0: - pos_Ch1 += 1 - else: - pos_Ch2 += 1 + matched_Ch1_idx = np.flatnonzero(has_match) + matched_Ch2_idx = idx_start[has_match] # First match only - return aaccepted_time_1, aaccepted_time_2, aaccepted_ampl_1, aaccepted_ampl_2 + return ( + Ch1_TIME[matched_Ch1_idx], + Ch2_TIME[matched_Ch2_idx], + Ch1_AMPL[matched_Ch1_idx], + Ch2_AMPL[matched_Ch2_idx], + ) def COINC_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window): - - pos_Ch1, pos_Ch2, pos_Ch3 = 0, 0, 0 - - length_Ch1 = len(Ch1_AMPL) - length_Ch2 = len(Ch2_AMPL) - length_Ch3 = len(Ch3_AMPL) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - aaccepted_ampl_3 = [] - aaccepted_time_3 = [] - - while pos_Ch1 < length_Ch1 and pos_Ch2 < length_Ch2 and pos_Ch3 < length_Ch3: - min_val = min(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3]) - max_val = max(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3]) - - if max_val - min_val <= t_window: - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) - - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) - - aaccepted_ampl_3.append(Ch3_AMPL[pos_Ch3]) - aaccepted_time_3.append(Ch3_TIME[pos_Ch3]) - - pos_Ch1 += 1 - pos_Ch2 += 1 - pos_Ch3 += 1 - else: - if min_val == Ch1_TIME[pos_Ch1]: - pos_Ch1 += 1 - if min_val == Ch2_TIME[pos_Ch2]: - pos_Ch2 += 1 - if min_val == Ch3_TIME[pos_Ch3]: - pos_Ch3 += 1 + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch3_TIME = np.asarray(Ch3_TIME) + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) + Ch3_AMPL = np.asarray(Ch3_AMPL) + + # For each Ch1 time, find window in Ch2 and Ch3 + idx_start_2 = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") + idx_end_2 = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") + + idx_start_3 = np.searchsorted(Ch3_TIME, Ch1_TIME - t_window, side="left") + idx_end_3 = np.searchsorted(Ch3_TIME, Ch1_TIME + t_window, side="right") + + # Valid coincidences: Ch1 has at least one match in both Ch2 and Ch3 + has_match = (idx_start_2 < idx_end_2) & (idx_start_3 < idx_end_3) + matched_Ch1_idx = np.flatnonzero(has_match) + matched_Ch2_idx = idx_start_2[has_match] + matched_Ch3_idx = idx_start_3[has_match] return ( - aaccepted_time_1, - aaccepted_time_2, - aaccepted_time_3, - aaccepted_ampl_1, - aaccepted_ampl_2, - aaccepted_ampl_3, + Ch1_TIME[matched_Ch1_idx], + Ch2_TIME[matched_Ch2_idx], + Ch3_TIME[matched_Ch3_idx], + Ch1_AMPL[matched_Ch1_idx], + Ch2_AMPL[matched_Ch2_idx], + Ch3_AMPL[matched_Ch3_idx], ) @@ -200,75 +169,47 @@ def COINC_4( Ch4_AMPL, t_window, ): + # Convert to NumPy arrays + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch3_TIME = np.asarray(Ch3_TIME) + Ch4_TIME = np.asarray(Ch4_TIME) + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) + Ch3_AMPL = np.asarray(Ch3_AMPL) + Ch4_AMPL = np.asarray(Ch4_AMPL) + + # For each Ch1 event, find index range in Ch2/Ch3/Ch4 within time window + idx_start_2 = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") + idx_end_2 = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") + + idx_start_3 = np.searchsorted(Ch3_TIME, Ch1_TIME - t_window, side="left") + idx_end_3 = np.searchsorted(Ch3_TIME, Ch1_TIME + t_window, side="right") + + idx_start_4 = np.searchsorted(Ch4_TIME, Ch1_TIME - t_window, side="left") + idx_end_4 = np.searchsorted(Ch4_TIME, Ch1_TIME + t_window, side="right") + + # Valid coincidences must have at least one match in Ch2, Ch3, and Ch4 + has_match = ( + (idx_start_2 < idx_end_2) + & (idx_start_3 < idx_end_3) + & (idx_start_4 < idx_end_4) + ) - pos_Ch1, pos_Ch2, pos_Ch3, pos_Ch4 = 0, 0, 0, 0 - - length_A = len(Ch1_AMPL) - length_B = len(Ch2_AMPL) - length_C = len(Ch3_AMPL) - length_D = len(Ch4_AMPL) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - aaccepted_ampl_3 = [] - aaccepted_time_3 = [] - - aaccepted_ampl_4 = [] - aaccepted_time_4 = [] - - while ( - pos_Ch1 < length_A - and pos_Ch2 < length_B - and pos_Ch3 < length_C - and pos_Ch4 < length_D - ): - min_val = min( - Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3], Ch4_TIME[pos_Ch4] - ) - max_val = max( - Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3], Ch4_TIME[pos_Ch4] - ) - - if max_val - min_val <= t_window: - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) - - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) - - aaccepted_ampl_3.append(Ch3_AMPL[pos_Ch3]) - aaccepted_time_3.append(Ch3_TIME[pos_Ch3]) - - aaccepted_ampl_4.append(Ch4_AMPL[pos_Ch4]) - aaccepted_time_4.append(Ch4_TIME[pos_Ch4]) - - pos_Ch1 += 1 - pos_Ch2 += 1 - pos_Ch3 += 1 - pos_Ch4 += 1 - else: - if min_val == Ch1_TIME[pos_Ch1]: - pos_Ch1 += 1 - if min_val == Ch2_TIME[pos_Ch2]: - pos_Ch2 += 1 - if min_val == Ch3_TIME[pos_Ch3]: - pos_Ch3 += 1 - if min_val == Ch4_TIME[pos_Ch4]: - pos_Ch4 += 1 + matched_Ch1_idx = np.flatnonzero(has_match) + matched_Ch2_idx = idx_start_2[has_match] + matched_Ch3_idx = idx_start_3[has_match] + matched_Ch4_idx = idx_start_4[has_match] return ( - aaccepted_time_1, - aaccepted_time_2, - aaccepted_time_3, - aaccepted_time_4, - aaccepted_ampl_1, - aaccepted_ampl_2, - aaccepted_ampl_3, - aaccepted_ampl_4, + Ch1_TIME[matched_Ch1_idx], + Ch2_TIME[matched_Ch2_idx], + Ch3_TIME[matched_Ch3_idx], + Ch4_TIME[matched_Ch4_idx], + Ch1_AMPL[matched_Ch1_idx], + Ch2_AMPL[matched_Ch2_idx], + Ch3_AMPL[matched_Ch3_idx], + Ch4_AMPL[matched_Ch4_idx], ) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index cbeb68d..1598c66 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -181,6 +181,13 @@ def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): expected: Expected output (time and amplitude matches). """ result = prt.COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) + # convert everything to list + result = ( + result[0].tolist(), + result[1].tolist(), + result[2].tolist(), + result[3].tolist(), + ) assert result == expected @@ -268,6 +275,15 @@ def test_COINC_3( result = prt.COINC_3( ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window ) + # convert everything to list + result = ( + result[0].tolist(), + result[1].tolist(), + result[2].tolist(), + result[3].tolist(), + result[4].tolist(), + result[5].tolist(), + ) assert result == expected @@ -386,4 +402,15 @@ def test_COINC_4( ch4_ampl, t_window, ) + # convert everything to list + result = ( + result[0].tolist(), + result[1].tolist(), + result[2].tolist(), + result[3].tolist(), + result[4].tolist(), + result[5].tolist(), + result[6].tolist(), + result[7].tolist(), + ) assert result == expected From 23d41272d2ccd5e1b1bc835aa6f90c33b5a21e05 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 13:33:57 -0400 Subject: [PATCH 013/137] lowercase function names --- .../neutron_detection/diamond/prt.py | 26 ++++++++++--------- test/neutron_detection/test_prt.py | 18 ++++++------- 2 files changed, 23 insertions(+), 21 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index 9dfaed0..372a916 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -100,10 +100,12 @@ def get_count_rate( **Contact:** [office@cividec.at](mailto:office@cividec.at)
**Author:** Julian Melbinger +Changes: +- vectorised functions using numpy for performance """ -def COINC_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): +def coinc_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): Ch1_TIME = np.asarray(Ch1_TIME) Ch2_TIME = np.asarray(Ch2_TIME) Ch1_AMPL = np.asarray(Ch1_AMPL) @@ -127,7 +129,7 @@ def COINC_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): ) -def COINC_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window): +def coinc_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window): Ch1_TIME = np.asarray(Ch1_TIME) Ch2_TIME = np.asarray(Ch2_TIME) Ch3_TIME = np.asarray(Ch3_TIME) @@ -158,7 +160,7 @@ def COINC_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window ) -def COINC_4( +def coinc_4( Ch1_TIME, Ch2_TIME, Ch3_TIME, @@ -213,7 +215,7 @@ def COINC_4( ) -def COINC_2_ANTI_1( +def coinc_2_ANTI_1( Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window ): @@ -264,7 +266,7 @@ def COINC_2_ANTI_1( return aaccepted_time_1, aaccepted_time_2, aaccepted_ampl_1, aaccepted_ampl_2 -def COINC_3_ANTI_1( +def coinc_3_ANTI_1( Ch1_TIME, Ch2_TIME, Ch3_TIME, @@ -337,7 +339,7 @@ def COINC_3_ANTI_1( ) -def COINC_2_ANTI_2( +def coinc_2_ANTI_2( Ch1_TIME, Ch2_TIME, Ch3_TIME, @@ -450,7 +452,7 @@ def calculate_coincidence( first_data = grouped_data[which_data_channels[0]] second_data = grouped_data[which_data_channels[1]] - result = COINC_2( + result = coinc_2( first_data[0], second_data[0], first_data[1], @@ -482,7 +484,7 @@ def calculate_coincidence( second_data = grouped_data[which_data_channels[1]] third_data = grouped_data[which_data_channels[2]] - result = COINC_3( + result = coinc_3( first_data[0], second_data[0], third_data[0], @@ -522,7 +524,7 @@ def calculate_coincidence( third_data = grouped_data[which_data_channels[2]] fourth_data = grouped_data[which_data_channels[3]] - result = COINC_4( + result = coinc_4( first_data[0], second_data[0], third_data[0], @@ -570,7 +572,7 @@ def calculate_coincidence( third_data = grouped_data[which_anti_data_channels[0]] - result = COINC_2_ANTI_1( + result = coinc_2_ANTI_1( first_data[0], second_data[0], third_data[0], @@ -613,7 +615,7 @@ def calculate_coincidence( fourth_data = grouped_data[which_anti_data_channels[0]] - result = COINC_3_ANTI_1( + result = coinc_3_ANTI_1( first_data[0], second_data[0], third_data[0], @@ -662,7 +664,7 @@ def calculate_coincidence( third_data = grouped_data[which_anti_data_channels[0]] fourth_data = grouped_data[which_anti_data_channels[1]] - result = COINC_2_ANTI_2( + result = coinc_2_ANTI_2( first_data[0], second_data[0], third_data[0], diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 1598c66..032d00a 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -166,9 +166,9 @@ def test_get_count_rate(bin_time: float, count_rate_real: float): ), ], ) -def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): +def test_coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): """ - Test the COINC_2 function. + Test the coinc_2 function. This function checks if the coincidence detection works correctly for two channels within a given time window. @@ -180,7 +180,7 @@ def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) + result = prt.coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) # convert everything to list result = ( result[0].tolist(), @@ -254,11 +254,11 @@ def test_COINC_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): ), ], ) -def test_COINC_3( +def test_coinc_3( ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected ): """ - Test the COINC_3 function. + Test the coinc_3 function. This function checks if the coincidence detection works correctly for three channels within a given time window. @@ -272,7 +272,7 @@ def test_COINC_3( t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.COINC_3( + result = prt.coinc_3( ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window ) # convert everything to list @@ -362,7 +362,7 @@ def test_COINC_3( ), ], ) -def test_COINC_4( +def test_coinc_4( ch1_time, ch2_time, ch3_time, @@ -375,7 +375,7 @@ def test_COINC_4( expected, ): """ - Test the COINC_4 function. + Test the coinc_4 function. This function checks if the coincidence detection works correctly for four channels within a given time window. @@ -391,7 +391,7 @@ def test_COINC_4( t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.COINC_4( + result = prt.coinc_4( ch1_time, ch2_time, ch3_time, From 17b55aeb4b57c6a7822f2958994eeea4d1199c77 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 14:12:33 -0400 Subject: [PATCH 014/137] refactored calculate_coincidence --- .../neutron_detection/diamond/prt.py | 341 ++++++------------ 1 file changed, 107 insertions(+), 234 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index 372a916..c64a47a 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -102,6 +102,7 @@ def get_count_rate( Changes: - vectorised functions using numpy for performance +- refactoring and abstraction of the common logic """ @@ -410,6 +411,58 @@ def coinc_2_ANTI_2( return aaccepted_time_1, aaccepted_time_2, aaccepted_ampl_1, aaccepted_ampl_2 +def process_coincidence( + grouped_data, coincidence_channels, t_window, coincidence_function +): + """ + Process coincidence for the given channels using the specified coincidence function. + + Args: + grouped_data: List of grouped data for all channels. + coincidence_channels: Indices of the channels involved in coincidence. + t_window: Time window for coincidence detection. + coincidence_function: Function to calculate coincidence. + + Returns: + Result of the coincidence function. + """ + data = [grouped_data[i] for i in coincidence_channels] + times = [d[0] for d in data] + amplitudes = [d[1] for d in data] + + return coincidence_function(*times, *amplitudes, t_window) + + +def process_anti_coincidence( + grouped_data, coincidence_channels, anti_channels, t_window, anti_function +): + """ + Process coincidence with anti-coincidence for the given channels. + + Args: + grouped_data: List of grouped data for all channels. + coincidence_channels: Indices of the channels involved in coincidence. + anti_channels: Indices of the channels involved in anti-coincidence. + t_window: Time window for coincidence detection. + anti_function: Function to calculate coincidence with anti-coincidence. + + Returns: + Result of the anti-coincidence function. + """ + coinc_data = [grouped_data[i] for i in coincidence_channels] + anti_data = [grouped_data[i] for i in anti_channels] + + coinc_times = [d[0] for d in coinc_data] + coinc_amplitudes = [d[1] for d in coinc_data] + + anti_times = [d[0] for d in anti_data] + anti_amplitudes = [d[1] for d in anti_data] + + return anti_function( + *coinc_times, *anti_times, *coinc_amplitudes, *anti_amplitudes, t_window + ) + + def calculate_coincidence( A_time, A_ampl, @@ -425,7 +478,7 @@ def calculate_coincidence( # Amplitude in mV # Time in s - Channel_names = ["A", "B", "C", "D"] + channel_names = ["A", "B", "C", "D"] coincidence_citeria = np.array(coincidence_citeria) grouped_data = [ @@ -435,254 +488,74 @@ def calculate_coincidence( [D_time, D_ampl], ] - number_of_ignore = len(np.where(np.array(coincidence_citeria) == 0)[0]) - number_of_coincidence = len(np.where(np.array(coincidence_citeria) == 1)[0]) - number_of_anti_coincidence = len(np.where(np.array(coincidence_citeria) == 2)[0]) + number_of_ignore = len(np.where(coincidence_citeria == 0)[0]) + number_of_coincidence = len(np.where(coincidence_citeria == 1)[0]) + number_of_anti_coincidence = len(np.where(coincidence_citeria == 2)[0]) + print( f"Ignore: {number_of_ignore}, Coincidence: {number_of_coincidence}, Anti-Coincidence: {number_of_anti_coincidence}" ) - # Coincidence between two data channels: - if number_of_coincidence == 2 and number_of_ignore == 2: - which_data_channels = np.where(coincidence_citeria == 1)[0] - - ch1 = Channel_names[which_data_channels[0]] - ch2 = Channel_names[which_data_channels[1]] - print(f"Coincidence between {ch1} and {ch2}") - first_data = grouped_data[which_data_channels[0]] - second_data = grouped_data[which_data_channels[1]] - - result = coinc_2( - first_data[0], - second_data[0], - first_data[1], - second_data[1], - t_window=coincidence_window, - ) + # Get indices of coincidence and anti-coincidence channels + coincidence_channels = np.where(coincidence_citeria == 1)[0] + anti_channels = np.where(coincidence_citeria == 2)[0] - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[2]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[3]), - "Sum_amplitude [mV]": np.array(result[2]) + np.array(result[3]), - } + # Handle different cases + if number_of_coincidence == 2 and number_of_anti_coincidence == 0: + result = process_coincidence( + grouped_data, coincidence_channels, coincidence_window, coinc_2 ) - return df - - # Coincidence between three data channels: - elif number_of_coincidence == 3 and number_of_ignore == 1: - which_data_channels = np.where(coincidence_citeria == 1)[0] - - ch1 = Channel_names[which_data_channels[0]] - ch2 = Channel_names[which_data_channels[1]] - ch3 = Channel_names[which_data_channels[2]] - - print(f"Coincidence between {ch1}, {ch2} and {ch3}") - first_data = grouped_data[which_data_channels[0]] - second_data = grouped_data[which_data_channels[1]] - third_data = grouped_data[which_data_channels[2]] - - result = coinc_3( - first_data[0], - second_data[0], - third_data[0], - first_data[1], - second_data[1], - third_data[1], - t_window=coincidence_window, + elif number_of_coincidence == 3 and number_of_anti_coincidence == 0: + result = process_coincidence( + grouped_data, coincidence_channels, coincidence_window, coinc_3 ) - - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[3]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[4]), - f"{ch3}_time [s]": np.array(result[2]), - f"{ch3}_amplitude [mV]": np.array(result[5]), - "Sum_amplitude [mV]": np.array(result[3]) - + np.array(result[4]) - + np.array(result[5]), - } - ) - return df - - # Coincidence between all four data channels: elif number_of_coincidence == 4: - which_data_channels = np.where(coincidence_citeria == 1)[0] - - ch1 = Channel_names[which_data_channels[0]] - ch2 = Channel_names[which_data_channels[1]] - ch3 = Channel_names[which_data_channels[2]] - ch4 = Channel_names[which_data_channels[3]] - - print(f"Coincidence between {ch1}, {ch2}, {ch3} and {ch4}") - first_data = grouped_data[which_data_channels[0]] - second_data = grouped_data[which_data_channels[1]] - third_data = grouped_data[which_data_channels[2]] - fourth_data = grouped_data[which_data_channels[3]] - - result = coinc_4( - first_data[0], - second_data[0], - third_data[0], - fourth_data[0], - first_data[1], - second_data[1], - third_data[1], - fourth_data[1], - t_window=coincidence_window, - ) - - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[4]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[5]), - f"{ch3}_time [s]": np.array(result[2]), - f"{ch3}_amplitude [mV]": np.array(result[6]), - f"{ch4}_time [s]": np.array(result[3]), - f"{ch4}_amplitude [mV]": np.array(result[7]), - "Sum_amplitude [mV]": np.array(result[4]) - + np.array(result[5]) - + np.array(result[6]) - + np.array(result[7]), - } + result = process_coincidence( + grouped_data, coincidence_channels, coincidence_window, coinc_4 ) - return df - - # Coincidence between two channels and anti-coincidence with a third one elif number_of_coincidence == 2 and number_of_anti_coincidence == 1: - which_coinc_data_channels = np.where(coincidence_citeria == 1)[0] - which_anti_data_channels = np.where(coincidence_citeria == 2)[0] - - # Coincidence channels: - ch1 = Channel_names[which_coinc_data_channels[0]] - ch2 = Channel_names[which_coinc_data_channels[1]] - - # Anti-coincidence channel - ch3 = Channel_names[which_anti_data_channels[0]] - - print(f"Coincidence between {ch1} and {ch2} and anti-coincidence with {ch3}") - first_data = grouped_data[which_coinc_data_channels[0]] - second_data = grouped_data[which_coinc_data_channels[1]] - - third_data = grouped_data[which_anti_data_channels[0]] - - result = coinc_2_ANTI_1( - first_data[0], - second_data[0], - third_data[0], - first_data[1], - second_data[1], - third_data[1], - t_window=coincidence_window, - ) - - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[2]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[3]), - "Sum_amplitude [mV]": np.array(result[2]) + np.array(result[3]), - } + result = process_anti_coincidence( + grouped_data, + coincidence_channels, + anti_channels, + coincidence_window, + coinc_2_ANTI_1, ) - return df - - # Coincidence between three channels and anti-coincidence with a fourth one elif number_of_coincidence == 3 and number_of_anti_coincidence == 1: - which_coinc_data_channels = np.where(coincidence_citeria == 1)[0] - which_anti_data_channels = np.where(coincidence_citeria == 2)[0] - - # Coincidence channels: - ch1 = Channel_names[which_coinc_data_channels[0]] - ch2 = Channel_names[which_coinc_data_channels[1]] - ch3 = Channel_names[which_coinc_data_channels[2]] - - # Anti-coincidence channel - ch4 = Channel_names[which_anti_data_channels[0]] - - print( - f"Coincidence between {ch1}, {ch2} and {ch3} and anti-coincidence with {ch4}" - ) - first_data = grouped_data[which_coinc_data_channels[0]] - second_data = grouped_data[which_coinc_data_channels[1]] - third_data = grouped_data[which_coinc_data_channels[2]] - - fourth_data = grouped_data[which_anti_data_channels[0]] - - result = coinc_3_ANTI_1( - first_data[0], - second_data[0], - third_data[0], - fourth_data[0], - first_data[1], - second_data[1], - third_data[1], - fourth_data[1], - t_window=coincidence_window, - ) - - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[3]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[4]), - f"{ch3}_time [s]": np.array(result[2]), - f"{ch3}_amplitude [mV]": np.array(result[5]), - "Sum_amplitude [mV]": np.array(result[3]) - + np.array(result[4]) - + np.array(result[5]), - } + result = process_anti_coincidence( + grouped_data, + coincidence_channels, + anti_channels, + coincidence_window, + coinc_3_ANTI_1, ) - return df - - # Coincidence between two channels and anti-coincidence with the remainign two channels elif number_of_coincidence == 2 and number_of_anti_coincidence == 2: - which_coinc_data_channels = np.where(coincidence_citeria == 1)[0] - which_anti_data_channels = np.where(coincidence_citeria == 2)[0] - - # Coincidence channels: - ch1 = Channel_names[which_coinc_data_channels[0]] - ch2 = Channel_names[which_coinc_data_channels[1]] - - # Anti-coincidence channel - ch3 = Channel_names[which_anti_data_channels[0]] - ch4 = Channel_names[which_anti_data_channels[1]] - - print( - f"Coincidence between {ch1} and {ch2} and anti-coincidence with {ch3} and {ch4} " + result = process_anti_coincidence( + grouped_data, + coincidence_channels, + anti_channels, + coincidence_window, + coinc_2_ANTI_2, ) - first_data = grouped_data[which_coinc_data_channels[0]] - second_data = grouped_data[which_coinc_data_channels[1]] - - third_data = grouped_data[which_anti_data_channels[0]] - fourth_data = grouped_data[which_anti_data_channels[1]] - - result = coinc_2_ANTI_2( - first_data[0], - second_data[0], - third_data[0], - fourth_data[0], - first_data[1], - second_data[1], - third_data[1], - fourth_data[1], - t_window=coincidence_window, + else: + raise ValueError("Unsupported combination of coincidence and anti-coincidence.") + + # Generate DataFrame dynamically + df_data = {} + for i, ch_idx in enumerate(coincidence_channels): + ch_name = channel_names[ch_idx] + df_data[f"{ch_name}_time [s]"] = np.array(result[i]) + df_data[f"{ch_name}_amplitude [mV]"] = np.array( + result[len(coincidence_channels) + i] ) - df = pd.DataFrame( - { - f"{ch1}_time [s]": np.array(result[0]), - f"{ch1}_amplitude [mV]": np.array(result[2]), - f"{ch2}_time [s]": np.array(result[1]), - f"{ch2}_amplitude [mV]": np.array(result[3]), - "Sum_amplitude [mV]": np.array(result[2]) + np.array(result[3]), - } + if number_of_anti_coincidence == 0: + df_data["Sum_amplitude [mV]"] = np.sum( + [ + np.array(result[len(coincidence_channels) + i]) + for i in range(len(coincidence_channels)) + ], + axis=0, ) - return df + + return pd.DataFrame(df_data) From 7e11d2ffc875485b54ebac958a899e9d4185056f Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 14:31:09 -0400 Subject: [PATCH 015/137] test for calculate coincidence --- test/neutron_detection/test_prt.py | 136 +++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 032d00a..2c7f7e3 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -414,3 +414,139 @@ def test_coinc_4( result[7].tolist(), ) assert result == expected + + +@pytest.mark.parametrize( + "A_time, A_ampl, B_time, B_ampl, C_time, C_ampl, D_time, D_ampl, coincidence_window, coincidence_citeria, expected", + [ + # Test case 1: Coincidence between A and B only + ( + [1.0, 2.0, 3.0], # A_time + [10, 20, 30], # A_ampl + [1.1, 2.1, 3.1], # B_time + [15, 25, 35], # B_ampl + [], # C_time + [], # C_ampl + [], # D_time + [], # D_ampl + 0.2, # coincidence_window + [1, 1, 0, 0], # coincidence_citeria + { + "A_time [s]": [1.0, 2.0, 3.0], + "A_amplitude [mV]": [10, 20, 30], + "B_time [s]": [1.1, 2.1, 3.1], + "B_amplitude [mV]": [15, 25, 35], + "Sum_amplitude [mV]": [25, 45, 65], + }, + ), + # Test case 2: Coincidence between A, B, and C + ( + [1.0, 2.0, 3.0], # A_time + [10, 20, 30], # A_ampl + [1.1, 2.1, 3.1], # B_time + [15, 25, 35], # B_ampl + [1.05, 2.05, 3.05], # C_time + [12, 22, 32], # C_ampl + [], # D_time + [], # D_ampl + 0.2, # coincidence_window + [1, 1, 1, 0], # coincidence_citeria + { + "A_time [s]": [1.0, 2.0, 3.0], + "A_amplitude [mV]": [10, 20, 30], + "B_time [s]": [1.1, 2.1, 3.1], + "B_amplitude [mV]": [15, 25, 35], + "C_time [s]": [1.05, 2.05, 3.05], + "C_amplitude [mV]": [12, 22, 32], + "Sum_amplitude [mV]": [37, 67, 97], + }, + ), + # Test case 3: Coincidence between A and B with anti-coincidence on C + ( + [1.0, 2.0, 3.0], # A_time + [10, 20, 30], # A_ampl + [1.1, 2.1, 3.1], # B_time + [15, 25, 35], # B_ampl + [1.05, 4, 5], # C_time + [12, 22, 32], # C_ampl + [], # D_time + [], # D_ampl + 0.2, # coincidence_window + [1, 1, 2, 0], # coincidence_citeria + { + "A_time [s]": [2.0, 3.0], + "A_amplitude [mV]": [20, 30], + "B_time [s]": [2.1, 3.1], + "B_amplitude [mV]": [25, 35], + }, + ), + # Test case 4: No matches due to time window + ( + [1.0, 2.0, 3.0], # A_time + [10, 20, 30], # A_ampl + [4.0, 5.0, 6.0], # B_time + [15, 25, 35], # B_ampl + [], # C_time + [], # C_ampl + [], # D_time + [], # D_ampl + 0.1, # coincidence_window + [1, 1, 0, 0], # coincidence_citeria + { + "A_time [s]": [], + "A_amplitude [mV]": [], + "B_time [s]": [], + "B_amplitude [mV]": [], + "Sum_amplitude [mV]": [], + }, + ), + ], +) +def test_calculate_coincidence( + A_time, + A_ampl, + B_time, + B_ampl, + C_time, + C_ampl, + D_time, + D_ampl, + coincidence_window, + coincidence_citeria, + expected, +): + """ + Test the calculate_coincidence function. + This function checks if the coincidence detection works correctly + for various combinations of channels and criteria. + + Args: + A_time: List of timestamps for channel A. + A_ampl: List of amplitudes for channel A. + B_time: List of timestamps for channel B. + B_ampl: List of amplitudes for channel B. + C_time: List of timestamps for channel C. + C_ampl: List of amplitudes for channel C. + D_time: List of timestamps for channel D. + D_ampl: List of amplitudes for channel D. + coincidence_window: Time window for coincidence detection. + coincidence_citeria: List of criteria for coincidence and anti-coincidence. + expected: Expected output as a dictionary. + """ + result_df = prt.calculate_coincidence( + A_time, + A_ampl, + B_time, + B_ampl, + C_time, + C_ampl, + D_time, + D_ampl, + coincidence_window, + coincidence_citeria, + ) + + # Convert result DataFrame to dictionary for comparison + result = {col: result_df[col].tolist() for col in result_df.columns} + + assert result == expected From 8362d275686a4225a76d05da8a54af8332916baa Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:00:57 -0400 Subject: [PATCH 016/137] vectorised anti_1 + tests for anti-coincidence --- .../neutron_detection/diamond/prt.py | 171 ++++---- test/neutron_detection/test_prt.py | 371 ++++++++++++++++++ 2 files changed, 450 insertions(+), 92 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index c64a47a..20e05f7 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -219,52 +219,37 @@ def coinc_4( def coinc_2_ANTI_1( Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window ): + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch3_TIME = np.asarray(Ch3_TIME) + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) - pos_Ch1, pos_Ch2, pos_Ch3 = 0, 0, 0 - - length_Ch1 = len(Ch1_AMPL) - length_Ch2 = len(Ch2_AMPL) - length_Ch3 = len(Ch3_TIME) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - while pos_Ch1 < length_Ch1 and pos_Ch2 < length_Ch2: - min_val = min(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2]) - max_val = max(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2]) - - CH3_IS_ANTI = True - while Ch3_TIME[pos_Ch3] <= min_val + t_window: - if Ch3_TIME[pos_Ch3] >= min_val: - CH3_IS_ANTI = False - break - - if pos_Ch3 < length_Ch3 - 1: - pos_Ch3 += 1 - else: - break - - if max_val - min_val <= t_window and CH3_IS_ANTI: - - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) + # Step 1: Find all time differences + time_diff = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + match_indices = np.where(time_diff <= t_window) + i1 = match_indices[0] + i2 = match_indices[1] - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) + if len(i1) == 0: + return np.array([]), np.array([]), np.array([]), np.array([]) - pos_Ch1 += 1 - pos_Ch2 += 1 + # Step 2: Compute t_min and t_max for matched pairs + t_min = np.minimum(Ch1_TIME[i1], Ch2_TIME[i2]) + t_max = t_min + t_window - else: - if min_val == Ch1_TIME[pos_Ch1]: - pos_Ch1 += 1 - if min_val == Ch2_TIME[pos_Ch2]: - pos_Ch2 += 1 + # Step 3: Use searchsorted to check if any Ch3 event is in [t_min, t_max] + idx_start = np.searchsorted(Ch3_TIME, t_min, side="left") + idx_end = np.searchsorted(Ch3_TIME, t_max, side="right") + is_anticoinc = idx_start == idx_end # True if no Ch3 event in window - return aaccepted_time_1, aaccepted_time_2, aaccepted_ampl_1, aaccepted_ampl_2 + # Step 4: Return only accepted coincidences + return ( + Ch1_TIME[i1[is_anticoinc]], + Ch2_TIME[i2[is_anticoinc]], + Ch1_AMPL[i1[is_anticoinc]], + Ch2_AMPL[i2[is_anticoinc]], + ) def coinc_3_ANTI_1( @@ -278,65 +263,67 @@ def coinc_3_ANTI_1( Ch4_AMPL, t_window, ): - pos_Ch1, pos_Ch2, pos_Ch3, pos_Ch4 = 0, 0, 0, 0 - - length_Ch1 = len(Ch1_AMPL) - length_Ch2 = len(Ch2_AMPL) - length_Ch3 = len(Ch3_AMPL) - length_Ch4 = len(Ch4_TIME) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - aaccepted_ampl_3 = [] - aaccepted_time_3 = [] - - while pos_Ch1 < length_Ch1 and pos_Ch2 < length_Ch2 and pos_Ch3 < length_Ch3: - min_val = min(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3]) - max_val = max(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2], Ch3_TIME[pos_Ch3]) + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch3_TIME = np.asarray(Ch3_TIME) + Ch4_TIME = np.sort(np.asarray(Ch4_TIME)) # must be sorted for searchsorted - CH4_IS_ANTI = True - while Ch4_TIME[pos_Ch4] <= min_val + t_window: - if Ch4_TIME[pos_Ch4] >= min_val: - CH4_IS_ANTI = False - break + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) + Ch3_AMPL = np.asarray(Ch3_AMPL) - if pos_Ch4 < length_Ch4 - 1: - pos_Ch4 += 1 - else: - break + # Step 1: Coincidences between Ch1 and Ch2 + diff12 = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + i1, i2 = np.where(diff12 <= t_window) + + if len(i1) == 0: + return ( + np.array([]), + np.array([]), + np.array([]), + np.array([]), + np.array([]), + np.array([]), + ) - if max_val - min_val <= t_window and CH4_IS_ANTI: - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) + # Step 2: Now for each (Ch1, Ch2) pair, find matching Ch3 + t12_avg = 0.5 * (Ch1_TIME[i1] + Ch2_TIME[i2]) + diff13 = np.abs(t12_avg[:, None] - Ch3_TIME[None, :]) + i_comb, i3 = np.where(diff13 <= t_window) + + # Keep only valid triplets (Ch1[i1[i_comb]], Ch2[i2[i_comb]], Ch3[i3]) + if len(i_comb) == 0: + return ( + np.array([]), + np.array([]), + np.array([]), + np.array([]), + np.array([]), + np.array([]), + ) - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) + final_i1 = i1[i_comb] + final_i2 = i2[i_comb] + final_i3 = i3 - aaccepted_ampl_3.append(Ch3_AMPL[pos_Ch3]) - aaccepted_time_3.append(Ch3_TIME[pos_Ch3]) + # Step 3: Anti-coincidence with Ch4 + t_min = np.minimum.reduce( + [Ch1_TIME[final_i1], Ch2_TIME[final_i2], Ch3_TIME[final_i3]] + ) + t_max = t_min + t_window - pos_Ch1 += 1 - pos_Ch2 += 1 - pos_Ch3 += 1 - else: - if min_val == Ch1_TIME[pos_Ch1]: - pos_Ch1 += 1 - if min_val == Ch2_TIME[pos_Ch2]: - pos_Ch2 += 1 - if min_val == Ch3_TIME[pos_Ch3]: - pos_Ch3 += 1 + idx_start = np.searchsorted(Ch4_TIME, t_min, side="left") + idx_end = np.searchsorted(Ch4_TIME, t_max, side="right") + is_anticoinc = idx_start == idx_end + # Step 4: Return accepted triples (not coincident with Ch4) return ( - aaccepted_time_1, - aaccepted_time_2, - aaccepted_time_3, - aaccepted_ampl_1, - aaccepted_ampl_2, - aaccepted_ampl_3, + Ch1_TIME[final_i1[is_anticoinc]], + Ch2_TIME[final_i2[is_anticoinc]], + Ch3_TIME[final_i3[is_anticoinc]], + Ch1_AMPL[final_i1[is_anticoinc]], + Ch2_AMPL[final_i2[is_anticoinc]], + Ch3_AMPL[final_i3[is_anticoinc]], ) diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 2c7f7e3..d60c048 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -416,6 +416,377 @@ def test_coinc_4( assert result == expected +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", + [ + # Test case 1: Coincidence between Ch1 and Ch2, no events in Ch3 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + 0.2, # t_window + ( + [1.0, 2.0, 3.0], # Ch1_TIME (coincidence) + [1.1, 2.1, 3.1], # Ch2_TIME (coincidence) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + ), + ), + # Test case 2: Coincidence between Ch1 and Ch2, but Ch3 overlaps for first event + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [1.05, 4, 5], # Ch3_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + 0.2, # t_window + ( + [2.0, 3.0], # Ch1_TIME (coincidence) + [2.1, 3.1], # Ch2_TIME (coincidence) + [20, 30], # Ch1_AMPL + [25, 35], # Ch2_AMPL + ), + ), + # Test case 3: No matches between Ch1 and Ch2 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) + [7.0, 8.0, 9.0], # Ch3_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + ), + ), + # Test case 4: Empty input + ( + [], # Ch1_TIME + [], # Ch2_TIME + [], # Ch3_TIME + [], # Ch1_AMPL + [], # Ch2_AMPL + [], # Ch3_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + ), + ), + ], +) +def test_coinc_2_ANTI_1( + ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected +): + """ + Test the coinc_2_ANTI_1 function. + This function checks if the coincidence detection works correctly + for two channels with anti-coincidence on a third channel. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch3_time: List of timestamps for channel 3 (anti-coincidence). + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + ch3_ampl: List of amplitudes for channel 3. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.coinc_2_ANTI_1( + ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window + ) + + # Convert result to lists for comparison + result = tuple(r.tolist() for r in result) + + assert result == expected + + +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + [ + # Test case 1: Coincidence between Ch1, Ch2, and Ch3, no events in Ch4 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [1.05, 2.05, 3.05], # Ch3_TIME + [4.0, 5.0, 6.0], # Ch4_TIME (no overlap) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [1.05, 2.05, 3.05], # Ch3_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + ), + ), + # Test case 2: Coincidence between Ch1, Ch2, and Ch3, but Ch4 overlaps + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [1.05, 2.05, 3.05], # Ch3_TIME + [1.02, 4, 5], # Ch4_TIME (overlaps with Ch1, Ch2, and Ch3) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [2.0, 3.0], # Ch1_TIME + [2.1, 3.1], # Ch2_TIME + [2.05, 3.05], # Ch3_TIME + [20, 30], # Ch1_AMPL + [25, 35], # Ch2_AMPL + [22, 32], # Ch3_AMPL + ), + ), + # Test case 3: No matches between Ch1, Ch2, and Ch3 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) + [7.0, 8.0, 9.0], # Ch3_TIME + [10.0, 11.0, 12.0], # Ch4_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch3_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + [], # No valid Ch3_AMPL + ), + ), + # Test case 4: Empty input + ( + [], # Ch1_TIME + [], # Ch2_TIME + [], # Ch3_TIME + [], # Ch4_TIME + [], # Ch1_AMPL + [], # Ch2_AMPL + [], # Ch3_AMPL + [], # Ch4_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch3_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + [], # No valid Ch3_AMPL + ), + ), + ], +) +def test_coinc_3_ANTI_1( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + expected, +): + """ + Test the coinc_3_ANTI_1 function. + This function checks if the coincidence detection works correctly + for three channels with anti-coincidence on a fourth channel. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch3_time: List of timestamps for channel 3. + ch4_time: List of timestamps for channel 4 (anti-coincidence). + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + ch3_ampl: List of amplitudes for channel 3. + ch4_ampl: List of amplitudes for channel 4. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.coinc_3_ANTI_1( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + ) + + # Convert result to lists for comparison + result = tuple(r.tolist() for r in result) + + assert result == expected + + +@pytest.mark.parametrize( + "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + [ + # Test case 1: Coincidence between Ch1 and Ch2, no events in Ch3 and Ch4 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) + [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + ), + ), + # Test case 2: Coincidence between Ch1 and Ch2, but Ch3 overlaps + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [1.05, 2.05, 5.0], # Ch3_TIME (overlaps with Ch1 and Ch2) + [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [3.0], # Ch1_TIME + [3.1], # Ch2_TIME + [30], # Ch1_AMPL + [35], # Ch2_AMPL + ), + ), + # Test case 3: Coincidence between Ch1 and Ch2, but Ch4 overlaps + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [1.1, 2.1, 3.1], # Ch2_TIME + [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) + [1.05, 2.05, 8.0], # Ch4_TIME (overlaps with Ch1 and Ch2) + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [3.0], # Ch1_TIME + [3.1], # Ch2_TIME + [30], # Ch1_AMPL + [35], # Ch2_AMPL + ), + ), + # Test case 4: No matches between Ch1 and Ch2 + ( + [1.0, 2.0, 3.0], # Ch1_TIME + [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) + [7.0, 8.0, 9.0], # Ch3_TIME + [10.0, 11.0, 12.0], # Ch4_TIME + [10, 20, 30], # Ch1_AMPL + [15, 25, 35], # Ch2_AMPL + [12, 22, 32], # Ch3_AMPL + [14, 24, 34], # Ch4_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + ), + ), + # Test case 5: Empty input + ( + [], # Ch1_TIME + [], # Ch2_TIME + [], # Ch3_TIME + [], # Ch4_TIME + [], # Ch1_AMPL + [], # Ch2_AMPL + [], # Ch3_AMPL + [], # Ch4_AMPL + 0.2, # t_window + ( + [], # No valid Ch1_TIME + [], # No valid Ch2_TIME + [], # No valid Ch1_AMPL + [], # No valid Ch2_AMPL + ), + ), + ], +) +def test_coinc_2_ANTI_2( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + expected, +): + """ + Test the coinc_2_ANTI_2 function. + This function checks if the coincidence detection works correctly + for two channels with anti-coincidence on two other channels. + + Args: + ch1_time: List of timestamps for channel 1. + ch2_time: List of timestamps for channel 2. + ch3_time: List of timestamps for channel 3 (anti-coincidence). + ch4_time: List of timestamps for channel 4 (anti-coincidence). + ch1_ampl: List of amplitudes for channel 1. + ch2_ampl: List of amplitudes for channel 2. + ch3_ampl: List of amplitudes for channel 3. + ch4_ampl: List of amplitudes for channel 4. + t_window: Time window for coincidence detection. + expected: Expected output (time and amplitude matches). + """ + result = prt.coinc_2_ANTI_2( + ch1_time, + ch2_time, + ch3_time, + ch4_time, + ch1_ampl, + ch2_ampl, + ch3_ampl, + ch4_ampl, + t_window, + ) + + # # Convert result to lists for comparison + # result = tuple(r.tolist() for r in result) + + assert result == expected + + @pytest.mark.parametrize( "A_time, A_ampl, B_time, B_ampl, C_time, C_ampl, D_time, D_ampl, coincidence_window, coincidence_citeria, expected", [ From e7f4d8eaa69f9e365ab71e07e947f4266035771e Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:04:29 -0400 Subject: [PATCH 017/137] tests + vectorised other functions --- .../neutron_detection/diamond/prt.py | 79 +++++++------------ test/neutron_detection/test_prt.py | 4 +- 2 files changed, 32 insertions(+), 51 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index 20e05f7..ef63b44 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -338,64 +338,45 @@ def coinc_2_ANTI_2( Ch4_AMPL, t_window, ): - # Ch3 and 4 is anti - - pos_Ch1, pos_Ch2, pos_Ch3, pos_Ch4 = 0, 0, 0, 0 - - length_Ch1 = len(Ch1_AMPL) - length_Ch2 = len(Ch2_AMPL) - length_Ch3 = len(Ch3_TIME) - length_Ch4 = len(Ch4_TIME) - - aaccepted_ampl_1 = [] - aaccepted_time_1 = [] - - aaccepted_ampl_2 = [] - aaccepted_time_2 = [] - - while pos_Ch1 < length_Ch1 and pos_Ch2 < length_Ch2: - min_val = min(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2]) - max_val = max(Ch1_TIME[pos_Ch1], Ch2_TIME[pos_Ch2]) + Ch1_TIME = np.asarray(Ch1_TIME) + Ch2_TIME = np.asarray(Ch2_TIME) + Ch3_TIME = np.asarray(Ch3_TIME) + Ch4_TIME = np.asarray(Ch4_TIME) - CH3_IS_ANTI = True - while Ch3_TIME[pos_Ch3] <= min_val + t_window: - if Ch3_TIME[pos_Ch3] >= min_val: - CH3_IS_ANTI = False - break + Ch1_AMPL = np.asarray(Ch1_AMPL) + Ch2_AMPL = np.asarray(Ch2_AMPL) - if pos_Ch3 < length_Ch3 - 1: - pos_Ch3 += 1 - else: - break + # Step 1: Find all coincidences between Ch1 and Ch2 + diff12 = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + i1, i2 = np.where(diff12 <= t_window) - CH4_IS_ANTI = True - while Ch4_TIME[pos_Ch4] <= min_val + t_window: - if Ch4_TIME[pos_Ch4] >= min_val: - CH4_IS_ANTI = False - break + if len(i1) == 0: + return np.array([]), np.array([]), np.array([]), np.array([]) - if pos_Ch4 < length_Ch4 - 1: - pos_Ch4 += 1 - else: - break + t_min = np.minimum(Ch1_TIME[i1], Ch2_TIME[i2]) + t_max = t_min + t_window - if max_val - min_val <= t_window and CH3_IS_ANTI and CH4_IS_ANTI: + # Step 2: Check anti-coincidence with Ch3 + idx3_start = np.searchsorted(Ch3_TIME, t_min, side="left") + idx3_end = np.searchsorted(Ch3_TIME, t_max, side="right") + anticoinc_3 = idx3_start == idx3_end - aaccepted_ampl_1.append(Ch1_AMPL[pos_Ch1]) - aaccepted_time_1.append(Ch1_TIME[pos_Ch1]) + # Step 3: Check anti-coincidence with Ch4 + idx4_start = np.searchsorted(Ch4_TIME, t_min, side="left") + idx4_end = np.searchsorted(Ch4_TIME, t_max, side="right") + anticoinc_4 = idx4_start == idx4_end - aaccepted_ampl_2.append(Ch2_AMPL[pos_Ch2]) - aaccepted_time_2.append(Ch2_TIME[pos_Ch2]) + is_anticoinc = anticoinc_3 & anticoinc_4 - pos_Ch1 += 1 - pos_Ch2 += 1 - else: - if min_val == Ch1_TIME[pos_Ch1]: - pos_Ch1 += 1 - if min_val == Ch2_TIME[pos_Ch2]: - pos_Ch2 += 1 + final_i1 = i1[is_anticoinc] + final_i2 = i2[is_anticoinc] - return aaccepted_time_1, aaccepted_time_2, aaccepted_ampl_1, aaccepted_ampl_2 + return ( + Ch1_TIME[final_i1], + Ch2_TIME[final_i2], + Ch1_AMPL[final_i1], + Ch2_AMPL[final_i2], + ) def process_coincidence( diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index d60c048..68dc669 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -781,8 +781,8 @@ def test_coinc_2_ANTI_2( t_window, ) - # # Convert result to lists for comparison - # result = tuple(r.tolist() for r in result) + # Convert result to lists for comparison + result = tuple(r.tolist() for r in result) assert result == expected From 69471da041b432958c26252bb9b84572b8882e2b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:15:48 -0400 Subject: [PATCH 018/137] removed unused arguments in anti-coincidence functions --- .../neutron_detection/diamond/prt.py | 13 +++------ test/neutron_detection/test_prt.py | 27 ++----------------- 2 files changed, 5 insertions(+), 35 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index ef63b44..f195d50 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -103,6 +103,7 @@ def get_count_rate( Changes: - vectorised functions using numpy for performance - refactoring and abstraction of the common logic +- removed unused arguments in anti-coincidence functions """ @@ -216,9 +217,7 @@ def coinc_4( ) -def coinc_2_ANTI_1( - Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window -): +def coinc_2_ANTI_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): Ch1_TIME = np.asarray(Ch1_TIME) Ch2_TIME = np.asarray(Ch2_TIME) Ch3_TIME = np.asarray(Ch3_TIME) @@ -260,7 +259,6 @@ def coinc_3_ANTI_1( Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, - Ch4_AMPL, t_window, ): Ch1_TIME = np.asarray(Ch1_TIME) @@ -334,8 +332,6 @@ def coinc_2_ANTI_2( Ch4_TIME, Ch1_AMPL, Ch2_AMPL, - Ch3_AMPL, - Ch4_AMPL, t_window, ): Ch1_TIME = np.asarray(Ch1_TIME) @@ -424,11 +420,8 @@ def process_anti_coincidence( coinc_amplitudes = [d[1] for d in coinc_data] anti_times = [d[0] for d in anti_data] - anti_amplitudes = [d[1] for d in anti_data] - return anti_function( - *coinc_times, *anti_times, *coinc_amplitudes, *anti_amplitudes, t_window - ) + return anti_function(*coinc_times, *anti_times, *coinc_amplitudes, t_window) def calculate_coincidence( diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 68dc669..b4ecac2 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -514,7 +514,7 @@ def test_coinc_2_ANTI_1( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", [ # Test case 1: Coincidence between Ch1, Ch2, and Ch3, no events in Ch4 ( @@ -525,7 +525,6 @@ def test_coinc_2_ANTI_1( [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [1.0, 2.0, 3.0], # Ch1_TIME @@ -545,7 +544,6 @@ def test_coinc_2_ANTI_1( [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [2.0, 3.0], # Ch1_TIME @@ -565,7 +563,6 @@ def test_coinc_2_ANTI_1( [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [], # No valid Ch1_TIME @@ -585,7 +582,6 @@ def test_coinc_2_ANTI_1( [], # Ch1_AMPL [], # Ch2_AMPL [], # Ch3_AMPL - [], # Ch4_AMPL 0.2, # t_window ( [], # No valid Ch1_TIME @@ -606,7 +602,6 @@ def test_coinc_3_ANTI_1( ch1_ampl, ch2_ampl, ch3_ampl, - ch4_ampl, t_window, expected, ): @@ -623,7 +618,6 @@ def test_coinc_3_ANTI_1( ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. ch3_ampl: List of amplitudes for channel 3. - ch4_ampl: List of amplitudes for channel 4. t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ @@ -635,7 +629,6 @@ def test_coinc_3_ANTI_1( ch1_ampl, ch2_ampl, ch3_ampl, - ch4_ampl, t_window, ) @@ -646,7 +639,7 @@ def test_coinc_3_ANTI_1( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, t_window, expected", [ # Test case 1: Coincidence between Ch1 and Ch2, no events in Ch3 and Ch4 ( @@ -656,8 +649,6 @@ def test_coinc_3_ANTI_1( [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [1.0, 2.0, 3.0], # Ch1_TIME @@ -674,8 +665,6 @@ def test_coinc_3_ANTI_1( [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [3.0], # Ch1_TIME @@ -692,8 +681,6 @@ def test_coinc_3_ANTI_1( [1.05, 2.05, 8.0], # Ch4_TIME (overlaps with Ch1 and Ch2) [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [3.0], # Ch1_TIME @@ -710,8 +697,6 @@ def test_coinc_3_ANTI_1( [10.0, 11.0, 12.0], # Ch4_TIME [10, 20, 30], # Ch1_AMPL [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL - [14, 24, 34], # Ch4_AMPL 0.2, # t_window ( [], # No valid Ch1_TIME @@ -728,8 +713,6 @@ def test_coinc_3_ANTI_1( [], # Ch4_TIME [], # Ch1_AMPL [], # Ch2_AMPL - [], # Ch3_AMPL - [], # Ch4_AMPL 0.2, # t_window ( [], # No valid Ch1_TIME @@ -747,8 +730,6 @@ def test_coinc_2_ANTI_2( ch4_time, ch1_ampl, ch2_ampl, - ch3_ampl, - ch4_ampl, t_window, expected, ): @@ -764,8 +745,6 @@ def test_coinc_2_ANTI_2( ch4_time: List of timestamps for channel 4 (anti-coincidence). ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. - ch3_ampl: List of amplitudes for channel 3. - ch4_ampl: List of amplitudes for channel 4. t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ @@ -776,8 +755,6 @@ def test_coinc_2_ANTI_2( ch4_time, ch1_ampl, ch2_ampl, - ch3_ampl, - ch4_ampl, t_window, ) From b9673c2d07e19b3a453a66b2a12b7c78be786f84 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:17:04 -0400 Subject: [PATCH 019/137] lowercase ANTI --- libra_toolbox/neutron_detection/diamond/prt.py | 12 ++++++------ test/neutron_detection/test_prt.py | 18 +++++++++--------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index f195d50..d5f397d 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -217,7 +217,7 @@ def coinc_4( ) -def coinc_2_ANTI_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): +def coinc_2_anti_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): Ch1_TIME = np.asarray(Ch1_TIME) Ch2_TIME = np.asarray(Ch2_TIME) Ch3_TIME = np.asarray(Ch3_TIME) @@ -251,7 +251,7 @@ def coinc_2_ANTI_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): ) -def coinc_3_ANTI_1( +def coinc_3_anti_1( Ch1_TIME, Ch2_TIME, Ch3_TIME, @@ -325,7 +325,7 @@ def coinc_3_ANTI_1( ) -def coinc_2_ANTI_2( +def coinc_2_anti_2( Ch1_TIME, Ch2_TIME, Ch3_TIME, @@ -480,7 +480,7 @@ def calculate_coincidence( coincidence_channels, anti_channels, coincidence_window, - coinc_2_ANTI_1, + coinc_2_anti_1, ) elif number_of_coincidence == 3 and number_of_anti_coincidence == 1: result = process_anti_coincidence( @@ -488,7 +488,7 @@ def calculate_coincidence( coincidence_channels, anti_channels, coincidence_window, - coinc_3_ANTI_1, + coinc_3_anti_1, ) elif number_of_coincidence == 2 and number_of_anti_coincidence == 2: result = process_anti_coincidence( @@ -496,7 +496,7 @@ def calculate_coincidence( coincidence_channels, anti_channels, coincidence_window, - coinc_2_ANTI_2, + coinc_2_anti_2, ) else: raise ValueError("Unsupported combination of coincidence and anti-coincidence.") diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index b4ecac2..953f8dd 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -485,11 +485,11 @@ def test_coinc_4( ), ], ) -def test_coinc_2_ANTI_1( +def test_coinc_2_anti_1( ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected ): """ - Test the coinc_2_ANTI_1 function. + Test the coinc_2_anti_1 function. This function checks if the coincidence detection works correctly for two channels with anti-coincidence on a third channel. @@ -503,7 +503,7 @@ def test_coinc_2_ANTI_1( t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.coinc_2_ANTI_1( + result = prt.coinc_2_anti_1( ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window ) @@ -594,7 +594,7 @@ def test_coinc_2_ANTI_1( ), ], ) -def test_coinc_3_ANTI_1( +def test_coinc_3_anti_1( ch1_time, ch2_time, ch3_time, @@ -606,7 +606,7 @@ def test_coinc_3_ANTI_1( expected, ): """ - Test the coinc_3_ANTI_1 function. + Test the coinc_3_anti_1 function. This function checks if the coincidence detection works correctly for three channels with anti-coincidence on a fourth channel. @@ -621,7 +621,7 @@ def test_coinc_3_ANTI_1( t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.coinc_3_ANTI_1( + result = prt.coinc_3_anti_1( ch1_time, ch2_time, ch3_time, @@ -723,7 +723,7 @@ def test_coinc_3_ANTI_1( ), ], ) -def test_coinc_2_ANTI_2( +def test_coinc_2_anti_2( ch1_time, ch2_time, ch3_time, @@ -734,7 +734,7 @@ def test_coinc_2_ANTI_2( expected, ): """ - Test the coinc_2_ANTI_2 function. + Test the coinc_2_anti_2 function. This function checks if the coincidence detection works correctly for two channels with anti-coincidence on two other channels. @@ -748,7 +748,7 @@ def test_coinc_2_ANTI_2( t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.coinc_2_ANTI_2( + result = prt.coinc_2_anti_2( ch1_time, ch2_time, ch3_time, From e539ed07f7bc06473560eb452aaaeb19e130b986 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:33:47 -0400 Subject: [PATCH 020/137] documentation + type hinting --- .../neutron_detection/diamond/prt.py | 478 ++++++++++++------ test/neutron_detection/test_prt.py | 393 +++++++------- 2 files changed, 531 insertions(+), 340 deletions(-) diff --git a/libra_toolbox/neutron_detection/diamond/prt.py b/libra_toolbox/neutron_detection/diamond/prt.py index d5f397d..b0a452c 100644 --- a/libra_toolbox/neutron_detection/diamond/prt.py +++ b/libra_toolbox/neutron_detection/diamond/prt.py @@ -1,4 +1,4 @@ -from typing import Tuple +from typing import Tuple, Union, List from pathlib import Path import h5py import numpy as np @@ -90,7 +90,6 @@ def get_count_rate( return count_rates, count_rate_bins -# TODO refactor/simplify/remove bits below that aren't needed """ # Coincidence Spectrum Analysis for Diamond Telescope Detector @@ -104,18 +103,45 @@ def get_count_rate( - vectorised functions using numpy for performance - refactoring and abstraction of the common logic - removed unused arguments in anti-coincidence functions +- documentation and type hinting +- snake_case for function names and variables """ -def coinc_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) +def coinc_2( + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Find coincidences between two channels within a specified time window. + For each Ch1 time, find similar times in Ch2 within the time window. + The function returns the matched times and amplitudes for both channels. + + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + t_window: the time window for coincidence detection (in seconds) + + Returns: + A tuple of four NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + """ + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) # For each Ch1 time, find window in Ch2 where match is possible - idx_start = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") - idx_end = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") + idx_start = np.searchsorted(ch2_times, ch1_times - t_window, side="left") + idx_end = np.searchsorted(ch2_times, ch1_times + t_window, side="right") # Keep only those with at least one match has_match = idx_start < idx_end @@ -124,27 +150,56 @@ def coinc_2(Ch1_TIME, Ch2_TIME, Ch1_AMPL, Ch2_AMPL, t_window): matched_Ch2_idx = idx_start[has_match] # First match only return ( - Ch1_TIME[matched_Ch1_idx], - Ch2_TIME[matched_Ch2_idx], - Ch1_AMPL[matched_Ch1_idx], - Ch2_AMPL[matched_Ch2_idx], + ch1_times[matched_Ch1_idx], + ch2_times[matched_Ch2_idx], + ch1_ampl[matched_Ch1_idx], + ch2_ampl[matched_Ch2_idx], ) -def coinc_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window): - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch3_TIME = np.asarray(Ch3_TIME) - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) - Ch3_AMPL = np.asarray(Ch3_AMPL) +def coinc_3( + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch3_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + ch3_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Find coincidences between three channels within a specified time window. + For each Ch1 time, find similar times in Ch2 and Ch3 within the time window. + The function returns the matched times and amplitudes for all three channels. + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch3_times: the times of events in channel 3 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + ch3_ampl: the amplitudes of events in channel 3 + t_window: the time window for coincidence detection (in seconds) + Returns: + A tuple of six NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch3 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + - matched Ch3 amplitudes + """ + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch3_times = np.asarray(ch3_times) + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) + ch3_ampl = np.asarray(ch3_ampl) # For each Ch1 time, find window in Ch2 and Ch3 - idx_start_2 = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") - idx_end_2 = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") + idx_start_2 = np.searchsorted(ch2_times, ch1_times - t_window, side="left") + idx_end_2 = np.searchsorted(ch2_times, ch1_times + t_window, side="right") - idx_start_3 = np.searchsorted(Ch3_TIME, Ch1_TIME - t_window, side="left") - idx_end_3 = np.searchsorted(Ch3_TIME, Ch1_TIME + t_window, side="right") + idx_start_3 = np.searchsorted(ch3_times, ch1_times - t_window, side="left") + idx_end_3 = np.searchsorted(ch3_times, ch1_times + t_window, side="right") # Valid coincidences: Ch1 has at least one match in both Ch2 and Ch3 has_match = (idx_start_2 < idx_end_2) & (idx_start_3 < idx_end_3) @@ -153,45 +208,81 @@ def coinc_3(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, Ch3_AMPL, t_window matched_Ch3_idx = idx_start_3[has_match] return ( - Ch1_TIME[matched_Ch1_idx], - Ch2_TIME[matched_Ch2_idx], - Ch3_TIME[matched_Ch3_idx], - Ch1_AMPL[matched_Ch1_idx], - Ch2_AMPL[matched_Ch2_idx], - Ch3_AMPL[matched_Ch3_idx], + ch1_times[matched_Ch1_idx], + ch2_times[matched_Ch2_idx], + ch3_times[matched_Ch3_idx], + ch1_ampl[matched_Ch1_idx], + ch2_ampl[matched_Ch2_idx], + ch3_ampl[matched_Ch3_idx], ) def coinc_4( - Ch1_TIME, - Ch2_TIME, - Ch3_TIME, - Ch4_TIME, - Ch1_AMPL, - Ch2_AMPL, - Ch3_AMPL, - Ch4_AMPL, - t_window, -): + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch3_times: Union[list, np.ndarray], + ch4_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + ch3_ampl: Union[list, np.ndarray], + ch4_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[ + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, + np.ndarray, +]: + """ + Find coincidences between four channels within a specified time window. + For each Ch1 time, find similar times in Ch2, Ch3, and Ch4 within the time window. + The function returns the matched times and amplitudes for all four channels. + + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch3_times: the times of events in channel 3 + ch4_times: the times of events in channel 4 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + ch3_ampl: the amplitudes of events in channel 3 + ch4_ampl: the amplitudes of events in channel 4 + t_window: the time window for coincidence detection (in seconds) + + Returns: + A tuple of eight NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch3 times + - matched Ch4 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + - matched Ch3 amplitudes + - matched Ch4 amplitudes + """ # Convert to NumPy arrays - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch3_TIME = np.asarray(Ch3_TIME) - Ch4_TIME = np.asarray(Ch4_TIME) - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) - Ch3_AMPL = np.asarray(Ch3_AMPL) - Ch4_AMPL = np.asarray(Ch4_AMPL) + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch3_times = np.asarray(ch3_times) + ch4_times = np.asarray(ch4_times) + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) + ch3_ampl = np.asarray(ch3_ampl) + ch4_ampl = np.asarray(ch4_ampl) # For each Ch1 event, find index range in Ch2/Ch3/Ch4 within time window - idx_start_2 = np.searchsorted(Ch2_TIME, Ch1_TIME - t_window, side="left") - idx_end_2 = np.searchsorted(Ch2_TIME, Ch1_TIME + t_window, side="right") + idx_start_2 = np.searchsorted(ch2_times, ch1_times - t_window, side="left") + idx_end_2 = np.searchsorted(ch2_times, ch1_times + t_window, side="right") - idx_start_3 = np.searchsorted(Ch3_TIME, Ch1_TIME - t_window, side="left") - idx_end_3 = np.searchsorted(Ch3_TIME, Ch1_TIME + t_window, side="right") + idx_start_3 = np.searchsorted(ch3_times, ch1_times - t_window, side="left") + idx_end_3 = np.searchsorted(ch3_times, ch1_times + t_window, side="right") - idx_start_4 = np.searchsorted(Ch4_TIME, Ch1_TIME - t_window, side="left") - idx_end_4 = np.searchsorted(Ch4_TIME, Ch1_TIME + t_window, side="right") + idx_start_4 = np.searchsorted(ch4_times, ch1_times - t_window, side="left") + idx_end_4 = np.searchsorted(ch4_times, ch1_times + t_window, side="right") # Valid coincidences must have at least one match in Ch2, Ch3, and Ch4 has_match = ( @@ -206,26 +297,53 @@ def coinc_4( matched_Ch4_idx = idx_start_4[has_match] return ( - Ch1_TIME[matched_Ch1_idx], - Ch2_TIME[matched_Ch2_idx], - Ch3_TIME[matched_Ch3_idx], - Ch4_TIME[matched_Ch4_idx], - Ch1_AMPL[matched_Ch1_idx], - Ch2_AMPL[matched_Ch2_idx], - Ch3_AMPL[matched_Ch3_idx], - Ch4_AMPL[matched_Ch4_idx], + ch1_times[matched_Ch1_idx], + ch2_times[matched_Ch2_idx], + ch3_times[matched_Ch3_idx], + ch4_times[matched_Ch4_idx], + ch1_ampl[matched_Ch1_idx], + ch2_ampl[matched_Ch2_idx], + ch3_ampl[matched_Ch3_idx], + ch4_ampl[matched_Ch4_idx], ) -def coinc_2_anti_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch3_TIME = np.asarray(Ch3_TIME) - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) +def coinc_2_anti_1( + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch3_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Find coincidences between two channels (Ch1 and Ch2) and check for anti-coincidence with a third channel (Ch3). + For each Ch1 time, find similar times in Ch2 within the time window and check if there are any Ch3 events in that window. + The function returns the matched times and amplitudes for Ch1 and Ch2, excluding any events that coincide with Ch3. + + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch3_times: the times of events in channel 3 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + t_window: the time window for coincidence detection (in seconds) + + Returns: + A tuple of four NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + """ + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch3_times = np.asarray(ch3_times) + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) # Step 1: Find all time differences - time_diff = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + time_diff = np.abs(ch1_times[:, None] - ch2_times[None, :]) match_indices = np.where(time_diff <= t_window) i1 = match_indices[0] i2 = match_indices[1] @@ -234,44 +352,69 @@ def coinc_2_anti_1(Ch1_TIME, Ch2_TIME, Ch3_TIME, Ch1_AMPL, Ch2_AMPL, t_window): return np.array([]), np.array([]), np.array([]), np.array([]) # Step 2: Compute t_min and t_max for matched pairs - t_min = np.minimum(Ch1_TIME[i1], Ch2_TIME[i2]) + t_min = np.minimum(ch1_times[i1], ch2_times[i2]) t_max = t_min + t_window # Step 3: Use searchsorted to check if any Ch3 event is in [t_min, t_max] - idx_start = np.searchsorted(Ch3_TIME, t_min, side="left") - idx_end = np.searchsorted(Ch3_TIME, t_max, side="right") + idx_start = np.searchsorted(ch3_times, t_min, side="left") + idx_end = np.searchsorted(ch3_times, t_max, side="right") is_anticoinc = idx_start == idx_end # True if no Ch3 event in window # Step 4: Return only accepted coincidences return ( - Ch1_TIME[i1[is_anticoinc]], - Ch2_TIME[i2[is_anticoinc]], - Ch1_AMPL[i1[is_anticoinc]], - Ch2_AMPL[i2[is_anticoinc]], + ch1_times[i1[is_anticoinc]], + ch2_times[i2[is_anticoinc]], + ch1_ampl[i1[is_anticoinc]], + ch2_ampl[i2[is_anticoinc]], ) def coinc_3_anti_1( - Ch1_TIME, - Ch2_TIME, - Ch3_TIME, - Ch4_TIME, - Ch1_AMPL, - Ch2_AMPL, - Ch3_AMPL, - t_window, -): - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch3_TIME = np.asarray(Ch3_TIME) - Ch4_TIME = np.sort(np.asarray(Ch4_TIME)) # must be sorted for searchsorted + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch3_times: Union[list, np.ndarray], + ch4_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + ch3_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Find coincidences between three channels (Ch1, Ch2, and Ch3) and check for anti-coincidence with a fourth channel (Ch4). + For each Ch1 time, find similar times in Ch2 and Ch3 within the time window and check if there are any Ch4 events in that window. + The function returns the matched times and amplitudes for Ch1, Ch2, and Ch3, excluding any events that coincide with Ch4. + + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch3_times: the times of events in channel 3 + ch4_times: the times of events in channel 4 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + ch3_ampl: the amplitudes of events in channel 3 + t_window: the time window for coincidence detection (in seconds) + + Returns: + A tuple of six NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch3 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + - matched Ch3 amplitudes + """ + + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch3_times = np.asarray(ch3_times) + ch4_times = np.sort(np.asarray(ch4_times)) # must be sorted for searchsorted - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) - Ch3_AMPL = np.asarray(Ch3_AMPL) + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) + ch3_ampl = np.asarray(ch3_ampl) # Step 1: Coincidences between Ch1 and Ch2 - diff12 = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + diff12 = np.abs(ch1_times[:, None] - ch2_times[None, :]) i1, i2 = np.where(diff12 <= t_window) if len(i1) == 0: @@ -285,8 +428,8 @@ def coinc_3_anti_1( ) # Step 2: Now for each (Ch1, Ch2) pair, find matching Ch3 - t12_avg = 0.5 * (Ch1_TIME[i1] + Ch2_TIME[i2]) - diff13 = np.abs(t12_avg[:, None] - Ch3_TIME[None, :]) + t12_avg = 0.5 * (ch1_times[i1] + ch2_times[i2]) + diff13 = np.abs(t12_avg[:, None] - ch3_times[None, :]) i_comb, i3 = np.where(diff13 <= t_window) # Keep only valid triplets (Ch1[i1[i_comb]], Ch2[i2[i_comb]], Ch3[i3]) @@ -306,60 +449,82 @@ def coinc_3_anti_1( # Step 3: Anti-coincidence with Ch4 t_min = np.minimum.reduce( - [Ch1_TIME[final_i1], Ch2_TIME[final_i2], Ch3_TIME[final_i3]] + [ch1_times[final_i1], ch2_times[final_i2], ch3_times[final_i3]] ) t_max = t_min + t_window - idx_start = np.searchsorted(Ch4_TIME, t_min, side="left") - idx_end = np.searchsorted(Ch4_TIME, t_max, side="right") + idx_start = np.searchsorted(ch4_times, t_min, side="left") + idx_end = np.searchsorted(ch4_times, t_max, side="right") is_anticoinc = idx_start == idx_end # Step 4: Return accepted triples (not coincident with Ch4) return ( - Ch1_TIME[final_i1[is_anticoinc]], - Ch2_TIME[final_i2[is_anticoinc]], - Ch3_TIME[final_i3[is_anticoinc]], - Ch1_AMPL[final_i1[is_anticoinc]], - Ch2_AMPL[final_i2[is_anticoinc]], - Ch3_AMPL[final_i3[is_anticoinc]], + ch1_times[final_i1[is_anticoinc]], + ch2_times[final_i2[is_anticoinc]], + ch3_times[final_i3[is_anticoinc]], + ch1_ampl[final_i1[is_anticoinc]], + ch2_ampl[final_i2[is_anticoinc]], + ch3_ampl[final_i3[is_anticoinc]], ) def coinc_2_anti_2( - Ch1_TIME, - Ch2_TIME, - Ch3_TIME, - Ch4_TIME, - Ch1_AMPL, - Ch2_AMPL, - t_window, -): - Ch1_TIME = np.asarray(Ch1_TIME) - Ch2_TIME = np.asarray(Ch2_TIME) - Ch3_TIME = np.asarray(Ch3_TIME) - Ch4_TIME = np.asarray(Ch4_TIME) + ch1_times: Union[list, np.ndarray], + ch2_times: Union[list, np.ndarray], + ch3_times: Union[list, np.ndarray], + ch4_times: Union[list, np.ndarray], + ch1_ampl: Union[list, np.ndarray], + ch2_ampl: Union[list, np.ndarray], + t_window: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """ + Find coincidences between two channels (Ch1 and Ch2) and check for anti-coincidence with two other channels (Ch3 and Ch4). + For each Ch1 time, find similar times in Ch2 within the time window and check if there are any Ch3 or Ch4 events in that window. + The function returns the matched times and amplitudes for Ch1 and Ch2, excluding any events that coincide with Ch3 or Ch4. - Ch1_AMPL = np.asarray(Ch1_AMPL) - Ch2_AMPL = np.asarray(Ch2_AMPL) + Args: + ch1_times: the times of events in channel 1 + ch2_times: the times of events in channel 2 + ch3_times: the times of events in channel 3 + ch4_times: the times of events in channel 4 + ch1_ampl: the amplitudes of events in channel 1 + ch2_ampl: the amplitudes of events in channel 2 + t_window: the time window for coincidence detection (in seconds) + + Returns: + A tuple of four NumPy arrays: + - matched Ch1 times + - matched Ch2 times + - matched Ch1 amplitudes + - matched Ch2 amplitudes + """ + + ch1_times = np.asarray(ch1_times) + ch2_times = np.asarray(ch2_times) + ch3_times = np.asarray(ch3_times) + ch4_times = np.asarray(ch4_times) + + ch1_ampl = np.asarray(ch1_ampl) + ch2_ampl = np.asarray(ch2_ampl) # Step 1: Find all coincidences between Ch1 and Ch2 - diff12 = np.abs(Ch1_TIME[:, None] - Ch2_TIME[None, :]) + diff12 = np.abs(ch1_times[:, None] - ch2_times[None, :]) i1, i2 = np.where(diff12 <= t_window) if len(i1) == 0: return np.array([]), np.array([]), np.array([]), np.array([]) - t_min = np.minimum(Ch1_TIME[i1], Ch2_TIME[i2]) + t_min = np.minimum(ch1_times[i1], ch2_times[i2]) t_max = t_min + t_window # Step 2: Check anti-coincidence with Ch3 - idx3_start = np.searchsorted(Ch3_TIME, t_min, side="left") - idx3_end = np.searchsorted(Ch3_TIME, t_max, side="right") + idx3_start = np.searchsorted(ch3_times, t_min, side="left") + idx3_end = np.searchsorted(ch3_times, t_max, side="right") anticoinc_3 = idx3_start == idx3_end # Step 3: Check anti-coincidence with Ch4 - idx4_start = np.searchsorted(Ch4_TIME, t_min, side="left") - idx4_end = np.searchsorted(Ch4_TIME, t_max, side="right") + idx4_start = np.searchsorted(ch4_times, t_min, side="left") + idx4_end = np.searchsorted(ch4_times, t_max, side="right") anticoinc_4 = idx4_start == idx4_end is_anticoinc = anticoinc_3 & anticoinc_4 @@ -368,15 +533,18 @@ def coinc_2_anti_2( final_i2 = i2[is_anticoinc] return ( - Ch1_TIME[final_i1], - Ch2_TIME[final_i2], - Ch1_AMPL[final_i1], - Ch2_AMPL[final_i2], + ch1_times[final_i1], + ch2_times[final_i2], + ch1_ampl[final_i1], + ch2_ampl[final_i2], ) def process_coincidence( - grouped_data, coincidence_channels, t_window, coincidence_function + grouped_data: List[List[Union[np.ndarray, list]]], + coincidence_channels: List[int], + t_window: float, + coincidence_function: callable, ): """ Process coincidence for the given channels using the specified coincidence function. @@ -398,7 +566,11 @@ def process_coincidence( def process_anti_coincidence( - grouped_data, coincidence_channels, anti_channels, t_window, anti_function + grouped_data: List[List[Union[np.ndarray, list]]], + coincidence_channels: List[int], + anti_channels: List[int], + t_window: float, + anti_function: callable, ): """ Process coincidence with anti-coincidence for the given channels. @@ -425,17 +597,41 @@ def process_anti_coincidence( def calculate_coincidence( - A_time, - A_ampl, - B_time, - B_ampl, - C_time, - C_ampl, - D_time, - D_ampl, - coincidence_window, - coincidence_citeria, -): + A_time: Union[list, np.ndarray], + A_ampl: Union[list, np.ndarray], + B_time: Union[list, np.ndarray], + B_ampl: Union[list, np.ndarray], + C_time: Union[list, np.ndarray], + C_ampl: Union[list, np.ndarray], + D_time: Union[list, np.ndarray], + D_ampl: Union[list, np.ndarray], + coincidence_window: float, + coincidence_citeria: Union[list, np.ndarray], +) -> pd.DataFrame: + """ + Calculate the coincidence spectrum of the PRT detector. + The function takes the time and amplitude data for four channels (A, B, C, D) and + applies the specified coincidence criteria to find coincidences and anti-coincidences. + The results are returned as a pandas DataFrame. + + Args: + A_time: time values for channel A + A_ampl: amplitude values for channel A + B_time: time values for channel B + B_ampl: amplitude values for channel B + C_time: time values for channel C + C_ampl: amplitude values for channel C + D_time: time values for channel D + D_ampl: amplitude values for channel D + coincidence_window: time window for coincidence detection (in seconds) + coincidence_citeria: criteria for coincidence and anti-coincidence + 0: ignore + 1: coincidence + 2: anti-coincidence + + Returns: + A pandas DataFrame containing the results of the coincidence analysis. + """ # Amplitude in mV # Time in s diff --git a/test/neutron_detection/test_prt.py b/test/neutron_detection/test_prt.py index 953f8dd..5b5065d 100644 --- a/test/neutron_detection/test_prt.py +++ b/test/neutron_detection/test_prt.py @@ -116,7 +116,7 @@ def test_get_count_rate(bin_time: float, count_rate_real: float): @pytest.mark.parametrize( - "ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected", + "ch1_times, ch2_times, ch1_ampl, ch2_ampl, t_window, expected", [ # Test case 1: Simple match within time window ( @@ -166,21 +166,21 @@ def test_get_count_rate(bin_time: float, count_rate_real: float): ), ], ) -def test_coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): +def test_coinc_2(ch1_times, ch2_times, ch1_ampl, ch2_ampl, t_window, expected): """ Test the coinc_2 function. This function checks if the coincidence detection works correctly for two channels within a given time window. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ - result = prt.coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window) + result = prt.coinc_2(ch1_times, ch2_times, ch1_ampl, ch2_ampl, t_window) # convert everything to list result = ( result[0].tolist(), @@ -192,7 +192,7 @@ def test_coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", + "ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", [ # Test case 1: All channels match within the time window ( @@ -255,7 +255,7 @@ def test_coinc_2(ch1_time, ch2_time, ch1_ampl, ch2_ampl, t_window, expected): ], ) def test_coinc_3( - ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected + ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected ): """ Test the coinc_3 function. @@ -263,9 +263,9 @@ def test_coinc_3( for three channels within a given time window. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. - ch3_time: List of timestamps for channel 3. + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. + ch3_times: List of timestamps for channel 3. ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. ch3_ampl: List of amplitudes for channel 3. @@ -273,7 +273,7 @@ def test_coinc_3( expected: Expected output (time and amplitude matches). """ result = prt.coinc_3( - ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window + ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, ch3_ampl, t_window ) # convert everything to list result = ( @@ -288,7 +288,7 @@ def test_coinc_3( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", + "ch1_times, ch2_times, ch3_times, ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, ch4_ampl, t_window, expected", [ # Test case 1: All channels match within the time window ( @@ -363,10 +363,10 @@ def test_coinc_3( ], ) def test_coinc_4( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, @@ -380,10 +380,10 @@ def test_coinc_4( for four channels within a given time window. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. - ch3_time: List of timestamps for channel 3. - ch4_time: List of timestamps for channel 4. + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. + ch3_times: List of timestamps for channel 3. + ch4_times: List of timestamps for channel 4. ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. ch3_ampl: List of amplitudes for channel 3. @@ -392,10 +392,10 @@ def test_coinc_4( expected: Expected output (time and amplitude matches). """ result = prt.coinc_4( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, @@ -417,76 +417,72 @@ def test_coinc_4( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", + "ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, t_window, expected", [ # Test case 1: Coincidence between Ch1 and Ch2, no events in Ch3 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [4.0, 5.0, 6.0], # ch3_times (no overlap) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [1.0, 2.0, 3.0], # Ch1_TIME (coincidence) - [1.1, 2.1, 3.1], # Ch2_TIME (coincidence) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times (coincidence) + [1.1, 2.1, 3.1], # ch2_times (coincidence) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl ), ), # Test case 2: Coincidence between Ch1 and Ch2, but Ch3 overlaps for first event ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [1.05, 4, 5], # Ch3_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [1.05, 4, 5], # ch3_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [2.0, 3.0], # Ch1_TIME (coincidence) - [2.1, 3.1], # Ch2_TIME (coincidence) - [20, 30], # Ch1_AMPL - [25, 35], # Ch2_AMPL + [2.0, 3.0], # ch1_times (coincidence) + [2.1, 3.1], # ch2_times (coincidence) + [20, 30], # ch1_ampl + [25, 35], # ch2_ampl ), ), # Test case 3: No matches between Ch1 and Ch2 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) - [7.0, 8.0, 9.0], # Ch3_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [4.0, 5.0, 6.0], # ch2_times (no overlap) + [7.0, 8.0, 9.0], # ch3_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl ), ), # Test case 4: Empty input ( - [], # Ch1_TIME - [], # Ch2_TIME - [], # Ch3_TIME - [], # Ch1_AMPL - [], # Ch2_AMPL - [], # Ch3_AMPL + [], # ch1_times + [], # ch2_times + [], # ch3_times + [], # ch1_ampl + [], # ch2_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl ), ), ], ) def test_coinc_2_anti_1( - ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected + ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, t_window, expected ): """ Test the coinc_2_anti_1 function. @@ -494,17 +490,16 @@ def test_coinc_2_anti_1( for two channels with anti-coincidence on a third channel. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. - ch3_time: List of timestamps for channel 3 (anti-coincidence). + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. + ch3_times: List of timestamps for channel 3 (anti-coincidence). ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. - ch3_ampl: List of amplitudes for channel 3. t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ result = prt.coinc_2_anti_1( - ch1_time, ch2_time, ch3_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window + ch1_times, ch2_times, ch3_times, ch1_ampl, ch2_ampl, t_window ) # Convert result to lists for comparison @@ -514,91 +509,91 @@ def test_coinc_2_anti_1( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", + "ch1_times, ch2_times, ch3_times, ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, t_window, expected", [ # Test case 1: Coincidence between Ch1, Ch2, and Ch3, no events in Ch4 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [1.05, 2.05, 3.05], # Ch3_TIME - [4.0, 5.0, 6.0], # Ch4_TIME (no overlap) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [1.05, 2.05, 3.05], # ch3_times + [4.0, 5.0, 6.0], # ch4_times (no overlap) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl + [12, 22, 32], # ch3_ampl 0.2, # t_window ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [1.05, 2.05, 3.05], # Ch3_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [1.05, 2.05, 3.05], # ch3_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl + [12, 22, 32], # ch3_ampl ), ), # Test case 2: Coincidence between Ch1, Ch2, and Ch3, but Ch4 overlaps ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [1.05, 2.05, 3.05], # Ch3_TIME - [1.02, 4, 5], # Ch4_TIME (overlaps with Ch1, Ch2, and Ch3) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [1.05, 2.05, 3.05], # ch3_times + [1.02, 4, 5], # ch4_times (overlaps with Ch1, Ch2, and Ch3) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl + [12, 22, 32], # ch3_ampl 0.2, # t_window ( - [2.0, 3.0], # Ch1_TIME - [2.1, 3.1], # Ch2_TIME - [2.05, 3.05], # Ch3_TIME - [20, 30], # Ch1_AMPL - [25, 35], # Ch2_AMPL - [22, 32], # Ch3_AMPL + [2.0, 3.0], # ch1_times + [2.1, 3.1], # ch2_times + [2.05, 3.05], # ch3_times + [20, 30], # ch1_ampl + [25, 35], # ch2_ampl + [22, 32], # ch3_ampl ), ), # Test case 3: No matches between Ch1, Ch2, and Ch3 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) - [7.0, 8.0, 9.0], # Ch3_TIME - [10.0, 11.0, 12.0], # Ch4_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL - [12, 22, 32], # Ch3_AMPL + [1.0, 2.0, 3.0], # ch1_times + [4.0, 5.0, 6.0], # ch2_times (no overlap) + [7.0, 8.0, 9.0], # ch3_times + [10.0, 11.0, 12.0], # ch4_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl + [12, 22, 32], # ch3_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch3_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL - [], # No valid Ch3_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch3_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl + [], # No valid ch3_ampl ), ), # Test case 4: Empty input ( - [], # Ch1_TIME - [], # Ch2_TIME - [], # Ch3_TIME - [], # Ch4_TIME - [], # Ch1_AMPL - [], # Ch2_AMPL - [], # Ch3_AMPL + [], # ch1_times + [], # ch2_times + [], # ch3_times + [], # ch4_times + [], # ch1_ampl + [], # ch2_ampl + [], # ch3_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch3_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL - [], # No valid Ch3_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch3_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl + [], # No valid ch3_ampl ), ), ], ) def test_coinc_3_anti_1( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, @@ -611,10 +606,10 @@ def test_coinc_3_anti_1( for three channels with anti-coincidence on a fourth channel. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. - ch3_time: List of timestamps for channel 3. - ch4_time: List of timestamps for channel 4 (anti-coincidence). + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. + ch3_times: List of timestamps for channel 3. + ch4_times: List of timestamps for channel 4 (anti-coincidence). ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. ch3_ampl: List of amplitudes for channel 3. @@ -622,10 +617,10 @@ def test_coinc_3_anti_1( expected: Expected output (time and amplitude matches). """ result = prt.coinc_3_anti_1( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, ch3_ampl, @@ -639,95 +634,95 @@ def test_coinc_3_anti_1( @pytest.mark.parametrize( - "ch1_time, ch2_time, ch3_time, ch4_time, ch1_ampl, ch2_ampl, t_window, expected", + "ch1_times, ch2_times, ch3_times, ch4_times, ch1_ampl, ch2_ampl, t_window, expected", [ # Test case 1: Coincidence between Ch1 and Ch2, no events in Ch3 and Ch4 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) - [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [4.0, 5.0, 6.0], # ch3_times (no overlap) + [7.0, 8.0, 9.0], # ch4_times (no overlap) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl ), ), # Test case 2: Coincidence between Ch1 and Ch2, but Ch3 overlaps ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [1.05, 2.05, 5.0], # Ch3_TIME (overlaps with Ch1 and Ch2) - [7.0, 8.0, 9.0], # Ch4_TIME (no overlap) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [1.05, 2.05, 5.0], # ch3_times (overlaps with Ch1 and Ch2) + [7.0, 8.0, 9.0], # ch4_times (no overlap) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [3.0], # Ch1_TIME - [3.1], # Ch2_TIME - [30], # Ch1_AMPL - [35], # Ch2_AMPL + [3.0], # ch1_times + [3.1], # ch2_times + [30], # ch1_ampl + [35], # ch2_ampl ), ), # Test case 3: Coincidence between Ch1 and Ch2, but Ch4 overlaps ( - [1.0, 2.0, 3.0], # Ch1_TIME - [1.1, 2.1, 3.1], # Ch2_TIME - [4.0, 5.0, 6.0], # Ch3_TIME (no overlap) - [1.05, 2.05, 8.0], # Ch4_TIME (overlaps with Ch1 and Ch2) - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times + [1.1, 2.1, 3.1], # ch2_times + [4.0, 5.0, 6.0], # ch3_times (no overlap) + [1.05, 2.05, 8.0], # ch4_times (overlaps with Ch1 and Ch2) + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [3.0], # Ch1_TIME - [3.1], # Ch2_TIME - [30], # Ch1_AMPL - [35], # Ch2_AMPL + [3.0], # ch1_times + [3.1], # ch2_times + [30], # ch1_ampl + [35], # ch2_ampl ), ), # Test case 4: No matches between Ch1 and Ch2 ( - [1.0, 2.0, 3.0], # Ch1_TIME - [4.0, 5.0, 6.0], # Ch2_TIME (no overlap) - [7.0, 8.0, 9.0], # Ch3_TIME - [10.0, 11.0, 12.0], # Ch4_TIME - [10, 20, 30], # Ch1_AMPL - [15, 25, 35], # Ch2_AMPL + [1.0, 2.0, 3.0], # ch1_times + [4.0, 5.0, 6.0], # ch2_times (no overlap) + [7.0, 8.0, 9.0], # ch3_times + [10.0, 11.0, 12.0], # ch4_times + [10, 20, 30], # ch1_ampl + [15, 25, 35], # ch2_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl ), ), # Test case 5: Empty input ( - [], # Ch1_TIME - [], # Ch2_TIME - [], # Ch3_TIME - [], # Ch4_TIME - [], # Ch1_AMPL - [], # Ch2_AMPL + [], # ch1_times + [], # ch2_times + [], # ch3_times + [], # ch4_times + [], # ch1_ampl + [], # ch2_ampl 0.2, # t_window ( - [], # No valid Ch1_TIME - [], # No valid Ch2_TIME - [], # No valid Ch1_AMPL - [], # No valid Ch2_AMPL + [], # No valid ch1_times + [], # No valid ch2_times + [], # No valid ch1_ampl + [], # No valid ch2_ampl ), ), ], ) def test_coinc_2_anti_2( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, t_window, @@ -739,20 +734,20 @@ def test_coinc_2_anti_2( for two channels with anti-coincidence on two other channels. Args: - ch1_time: List of timestamps for channel 1. - ch2_time: List of timestamps for channel 2. - ch3_time: List of timestamps for channel 3 (anti-coincidence). - ch4_time: List of timestamps for channel 4 (anti-coincidence). + ch1_times: List of timestamps for channel 1. + ch2_times: List of timestamps for channel 2. + ch3_times: List of timestamps for channel 3 (anti-coincidence). + ch4_times: List of timestamps for channel 4 (anti-coincidence). ch1_ampl: List of amplitudes for channel 1. ch2_ampl: List of amplitudes for channel 2. t_window: Time window for coincidence detection. expected: Expected output (time and amplitude matches). """ result = prt.coinc_2_anti_2( - ch1_time, - ch2_time, - ch3_time, - ch4_time, + ch1_times, + ch2_times, + ch3_times, + ch4_times, ch1_ampl, ch2_ampl, t_window, From 92623ed51499a5d4e2d925fd7614d0181bc4e9fb Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 18 Apr 2025 15:36:52 -0400 Subject: [PATCH 021/137] re-ran notebook --- docs/examples/prt.ipynb | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/examples/prt.ipynb b/docs/examples/prt.ipynb index 4b5e200..6480cd6 100644 --- a/docs/examples/prt.ipynb +++ b/docs/examples/prt.ipynb @@ -283,8 +283,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Ignore: 1, Coincidence: 3, Anti-Coincidence: 0\n", - "Coincidence between A, B and C\n" + "Ignore: 1, Coincidence: 3, Anti-Coincidence: 0\n" ] } ], @@ -436,7 +435,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 6418 coincidence events\n" + "Found 6447 coincidence events\n" ] } ], @@ -458,7 +457,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] From 8b0c8521717432bd5750a14213630bf289d3f56f Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Apr 2025 11:36:29 -0400 Subject: [PATCH 022/137] Added get_start_stop_time function --- .../activation_foils/compass.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 8af0ebd..934ac1b 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -2,6 +2,7 @@ import os import pandas as pd from typing import Tuple, Dict +import datetime def get_channel(filename): @@ -108,3 +109,30 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar energy_values[ch] = np.concatenate([energy_values[ch], energy_data]) return time_values, energy_values + + +def get_start_stop_time(directory): + """ Obtains count start and stop time from the run.info file.""" + + info_file = os.path.join(directory, '../run.info') + if os.path.isfile(info_file): + time_format = "%Y/%m/%d %H:%M:%S.%f%z" + with open(info_file, 'r') as file: + continue_search = True + while continue_search: + line = file.readline() + if 'time.start=' in line: + # get start time string while cutting off '\n' newline + time_string = line.split('=')[1][:-1] + start_time = datetime.datetime.strptime(time_string, time_format) + elif 'time.stop=' in line: + # get stop time string while cutting off '\n' newline + time_string = line.split('=')[1][:-1] + stop_time = datetime.datetime.strptime(time_string, time_format) + continue_search = False + elif len(line) == 0: + continue_search = False + else: + raise LookupError('Could not find run.info file') + + return start_time, stop_time From 85c5fca51831a181ff73cc5a9718ec02a1729e6a Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Apr 2025 12:15:57 -0400 Subject: [PATCH 023/137] Reformatted file structure and added test --- ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 0 ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 0 ..._Co60_0_872uCi_19Mar2014_250318_run2_1.csv | 0 .../times/test1/UNFILTERED/DO_NOT_DELETE.txt | 1 + .../compass_test_data/times/test1/run.info | 31 +++++++++++++++++++ .../times/test2/UNFILTERED/DO_NOT_DELETE.txt | 1 + .../compass_test_data/times/test2/run.info | 31 +++++++++++++++++++ 7 files changed, 64 insertions(+) rename test/neutron_detection/compass_test_data/{ => events}/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv (100%) rename test/neutron_detection/compass_test_data/{ => events}/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv (100%) rename test/neutron_detection/compass_test_data/{ => events}/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv (100%) create mode 100644 test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt create mode 100755 test/neutron_detection/compass_test_data/times/test1/run.info create mode 100644 test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt create mode 100755 test/neutron_detection/compass_test_data/times/test2/run.info diff --git a/test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/events/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv similarity index 100% rename from test/neutron_detection/compass_test_data/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv rename to test/neutron_detection/compass_test_data/events/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv diff --git a/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv similarity index 100% rename from test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv rename to test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv diff --git a/test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv b/test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv similarity index 100% rename from test/neutron_detection/compass_test_data/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv rename to test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv diff --git a/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt b/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt new file mode 100644 index 0000000..bf3b998 --- /dev/null +++ b/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt @@ -0,0 +1 @@ +DO NOT DELETE TO ENSURE test_get_start_stop_time() works \ No newline at end of file diff --git a/test/neutron_detection/compass_test_data/times/test1/run.info b/test/neutron_detection/compass_test_data/times/test1/run.info new file mode 100755 index 0000000..c267566 --- /dev/null +++ b/test/neutron_detection/compass_test_data/times/test1/run.info @@ -0,0 +1,31 @@ +id=Co60_0_872uCi_19Mar14_241107 +time.start=2024/11/07 15:47:21.127-0500 +time.stop=2024/11/07 16:02:21.133-0500 +time.real=00:15:00 +board.0-14-292.readout.rate=132.731 kb/s +board.0-14-292.1.rejections.singles=0.0 +board.0-14-292.1.rejections.pileup=0.0 +board.0-14-292.1.rejections.saturation=1729.15 +board.0-14-292.1.rejections.energy=0.0 +board.0-14-292.1.rejections.psd=0.0 +board.0-14-292.1.rejections.timedistribution=0.0 +board.0-14-292.1.throughput=6950.66 +board.0-14-292.1.icr=7424.44 +board.0-14-292.1.ocr=5253.24 +board.0-14-292.1.calibration.energy.c0=0.0 +board.0-14-292.1.calibration.energy.c1=1.0 +board.0-14-292.1.calibration.energy.c2=0.0 +board.0-14-292.1.calibration.energy.uom=keV +board.0-14-292.2.rejections.singles=0.0 +board.0-14-292.2.rejections.pileup=0.0 +board.0-14-292.2.rejections.saturation=8.2202 +board.0-14-292.2.rejections.energy=0.0 +board.0-14-292.2.rejections.psd=0.0 +board.0-14-292.2.rejections.timedistribution=0.0 +board.0-14-292.2.throughput=3958.96 +board.0-14-292.2.icr=3981.66 +board.0-14-292.2.ocr=3952.89 +board.0-14-292.2.calibration.energy.c0=0.0 +board.0-14-292.2.calibration.energy.c1=1.0 +board.0-14-292.2.calibration.energy.c2=0.0 +board.0-14-292.2.calibration.energy.uom=keV diff --git a/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt b/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt new file mode 100644 index 0000000..bf3b998 --- /dev/null +++ b/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt @@ -0,0 +1 @@ +DO NOT DELETE TO ENSURE test_get_start_stop_time() works \ No newline at end of file diff --git a/test/neutron_detection/compass_test_data/times/test2/run.info b/test/neutron_detection/compass_test_data/times/test2/run.info new file mode 100755 index 0000000..335d092 --- /dev/null +++ b/test/neutron_detection/compass_test_data/times/test2/run.info @@ -0,0 +1,31 @@ +id=Zirconium_250318_2219_count2 +time.start=2025/03/18 22:19:03.947-0400 +time.stop=2025/03/19 09:21:06.558-0400 +time.real=11:02:02 +board.0-14-292.readout.rate=36.971 kb/s +board.0-14-292.4.rejections.singles=0.0 +board.0-14-292.4.rejections.pileup=0.0 +board.0-14-292.4.rejections.saturation=1130.67 +board.0-14-292.4.rejections.energy=0.0 +board.0-14-292.4.rejections.psd=0.0 +board.0-14-292.4.rejections.timedistribution=0.0 +board.0-14-292.4.throughput=1935.08 +board.0-14-292.4.icr=1021.38 +board.0-14-292.4.ocr=783.92 +board.0-14-292.4.calibration.energy.c0=0.0 +board.0-14-292.4.calibration.energy.c1=1.0 +board.0-14-292.4.calibration.energy.c2=0.0 +board.0-14-292.4.calibration.energy.uom=keV +board.0-14-292.5.rejections.singles=0.0 +board.0-14-292.5.rejections.pileup=0.0 +board.0-14-292.5.rejections.saturation=727.287 +board.0-14-292.5.rejections.energy=0.0 +board.0-14-292.5.rejections.psd=0.0 +board.0-14-292.5.rejections.timedistribution=0.0 +board.0-14-292.5.throughput=1063.43 +board.0-14-292.5.icr=1069.07 +board.0-14-292.5.ocr=338.805 +board.0-14-292.5.calibration.energy.c0=0.0 +board.0-14-292.5.calibration.energy.c1=1.0 +board.0-14-292.5.calibration.energy.c2=0.0 +board.0-14-292.5.calibration.energy.uom=keV From 4209f206eaadf714c69faee15b21c05c7f8194be Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Apr 2025 12:17:01 -0400 Subject: [PATCH 024/137] Actually added test_get_start_stop_time() --- test/neutron_detection/test_compass.py | 69 +++++++++++++++++++++++++- 1 file changed, 68 insertions(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 818583f..e634f37 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -3,6 +3,7 @@ import os from libra_toolbox.neutron_detection.activation_foils import compass from pathlib import Path +import datetime @pytest.mark.parametrize( @@ -102,7 +103,7 @@ def test_get_events(expected_time, expected_energy, expected_idx): Test the get_events function from the compass module. Checks that specific time and energy values are returned for a given channel """ - test_directory = Path(__file__).parent / "compass_test_data" + test_directory = Path(__file__).parent / "compass_test_data/events" times, energies = compass.get_events(test_directory) assert isinstance(times, dict) assert isinstance(energies, dict) @@ -115,3 +116,69 @@ def test_get_events(expected_time, expected_energy, expected_idx): ch = 5 assert times[ch][expected_idx] == expected_time assert energies[ch][expected_idx] == expected_energy + + +@pytest.mark.parametrize( + "directory, expected_start, expected_stop", + [ + ( + Path(__file__).parent / "compass_test_data/times/test1/UNFILTERED", + datetime.datetime( + 2024, + 11, + 7, + 15, + 47, + 21, + 127000, + tzinfo=datetime.timezone(datetime.timedelta(hours=-5)), + ), + datetime.datetime( + 2024, + 11, + 7, + 16, + 2, + 21, + 133000, + tzinfo=datetime.timezone(datetime.timedelta(hours=-5)), + ), + ), + ( + Path(__file__).parent / "compass_test_data/times/test2/UNFILTERED", + datetime.datetime( + 2025, + 3, + 18, + 22, + 19, + 3, + 947000, + tzinfo=datetime.timezone(datetime.timedelta(hours=-4)), + ), + datetime.datetime( + 2025, + 3, + 19, + 9, + 21, + 6, + 558000, + tzinfo=datetime.timezone(datetime.timedelta(hours=-4)), + ), + ), + ], +) +def test_get_start_stop_time(directory, expected_start, expected_stop): + """ + Test the get_start_stop_time function from the compass module. + Checks that start and stop datetime.datetime objects are correctly + obtained from the run.info file. + """ + start_time, stop_time = compass.get_start_stop_time(directory) + + assert isinstance(start_time, datetime.datetime) + assert start_time == expected_start + + assert isinstance(stop_time, datetime.datetime) + assert stop_time == expected_stop From 71a590c45f86b13827b76f0de8213c4f607f5549 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:18:26 -0400 Subject: [PATCH 025/137] better error message --- .../activation_foils/compass.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 934ac1b..2253a53 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -112,27 +112,27 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar def get_start_stop_time(directory): - """ Obtains count start and stop time from the run.info file.""" + """Obtains count start and stop time from the run.info file.""" - info_file = os.path.join(directory, '../run.info') + info_file = os.path.join(directory, "../run.info") if os.path.isfile(info_file): time_format = "%Y/%m/%d %H:%M:%S.%f%z" - with open(info_file, 'r') as file: + with open(info_file, "r") as file: continue_search = True while continue_search: line = file.readline() - if 'time.start=' in line: + if "time.start=" in line: # get start time string while cutting off '\n' newline - time_string = line.split('=')[1][:-1] + time_string = line.split("=")[1][:-1] start_time = datetime.datetime.strptime(time_string, time_format) - elif 'time.stop=' in line: + elif "time.stop=" in line: # get stop time string while cutting off '\n' newline - time_string = line.split('=')[1][:-1] + time_string = line.split("=")[1][:-1] stop_time = datetime.datetime.strptime(time_string, time_format) continue_search = False elif len(line) == 0: continue_search = False else: - raise LookupError('Could not find run.info file') - + raise FileNotFoundError(f"Could not find run.info file in {directory}") + return start_time, stop_time From 19d26ec1ce886b88145a78eba25297f59e2ea096 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:21:23 -0400 Subject: [PATCH 026/137] pathlib --- .../neutron_detection/activation_foils/compass.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 2253a53..2aa6009 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -1,5 +1,6 @@ import numpy as np import os +from pathlib import Path import pandas as pd from typing import Tuple, Dict import datetime @@ -111,11 +112,11 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar return time_values, energy_values -def get_start_stop_time(directory): +def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.datetime]: """Obtains count start and stop time from the run.info file.""" - info_file = os.path.join(directory, "../run.info") - if os.path.isfile(info_file): + info_file = Path(directory).parent / "run.info" + if info_file.exists(): time_format = "%Y/%m/%d %H:%M:%S.%f%z" with open(info_file, "r") as file: continue_search = True From b2c1d0d7bff0a5e85db5672381319c4edccf052c Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:24:12 -0400 Subject: [PATCH 027/137] added test --- test/neutron_detection/test_compass.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index e634f37..77fc9fc 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -182,3 +182,10 @@ def test_get_start_stop_time(directory, expected_start, expected_stop): assert isinstance(stop_time, datetime.datetime) assert stop_time == expected_stop + + +def test_filenotfound_error_info(): + with pytest.raises(FileNotFoundError, match="Could not find run.info"): + compass.get_start_stop_time( + directory=Path(__file__).parent / "compass_test_data/events" + ) From b3ca47e71039bbb497181ffcb142e2b6ef6c6cd4 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:32:28 -0400 Subject: [PATCH 028/137] refactoring --- .../activation_foils/compass.py | 31 ++++++++++--------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 2aa6009..da73786 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -119,21 +119,22 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat if info_file.exists(): time_format = "%Y/%m/%d %H:%M:%S.%f%z" with open(info_file, "r") as file: - continue_search = True - while continue_search: - line = file.readline() - if "time.start=" in line: - # get start time string while cutting off '\n' newline - time_string = line.split("=")[1][:-1] - start_time = datetime.datetime.strptime(time_string, time_format) - elif "time.stop=" in line: - # get stop time string while cutting off '\n' newline - time_string = line.split("=")[1][:-1] - stop_time = datetime.datetime.strptime(time_string, time_format) - continue_search = False - elif len(line) == 0: - continue_search = False + lines = file.readlines() else: raise FileNotFoundError(f"Could not find run.info file in {directory}") - return start_time, stop_time + start_time, stop_time = None, None + for line in lines: + if "time.start=" in line: + # get start time string while cutting off '\n' newline + time_string = line.split("=")[1].replace("\n", "") + start_time = datetime.datetime.strptime(time_string, time_format) + elif "time.stop=" in line: + # get stop time string while cutting off '\n' newline + time_string = line.split("=")[1].replace("\n", "") + stop_time = datetime.datetime.strptime(time_string, time_format) + + if None in (start_time, stop_time): + raise ValueError(f"Could not find time.start or time.stop in file {info_file}.") + else: + return start_time, stop_time From e326e2488ea95e3fb34a940608fcb418d053c8e3 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:38:55 -0400 Subject: [PATCH 029/137] new test --- test/neutron_detection/test_compass.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 77fc9fc..88f25d9 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -189,3 +189,22 @@ def test_filenotfound_error_info(): compass.get_start_stop_time( directory=Path(__file__).parent / "compass_test_data/events" ) + + +def test_get_start_stop_time_with_notime(tmpdir): + """Creates an empty file run.info and check that an error is raised if can't find time""" + + # Create another temporary directory + + tmpdir2 = os.path.join(tmpdir, "tmpdir2") + + # create an empty run.info file + run_info_path = os.path.join(tmpdir, "run.info") + + # add some stuff + with open(run_info_path, "w") as f: + f.write("coucou\ncoucou\n") + + # run + with pytest.raises(ValueError, match="Could not find time.start or time.stop"): + compass.get_start_stop_time(tmpdir2) From fc61f32b810db36b0fb9801b5ed30aa255b5fa68 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 13:54:54 -0400 Subject: [PATCH 030/137] instead of storing files we can create them --- .../times/test1/UNFILTERED/DO_NOT_DELETE.txt | 1 - .../compass_test_data/times/test1/run.info | 31 -------- .../times/test2/UNFILTERED/DO_NOT_DELETE.txt | 1 - .../compass_test_data/times/test2/run.info | 31 -------- test/neutron_detection/test_compass.py | 74 ++++++++++++++++--- 5 files changed, 62 insertions(+), 76 deletions(-) delete mode 100644 test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt delete mode 100755 test/neutron_detection/compass_test_data/times/test1/run.info delete mode 100644 test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt delete mode 100755 test/neutron_detection/compass_test_data/times/test2/run.info diff --git a/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt b/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt deleted file mode 100644 index bf3b998..0000000 --- a/test/neutron_detection/compass_test_data/times/test1/UNFILTERED/DO_NOT_DELETE.txt +++ /dev/null @@ -1 +0,0 @@ -DO NOT DELETE TO ENSURE test_get_start_stop_time() works \ No newline at end of file diff --git a/test/neutron_detection/compass_test_data/times/test1/run.info b/test/neutron_detection/compass_test_data/times/test1/run.info deleted file mode 100755 index c267566..0000000 --- a/test/neutron_detection/compass_test_data/times/test1/run.info +++ /dev/null @@ -1,31 +0,0 @@ -id=Co60_0_872uCi_19Mar14_241107 -time.start=2024/11/07 15:47:21.127-0500 -time.stop=2024/11/07 16:02:21.133-0500 -time.real=00:15:00 -board.0-14-292.readout.rate=132.731 kb/s -board.0-14-292.1.rejections.singles=0.0 -board.0-14-292.1.rejections.pileup=0.0 -board.0-14-292.1.rejections.saturation=1729.15 -board.0-14-292.1.rejections.energy=0.0 -board.0-14-292.1.rejections.psd=0.0 -board.0-14-292.1.rejections.timedistribution=0.0 -board.0-14-292.1.throughput=6950.66 -board.0-14-292.1.icr=7424.44 -board.0-14-292.1.ocr=5253.24 -board.0-14-292.1.calibration.energy.c0=0.0 -board.0-14-292.1.calibration.energy.c1=1.0 -board.0-14-292.1.calibration.energy.c2=0.0 -board.0-14-292.1.calibration.energy.uom=keV -board.0-14-292.2.rejections.singles=0.0 -board.0-14-292.2.rejections.pileup=0.0 -board.0-14-292.2.rejections.saturation=8.2202 -board.0-14-292.2.rejections.energy=0.0 -board.0-14-292.2.rejections.psd=0.0 -board.0-14-292.2.rejections.timedistribution=0.0 -board.0-14-292.2.throughput=3958.96 -board.0-14-292.2.icr=3981.66 -board.0-14-292.2.ocr=3952.89 -board.0-14-292.2.calibration.energy.c0=0.0 -board.0-14-292.2.calibration.energy.c1=1.0 -board.0-14-292.2.calibration.energy.c2=0.0 -board.0-14-292.2.calibration.energy.uom=keV diff --git a/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt b/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt deleted file mode 100644 index bf3b998..0000000 --- a/test/neutron_detection/compass_test_data/times/test2/UNFILTERED/DO_NOT_DELETE.txt +++ /dev/null @@ -1 +0,0 @@ -DO NOT DELETE TO ENSURE test_get_start_stop_time() works \ No newline at end of file diff --git a/test/neutron_detection/compass_test_data/times/test2/run.info b/test/neutron_detection/compass_test_data/times/test2/run.info deleted file mode 100755 index 335d092..0000000 --- a/test/neutron_detection/compass_test_data/times/test2/run.info +++ /dev/null @@ -1,31 +0,0 @@ -id=Zirconium_250318_2219_count2 -time.start=2025/03/18 22:19:03.947-0400 -time.stop=2025/03/19 09:21:06.558-0400 -time.real=11:02:02 -board.0-14-292.readout.rate=36.971 kb/s -board.0-14-292.4.rejections.singles=0.0 -board.0-14-292.4.rejections.pileup=0.0 -board.0-14-292.4.rejections.saturation=1130.67 -board.0-14-292.4.rejections.energy=0.0 -board.0-14-292.4.rejections.psd=0.0 -board.0-14-292.4.rejections.timedistribution=0.0 -board.0-14-292.4.throughput=1935.08 -board.0-14-292.4.icr=1021.38 -board.0-14-292.4.ocr=783.92 -board.0-14-292.4.calibration.energy.c0=0.0 -board.0-14-292.4.calibration.energy.c1=1.0 -board.0-14-292.4.calibration.energy.c2=0.0 -board.0-14-292.4.calibration.energy.uom=keV -board.0-14-292.5.rejections.singles=0.0 -board.0-14-292.5.rejections.pileup=0.0 -board.0-14-292.5.rejections.saturation=727.287 -board.0-14-292.5.rejections.energy=0.0 -board.0-14-292.5.rejections.psd=0.0 -board.0-14-292.5.rejections.timedistribution=0.0 -board.0-14-292.5.throughput=1063.43 -board.0-14-292.5.icr=1069.07 -board.0-14-292.5.ocr=338.805 -board.0-14-292.5.calibration.energy.c0=0.0 -board.0-14-292.5.calibration.energy.c1=1.0 -board.0-14-292.5.calibration.energy.c2=0.0 -board.0-14-292.5.calibration.energy.uom=keV diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 88f25d9..9104e26 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -119,10 +119,9 @@ def test_get_events(expected_time, expected_energy, expected_idx): @pytest.mark.parametrize( - "directory, expected_start, expected_stop", + "start_time, stop_time", [ ( - Path(__file__).parent / "compass_test_data/times/test1/UNFILTERED", datetime.datetime( 2024, 11, @@ -145,7 +144,6 @@ def test_get_events(expected_time, expected_energy, expected_idx): ), ), ( - Path(__file__).parent / "compass_test_data/times/test2/UNFILTERED", datetime.datetime( 2025, 3, @@ -169,19 +167,71 @@ def test_get_events(expected_time, expected_energy, expected_idx): ), ], ) -def test_get_start_stop_time(directory, expected_start, expected_stop): +def test_get_start_stop_time(tmpdir, start_time, stop_time): """ - Test the get_start_stop_time function from the compass module. - Checks that start and stop datetime.datetime objects are correctly - obtained from the run.info file. + Tests the get_start_stop_time function from the compass module. + Checks that the start and stop times are correctly parsed from the run.info file. """ - start_time, stop_time = compass.get_start_stop_time(directory) + # BUILD + content = _run_info_content(start_time, stop_time) - assert isinstance(start_time, datetime.datetime) - assert start_time == expected_start + # Create another temporary directory + tmpdir2 = os.path.join(tmpdir, "tmpdir2") + + # create an empty run.info file + run_info_path = os.path.join(tmpdir, "run.info") + + # add some stuff + with open(run_info_path, "w") as f: + f.write(content) + + # RUN + start_time_out, stop_time_out = compass.get_start_stop_time(tmpdir2) + + # TEST + assert isinstance(start_time_out, datetime.datetime) + assert start_time_out == start_time + + assert isinstance(stop_time_out, datetime.datetime) + assert stop_time_out == stop_time - assert isinstance(stop_time, datetime.datetime) - assert stop_time == expected_stop + +def _run_info_content(start_time: datetime.datetime, stop_time: datetime.datetime): + """ + Creates a string that simulates the content of a run.info file. + """ + return f"""id=Co60_0_872uCi_19Mar14_241107 +time.start={start_time.strftime("%Y/%m/%d %H:%M:%S.%f%z")} +time.stop={stop_time.strftime("%Y/%m/%d %H:%M:%S.%f%z")} +time.real=00:15:00 +board.0-14-292.readout.rate=132.731 kb/s +board.0-14-292.1.rejections.singles=0.0 +board.0-14-292.1.rejections.pileup=0.0 +board.0-14-292.1.rejections.saturation=1729.15 +board.0-14-292.1.rejections.energy=0.0 +board.0-14-292.1.rejections.psd=0.0 +board.0-14-292.1.rejections.timedistribution=0.0 +board.0-14-292.1.throughput=6950.66 +board.0-14-292.1.icr=7424.44 +board.0-14-292.1.ocr=5253.24 +board.0-14-292.1.calibration.energy.c0=0.0 +board.0-14-292.1.calibration.energy.c1=1.0 +board.0-14-292.1.calibration.energy.c2=0.0 +board.0-14-292.1.calibration.energy.uom=keV +board.0-14-292.2.rejections.singles=0.0 +board.0-14-292.2.rejections.pileup=0.0 +board.0-14-292.2.rejections.saturation=8.2202 +board.0-14-292.2.rejections.energy=0.0 +board.0-14-292.2.rejections.psd=0.0 +board.0-14-292.2.rejections.timedistribution=0.0 +board.0-14-292.2.throughput=3958.96 +board.0-14-292.2.icr=3981.66 +board.0-14-292.2.ocr=3952.89 +board.0-14-292.2.calibration.energy.c0=0.0 +board.0-14-292.2.calibration.energy.c1=1.0 +board.0-14-292.2.calibration.energy.c2=0.0 +board.0-14-292.2.calibration.energy.uom=keV +""" def test_filenotfound_error_info(): From d0984f328d3cb243923758bb883a6fce91768e39 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 25 Apr 2025 14:28:00 -0400 Subject: [PATCH 031/137] slight refactor --- test/neutron_detection/test_compass.py | 40 ++++++-------------------- 1 file changed, 8 insertions(+), 32 deletions(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 9104e26..0a2bd88 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -118,51 +118,27 @@ def test_get_events(expected_time, expected_energy, expected_idx): assert energies[ch][expected_idx] == expected_energy +utc_minus5 = datetime.timezone(datetime.timedelta(hours=-5)) +utc_minus4 = datetime.timezone(datetime.timedelta(hours=-4)) + + @pytest.mark.parametrize( "start_time, stop_time", [ ( datetime.datetime( - 2024, - 11, - 7, - 15, - 47, - 21, - 127000, - tzinfo=datetime.timezone(datetime.timedelta(hours=-5)), + 2024, 11, 7, 15, 47, 21, microsecond=127000, tzinfo=utc_minus5 ), datetime.datetime( - 2024, - 11, - 7, - 16, - 2, - 21, - 133000, - tzinfo=datetime.timezone(datetime.timedelta(hours=-5)), + 2024, 11, 7, 16, 2, 21, microsecond=133000, tzinfo=utc_minus5 ), ), ( datetime.datetime( - 2025, - 3, - 18, - 22, - 19, - 3, - 947000, - tzinfo=datetime.timezone(datetime.timedelta(hours=-4)), + 2025, 3, 18, 22, 19, 3, microsecond=947000, tzinfo=utc_minus4 ), datetime.datetime( - 2025, - 3, - 19, - 9, - 21, - 6, - 558000, - tzinfo=datetime.timezone(datetime.timedelta(hours=-4)), + 2025, 3, 19, 9, 21, 6, microsecond=558000, tzinfo=utc_minus4 ), ), ], From fe21c5a7321d77df6acb2e5b33e8e7fc7794193b Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Sat, 26 Apr 2025 18:22:51 -0400 Subject: [PATCH 032/137] Added test and test files for get_livetime func --- .../activation_foils/compass.py | 13 ++++++ .../times/Hcompass_Co60_20241107.root | Bin 0 -> 83399 bytes .../times/Hcompass_Zirconium_20250319.root | Bin 0 -> 90677 bytes test/neutron_detection/test_compass.py | 41 ++++++++++++++++++ 4 files changed, 54 insertions(+) create mode 100755 test/neutron_detection/compass_test_data/times/Hcompass_Co60_20241107.root create mode 100755 test/neutron_detection/compass_test_data/times/Hcompass_Zirconium_20250319.root diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index da73786..60d3162 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -4,6 +4,7 @@ import pandas as pd from typing import Tuple, Dict import datetime +import uproot def get_channel(filename): @@ -138,3 +139,15 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat raise ValueError(f"Could not find time.start or time.stop in file {info_file}.") else: return start_time, stop_time + + +def get_live_time_from_root(root_filename, channel: int): + """Gets live and real count time from Compass root file. + Live time is defined as the difference between the actual time that + a count is occuring and the "dead time," in which the output of detector + pulses is saturated such that additional signals cannot be processed.""" + + with uproot.open(root_filename) as root_file: + live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 + real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 + return live_count_time, real_count_time diff --git a/test/neutron_detection/compass_test_data/times/Hcompass_Co60_20241107.root 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zvlF-=Im+A)uMacE7UN*~zv8BU!>QW3TmFZ*V)5T_|7Z{NZ`&(=`Wx;a?fr=(1fS_g zNc<1~5Sn_N`RHHy2M=I=cm{had=5KXY)U|U{|t6sqK@V-hsr7nO6l`~`9W}g5q>-u z0?%_(a-U;V&oJ!PXBb*pgoCZQ3j$+{a*{<^T3Oj5Z7rQJ6u({mC-dHk`m_DwPygIL z=-;&8WB8x8|EJjq63%t8IoIVZMhW3XXL0(Q+5Q%zL<9ti!FUKE(O;Rsf3E)I8RXn# z*Up0BPlHK`{fX%5{S)zrim~Uktj=kj0wR?!^CNZ9=@))6OY5oKre4W@t8YmUX{T`Xl4;v~ClfY5v<ee_qS{cV_?h k_0M>6&FAE Date: Sat, 26 Apr 2025 18:26:16 -0400 Subject: [PATCH 033/137] Fixed typo in doc string --- libra_toolbox/neutron_detection/activation_foils/compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 60d3162..b0304b9 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -144,7 +144,7 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat def get_live_time_from_root(root_filename, channel: int): """Gets live and real count time from Compass root file. Live time is defined as the difference between the actual time that - a count is occuring and the "dead time," in which the output of detector + a count is occurring and the "dead time," in which the output of detector pulses is saturated such that additional signals cannot be processed.""" with uproot.open(root_filename) as root_file: From f97a7792314e16a48b9546943f40606c4fa7fee5 Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Sat, 26 Apr 2025 18:36:18 -0400 Subject: [PATCH 034/137] I think I added the uproot dependency --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 8b0e55d..1a9a939 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ description = "Design and analysis tools for LIBRA project" license = {file = "LICENSE"} requires-python = ">=3.10" dynamic = ["version"] -dependencies = ["numpy", "pint", "scipy", "matplotlib", "sympy", "pandas", "h5py"] +dependencies = ["numpy", "pint", "scipy", "matplotlib", "sympy", "pandas", "h5py", "uproot"] [project.optional-dependencies] neutronics = ["openmc-data-downloader"] From 150675f4f8b20dcd00573f820fb4751cf670dcb9 Mon Sep 17 00:00:00 2001 From: Collin Dunn <94483121+cdunn314@users.noreply.github.com> Date: Tue, 29 Apr 2025 10:38:24 -0400 Subject: [PATCH 035/137] Update libra_toolbox/neutron_detection/activation_foils/compass.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Rémi Delaporte-Mathurin <40028739+RemDelaporteMathurin@users.noreply.github.com> --- libra_toolbox/neutron_detection/activation_foils/compass.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b0304b9..0c2426b 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -141,13 +141,13 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat return start_time, stop_time -def get_live_time_from_root(root_filename, channel: int): +def get_live_time_from_root(filename, channel: int): """Gets live and real count time from Compass root file. Live time is defined as the difference between the actual time that a count is occurring and the "dead time," in which the output of detector pulses is saturated such that additional signals cannot be processed.""" - with uproot.open(root_filename) as root_file: + with uproot.open(filename) as root_file: live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 return live_count_time, real_count_time From 242345c1c5bfdab719a807c85550fab42f9873dd Mon Sep 17 00:00:00 2001 From: Collin Dunn <94483121+cdunn314@users.noreply.github.com> Date: Tue, 29 Apr 2025 10:39:06 -0400 Subject: [PATCH 036/137] Update libra_toolbox/neutron_detection/activation_foils/compass.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Rémi Delaporte-Mathurin <40028739+RemDelaporteMathurin@users.noreply.github.com> --- .../neutron_detection/activation_foils/compass.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 0c2426b..825e22a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -142,10 +142,13 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat def get_live_time_from_root(filename, channel: int): - """Gets live and real count time from Compass root file. + """ + Gets live and real count time from Compass root file. + Times are given in seconds. Live time is defined as the difference between the actual time that a count is occurring and the "dead time," in which the output of detector - pulses is saturated such that additional signals cannot be processed.""" + pulses is saturated such that additional signals cannot be processed. + """ with uproot.open(filename) as root_file: live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 From f30a84c220dcfdcd1b2774d2fecb124a25d204f2 Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Wed, 7 May 2025 10:46:40 -0400 Subject: [PATCH 037/137] Initial add --- example.py | 7 + .../activation_foils/compass.py | 142 +++++++++++++++++- 2 files changed, 148 insertions(+), 1 deletion(-) create mode 100644 example.py diff --git a/example.py b/example.py new file mode 100644 index 0000000..342fa2e --- /dev/null +++ b/example.py @@ -0,0 +1,7 @@ +from libra_toolbox.neutron_detection.activation_foils.compass import Measurement + + +my_measurement = Measurement(name="check_source_from_last_friday", events=[], ch=2) + + +my_other_measurement = Measurement.from_directory("path_to_directory") \ No newline at end of file diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b0304b9..2bde8e6 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -2,9 +2,10 @@ import os from pathlib import Path import pandas as pd -from typing import Tuple, Dict +from typing import Tuple, Dict, List import datetime import uproot +import glob def get_channel(filename): @@ -151,3 +152,142 @@ def get_live_time_from_root(root_filename, channel: int): live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 return live_count_time, real_count_time + + +class Detector: + events: np.ndarray + channel_nb: int + live_count_time: float + real_count_time: float + +class Measurement: + start_time: datetime.datetime + stop_time: datetime.datetime + name: str + detectors: List[Detector] + + def __init__(self, name: str, ch: int) -> None: + self.name = name + self.channel_nb = ch + self.detectors = [] + + @classmethod + def from_directory(cls, source_dir: str, name: str): + #print('Reading in files for {}'.format(source)) + + # Get events + time_values, energy_values = get_events(source_dir) + + # Get start and stop time + start_time, stop_time = get_start_stop_time(source_dir) + + # Get live and real count times + root_filename = glob.glob(os.path.join(source_dir, '*.root'))[0] + if os.path.isfile(root_filename): + for channel in time_values.keys(): + live_count_time, real_count_time = get_live_time_from_root(root_filename, channel) + else: + real_count_time = (stop_time - start_time).total_seconds() + for channel in time_values.keys(): + # Assume first and last event correspond to start and stop time of live counts + # and convert from picoseconds to seconds + live_count_time = (time_values[channel][-1] - time_values[channel][0]) / 1e12 + + measurement_object = cls(name=name, ) + + return cls() + + +my_check_source_measurement = Measurement.from_directory(path) + +def get_all_spectra_from_raw(directories): + + data = {} + + # Iterate through all CSV files in the directory + for source in directories.keys(): + print('Reading in files for {}'.format(source)) + data[source] = {} + + # Get events + time_values, energy_values = get_events(directories[source]) + + # Get start and stop time + start_time, stop_time = get_start_stop_time(directories[source]) + + # Get live and real count times + root_filename = glob.glob(os.path.join(directories[source], '*.root'))[0] + if os.path.isfile(root_filename): + for channel in time_values.keys(): + live_count_time, real_count_time = get_live_time_from_root(root_filename, channel) + else: + real_count_time = (stop_time - start_time).total_seconds() + for channel in time_values.keys(): + # Assume first and last event correspond to start and stop time of live counts + # and convert from picoseconds to seconds + live_count_time = (time_values[channel][-1] - time_values[channel][0]) / 1e12 + + + # Create a histogram to represent the combined energy spectrum + for ch in time_values[source].keys(): + # sort data based on timestamp + inds = np.argsort(time_values[source][ch]) + # convert times to seconds + time_values[source][ch] = np.array(time_values[source][ch])[inds] /1e12 + energy_values[source][ch] = np.array(energy_values[source][ch])[inds] + # print(np.nanmax(energy_values[source])) + + energy_values[source][ch] = np.nan_to_num(energy_values[source][ch], nan=0) + + if isinstance(bins, int): + hist, bin_edges = np.histogram(energy_values[source][ch], bins=bins) + elif bins=='double': + hist, bin_edges = np.histogram(energy_values[source][ch], bins=int(np.nanmax(energy_values[source][ch])/2)) + else: + # b = np.arange(0, max_channel[ch]) + b = np.arange(0, np.max(energy_values[source][ch])) + hist, bin_edges = np.histogram(energy_values[source][ch], bins=b) + + total_time = np.max(time_values[source][ch]) - np.min(time_values[source][ch]) + # counts[source][ch]['count_time'] = total_time + if np.abs(total_time - counts[source][ch]['real_count_time']) > 0.1: + print(f'Total time = {total_time}\n', + 'Real Count Time (root) = {}'.format(counts[source][ch]['real_count_time'])) + raise Exception(f'Total time different from root file real count time') + if count_rate: + counts[source][ch]['hist'] = hist / total_time + else: + counts[source][ch]['hist'] = hist + counts[source][ch]['bin_edges'] = bin_edges + # Get count start time + start_time, stop_time = get_start_stop_time(directories[source]) + for ch in counts[source].keys(): + counts[source][ch]['start_time'] = start_time + counts[source][ch]['stop_time'] = stop_time + + + # save data for faster opening in future + if savefile is not None: + with open(savefile, 'wb') as file: + pickle.dump(counts, file) + + return counts + +def get_all_spectra(directories:dict, savefile=None): + + """ Obtain detector counts from .CSV file saved in CoMPASS.""" + + get_raw_data = False + + if savefile is not None: + if os.path.isfile(savefile): + with open(savefile, 'rb') as file: + data = pickle.load(file) + else: + get_raw_data = True + + if get_raw_data: + counts = get_all_spectra_from_raw(directories) + + return counts + From 15b2a8fe6f0b14cc7e8dbdab581df7b87c032c0e Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 10:50:53 -0400 Subject: [PATCH 038/137] black --- .../activation_foils/compass.py | 83 +++++++++++-------- 1 file changed, 49 insertions(+), 34 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 2bde8e6..8dfab28 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -160,6 +160,7 @@ class Detector: live_count_time: float real_count_time: float + class Measurement: start_time: datetime.datetime stop_time: datetime.datetime @@ -173,7 +174,7 @@ def __init__(self, name: str, ch: int) -> None: @classmethod def from_directory(cls, source_dir: str, name: str): - #print('Reading in files for {}'.format(source)) + # print('Reading in files for {}'.format(source)) # Get events time_values, energy_values = get_events(source_dir) @@ -182,31 +183,35 @@ def from_directory(cls, source_dir: str, name: str): start_time, stop_time = get_start_stop_time(source_dir) # Get live and real count times - root_filename = glob.glob(os.path.join(source_dir, '*.root'))[0] + root_filename = glob.glob(os.path.join(source_dir, "*.root"))[0] if os.path.isfile(root_filename): for channel in time_values.keys(): - live_count_time, real_count_time = get_live_time_from_root(root_filename, channel) + live_count_time, real_count_time = get_live_time_from_root( + root_filename, channel + ) else: real_count_time = (stop_time - start_time).total_seconds() for channel in time_values.keys(): # Assume first and last event correspond to start and stop time of live counts # and convert from picoseconds to seconds - live_count_time = (time_values[channel][-1] - time_values[channel][0]) / 1e12 + live_count_time = ( + time_values[channel][-1] - time_values[channel][0] + ) / 1e12 - measurement_object = cls(name=name, ) + measurement_object = cls( + name=name, + ) return cls() -my_check_source_measurement = Measurement.from_directory(path) - def get_all_spectra_from_raw(directories): data = {} # Iterate through all CSV files in the directory for source in directories.keys(): - print('Reading in files for {}'.format(source)) + print("Reading in files for {}".format(source)) data[source] = {} # Get events @@ -216,24 +221,27 @@ def get_all_spectra_from_raw(directories): start_time, stop_time = get_start_stop_time(directories[source]) # Get live and real count times - root_filename = glob.glob(os.path.join(directories[source], '*.root'))[0] + root_filename = glob.glob(os.path.join(directories[source], "*.root"))[0] if os.path.isfile(root_filename): for channel in time_values.keys(): - live_count_time, real_count_time = get_live_time_from_root(root_filename, channel) + live_count_time, real_count_time = get_live_time_from_root( + root_filename, channel + ) else: real_count_time = (stop_time - start_time).total_seconds() for channel in time_values.keys(): # Assume first and last event correspond to start and stop time of live counts # and convert from picoseconds to seconds - live_count_time = (time_values[channel][-1] - time_values[channel][0]) / 1e12 - + live_count_time = ( + time_values[channel][-1] - time_values[channel][0] + ) / 1e12 # Create a histogram to represent the combined energy spectrum for ch in time_values[source].keys(): # sort data based on timestamp inds = np.argsort(time_values[source][ch]) # convert times to seconds - time_values[source][ch] = np.array(time_values[source][ch])[inds] /1e12 + time_values[source][ch] = np.array(time_values[source][ch])[inds] / 1e12 energy_values[source][ch] = np.array(energy_values[source][ch])[inds] # print(np.nanmax(energy_values[source])) @@ -241,47 +249,55 @@ def get_all_spectra_from_raw(directories): if isinstance(bins, int): hist, bin_edges = np.histogram(energy_values[source][ch], bins=bins) - elif bins=='double': - hist, bin_edges = np.histogram(energy_values[source][ch], bins=int(np.nanmax(energy_values[source][ch])/2)) + elif bins == "double": + hist, bin_edges = np.histogram( + energy_values[source][ch], + bins=int(np.nanmax(energy_values[source][ch]) / 2), + ) else: # b = np.arange(0, max_channel[ch]) b = np.arange(0, np.max(energy_values[source][ch])) hist, bin_edges = np.histogram(energy_values[source][ch], bins=b) - total_time = np.max(time_values[source][ch]) - np.min(time_values[source][ch]) + total_time = np.max(time_values[source][ch]) - np.min( + time_values[source][ch] + ) # counts[source][ch]['count_time'] = total_time - if np.abs(total_time - counts[source][ch]['real_count_time']) > 0.1: - print(f'Total time = {total_time}\n', - 'Real Count Time (root) = {}'.format(counts[source][ch]['real_count_time'])) - raise Exception(f'Total time different from root file real count time') + if np.abs(total_time - counts[source][ch]["real_count_time"]) > 0.1: + print( + f"Total time = {total_time}\n", + "Real Count Time (root) = {}".format( + counts[source][ch]["real_count_time"] + ), + ) + raise Exception(f"Total time different from root file real count time") if count_rate: - counts[source][ch]['hist'] = hist / total_time + counts[source][ch]["hist"] = hist / total_time else: - counts[source][ch]['hist'] = hist - counts[source][ch]['bin_edges'] = bin_edges + counts[source][ch]["hist"] = hist + counts[source][ch]["bin_edges"] = bin_edges # Get count start time start_time, stop_time = get_start_stop_time(directories[source]) for ch in counts[source].keys(): - counts[source][ch]['start_time'] = start_time - counts[source][ch]['stop_time'] = stop_time - - + counts[source][ch]["start_time"] = start_time + counts[source][ch]["stop_time"] = stop_time + # save data for faster opening in future if savefile is not None: - with open(savefile, 'wb') as file: - pickle.dump(counts, file) + with open(savefile, "wb") as file: + pickle.dump(counts, file) return counts -def get_all_spectra(directories:dict, savefile=None): - """ Obtain detector counts from .CSV file saved in CoMPASS.""" +def get_all_spectra(directories: dict, savefile=None): + """Obtain detector counts from .CSV file saved in CoMPASS.""" get_raw_data = False - + if savefile is not None: if os.path.isfile(savefile): - with open(savefile, 'rb') as file: + with open(savefile, "rb") as file: data = pickle.load(file) else: get_raw_data = True @@ -290,4 +306,3 @@ def get_all_spectra(directories:dict, savefile=None): counts = get_all_spectra_from_raw(directories) return counts - From 34ed2e04666c4bb41c921d3487eaa0e1040b3702 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 11:20:24 -0400 Subject: [PATCH 039/137] create the detector objects --- .../activation_foils/compass.py | 48 ++++++++++++++----- 1 file changed, 35 insertions(+), 13 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 8dfab28..5cb3f19 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -1,4 +1,5 @@ import numpy as np +from numpy.typing import NDArray import os from pathlib import Path import pandas as pd @@ -155,11 +156,17 @@ def get_live_time_from_root(root_filename, channel: int): class Detector: - events: np.ndarray + events: NDArray[Tuple[float, float]] # type: ignore # Array of (time, energy) pairs channel_nb: int live_count_time: float real_count_time: float + def __init__(self, channel_nb) -> None: + self.channel_nb = channel_nb + self.events = np.empty((0, 2)) # Initialize as empty 2D array with 2 columns + self.live_count_time = None + self.real_count_time = None + class Measurement: start_time: datetime.datetime @@ -173,36 +180,51 @@ def __init__(self, name: str, ch: int) -> None: self.detectors = [] @classmethod - def from_directory(cls, source_dir: str, name: str): - # print('Reading in files for {}'.format(source)) - + def from_directory(cls, source_dir: str, name: str) -> "Measurement": # Get events time_values, energy_values = get_events(source_dir) # Get start and stop time start_time, stop_time = get_start_stop_time(source_dir) + detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] + # Get live and real count times root_filename = glob.glob(os.path.join(source_dir, "*.root"))[0] - if os.path.isfile(root_filename): - for channel in time_values.keys(): + if not os.path.isfile(root_filename): + print("No root file found, assuming all counts are live") + + for detector in detectors: + detector.events = np.column_stack( + (time_values[detector.channel_nb], energy_values[detector.channel_nb]) + ) + + if os.path.isfile(root_filename): live_count_time, real_count_time = get_live_time_from_root( - root_filename, channel + root_filename, detector.channel_nb ) - else: - real_count_time = (stop_time - start_time).total_seconds() - for channel in time_values.keys(): + detector.live_count_time = live_count_time + detector.real_count_time = real_count_time + else: + real_count_time = (stop_time - start_time).total_seconds() # Assume first and last event correspond to start and stop time of live counts # and convert from picoseconds to seconds + ps_to_seconds = 1e-12 live_count_time = ( - time_values[channel][-1] - time_values[channel][0] - ) / 1e12 + time_values[detector.channel_nb][-1] + - time_values[detector.channel_nb][0] + ) * ps_to_seconds + detector.live_count_time = live_count_time + detector.real_count_time = real_count_time measurement_object = cls( name=name, ) + measurement_object.start_time = start_time + measurement_object.stop_time = stop_time + measurement_object.detectors = detectors - return cls() + return measurement_object def get_all_spectra_from_raw(directories): From cc296f4796a55b3a5350a4de21af0886b064ae0b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 11:22:22 -0400 Subject: [PATCH 040/137] minor refactor --- .../neutron_detection/activation_foils/compass.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 5cb3f19..1bbc784 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -181,12 +181,17 @@ def __init__(self, name: str, ch: int) -> None: @classmethod def from_directory(cls, source_dir: str, name: str) -> "Measurement": + measurement_object = cls(name=name) + # Get events time_values, energy_values = get_events(source_dir) # Get start and stop time start_time, stop_time = get_start_stop_time(source_dir) + measurement_object.start_time = start_time + measurement_object.stop_time = stop_time + # Create detectors detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] # Get live and real count times @@ -217,11 +222,6 @@ def from_directory(cls, source_dir: str, name: str) -> "Measurement": detector.live_count_time = live_count_time detector.real_count_time = real_count_time - measurement_object = cls( - name=name, - ) - measurement_object.start_time = start_time - measurement_object.stop_time = stop_time measurement_object.detectors = detectors return measurement_object From bcbde2a74391f7710bfa5ebd39fe2e32855a6e5b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 11:40:24 -0400 Subject: [PATCH 041/137] added histogramme --- .../activation_foils/compass.py | 124 +++--------------- 1 file changed, 21 insertions(+), 103 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 1bbc784..4ad9f24 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -167,6 +167,27 @@ def __init__(self, channel_nb) -> None: self.live_count_time = None self.real_count_time = None + def get_energy_hist(self, bins) -> Tuple[np.ndarray, np.ndarray]: + + energy_values = self.events[:, 1].copy() + time_values = self.events[:, 0].copy() + # sort data based on timestamp + inds = np.argsort(time_values) + time_values = time_values[inds] + energy_values = energy_values[inds] + # print(np.nanmax(energy_values[source])) + + energy_values = np.nan_to_num(energy_values, nan=0) + + if isinstance(bins, int): + real_bins = bins + elif bins == "double": + real_bins = int(np.nanmax(energy_values) / 2) + else: + real_bins = np.arange(0, np.max(energy_values)) + + return np.histogram(energy_values, bins=real_bins) + class Measurement: start_time: datetime.datetime @@ -225,106 +246,3 @@ def from_directory(cls, source_dir: str, name: str) -> "Measurement": measurement_object.detectors = detectors return measurement_object - - -def get_all_spectra_from_raw(directories): - - data = {} - - # Iterate through all CSV files in the directory - for source in directories.keys(): - print("Reading in files for {}".format(source)) - data[source] = {} - - # Get events - time_values, energy_values = get_events(directories[source]) - - # Get start and stop time - start_time, stop_time = get_start_stop_time(directories[source]) - - # Get live and real count times - root_filename = glob.glob(os.path.join(directories[source], "*.root"))[0] - if os.path.isfile(root_filename): - for channel in time_values.keys(): - live_count_time, real_count_time = get_live_time_from_root( - root_filename, channel - ) - else: - real_count_time = (stop_time - start_time).total_seconds() - for channel in time_values.keys(): - # Assume first and last event correspond to start and stop time of live counts - # and convert from picoseconds to seconds - live_count_time = ( - time_values[channel][-1] - time_values[channel][0] - ) / 1e12 - - # Create a histogram to represent the combined energy spectrum - for ch in time_values[source].keys(): - # sort data based on timestamp - inds = np.argsort(time_values[source][ch]) - # convert times to seconds - time_values[source][ch] = np.array(time_values[source][ch])[inds] / 1e12 - energy_values[source][ch] = np.array(energy_values[source][ch])[inds] - # print(np.nanmax(energy_values[source])) - - energy_values[source][ch] = np.nan_to_num(energy_values[source][ch], nan=0) - - if isinstance(bins, int): - hist, bin_edges = np.histogram(energy_values[source][ch], bins=bins) - elif bins == "double": - hist, bin_edges = np.histogram( - energy_values[source][ch], - bins=int(np.nanmax(energy_values[source][ch]) / 2), - ) - else: - # b = np.arange(0, max_channel[ch]) - b = np.arange(0, np.max(energy_values[source][ch])) - hist, bin_edges = np.histogram(energy_values[source][ch], bins=b) - - total_time = np.max(time_values[source][ch]) - np.min( - time_values[source][ch] - ) - # counts[source][ch]['count_time'] = total_time - if np.abs(total_time - counts[source][ch]["real_count_time"]) > 0.1: - print( - f"Total time = {total_time}\n", - "Real Count Time (root) = {}".format( - counts[source][ch]["real_count_time"] - ), - ) - raise Exception(f"Total time different from root file real count time") - if count_rate: - counts[source][ch]["hist"] = hist / total_time - else: - counts[source][ch]["hist"] = hist - counts[source][ch]["bin_edges"] = bin_edges - # Get count start time - start_time, stop_time = get_start_stop_time(directories[source]) - for ch in counts[source].keys(): - counts[source][ch]["start_time"] = start_time - counts[source][ch]["stop_time"] = stop_time - - # save data for faster opening in future - if savefile is not None: - with open(savefile, "wb") as file: - pickle.dump(counts, file) - - return counts - - -def get_all_spectra(directories: dict, savefile=None): - """Obtain detector counts from .CSV file saved in CoMPASS.""" - - get_raw_data = False - - if savefile is not None: - if os.path.isfile(savefile): - with open(savefile, "rb") as file: - data = pickle.load(file) - else: - get_raw_data = True - - if get_raw_data: - counts = get_all_spectra_from_raw(directories) - - return counts From 2b2836a09cb7830b4e01a20be7c87c91cd11d20a Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 11:44:54 -0400 Subject: [PATCH 042/137] documentation to get_live_time from root --- .../neutron_detection/activation_foils/compass.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 4ad9f24..bb58cee 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -143,11 +143,20 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat return start_time, stop_time -def get_live_time_from_root(root_filename, channel: int): - """Gets live and real count time from Compass root file. +def get_live_time_from_root(root_filename: str, channel: int) -> Tuple[float, float]: + """ + Gets live and real count time from Compass root file. Live time is defined as the difference between the actual time that a count is occurring and the "dead time," in which the output of detector - pulses is saturated such that additional signals cannot be processed.""" + pulses is saturated such that additional signals cannot be processed. + + Args: + root_filename: the path to the .root file + channel: the channel number to get the live time for + + Returns: + the live count time in seconds, the real count time in seconds + """ with uproot.open(root_filename) as root_file: live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 From 995d781d3a534d85240c0e4e62413dcdd053808c Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 12:29:53 -0400 Subject: [PATCH 043/137] working example --- example.py | 9 ++++++--- .../neutron_detection/activation_foils/compass.py | 3 +-- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/example.py b/example.py index 342fa2e..bacbcbc 100644 --- a/example.py +++ b/example.py @@ -1,7 +1,10 @@ from libra_toolbox.neutron_detection.activation_foils.compass import Measurement -my_measurement = Measurement(name="check_source_from_last_friday", events=[], ch=2) +my_measurement = Measurement.from_directory( + "250317_BABY_1L_run3/DAQ/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED", + name="test", +) - -my_other_measurement = Measurement.from_directory("path_to_directory") \ No newline at end of file +print(my_measurement.start_time) +print(my_measurement.stop_time) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index bb58cee..98f02c5 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -204,9 +204,8 @@ class Measurement: name: str detectors: List[Detector] - def __init__(self, name: str, ch: int) -> None: + def __init__(self, name: str) -> None: self.name = name - self.channel_nb = ch self.detectors = [] @classmethod From 6c38398b21e9ff65d557d9096538ab95695e3f9a Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 12:33:15 -0400 Subject: [PATCH 044/137] docstrings --- .../activation_foils/compass.py | 32 +++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 98f02c5..7e36c77 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -3,7 +3,7 @@ import os from pathlib import Path import pandas as pd -from typing import Tuple, Dict, List +from typing import Tuple, Dict, List, Union import datetime import uproot import glob @@ -171,12 +171,24 @@ class Detector: real_count_time: float def __init__(self, channel_nb) -> None: + """ + Initialize a Detector object. + Args: + channel_nb: channel number of the detector + """ self.channel_nb = channel_nb self.events = np.empty((0, 2)) # Initialize as empty 2D array with 2 columns self.live_count_time = None self.real_count_time = None - def get_energy_hist(self, bins) -> Tuple[np.ndarray, np.ndarray]: + def get_energy_hist(self, bins: Union[int, str]) -> Tuple[np.ndarray, np.ndarray]: + """ + Get the energy histogram of the detector events. + Args: + bins: number of bins or "double" to use half the max energy as bin size + Returns: + Tuple of histogram values and bin edges + """ energy_values = self.events[:, 1].copy() time_values = self.events[:, 0].copy() @@ -193,6 +205,7 @@ def get_energy_hist(self, bins) -> Tuple[np.ndarray, np.ndarray]: elif bins == "double": real_bins = int(np.nanmax(energy_values) / 2) else: + # NOTE I don't think this is used real_bins = np.arange(0, np.max(energy_values)) return np.histogram(energy_values, bins=real_bins) @@ -205,11 +218,26 @@ class Measurement: detectors: List[Detector] def __init__(self, name: str) -> None: + """ + Initialize a Measurement object. + Args: + name: name of the measurement + """ + self.start_time = None + self.stop_time = None self.name = name self.detectors = [] @classmethod def from_directory(cls, source_dir: str, name: str) -> "Measurement": + """ + Create a Measurement object from a directory containing Compass data. + Args: + source_dir: directory containing Compass data + name: name of the measurement + Returns: + Measurement object + """ measurement_object = cls(name=name) # Get events From ec8de0da40d9845a6aa2f35f5d5e246fe98869a2 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 12:36:56 -0400 Subject: [PATCH 045/137] removed example file --- example.py | 10 ---------- 1 file changed, 10 deletions(-) delete mode 100644 example.py diff --git a/example.py b/example.py deleted file mode 100644 index bacbcbc..0000000 --- a/example.py +++ /dev/null @@ -1,10 +0,0 @@ -from libra_toolbox.neutron_detection.activation_foils.compass import Measurement - - -my_measurement = Measurement.from_directory( - "250317_BABY_1L_run3/DAQ/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED", - name="test", -) - -print(my_measurement.start_time) -print(my_measurement.stop_time) From 9c8e5b3e4604abf770cd905c884fe50697c6b82b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 12:48:06 -0400 Subject: [PATCH 046/137] fixed error message --- libra_toolbox/neutron_detection/activation_foils/compass.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index bf7b9f7..f181409 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -124,7 +124,9 @@ def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.dat with open(info_file, "r") as file: lines = file.readlines() else: - raise FileNotFoundError(f"Could not find run.info file in {directory}") + raise FileNotFoundError( + f"Could not find run.info file in parent directory {Path(directory).parent}" + ) start_time, stop_time = None, None for line in lines: From 1dee84895f3bd3cd11921758006807312c09ba4e Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 12:52:14 -0400 Subject: [PATCH 047/137] added test --- ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 11 +++++++ ..._Co60_0_872uCi_19Mar2014_250318_run2_1.csv | 4 +++ .../data/Hcompass_Co60_20241107.root | Bin 0 -> 83399 bytes .../complete_measurement/run.info | 31 ++++++++++++++++++ test/neutron_detection/test_compass.py | 13 ++++++++ 5 files changed, 59 insertions(+) create mode 100644 test/neutron_detection/compass_test_data/complete_measurement/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv create mode 100644 test/neutron_detection/compass_test_data/complete_measurement/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv create mode 100755 test/neutron_detection/compass_test_data/complete_measurement/data/Hcompass_Co60_20241107.root create mode 100644 test/neutron_detection/compass_test_data/complete_measurement/run.info diff --git a/test/neutron_detection/compass_test_data/complete_measurement/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/complete_measurement/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv new file mode 100644 index 0000000..fadcce6 --- /dev/null +++ b/test/neutron_detection/compass_test_data/complete_measurement/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv @@ -0,0 +1,11 @@ +BOARD;CHANNEL;TIMETAG;ENERGY;ENERGYSHORT;FLAGS +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 +0;5;5997211248;375;251;0x4000 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zv(h2X`@7Pq{j<^mSN>c2mx#Bg?JM95YcFe{n$dlrgp|0r zm=r`+MfE8}To3Zf+f^KRU*#cC%}quOA_kF@mGD+`gow-QTfY*Qg8a=b3w-=QLtD>S z{gL{8pq7oBi@UX_C**$xfJlh_2MH0E5tEP*7k%aC=0%`KL{Oz5a{I%a=s%&!B?-(4 z;m`@!FQ|?0JHE2D@p60RN1^U&`^x_R`5+`Z{{sU210gZ{7liCjO!L2QgapTbWEc6H zz58F;NePM^e_n*!F8|2<$KaQs>+!G5OKd++RTY9{{=W9}3iu zY^`7Z`2rEAb$jkaC=a)nqt{E@|4-cVKX7`EuWkPiaapYY!2M5o68~FySyunR{ZD!S z#u3Z@DIYP}-}3!wA7WLiKW+5CY99iCTmKL2t=wPOAH5kOGJ!v^zlPJ-{?njpTH^Oi z#APL Date: Wed, 7 May 2025 12:54:24 -0400 Subject: [PATCH 048/137] extended test --- test/neutron_detection/test_compass.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 1d26785..ebf5cbc 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -286,5 +286,12 @@ def test_measurement_object_from_directory(): Path(__file__).parent / "compass_test_data/complete_measurement/data" ) - # RUN measurement = compass.Measurement.from_directory(test_directory, name="test") + + assert len(measurement.detectors) == 1 + assert isinstance(measurement.detectors[0], compass.Detector) + assert measurement.detectors[0].channel_nb == 1 + + assert measurement.detectors[0].events.shape[1] == 2 + + measurement.detectors[0].get_energy_hist(bins="double") From 723a3321d0fdd05554659588bedb96bb8e569ef0 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 13:07:43 -0400 Subject: [PATCH 049/137] test no root file case --- .../activation_foils/compass.py | 9 ++++-- ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 11 +++++++ ..._Co60_0_872uCi_19Mar2014_250318_run2_1.csv | 4 +++ .../complete_measurement_no_root/run.info | 31 +++++++++++++++++++ test/neutron_detection/test_compass.py | 16 +++++++--- 5 files changed, 63 insertions(+), 8 deletions(-) create mode 100644 test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv create mode 100644 test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv create mode 100644 test/neutron_detection/compass_test_data/complete_measurement_no_root/run.info diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index f181409..4c93d66 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -246,8 +246,11 @@ def from_directory(cls, source_dir: str, name: str) -> "Measurement": detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] # Get live and real count times - root_filename = glob.glob(os.path.join(source_dir, "*.root"))[0] - if not os.path.isfile(root_filename): + all_root_filenames = glob.glob(os.path.join(source_dir, "*.root")) + if len(all_root_filenames) == 1: + root_filename = all_root_filenames[0] + else: + root_filename = None print("No root file found, assuming all counts are live") for detector in detectors: @@ -255,7 +258,7 @@ def from_directory(cls, source_dir: str, name: str) -> "Measurement": (time_values[detector.channel_nb], energy_values[detector.channel_nb]) ) - if os.path.isfile(root_filename): + if root_filename: live_count_time, real_count_time = get_live_time_from_root( root_filename, detector.channel_nb ) diff --git a/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv new file mode 100644 index 0000000..fadcce6 --- /dev/null +++ b/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv @@ -0,0 +1,11 @@ +BOARD;CHANNEL;TIMETAG;ENERGY;ENERGYSHORT;FLAGS +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 +0;5;5997211248;375;251;0x4000 +0;5;6685836624;515;340;0x4000 +0;5;11116032249;568;380;0x4000 +0;5;11281099382;1;0;0x4000 +0;5;12783039350;5;0;0x4000 +0;5;18306299412;2;0;0x4000 diff --git a/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv b/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv new file mode 100644 index 0000000..7c2f11b --- /dev/null +++ b/test/neutron_detection/compass_test_data/complete_measurement_no_root/data/Data_CH1@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv @@ -0,0 +1,4 @@ +0;5;234859459;2;2;0x4000 +0;5;421999310;0;1;0x4000 +0;5;535148093;1237;810;0x4000 +0;5;1623550122;589;396;0x4000 diff --git a/test/neutron_detection/compass_test_data/complete_measurement_no_root/run.info b/test/neutron_detection/compass_test_data/complete_measurement_no_root/run.info new file mode 100644 index 0000000..b004182 --- /dev/null +++ b/test/neutron_detection/compass_test_data/complete_measurement_no_root/run.info @@ -0,0 +1,31 @@ +id=Co60_0_872uCi_19Marc2014_250319_run3 +time.start=2025/03/19 09:47:46.724-0400 +time.stop=2025/03/19 09:53:05.651-0400 +time.real=00:05:18 +board.0-14-292.readout.rate=51.149 kb/s +board.0-14-292.4.rejections.singles=0.0 +board.0-14-292.4.rejections.pileup=0.0 +board.0-14-292.4.rejections.saturation=1301.87 +board.0-14-292.4.rejections.energy=0.0 +board.0-14-292.4.rejections.psd=0.0 +board.0-14-292.4.rejections.timedistribution=0.0 +board.0-14-292.4.throughput=2627.31 +board.0-14-292.4.icr=3059.24 +board.0-14-292.4.ocr=1293.91 +board.0-14-292.4.calibration.energy.c0=0.0 +board.0-14-292.4.calibration.energy.c1=1.0 +board.0-14-292.4.calibration.energy.c2=0.0 +board.0-14-292.4.calibration.energy.uom=keV +board.0-14-292.5.rejections.singles=0.0 +board.0-14-292.5.rejections.pileup=0.0 +board.0-14-292.5.rejections.saturation=717.247 +board.0-14-292.5.rejections.energy=0.0 +board.0-14-292.5.rejections.psd=0.0 +board.0-14-292.5.rejections.timedistribution=0.0 +board.0-14-292.5.throughput=1694.65 +board.0-14-292.5.icr=1703.71 +board.0-14-292.5.ocr=984.476 +board.0-14-292.5.calibration.energy.c0=0.0 +board.0-14-292.5.calibration.energy.c1=1.0 +board.0-14-292.5.calibration.energy.c2=0.0 +board.0-14-292.5.calibration.energy.uom=keV diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index ebf5cbc..c3e6466 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -277,14 +277,20 @@ def test_get_live_time_from_root(root_filename, channel, live_time, real_time): assert real_time_out == real_time -def test_measurement_object_from_directory(): +@pytest.mark.parametrize("no_root", [True, False]) +def test_measurement_object_from_directory(no_root): """ Test the Measurement object creation from a directory. """ - - test_directory = ( - Path(__file__).parent / "compass_test_data/complete_measurement/data" - ) + if no_root: + test_directory = ( + Path(__file__).parent + / "compass_test_data/complete_measurement_no_root/data" + ) + else: + test_directory = ( + Path(__file__).parent / "compass_test_data/complete_measurement/data" + ) measurement = compass.Measurement.from_directory(test_directory, name="test") From 9edff035af7dffc6b91f3225d8912e30667089c6 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 13:20:39 -0400 Subject: [PATCH 050/137] test for different bins --- .../activation_foils/compass.py | 13 ++++---- test/neutron_detection/test_compass.py | 30 +++++++++++++++++++ 2 files changed, 36 insertions(+), 7 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 4c93d66..78687c4 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -159,7 +159,7 @@ def get_live_time_from_root(root_filename: str, channel: int) -> Tuple[float, fl class Detector: - events: NDArray[Tuple[float, float]] # type: ignore # Array of (time, energy) pairs + events: NDArray[Tuple[float, float]] # type: ignore # Array of (time in ps, energy) pairs channel_nb: int live_count_time: float real_count_time: float @@ -175,7 +175,9 @@ def __init__(self, channel_nb) -> None: self.live_count_time = None self.real_count_time = None - def get_energy_hist(self, bins: Union[int, str]) -> Tuple[np.ndarray, np.ndarray]: + def get_energy_hist( + self, bins: Union[int, str, NDArray[np.float64]] + ) -> Tuple[np.ndarray, np.ndarray]: """ Get the energy histogram of the detector events. Args: @@ -186,21 +188,18 @@ def get_energy_hist(self, bins: Union[int, str]) -> Tuple[np.ndarray, np.ndarray energy_values = self.events[:, 1].copy() time_values = self.events[:, 0].copy() + # sort data based on timestamp inds = np.argsort(time_values) time_values = time_values[inds] energy_values = energy_values[inds] - # print(np.nanmax(energy_values[source])) energy_values = np.nan_to_num(energy_values, nan=0) - if isinstance(bins, int): + if isinstance(bins, (np.ndarray, int)): real_bins = bins elif bins == "double": real_bins = int(np.nanmax(energy_values) / 2) - else: - # NOTE I don't think this is used - real_bins = np.arange(0, np.max(energy_values)) return np.histogram(energy_values, bins=real_bins) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index c3e6466..0bcfabd 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -301,3 +301,33 @@ def test_measurement_object_from_directory(no_root): assert measurement.detectors[0].events.shape[1] == 2 measurement.detectors[0].get_energy_hist(bins="double") + + +@pytest.mark.parametrize( + "bins", + [ + 10, + 20, + 50, + 100, + "double", + np.arange(0, 10, 1), + np.linspace(0, 10, num=100), + ], +) +def test_detector_get_energy_hist(bins): + """ + Test the get_energy_hist method of the Detector class. + """ + my_detector = compass.Detector(channel_nb=1) + my_detector.events = np.array( + [ + [1, 2], + [3, 4], + [5, 6], + [7, 8], + [9, 10], + ] + ) + + my_detector.get_energy_hist(bins=bins) From 86add5ecdeb46fe475f5ade6b25a9343ba42709c Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 14:43:58 -0400 Subject: [PATCH 051/137] black --- .../activation_foils/calibration.py | 73 +++++++++++-------- 1 file changed, 43 insertions(+), 30 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 904cbac..473c2ac 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -1,39 +1,52 @@ import datetime -def get_decay_lines(nuclides:list[str])->dict: - """ Creates dictionary of check source data - given a list of check source nuclides. """ + +def get_decay_lines(nuclides: list[str]) -> dict: + """Creates dictionary of check source data + given a list of check source nuclides.""" # energy is the gamma energy in units of eV # intensity is the percentage of decays that result in this energy gamma - all_decay_lines = {'Ba133':{'energy':[80.9979, 276.3989, 302.8508, 356.0129, 383.8485], - 'intensity':[0.329, 0.0716, 0.1834, 0.6205, 0.0894], - 'half_life':[10.551*365.25*24*3600], - 'activity_date':datetime.date(2014, 3, 19), - 'activity':1 * 3.7e4}, - 'Co60':{'energy':[1173.228, 1332.492], - 'intensity':[0.9985, 0.999826], - 'half_life':[1925.28*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity':0.872 * 3.7e4}, - 'Na22':{'energy':[511, 1274.537], - 'intensity':[1.80, 0.9994], - 'half_life':[2.6018*365.25*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity': 5 * 3.7e4}, - 'Cs137':{'energy':[661.657], - 'intensity':[0.851], - 'half_life':[30.08*365.25*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity':4.66 * 3.7e4}, - 'Mn54':{'energy':[834.848], - 'intensity':[0.99976], - 'half_life':[312.20*24*3600], - 'actvity_date':datetime.date(2016, 5, 2), - 'activity':6.27 * 3.7e4}} + all_decay_lines = { + "Ba133": { + "energy": [80.9979, 276.3989, 302.8508, 356.0129, 383.8485], + "intensity": [0.329, 0.0716, 0.1834, 0.6205, 0.0894], + "half_life": [10.551 * 365.25 * 24 * 3600], + "activity_date": datetime.date(2014, 3, 19), + "activity": 1 * 3.7e4, + }, + "Co60": { + "energy": [1173.228, 1332.492], + "intensity": [0.9985, 0.999826], + "half_life": [1925.28 * 24 * 3600], + "actvity_date": datetime.date(2014, 3, 19), + "activity": 0.872 * 3.7e4, + }, + "Na22": { + "energy": [511, 1274.537], + "intensity": [1.80, 0.9994], + "half_life": [2.6018 * 365.25 * 24 * 3600], + "actvity_date": datetime.date(2014, 3, 19), + "activity": 5 * 3.7e4, + }, + "Cs137": { + "energy": [661.657], + "intensity": [0.851], + "half_life": [30.08 * 365.25 * 24 * 3600], + "actvity_date": datetime.date(2014, 3, 19), + "activity": 4.66 * 3.7e4, + }, + "Mn54": { + "energy": [834.848], + "intensity": [0.99976], + "half_life": [312.20 * 24 * 3600], + "actvity_date": datetime.date(2016, 5, 2), + "activity": 6.27 * 3.7e4, + }, + } decay_lines = {} for nuclide in nuclides: if nuclide in all_decay_lines.keys(): decay_lines[nuclide] = all_decay_lines[nuclide] else: - raise ValueError(f'{nuclide} not yet added to get_decay_lines()') - return decay_lines \ No newline at end of file + raise ValueError(f"{nuclide} not yet added to get_decay_lines()") + return decay_lines From c05f5080e558a780e687481a356645e6305abdf5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 16:38:19 -0400 Subject: [PATCH 052/137] background substraction initial --- .../activation_foils/compass.py | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 78687c4..f86532b 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -203,6 +203,36 @@ def get_energy_hist( return np.histogram(energy_values, bins=real_bins) + def get_energy_hist_background_substract( + self, + background_detector: "Detector", + bins: Union[int, str, NDArray[np.float64]], + ) -> Tuple[np.ndarray, np.ndarray]: + + raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) + background_times = background_detector.events[:, 0].copy() + background_energies = background_detector.events[:, 1].copy() + + if self.real_count_time < background_detector.real_count_time: + # get background counts for the duration of the sample count + + end_ind = np.nanargmin( + np.abs(self.real_count_time - (background_times - background_times[0])) + ) + b_hist, b_edges = np.histogram( + background_energies[: end_ind + 1], + bins=raw_bin_edges, + ) + else: + b_hist, b_edges = np.histogram(background_energies, bins=raw_bin_edges) + b_hist = b_hist * ( + self.real_count_time / background_detector.real_count_time + ) + + hist_background_substracted = raw_hist - b_hist + + return hist_background_substracted, raw_bin_edges + class Measurement: start_time: datetime.datetime From 899d106a6a92d467b3325be47d8287cb7c974cc5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 16:43:13 -0400 Subject: [PATCH 053/137] info file can be optional --- .../activation_foils/compass.py | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 78687c4..7027608 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -8,6 +8,8 @@ import uproot import glob +import warnings + def get_channel(filename): """ @@ -222,12 +224,16 @@ def __init__(self, name: str) -> None: self.detectors = [] @classmethod - def from_directory(cls, source_dir: str, name: str) -> "Measurement": + def from_directory( + cls, source_dir: str, name: str, info_file_optional: bool = False + ) -> "Measurement": """ Create a Measurement object from a directory containing Compass data. Args: source_dir: directory containing Compass data name: name of the measurement + info_file_optional: if True, the function will not raise an error + if the run.info file is not found Returns: Measurement object """ @@ -237,9 +243,15 @@ def from_directory(cls, source_dir: str, name: str) -> "Measurement": time_values, energy_values = get_events(source_dir) # Get start and stop time - start_time, stop_time = get_start_stop_time(source_dir) - measurement_object.start_time = start_time - measurement_object.stop_time = stop_time + try: + start_time, stop_time = get_start_stop_time(source_dir) + measurement_object.start_time = start_time + measurement_object.stop_time = stop_time + except FileNotFoundError: + if info_file_optional: + warnings.warn( + "run.info file not found. Assuming start and stop time are not needed." + ) # Create detectors detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] From be29ab53a35e3aff11fafc578e11ab232672445f Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 16:50:23 -0400 Subject: [PATCH 054/137] added old peak fitting functions --- .../activation_foils/peak_fitting.py | 441 ++++++++++++++++++ 1 file changed, 441 insertions(+) create mode 100644 libra_toolbox/neutron_detection/activation_foils/peak_fitting.py diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py new file mode 100644 index 0000000..2eab453 --- /dev/null +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -0,0 +1,441 @@ +import numpy as np +from scipy.signal import find_peaks +from scipy.optimize import curve_fit + +from typing import List, Dict +from libra_toolbox.neutron_detection.activation_foils.compass import ( + Detector, + Measurement, +) + + +def get_peak_inputs(samples): + default_inputs = { + "Na22": {"prom_factor": 0.075, "width": [10, 150], "start_index": 100}, + "Co60": {"prom_factor": 0.2, "width": [10, 150], "start_index": 400}, + "Ba133": {"prom_factor": 0.1, "width": [10, 200], "start_index": 100}, + "Mn54": {"prom_factor": 0.2, "width": [10, 100], "start_index": 100}, + } + + defaults = {"prom_factor": 0.075, "width": [10, 150], "start_index": 100} + + peak_inputs = {} + for sample in samples: + if sample in default_inputs.keys(): + peak_inputs[sample] = default_inputs[sample] + else: + peak_inputs[sample] = defaults + return peak_inputs + + +def get_peaks(hist, source): + start_index = 100 + prominence = 0.10 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + width = [10, 150] + indices = None + distance = 30 + if "na22" in source.lower(): + # find 511 keV peak first + prominence = 0.01 * np.max(hist[start_index:]) + height = 0.9 * np.max(hist[start_index:]) + width = [10, 200] + elif "co60" in source.lower(): + start_index = 400 + height = 0.60 * np.max(hist[start_index:]) + prominence = None + elif "ba133" in source.lower(): + width = [10, 200] + elif "mn54" in source.lower(): + height = 0.6 * np.max(hist[start_index:]) + peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + peaks = np.array(peaks) + start_index + if "na22" in source.lower(): + # Find 1275 keV peak + peak_511 = peaks[0] + start_index = peak_511 + 100 + prominence = 0.5 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + + high_peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + high_peaks = np.array(high_peaks) + start_index + peaks = [peak_511, high_peaks[0]] + + if indices: + peaks = peaks[[indices]][0] + + return peaks + + +def calibrate_counts_old(counts, decay_lines): + + calibration_energies = {} + calibration_channels = {} + coeff = {} + + # find what digitizer channels were used (ex. Ch0 and Ch1) + ch_keys = counts[list(counts.keys())[0]].keys() + for ch in ch_keys: + calibration_energies[ch] = [] + calibration_channels[ch] = [] + + for sample in decay_lines.keys(): + # print('\n', sample) + if "channel" in decay_lines[sample].keys(): + + # If the peak chanels are already included in decay_lines, use them. + calibration_channels[ch] += decay_lines[sample]["channel"] + calibration_energies[ch] += decay_lines[sample]["energy"] + else: + peaks = get_peaks(counts[sample][ch]["hist"], sample) + # print(ch, sample, peaks) + if len(peaks) != len(decay_lines[sample]["energy"]): + raise LookupError( + "SciPy find_peaks() found {} photon peaks, while {} were expected".format( + len(peaks), len(decay_lines[sample]["energy"]) + ) + ) + calibration_channels[ch] += list(peaks) + calibration_energies[ch] += decay_lines[sample]["energy"] + + # print('Channel: ', calibration_channels[-1], ', Energy: ', calibration_energies[-1]) + inds = np.argsort(calibration_channels[ch]) + calibration_channels[ch] = np.array(calibration_channels[ch])[inds] + calibration_energies[ch] = np.array(calibration_energies[ch])[inds] + + # print(ch) + # print(calibration_channels[ch]) + # print(calibration_energies[ch]) + + # linear fit for calibration curve + coeff[ch] = np.polyfit(calibration_channels[ch], calibration_energies[ch], 1) + + for source in counts.keys(): + counts[source][ch]["calibrated_bin_edges"] = np.polyval( + coeff[ch], counts[source][ch]["bin_edges"] + ) + + return counts, coeff + + +def calibrate_counts(measurements: List[Measurement], decay_lines): + + calibration_energies = {} + calibration_channels = {} + coeff = {} + + for measurement in measurements: + print(measurement.name) + # find what digitizer channels were used (ex. Ch0 and Ch1) + for detector in measurement.detectors: + calibration_energies[detector.channel_nb] = [] + calibration_channels[detector.channel_nb] = [] + + sample = measurement.name[:-2] + print(detector.channel_nb, sample) + hist, _ = detector.get_energy_hist(bins="double") + peaks = get_peaks(hist, sample) + + if len(peaks) != len(decay_lines[sample]["energy"]): + raise ValueError( + f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(decay_lines[sample]["energy"])} were expected" + ) + calibration_channels[detector.channel_nb] += list(peaks) + calibration_energies[detector.channel_nb] += decay_lines[sample]["energy"] + + inds = np.argsort(calibration_channels[detector.channel_nb]) + calibration_channels[detector.channel_nb] = np.array( + calibration_channels[detector.channel_nb] + )[inds] + calibration_energies[detector.channel_nb] = np.array( + calibration_energies[detector.channel_nb] + )[inds] + + # linear fit for calibration curve + coeff[detector.channel_nb] = np.polyfit( + calibration_channels[detector.channel_nb], + calibration_energies[detector.channel_nb], + 1, + ) + print(coeff) + + return coeff + + +def gauss1(x, H, m, A, x0, sigma): + return H + m * x + A * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) + + +def gauss2(x, H, m, A1, x1, sigma1, A2, x2, sigma2): + out = ( + H + + m * x + + A1 * np.exp(-((x - x1) ** 2) / (2 * sigma1**2)) + + A2 * np.exp(-((x - x2) ** 2) / (2 * sigma2**2)) + ) + return out + + +def gauss(x, b, m, *args): + """Creates a multipeak gaussian with a linear addition of the form: + m * x + b + Sum_i (A_i * exp(-(x - x_i)**2) / (2 * sigma_i**2)""" + + out = m * x + b + if np.mod(len(args), 3) == 0: + for i in range(int(len(args) / 3)): + out += args[i * 3 + 0] * np.exp( + -((x - args[i * 3 + 1]) ** 2) / (2 * args[i * 3 + 2] ** 2) + ) + else: + raise ValueError("Incorrect number of gaussian arguments given.") + return out + + +def get_singlepeak_area(hist, bins, peak_erg, search_width=300): + + # get midpoints of every bin + xvals = np.diff(bins) / 2 + bins[:-1] + + peak_ind = np.argmin(np.abs((peak_erg) - xvals)) + search_start = np.argmin(np.abs((peak_erg - search_width / 2) - xvals)) + search_end = np.argmin(np.abs((peak_erg + search_width / 2) - xvals)) + + slope_guess = (hist[search_end] - hist[search_start]) / ( + xvals[search_end] - xvals[search_start] + ) + + guess_parameters = [0, slope_guess, hist[peak_ind], peak_erg, search_width / 6] + # print(guess_parameters) + + parameters, covariance = curve_fit( + gauss1, + xvals[search_start:search_end], + hist[search_start:search_end], + p0=guess_parameters, + ) + # print(parameters) + + mean = parameters[3] + sigma = parameters[4] + + peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) + peak_end = np.argmin(np.abs((mean + 3 * sigma) - xvals)) + + gross_area = np.trapezoid(hist[peak_start:peak_end], x=xvals[peak_start:peak_end]) + # trap_cutoff_area = (hist[peak_start] + hist[peak_end])/2 * (bins[peak_end] - bins[peak_start]) + trap_cutoff_area = np.trapezoid( + parameters[0] + parameters[1] * xvals[peak_start:peak_end], + x=xvals[peak_start:peak_end], + ) + area = gross_area - trap_cutoff_area + + return area + + +def get_multipeak_area(hist, bins, peak_ergs, search_width=600): + # get midpoints of every bin + xvals = np.diff(bins) / 2 + bins[:-1] + + search_start = np.argmin( + np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals) + ) + search_end = np.argmin( + np.abs((peak_ergs[-1] + search_width / (2 * len(peak_ergs))) - xvals) + ) + + guess_slope = (hist[search_end] - hist[search_start]) / ( + xvals[search_end] - xvals[search_start] + ) + + guess_parameters = [0, guess_slope] + + for i in range(len(peak_ergs)): + peak_ind = np.argmin(np.abs((peak_ergs[i]) - xvals)) + guess_parameters += [ + hist[peak_ind], + peak_ergs[i], + search_width / (3 * len(peak_ergs)), + ] + + # print(guess_parameters) + + parameters, covariance = curve_fit( + gauss, + xvals[search_start:search_end], + hist[search_start:search_end], + p0=guess_parameters, + ) + # print(parameters) + + areas = [] + peak_starts = [] + peak_ends = [] + all_peak_params = [] + peak_amplitudes = [] + for i in range(len(peak_ergs)): + peak_amplitudes += [parameters[2 + 3 * i]] + mean = parameters[2 + 3 * i + 1] + sigma = np.abs(parameters[2 + 3 * i + 2]) + peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) + peak_end = np.argmin(np.abs((mean + 3 * sigma) - xvals)) + + peak_starts += [peak_start] + peak_ends += [peak_end] + + # Use unimodal gaussian to estimate counts from just one peak + peak_params = [parameters[0], parameters[1], parameters[2 + 3 * i], mean, sigma] + all_peak_params += [peak_params] + gross_area = np.trapezoid( + gauss(xvals[peak_start:peak_end], *peak_params), + x=xvals[peak_start:peak_end], + ) + + # Cut off trapezoidal area due to compton scattering and noise + trap_cutoff_area = np.trapezoid( + parameters[0] + parameters[1] * xvals[peak_start:peak_end], + x=xvals[peak_start:peak_end], + ) + area = gross_area - trap_cutoff_area + areas += [area] + + return areas + + +def group_close_values(data, threshold=200): + # Sort the data to group values sequentially + data.sort() + + # Initialize groups and a temporary group + groups = [] + temp_group = [data[0]] + + for i in range(1, len(data)): + # Check if the current value is within the threshold of the last value in the temp group + if abs(data[i] - temp_group[-1]) < threshold: + temp_group.append(data[i]) + else: + # Commit the temp group to groups and start a new group + groups.append(tuple(temp_group)) + temp_group = [data[i]] + + # Add the last group + groups.append(tuple(temp_group)) + + return groups + + +def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): + + areas = [] + # organize peak energies into tuples, in which peak energies close enough + # to have overlapping peaks will be paired together + erg_groups = group_close_values(peak_ergs, threshold=overlap_width) + # print(erg_groups) + + for erg_group in erg_groups: + areas += get_multipeak_area( + hist, bins, erg_group, search_width=len(erg_group) * search_width + ) + # print(areas) + return areas + + +def energy_efficiency( + counts, + decay_lines, + nuclides=None, + overlap_width=200, + search_width=400, + degree=2, + count_sum_peak=False, +): + if nuclides is None: + nuclides = decay_lines.keys() + + effs = {} + eff_errs = {} + energies = {} + for nuc in nuclides: + sum_peak = False + if len(decay_lines[nuc]["energy"]) > 1 and count_sum_peak: + peak_energies = decay_lines[nuc]["energy"] + [ + np.sum(decay_lines[nuc]["energy"]) + ] + sum_peak = True + else: + peak_energies = decay_lines[nuc]["energy"] + for ch in counts[nuc].keys(): + # initialize efficiency list + if ch not in effs.keys(): + effs[ch] = [] + eff_errs[ch] = [] + energies[ch] = [] + + # print(nuc, ' Ch ', ch ) + areas = get_peak_areas( + counts[nuc][ch]["hist"], + counts[nuc][ch]["calibrated_bin_edges"], + peak_energies, + overlap_width=overlap_width, + search_width=search_width, + ) + if sum_peak: + areas = np.array(areas)[:-1] + areas[-1] / len(areas[:-1]) + # print('Peak areas: ', areas) + # measured activity + # I think this should be divided by live count time, but maybe it should + # be divided by real count time?? Should go over this again + act_meas = np.array(areas) / ( + np.array(decay_lines[nuc]["intensity"]) + * counts[nuc][ch]["live_count_time"] + ) + # print('Activity measured: ', act_meas) + act_meas_err = np.sqrt(np.array(areas)) / ( + np.array(decay_lines[nuc]["intensity"]) + * counts[nuc][ch]["live_count_time"] + ) + # expected activity + l = np.log(2) / decay_lines[nuc]["half_life"] + # print('decay constant: ', l) + time = ( + counts[nuc][ch]["start_time"] - decay_lines[nuc]["activity_date"] + ).total_seconds() + # print('count time: ', time) + act_expec = decay_lines[nuc]["activity"] * np.exp(-l * time) + # print('Activity expected: ', act_expec) + # print('efficiency: ', act_meas/act_expec) + + effs[ch] += list(act_meas / act_expec) + eff_errs[ch] += list(act_meas_err / act_expec) + energies[ch] += decay_lines[nuc]["energy"] + + # Sort the data + # print('effs: ', effs) + # print('energies: ', energies) + coeff = {} + bounds = {} + for ch in effs.keys(): + ind = np.argsort(energies[ch]) + energies[ch] = np.array(energies[ch])[ind] + effs[ch] = np.array(effs[ch])[ind] + eff_errs[ch] = np.array(eff_errs[ch])[ind] + + # Create polynomial fit + coeff[ch] = np.polyfit(energies[ch], effs[ch], degree) + + # Get bounds of fit for interpolation + bounds[ch] = [np.min(energies[ch]), np.max(energies[ch])] + + return effs, eff_errs, coeff, bounds From 0923cb9a3a37ce76bbc931a82af7340e65cc8e78 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 17:03:13 -0400 Subject: [PATCH 055/137] raise error if not optional --- libra_toolbox/neutron_detection/activation_foils/compass.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 7027608..a5a1650 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -247,11 +247,13 @@ def from_directory( start_time, stop_time = get_start_stop_time(source_dir) measurement_object.start_time = start_time measurement_object.stop_time = stop_time - except FileNotFoundError: + except FileNotFoundError as e: if info_file_optional: warnings.warn( "run.info file not found. Assuming start and stop time are not needed." ) + else: + raise FileNotFoundError(e) # Create detectors detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] From 6719adc8dbcb8f7b79c74e8f87fd2a5f48920634 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 17:21:51 -0400 Subject: [PATCH 056/137] fixed background substraction --- .../activation_foils/compass.py | 59 +++++++++++++++++-- 1 file changed, 54 insertions(+), 5 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 9049d30..40e5d3d 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -210,23 +210,25 @@ def get_energy_hist_background_substract( background_detector: "Detector", bins: Union[int, str, NDArray[np.float64]], ) -> Tuple[np.ndarray, np.ndarray]: - + ps_to_seconds = 1e-12 raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) background_times = background_detector.events[:, 0].copy() background_energies = background_detector.events[:, 1].copy() if self.real_count_time < background_detector.real_count_time: # get background counts for the duration of the sample count - end_ind = np.nanargmin( - np.abs(self.real_count_time - (background_times - background_times[0])) + np.abs( + self.real_count_time / ps_to_seconds + - (background_times - background_times[0]) + ) ) - b_hist, b_edges = np.histogram( + b_hist, _ = np.histogram( background_energies[: end_ind + 1], bins=raw_bin_edges, ) else: - b_hist, b_edges = np.histogram(background_energies, bins=raw_bin_edges) + b_hist, _ = np.histogram(background_energies, bins=raw_bin_edges) b_hist = b_hist * ( self.real_count_time / background_detector.real_count_time ) @@ -322,3 +324,50 @@ def from_directory( measurement_object.detectors = detectors return measurement_object + + +def subtract_background(counts, background_directory, savefile=None): + # Check if background subtracted counts have already been saved + if savefile: + if os.path.isfile(savefile): + with open(savefile, "rb") as file: + counts = pickle.load(file) + return counts + times, energies = get_events(background_directory) + b_count_time = {} + for ch in times.keys(): + if os.path.isfile(os.path.join(background_directory, "run.info")): + start_time, stop_time = get_start_stop_time(background_directory) + b_count_time[ch] = (stop_time - start_time) * 1e12 + else: + b_count_time[ch] = times[ch][-1] - times[ch][0] + print(b_count_time) + for sample in counts.keys(): + for ch in counts[sample].keys(): + if counts[sample][ch]["real_count_time"] * 1e12 < b_count_time[ch]: + # get background counts for the duration of the sample count + end_ind = np.nanargmin( + np.abs( + counts[sample][ch]["real_count_time"] * 1e12 + - (times[ch] - times[ch][0]) + ) + ) + b_hist, b_edges = np.histogram( + energies[ch][: end_ind + 1], bins=counts[sample][ch]["bin_edges"] + ) + else: + b_hist, b_edges = np.histogram( + energies[ch], bins=counts[sample][ch]["bin_edges"] + ) + print( + "Sample count time: ", counts[sample][ch]["real_count_time"] * 1e12 + ) + print("Background count time: ", b_count_time[ch]) + b_hist = b_hist * ( + counts[sample][ch]["real_count_time"] * 1e12 / b_count_time[ch] + ) + counts[sample][ch]["hist"] = counts[sample][ch]["hist"] - b_hist + if savefile: + with open(savefile, "wb") as file: + pickle.dump(counts, file) + return counts From 975a028b37e05cbd192eb7f67f3def5fe460e02b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 17:36:16 -0400 Subject: [PATCH 057/137] calibrate per channel nb --- .../activation_foils/peak_fitting.py | 64 ++++++++++--------- 1 file changed, 35 insertions(+), 29 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index 2eab453..78cfaf2 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -130,46 +130,52 @@ def calibrate_counts_old(counts, decay_lines): return counts, coeff -def calibrate_counts(measurements: List[Measurement], decay_lines): +def calibrate_counts( + check_source_measurements: List[Measurement], + background_measurement: Measurement, + channel_nb: int, + decay_lines, +): - calibration_energies = {} - calibration_channels = {} - coeff = {} + background_detector = [ + detector + for detector in background_measurement.detectors + if detector.channel_nb == detector.channel_nb + ][0] - for measurement in measurements: - print(measurement.name) - # find what digitizer channels were used (ex. Ch0 and Ch1) + calibration_energies = [] + calibration_channels = [] + coeff = [] + + for measurement in check_source_measurements.values(): for detector in measurement.detectors: - calibration_energies[detector.channel_nb] = [] - calibration_channels[detector.channel_nb] = [] + if detector.channel_nb != channel_nb: + continue sample = measurement.name[:-2] - print(detector.channel_nb, sample) - hist, _ = detector.get_energy_hist(bins="double") + + hist, _ = detector.get_energy_hist_background_substract( + background_detector, bins="double" + ) peaks = get_peaks(hist, sample) if len(peaks) != len(decay_lines[sample]["energy"]): raise ValueError( f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(decay_lines[sample]["energy"])} were expected" ) - calibration_channels[detector.channel_nb] += list(peaks) - calibration_energies[detector.channel_nb] += decay_lines[sample]["energy"] - - inds = np.argsort(calibration_channels[detector.channel_nb]) - calibration_channels[detector.channel_nb] = np.array( - calibration_channels[detector.channel_nb] - )[inds] - calibration_energies[detector.channel_nb] = np.array( - calibration_energies[detector.channel_nb] - )[inds] - - # linear fit for calibration curve - coeff[detector.channel_nb] = np.polyfit( - calibration_channels[detector.channel_nb], - calibration_energies[detector.channel_nb], - 1, - ) - print(coeff) + calibration_channels += list(peaks) + calibration_energies += decay_lines[sample]["energy"] + + inds = np.argsort(calibration_channels) + calibration_channels = np.array(calibration_channels)[inds] + calibration_energies = np.array(calibration_energies)[inds] + + # linear fit for calibration curve + coeff = np.polyfit( + calibration_channels, + calibration_energies, + 1, + ) return coeff From f406e22e6772479b35f963910f4e68c70a544e3b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 17:39:15 -0400 Subject: [PATCH 058/137] example notebook --- example.ipynb | 478 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 478 insertions(+) create mode 100644 example.ipynb diff --git a/example.ipynb b/example.ipynb new file mode 100644 index 0000000..61933a1 --- /dev/null +++ b/example.ipynb @@ -0,0 +1,478 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from libra_toolbox.neutron_detection.activation_foils.compass import Measurement\n", + "\n", + "run_dir = \"250317_BABY_1L_run3/DAQ\"\n", + "\n", + "directories = {}\n", + "directories[\"Co60_1\"] = f\"{run_dir}/Co60_0_872uCi_19Mar2014_240317/UNFILTERED\"\n", + "directories[\"Co60_2\"] = f\"{run_dir}/Co60_0_872uCi_19Mar2014_250318_run2/UNFILTERED\"\n", + "directories[\"Co60_3\"] = f\"{run_dir}/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\"\n", + "directories[\"Co60_4\"] = f\"{run_dir}/Co60_0_872uCi_19Marc2014_250320_run4/UNFILTERED\"\n", + "directories[\"Cs137_1\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep23_240317/UNFILTERED\"\n", + "directories[\"Cs137_2\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run2/UNFILTERED\"\n", + "directories[\"Cs137_3\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run3/UNFILTERED\"\n", + "directories[\"Cs137_4\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250319_run5/UNFILTERED\"\n", + "directories['Mn54_1'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250318/UNFILTERED'\n", + "directories['Mn54_2'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250319_run2/UNFILTERED'\n", + "directories['Mn54_3'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250320_run3/UNFILTERED'\n", + "directories[\"Na22_1\"] = f\"{run_dir}/Na22_9_98uCi_29Sep23_240317/UNFILTERED\"\n", + "directories[\"Na22_2\"] = f\"{run_dir}/Na22_9_98uCi_29Sep23_240317_run2/UNFILTERED\"\n", + "directories[\"Na22_3\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run3/UNFILTERED\"\n", + "directories[\"Na22_4\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run4/UNFILTERED\"\n", + "directories[\"Na22_5\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250319_run5/UNFILTERED\"\n", + "\n", + "background_dir = f\"{run_dir}/Background_250322/UNFILTERED\"\n", + "\n", + "check_source_Co60_meas = Measurement.from_directory(\n", + " \"250317_BABY_1L_run3/DAQ/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\",\n", + " name=\"test\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Co60_1...\n", + "No root file found, assuming all counts are live\n", + "\n", + "Processing Co60_2...\n", + "\n", + "Processing Co60_3...\n", + "\n", + "Processing Co60_4...\n", + "\n", + "Processing Cs137_1...\n", + "No root file found, assuming all counts are live\n", + "\n", + "Processing Cs137_2...\n", + "\n", + "Processing Cs137_3...\n", + "\n", + "Processing Cs137_4...\n", + "\n", + "Processing Mn54_1...\n", + "\n", + "Processing Mn54_2...\n", + "\n", + "Processing Mn54_3...\n", + "\n", + "Processing Na22_1...\n", + "No root file found, assuming all counts are live\n", + "\n", + "Processing Na22_2...\n", + "No root file found, assuming all counts are live\n", + "\n", + "Processing Na22_3...\n", + "\n", + "Processing Na22_4...\n", + "\n", + "Processing Na22_5...\n", + "\n", + "Processing background...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:284: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "all_measurements = {}\n", + "\n", + "for source, directory in directories.items():\n", + " print(f\"Processing {source}...\")\n", + " meas = Measurement.from_directory(directory, name=source)\n", + " print(meas)\n", + " all_measurements[source] = meas\n", + "\n", + "print(f\"Processing background...\")\n", + "background_meas = Measurement.from_directory(\n", + " background_dir,\n", + " name=\"Background\",\n", + " info_file_optional=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from libra_toolbox.neutron_detection.activation_foils import peak_fitting\n", + "\n", + "for detector in check_source_Co60_meas.detectors:\n", + " hist, bin_edges = detector.get_energy_hist(bins=\"double\")\n", + "\n", + " plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb}\",\n", + " )\n", + " peaks = peak_fitting.get_peaks(hist, source=\"Co60_0_872uCi_19Marc2014_250319_run3\")\n", + " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", + "\n", + " from scipy.signal import find_peaks\n", + " import numpy as np\n", + "\n", + " start_index = 400\n", + " height = 0.60 * np.max(hist[start_index:])\n", + " prominence = None\n", + " width = [10, 150]\n", + " distance = 30\n", + " peaks, peak_data = find_peaks(\n", + " hist[start_index:],\n", + " prominence=prominence,\n", + " height=height,\n", + " width=width,\n", + " distance=distance,\n", + " )\n", + " plt.plot(bin_edges[start_index:][peaks], peak_data[\"peak_heights\"], \".\", ms=10)\n", + "\n", + " for i, p in enumerate(peaks):\n", + " width = peak_data[\"widths\"][i]\n", + " plt.axvspan(\n", + " bin_edges[start_index:][p] - width,\n", + " bin_edges[start_index:][p] + width,\n", + " color=\"red\",\n", + " alpha=0.2,\n", + " label=\"Peak range\",\n", + " )\n", + "\n", + "plt.legend()\n", + "# plt.yscale(\"log\")\n", + "plt.ylim(top=2100)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\n", + "\n", + "\n", + "plt.sca(axs[0])\n", + "hist, bin_edges = background_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"background\",\n", + ")\n", + "plt.ylim(top=2100)\n", + "plt.ylabel(\"Counts\")\n", + "plt.legend()\n", + "\n", + "plt.sca(axs[1])\n", + "\n", + "\n", + "background_time = background_meas.detectors[1].real_count_time\n", + "bg_hist_scale = hist * check_source_Co60_meas.detectors[1].real_count_time / background_time \n", + "plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=bg_hist_scale,\n", + " histtype=\"step\",\n", + " label=f\"background\",\n", + ")\n", + "plt.ylim(top=100)\n", + "plt.ylabel(\"Counts (rescaled)\")\n", + "plt.legend()\n", + "\n", + "plt.sca(axs[2])\n", + "\n", + "hist, bin_edges = check_source_Co60_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb} - raw\",\n", + ")\n", + "\n", + "\n", + "background_detector = background_meas.detectors[1]\n", + "\n", + "hist_background_substracted, bin_edges_bg_sub = check_source_Co60_meas.detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges_bg_sub[:-1],\n", + " bins=bin_edges_bg_sub,\n", + " weights=hist_background_substracted,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb} - background substracted\",\n", + ")\n", + "plt.ylabel(\"Counts\")\n", + "\n", + "plt.legend()\n", + "# plt.yscale(\"log\")\n", + "plt.ylim(top=600)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_863944/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " plt.legend()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\n", + "\n", + "\n", + "plt.sca(axs[0])\n", + "hist, bin_edges = background_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"background\",\n", + ")\n", + "plt.ylim(top=2100)\n", + "plt.ylabel(\"Counts\")\n", + "plt.legend()\n", + "\n", + "plt.sca(axs[1])\n", + "\n", + "\n", + "# background_time = background_meas.detectors[1].real_count_time\n", + "# bg_hist_scale = hist * all_measurements[\"Mn54_1\"].detectors[1].real_count_time / background_time \n", + "# plt.hist(\n", + "# bin_edges[:-1],\n", + "# bins=bin_edges,\n", + "# weights=bg_hist_scale,\n", + "# histtype=\"step\",\n", + "# label=f\"background\",\n", + "# )\n", + "plt.ylim(top=100)\n", + "plt.ylabel(\"Counts (rescaled)\")\n", + "plt.legend()\n", + "\n", + "plt.sca(axs[2])\n", + "\n", + "hist, bin_edges = all_measurements[\"Mn54_1\"].detectors[1].get_energy_hist(bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb} - raw\",\n", + ")\n", + "\n", + "\n", + "background_detector = background_meas.detectors[1]\n", + "\n", + "hist_background_substracted, bin_edges_bg_sub = all_measurements[\"Mn54_1\"].detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", + "\n", + "plt.hist(\n", + " bin_edges_bg_sub[:-1],\n", + " bins=bin_edges_bg_sub,\n", + " weights=hist_background_substracted,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb} - background substracted\",\n", + ")\n", + "plt.ylabel(\"Counts\")\n", + "\n", + "plt.legend()\n", + "# plt.yscale(\"log\")\n", + "plt.ylim(bottom=0, top=1500)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from libra_toolbox.neutron_detection.activation_foils.calibration import get_decay_lines\n", + "\n", + "decay_lines = get_decay_lines([\"Co60\", \"Cs137\", \"Mn54\", \"Na22\"])\n", + "\n", + "\n", + "coeff_4 = peak_fitting.calibrate_counts(\n", + " all_measurements,\n", + " background_measurement=background_meas,\n", + " decay_lines=decay_lines,\n", + " channel_nb=4,\n", + ")\n", + "\n", + "coeff_5 = peak_fitting.calibrate_counts(\n", + " all_measurements,\n", + " background_measurement=background_meas,\n", + " decay_lines=decay_lines,\n", + " channel_nb=5,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Mw+CDXw4y6ZvdFFht1Az0Zmb/aNrUrmp2aU5FgUhERMQkWecLGf3JVpbvSAXgjuYhTL0/igAfd5Mrcz4KRCIiIiZIPpJJ/IJEjp45j7urhee6NWNgh7oaIjOJApGIiEg5MgyD934+wKvf7qbIZlA7yIdZ/aNpVSvQ7NKcmgKRiIhIOcnMLeCZJVv4flc6AN0iQ3m1Vyv8vTREZjYFIhERkXKw+VAGQxYkcTwrDw83F8bd1ZwH29fWEJmDUCASEREpQzabwdy1+5m6IgWrzaBedV9m9Y+mRXiA2aXJHygQiYiIlJHTOfmMXLKFH1NOAtAjKpxJ90Xi56mvX0ejn4iIiEgZSNh/mqGLkkjLzsfTzYUJPVrQ57oIDZE5KAUiERGRUmS1Gbz9wz7e/H4PNgMa1PBldmwbmob6m12a/AUFIhERkVJy8mw+Ty9O5ud9pwC4r01NXu7ZEl8NkTk8/YRERERKwS/7TjFscTInz+bj7e7KP3u24IF2EWaXJSXkYubK16xZw9133014eDgWi4WlS5fa2woLCxk9ejSRkZH4+voSHh7Oww8/zPHjx4u9R0ZGBrGxsfj7+xMYGMjgwYPJyckp1mfr1q3cdNNNeHl5ERERwZQpU8pj80RExAlYbQZvrNxD7HsJnDybT+MQP76M76gwVMGYGojOnTtHVFQUs2fPvqQtNzeXxMRExo0bR2JiIp999hkpKSn06NGjWL/Y2Fh27NjBypUrWbZsGWvWrOGxxx6zt2dnZ3PHHXdQp04dNm/ezNSpU3nppZeYO3dumW+fiIhUbmnZecT++1dmrNqLYUCfdhF8EXcjjUKqmF2aXCGLYRiG2UUAWCwWPv/8c+65554/7bNx40auv/56Dh06RO3atdm1axfNmzdn48aNtGvXDoDly5fTrVs3jh49Snh4OHPmzOH5558nNTUVDw8PAMaMGcPSpUvZvXv3ZdeTn59Pfn6+fTo7O5uIiAiysrLw99dJcSIiAmv2nOTpxcmcPleAj4crk+6N5J7ommaXJX+QnZ1NQEBAib6/TT1CdKWysrKwWCwEBgYCsH79egIDA+1hCKBz5864uLiQkJBg73PzzTfbwxBAly5dSElJ4cyZM5ddz+TJkwkICLC/IiJ02FNERH5XZLUxZfluHv7PBk6fK6BZmD/LhtyoMFTBVZhAlJeXx+jRo+nXr5895aWmphIcHFysn5ubG0FBQaSmptr7hISEFOtzYfpCn4uNHTuWrKws++vIkSOlvTkiIlIBncg6T79//crbP/4GQP/2tfn8qQ7Ur+FncmVyrSrEVWaFhYX07t0bwzCYM2dOma/P09MTT0/PMl+PiIhUHD/sTmfEx8mcyS3Ez9ONV3tFclercLPLklLi8IHoQhg6dOgQq1evLjYGGBoaSnp6erH+RUVFZGRkEBoaau+TlpZWrM+F6Qt9RERE/kyh1ca0FSm8u2Y/AC1r+jOrXxvqVvc1uTIpTQ49ZHYhDO3du5fvv/+eatWqFWuPiYkhMzOTzZs32+etXr0am81G+/bt7X3WrFlDYWGhvc/KlStp0qQJVatWLZ8NERGRCunomVx6v7veHoYGdqjLp092UBiqhEwNRDk5OSQnJ5OcnAzAgQMHSE5O5vDhwxQWFnL//fezadMm5s+fj9VqJTU1ldTUVAoKCgBo1qwZXbt25dFHH2XDhg2sW7eO+Ph4+vbtS3j474cx+/fvj4eHB4MHD2bHjh0sXryY6dOnM2LECLM2W0REKoDvdqTSfcbPJB3OpIqXG+882IaXerTA083V7NKkDJh62f2PP/7Ibbfddsn8AQMG8NJLL1GvXr3LLvfDDz9w6623Ar/fmDE+Pp6vvvoKFxcXevXqxYwZM/Dz+98Jblu3biUuLo6NGzdSvXp1hgwZwujRo0tc55VcticiIhVbQZGNV7/dzX/WHQAgKiKQWf2iiQjyMbkyuVJX8v3tMPchcmQKRCIizuHw6VziFyay9WgWAI/eVI9RXZri4ebQZ5jIn7iS72+HP6laRESkPHy77QTPfrKVs/lFBHi78/oDUXRuHvL3C0qloEAkIiJOLa/QyqRvdvHf9YcAaFunKjP6RVMz0NvkyqQ8KRCJiIjTOnDqHPELEtlxPBuAJ25pwMg7GuPuqiEyZ6NAJCIiTunLLcd57rNt5OQXEeTrwRu9o7i1SfDfLyiVkgKRiIg4lbxCKxO+2snCDYcBuL5eEDP6RhMa4GVyZWImBSIREXEa+9JziF+QyO7Us1gsEH9bQ4Z1aoSbhsicngKRiIg4hc8Sj/LC0u3kFlip7ufBW32iubFRdbPLEgehQCQiIpVabkERL36xgyWbjwLQoUE13urTmmB/DZHJ/ygQiYhIpbUn7Sxx8xPZm56DiwWGdWpM/O0NcXWxmF2aOBgFIhERqXQMw2DJpqOM/3I7eYU2gqt4Mr1vNDENqv39wuKUFIhERKRSOZdfxPOfb2Np8nEAbmpUnTf7tKa6n6fJlYkjUyASEZFKY+fxbOIXJLL/1DlcXSyM+EdjnrylAS4aIpO/oUAkIiIVnmEYLNhwmAlf7aSgyEaovxcz+0dzXd0gs0uTCkKBSEREKrSzeYWM+WwbX289AcBtTWrweu/WBPl6mFyZVCQKRCIiUmFtP5ZF3IJEDp3Oxc3FwqguTXj0pvoaIpMrpkAkIiIVjmEY/Hf9ISZ+vYsCq42agd7M6BdN2zpVzS5NKigFIhERqVCyzhcy+pOtLN+RCsA/mocw9f5WBPpoiEyungKRiIhUGMlHMolfkMjRM+dxd7Uw9s5mPNKxLhaLhsjk2igQiYiIwzMMg/d+PsBry3dTaDWICPJmVr82REUEml2aVBIKRCIi4tAycwt4ZslWvt+VBkC3yFBe7dUKfy93kyuTykSBSEREHNbmQxkMWZDE8aw8PFxdGHdXMx68oY6GyKTUKRCJiIjDsdkM5q7dz9QVKVhtBnWr+TCrfxta1gwwuzSppBSIRETEoZzOyWfkki38mHISgB5R4Uy6LxI/T31lSdnRp0tERBxGwv7TDF2URFp2Pp5uLrzUowV9r4vQEJmUOQUiERExnc1m8PaP+3hj5R5sBtSv4cvs/m1oFuZvdmniJBSIRETEVCfP5jPi42TW7j0FwH3RNXn5npb4aohMypE+bSIiYppf9p1i2OJkTp7Nx8vdhZd7tuSBdhFmlyVOSIFIRETKndVmMGPVXmas3othQKNgP96ObUOjkCpmlyZOSoFIRETKVXp2HkMXJfHr/gwAererxYQeLfH2cDW5MnFmCkQiIlJu1u49ydOLkzmVU4CPhysT723JvdG1zC5LRIFIRETKXpHVxlvf72X2j/swDGgaWoXZsW1oUMPP7NJEAAUiEREpYyeyzjNsYTIbDv4+RNa/fW3G39UcL3cNkYnjUCASEZEy80NKOiMWJ3MmtxA/Tzcm3xfJ3VHhZpclcgkFIhERKXWFVhvTvkvh3Z/2A9Cypj+z+rWhbnVfkysTuTwFIhERKVXHMs8zZEEiiYczARgQU4fnujfD001DZOK4FIhERKTUrNyZxjNLtpB1vpAqXm5M6dWKOyPDzC5L5G8pEImIyDUrKLLx6re7+c+6AwBE1QpgVv82RAT5mFyZSMkoEImIyDU5fDqX+IWJbD2aBcDgG+sxumtTPNxcTK5MpOQUiERE5Kp9u+0Ez366lbN5RQR4uzPtgSj+0TzE7LJErpgCkYiIXLG8QiuTvtnFf9cfAqBN7UBm9IumVlUNkUnFpEAkIiJX5OCpc8QtSGTH8WwAHr+lPs/c0QR3Vw2RScWlQCQiIiX25ZbjPPfZNnLyi6jq484bvVtzW9Ngs8sSuWYKRCIi8rfyCq1M+GonCzccBuC6ulWZ0S+asABvkysTKR0KRCIi8pd+O5lD3PxEdqeexWKBp25twNOdG+OmITKpRBSIRETkT32edJTnP99OboGV6n4evNmnNTc1qmF2WSKlToFIREQucb7AyotfbufjTUcBiKlfjel9WxPs72VyZSJlQ4FIRESK2ZN2lrj5iexNz8FigWGdGjHk9ka4uljMLk2kzCgQiYgIAIZhsGTzUcZ/sZ28Qhs1qngyvW9rOjSobnZpImVOgUhERDiXX8S4pdv5LOkYADc1qs6bfVpT3c/T5MpEyocCkYiIk9t1Ipu4BYnsP3kOFwuMvKMJT97SABcNkYkTUSASEXFShmGwcMMRJny1g/wiG6H+XszoF8319YLMLk2k3CkQiYg4obN5hTz3+Xa+2nIcgNua1OD13q0J8vUwuTIRcygQiYg4me3HsohfkMjB07m4uVgY1aUJj95UX0Nk4tQUiEREnIRhGHz46yFeWbaLAquNmoHezOgXTds6Vc0uTcR0CkQiIk4gO6+QMZ9u5ZttqQB0bhbCtAdaEeijITIRUCASEan0thzJJH5hIkcyzuPuamHMnc0Y1LEuFouGyEQuUCASEamkDMPg/XUHmfztLgqtBhFB3szq14aoiECzSxNxOApEIiKVUGZuAaM+2crKnWkAdG0Rymv3tyLA293kykQckwKRiEglk3j4DEMWJHEs8zweri68cFczHrqhjobIRP6CApGISCVhsxn8a+1+pq5IochmUKeaD7P7t6FlzQCzSxNxeApEIiKVQMa5Ap5ZsoXVu9MBuKtVGJPvi6SKl4bIRErCxcyVr1mzhrvvvpvw8HAsFgtLly4t1m4YBuPHjycsLAxvb286d+7M3r17i/XJyMggNjYWf39/AgMDGTx4MDk5OcX6bN26lZtuugkvLy8iIiKYMmVKWW+aiEi52Xgwg27T17J6dzoebi5MujeSmf2iFYZEroCpgejcuXNERUUxe/bsy7ZPmTKFGTNm8M4775CQkICvry9dunQhLy/P3ic2NpYdO3awcuVKli1bxpo1a3jsscfs7dnZ2dxxxx3UqVOHzZs3M3XqVF566SXmzp1b5tsnIlKWbDaD2T/so+/cX0nNzqN+DV++iOtI//a1db6QyBWyGIZhmF0EgMVi4fPPP+eee+4Bfj86FB4ezsiRI3nmmWcAyMrKIiQkhHnz5tG3b1927dpF8+bN2bhxI+3atQNg+fLldOvWjaNHjxIeHs6cOXN4/vnnSU1NxcPj9xuQjRkzhqVLl7J79+7L1pKfn09+fr59Ojs7m4iICLKysvD39y/DvSAiUjKncvJ5enEya/eeAuDe6Jq8ck9LfD11JoTIBdnZ2QQEBJTo+9vUI0R/5cCBA6SmptK5c2f7vICAANq3b8/69esBWL9+PYGBgfYwBNC5c2dcXFxISEiw97n55pvtYQigS5cupKSkcObMmcuue/LkyQQEBNhfERERZbGJIiJXZf1vp+k2fS1r957Cy92FKfe34o3eUQpDItfAYQNRaurvt5cPCQkpNj8kJMTelpqaSnBwcLF2Nzc3goKCivW53Hv8cR0XGzt2LFlZWfbXkSNHrn2DRESukdVm8Nb3e4j996+kn82nUbAfX8bfSO92ERoiE7lG+nPiMjw9PfH09DS7DBERu/TsPIYvTuaX304D0LtdLSb0aIm3h6vJlYlUDg4biEJDQwFIS0sjLCzMPj8tLY3WrVvb+6SnpxdbrqioiIyMDPvyoaGhpKWlFetzYfpCHxERR7Z270meXpzMqZwCfDxceeWeltzXppbZZYlUKg47ZFavXj1CQ0NZtWqVfV52djYJCQnExMQAEBMTQ2ZmJps3b7b3Wb16NTabjfbt29v7rFmzhsLCQnuflStX0qRJE6pWrVpOWyMicuWKrDamrUjh4f9s4FROAU1Dq/Bl/I0KQyJlwNRAlJOTQ3JyMsnJycDvJ1InJydz+PBhLBYLw4cP55VXXuHLL79k27ZtPPzww4SHh9uvRGvWrBldu3bl0UcfZcOGDaxbt474+Hj69u1LeHg4AP3798fDw4PBgwezY8cOFi9ezPTp0xkxYoRJWy0i8vdSs/Lo/68EZv2wD8OA/u1rszSuIw2D/cwuTaRSMvWy+x9//JHbbrvtkvkDBgxg3rx5GIbBiy++yNy5c8nMzOTGG2/k7bffpnHjxva+GRkZxMfH89VXX+Hi4kKvXr2YMWMGfn7/+6WxdetW4uLi2LhxI9WrV2fIkCGMHj26xHVeyWV7IiLX6oeUdEZ+vIWMcwX4ebox6b5IekSFm12WSIVzJd/fDnMfIkemQCQi5aHQamPadym8+9N+AFqE+zO7fxvqVvc1uTKRiulKvr8d9qRqERFncizzPEMWJJJ4OBOAATF1GNutGV7uuopMpDwoEImImGzlzjSeWbKFrPOFVPFyY0qvVtwZGfb3C4pIqVEgEhExSUGRjdeW7+a9nw8AEFUrgJn92lC7mo/JlYk4HwUiERETHMnIJX5hEluOZAIwqGM9xtzZFA83h70bikilpkAkIlLOlm8/wahPtnI2rwh/LzemPRDFHS10o1gRMykQiYiUk/wiK5O+3sUH6w8BEF07kJn9oqlVVUNkIma7qkB07tw5fH11GaiISEkdPHWO+IWJbD+WDcDjt9TnmTua4O6qITIRR3BV/xNDQkIYNGgQP//8c2nXIyJS6Szbepy7Zv7M9mPZVPVx5/2B1zH2zmYKQyIO5Kr+N3700UdkZGRw++2307hxY1599VWOHz9e2rWJiFRoeYVWnvt8G/ELksjJL+K6ulX5ZthN3NY02OzSROQi13Sn6pMnT/Lhhx8yb948du3aRZcuXRg0aBA9evTAza3ynJ6kO1WLyJX67WQOcfMT2Z16FosF4m5tyPDOjXDTUSGRcmPKoztmzpzJqFGjKCgooHr16jzxxBOMGTMGH5+Kf7KgApGIXInPk47y/OfbyS2wUs3Xgzf7tObmxjXMLkvE6ZTbozvS0tL44IMPmDdvHocOHeL+++9n8ODBHD16lNdee41ff/2V77777lpWISJSYZwvsPLil9v5eNNRAGLqV2N639YE+3uZXJmI/J2rCkSfffYZ77//PitWrKB58+Y89dRTPPjggwQGBtr7dOjQgWbNmpVWnSIiDm1v2lniFiSyJy0HiwWG3t6IoZ0a4epiMbs0ESmBqwpEjzzyCH379mXdunVcd911l+0THh7O888/f03FiYg4OsMwWLL5KOO/2E5eoY0aVTyZ3qc1HRpWN7s0EbkCV3UOUW5ubqU4N6ikdA6RiFzOufwixi3dzmdJxwC4qVF13ujdmhpVPE2uTESgHM4hKioqIjs7+5L5FosFT09PPDw8ruZtRUQqjN2p2cTNT+S3k+dwscCIfzTmqVsb4qIhMpEK6aoCUWBgIBbLn/+nr1WrFgMHDuTFF1/ExUWXmIpI5WEYBos2HuGlL3eQX2QjxN+TGX2jaV+/mtmlicg1uKpANG/ePJ5//nkGDhzI9ddfD8CGDRv44IMPeOGFFzh58iTTpk3D09OT5557rlQLFhExS05+Ec99to0vt/x+I9pbm9Tg9QeiqOanITKRiu6qAtEHH3zA66+/Tu/eve3z7r77biIjI3n33XdZtWoVtWvXZuLEiQpEIlIp7DieRfyCJA6cOoeri4VRXZrw2E31NUQmUklc1XjWL7/8QnR09CXzo6OjWb9+PQA33ngjhw8fvrbqRERMZhgGH64/yL1v/8KBU+cID/Di48dv4IlbGigMiVQiVxWIIiIieO+99y6Z/9577xEREQHA6dOnqVq16rVVJyJiouy8QuIWJDLuix0UFNno3CyYr4feRNs6QWaXJiKl7KqGzKZNm8YDDzzAt99+a78P0aZNm9i9ezeffPIJABs3bqRPnz6lV6mISDnaejST+AVJHM7Ixd3VwuiuTRl8Y72/vKBERCquq36W2cGDB3n33XdJSUkBoEmTJjz++OPUrVu3NOtzCLoPkYjzMAyD99cdZPK3uyi0GtSq6s2s/m1oHRFodmkicoXK9D5EhYWFdO3alXfeeYfJkydfdZEiIo4mK7eQUZ9s4budaQB0bRHKa/e3IsDb3eTKRKSsXXEgcnd3Z+vWrWVRi4iIaRIPn2HIgiSOZZ7Hw9WF57s34+GYOhoiE3ESV3VS9YMPPnjZk6pFRCoam81g7prf6P3Oeo5lnqdONR8+fbIDAzrUVRgScSJX/eiO//znP3z//fe0bdsWX1/fYu1vvPFGqRQnIlKWzpwrYOSSLazenQ5A91ZhvHpfJFW8NEQm4myuKhBt376dNm3aALBnz55ibfqLSkQqgo0HMxi6MIkTWXl4uLnw4t3N6X99bf0OE3FSVxWIfvjhh9KuQ0SkXNhsBnN++o03Vu7BajOoX92XWf3b0DxcV5CKOLOrCkQX7Nu3j99++42bb74Zb29vDMPQX1ci4rBO5eQz4uMtrNlzEoB7o2vyyj0t8fW8pl+FIlIJXNVvgdOnT9O7d29++OEHLBYLe/fupX79+gwePJiqVavy+uuvl3adIiLXZP1vpxm2KIn0s/l4ubvwzx4teaBdLf0RJyLAVV5l9vTTT+Pu7s7hw4fx8fGxz+/Tpw/Lly8vteJERK6V1WYw/fu9xP77V9LP5tMw2I8v4m6k93URCkMiYndVR4i+++47VqxYQa1atYrNb9SoEYcOHSqVwkRErlX62TyGL0rml99OA3B/21r8s2cLfDw0RCYixV3Vb4Vz584VOzJ0QUZGBp6entdclIjItfp57ymGL07iVE4B3u6uvHJPS3q1rfX3C4qIU7qqIbObbrqJ//73v/Zpi8WCzWZjypQp3HbbbaVWnIjIlSqy2pi2IoWH/pPAqZwCmoRU4ashNyoMichfuqojRFOmTKFTp05s2rSJgoICnn32WXbs2EFGRgbr1q0r7RpFREokNSuPoYuS2HAgA4B+10fw4t0t8HJ3NbkyEXF0VxWIWrZsyZ49e5g1axZVqlQhJyeH++67j7i4OMLCwkq7RhGRv/VDSjojP95CxrkCfD1cmXRfJD1b1zS7LBGpICyGYRhmF+HosrOzCQgIICsrC39/3bxNxJEUWm1M+y6Fd3/aD0DzMH9mx7ahXnXfv1lSRCq7K/n+vupLLTIzM9mwYQPp6enYbLZibQ8//PDVvq2ISIkdyzzP0IVJbD50BoCHbqjD892baYhMRK7YVQWir776itjYWHJycvD39y92Lw+LxaJAJCJl7vudaTzzyRYycwup4unGa/e3olukhuxF5OpcVSAaOXIkgwYNYtKkSZe9/F5EpKwUFNmYsnw3//75AACtagUwq18balfT7yIRuXpXFYiOHTvG0KFDFYZEpFwdycglfmESW45kAvBIx7qMubMpnm4aIhORa3NVgahLly5s2rSJ+vXrl3Y9IiKXtXx7KqM+2cLZvCL8vdyY+kAUXVqEml2WiFQSVxWIunfvzqhRo9i5cyeRkZG4u7sXa+/Ro0epFCcikl9kZfI3u5n3y0EAomsHMrNfNLWq6gi1iJSeq7rs3sXlz29wbbFYsFqt11SUo9Fl9yLmOHT6HPELkth2LAuAx26uz6guTXB3vaqb7IuIkynzy+4vvsxeRKS0fb31BGM+3crZ/CKq+rjzeu8obm8aYnZZIlJJXdGfWd26dSMrK8s+/eqrr5KZmWmfPn36NM2bNy+14kTE+eQVWnn+823ELUjkbH4R7epU5ZthNykMiUiZuqJAtGLFCvLz8+3TkyZNIiMjwz5dVFRESkpK6VUnIk5l/8kc7n37F+YnHAbgqVsbsOixGwgL8Da5MhGp7K5oyOzi04301A8RKS1Lk47x3OfbyC2wUs3Xgzf6tOaWxjXMLktEnMRVP7pDRKQ0nC+w8tKXO1i86QgAN9QPYnrfaEL8vUyuTEScyRUFIovFUuwxHRfmiYhcjX3pZ4mbn0RK2lksFhh6eyOGdmqEq4t+r4hI+briIbOBAwfi6ekJQF5eHk888QS+vr8/VfqP5xeJiPyVTzYfZdzS7ZwvtFKjiifT+7SmQ8PqZpclIk7qigLRgAEDik0/+OCDl/TRg11F5K/kFhTxwtLtfJZ4DIAbG1bnzT6tqVHF0+TKRMSZXVEgev/998uqDhFxArtTs4mbn8hvJ8/hYoER/2jMk7c21BCZiJhOJ1WLSJkzDIPFG4/w4pc7yC+yEeLvyYy+0bSvX83s0kREAAUiESljOflFPP/5Nr5IPg7ALY1r8EbvKKr5aYhMRByHApGIlJkdx7OIX5DEgVPncHWx8MwdTXj85vq4aIhMRByMApGIlDrDMPgo4TAvL9tJQZGN8AAvZvaPpm2dILNLExG5LAUiESlV2XmFjP10G19vOwFA52bBTHsgikAfD5MrExH5cwpEIlJqth7NJH5BEoczcnF3tTC6a1MG31hPN3AVEYd3RQ93LW9Wq5Vx48ZRr149vL29adCgAS+//HKxZ6gZhsH48eMJCwvD29ubzp07s3fv3mLvk5GRQWxsLP7+/gQGBjJ48GBycnLKe3NEKi3DMHh/3QF6zfmFwxm51KrqzZInOvB/N9VXGBKRCsGhA9Frr73GnDlzmDVrFrt27eK1115jypQpzJw5095nypQpzJgxg3feeYeEhAR8fX3p0qULeXl59j6xsbHs2LGDlStXsmzZMtasWcNjjz1mxiaJVDpZuYU8/uFmJny1k0KrQZcWIXw99CZaRwSaXZqISIlZDAd+ZP1dd91FSEgI7733nn1er1698Pb25qOPPsIwDMLDwxk5ciTPPPMMAFlZWYSEhDBv3jz69u3Lrl27aN68ORs3bqRdu3YALF++nG7dunH06FHCw8P/to7s7GwCAgLIysrC39+/bDZWpAJKOnyG+AVJHMs8j4erC893b8bDMXV0VEhEHMKVfH879BGiDh06sGrVKvbs2QPAli1b+Pnnn7nzzjsBOHDgAKmpqXTu3Nm+TEBAAO3bt2f9+vUArF+/nsDAQHsYAujcuTMuLi4kJCRcdr35+flkZ2cXe4nI/9hsBv9as58H3lnPsczz1Knmw6dPdmBAh7oKQyJSITn0SdVjxowhOzubpk2b4urqitVqZeLEicTGxgKQmpoKQEhISLHlQkJC7G2pqakEBwcXa3dzcyMoKMje52KTJ09mwoQJpb05IpXCmXMFjFyyhdW70wHo3iqMyfdF4u/lbnJlIiJXz6ED0ccff8z8+fNZsGABLVq0IDk5meHDhxMeHn7Jg2ZL09ixYxkxYoR9Ojs7m4iIiDJbn0hFselgBkMWJnEiKw8PNxfG3dWcB9vX1lEhEanwHDoQjRo1ijFjxtC3b18AIiMjOXToEJMnT2bAgAGEhoYCkJaWRlhYmH25tLQ0WrduDUBoaCjp6enF3reoqIiMjAz78hfz9PTE01OPFRC5wGYzeGfNb7z+3R6sNoN61X2Z1T+aFuEBZpcmIlIqHPocotzcXFxcipfo6uqKzWYDoF69eoSGhrJq1Sp7e3Z2NgkJCcTExAAQExNDZmYmmzdvtvdZvXo1NpuN9u3bl8NWiFRsp3PyeWTeRqYsT8FqM+jZOpyvhtyoMCQilYpDHyG6++67mThxIrVr16ZFixYkJSXxxhtvMGjQIAAsFgvDhw/nlVdeoVGjRtSrV49x48YRHh7OPffcA0CzZs3o2rUrjz76KO+88w6FhYXEx8fTt2/fEl1hJuLMft1/mmGLkkjLzsfL3YUJPVrQu12EhshEpNJx6EA0c+ZMxo0bx1NPPUV6ejrh4eE8/vjjjB8/3t7n2Wef5dy5czz22GNkZmZy4403snz5cry8vOx95s+fT3x8PJ06dcLFxYVevXoxY8YMMzZJpEKw2gxm/7CPt77fg82AhsF+zO7fhiahVcwuTUSkTDj0fYgche5DJM4k/WweTy9OZt2+0wDc37YW/+zZAh8Ph/77SUTkElfy/a3fcCJit27fKYYtSuZUTj7e7q68ck9LerWtZXZZIiJlToFIRCiy2pixai8zf9iHYUCTkCrMjm1Dw2A/s0sTESkXCkQiTi4tO48hC5PYcCADgH7XR/Di3S3wcnc1uTIRkfKjQCTixH5MSWfEx1vIOFeAr4crk+6LpGfrmmaXJSJS7hSIRJxQodXGGyv3MOfH3wBoHubP7Ng21Kvua3JlIiLmUCAScTLHM88zZGESmw+dAeChG+rwfPdmGiITEaemQCTiRFbtSmPkki1k5hZSxdONV3u1onursL9fUESkklMgEnECBUU2pizfzb9/PgBAZM0AZvWPpk41DZGJiIACkUildyQjlyELk0g+kgnAwA51GdutKZ5uGiITEblAgUikEluxI5VRS7aQnVeEv5cbU+6PomvLULPLEhFxOApEIpVQfpGVyd/sZt4vBwGIighkVr9oIoJ8zC1MRMRBKRCJVDKHTp8jfkES245lAfDoTfUY1aUpHm4uJlcmIuK4FIhEKpGvt55gzKdbOZtfRKCPO68/EEWnZiFmlyUi4vAUiEQqgbxCK698vZOPfj0MQLs6VZnRL5rwQG+TKxMRqRgUiEQquP0nc4hbkMSuE9kAPHVrA0b8ozFurhoiExEpKQUikQrsi+RjPPfZNs4VWKnm68EbfVpzS+MaZpclIlLhKBCJVEDnC6xM+GoHizYeAeCG+kFM7xtNiL+XyZWJiFRMCkQiFcy+9LPEzU8iJe0sFgsMub0Rwzo1wtXFYnZpIiIVlgKRSAXyyeajjFu6nfOFVqr7eTK9b2s6NqxudlkiIhWeApFIBZBbUMS4pTv4NPEoAB0bVuPNPq0JrqIhMhGR0qBAJOLgUlLPErcgkX3pObhY4OnOjXnqtoYaIhMRKUUKRCIOyjAMFm88wotf7iC/yEaIvyfT+0ZzQ/1qZpcmIlLpKBCJOKCc/CKe/3wbXyQfB+CWxjV4o3cU1fw8Ta5MRKRyUiAScTA7jmcxZEES+0+dw9XFwjN3NOHxm+vjoiEyEZEyo0Ak4iAMw+CjhMO8vGwnBUU2wgK8mNkvmnZ1g8wuTUSk0lMgEnEA2XmFjP10G19vOwFAp6bBTHsgiqq+HiZXJiLiHBSIREy29Wgm8QuSOJyRi5uLhTF3NmXwjfWwWDREJiJSXhSIRExiGAbzfjnIpG92UWg1qBnozaz+0UTXrmp2aSIiTkeBSMQEWbmFPPvpFlbsSAPgjuYhTL0/igAfd5MrExFxTgpEIuUs6fAZ4hckcSzzPO6uFp7r1oyBHepqiExExEQKRCLlxDAM/r32AK8t302RzaB2kA+z+kfTqlag2aWJiDg9BSKRcnDmXAHPLNnCqt3pAHSPDGNyr0j8vTREJiLiCBSIRMrYpoMZDF2YxPGsPDzcXBh/V3Ni29fWEJmIiANRIBIpIzabwbtr9jPtuxSsNoN61X2Z1T+aFuEBZpcmIiIXUSASKQOnc/IZ8fEWftpzEoCercOZeG8kfp76Lyci4oj021mklCXsP83QRUmkZefj6ebChB4t6HNdhIbIREQcmAKRSCmx2gze/mEfb36/B5sBDYP9mN2/DU1Cq5hdmoiI/A0FIpFScPJsPsMXJ7Fu32kAerWpxcv3tMDHQ//FREQqAv22FrlG6/adYtiiZE7l5OPt7srL97Tk/ra1zC5LRESugAKRyFWy2gymr9rLzNV7MQxoElKF2bHRNAzWEJmISEWjQCRyFdKy8xi2KIlf92cA0Pe6CF68uwXeHq4mVyYiIldDgUjkCv205yQjFidz+lwBvh6uTLovkp6ta5pdloiIXAMFIpESKrLaeGPlHt7+8TcAmoX5M7t/NPVr+JlcmYiIXCsFIpESOJF1nqELk9h48AwAD95Qmxe6N8fLXUNkIiKVgQKRyN9YvTuNkR9v4UxuIVU83ZjcK5K7WoWbXZaIiJQiBSKRP1FotTF1RQpz1+wHILJmALP6R1Onmq/JlYmISGlTIBK5jKNnchmyMImkw5kADOxQl7HdmuLppiEyEZHKSIFI5CLf7UjlmSVbyM4rwt/LjSn3R9G1ZajZZYmISBlSIBL5/wqKbEz+dhfvrzsIQFREILP6RRMR5GNuYSIiUuYUiESAw6dziV+YyNajWQD83431eLZrUzzcXEyuTEREyoMCkTi9b7adYPQnWzmbX0SgjzvT7o+ic/MQs8sSEZFypEAkTiuv0MrEr3fx4a+HAGhbpyoz+0UTHuhtcmUiIlLeFIjEKR04dY64+YnsPJENwJO3NmDEPxrj7qohMhERZ6RAJE7ni+RjPPfZNs4VWAny9eCN3lHc2iTY7LJERMRECkTiNPIKrUz4agcLNxwB4Pp6QczoG01ogJfJlYmIiNkUiMQp7Es/S9z8JFLSzmKxQPxtDRnWqRFuGiITEREUiMQJfLr5KC8s3c75QivV/Tx5q09rbmxU3eyyRETEgSgQSaWVW1DE+C928MnmowB0aFCNt/q2JriKhshERKQ4BSKplFJSzxK3IJF96Tm4WGBYp8bE394QVxeL2aWJiIgDUiCSSsUwDD7edITxX+wgv8hGcBVPpveNJqZBNbNLExERB6ZAJJVGTn4RL3y+jaXJxwG4qVF13uzTmup+niZXJiIijs7hL7E5duwYDz74INWqVcPb25vIyEg2bdpkbzcMg/HjxxMWFoa3tzedO3dm7969xd4jIyOD2NhY/P39CQwMZPDgweTk5JT3pkgZ2nk8mx4zf2Zp8nFcXSw827UJHzxyvcKQiIiUiEMHojNnztCxY0fc3d359ttv2blzJ6+//jpVq1a195kyZQozZszgnXfeISEhAV9fX7p06UJeXp69T2xsLDt27GDlypUsW7aMNWvW8Nhjj5mxSVLKDMPgo18Pcc/b69h/6hxhAV4seuwGnrq1IS46X0hERErIYhiGYXYRf2bMmDGsW7eOtWvXXrbdMAzCw8MZOXIkzzzzDABZWVmEhIQwb948+vbty65du2jevDkbN26kXbt2ACxfvpxu3bpx9OhRwsPD/7aO7OxsAgICyMrKwt/fv/Q2UK5Jdl4hYz/bxtdbTwBwe9Ngpj0QRZCvh8mViYiII7iS72+HPkL05Zdf0q5dOx544AGCg4OJjo7mX//6l739wIEDpKam0rlzZ/u8gIAA2rdvz/r16wFYv349gYGB9jAE0LlzZ1xcXEhISLjsevPz88nOzi72Esey7WgWd8/8ma+3nsDNxcLz3Zrx74fbKQyJiMhVcehAtH//fubMmUOjRo1YsWIFTz75JEOHDuWDDz4AIDU1FYCQkJBiy4WEhNjbUlNTCQ4u/pwqNzc3goKC7H0uNnnyZAICAuyviIiI0t40uUqGYTBv3QF6zfmFQ6dzqRnozcdPxPDozfU1RCYiIlfNoa8ys9lstGvXjkmTJgEQHR3N9u3beeeddxgwYECZrXfs2LGMGDHCPp2dna1Q5ACyzhcy+pOtLN/xe5C9o3kIU++PIsDH3eTKRESkonPoQBQWFkbz5s2LzWvWrBmffvopAKGhoQCkpaURFhZm75OWlkbr1q3tfdLT04u9R1FRERkZGfblL+bp6Ymnp65OciTJRzKJX5DI0TPncXe18Fy3ZgzsUBeLRUeFRETk2jn0kFnHjh1JSUkpNm/Pnj3UqVMHgHr16hEaGsqqVavs7dnZ2SQkJBATEwNATEwMmZmZbN682d5n9erV2Gw22rdvXw5bIdfCMAz+vXY/D7zzC0fPnKd2kA+fPtmBRzrWUxgSEZFS49BHiJ5++mk6dOjApEmT6N27Nxs2bGDu3LnMnTsXAIvFwvDhw3nllVdo1KgR9erVY9y4cYSHh3PPPfcAvx9R6tq1K48++ijvvPMOhYWFxMfH07dv3xJdYSbmycwt4JklW/h+1+9H+LpHhjG5VyT+XhoiExGR0uXQl90DLFu2jLFjx7J3717q1avHiBEjePTRR+3thmHw4osvMnfuXDIzM7nxxht5++23ady4sb1PRkYG8fHxfPXVV7i4uNCrVy9mzJiBn59fiWrQZfflb/OhDIYsSOJ4Vh4ebi6Mu6s5D7avraNCIiJSYlfy/e3wgcgRKBCVH5vNYO7a/UxdkYLVZlCvui+z+kfTIjzA7NJERKSCuZLvb4ceMhPncjonn5FLtvBjykkAerYOZ+K9kfh56mMqIiJlS9804hAS9p9m6KIk0rLz8XRzYUKPFvS5LkJDZCIiUi4UiMRUVpvB2z/s483v92AzoEENX2bHtqFpqIYmRUSk/CgQiWlOns3n6cXJ/LzvFAC92tTi5Xta4OOhj6WIiJQvffOIKX7Zd4phi5M5eTYfb3dX/tmzBQ+0093ARUTEHApEUq6sNoPpq/Yyc/VeDAMah/gxu38bGoVUMbs0ERFxYgpEUm7SsvMYtiiJX/dnAND3ughevLsF3h6uJlcmIiLOToFIysWaPSd5enEyp88V4OvhyqT7IunZuqbZZYmIiAAKRFLGiqw23vx+D2//+BuGAc3C/JndP5r6NUp2l3AREZHyoEAkZeZE1nmGLkxi48EzAMS2r824u5rj5a4hMhERcSwKRFImftidzoiPkzmTW4ifpxuv9orkrlZ6mK6IiDgmBSIpVYVWG9NWpPDumv0AtKzpz+z+bahTzdfkykRERP6cApGUmqNnchmyMImkw5kADOxQl7HdmuLppiEyERFxbApEUiq+25HKqE+2knW+kCpebky9vxVdW4aZXZaIiEiJKBDJNSkosjH52128v+4gAFERgczqF01EkI+5hYmIiFwBBSK5aodP5xK/MJGtR7MA+L8b6/Fs16Z4uLmYXJmIiMiVUSCSq/LtthM8+8lWzuYXEeDtzusPRNG5eYjZZYmIiFwVBSK5InmFViZ+vYsPfz0EQNs6VZnRL5qagd4mVyYiInL1FIikxA6cOkf8gkR2HM8G4PFb6vPMHU1wd9UQmYiIVGwKRFIiX245zthPt3KuwEqQrwev947itibBZpclIiJSKhSI5C/lFVqZ8NVOFm44DMD1dYOY0S+a0AAvkysTEREpPQpE8qf2pecQvyCR3alnsVgg/raGDOvUCDcNkYmISCWjQCSX9VniUV5Yup3cAivV/Tx4q080NzaqbnZZIiIiZUKBSIrJLSjixS92sGTzUQA6NKjGW31aE+yvITIREam8FIjEbk/aWeLmJ7I3PQcXCwzr1Jj42xvi6mIxuzQREZEypUAkGIbBkk1HGf/ldvIKbQRX8WR632hiGlQzuzQREZFyoUDk5M7lF/HC0u18nnQMgJsaVefNPq2p7udpcmUiIiLlR4HIie06kU3c/ET2nzqHq4uFEf9ozJO3NMBFQ2QiIuJkFIickGEYLNhwmAlf7aSgyEaovxcz+0dzXd0gs0sTERExhQKRkzmbV8jYz7axbOsJAG5vGsy0B6II8vUwuTIRERHzKBA5ke3HsohfkMjB07m4uVh4tmsT/u/G+hoiExERp6dA5AQMw+C/6w8x8etdFFht1Az0Zmb/aNrUrmp2aSIiIg5BgaiSyzpfyOhPtrJ8RyoA/2gewtT7WxHooyEyERGRCxSIKrHkI5nEL0jk6JnzuLtaGHtnMx7pWBeLRUNkIiIif6RAVAkZhsF7Px/gteW7KbQaRAR5M6tfG6IiAs0uTURExCEpEFUymbkFPLNkC9/vSgfgzpahvNqrFQHe7iZXJiIi4rgUiCqRzYcyGLIgieNZeXi4ujDurmY8eEMdDZGJiIj8DQWiSsBmM5i7dj9TV6RgtRnUrebDrP5taFkzwOzSREREKgQFogrudE4+I5ds4ceUkwDcHRXOpHtbUsVLQ2QiIiIlpUBUgW04kMGQhYmkZefj6ebCi3e3oN/1ERoiExERuUIKRBWQzWbw9o/7eGPlHmwG1K/hy+z+bWgW5m92aSIiIhWSAlEFc/JsPiM+Tmbt3lMA3Bddk5fvaYmvp36UIiIiV0vfohXIL/tOMWxxMifP5uPl7sLLPVvyQLsIs8sSERGp8BSIKgCrzWDGqr3MWL0Xw4DGIX7M7t+GRiFVzC5NRESkUlAgcnDp2XkMW5TM+v2nAejTLoKXerTA28PV5MpEREQqDwUiB7Z270meXpzMqZwCfDxcmXRvJPdE1zS7LBERkUpHgcgBFVltvPX9Xmb/uA/DgKahVZgd24YGNfzMLk1ERKRSUiByMCeyzjNsYTIbDmYAENu+NuPuao6Xu4bIREREyooCkQP5ISWdEYuTOZNbiJ+nG6/2iuSuVuFmlyUiIlLpKRA5gEKrjWnfpfDuT/sBaFnTn1n92lC3uq/JlYmIiDgHBSKTHcs8z5AFiSQezgRgYIe6jO3WFE83DZGJiIiUFwUiEyUdPsPA9zeSdb6QKl5uTL2/FV1bhpldloiIiNNRIDJRg2A//L3dqFvNh1n92xAR5GN2SSIiIk5JgchE/l7uLPi/Gwjx98LDzcXsckRERJyWApHJdFRIRETEfDosISIiIk5PgUhEREScngKRiIiIOD0FIhEREXF6CkQiIiLi9BSIRERExOlVqED06quvYrFYGD58uH1eXl4ecXFxVKtWDT8/P3r16kVaWlqx5Q4fPkz37t3x8fEhODiYUaNGUVRUVM7Vi4iIiKOqMIFo48aNvPvuu7Rq1arY/KeffpqvvvqKJUuW8NNPP3H8+HHuu+8+e7vVaqV79+4UFBTwyy+/8MEHHzBv3jzGjx9f3psgIiIiDqpCBKKcnBxiY2P517/+RdWqVe3zs7KyeO+993jjjTe4/fbbadu2Le+//z6//PILv/76KwDfffcdO3fu5KOPPqJ169bceeedvPzyy8yePZuCggKzNklEREQcSIUIRHFxcXTv3p3OnTsXm79582YKCwuLzW/atCm1a9dm/fr1AKxfv57IyEhCQkLsfbp06UJ2djY7duy47Pry8/PJzs4u9hIREZHKy+Ef3bFo0SISExPZuHHjJW2pqal4eHgQGBhYbH5ISAipqan2Pn8MQxfaL7RdzuTJk5kwYUIpVC8iIiIVgUMfITpy5AjDhg1j/vz5eHl5ldt6x44dS1ZWlv115MiRclu3iIiIlD+HDkSbN28mPT2dNm3a4ObmhpubGz/99BMzZszAzc2NkJAQCgoKyMzMLLZcWloaoaGhAISGhl5y1dmF6Qt9Lubp6Ym/v3+xl4iIiFReDj1k1qlTJ7Zt21Zs3iOPPELTpk0ZPXo0ERERuLu7s2rVKnr16gVASkoKhw8fJiYmBoCYmBgmTpxIeno6wcHBAKxcuRJ/f3+aN29eojoMwwDQuUQiIiIVyIXv7Qvf43/JqGBuueUWY9iwYfbpJ554wqhdu7axevVqY9OmTUZMTIwRExNjby8qKjJatmxp3HHHHUZycrKxfPlyo0aNGsbYsWNLvM4jR44YgF566aWXXnrpVQFfR44c+dvveoc+QlQSb775Ji4uLvTq1Yv8/Hy6dOnC22+/bW93dXVl2bJlPPnkk8TExODr68uAAQP45z//WeJ1hIeHc+TIEapUqYLFYinV+rOzs4mIiODIkSMamvsb2lclp31VctpXV0b7q+S0r0qurPaVYRicPXuW8PDwv+1rMYySHEeSspKdnU1AQABZWVn6D/M3tK9KTvuq5LSvroz2V8lpX5WcI+wrhz6pWkRERKQ8KBCJiIiI01MgMpmnpycvvvginp6eZpfi8LSvSk77quS0r66M9lfJaV+VnCPsK51DJCIiIk5PR4hERETE6SkQiYiIiNNTIBIRERGnp0AkIiIiTk+BqAzMmTOHVq1a2R8MGxMTw7fffmtvz8vLIy4ujmrVquHn50evXr0ueQDt4cOH6d69Oz4+PgQHBzNq1CiKiorKe1PK3auvvorFYmH48OH2edpfv3vppZewWCzFXk2bNrW3az8Vd+zYMR588EGqVauGt7c3kZGRbNq0yd5uGAbjx48nLCwMb29vOnfuzN69e4u9R0ZGBrGxsfj7+xMYGMjgwYPJyckp700pc3Xr1r3ks2WxWIiLiwP02fojq9XKuHHjqFevHt7e3jRo0ICXX3652LOy9Nn6n7NnzzJ8+HDq1KmDt7c3HTp0YOPGjfZ2h9pXJX6gl5TYl19+aXz99dfGnj17jJSUFOO5554z3N3dje3btxuG8fvz1yIiIoxVq1YZmzZtMm644QajQ4cO9uUvPH+tc+fORlJSkvHNN98Y1atXv6Lnr1VEGzZsMOrWrWu0atXqkufVaX8Zxosvvmi0aNHCOHHihP118uRJe7v20/9kZGQYderUMQYOHGgkJCQY+/fvN1asWGHs27fP3ufVV181AgICjKVLlxpbtmwxevToYdSrV884f/68vU/Xrl2NqKgo49dffzXWrl1rNGzY0OjXr58Zm1Sm0tPTi32uVq5caQDGDz/8YBiGPlt/NHHiRKNatWrGsmXLjAMHDhhLliwx/Pz8jOnTp9v76LP1P7179zaaN29u/PTTT8bevXuNF1980fD39zeOHj1qGIZj7SsFonJStWpV49///reRmZlpuLu7G0uWLLG37dq1ywCM9evXG4ZhGN98843h4uJipKam2vvMmTPH8Pf3N/Lz88u99vJw9uxZo1GjRsbKlSuLPcBX++t/XnzxRSMqKuqybdpPxY0ePdq48cYb/7TdZrMZoaGhxtSpU+3zMjMzDU9PT2PhwoWGYRjGzp07DcDYuHGjvc+3335rWCwW49ixY2VXvAMYNmyY0aBBA8Nms+mzdZHu3bsbgwYNKjbvvvvuM2JjYw3D0Gfrj3Jzcw1XV1dj2bJlxea3adPGeP755x1uX2nIrIxZrVYWLVrEuXPniImJYfPmzRQWFtK5c2d7n6ZNm1K7dm3Wr18PwPr164mMjCQkJMTep0uXLmRnZ7Njx45y34byEBcXR/fu3YvtF0D76yJ79+4lPDyc+vXrExsby+HDhwHtp4t9+eWXtGvXjgceeIDg4GCio6P517/+ZW8/cOAAqampxfZXQEAA7du3L7a/AgMDadeunb1P586dcXFxISEhofw2ppwVFBTw0UcfMWjQICwWiz5bF+nQoQOrVq1iz549AGzZsoWff/6ZO++8E9Bn64+KioqwWq14eXkVm+/t7c3PP//scPuqwj/t3lFt27aNmJgY8vLy8PPz4/PPP6d58+YkJyfj4eFBYGBgsf4hISGkpqYCkJqaWuwXy4X2C22VzaJFi0hMTCw2rnxBamqq9tf/1759e+bNm0eTJk04ceIEEyZM4KabbmL79u3aTxfZv38/c+bMYcSIETz33HNs3LiRoUOH4uHhwYABA+zbe7n98cf9FRwcXKzdzc2NoKCgSre//mjp0qVkZmYycOBAQP8HLzZmzBiys7Np2rQprq6uWK1WJk6cSGxsLIA+W39QpUoVYmJiePnll2nWrBkhISEsXLiQ9evX07BhQ4fbVwpEZaRJkyYkJyeTlZXFJ598woABA/jpp5/MLsvhHDlyhGHDhrFy5cpL/oqQ4i78BQrQqlUr2rdvT506dfj444/x9vY2sTLHY7PZaNeuHZMmTQIgOjqa7du388477zBgwACTq3Ns7733HnfeeSfh4eFml+KQPv74Y+bPn8+CBQto0aIFycnJDB8+nPDwcH22LuPDDz9k0KBB1KxZE1dXV9q0aUO/fv3YvHmz2aVdQkNmZcTDw4OGDRvStm1bJk+eTFRUFNOnTyc0NJSCggIyMzOL9U9LSyM0NBSA0NDQS67guDB9oU9lsXnzZtLT02nTpg1ubm64ubnx008/MWPGDNzc3AgJCdH++hOBgYE0btyYffv26XN1kbCwMJo3b15sXrNmzexDjBe293L744/7Kz09vVh7UVERGRkZlW5/XXDo0CG+//57/u///s8+T5+t4kaNGsWYMWPo27cvkZGRPPTQQzz99NNMnjwZ0GfrYg0aNOCnn34iJyeHI0eOsGHDBgoLC6lfv77D7SsFonJis9nIz8+nbdu2uLu7s2rVKntbSkoKhw8fJiYmBoCYmBi2bdtW7EOwcuVK/P39L/klX9F16tSJbdu2kZycbH+1a9eO2NhY+7+1vy4vJyeH3377jbCwMH2uLtKxY0dSUlKKzduzZw916tQBoF69eoSGhhbbX9nZ2SQkJBTbX5mZmcX+kl29ejU2m4327duXw1aUv/fff5/g4GC6d+9un6fPVnG5ubm4uBT/6nR1dcVmswH6bP0ZX19fwsLCOHPmDCtWrKBnz56Ot69K9RRtMQzDMMaMGWP89NNPxoEDB4ytW7caY8aMMSwWi/Hdd98ZhvH7Jay1a9c2Vq9ebWzatMmIiYkxYmJi7MtfuIT1jjvuMJKTk43ly5cbNWrUqJSXsF7OH68yMwztrwtGjhxp/Pjjj8aBAweMdevWGZ07dzaqV69upKenG4ah/fRHGzZsMNzc3IyJEycae/fuNebPn2/4+PgYH330kb3Pq6++agQGBhpffPGFsXXrVqNnz56Xvdw3OjraSEhIMH7++WejUaNGlfLSaMMwDKvVatSuXdsYPXr0JW36bP3PgAEDjJo1a9ovu//ss8+M6tWrG88++6y9jz5b/7N8+XLj22+/Nfbv32989913RlRUlNG+fXujoKDAMAzH2lcKRGVg0KBBRp06dQwPDw+jRo0aRqdOnexhyDAM4/z588ZTTz1lVK1a1fDx8THuvfde48SJE8Xe4+DBg8add95peHt7G9WrVzdGjhxpFBYWlvemmOLiQKT99bs+ffoYYWFhhoeHh1GzZk2jT58+xe6ro/1U3FdffWW0bNnS8PT0NJo2bWrMnTu3WLvNZjPGjRtnhISEGJ6enkanTp2MlJSUYn1Onz5t9OvXz/Dz8zP8/f2NRx55xDh79mx5bka5WbFihQFcsg8MQ5+tP8rOzjaGDRtm1K5d2/Dy8jLq169vPP/888VuL6DP1v8sXrzYqF+/vuHh4WGEhoYacXFxRmZmpr3dkfaVxTD+cHtNERERESekc4hERETE6SkQiYiIiNNTIBIRERGnp0AkIiIiTk+BSERERJyeApGIiIg4PQUiERERcXoKRCIiIuL0FIhEpNRYLBaWLl1qdhklMnDgQO655x6zy7isefPmERgYaHYZIk5FgUhESiQ1NZUhQ4ZQv359PD09iYiI4O677y72YEYRkYrKzewCRMTxHTx4kI4dOxIYGMjUqVOJjIyksLCQFStWEBcXx+7du80uUUqgsLAQd3d3s8sQcUg6QiQif+upp57CYrGwYcMGevXqRePGjWnRogUjRozg119/Ldb31KlT3Hvvvfj4+NCoUSO+/PJLe5vVamXw4MHUq1cPb29vmjRpwvTp04stf2Eoa9q0aYSFhVGtWjXi4uIoLCy096lbty6TJk1i0KBBVKlShdq1azN37txi73PkyBF69+5NYGAgQUFB9OzZk4MHD5Z4my8MW61YsYJmzZrh5+dH165dOXHihL3PrbfeyvDhw4std8899zBw4MBitb7yyis8/PDD+Pn5UadOHb788ktOnjxJz5498fPzo1WrVmzatOmSGpYuXUqjRo3w8vKiS5cuHDlypFj7F198QZs2bfDy8qJ+/fpMmDCBoqIie7vFYmHOnDn06NEDX19fJk6cWOLtF3E2CkQi8pcyMjJYvnw5cXFx+Pr6XtJ+8bkuEyZMoHfv3mzdupVu3boRGxtLRkYGADabjVq1arFkyRJ27tzJ+PHjee655/j444+LvccPP/zAb7/9xg8//MAHH3zAvHnzmDdvXrE+r7/+Ou3atSMpKYmnnnqKJ598kpSUFOD3IyFdunShSpUqrF27lnXr1tkDTUFBQYm3PTc3l2nTpvHhhx+yZs0aDh8+zDPPPFPi5S9488036dixI0lJSXTv3p2HHnqIhx9+mAcffJDExEQaNGjAww8/zB+ftZ2bm8vEiRP573//y7p168jMzKRv37729rVr1/Lwww8zbNgwdu7cybvvvsu8efMuCT0vvfQS9957L9u2bWPQoEFXXLuI0zBERP5CQkKCARifffbZ3/YFjBdeeME+nZOTYwDGt99++6fLxMXFGb169bJPDxgwwKhTp45RVFRkn/fAAw8Yffr0sU/XqVPHePDBB+3TNpvNCA4ONubMmWMYhmF8+OGHRpMmTQybzWbvk5+fb3h7exsrVqywr6dnz55/Wtf7779vAMa+ffvs82bPnm2EhITYp2+55RZj2LBhxZbr2bOnMWDAgD+t9cSJEwZgjBs3zj5v/fr1BmCcOHGi2Lp//fVXe59du3YZgJGQkGAYhmF06tTJmDRpUrF1f/jhh0ZYWJh9GjCGDx/+p9soIv+jc4hE5C8ZfzhqURKtWrWy/9vX1xd/f3/S09Pt82bPns1//vMfDh8+zPnz5ykoKKB169bF3qNFixa4urrap8PCwti2bdufrsdisRAaGmpfz5YtW9i3bx9VqlQptkxeXh6//fZbibfFx8eHBg0aFKvjj9tSUn+sNSQkBIDIyMhL5qWnpxMaGgqAm5sb1113nb1P06ZNCQwMZNeuXVx//fVs2bKFdevWFTsiZLVaycvLIzc3Fx8fHwDatWt3xfWKOCMFIhH5S40aNcJisZT4xOmLT9q1WCzYbDYAFi1axDPPPMPrr79OTEwMVapUYerUqSQkJJT4PUrSJycnh7Zt2zJ//vxL6qtRo0aJtuPP1vHHgOji4nJJYPzjuU6Xex+LxfKn8y7exr+Sk5PDhAkTuO+++y5p8/Lysv/7csOcInIpBSIR+UtBQUF06dKF2bNnM3To0Eu+YDMzM0t8z5x169bRoUMHnnrqKfu8KzliU1Jt2rRh8eLFBAcH4+/vX+rvf0GNGjWKnWRttVrZvn07t9122zW/d1FREZs2beL6668HICUlhczMTJo1awb8vo0pKSk0bNjwmtclIjqpWkRKYPbs2VitVq6//no+/fRT9u7dy65du5gxYwYxMTElfp9GjRqxadMmVqxYwZ49exg3bhwbN24s9XpjY2OpXr06PXv2ZO3atRw4cIAff/yRoUOHcvTo0VJbz+23387XX3/N119/ze7du3nyySfJzMwslfd2d3dnyJAhJCQksHnzZgYOHMgNN9xgD0jjx4/nv//9LxMmTGDHjh3s2rWLRYsW8cILL5TK+kWcjQKRiPyt+vXrk5iYyG233cbIkSNp2bIl//jHP1i1ahVz5swp8fs8/vjj3HffffTp04f27dtz+vTpYkeLSouPjw9r1qyhdu3a3HfffTRr1ozBgweTl5dXqkeMBg0axIABA3j44Ye55ZZbqF+/fqkcHYLft2H06NH079+fjh074ufnx+LFi+3tXbp0YdmyZXz33Xdcd9113HDDDbz55pvUqVOnVNYv4mwsxpWeMSkiIiJSyegIkYiIiDg9BSIRERFxegpEIiIi4vQUiERERMTpKRCJiIiI01MgEhEREaenQCQiIiJOT4FIREREnJ4CkYiIiDg9BSIRERFxegpEIiIi4vT+H8cWDKkCqY+5AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for nb in [4]:\n", + "\n", + " xs = np.linspace(\n", + " 300,\n", + " 900,\n", + " )\n", + " plt.plot(\n", + " xs,\n", + " np.polyval(coeff_4, xs),\n", + " label=f\"Ch {nb} fit\",\n", + " )\n", + " # plt.scatter(\n", + " # calibration_channels[nb],\n", + " # calibration_energies[nb],\n", + " # label=f\"Ch {nb} data\",\n", + " # alpha=0.5,\n", + " # )\n", + "plt.xlabel(\"Channel number\")\n", + "plt.ylabel(\"Energy\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for nb in [4, 5]:\n", + " xs = np.linspace(\n", + " calibration_channels[nb][0],\n", + " calibration_channels[nb][-1],\n", + " )\n", + " plt.plot(\n", + " xs,\n", + " np.polyval(coeff[nb], xs),\n", + " label=f\"Ch {nb} fit\",\n", + " )\n", + " plt.scatter(\n", + " calibration_channels[nb],\n", + " calibration_energies[nb],\n", + " label=f\"Ch {nb} data\",\n", + " alpha=0.5,\n", + " )\n", + "plt.xlabel(\"Channel number\")\n", + "plt.ylabel(\"Energy\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "libra-toolbox-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0ee985e8721b32caf6bc415a37e4b5760fd17d17 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 7 May 2025 17:44:11 -0400 Subject: [PATCH 059/137] split into two function for calib. data --- example.ipynb | 132 ++++++------------ .../activation_foils/peak_fitting.py | 72 +++------- 2 files changed, 58 insertions(+), 146 deletions(-) diff --git a/example.ipynb b/example.ipynb index 61933a1..b153e1e 100644 --- a/example.ipynb +++ b/example.ipynb @@ -47,40 +47,40 @@ "text": [ "Processing Co60_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Cs137_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -267,7 +267,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_863944/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_865648/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -350,38 +350,12 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from libra_toolbox.neutron_detection.activation_foils.calibration import get_decay_lines\n", - "\n", - "decay_lines = get_decay_lines([\"Co60\", \"Cs137\", \"Mn54\", \"Na22\"])\n", - "\n", - "\n", - "coeff_4 = peak_fitting.calibrate_counts(\n", - " all_measurements,\n", - " background_measurement=background_meas,\n", - " decay_lines=decay_lines,\n", - " channel_nb=4,\n", - ")\n", - "\n", - "coeff_5 = peak_fitting.calibrate_counts(\n", - " all_measurements,\n", - " background_measurement=background_meas,\n", - " decay_lines=decay_lines,\n", - " channel_nb=5,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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", 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" ] @@ -391,66 +365,40 @@ } ], "source": [ - "for nb in [4]:\n", + "from libra_toolbox.neutron_detection.activation_foils.calibration import get_decay_lines\n", "\n", - " xs = np.linspace(\n", - " 300,\n", - " 900,\n", - " )\n", - " plt.plot(\n", - " xs,\n", - " np.polyval(coeff_4, xs),\n", - " label=f\"Ch {nb} fit\",\n", + "decay_lines = get_decay_lines([\"Co60\", \"Cs137\", \"Mn54\", \"Na22\"])\n", + "\n", + "\n", + "for channel_nb in [4, 5]:\n", + " calibration_channels, calibration_energies = peak_fitting.get_calibration_data(\n", + " all_measurements,\n", + " background_measurement=background_meas,\n", + " decay_lines=decay_lines,\n", + " channel_nb=channel_nb,\n", " )\n", - " # plt.scatter(\n", - " # calibration_channels[nb],\n", - " # calibration_energies[nb],\n", - " # label=f\"Ch {nb} data\",\n", - " # alpha=0.5,\n", - " # )\n", - "plt.xlabel(\"Channel number\")\n", - "plt.ylabel(\"Energy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for nb in [4, 5]:\n", + "\n", + " coeff = np.polyfit(calibration_channels, calibration_energies, 1)\n", + "\n", " xs = np.linspace(\n", - " calibration_channels[nb][0],\n", - " calibration_channels[nb][-1],\n", + " calibration_channels[0],\n", + " calibration_channels[-1],\n", " )\n", " plt.plot(\n", " xs,\n", - " np.polyval(coeff[nb], xs),\n", - " label=f\"Ch {nb} fit\",\n", + " np.polyval(coeff, xs),\n", + " label=f\"Ch {channel_nb} fit\",\n", " )\n", " plt.scatter(\n", - " calibration_channels[nb],\n", - " calibration_energies[nb],\n", - " label=f\"Ch {nb} data\",\n", + " calibration_channels,\n", + " calibration_energies,\n", + " label=f\"Ch {channel_nb} data\",\n", " alpha=0.5,\n", " )\n", "plt.xlabel(\"Channel number\")\n", "plt.ylabel(\"Energy\")\n", "plt.legend()\n", - "plt.show()" + "plt.show()\n" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index 78cfaf2..c5d7659 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -79,64 +79,12 @@ def get_peaks(hist, source): return peaks -def calibrate_counts_old(counts, decay_lines): - - calibration_energies = {} - calibration_channels = {} - coeff = {} - - # find what digitizer channels were used (ex. Ch0 and Ch1) - ch_keys = counts[list(counts.keys())[0]].keys() - for ch in ch_keys: - calibration_energies[ch] = [] - calibration_channels[ch] = [] - - for sample in decay_lines.keys(): - # print('\n', sample) - if "channel" in decay_lines[sample].keys(): - - # If the peak chanels are already included in decay_lines, use them. - calibration_channels[ch] += decay_lines[sample]["channel"] - calibration_energies[ch] += decay_lines[sample]["energy"] - else: - peaks = get_peaks(counts[sample][ch]["hist"], sample) - # print(ch, sample, peaks) - if len(peaks) != len(decay_lines[sample]["energy"]): - raise LookupError( - "SciPy find_peaks() found {} photon peaks, while {} were expected".format( - len(peaks), len(decay_lines[sample]["energy"]) - ) - ) - calibration_channels[ch] += list(peaks) - calibration_energies[ch] += decay_lines[sample]["energy"] - - # print('Channel: ', calibration_channels[-1], ', Energy: ', calibration_energies[-1]) - inds = np.argsort(calibration_channels[ch]) - calibration_channels[ch] = np.array(calibration_channels[ch])[inds] - calibration_energies[ch] = np.array(calibration_energies[ch])[inds] - - # print(ch) - # print(calibration_channels[ch]) - # print(calibration_energies[ch]) - - # linear fit for calibration curve - coeff[ch] = np.polyfit(calibration_channels[ch], calibration_energies[ch], 1) - - for source in counts.keys(): - counts[source][ch]["calibrated_bin_edges"] = np.polyval( - coeff[ch], counts[source][ch]["bin_edges"] - ) - - return counts, coeff - - -def calibrate_counts( +def get_calibration_data( check_source_measurements: List[Measurement], background_measurement: Measurement, channel_nb: int, decay_lines, ): - background_detector = [ detector for detector in background_measurement.detectors @@ -145,7 +93,6 @@ def calibrate_counts( calibration_energies = [] calibration_channels = [] - coeff = [] for measurement in check_source_measurements.values(): for detector in measurement.detectors: @@ -170,6 +117,23 @@ def calibrate_counts( calibration_channels = np.array(calibration_channels)[inds] calibration_energies = np.array(calibration_energies)[inds] + return calibration_channels, calibration_energies + + +def get_calibration_curve( + check_source_measurements: List[Measurement], + background_measurement: Measurement, + channel_nb: int, + decay_lines, +): + + calibration_channels, calibration_energies = get_calibration_data( + check_source_measurements, + background_measurement, + channel_nb, + decay_lines, + ) + # linear fit for calibration curve coeff = np.polyfit( calibration_channels, From 1864edc6bcd68c7eae3629a3d2cd6d0564e8ad1a Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 09:05:51 -0400 Subject: [PATCH 060/137] added documentation + fix indices vs channel --- .../activation_foils/peak_fitting.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index c5d7659..ff2604a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -28,7 +28,16 @@ def get_peak_inputs(samples): return peak_inputs -def get_peaks(hist, source): +def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: + """Returns the peak indices of the histogram + + Args: + hist: a histogram + source: the type of source (eg. "Na22", "Co60", "Ba133", "Mn54") + + Returns: + the peak indices in ``hist`` + """ start_index = 100 prominence = 0.10 * np.max(hist[start_index:]) height = 0.10 * np.max(hist[start_index:]) @@ -101,10 +110,11 @@ def get_calibration_data( sample = measurement.name[:-2] - hist, _ = detector.get_energy_hist_background_substract( + hist, bin_edges = detector.get_energy_hist_background_substract( background_detector, bins="double" ) - peaks = get_peaks(hist, sample) + peaks_ind = get_peaks(hist, sample) + peaks = bin_edges[peaks_ind] if len(peaks) != len(decay_lines[sample]["energy"]): raise ValueError( From 6fcee62941e47a51aca5d3547bb6d3f0f54965ac Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 10:30:41 -0400 Subject: [PATCH 061/137] fixed typo --- .../activation_foils/calibration.py | 8 +- .../activation_foils/peak_fitting.py | 115 +++--------------- 2 files changed, 20 insertions(+), 103 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 473c2ac..b9b1763 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -18,28 +18,28 @@ def get_decay_lines(nuclides: list[str]) -> dict: "energy": [1173.228, 1332.492], "intensity": [0.9985, 0.999826], "half_life": [1925.28 * 24 * 3600], - "actvity_date": datetime.date(2014, 3, 19), + "activity_date": datetime.date(2014, 3, 19), "activity": 0.872 * 3.7e4, }, "Na22": { "energy": [511, 1274.537], "intensity": [1.80, 0.9994], "half_life": [2.6018 * 365.25 * 24 * 3600], - "actvity_date": datetime.date(2014, 3, 19), + "activity_date": datetime.date(2014, 3, 19), "activity": 5 * 3.7e4, }, "Cs137": { "energy": [661.657], "intensity": [0.851], "half_life": [30.08 * 365.25 * 24 * 3600], - "actvity_date": datetime.date(2014, 3, 19), + "activity_date": datetime.date(2014, 3, 19), "activity": 4.66 * 3.7e4, }, "Mn54": { "energy": [834.848], "intensity": [0.99976], "half_life": [312.20 * 24 * 3600], - "actvity_date": datetime.date(2016, 5, 2), + "activity_date": datetime.date(2016, 5, 2), "activity": 6.27 * 3.7e4, }, } diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index ff2604a..734e253 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -224,9 +224,7 @@ def get_singlepeak_area(hist, bins, peak_erg, search_width=300): return area -def get_multipeak_area(hist, bins, peak_ergs, search_width=600): - # get midpoints of every bin - xvals = np.diff(bins) / 2 + bins[:-1] +def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): search_start = np.argmin( np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals) @@ -235,11 +233,11 @@ def get_multipeak_area(hist, bins, peak_ergs, search_width=600): np.abs((peak_ergs[-1] + search_width / (2 * len(peak_ergs))) - xvals) ) - guess_slope = (hist[search_end] - hist[search_start]) / ( + slope_guess = (hist[search_end] - hist[search_start]) / ( xvals[search_end] - xvals[search_start] ) - guess_parameters = [0, guess_slope] + guess_parameters = [0, slope_guess] for i in range(len(peak_ergs)): peak_ind = np.argmin(np.abs((peak_ergs[i]) - xvals)) @@ -249,23 +247,31 @@ def get_multipeak_area(hist, bins, peak_ergs, search_width=600): search_width / (3 * len(peak_ergs)), ] - # print(guess_parameters) - parameters, covariance = curve_fit( gauss, xvals[search_start:search_end], hist[search_start:search_end], p0=guess_parameters, ) - # print(parameters) + + return parameters, covariance + + +def get_multipeak_area(hist, bins, peak_ergs, search_width=600): + # get midpoints of every bin + xvals = np.diff(bins) / 2 + bins[:-1] + + parameters, covariance = fit_peak_gauss( + hist, xvals, peak_ergs, search_width=search_width + ) areas = [] peak_starts = [] peak_ends = [] all_peak_params = [] - peak_amplitudes = [] + # peak_amplitudes = [] for i in range(len(peak_ergs)): - peak_amplitudes += [parameters[2 + 3 * i]] + # peak_amplitudes += [parameters[2 + 3 * i]] mean = parameters[2 + 3 * i + 1] sigma = np.abs(parameters[2 + 3 * i + 2]) peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) @@ -330,92 +336,3 @@ def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): ) # print(areas) return areas - - -def energy_efficiency( - counts, - decay_lines, - nuclides=None, - overlap_width=200, - search_width=400, - degree=2, - count_sum_peak=False, -): - if nuclides is None: - nuclides = decay_lines.keys() - - effs = {} - eff_errs = {} - energies = {} - for nuc in nuclides: - sum_peak = False - if len(decay_lines[nuc]["energy"]) > 1 and count_sum_peak: - peak_energies = decay_lines[nuc]["energy"] + [ - np.sum(decay_lines[nuc]["energy"]) - ] - sum_peak = True - else: - peak_energies = decay_lines[nuc]["energy"] - for ch in counts[nuc].keys(): - # initialize efficiency list - if ch not in effs.keys(): - effs[ch] = [] - eff_errs[ch] = [] - energies[ch] = [] - - # print(nuc, ' Ch ', ch ) - areas = get_peak_areas( - counts[nuc][ch]["hist"], - counts[nuc][ch]["calibrated_bin_edges"], - peak_energies, - overlap_width=overlap_width, - search_width=search_width, - ) - if sum_peak: - areas = np.array(areas)[:-1] + areas[-1] / len(areas[:-1]) - # print('Peak areas: ', areas) - # measured activity - # I think this should be divided by live count time, but maybe it should - # be divided by real count time?? Should go over this again - act_meas = np.array(areas) / ( - np.array(decay_lines[nuc]["intensity"]) - * counts[nuc][ch]["live_count_time"] - ) - # print('Activity measured: ', act_meas) - act_meas_err = np.sqrt(np.array(areas)) / ( - np.array(decay_lines[nuc]["intensity"]) - * counts[nuc][ch]["live_count_time"] - ) - # expected activity - l = np.log(2) / decay_lines[nuc]["half_life"] - # print('decay constant: ', l) - time = ( - counts[nuc][ch]["start_time"] - decay_lines[nuc]["activity_date"] - ).total_seconds() - # print('count time: ', time) - act_expec = decay_lines[nuc]["activity"] * np.exp(-l * time) - # print('Activity expected: ', act_expec) - # print('efficiency: ', act_meas/act_expec) - - effs[ch] += list(act_meas / act_expec) - eff_errs[ch] += list(act_meas_err / act_expec) - energies[ch] += decay_lines[nuc]["energy"] - - # Sort the data - # print('effs: ', effs) - # print('energies: ', energies) - coeff = {} - bounds = {} - for ch in effs.keys(): - ind = np.argsort(energies[ch]) - energies[ch] = np.array(energies[ch])[ind] - effs[ch] = np.array(effs[ch])[ind] - eff_errs[ch] = np.array(eff_errs[ch])[ind] - - # Create polynomial fit - coeff[ch] = np.polyfit(energies[ch], effs[ch], degree) - - # Get bounds of fit for interpolation - bounds[ch] = [np.min(energies[ch]), np.max(energies[ch])] - - return effs, eff_errs, coeff, bounds From b31003e9c8990600cb25a568cf1058c5c6337774 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 11:16:07 -0400 Subject: [PATCH 062/137] added detection efficiency calculation --- example.ipynb | 189 ++++++++++++++++-- .../activation_foils/peak_fitting.py | 141 +++++++++---- 2 files changed, 267 insertions(+), 63 deletions(-) diff --git a/example.ipynb b/example.ipynb index b153e1e..55ce8b3 100644 --- a/example.ipynb +++ b/example.ipynb @@ -47,40 +47,40 @@ "text": [ "Processing Co60_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Cs137_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -267,7 +267,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_865648/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_908726/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -350,12 +350,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -369,6 +369,7 @@ "\n", "decay_lines = get_decay_lines([\"Co60\", \"Cs137\", \"Mn54\", \"Na22\"])\n", "\n", + "calibration_coeffs = {}\n", "\n", "for channel_nb in [4, 5]:\n", " calibration_channels, calibration_energies = peak_fitting.get_calibration_data(\n", @@ -379,7 +380,8 @@ " )\n", "\n", " coeff = np.polyfit(calibration_channels, calibration_energies, 1)\n", - "\n", + " calibration_coeffs[channel_nb] = coeff\n", + " \n", " xs = np.linspace(\n", " calibration_channels[0],\n", " calibration_channels[-1],\n", @@ -396,10 +398,155 @@ " alpha=0.5,\n", " )\n", "plt.xlabel(\"Channel number\")\n", - "plt.ylabel(\"Energy\")\n", + "plt.ylabel(\"Energy (keV)\")\n", "plt.legend()\n", "plt.show()\n" ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ch_nb = 5\n", + "\n", + "background_detector = background_meas.detectors[0]\n", + "check_source_detector = check_source_Co60_meas.detectors[0]\n", + "check_source_data = decay_lines[\"Co60\"]\n", + "\n", + "assert background_detector.channel_nb == check_source_detector.channel_nb\n", + "assert (\n", + " background_detector.channel_nb == ch_nb\n", + "), f\"Channel number mismatch: {background_detector.channel_nb} != {ch_nb}\"\n", + "\n", + "hist, bin_edges = check_source_detector.get_energy_hist_background_substract(\n", + " background_detector, bins=\"double\"\n", + ")\n", + "\n", + "calibrated_bin_bedges = np.polyval(calibration_coeffs[ch_nb], bin_edges)\n", + "\n", + "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", + "\n", + "parameters, covariance = peak_fitting.fit_peak_gauss(\n", + " hist, xvals, check_source_data[\"energy\"], search_width=800\n", + ")\n", + "\n", + "# plotting\n", + "\n", + "peak_start = 520\n", + "peak_end = 800\n", + "plt.fill_between(\n", + " xvals[peak_start:peak_end],\n", + " peak_fitting.gauss(xvals[peak_start:peak_end], *parameters),\n", + " alpha=0.5,\n", + ")\n", + "\n", + "plt.hist(\n", + " calibrated_bin_bedges[:-1],\n", + " bins=calibrated_bin_bedges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb} - calibrated\",\n", + ")\n", + "plt.ylabel(\"Counts\")\n", + "\n", + "plt.legend()\n", + "plt.xlabel(\"Energy (keV)\")\n", + "plt.ylim(bottom=0, top=1500)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Detection efficiency: [0.02704431 0.02559813]\n" + ] + } + ], + "source": [ + "ch_nb = 5\n", + "\n", + "background_detector = background_meas.detectors[0]\n", + "check_source_detector = check_source_Co60_meas.detectors[0]\n", + "check_source_data = decay_lines[\"Co60\"]\n", + "\n", + "assert background_detector.channel_nb == check_source_detector.channel_nb\n", + "assert (\n", + " background_detector.channel_nb == ch_nb\n", + "), f\"Channel number mismatch: {background_detector.channel_nb} != {ch_nb}\"\n", + "\n", + "\n", + "efficiency = peak_fitting.compute_detection_efficiency(\n", + " check_source_detector=check_source_detector,\n", + " background_detector=background_detector,\n", + " check_source_meas=check_source_Co60_meas,\n", + " check_source_data=check_source_data,\n", + " calibration_coeffs=calibration_coeffs[ch_nb],\n", + ")\n", + "\n", + "print(f\"Detection efficiency: {efficiency}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing niobium_1...\n", + "\n", + "Processing niobium_2...\n", + "\n", + "Processing niobium_3...\n", + "\n", + "Processing zirconium_1...\n", + "\n", + "Processing zirconium_2...\n", + "\n", + "Processing zirconium_3...\n", + "\n" + ] + } + ], + "source": [ + "sample_measurements_directories = {\n", + " \"niobium_1\": f\"{run_dir}/Niobium_250318_1253_count1/UNFILTERED\",\n", + " \"niobium_2\": f\"{run_dir}/Niobium_250319_1124_count2/UNFILTERED\",\n", + " \"niobium_3\": f\"{run_dir}/Niobium_250321_0935_count3/UNFILTERED\",\n", + " \"zirconium_1\": f\"{run_dir}/Zirconium_1L_3_240317_2312/UNFILTERED\",\n", + " \"zirconium_2\": f\"{run_dir}/Zirconium_250318_2219_count2/UNFILTERED\",\n", + " \"zirconium_3\": f\"{run_dir}/Zirconium_250320_1042_count3/UNFILTERED\",\n", + "}\n", + "\n", + "all_sample_measurements = {}\n", + "\n", + "for sample, directory in sample_measurements_directories.items():\n", + " print(f\"Processing {sample}...\")\n", + " meas = Measurement.from_directory(directory, name=sample)\n", + " print(meas)\n", + " all_sample_measurements[sample] = meas\n" + ] } ], "metadata": { diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index 734e253..5664d92 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -2,7 +2,8 @@ from scipy.signal import find_peaks from scipy.optimize import curve_fit -from typing import List, Dict +from typing import List, Dict, Union +import datetime from libra_toolbox.neutron_detection.activation_foils.compass import ( Detector, Measurement, @@ -183,47 +184,6 @@ def gauss(x, b, m, *args): return out -def get_singlepeak_area(hist, bins, peak_erg, search_width=300): - - # get midpoints of every bin - xvals = np.diff(bins) / 2 + bins[:-1] - - peak_ind = np.argmin(np.abs((peak_erg) - xvals)) - search_start = np.argmin(np.abs((peak_erg - search_width / 2) - xvals)) - search_end = np.argmin(np.abs((peak_erg + search_width / 2) - xvals)) - - slope_guess = (hist[search_end] - hist[search_start]) / ( - xvals[search_end] - xvals[search_start] - ) - - guess_parameters = [0, slope_guess, hist[peak_ind], peak_erg, search_width / 6] - # print(guess_parameters) - - parameters, covariance = curve_fit( - gauss1, - xvals[search_start:search_end], - hist[search_start:search_end], - p0=guess_parameters, - ) - # print(parameters) - - mean = parameters[3] - sigma = parameters[4] - - peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) - peak_end = np.argmin(np.abs((mean + 3 * sigma) - xvals)) - - gross_area = np.trapezoid(hist[peak_start:peak_end], x=xvals[peak_start:peak_end]) - # trap_cutoff_area = (hist[peak_start] + hist[peak_end])/2 * (bins[peak_end] - bins[peak_start]) - trap_cutoff_area = np.trapezoid( - parameters[0] + parameters[1] * xvals[peak_start:peak_end], - x=xvals[peak_start:peak_end], - ) - area = gross_area - trap_cutoff_area - - return area - - def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): search_start = np.argmin( @@ -336,3 +296,100 @@ def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): ) # print(areas) return areas + + +# should be a method of a class called CheckSourceMeasurement +def get_expected_activity( + check_source_data: Dict[str, Union[float, List[float], datetime.date]], + check_source_meas: Measurement, +) -> float: + """ + Calculates the expected activity of a check source given the + half-life and the date of the measurement. + The expected activity is calculated using the formula: + .. math:: A(t) = A_0 e^{-\\lambda t} + + where :math:`A_0` is the initial activity, :math:`\\lambda` is the decay constant + and :math:`t` is the time since the measurement date. + + Args: + check_source_data: _description_ + check_source_meas: _description_ + + Returns: + the expected activity of the check source in Bq + """ + # expected activity + decay_constant = np.log(2) / check_source_data["half_life"] + + # Convert date to datetime if needed + if isinstance(check_source_data["activity_date"], datetime.date) and not isinstance( + check_source_data["activity_date"], datetime.datetime + ): + activity_datetime = datetime.datetime.combine( + check_source_data["activity_date"], datetime.time.min + ) + # add a timezone + activity_datetime = activity_datetime.replace(tzinfo=datetime.timezone.utc) + else: + activity_datetime = check_source_data["activity_date"] + + time = (check_source_meas.start_time - activity_datetime).total_seconds() + act_expec = check_source_data["activity"] * np.exp(-decay_constant * time) + return act_expec + + +# should be a method of a class called CheckSourceMeasurement +def compute_detection_efficiency( + check_source_detector: Detector, + background_detector: Detector, + check_source_meas: Measurement, + check_source_data: Dict[str, Union[float, List[float], datetime.date]], + calibration_coeffs: np.ndarray, +) -> Union[np.ndarray, float]: + """ + Computes the detection efficiency of a check source given the + check source data and the calibration coefficients. + The detection efficiency is calculated using the formula: + .. math:: \\eta = \\frac{A_{meas}}{A_{expec}} + + where :math:`A_{meas}` is the measured activity and :math:`A_{expec}` is the expected activity. + The measured activity is calculated using the formula: + .. math:: A_{meas} = \\frac{A_{peak}}{I \\cdot t_{live}} + + where :math:`A_{peak}` is the area of the peak, :math:`I` is the intensity of the check source + and :math:`t_{live}` is the live count time of the detector. + + Args: + check_source_detector: _description_ + background_detector: _description_ + check_source_meas: _description_ + check_source_data: _description_ + calibration_coeffs: _description_ + + Returns: + the detection efficiency + """ + + hist, bin_edges = check_source_detector.get_energy_hist_background_substract( + background_detector, bins="double" + ) + + calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + + areas = get_multipeak_area( + hist, calibrated_bin_bedges, check_source_data["energy"], search_width=800 + ) + + act_meas = np.array(areas) / ( + np.array(check_source_data["intensity"]) * check_source_detector.live_count_time + ) + act_meas_err = np.sqrt(np.array(areas)) / ( + np.array(check_source_data["intensity"]) * check_source_detector.live_count_time + ) + + act_expec = get_expected_activity(check_source_data, check_source_meas) + + detection_efficiency = act_meas / act_expec + + return detection_efficiency From 98b521a4c2f0ad4e6eb1700e3a939e032aa546ba Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 11:23:06 -0400 Subject: [PATCH 063/137] removed unneeded functions --- .../activation_foils/peak_fitting.py | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index 5664d92..01c0ef6 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -155,20 +155,6 @@ def get_calibration_curve( return coeff -def gauss1(x, H, m, A, x0, sigma): - return H + m * x + A * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) - - -def gauss2(x, H, m, A1, x1, sigma1, A2, x2, sigma2): - out = ( - H - + m * x - + A1 * np.exp(-((x - x1) ** 2) / (2 * sigma1**2)) - + A2 * np.exp(-((x - x2) ** 2) / (2 * sigma2**2)) - ) - return out - - def gauss(x, b, m, *args): """Creates a multipeak gaussian with a linear addition of the form: m * x + b + Sum_i (A_i * exp(-(x - x_i)**2) / (2 * sigma_i**2)""" From ecc757d08e4b00d29e40c599fae0854a682ad9a5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 11:37:29 -0400 Subject: [PATCH 064/137] added classes for check sources --- .../activation_foils/__init__.py | 29 +++++++ .../activation_foils/calibration.py | 77 ++++++++++--------- 2 files changed, 69 insertions(+), 37 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 049aafc..3c51405 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -1 +1,30 @@ +from datetime import datetime + + +class CheckSource: + nuclide: str + energy: list + intensity: list + activity_date: datetime.date + activity: float + + def __init__( + self, + nuclide: str, + energy: list, + intensity: list, + activity_date: datetime.date, + activity: float, + ): + self.nuclide = nuclide + self.energy = energy + self.intensity = intensity + self.activity_date = activity_date + self.activity = activity + + +class ActivationFoil: + nuclide: str + + from . import explicit, settings, calculations diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 904cbac..172d458 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -1,39 +1,42 @@ import datetime -def get_decay_lines(nuclides:list[str])->dict: - """ Creates dictionary of check source data - given a list of check source nuclides. """ - # energy is the gamma energy in units of eV - # intensity is the percentage of decays that result in this energy gamma - all_decay_lines = {'Ba133':{'energy':[80.9979, 276.3989, 302.8508, 356.0129, 383.8485], - 'intensity':[0.329, 0.0716, 0.1834, 0.6205, 0.0894], - 'half_life':[10.551*365.25*24*3600], - 'activity_date':datetime.date(2014, 3, 19), - 'activity':1 * 3.7e4}, - 'Co60':{'energy':[1173.228, 1332.492], - 'intensity':[0.9985, 0.999826], - 'half_life':[1925.28*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity':0.872 * 3.7e4}, - 'Na22':{'energy':[511, 1274.537], - 'intensity':[1.80, 0.9994], - 'half_life':[2.6018*365.25*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity': 5 * 3.7e4}, - 'Cs137':{'energy':[661.657], - 'intensity':[0.851], - 'half_life':[30.08*365.25*24*3600], - 'actvity_date':datetime.date(2014, 3, 19), - 'activity':4.66 * 3.7e4}, - 'Mn54':{'energy':[834.848], - 'intensity':[0.99976], - 'half_life':[312.20*24*3600], - 'actvity_date':datetime.date(2016, 5, 2), - 'activity':6.27 * 3.7e4}} - decay_lines = {} - for nuclide in nuclides: - if nuclide in all_decay_lines.keys(): - decay_lines[nuclide] = all_decay_lines[nuclide] - else: - raise ValueError(f'{nuclide} not yet added to get_decay_lines()') - return decay_lines \ No newline at end of file +from libra_toolbox.neutron_detection.activation_foils import CheckSource + +uCi_to_Bq = 3.7e4 + +check_source_ba133 = CheckSource( + nuclide="Ba133", + energy=[80.9979, 276.3989, 302.8508, 356.0129, 383.8485], + intensity=[0.329, 0.0716, 0.1834, 0.6205, 0.0894], + activity_date=datetime.date(2014, 3, 19), + activity=1 * uCi_to_Bq, +) + +check_source_co60 = CheckSource( + nuclide="Co60", + energy=[1173.228, 1332.492], + intensity=[0.9985, 0.999826], + activity_date=datetime.date(2014, 3, 19), + activity=0.872 * uCi_to_Bq, +) +check_source_na22 = CheckSource( + nuclide="Na22", + energy=[511, 1274.537], + intensity=[1.80, 0.9994], + activity_date=datetime.date(2014, 3, 19), + activity=5 * uCi_to_Bq, +) +check_source_cs137 = CheckSource( + nuclide="Cs137", + energy=[661.657], + intensity=[0.851], + activity_date=datetime.date(2014, 3, 19), + activity=4.66 * uCi_to_Bq, +) +check_source_mn54 = CheckSource( + nuclide="Mn54", + energy=[834.848], + intensity=[0.99976], + activity_date=datetime.date(2016, 5, 2), + activity=6.27 * uCi_to_Bq, +) From 383703a329f83bad8ff21ea74fcebba2500c1b94 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 11:38:07 -0400 Subject: [PATCH 065/137] mass attribute to activation foil --- libra_toolbox/neutron_detection/activation_foils/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 3c51405..877dd33 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -25,6 +25,7 @@ def __init__( class ActivationFoil: nuclide: str + mass: float from . import explicit, settings, calculations From 31982bbe64675a04c8d12d7dd87d373266e5e5ef Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 12:15:20 -0400 Subject: [PATCH 066/137] make use of check source class --- example.ipynb | 191 +++++++++++------- .../activation_foils/compass.py | 6 + .../activation_foils/peak_fitting.py | 49 ++--- test/neutron_detection/test_calibration.py | 9 - 4 files changed, 153 insertions(+), 102 deletions(-) delete mode 100644 test/neutron_detection/test_calibration.py diff --git a/example.ipynb b/example.ipynb index 55ce8b3..a5dce2a 100644 --- a/example.ipynb +++ b/example.ipynb @@ -6,34 +6,86 @@ "metadata": {}, "outputs": [], "source": [ - "from libra_toolbox.neutron_detection.activation_foils.compass import Measurement\n", + "from libra_toolbox.neutron_detection.activation_foils.calibration import (\n", + " check_source_ba133,\n", + " check_source_co60,\n", + " check_source_cs137,\n", + " check_source_na22,\n", + " check_source_mn54,\n", + ")\n", + "from libra_toolbox.neutron_detection.activation_foils.compass import Measurement, CheckSourceMeasurement\n", "\n", "run_dir = \"250317_BABY_1L_run3/DAQ\"\n", "\n", - "directories = {}\n", - "directories[\"Co60_1\"] = f\"{run_dir}/Co60_0_872uCi_19Mar2014_240317/UNFILTERED\"\n", - "directories[\"Co60_2\"] = f\"{run_dir}/Co60_0_872uCi_19Mar2014_250318_run2/UNFILTERED\"\n", - "directories[\"Co60_3\"] = f\"{run_dir}/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\"\n", - "directories[\"Co60_4\"] = f\"{run_dir}/Co60_0_872uCi_19Marc2014_250320_run4/UNFILTERED\"\n", - "directories[\"Cs137_1\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep23_240317/UNFILTERED\"\n", - "directories[\"Cs137_2\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run2/UNFILTERED\"\n", - "directories[\"Cs137_3\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run3/UNFILTERED\"\n", - "directories[\"Cs137_4\"] = f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250319_run5/UNFILTERED\"\n", - "directories['Mn54_1'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250318/UNFILTERED'\n", - "directories['Mn54_2'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250319_run2/UNFILTERED'\n", - "directories['Mn54_3'] = f'{run_dir}/Mn54_6_27uCi_2May2016_250320_run3/UNFILTERED'\n", - "directories[\"Na22_1\"] = f\"{run_dir}/Na22_9_98uCi_29Sep23_240317/UNFILTERED\"\n", - "directories[\"Na22_2\"] = f\"{run_dir}/Na22_9_98uCi_29Sep23_240317_run2/UNFILTERED\"\n", - "directories[\"Na22_3\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run3/UNFILTERED\"\n", - "directories[\"Na22_4\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run4/UNFILTERED\"\n", - "directories[\"Na22_5\"] = f\"{run_dir}/Na22_9_98uCi_29Sep2023_250319_run5/UNFILTERED\"\n", - "\n", - "background_dir = f\"{run_dir}/Background_250322/UNFILTERED\"\n", - "\n", - "check_source_Co60_meas = Measurement.from_directory(\n", - " \"250317_BABY_1L_run3/DAQ/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\",\n", - " name=\"test\",\n", - ")" + "\n", + "check_source_measurements = {\n", + " \"Co60_1\": {\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_240317/UNFILTERED\",\n", + " \"check_source\": check_source_co60,\n", + " },\n", + " \"Co60_2\": {\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_250318_run2/UNFILTERED\",\n", + " \"check_source\": check_source_co60,\n", + " },\n", + " \"Co60_3\": {\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\",\n", + " \"check_source\": check_source_co60,\n", + " },\n", + " \"Co60_4\": {\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Marc2014_250320_run4/UNFILTERED\",\n", + " \"check_source\": check_source_co60,\n", + " },\n", + " \"Cs137_1\": {\n", + " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep23_240317/UNFILTERED\",\n", + " \"check_source\": check_source_cs137,\n", + " },\n", + " \"Cs137_2\": {\n", + " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run2/UNFILTERED\",\n", + " \"check_source\": check_source_cs137,\n", + " },\n", + " \"Cs137_3\": {\n", + " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run3/UNFILTERED\",\n", + " \"check_source\": check_source_cs137,\n", + " },\n", + " \"Cs137_4\": {\n", + " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250319_run5/UNFILTERED\",\n", + " \"check_source\": check_source_cs137,\n", + " },\n", + " \"Mn54_1\": {\n", + " \"directory\": f\"{run_dir}/Mn54_6_27uCi_2May2016_250318/UNFILTERED\",\n", + " \"check_source\": check_source_mn54,\n", + " },\n", + " \"Mn54_2\": {\n", + " \"directory\": f\"{run_dir}/Mn54_6_27uCi_2May2016_250319_run2/UNFILTERED\",\n", + " \"check_source\": check_source_mn54,\n", + " },\n", + " \"Mn54_3\": {\n", + " \"directory\": f\"{run_dir}/Mn54_6_27uCi_2May2016_250320_run3/UNFILTERED\",\n", + " \"check_source\": check_source_mn54,\n", + " },\n", + " \"Na22_1\": {\n", + " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep23_240317/UNFILTERED\",\n", + " \"check_source\": check_source_na22,\n", + " },\n", + " \"Na22_2\": {\n", + " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep23_240317_run2/UNFILTERED\",\n", + " \"check_source\": check_source_na22,\n", + " },\n", + " \"Na22_3\": {\n", + " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run3/UNFILTERED\",\n", + " \"check_source\": check_source_na22,\n", + " },\n", + " \"Na22_4\": {\n", + " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run4/UNFILTERED\",\n", + " \"check_source\": check_source_na22,\n", + " },\n", + " \"Na22_5\": {\n", + " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250319_run5/UNFILTERED\",\n", + " \"check_source\": check_source_na22,\n", + " },\n", + "}\n", + "\n", + "background_dir = f\"{run_dir}/Background_250322/UNFILTERED\"\n" ] }, { @@ -47,40 +99,40 @@ "text": [ "Processing Co60_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Cs137_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -88,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:284: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:286: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -96,11 +148,12 @@ "source": [ "all_measurements = {}\n", "\n", - "for source, directory in directories.items():\n", - " print(f\"Processing {source}...\")\n", - " meas = Measurement.from_directory(directory, name=source)\n", + "for name, values in check_source_measurements.items():\n", + " print(f\"Processing {name}...\")\n", + " meas = CheckSourceMeasurement.from_directory(values[\"directory\"], name=name)\n", + " meas.check_source = values[\"check_source\"]\n", " print(meas)\n", - " all_measurements[source] = meas\n", + " all_measurements[name] = meas\n", "\n", "print(f\"Processing background...\")\n", "background_meas = Measurement.from_directory(\n", @@ -130,7 +183,7 @@ "import matplotlib.pyplot as plt\n", "from libra_toolbox.neutron_detection.activation_foils import peak_fitting\n", "\n", - "for detector in check_source_Co60_meas.detectors:\n", + "for detector in all_measurements[\"Co60_3\"].detectors:\n", " hist, bin_edges = detector.get_energy_hist(bins=\"double\")\n", "\n", " plt.hist(\n", @@ -140,7 +193,7 @@ " histtype=\"step\",\n", " label=f\"Ch {detector.channel_nb}\",\n", " )\n", - " peaks = peak_fitting.get_peaks(hist, source=\"Co60_0_872uCi_19Marc2014_250319_run3\")\n", + " peaks = peak_fitting.get_peaks(hist, source=all_measurements[\"Co60_3\"].check_source.nuclide)\n", " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", "\n", " from scipy.signal import find_peaks\n", @@ -214,7 +267,7 @@ "\n", "\n", "background_time = background_meas.detectors[1].real_count_time\n", - "bg_hist_scale = hist * check_source_Co60_meas.detectors[1].real_count_time / background_time \n", + "bg_hist_scale = hist * all_measurements[\"Co60_3\"].detectors[1].real_count_time / background_time \n", "plt.hist(\n", " bin_edges[:-1],\n", " bins=bin_edges,\n", @@ -228,7 +281,7 @@ "\n", "plt.sca(axs[2])\n", "\n", - "hist, bin_edges = check_source_Co60_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "hist, bin_edges = all_measurements[\"Co60_3\"].detectors[1].get_energy_hist(bins=\"double\")\n", "\n", "plt.hist(\n", " bin_edges[:-1],\n", @@ -241,7 +294,7 @@ "\n", "background_detector = background_meas.detectors[1]\n", "\n", - "hist_background_substracted, bin_edges_bg_sub = check_source_Co60_meas.detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", + "hist_background_substracted, bin_edges_bg_sub = all_measurements[\"Co60_3\"].detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", "\n", "plt.hist(\n", " bin_edges_bg_sub[:-1],\n", @@ -267,7 +320,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_908726/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_923352/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -365,17 +418,12 @@ } ], "source": [ - "from libra_toolbox.neutron_detection.activation_foils.calibration import get_decay_lines\n", - "\n", - "decay_lines = get_decay_lines([\"Co60\", \"Cs137\", \"Mn54\", \"Na22\"])\n", - "\n", "calibration_coeffs = {}\n", "\n", "for channel_nb in [4, 5]:\n", " calibration_channels, calibration_energies = peak_fitting.get_calibration_data(\n", " all_measurements,\n", " background_measurement=background_meas,\n", - " decay_lines=decay_lines,\n", " channel_nb=channel_nb,\n", " )\n", "\n", @@ -405,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -423,8 +471,7 @@ "ch_nb = 5\n", "\n", "background_detector = background_meas.detectors[0]\n", - "check_source_detector = check_source_Co60_meas.detectors[0]\n", - "check_source_data = decay_lines[\"Co60\"]\n", + "check_source_detector = all_measurements[\"Co60_3\"].detectors[0]\n", "\n", "assert background_detector.channel_nb == check_source_detector.channel_nb\n", "assert (\n", @@ -440,7 +487,7 @@ "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", "\n", "parameters, covariance = peak_fitting.fit_peak_gauss(\n", - " hist, xvals, check_source_data[\"energy\"], search_width=800\n", + " hist, xvals, all_measurements[\"Co60_3\"].check_source.energy, search_width=800\n", ")\n", "\n", "# plotting\n", @@ -470,14 +517,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Detection efficiency: [0.02704431 0.02559813]\n" + "ename": "AttributeError", + "evalue": "'CheckSource' object has no attribute 'half_life'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m background_detector.channel_nb == check_source_detector.channel_nb\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[32m 8\u001b[39m background_detector.channel_nb == ch_nb\n\u001b[32m 9\u001b[39m ), \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mChannel number mismatch: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackground_detector.channel_nb\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m != \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mch_nb\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m efficiency = \u001b[43mpeak_fitting\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute_detection_efficiency\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 13\u001b[39m \u001b[43m \u001b[49m\u001b[43mcheck_source_detector\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcheck_source_detector\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackground_detector\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackground_detector\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 15\u001b[39m \u001b[43m \u001b[49m\u001b[43mcheck_source_meas\u001b[49m\u001b[43m=\u001b[49m\u001b[43mall_measurements\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCo60_3\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[43mcalibration_coeffs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcalibration_coeffs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mch_nb\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mDetection efficiency: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mefficiency\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py:380\u001b[39m, in \u001b[36mcompute_detection_efficiency\u001b[39m\u001b[34m(check_source_detector, background_detector, check_source_meas, calibration_coeffs)\u001b[39m\n\u001b[32m 371\u001b[39m act_meas = np.array(areas) / (\n\u001b[32m 372\u001b[39m np.array(check_source_meas.check_source.intensity)\n\u001b[32m 373\u001b[39m * check_source_detector.live_count_time\n\u001b[32m 374\u001b[39m )\n\u001b[32m 375\u001b[39m act_meas_err = np.sqrt(np.array(areas)) / (\n\u001b[32m 376\u001b[39m np.array(check_source_meas.check_source.intensity)\n\u001b[32m 377\u001b[39m * check_source_detector.live_count_time\n\u001b[32m 378\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m380\u001b[39m act_expec = \u001b[43mget_expected_activity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheck_source_meas\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 382\u001b[39m detection_efficiency = act_meas / act_expec\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m detection_efficiency\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py:306\u001b[39m, in \u001b[36mget_expected_activity\u001b[39m\u001b[34m(check_source_meas)\u001b[39m\n\u001b[32m 290\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 291\u001b[39m \u001b[33;03mCalculates the expected activity of a check source given the\u001b[39;00m\n\u001b[32m 292\u001b[39m \u001b[33;03mhalf-life and the date of the measurement.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 303\u001b[39m \u001b[33;03m the expected activity of the check source in Bq\u001b[39;00m\n\u001b[32m 304\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 305\u001b[39m \u001b[38;5;66;03m# expected activity\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m306\u001b[39m decay_constant = np.log(\u001b[32m2\u001b[39m) / \u001b[43mcheck_source_meas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcheck_source\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhalf_life\u001b[49m\n\u001b[32m 308\u001b[39m \u001b[38;5;66;03m# Convert date to datetime if needed\u001b[39;00m\n\u001b[32m 309\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 310\u001b[39m check_source_meas.check_source.activity_date, datetime.date\n\u001b[32m 311\u001b[39m ) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 312\u001b[39m check_source_meas.check_source.activity_date, datetime.datetime\n\u001b[32m 313\u001b[39m ):\n", + "\u001b[31mAttributeError\u001b[39m: 'CheckSource' object has no attribute 'half_life'" ] } ], @@ -485,8 +538,7 @@ "ch_nb = 5\n", "\n", "background_detector = background_meas.detectors[0]\n", - "check_source_detector = check_source_Co60_meas.detectors[0]\n", - "check_source_data = decay_lines[\"Co60\"]\n", + "check_source_detector = all_measurements[\"Co60_3\"].detectors[0]\n", "\n", "assert background_detector.channel_nb == check_source_detector.channel_nb\n", "assert (\n", @@ -497,8 +549,7 @@ "efficiency = peak_fitting.compute_detection_efficiency(\n", " check_source_detector=check_source_detector,\n", " background_detector=background_detector,\n", - " check_source_meas=check_source_Co60_meas,\n", - " check_source_data=check_source_data,\n", + " check_source_meas=all_measurements[\"Co60_3\"],\n", " calibration_coeffs=calibration_coeffs[ch_nb],\n", ")\n", "\n", diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 40e5d3d..2f5cfc0 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -10,6 +10,8 @@ import warnings +from libra_toolbox.neutron_detection.activation_foils import CheckSource + def get_channel(filename): """ @@ -326,6 +328,10 @@ def from_directory( return measurement_object +class CheckSourceMeasurement(Measurement): + check_source: CheckSource + + def subtract_background(counts, background_directory, savefile=None): # Check if background subtracted counts have already been saved if savefile: diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py index 01c0ef6..6cfe78f 100644 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py @@ -4,9 +4,11 @@ from typing import List, Dict, Union import datetime +from libra_toolbox.neutron_detection.activation_foils import CheckSource from libra_toolbox.neutron_detection.activation_foils.compass import ( Detector, Measurement, + CheckSourceMeasurement, ) @@ -90,10 +92,9 @@ def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: def get_calibration_data( - check_source_measurements: List[Measurement], + check_source_measurements: List[CheckSourceMeasurement], background_measurement: Measurement, channel_nb: int, - decay_lines, ): background_detector = [ detector @@ -117,12 +118,12 @@ def get_calibration_data( peaks_ind = get_peaks(hist, sample) peaks = bin_edges[peaks_ind] - if len(peaks) != len(decay_lines[sample]["energy"]): + if len(peaks) != len(measurement.check_source.energy): raise ValueError( - f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(decay_lines[sample]["energy"])} were expected" + f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(measurement.check_source.energy)} were expected" ) calibration_channels += list(peaks) - calibration_energies += decay_lines[sample]["energy"] + calibration_energies += measurement.check_source.energy inds = np.argsort(calibration_channels) calibration_channels = np.array(calibration_channels)[inds] @@ -132,17 +133,15 @@ def get_calibration_data( def get_calibration_curve( - check_source_measurements: List[Measurement], + check_source_measurements: List[CheckSourceMeasurement], background_measurement: Measurement, channel_nb: int, - decay_lines, ): calibration_channels, calibration_energies = get_calibration_data( check_source_measurements, background_measurement, channel_nb, - decay_lines, ) # linear fit for calibration curve @@ -286,8 +285,7 @@ def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): # should be a method of a class called CheckSourceMeasurement def get_expected_activity( - check_source_data: Dict[str, Union[float, List[float], datetime.date]], - check_source_meas: Measurement, + check_source_meas: CheckSourceMeasurement, ) -> float: """ Calculates the expected activity of a check source given the @@ -299,29 +297,30 @@ def get_expected_activity( and :math:`t` is the time since the measurement date. Args: - check_source_data: _description_ check_source_meas: _description_ Returns: the expected activity of the check source in Bq """ # expected activity - decay_constant = np.log(2) / check_source_data["half_life"] + decay_constant = np.log(2) / check_source_meas.check_source.half_life # Convert date to datetime if needed - if isinstance(check_source_data["activity_date"], datetime.date) and not isinstance( - check_source_data["activity_date"], datetime.datetime + if isinstance( + check_source_meas.check_source.activity_date, datetime.date + ) and not isinstance( + check_source_meas.check_source.activity_date, datetime.datetime ): activity_datetime = datetime.datetime.combine( - check_source_data["activity_date"], datetime.time.min + check_source_meas.check_source.activity_date, datetime.time.min ) # add a timezone activity_datetime = activity_datetime.replace(tzinfo=datetime.timezone.utc) else: - activity_datetime = check_source_data["activity_date"] + activity_datetime = check_source_meas.check_source.activity_date time = (check_source_meas.start_time - activity_datetime).total_seconds() - act_expec = check_source_data["activity"] * np.exp(-decay_constant * time) + act_expec = check_source_meas.check_source.activity * np.exp(-decay_constant * time) return act_expec @@ -329,8 +328,7 @@ def get_expected_activity( def compute_detection_efficiency( check_source_detector: Detector, background_detector: Detector, - check_source_meas: Measurement, - check_source_data: Dict[str, Union[float, List[float], datetime.date]], + check_source_meas: CheckSourceMeasurement, calibration_coeffs: np.ndarray, ) -> Union[np.ndarray, float]: """ @@ -364,17 +362,22 @@ def compute_detection_efficiency( calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) areas = get_multipeak_area( - hist, calibrated_bin_bedges, check_source_data["energy"], search_width=800 + hist, + calibrated_bin_bedges, + check_source_meas.check_source.energy, + search_width=800, ) act_meas = np.array(areas) / ( - np.array(check_source_data["intensity"]) * check_source_detector.live_count_time + np.array(check_source_meas.check_source.intensity) + * check_source_detector.live_count_time ) act_meas_err = np.sqrt(np.array(areas)) / ( - np.array(check_source_data["intensity"]) * check_source_detector.live_count_time + np.array(check_source_meas.check_source.intensity) + * check_source_detector.live_count_time ) - act_expec = get_expected_activity(check_source_data, check_source_meas) + act_expec = get_expected_activity(check_source_meas) detection_efficiency = act_meas / act_expec diff --git a/test/neutron_detection/test_calibration.py b/test/neutron_detection/test_calibration.py deleted file mode 100644 index c30625c..0000000 --- a/test/neutron_detection/test_calibration.py +++ /dev/null @@ -1,9 +0,0 @@ -from libra_toolbox.neutron_detection.activation_foils import calibration - - -def test_get_decay_lines(): - decay_lines = calibration.get_decay_lines(['Na22', 'Cs137']) - assert isinstance(decay_lines, dict) - assert 'energy' in decay_lines['Na22'].keys() - - From 4ca727d667bcdd6f410c205fb2b5d7f4fc321564 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 12:20:15 -0400 Subject: [PATCH 067/137] added half life --- libra_toolbox/neutron_detection/activation_foils/__init__.py | 3 +++ .../neutron_detection/activation_foils/calibration.py | 5 +++++ 2 files changed, 8 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 877dd33..9e5c3d6 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -7,6 +7,7 @@ class CheckSource: intensity: list activity_date: datetime.date activity: float + half_life: float def __init__( self, @@ -15,12 +16,14 @@ def __init__( intensity: list, activity_date: datetime.date, activity: float, + half_life: float, ): self.nuclide = nuclide self.energy = energy self.intensity = intensity self.activity_date = activity_date self.activity = activity + self.half_life = half_life class ActivationFoil: diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 172d458..cae463f 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -10,6 +10,7 @@ intensity=[0.329, 0.0716, 0.1834, 0.6205, 0.0894], activity_date=datetime.date(2014, 3, 19), activity=1 * uCi_to_Bq, + half_life=10.551 * 365.25 * 24 * 3600, ) check_source_co60 = CheckSource( @@ -18,6 +19,7 @@ intensity=[0.9985, 0.999826], activity_date=datetime.date(2014, 3, 19), activity=0.872 * uCi_to_Bq, + half_life=1925.28 * 24 * 3600, ) check_source_na22 = CheckSource( nuclide="Na22", @@ -25,6 +27,7 @@ intensity=[1.80, 0.9994], activity_date=datetime.date(2014, 3, 19), activity=5 * uCi_to_Bq, + half_life=2.6018 * 365.25 * 24 * 3600, ) check_source_cs137 = CheckSource( nuclide="Cs137", @@ -32,6 +35,7 @@ intensity=[0.851], activity_date=datetime.date(2014, 3, 19), activity=4.66 * uCi_to_Bq, + half_life=30.08 * 365.25 * 24 * 3600, ) check_source_mn54 = CheckSource( nuclide="Mn54", @@ -39,4 +43,5 @@ intensity=[0.99976], activity_date=datetime.date(2016, 5, 2), activity=6.27 * uCi_to_Bq, + half_life=312.20 * 24 * 3600, ) From 3cb53d2e5005c29645de650dca702531f935fba8 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 12:21:05 -0400 Subject: [PATCH 068/137] removed test --- test/neutron_detection/test_calibration.py | 9 --------- 1 file changed, 9 deletions(-) delete mode 100644 test/neutron_detection/test_calibration.py diff --git a/test/neutron_detection/test_calibration.py b/test/neutron_detection/test_calibration.py deleted file mode 100644 index c30625c..0000000 --- a/test/neutron_detection/test_calibration.py +++ /dev/null @@ -1,9 +0,0 @@ -from libra_toolbox.neutron_detection.activation_foils import calibration - - -def test_get_decay_lines(): - decay_lines = calibration.get_decay_lines(['Na22', 'Cs137']) - assert isinstance(decay_lines, dict) - assert 'energy' in decay_lines['Na22'].keys() - - From 8400b7bf3a7817cef9096114d5d3e80c4e000a51 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 12:23:45 -0400 Subject: [PATCH 069/137] re-ran notebook --- example.ipynb | 65 +++++++++++++++++++++------------------------------ 1 file changed, 26 insertions(+), 39 deletions(-) diff --git a/example.ipynb b/example.ipynb index a5dce2a..e7cd4c7 100644 --- a/example.ipynb +++ b/example.ipynb @@ -99,40 +99,40 @@ "text": [ "Processing Co60_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Cs137_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -320,7 +320,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_923352/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_927866/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -517,20 +517,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'CheckSource' object has no attribute 'half_life'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m background_detector.channel_nb == check_source_detector.channel_nb\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[32m 8\u001b[39m background_detector.channel_nb == ch_nb\n\u001b[32m 9\u001b[39m ), \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mChannel number mismatch: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mbackground_detector.channel_nb\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m != \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mch_nb\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m efficiency = \u001b[43mpeak_fitting\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcompute_detection_efficiency\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 13\u001b[39m \u001b[43m \u001b[49m\u001b[43mcheck_source_detector\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcheck_source_detector\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 14\u001b[39m \u001b[43m \u001b[49m\u001b[43mbackground_detector\u001b[49m\u001b[43m=\u001b[49m\u001b[43mbackground_detector\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 15\u001b[39m \u001b[43m \u001b[49m\u001b[43mcheck_source_meas\u001b[49m\u001b[43m=\u001b[49m\u001b[43mall_measurements\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCo60_3\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 16\u001b[39m \u001b[43m \u001b[49m\u001b[43mcalibration_coeffs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcalibration_coeffs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mch_nb\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 19\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mDetection efficiency: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mefficiency\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py:380\u001b[39m, in \u001b[36mcompute_detection_efficiency\u001b[39m\u001b[34m(check_source_detector, background_detector, check_source_meas, calibration_coeffs)\u001b[39m\n\u001b[32m 371\u001b[39m act_meas = np.array(areas) / (\n\u001b[32m 372\u001b[39m np.array(check_source_meas.check_source.intensity)\n\u001b[32m 373\u001b[39m * check_source_detector.live_count_time\n\u001b[32m 374\u001b[39m )\n\u001b[32m 375\u001b[39m act_meas_err = np.sqrt(np.array(areas)) / (\n\u001b[32m 376\u001b[39m np.array(check_source_meas.check_source.intensity)\n\u001b[32m 377\u001b[39m * check_source_detector.live_count_time\n\u001b[32m 378\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m380\u001b[39m act_expec = \u001b[43mget_expected_activity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheck_source_meas\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 382\u001b[39m detection_efficiency = act_meas / act_expec\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m detection_efficiency\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py:306\u001b[39m, in \u001b[36mget_expected_activity\u001b[39m\u001b[34m(check_source_meas)\u001b[39m\n\u001b[32m 290\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 291\u001b[39m \u001b[33;03mCalculates the expected activity of a check source given the\u001b[39;00m\n\u001b[32m 292\u001b[39m \u001b[33;03mhalf-life and the date of the measurement.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 303\u001b[39m \u001b[33;03m the expected activity of the check source in Bq\u001b[39;00m\n\u001b[32m 304\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 305\u001b[39m \u001b[38;5;66;03m# expected activity\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m306\u001b[39m decay_constant = np.log(\u001b[32m2\u001b[39m) / \u001b[43mcheck_source_meas\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcheck_source\u001b[49m\u001b[43m.\u001b[49m\u001b[43mhalf_life\u001b[49m\n\u001b[32m 308\u001b[39m \u001b[38;5;66;03m# Convert date to datetime if needed\u001b[39;00m\n\u001b[32m 309\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 310\u001b[39m check_source_meas.check_source.activity_date, datetime.date\n\u001b[32m 311\u001b[39m ) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m 312\u001b[39m check_source_meas.check_source.activity_date, datetime.datetime\n\u001b[32m 313\u001b[39m ):\n", - "\u001b[31mAttributeError\u001b[39m: 'CheckSource' object has no attribute 'half_life'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Detection efficiency: [0.02704431 0.02559813]\n" ] } ], @@ -566,17 +560,10 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", - "Processing niobium_3...\n", - "\n", - "Processing zirconium_1...\n", - "\n", - "Processing zirconium_2...\n", - "\n", - "Processing zirconium_3...\n", - "\n" + "\n", + "Processing niobium_3...\n" ] } ], From 8dadd43324a12f382fafc6f92aabb86e40d250b5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 13:29:55 -0400 Subject: [PATCH 070/137] everything in one file for simplicity --- example.ipynb | 86 +-- .../activation_foils/__init__.py | 33 - .../activation_foils/calibration.py | 31 +- .../activation_foils/compass.py | 668 ++++++++++++++---- .../activation_foils/peak_fitting.py | 384 ---------- 5 files changed, 583 insertions(+), 619 deletions(-) delete mode 100644 libra_toolbox/neutron_detection/activation_foils/peak_fitting.py diff --git a/example.ipynb b/example.ipynb index e7cd4c7..afab135 100644 --- a/example.ipynb +++ b/example.ipynb @@ -99,40 +99,40 @@ "text": [ "Processing Co60_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Cs137_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_1...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -140,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:286: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:139: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -181,7 +181,7 @@ ], "source": [ "import matplotlib.pyplot as plt\n", - "from libra_toolbox.neutron_detection.activation_foils import peak_fitting\n", + "from libra_toolbox.neutron_detection.activation_foils import compass\n", "\n", "for detector in all_measurements[\"Co60_3\"].detectors:\n", " hist, bin_edges = detector.get_energy_hist(bins=\"double\")\n", @@ -193,7 +193,7 @@ " histtype=\"step\",\n", " label=f\"Ch {detector.channel_nb}\",\n", " )\n", - " peaks = peak_fitting.get_peaks(hist, source=all_measurements[\"Co60_3\"].check_source.nuclide)\n", + " peaks = compass.get_peaks(hist, source=all_measurements[\"Co60_3\"].check_source.nuclide)\n", " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", "\n", " from scipy.signal import find_peaks\n", @@ -231,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -313,14 +313,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_927866/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_943186/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -421,7 +421,7 @@ "calibration_coeffs = {}\n", "\n", "for channel_nb in [4, 5]:\n", - " calibration_channels, calibration_energies = peak_fitting.get_calibration_data(\n", + " calibration_channels, calibration_energies = compass.get_calibration_data(\n", " all_measurements,\n", " background_measurement=background_meas,\n", " channel_nb=channel_nb,\n", @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -486,7 +486,7 @@ "\n", "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", "\n", - "parameters, covariance = peak_fitting.fit_peak_gauss(\n", + "parameters, covariance = compass.fit_peak_gauss(\n", " hist, xvals, all_measurements[\"Co60_3\"].check_source.energy, search_width=800\n", ")\n", "\n", @@ -496,7 +496,7 @@ "peak_end = 800\n", "plt.fill_between(\n", " xvals[peak_start:peak_end],\n", - " peak_fitting.gauss(xvals[peak_start:peak_end], *parameters),\n", + " compass.gauss(xvals[peak_start:peak_end], *parameters),\n", " alpha=0.5,\n", ")\n", "\n", @@ -517,34 +517,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Detection efficiency: [0.02704431 0.02559813]\n" + "Detection efficiency: [0.02047002 0.01863007]\n" ] } ], "source": [ - "ch_nb = 5\n", - "\n", - "background_detector = background_meas.detectors[0]\n", - "check_source_detector = all_measurements[\"Co60_3\"].detectors[0]\n", - "\n", - "assert background_detector.channel_nb == check_source_detector.channel_nb\n", - "assert (\n", - " background_detector.channel_nb == ch_nb\n", - "), f\"Channel number mismatch: {background_detector.channel_nb} != {ch_nb}\"\n", - "\n", - "\n", - "efficiency = peak_fitting.compute_detection_efficiency(\n", - " check_source_detector=check_source_detector,\n", - " background_detector=background_detector,\n", - " check_source_meas=all_measurements[\"Co60_3\"],\n", - " calibration_coeffs=calibration_coeffs[ch_nb],\n", + "efficiency = all_measurements[\"Co60_3\"].compute_detection_efficiency(\n", + " background_measurement=background_meas,\n", + " calibration_coeffs=calibration_coeffs[4],\n", + " channel_nb=4,\n", ")\n", "\n", "print(f\"Detection efficiency: {efficiency}\")" @@ -559,11 +547,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing niobium_1...\n", - "\n", - "Processing niobium_2...\n", - "\n", - "Processing niobium_3...\n" + "Processing niobium_1...\n" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 9e5c3d6..049aafc 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -1,34 +1 @@ -from datetime import datetime - - -class CheckSource: - nuclide: str - energy: list - intensity: list - activity_date: datetime.date - activity: float - half_life: float - - def __init__( - self, - nuclide: str, - energy: list, - intensity: list, - activity_date: datetime.date, - activity: float, - half_life: float, - ): - self.nuclide = nuclide - self.energy = energy - self.intensity = intensity - self.activity_date = activity_date - self.activity = activity - self.half_life = half_life - - -class ActivationFoil: - nuclide: str - mass: float - - from . import explicit, settings, calculations diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index cae463f..1c10fb1 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -1,6 +1,35 @@ import datetime -from libra_toolbox.neutron_detection.activation_foils import CheckSource + +class CheckSource: + nuclide: str + energy: list + intensity: list + activity_date: datetime.date + activity: float + half_life: float + + def __init__( + self, + nuclide: str, + energy: list, + intensity: list, + activity_date: datetime.date, + activity: float, + half_life: float, + ): + self.nuclide = nuclide + self.energy = energy + self.intensity = intensity + self.activity_date = activity_date + self.activity = activity + self.half_life = half_life + + +class ActivationFoil: + nuclide: str + mass: float + uCi_to_Bq = 3.7e4 diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 2f5cfc0..174f34a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -9,157 +9,10 @@ import glob import warnings +from libra_toolbox.neutron_detection.activation_foils.calibration import CheckSource -from libra_toolbox.neutron_detection.activation_foils import CheckSource - - -def get_channel(filename): - """ - Extract the channel number from a given filename string. - - Parameters - ---------- - filename : str - The input filename string containing the channel information. - Should look something like : "Data_CH@V...CSV" - - Returns - ------- - int - The extracted channel number. - - Example - ------- - >>> get_channel("Data_CH4@V1725_292_Background_250322.CSV") - 4 - """ - return int(filename.split("@")[0][7:]) - - -def sort_compass_files(directory: str) -> dict: - """Gets Compass csv data filenames - and sorts them according to channel and ending number. - The filenames need to be sorted by ending number because only - the first csv file for each channel contains a header. - - Example of sorted filenames in directory: - 1st file: Data_CH4@...22.CSV - 2nd file: Data_CH4@...22_1.CSV - 3rd file: Data_CH4@...22_2.CSV""" - - filenames = os.listdir(directory) - data_filenames = {} - for filename in filenames: - if filename.lower().endswith(".csv"): - ch = get_channel(filename) - # initialize filenames for each channel - if ch not in data_filenames.keys(): - data_filenames[ch] = [] - - data_filenames[ch].append(filename) - # Sort filenames by number at end - for ch in data_filenames.keys(): - data_filenames[ch] = np.sort(data_filenames[ch]) - - return data_filenames - - -def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndarray]]: - """ - From a directory with unprocessed Compass data CSV files, - this returns dictionaries of detector pulse times and energies - with digitizer channels as the keys to the dictionaries. - - This function is also built to be able to read-in problematic - Compass CSV files that have been incorrectly post-processed to - reduce waveform data. - - Args: - directory: directory containing CSV files with Compass data - - Returns: - time values and energy values for each channel - """ - - time_values = {} - energy_values = {} - - data_filenames = sort_compass_files(directory) - - for ch in data_filenames.keys(): - # Initialize time_values and energy_values for each channel - time_values[ch] = np.empty(0) - energy_values[ch] = np.empty(0) - for i, filename in enumerate(data_filenames[ch]): - - # only the first file has a header - if i == 0: - header = 0 - else: - header = None - - csv_file_path = os.path.join(directory, filename) - - df = pd.read_csv(csv_file_path, delimiter=";", header=header) - - # read the header and store in names - if i == 0: - names = df.columns.values - else: - # apply the column names if not the first file - df.columns = names - - time_data = df["TIMETAG"].to_numpy() - energy_data = df["ENERGY"].to_numpy() - - # Extract and append the energy data to the list - time_values[ch] = np.concatenate([time_values[ch], time_data]) - energy_values[ch] = np.concatenate([energy_values[ch], energy_data]) - - return time_values, energy_values - - -def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.datetime]: - """Obtains count start and stop time from the run.info file.""" - - info_file = Path(directory).parent / "run.info" - if info_file.exists(): - time_format = "%Y/%m/%d %H:%M:%S.%f%z" - with open(info_file, "r") as file: - lines = file.readlines() - else: - raise FileNotFoundError( - f"Could not find run.info file in parent directory {Path(directory).parent}" - ) - - start_time, stop_time = None, None - for line in lines: - if "time.start=" in line: - # get start time string while cutting off '\n' newline - time_string = line.split("=")[1].replace("\n", "") - start_time = datetime.datetime.strptime(time_string, time_format) - elif "time.stop=" in line: - # get stop time string while cutting off '\n' newline - time_string = line.split("=")[1].replace("\n", "") - stop_time = datetime.datetime.strptime(time_string, time_format) - - if None in (start_time, stop_time): - raise ValueError(f"Could not find time.start or time.stop in file {info_file}.") - else: - return start_time, stop_time - - -def get_live_time_from_root(root_filename: str, channel: int) -> Tuple[float, float]: - """ - Gets live and real count time from Compass root file. - Live time is defined as the difference between the actual time that - a count is occurring and the "dead time," in which the output of detector - pulses is saturated such that additional signals cannot be processed.""" - - with uproot.open(root_filename) as root_file: - live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 - real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 - return live_count_time, real_count_time +from scipy.signal import find_peaks +from scipy.optimize import curve_fit class Detector: @@ -331,6 +184,521 @@ def from_directory( class CheckSourceMeasurement(Measurement): check_source: CheckSource + def get_expected_activity(self) -> float: + """ + Calculates the expected activity of a check source given the + half-life and the date of the measurement. + The expected activity is calculated using the formula: + .. math:: A(t) = A_0 e^{-\\lambda t} + + where :math:`A_0` is the initial activity, :math:`\\lambda` is the decay constant + and :math:`t` is the time since the measurement date. + + Returns: + the expected activity of the check source in Bq + """ + decay_constant = np.log(2) / self.check_source.half_life + + # Convert date to datetime if needed + if isinstance( + self.check_source.activity_date, datetime.date + ) and not isinstance(self.check_source.activity_date, datetime.datetime): + activity_datetime = datetime.datetime.combine( + self.check_source.activity_date, datetime.time.min + ) + # add a timezone + activity_datetime = activity_datetime.replace(tzinfo=datetime.timezone.utc) + else: + activity_datetime = self.check_source.activity_date + + time = (self.start_time - activity_datetime).total_seconds() + act_expec = self.check_source.activity * np.exp(-decay_constant * time) + return act_expec + + # should be a method of a class called CheckSourceMeasurement + def compute_detection_efficiency( + self, + background_measurement: Measurement, + calibration_coeffs: np.ndarray, + channel_nb: int, + ) -> Union[np.ndarray, float]: + """ + Computes the detection efficiency of a check source given the + check source data and the calibration coefficients. + The detection efficiency is calculated using the formula: + .. math:: \\eta = \\frac{A_{meas}}{A_{expec}} + + where :math:`A_{meas}` is the measured activity and :math:`A_{expec}` is the expected activity. + The measured activity is calculated using the formula: + .. math:: A_{meas} = \\frac{A_{peak}}{I \\cdot t_{live}} + + where :math:`A_{peak}` is the area of the peak, :math:`I` is the intensity of the check source + and :math:`t_{live}` is the live count time of the detector. + + Args: + background_measurement: _description_ + calibration_coeffs: _description_ + + Returns: + the detection efficiency + """ + # find right background detector + + background_detector = [ + d for d in background_measurement.detectors if d.channel_nb == channel_nb + ][0] + check_source_detector = [ + d for d in self.detectors if d.channel_nb == channel_nb + ][0] + + hist, bin_edges = check_source_detector.get_energy_hist_background_substract( + background_detector, bins="double" + ) + + calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + + areas = get_multipeak_area( + hist, + calibrated_bin_bedges, + self.check_source.energy, + search_width=800, + ) + + act_meas = np.array(areas) / ( + np.array(self.check_source.intensity) + * check_source_detector.live_count_time + ) + act_meas_err = np.sqrt(np.array(areas)) / ( + np.array(self.check_source.intensity) + * check_source_detector.live_count_time + ) + + act_expec = self.get_expected_activity() + + detection_efficiency = act_meas / act_expec + + return detection_efficiency + + +def get_peak_inputs(samples): + default_inputs = { + "Na22": {"prom_factor": 0.075, "width": [10, 150], "start_index": 100}, + "Co60": {"prom_factor": 0.2, "width": [10, 150], "start_index": 400}, + "Ba133": {"prom_factor": 0.1, "width": [10, 200], "start_index": 100}, + "Mn54": {"prom_factor": 0.2, "width": [10, 100], "start_index": 100}, + } + + defaults = {"prom_factor": 0.075, "width": [10, 150], "start_index": 100} + + peak_inputs = {} + for sample in samples: + if sample in default_inputs.keys(): + peak_inputs[sample] = default_inputs[sample] + else: + peak_inputs[sample] = defaults + return peak_inputs + + +def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: + """Returns the peak indices of the histogram + + Args: + hist: a histogram + source: the type of source (eg. "Na22", "Co60", "Ba133", "Mn54") + + Returns: + the peak indices in ``hist`` + """ + start_index = 100 + prominence = 0.10 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + width = [10, 150] + indices = None + distance = 30 + if "na22" in source.lower(): + # find 511 keV peak first + prominence = 0.01 * np.max(hist[start_index:]) + height = 0.9 * np.max(hist[start_index:]) + width = [10, 200] + elif "co60" in source.lower(): + start_index = 400 + height = 0.60 * np.max(hist[start_index:]) + prominence = None + elif "ba133" in source.lower(): + width = [10, 200] + elif "mn54" in source.lower(): + height = 0.6 * np.max(hist[start_index:]) + peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + peaks = np.array(peaks) + start_index + if "na22" in source.lower(): + # Find 1275 keV peak + peak_511 = peaks[0] + start_index = peak_511 + 100 + prominence = 0.5 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + + high_peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + high_peaks = np.array(high_peaks) + start_index + peaks = [peak_511, high_peaks[0]] + + if indices: + peaks = peaks[[indices]][0] + + return peaks + + +def get_calibration_data( + check_source_measurements: List[CheckSourceMeasurement], + background_measurement: Measurement, + channel_nb: int, +): + background_detector = [ + detector + for detector in background_measurement.detectors + if detector.channel_nb == detector.channel_nb + ][0] + + calibration_energies = [] + calibration_channels = [] + + for measurement in check_source_measurements.values(): + for detector in measurement.detectors: + if detector.channel_nb != channel_nb: + continue + + sample = measurement.name[:-2] + + hist, bin_edges = detector.get_energy_hist_background_substract( + background_detector, bins="double" + ) + peaks_ind = get_peaks(hist, sample) + peaks = bin_edges[peaks_ind] + + if len(peaks) != len(measurement.check_source.energy): + raise ValueError( + f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(measurement.check_source.energy)} were expected" + ) + calibration_channels += list(peaks) + calibration_energies += measurement.check_source.energy + + inds = np.argsort(calibration_channels) + calibration_channels = np.array(calibration_channels)[inds] + calibration_energies = np.array(calibration_energies)[inds] + + return calibration_channels, calibration_energies + + +def get_calibration_curve( + check_source_measurements: List[CheckSourceMeasurement], + background_measurement: Measurement, + channel_nb: int, +): + + calibration_channels, calibration_energies = get_calibration_data( + check_source_measurements, + background_measurement, + channel_nb, + ) + + # linear fit for calibration curve + coeff = np.polyfit( + calibration_channels, + calibration_energies, + 1, + ) + + return coeff + + +def gauss(x, b, m, *args): + """Creates a multipeak gaussian with a linear addition of the form: + m * x + b + Sum_i (A_i * exp(-(x - x_i)**2) / (2 * sigma_i**2)""" + + out = m * x + b + if np.mod(len(args), 3) == 0: + for i in range(int(len(args) / 3)): + out += args[i * 3 + 0] * np.exp( + -((x - args[i * 3 + 1]) ** 2) / (2 * args[i * 3 + 2] ** 2) + ) + else: + raise ValueError("Incorrect number of gaussian arguments given.") + return out + + +def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): + + search_start = np.argmin( + np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals) + ) + search_end = np.argmin( + np.abs((peak_ergs[-1] + search_width / (2 * len(peak_ergs))) - xvals) + ) + + slope_guess = (hist[search_end] - hist[search_start]) / ( + xvals[search_end] - xvals[search_start] + ) + + guess_parameters = [0, slope_guess] + + for i in range(len(peak_ergs)): + peak_ind = np.argmin(np.abs((peak_ergs[i]) - xvals)) + guess_parameters += [ + hist[peak_ind], + peak_ergs[i], + search_width / (3 * len(peak_ergs)), + ] + + parameters, covariance = curve_fit( + gauss, + xvals[search_start:search_end], + hist[search_start:search_end], + p0=guess_parameters, + ) + + return parameters, covariance + + +def get_multipeak_area(hist, bins, peak_ergs, search_width=600): + # get midpoints of every bin + xvals = np.diff(bins) / 2 + bins[:-1] + + parameters, covariance = fit_peak_gauss( + hist, xvals, peak_ergs, search_width=search_width + ) + + areas = [] + peak_starts = [] + peak_ends = [] + all_peak_params = [] + # peak_amplitudes = [] + for i in range(len(peak_ergs)): + # peak_amplitudes += [parameters[2 + 3 * i]] + mean = parameters[2 + 3 * i + 1] + sigma = np.abs(parameters[2 + 3 * i + 2]) + peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) + peak_end = np.argmin(np.abs((mean + 3 * sigma) - xvals)) + + peak_starts += [peak_start] + peak_ends += [peak_end] + + # Use unimodal gaussian to estimate counts from just one peak + peak_params = [parameters[0], parameters[1], parameters[2 + 3 * i], mean, sigma] + all_peak_params += [peak_params] + gross_area = np.trapezoid( + gauss(xvals[peak_start:peak_end], *peak_params), + x=xvals[peak_start:peak_end], + ) + + # Cut off trapezoidal area due to compton scattering and noise + trap_cutoff_area = np.trapezoid( + parameters[0] + parameters[1] * xvals[peak_start:peak_end], + x=xvals[peak_start:peak_end], + ) + area = gross_area - trap_cutoff_area + areas += [area] + + return areas + + +def group_close_values(data, threshold=200): + # Sort the data to group values sequentially + data.sort() + + # Initialize groups and a temporary group + groups = [] + temp_group = [data[0]] + + for i in range(1, len(data)): + # Check if the current value is within the threshold of the last value in the temp group + if abs(data[i] - temp_group[-1]) < threshold: + temp_group.append(data[i]) + else: + # Commit the temp group to groups and start a new group + groups.append(tuple(temp_group)) + temp_group = [data[i]] + + # Add the last group + groups.append(tuple(temp_group)) + + return groups + + +def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): + + areas = [] + # organize peak energies into tuples, in which peak energies close enough + # to have overlapping peaks will be paired together + erg_groups = group_close_values(peak_ergs, threshold=overlap_width) + # print(erg_groups) + + for erg_group in erg_groups: + areas += get_multipeak_area( + hist, bins, erg_group, search_width=len(erg_group) * search_width + ) + # print(areas) + return areas + + +def get_channel(filename): + """ + Extract the channel number from a given filename string. + + Parameters + ---------- + filename : str + The input filename string containing the channel information. + Should look something like : "Data_CH@V...CSV" + + Returns + ------- + int + The extracted channel number. + + Example + ------- + >>> get_channel("Data_CH4@V1725_292_Background_250322.CSV") + 4 + """ + return int(filename.split("@")[0][7:]) + + +def sort_compass_files(directory: str) -> dict: + """Gets Compass csv data filenames + and sorts them according to channel and ending number. + The filenames need to be sorted by ending number because only + the first csv file for each channel contains a header. + + Example of sorted filenames in directory: + 1st file: Data_CH4@...22.CSV + 2nd file: Data_CH4@...22_1.CSV + 3rd file: Data_CH4@...22_2.CSV""" + + filenames = os.listdir(directory) + data_filenames = {} + for filename in filenames: + if filename.lower().endswith(".csv"): + ch = get_channel(filename) + # initialize filenames for each channel + if ch not in data_filenames.keys(): + data_filenames[ch] = [] + + data_filenames[ch].append(filename) + # Sort filenames by number at end + for ch in data_filenames.keys(): + data_filenames[ch] = np.sort(data_filenames[ch]) + + return data_filenames + + +def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndarray]]: + """ + From a directory with unprocessed Compass data CSV files, + this returns dictionaries of detector pulse times and energies + with digitizer channels as the keys to the dictionaries. + + This function is also built to be able to read-in problematic + Compass CSV files that have been incorrectly post-processed to + reduce waveform data. + + Args: + directory: directory containing CSV files with Compass data + + Returns: + time values and energy values for each channel + """ + + time_values = {} + energy_values = {} + + data_filenames = sort_compass_files(directory) + + for ch in data_filenames.keys(): + # Initialize time_values and energy_values for each channel + time_values[ch] = np.empty(0) + energy_values[ch] = np.empty(0) + for i, filename in enumerate(data_filenames[ch]): + + # only the first file has a header + if i == 0: + header = 0 + else: + header = None + + csv_file_path = os.path.join(directory, filename) + + df = pd.read_csv(csv_file_path, delimiter=";", header=header) + + # read the header and store in names + if i == 0: + names = df.columns.values + else: + # apply the column names if not the first file + df.columns = names + + time_data = df["TIMETAG"].to_numpy() + energy_data = df["ENERGY"].to_numpy() + + # Extract and append the energy data to the list + time_values[ch] = np.concatenate([time_values[ch], time_data]) + energy_values[ch] = np.concatenate([energy_values[ch], energy_data]) + + return time_values, energy_values + + +def get_start_stop_time(directory: str) -> Tuple[datetime.datetime, datetime.datetime]: + """Obtains count start and stop time from the run.info file.""" + + info_file = Path(directory).parent / "run.info" + if info_file.exists(): + time_format = "%Y/%m/%d %H:%M:%S.%f%z" + with open(info_file, "r") as file: + lines = file.readlines() + else: + raise FileNotFoundError( + f"Could not find run.info file in parent directory {Path(directory).parent}" + ) + + start_time, stop_time = None, None + for line in lines: + if "time.start=" in line: + # get start time string while cutting off '\n' newline + time_string = line.split("=")[1].replace("\n", "") + start_time = datetime.datetime.strptime(time_string, time_format) + elif "time.stop=" in line: + # get stop time string while cutting off '\n' newline + time_string = line.split("=")[1].replace("\n", "") + stop_time = datetime.datetime.strptime(time_string, time_format) + + if None in (start_time, stop_time): + raise ValueError(f"Could not find time.start or time.stop in file {info_file}.") + else: + return start_time, stop_time + + +def get_live_time_from_root(root_filename: str, channel: int) -> Tuple[float, float]: + """ + Gets live and real count time from Compass root file. + Live time is defined as the difference between the actual time that + a count is occurring and the "dead time," in which the output of detector + pulses is saturated such that additional signals cannot be processed.""" + + with uproot.open(root_filename) as root_file: + live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 + real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 + return live_count_time, real_count_time + def subtract_background(counts, background_directory, savefile=None): # Check if background subtracted counts have already been saved diff --git a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py b/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py deleted file mode 100644 index 6cfe78f..0000000 --- a/libra_toolbox/neutron_detection/activation_foils/peak_fitting.py +++ /dev/null @@ -1,384 +0,0 @@ -import numpy as np -from scipy.signal import find_peaks -from scipy.optimize import curve_fit - -from typing import List, Dict, Union -import datetime -from libra_toolbox.neutron_detection.activation_foils import CheckSource -from libra_toolbox.neutron_detection.activation_foils.compass import ( - Detector, - Measurement, - CheckSourceMeasurement, -) - - -def get_peak_inputs(samples): - default_inputs = { - "Na22": {"prom_factor": 0.075, "width": [10, 150], "start_index": 100}, - "Co60": {"prom_factor": 0.2, "width": [10, 150], "start_index": 400}, - "Ba133": {"prom_factor": 0.1, "width": [10, 200], "start_index": 100}, - "Mn54": {"prom_factor": 0.2, "width": [10, 100], "start_index": 100}, - } - - defaults = {"prom_factor": 0.075, "width": [10, 150], "start_index": 100} - - peak_inputs = {} - for sample in samples: - if sample in default_inputs.keys(): - peak_inputs[sample] = default_inputs[sample] - else: - peak_inputs[sample] = defaults - return peak_inputs - - -def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: - """Returns the peak indices of the histogram - - Args: - hist: a histogram - source: the type of source (eg. "Na22", "Co60", "Ba133", "Mn54") - - Returns: - the peak indices in ``hist`` - """ - start_index = 100 - prominence = 0.10 * np.max(hist[start_index:]) - height = 0.10 * np.max(hist[start_index:]) - width = [10, 150] - indices = None - distance = 30 - if "na22" in source.lower(): - # find 511 keV peak first - prominence = 0.01 * np.max(hist[start_index:]) - height = 0.9 * np.max(hist[start_index:]) - width = [10, 200] - elif "co60" in source.lower(): - start_index = 400 - height = 0.60 * np.max(hist[start_index:]) - prominence = None - elif "ba133" in source.lower(): - width = [10, 200] - elif "mn54" in source.lower(): - height = 0.6 * np.max(hist[start_index:]) - peaks, peak_data = find_peaks( - hist[start_index:], - prominence=prominence, - height=height, - width=width, - distance=distance, - ) - peaks = np.array(peaks) + start_index - if "na22" in source.lower(): - # Find 1275 keV peak - peak_511 = peaks[0] - start_index = peak_511 + 100 - prominence = 0.5 * np.max(hist[start_index:]) - height = 0.10 * np.max(hist[start_index:]) - - high_peaks, peak_data = find_peaks( - hist[start_index:], - prominence=prominence, - height=height, - width=width, - distance=distance, - ) - high_peaks = np.array(high_peaks) + start_index - peaks = [peak_511, high_peaks[0]] - - if indices: - peaks = peaks[[indices]][0] - - return peaks - - -def get_calibration_data( - check_source_measurements: List[CheckSourceMeasurement], - background_measurement: Measurement, - channel_nb: int, -): - background_detector = [ - detector - for detector in background_measurement.detectors - if detector.channel_nb == detector.channel_nb - ][0] - - calibration_energies = [] - calibration_channels = [] - - for measurement in check_source_measurements.values(): - for detector in measurement.detectors: - if detector.channel_nb != channel_nb: - continue - - sample = measurement.name[:-2] - - hist, bin_edges = detector.get_energy_hist_background_substract( - background_detector, bins="double" - ) - peaks_ind = get_peaks(hist, sample) - peaks = bin_edges[peaks_ind] - - if len(peaks) != len(measurement.check_source.energy): - raise ValueError( - f"SciPy find_peaks() found {len(peaks)} photon peaks, while {len(measurement.check_source.energy)} were expected" - ) - calibration_channels += list(peaks) - calibration_energies += measurement.check_source.energy - - inds = np.argsort(calibration_channels) - calibration_channels = np.array(calibration_channels)[inds] - calibration_energies = np.array(calibration_energies)[inds] - - return calibration_channels, calibration_energies - - -def get_calibration_curve( - check_source_measurements: List[CheckSourceMeasurement], - background_measurement: Measurement, - channel_nb: int, -): - - calibration_channels, calibration_energies = get_calibration_data( - check_source_measurements, - background_measurement, - channel_nb, - ) - - # linear fit for calibration curve - coeff = np.polyfit( - calibration_channels, - calibration_energies, - 1, - ) - - return coeff - - -def gauss(x, b, m, *args): - """Creates a multipeak gaussian with a linear addition of the form: - m * x + b + Sum_i (A_i * exp(-(x - x_i)**2) / (2 * sigma_i**2)""" - - out = m * x + b - if np.mod(len(args), 3) == 0: - for i in range(int(len(args) / 3)): - out += args[i * 3 + 0] * np.exp( - -((x - args[i * 3 + 1]) ** 2) / (2 * args[i * 3 + 2] ** 2) - ) - else: - raise ValueError("Incorrect number of gaussian arguments given.") - return out - - -def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): - - search_start = np.argmin( - np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals) - ) - search_end = np.argmin( - np.abs((peak_ergs[-1] + search_width / (2 * len(peak_ergs))) - xvals) - ) - - slope_guess = (hist[search_end] - hist[search_start]) / ( - xvals[search_end] - xvals[search_start] - ) - - guess_parameters = [0, slope_guess] - - for i in range(len(peak_ergs)): - peak_ind = np.argmin(np.abs((peak_ergs[i]) - xvals)) - guess_parameters += [ - hist[peak_ind], - peak_ergs[i], - search_width / (3 * len(peak_ergs)), - ] - - parameters, covariance = curve_fit( - gauss, - xvals[search_start:search_end], - hist[search_start:search_end], - p0=guess_parameters, - ) - - return parameters, covariance - - -def get_multipeak_area(hist, bins, peak_ergs, search_width=600): - # get midpoints of every bin - xvals = np.diff(bins) / 2 + bins[:-1] - - parameters, covariance = fit_peak_gauss( - hist, xvals, peak_ergs, search_width=search_width - ) - - areas = [] - peak_starts = [] - peak_ends = [] - all_peak_params = [] - # peak_amplitudes = [] - for i in range(len(peak_ergs)): - # peak_amplitudes += [parameters[2 + 3 * i]] - mean = parameters[2 + 3 * i + 1] - sigma = np.abs(parameters[2 + 3 * i + 2]) - peak_start = np.argmin(np.abs((mean - 3 * sigma) - xvals)) - peak_end = np.argmin(np.abs((mean + 3 * sigma) - xvals)) - - peak_starts += [peak_start] - peak_ends += [peak_end] - - # Use unimodal gaussian to estimate counts from just one peak - peak_params = [parameters[0], parameters[1], parameters[2 + 3 * i], mean, sigma] - all_peak_params += [peak_params] - gross_area = np.trapezoid( - gauss(xvals[peak_start:peak_end], *peak_params), - x=xvals[peak_start:peak_end], - ) - - # Cut off trapezoidal area due to compton scattering and noise - trap_cutoff_area = np.trapezoid( - parameters[0] + parameters[1] * xvals[peak_start:peak_end], - x=xvals[peak_start:peak_end], - ) - area = gross_area - trap_cutoff_area - areas += [area] - - return areas - - -def group_close_values(data, threshold=200): - # Sort the data to group values sequentially - data.sort() - - # Initialize groups and a temporary group - groups = [] - temp_group = [data[0]] - - for i in range(1, len(data)): - # Check if the current value is within the threshold of the last value in the temp group - if abs(data[i] - temp_group[-1]) < threshold: - temp_group.append(data[i]) - else: - # Commit the temp group to groups and start a new group - groups.append(tuple(temp_group)) - temp_group = [data[i]] - - # Add the last group - groups.append(tuple(temp_group)) - - return groups - - -def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): - - areas = [] - # organize peak energies into tuples, in which peak energies close enough - # to have overlapping peaks will be paired together - erg_groups = group_close_values(peak_ergs, threshold=overlap_width) - # print(erg_groups) - - for erg_group in erg_groups: - areas += get_multipeak_area( - hist, bins, erg_group, search_width=len(erg_group) * search_width - ) - # print(areas) - return areas - - -# should be a method of a class called CheckSourceMeasurement -def get_expected_activity( - check_source_meas: CheckSourceMeasurement, -) -> float: - """ - Calculates the expected activity of a check source given the - half-life and the date of the measurement. - The expected activity is calculated using the formula: - .. math:: A(t) = A_0 e^{-\\lambda t} - - where :math:`A_0` is the initial activity, :math:`\\lambda` is the decay constant - and :math:`t` is the time since the measurement date. - - Args: - check_source_meas: _description_ - - Returns: - the expected activity of the check source in Bq - """ - # expected activity - decay_constant = np.log(2) / check_source_meas.check_source.half_life - - # Convert date to datetime if needed - if isinstance( - check_source_meas.check_source.activity_date, datetime.date - ) and not isinstance( - check_source_meas.check_source.activity_date, datetime.datetime - ): - activity_datetime = datetime.datetime.combine( - check_source_meas.check_source.activity_date, datetime.time.min - ) - # add a timezone - activity_datetime = activity_datetime.replace(tzinfo=datetime.timezone.utc) - else: - activity_datetime = check_source_meas.check_source.activity_date - - time = (check_source_meas.start_time - activity_datetime).total_seconds() - act_expec = check_source_meas.check_source.activity * np.exp(-decay_constant * time) - return act_expec - - -# should be a method of a class called CheckSourceMeasurement -def compute_detection_efficiency( - check_source_detector: Detector, - background_detector: Detector, - check_source_meas: CheckSourceMeasurement, - calibration_coeffs: np.ndarray, -) -> Union[np.ndarray, float]: - """ - Computes the detection efficiency of a check source given the - check source data and the calibration coefficients. - The detection efficiency is calculated using the formula: - .. math:: \\eta = \\frac{A_{meas}}{A_{expec}} - - where :math:`A_{meas}` is the measured activity and :math:`A_{expec}` is the expected activity. - The measured activity is calculated using the formula: - .. math:: A_{meas} = \\frac{A_{peak}}{I \\cdot t_{live}} - - where :math:`A_{peak}` is the area of the peak, :math:`I` is the intensity of the check source - and :math:`t_{live}` is the live count time of the detector. - - Args: - check_source_detector: _description_ - background_detector: _description_ - check_source_meas: _description_ - check_source_data: _description_ - calibration_coeffs: _description_ - - Returns: - the detection efficiency - """ - - hist, bin_edges = check_source_detector.get_energy_hist_background_substract( - background_detector, bins="double" - ) - - calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) - - areas = get_multipeak_area( - hist, - calibrated_bin_bedges, - check_source_meas.check_source.energy, - search_width=800, - ) - - act_meas = np.array(areas) / ( - np.array(check_source_meas.check_source.intensity) - * check_source_detector.live_count_time - ) - act_meas_err = np.sqrt(np.array(areas)) / ( - np.array(check_source_meas.check_source.intensity) - * check_source_detector.live_count_time - ) - - act_expec = get_expected_activity(check_source_meas) - - detection_efficiency = act_meas / act_expec - - return detection_efficiency From e77832667b4d19cd9c64520b15e700fa9f493e87 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 14:10:37 -0400 Subject: [PATCH 071/137] ran notebook --- example.ipynb | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 69 insertions(+), 1 deletion(-) diff --git a/example.ipynb b/example.ipynb index afab135..b1c52d3 100644 --- a/example.ipynb +++ b/example.ipynb @@ -542,12 +542,80 @@ "cell_type": "code", "execution_count": null, "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:271: RuntimeWarning: invalid value encountered in sqrt\n", + " act_meas_err = np.sqrt(np.array(areas)) / (\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.0, 10.405620768468069)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "channel_nb = 4\n", + "for name, measurement in all_measurements.items():\n", + " try:\n", + " efficiency = measurement.compute_detection_efficiency(\n", + " background_measurement=background_meas,\n", + " calibration_coeffs=calibration_coeffs[channel_nb],\n", + " channel_nb=channel_nb,\n", + " )\n", + " plt.scatter(\n", + " measurement.check_source.energy,\n", + " efficiency * 100,\n", + " label=name,\n", + " )\n", + " except:\n", + " continue\n", + "plt.xlabel(\"Energy (keV)\")\n", + "plt.ylabel(\"Detection efficiency (%)\")\n", + "plt.legend()\n", + "plt.ylim(bottom=0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Processing niobium_1...\n" + "Processing niobium_1...\n", + "\n", + "Processing niobium_2...\n", + "\n", + "Processing niobium_3...\n", + "\n", + "Processing zirconium_1...\n", + "\n", + "Processing zirconium_2...\n", + "\n", + "Processing zirconium_3...\n", + "\n" ] } ], From e6ec09ed3fe7e2cdec6fbc42ec37b9d9c696d91b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 14:18:50 -0400 Subject: [PATCH 072/137] nuclide class --- .../activation_foils/__init__.py | 27 +++-------- .../activation_foils/calibration.py | 47 +++++++++---------- 2 files changed, 29 insertions(+), 45 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 9e5c3d6..34b4b03 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -1,33 +1,18 @@ from datetime import datetime +from . import calibration +from dataclasses import dataclass + +@dataclass class CheckSource: - nuclide: str - energy: list - intensity: list + nuclide: calibration.Nuclide activity_date: datetime.date activity: float - half_life: float - - def __init__( - self, - nuclide: str, - energy: list, - intensity: list, - activity_date: datetime.date, - activity: float, - half_life: float, - ): - self.nuclide = nuclide - self.energy = energy - self.intensity = intensity - self.activity_date = activity_date - self.activity = activity - self.half_life = half_life class ActivationFoil: - nuclide: str + nuclide: calibration.Nuclide mass: float diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index cae463f..bb089ee 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -1,47 +1,46 @@ -import datetime +from dataclasses import dataclass +from typing import List -from libra_toolbox.neutron_detection.activation_foils import CheckSource -uCi_to_Bq = 3.7e4 +@dataclass +class Nuclide: + """ + Class to hold the information of a nuclide. + """ -check_source_ba133 = CheckSource( - nuclide="Ba133", + name: str + energy: List[float] + intensity: List[float] + half_life: float + + +ba133 = Nuclide( + name="Ba133", energy=[80.9979, 276.3989, 302.8508, 356.0129, 383.8485], intensity=[0.329, 0.0716, 0.1834, 0.6205, 0.0894], - activity_date=datetime.date(2014, 3, 19), - activity=1 * uCi_to_Bq, half_life=10.551 * 365.25 * 24 * 3600, ) - -check_source_co60 = CheckSource( - nuclide="Co60", +co60 = Nuclide( + name="Co60", energy=[1173.228, 1332.492], intensity=[0.9985, 0.999826], - activity_date=datetime.date(2014, 3, 19), - activity=0.872 * uCi_to_Bq, half_life=1925.28 * 24 * 3600, ) -check_source_na22 = CheckSource( - nuclide="Na22", +na22 = Nuclide( + name="Na22", energy=[511, 1274.537], intensity=[1.80, 0.9994], - activity_date=datetime.date(2014, 3, 19), - activity=5 * uCi_to_Bq, half_life=2.6018 * 365.25 * 24 * 3600, ) -check_source_cs137 = CheckSource( - nuclide="Cs137", +cs137 = Nuclide( + name="Cs137", energy=[661.657], intensity=[0.851], - activity_date=datetime.date(2014, 3, 19), - activity=4.66 * uCi_to_Bq, half_life=30.08 * 365.25 * 24 * 3600, ) -check_source_mn54 = CheckSource( - nuclide="Mn54", +mn54 = Nuclide( + name="Mn54", energy=[834.848], intensity=[0.99976], - activity_date=datetime.date(2016, 5, 2), - activity=6.27 * uCi_to_Bq, half_life=312.20 * 24 * 3600, ) From b5432e9a96d75db3ea43cfa1479cb63d2ecc1c52 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 14:19:51 -0400 Subject: [PATCH 073/137] docstrings --- .../neutron_detection/activation_foils/calibration.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index bb089ee..4b2fbed 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -6,6 +6,17 @@ class Nuclide: """ Class to hold the information of a nuclide. + + Attributes + ---------- + name : + The name of the nuclide. + energy : + The energy of the gamma rays emitted by the nuclide. + intensity : + The intensity of the gamma rays emitted by the nuclide. (must sum to 1) + half_life : + The half-life of the nuclide in seconds. """ name: str From f9f939cb8b5765b9dd07fbb933a9a07b46e73172 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 15:04:04 -0400 Subject: [PATCH 074/137] changed check source files used --- example.ipynb | 105 ++++++++++++++++++++++---------------------------- 1 file changed, 47 insertions(+), 58 deletions(-) diff --git a/example.ipynb b/example.ipynb index c1f19cf..0ae9427 100644 --- a/example.ipynb +++ b/example.ipynb @@ -22,24 +22,28 @@ "\n", "check_source_measurements = {\n", " \"Co60_1\": {\n", - " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_240317/UNFILTERED\",\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_250318_run2/UNFILTERED\",\n", " \"check_source\": co60_checksource,\n", " },\n", " \"Co60_2\": {\n", - " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_250318_run2/UNFILTERED\",\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_250319_run3/UNFILTERED\",\n", " \"check_source\": co60_checksource,\n", " },\n", " \"Co60_3\": {\n", - " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Marc2014_250319_run3/UNFILTERED\",\n", + " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Mar2014_250320_run4/UNFILTERED\",\n", " \"check_source\": co60_checksource,\n", " },\n", " \"Co60_4\": {\n", - " \"directory\": f\"{run_dir}/Co60_0_872uCi_19Marc2014_250320_run4/UNFILTERED\",\n", - " \"check_source\": co60_checksource,\n", + " \"directory\": f\"{run_dir}/Co60_1_0uCi_Jan2006_250318/UNFILTERED\",\n", + " \"check_source\": CheckSource(co60, activity_date=date(2006, 1, 1), activity=1.0*uCi_to_Bq),\n", + " },\n", + " \"Co60_5\": {\n", + " \"directory\": f\"{run_dir}/Co60_1_0uCi_Feb2006_250320_run1/UNFILTERED\",\n", + " \"check_source\": CheckSource(co60, activity_date=date(2006, 2, 1), activity=1.0*uCi_to_Bq),\n", " },\n", " \"Cs137_1\": {\n", - " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep23_240317/UNFILTERED\",\n", - " \"check_source\": cs137_checksource,\n", + " \"directory\": f\"{run_dir}/Cs137_4_66uCi_19Mar2014_250318/UNFILTERED\",\n", + " \"check_source\": CheckSource(cs137, activity_date=date(2014, 3, 19), activity=4.66*uCi_to_Bq),\n", " },\n", " \"Cs137_2\": {\n", " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run2/UNFILTERED\",\n", @@ -65,23 +69,16 @@ " \"directory\": f\"{run_dir}/Mn54_6_27uCi_2May2016_250320_run3/UNFILTERED\",\n", " \"check_source\": mn54_checksource,\n", " },\n", - " \"Na22_1\": {\n", - " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep23_240317/UNFILTERED\",\n", - " \"check_source\": na22_checksource,\n", - " },\n", + "\n", " \"Na22_2\": {\n", - " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep23_240317_run2/UNFILTERED\",\n", - " \"check_source\": na22_checksource,\n", - " },\n", - " \"Na22_3\": {\n", " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run3/UNFILTERED\",\n", " \"check_source\": na22_checksource,\n", " },\n", - " \"Na22_4\": {\n", + " \"Na22_3\": {\n", " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run4/UNFILTERED\",\n", " \"check_source\": na22_checksource,\n", " },\n", - " \"Na22_5\": {\n", + " \"Na22_4\": {\n", " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250319_run5/UNFILTERED\",\n", " \"check_source\": na22_checksource,\n", " },\n", @@ -100,41 +97,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", + "Processing Co60_5...\n", + "\n", "Processing Cs137_1...\n", - "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", - "Processing Na22_1...\n", - "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_2...\n", - "No root file found, assuming all counts are live\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", - "Processing Na22_5...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -172,7 +163,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -322,7 +313,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_958975/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_964087/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -410,7 +401,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -455,12 +446,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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KyIWp1WqEh4fzFCA5nEqluqEeqkoMVURE5DByuZy3+6ImgyexiYiIiOyAoYqIiIjIDhiqiIiIiOyAoYqIiIjIDhiqiIiIiOyAoYpcSpmZt7QgIiLnxFBFLiX5gsHRJRAREVnFUEUuRUDAQ33jE7QRERHZG0MVuRQZZFArK35tjUVlDq6GiIjobwxV5FIEBAI8NQCAU5fyHVwNERHR3xiqyOUo5DLIHF0EERHRdRiqyKUIXvxHREROiqGKiIiIyA4YqsilsKOKiIicFUMVERERkR0wVJFLERxURUREToqhioiIiMgOGKrIpbCfioiInBVDFbmU9JwiR5dARERkFUMVuZSsvBJoee8/IiJyQgxV5FIUchl83FSOLoOIiKgKhioiIiIiO2CoIpckAKTq8xxdBhERkYShilySr7sKBSVlji6DiIhIwlBFLslDo3R0CURERBYYqoiIiIjsgKGKXBNnASUiIifDUEUuiZmKiIicDUMVERERkR0wVBERERHZAUMVuSTBE4BERORkGKqIiIiI7IChilxKubmih0qwo4qIiJwMQxW5jKy8YgCAXCZzcCVERERVMVSRy8g0lAAAAnUaB1dCRERUFUMVuRwZ2FNFRETOh6GKXBaHVRERkTNhqCLXxdHqRETkRBiqyGUxUhERkTNhqCKXJARwNMPo6DKIiIgkDFXkknRaJYpM5Y4ug4iISMJQRS7JQ6uEXM6rAImIyHkwVBERERHZAUMVuS6OVCciIifCUEVERERkBwxVRERERHbAUEUui2f/iIjImTBUEREREdkBQxURERGRHTh1qCovL8cbb7yBsLAwuLm5oXXr1pg1axbENfd8E0JgxowZaN68Odzc3BAZGYm//vrLYjvZ2dkYOXIkdDodfHx8MG7cOOTn51u0OXz4MO6++25otVqEhoZi7ty5VepZvXo12rVrB61Wi86dO2Pjxo0N88aJiIjI5Th1qHr//fexZMkSfPLJJzh27Bjef/99zJ07F4sWLZLazJ07FwsXLsTSpUuxZ88eeHh4ICoqCsXFxVKbkSNH4siRI9i0aRPWr1+PHTt24Nlnn5XWG41GDBo0CC1btkRiYiI++OADvPnmm/j888+lNrt378Zjjz2GcePG4eDBgxg6dCiGDh2KlJSUxvkwqCreUJmIiJyITAjn/WZ64IEHEBQUhP/7v/+Tlg0fPhxubm745ptvIIRASEgIXn75Zfz3v/8FABgMBgQFBSE2NhYjRozAsWPH0KFDB+zbtw+9evUCAMTFxWHIkCG4cOECQkJCsGTJErz22mvQ6/VQq9UAgGnTpmHt2rU4fvw4AODRRx9FQUEB1q9fL9Vyxx13oFu3bli6dKlN78doNMLb2xsGgwE6nc4un9HNJPmCAQ9+8gce79MCx/RG5BSYsG3KAEeXRURETZyt399O3VN15513Ij4+HidOnAAAHDp0CH/88Qfuv/9+AEBaWhr0ej0iIyOl13h7e6Nv375ISEgAACQkJMDHx0cKVAAQGRkJuVyOPXv2SG369+8vBSoAiIqKQmpqKnJycqQ21+6nsk3lfqwpKSmB0Wi0eBAREVHTpHR0ATWZNm0ajEYj2rVrB4VCgfLycsyePRsjR44EAOj1egBAUFCQxeuCgoKkdXq9HoGBgRbrlUol/Pz8LNqEhYVV2UblOl9fX+j1+hr3Y82cOXPw1ltv1fVtUzWKSnkDZSIicl5O3VP1/fffY8WKFVi5ciUOHDiA5cuX48MPP8Ty5csdXZpNpk+fDoPBID3Onz/v6JJc2rGMip4+bzeVgyshIiKqyql7qqZMmYJp06ZhxIgRAIDOnTvj7NmzmDNnDkaPHo3g4GAAQGZmJpo3by69LjMzE926dQMABAcHIysry2K7ZWVlyM7Oll4fHByMzMxMizaVz2trU7neGo1GA41GU9e3TTVQymVQKyv+FnDawYBERHRTcuqeqsLCQsjlliUqFAqYzWYAQFhYGIKDgxEfHy+tNxqN2LNnDyIiIgAAERERyM3NRWJiotRmy5YtMJvN6Nu3r9Rmx44dKC0tldps2rQJbdu2ha+vr9Tm2v1UtqncDzW+s1cKUW5mtCIiIufg1KHqwQcfxOzZs7FhwwacOXMGa9aswUcffYSHHnoIACCTyTBx4kS88847WLduHZKTk/Hkk08iJCQEQ4cOBQC0b98egwcPxjPPPIO9e/di165dGD9+PEaMGIGQkBAAwOOPPw61Wo1x48bhyJEj+O6777BgwQJMnjxZquWll15CXFwc5s2bh+PHj+PNN9/E/v37MX78+Eb/XAjwc6+4qKDAVObgSoiIiCo49em/RYsW4Y033sB//vMfZGVlISQkBM899xxmzJghtXnllVdQUFCAZ599Frm5ubjrrrsQFxcHrVYrtVmxYgXGjx+PgQMHQi6XY/jw4Vi4cKG03tvbG7///jtiYmLQs2dP+Pv7Y8aMGRZzWd15551YuXIlXn/9dbz66qsIDw/H2rVr0alTp8b5MMiCRunUfw8QEdFNyKnnqWpqOE/VjVm++wxmrT+KmAFt8FdmHjam6HH4zUHQaTlwnYiIGk6TmKeKiIiIyFUwVBERERHZAUMVERERkR0wVBERERHZAUMVERERkR0wVBERERHZAUMVERERkR0wVBERERHZAUMVERERkR0wVBERERHZAUMVERERkR0wVJHLOHUpH2Vm3qqSiIicE0MVuQxjUSn8PNSOLoOIiMgqhipyKTqt0tElEBERWcVQRURERGQHDFXkUq4fUVVsKndIHURERNdjqCLXcjVVVY6tOqbPc2AxREREf2OoIpekVvJXl4iInAu/mcilcEIFIiJyVgxVRERERHbAUEVERERkBwxV5FIETwASEZGTYqgi18JMRUREToqhioiIiMgOGKqIiIiI7IChilwKz/4REZGzYqgiIiIisgOGKnIZQvofIiIi58NQRURERGQHDFVEREREdsBQRS5D8NQfERE5MYYqIiIiIjtgqCKXkVdcynHqRETktBiqyGX8lZUPmaOLICIiqgZDFbmMcrNAoE7j6DKIiIisYqgil6GUyyCXsa+KiIicE0MVuYxrx1NVhqszlwscUwwREdF1GKrIJXlolJDJgOwCk6NLISIiAsBQRS5Mp1U5ugQiIiIJQxURERGRHTBUEREREdkBQxURERGRHTBUkcvgvf+IiMiZMVSRyxK8aQ0RETkRhipyXcxURETkRBiqiIiIiOyAoYqIiIjIDhiqiIiIiOyAoYpcBgemExGRM2OoIiIiIrIDhipyWey3IiIiZ8JQRURERGQHDFXkOtg1RUREToyhioiIiMgOGKrIZeSXlDm6BCIiomoxVJFLyCsuhbG4DEq5zNGlEBERWcVQRS7BVGYGAAR4aaRl5WaB3EKTo0oiIiKywFBFLkshk+H05QJHl0FERASAoYpcWIiPG2Qyng4kIiLnwFBFLou3rSEiImfi9KEqPT0dTzzxBJo1awY3Nzd07twZ+/fvl9YLITBjxgw0b94cbm5uiIyMxF9//WWxjezsbIwcORI6nQ4+Pj4YN24c8vPzLdocPnwYd999N7RaLUJDQzF37twqtaxevRrt2rWDVqtF586dsXHjxoZ500RERORynDpU5eTkoF+/flCpVPj1119x9OhRzJs3D76+vlKbuXPnYuHChVi6dCn27NkDDw8PREVFobi4WGozcuRIHDlyBJs2bcL69euxY8cOPPvss9J6o9GIQYMGoWXLlkhMTMQHH3yAN998E59//rnUZvfu3Xjssccwbtw4HDx4EEOHDsXQoUORkpLSOB8GEREROTWZEMJpz6FMmzYNu3btws6dO62uF0IgJCQEL7/8Mv773/8CAAwGA4KCghAbG4sRI0bg2LFj6NChA/bt24devXoBAOLi4jBkyBBcuHABISEhWLJkCV577TXo9Xqo1Wpp32vXrsXx48cBAI8++igKCgqwfv16af933HEHunXrhqVLl1qtr6SkBCUlJdJzo9GI0NBQGAwG6HS6G/+AbiJX8kvQ853NeLBLc9wW4AkA2JicgeY+bvh6bB8HV0dERE2Z0WiEt7d3rd/fTt1TtW7dOvTq1Qv//ve/ERgYiO7du+OLL76Q1qelpUGv1yMyMlJa5u3tjb59+yIhIQEAkJCQAB8fHylQAUBkZCTkcjn27Nkjtenfv78UqAAgKioKqampyMnJkdpcu5/KNpX7sWbOnDnw9vaWHqGhoTfwaRAREZEzc+pQdfr0aSxZsgTh4eH47bff8MILL+DFF1/E8uXLAQB6vR4AEBQUZPG6oKAgaZ1er0dgYKDFeqVSCT8/P4s21rZx7T6qa1O53prp06fDYDBIj/Pnz9fp/RMREZHrUDq6gJqYzWb06tUL7777LgCge/fuSElJwdKlSzF69GgHV1c7jUYDjUZTe0OqFwEAznv2moiIbjJO3VPVvHlzdOjQwWJZ+/btce7cOQBAcHAwACAzM9OiTWZmprQuODgYWVlZFuvLysqQnZ1t0cbaNq7dR3VtKtcTERHRzc2pQ1W/fv2QmppqsezEiRNo2bIlACAsLAzBwcGIj4+X1huNRuzZswcREREAgIiICOTm5iIxMVFqs2XLFpjNZvTt21dqs2PHDpSWlkptNm3ahLZt20pXGkZERFjsp7JN5X7IMdhPRUREzsKpQ9WkSZPw559/4t1338XJkyexcuVKfP7554iJiQEAyGQyTJw4Ee+88w7WrVuH5ORkPPnkkwgJCcHQoUMBVPRsDR48GM888wz27t2LXbt2Yfz48RgxYgRCQkIAAI8//jjUajXGjRuHI0eO4LvvvsOCBQswefJkqZaXXnoJcXFxmDdvHo4fP44333wT+/fvx/jx4xv9cyEiIiLnU69QdeDAASQnJ0vPf/75ZwwdOhSvvvoqTCb73eC2d+/eWLNmDb799lt06tQJs2bNwscff4yRI0dKbV555RVMmDABzz77LHr37o38/HzExcVBq9VKbVasWIF27dph4MCBGDJkCO666y6LOai8vb3x+++/Iy0tDT179sTLL7+MGTNmWMxldeedd0qhrmvXrvjhhx+wdu1adOrUyW7vl4iIiFxXveap6t27N6ZNm4bhw4fj9OnT6NixIx566CHs27cP0dHR+PjjjxugVNdn6zwXVJW1eao2JGcg2FuLb8b1dXB1RETUlDXoPFUnTpxAt27dAFTcuqV///5YuXIlYmNj8eOPP9arYKJ64aAqIiJyEvUKVUIImM1mAMDmzZsxZMgQAEBoaCguX75sv+qIiIiIXES9QlWvXr3wzjvv4H//+x+2b9+O6OhoABUznF8/QSZRQxLsqiIiIidRr1A1f/58HDhwAOPHj8drr72GNm3aAAB++OEH3HnnnXYtkIiIiMgV1GtG9a5du1pc/Vfpgw8+gFLp1JO0k4u6UlDNVaXsqCIiIidRr56q2267DVeuXKmyvLi4GLfffvsNF0V0vbNXCgEAQTqtxXJmKiIichb1ClVnzpxBeXl5leUlJSW4cOHCDRdFVB2ZzNEVEBERWVenc3Xr1q2Tfv7tt9/g7e0tPS8vL0d8fDzCwsLsVx1RLUpKq4Z7IiIiR6hTqKq89YtMJsPo0aMt1qlUKrRq1Qrz5s2zW3FEtTmuz3N0CURERADqGKoq56YKCwvDvn374O/v3yBFEdki0EuDs1cKHF0GERERgHpe/ZeWlmbvOojqTCGTQa106nuCExHRTaTe8x/Ex8cjPj4eWVlZUg9Wpa+++uqGCyMiIiJyJfUKVW+99Rbefvtt9OrVC82bN4eMl2QRERHRTa5eoWrp0qWIjY3FqFGj7F0PERERkUuq14AUk8nE29EQERERXaNeoerpp5/GypUr7V0LERERkcuq1+m/4uJifP7559i8eTO6dOkClUplsf6jjz6yS3FERERErqJeoerw4cPo1q0bACAlJcViHQetU6Pizf+IiMhJ1CtUbd261d51EBEREbk0zpxIREREZAf16qkaMGBAjaf5tmzZUu+CiIiIiFxRvUJV5XiqSqWlpUhKSkJKSkqVGy0TERER3QzqFarmz59vdfmbb76J/Pz8GyqIiIiIyBXZdUzVE088wfv+UYMwC+uX+fHiPyIichZ2DVUJCQnQarX23CQRAODwhVwAgErBayuIiMg51ev037BhwyyeCyGQkZGB/fv344033rBLYUTXEgLwdlMxVBERkdOqV6jy9va2eC6Xy9G2bVu8/fbbGDRokF0KI7qeUs6JZYmIyHnVK1QtW7bM3nUQ1Yrjp4iIyJnVK1RVSkxMxLFjxwAAHTt2RPfu3e1SFJGtCk3lji6BiIgIQD1DVVZWFkaMGIFt27bBx8cHAJCbm4sBAwZg1apVCAgIsGeNRFZ5aCp+fdMuFyDM38PB1RAR0c2uXqN+J0yYgLy8PBw5cgTZ2dnIzs5GSkoKjEYjXnzxRXvXSGSVl7YiVJWbzQ6uhIiIqJ49VXFxcdi8eTPat28vLevQoQMWL17MgepERER0U6pXT5XZbIZKpaqyXKVSwcxeA2ogopoJQImIiJxBvULVfffdh5deegkXL16UlqWnp2PSpEkYOHCg3YojIiIichX1ClWffPIJjEYjWrVqhdatW6N169YICwuD0WjEokWL7F0jERERkdOr15iq0NBQHDhwAJs3b8bx48cBAO3bt0dkZKRdiyMiIiJyFXXqqdqyZQs6dOgAo9EImUyGf/zjH5gwYQImTJiA3r17o2PHjti5c2dD1UpERETktOoUqj7++GM888wz0Ol0VdZ5e3vjueeew0cffWS34oiIiIhcRZ1C1aFDhzB48OBq1w8aNAiJiYk3XBQRERGRq6lTqMrMzLQ6lUIlpVKJS5cu3XBRRNZwQgUiInJmdQpVt9xyC1JSUqpdf/jwYTRv3vyGiyIiIiJyNXUKVUOGDMEbb7yB4uLiKuuKioowc+ZMPPDAA3YrjoiIiMhV1GlKhddffx0//fQTbr/9dowfPx5t27YFABw/fhyLFy9GeXk5XnvttQYplIjn/4iIyJnVKVQFBQVh9+7deOGFFzB9+nTptiEymQxRUVFYvHgxgoKCGqRQIiIiImdW58k/W7ZsiY0bNyInJwcnT56EEALh4eHw9fVtiPqIAAApF40w895/RETkxOo1ozoA+Pr6onfv3vashahaJaXl8HazvPJUIZcBAC7mFqNNoJcjyiIiIpLU695/RI1NJgM8NJZ/AwR6aQAAekPVCyeIiIgaG0MVERERkR0wVJHL4IgqIiJyZgxV5DqYqoiIyIkxVJFrYKAiIiInx1BFLoGZioiInB1DFREREZEdMFQRERER2QFDFbkEnv4jIiJnx1BFREREZAcMVeQaeN8/IiJycgxV5BIYqYiIyNkxVBERERHZgUuFqvfeew8ymQwTJ06UlhUXFyMmJgbNmjWDp6cnhg8fjszMTIvXnTt3DtHR0XB3d0dgYCCmTJmCsrIyizbbtm1Djx49oNFo0KZNG8TGxlbZ/+LFi9GqVStotVr07dsXe/fubYi3SURERC7IZULVvn378Nlnn6FLly4WyydNmoRffvkFq1evxvbt23Hx4kUMGzZMWl9eXo7o6GiYTCbs3r0by5cvR2xsLGbMmCG1SUtLQ3R0NAYMGICkpCRMnDgRTz/9NH777TepzXfffYfJkydj5syZOHDgALp27YqoqChkZWU1/JsnDqkiIiKn5xKhKj8/HyNHjsQXX3wBX19fabnBYMD//d//4aOPPsJ9992Hnj17YtmyZdi9ezf+/PNPAMDvv/+Oo0eP4ptvvkG3bt1w//33Y9asWVi8eDFMJhMAYOnSpQgLC8O8efPQvn17jB8/Hg8//DDmz58v7eujjz7CM888gzFjxqBDhw5YunQp3N3d8dVXXzXuh0FEREROySVCVUxMDKKjoxEZGWmxPDExEaWlpRbL27VrhxYtWiAhIQEAkJCQgM6dOyMoKEhqExUVBaPRiCNHjkhtrt92VFSUtA2TyYTExESLNnK5HJGRkVIba0pKSmA0Gi0eRERE1DQ5fahatWoVDhw4gDlz5lRZp9froVar4ePjY7E8KCgIer1eanNtoKpcX7mupjZGoxFFRUW4fPkyysvLrbap3IY1c+bMgbe3t/QIDQ217U1TFScy8xxdAhERUY2cOlSdP38eL730ElasWAGtVuvocups+vTpMBgM0uP8+fOOLsllGYvLoHNTWl13uaCkkashIiKqyqlDVWJiIrKystCjRw8olUoolUps374dCxcuhFKpRFBQEEwmE3Jzcy1el5mZieDgYABAcHBwlasBK5/X1kan08HNzQ3+/v5QKBRW21RuwxqNRgOdTmfxoPrRKOXwUFcNVUq5DOezCx1QERERkSWnDlUDBw5EcnIykpKSpEevXr0wcuRI6WeVSoX4+HjpNampqTh37hwiIiIAABEREUhOTra4Sm/Tpk3Q6XTo0KGD1ObabVS2qdyGWq1Gz549LdqYzWbEx8dLbajxyWQyBHu7Xg8mERE1TdbPpzgJLy8vdOrUyWKZh4cHmjVrJi0fN24cJk+eDD8/P+h0OkyYMAERERG44447AACDBg1Chw4dMGrUKMydOxd6vR6vv/46YmJioNFoAADPP/88PvnkE7zyyisYO3YstmzZgu+//x4bNmyQ9jt58mSMHj0avXr1Qp8+ffDxxx+joKAAY8aMaaRPg4iIiJyZU4cqW8yfPx9yuRzDhw9HSUkJoqKi8Omnn0rrFQoF1q9fjxdeeAERERHw8PDA6NGj8fbbb0ttwsLCsGHDBkyaNAkLFizArbfeii+//BJRUVFSm0cffRSXLl3CjBkzoNfr0a1bN8TFxVUZvE4No9ppqjh/FREROQmZEJxWsbEYjUZ4e3vDYDBwfFUd3f76r7jztmboGupjsfyHxAvof7s/5gzrYv2FREREN8jW72+nHlNFVDv+TUBERM6BoYpcGiMVERE5C4YqIiIiIjtgqCIiIiKyA4Yqcm08/0dERE6CoYpcQzXhiZmKiIicBUMVERERkR0wVBERERHZAUMVERERkR0wVJFL4/0AiIjIWTBUkUsQHJJOREROjqGKnF52gQml5dZDFcMWERE5C4YqcnoZhiIAQJBOa3U9TwESEZEzYKgilyGTVV0mBHDkorHxiyEiIroOQxW5NJ1WhUJTmaPLICIiYqgi1+apUUIht9KFRURE1MgYqoiIiIjsgKGKXB4HqhMRkTNgqCIiIiKyA4YqIiIiIjtgqCKXx7N/RETkDBiqiIiIiOyAoYqIiIjIDhiqyPXx/B8RETkBhipyebypMhEROQOGKnJ6OQWlji6BiIioVgxV5PTOXCkAAAR4aRxcCRERUfUYqsglKGQyyGXW7/F35kohBKdVJyIiB2OoIpdQ3bgpLzclys0CRaXljVwRERGRJYYqcmkeaqWjSyAiIgLAUEVERERkFwxVRERERHbAUEVERERkBwxVRERERHbAUEVERERkBwxVRERERHbAUEVERERkBwxVRERERHbAUEVERERkBwxVRERERHbAUEVOTwAw837JRETk5BiqyOkdSTc4ugQiIqJaMVSR0zOVmxHgqXF0GURERDViqCKXoFVb/1VVKWQAgHPZhY1ZDhERURUMVeQaqhlT5X+1B+tynqkRiyEiIqqKoYqIiIjIDhiqyCXw4j8iInJ2DFVEREREdsBQRS5BsKuKiIicHEMVERERkR0wVJFLYEcVERE5O4Yqcn4CPP9HREROj6GKiIiIyA4Yqsjp5RWX8fQfERE5PYYqcnp/ZeVBVkub7ELOqE5ERI7FUEVOzyyAQJ3W6jpPjRIAcPZyQWOWREREVAVDFTk9uQxQyKz3VcnlMui0ykauiIiIqCqGKnJ6HE9FRESugKGKXB5DFxEROQOGKnJ+TE1EROQCnDpUzZkzB71794aXlxcCAwMxdOhQpKamWrQpLi5GTEwMmjVrBk9PTwwfPhyZmZkWbc6dO4fo6Gi4u7sjMDAQU6ZMQVlZmUWbbdu2oUePHtBoNGjTpg1iY2Or1LN48WK0atUKWq0Wffv2xd69e+3+nqkqZioiInIFTh2qtm/fjpiYGPz555/YtGkTSktLMWjQIBQU/H2l16RJk/DLL79g9erV2L59Oy5evIhhw4ZJ68vLyxEdHQ2TyYTdu3dj+fLliI2NxYwZM6Q2aWlpiI6OxoABA5CUlISJEyfi6aefxm+//Sa1+e677zB58mTMnDkTBw4cQNeuXREVFYWsrKzG+TCoekxdRETkBGRCuM79Py5duoTAwEBs374d/fv3h8FgQEBAAFauXImHH34YAHD8+HG0b98eCQkJuOOOO/Drr7/igQcewMWLFxEUFAQAWLp0KaZOnYpLly5BrVZj6tSp2LBhA1JSUqR9jRgxArm5uYiLiwMA9O3bF71798Ynn3wCADCbzQgNDcWECRMwbdo0q/WWlJSgpKREem40GhEaGgqDwQCdTtcgn1FT1H/uVgR4anBXuL/V9V/9kYZn+9+GCQPDG7kyIiK6GRiNRnh7e9f6/e3UPVXXMxgMAAA/Pz8AQGJiIkpLSxEZGSm1adeuHVq0aIGEhAQAQEJCAjp37iwFKgCIioqC0WjEkSNHpDbXbqOyTeU2TCYTEhMTLdrI5XJERkZKbayZM2cOvL29pUdoaOiNvP2bVm2p32X+KiAioibNZUKV2WzGxIkT0a9fP3Tq1AkAoNfroVar4ePjY9E2KCgIer1eanNtoKpcX7mupjZGoxFFRUW4fPkyysvLrbap3IY106dPh8FgkB7nz5+v+xunWm+mbBYCZiYrIiJyMJeZNTEmJgYpKSn4448/HF2KzTQaDTQajaPLcHmZxmIEelmfUR0AZAAOXchttHqIiIiscYmeqvHjx2P9+vXYunUrbr31Vml5cHAwTCYTcnNzLdpnZmYiODhYanP91YCVz2tro9Pp4ObmBn9/fygUCqttKrdBDcNQVApTuYC7WlFtG39PDcyuMzSQiIiaKKcOVUIIjB8/HmvWrMGWLVsQFhZmsb5nz55QqVSIj4+XlqWmpuLcuXOIiIgAAERERCA5OdniKr1NmzZBp9OhQ4cOUptrt1HZpnIbarUaPXv2tGhjNpsRHx8vtaGGUXkdhc5NVW0bjUpe6w2XiYiIGppTn/6LiYnBypUr8fPPP8PLy0sav+Tt7Q03Nzd4e3tj3LhxmDx5Mvz8/KDT6TBhwgRERETgjjvuAAAMGjQIHTp0wKhRozB37lzo9Xq8/vrriImJkU7NPf/88/jkk0/wyiuvYOzYsdiyZQu+//57bNiwQapl8uTJGD16NHr16oU+ffrg448/RkFBAcaMGdP4HwxZYB8VERE5A6cOVUuWLAEA3HvvvRbLly1bhqeeegoAMH/+fMjlcgwfPhwlJSWIiorCp59+KrVVKBRYv349XnjhBURERMDDwwOjR4/G22+/LbUJCwvDhg0bMGnSJCxYsAC33norvvzyS0RFRUltHn30UVy6dAkzZsyAXq9Ht27dEBcXV2XwOjU+nvkjIiJn4FLzVLk6W+e5oL/lFprQ7e1NiO7cHG0CPa222XD4IkL93LFsTJ9Gro6IiG4GTXKeKiJr+FcBERE5A4YqahIYrIiIyNEYqqhJ4ElsIiJyNIYqahI4NLD+FsX/hdfXJvMzJCK6QU599R9RXnGZTe0OXTA0cCVNU0q6AfM2nQAABHpp8SJvSk1EVG/sqSKndjIrHwAQ4FX97X78PNQwlZkbq6QmZedfl6WfV+0958BKiIhcH0MVuQSlvPo509VKOdRK/irXR3J6LtxUFbcAumgodnA1RESujd9ERDexklIzgr016Ne6WcXzsnIHV0RE5LoYqqhJEJxUoc5MZWbEH8+CEEArfw8AwIbDGQ6uiojIdTFUUdPATFVnVwpKAABtAj3h56EGABw6n+vAioiIXBtDFdFN6nhGHgAgwFMDuUwGbzcV9p7JdnBVRESui6GK6CZVaKoYP+XtrgIABHppUFrGLj8iovpiqCKnZutYKWNxGcrKOa1CXRy+kAsAkKHiyko3tQKG4lIHVkRE5NoYqsipJV8wAgCUiuqnVPBxqxgPdC67sFFqair2ncmGm0ohTUehlMtwKa+Ec34REdUTQxU5tTKzGe5qBTRKRbVtKudZoro5c6UQtwV4SM9DfNyuLi9wVElERC6NoYrsav+ZbLSatgF3vBtvt3vJqRT8NW0I2QUmi0Da7OoVgJfzSxxVEhGRS+O3FdnNgXM5eHhpAgBAbyxGz3c2I7fQ5OCqqCY6N5X0c2V4TdXnOaocIiKXxlBFdvHf1Ycw7NPdAIDerXyh0yqRXWDCqUs3fiqJE3vaX+U9FbXX3N7HQ6OEQiZDQYltN7EmIiJLDFV0wxZvPYkfEi8AACJua4bZD3VG/Mv3AgA2Jtthhm5mKrs7f3VQv+/VU36VFHIZktMNjiiJiMjlKR1dALkmIQR2n7qCkV/ukZb9MXUAbvV1l54HemlwIvPGTiWdyMxDuY1jsyrnXaLapV2u6EH0c7cMVT7uKmQZOaaKiKg+GKqoXh5emoDEsznS81PvDoFCbjntQdtgL/xx8vIN7Se/uAxeWlWNbZp5VgSDVH0eOt3ifUP7u5moFXLIrztmHholTl/m1X9ERPXB039UZ59tPyUFqln/6ojNk/tXCVQAMKBtIIQALuXdWM+HTlNz9ufVgXWXaSxGqZXJUoN1WhiKOAEoEVF9sKeK6uRSXgnm/HocAHDkrSh41BB4Ot9a0Wu06+RlDO1+S732Z+Z4qgaRnG6Au7rq/F6VyzIMRWju7dbYZRERuTT+iU91Mm75PgDA5sn9awxUANC7lR8A4Nu95+q9P1751zBOXyqAv6emynLfq2OsLuQUNXZJREQuj6GKbHYprwSHLxjQu5Uv2gR62fSae24PQNkNdDel1WF8Tzm7tWxmKjcjUFc1VAVdXZbOUEVEVGcMVWSzL3aeBgAseqyHza8J8NIg8WxOvWdXv5RXAvdaesRkV4dzHbp6g2CqWX5JGbILTJDJqr+fook3pyYiqjOGKrLZH39dRoCXBsHeWptfc2/bAADAkYvGeu1To1RAp605VMllMqmHhWpXeHVyT2ufmfxq0Eq+wLmqiIjqiqGKbJKVV4yjGUb0DfOr0+vaBHoCAM5dnWyyrmwdU6WoodeFLF26em8/Oap+ZnK5DH7uamQYePqPiKiuGKrIJknncgEAYf4edXpd26CKsVfrD1+0d0kWOJrKdpXj1KyNqQIAlVJW7xBMRHQzY6gim1SOV3rh3tZ1ep1MJkOvlr7483R2nfdZZCpHcaltY3vKzaJOg9pvZpWn9tTVzO8V6KVFTgFvhE1EVFcMVWSTxVtPAQDc1XWf2qxbqA+yC0zIr+ONeit7S6xd+n89rUqBbAYBm/l5qKGsJlSpFDJcyjehjIPViYjqhKGKbGZt1nRb3NmmGQDgQk79TinZMmO6j7tKGmRNNTMWl8Jcw/QTza6GWF4BSERUNwxVVKuzVypOq330SNd6vb5ny4rB7av2nq/T6wpNdevZYgiwzcmsfFgZoy6pvMlySnr9rtgkIrpZMVRRrX5Oqhhk3jbYtgk/r+ftpoKvuwo7Tlyq0+uO6/MAVJyqssXJrHyesrLBhZwiBHpVf0pVo6r4z0JWXnFjlURE1CQwVFGtDl8dpN4uWFfvbdzfuTlOXy5Aem7dL9VXK2v/Na0MCTcye/vNIsNQXOPYOB83FQDgDAf+ExHVCUMV1WrL8Sz0aVW3+amuN/3+dgCAN9cdsUdJVXCeKttU3srH111VbRuZTAaNUo7DnACUiKhOGKqoRrmFJpgFMKhj0A1tx0urQtsgL2w6mmnzgPUjF/mlbm9Hr85s7+1WfagCKq64vFiPXkUiopsZQxXV6I+TlwEAHUO8b3hbr0W3BwDc9f5WlNow9slUZq6xR+VaGpUCAHD6Ek9Z1aSotBwA4KZW1NjOTa3Aiaz8xiiJiKjJYKiiGiWnV/QWRbRudsPb6n97gPTzluNZtbY/c6UQGmXNX/6VKsOXoai0fsXdJI5lVPRU+bjVPPg/yEsDU5kZhkJ+nkREtmKooholnLqCIJ2mztMbVOfIW1EAgOf+l4iCWiYDPXLRYNMgdbJd5QSstX2uAVcH/ueVMFQREdmK31hUo6MXjXBTKfDZ9tP4fMcp/Jh4AdtPXEJKugGZxmKbTuNdy0OjxLAetwAAOs78DUJYv1qv0FSGgpJyhHhrbdpu5QShf2Xl1amem82JzDybJnGVXR34n1PAUEVEZKu633OEbhpX8ktQZhYI9XMHABSUlKOgpNDiZrsyWcUl+P5eGjTz0CDASw1/Tw283VTSF/P1PnqkG346kA4ACJu+EWlzhlRpW3mVmq1zVHloKn6V84rt06PWVB25aESwrvagGnT1ZsvbUrPQ+dYbH09HRHQzYKiiau34q2KyzjYBntW2EQLIKSxFTmEp/sLfA5tVChmaeWrg76lBM081Aq7+f+X8SNun3It7PtgGoCJYxb98D1pfs5+9aRU3YK7LvQa1SjkOnc+1uf3NKKfABP8aJv6spFEqoFHKcYX3UyQishlDFVWrckZzXS2X31tTWi6gNxRDb7CcldtDo0AzDw38vTTY8OJdWHMgHV/+kYaB87bj22fuQM+WvlAr5XhpVRIAINjG038A4OuhRlZeSZ1rvVkUmcpxpcCEMH8Pm9p7apRSuCUiotoxVFG1th7Pgo+NUxrY6vpTiB4aJf7ZtTnWHcrAY1/8CQDQKOUoKTOjmYe6TjdxdlcrkMSeqmpVXmwQZMPpP6DiJtVHM4wQQlR7KpeIiP7GgepUrYzcYnQP9Wnw/YT5e+L+TsHS85IyM5RyGR7r06JO22nVrKIHhpNWWncxt6LXUKmwLSC19Kv4PHM4rQIRkU3YU0VWnc8uRF5JGW71dWuU/d0e5IXbg7wghICpzAyVUg55HXtHKmcJT7ucjxCfxqnblVSOkWtuY09V5anXYxlG9Gvj32B1ERE1FeypIqsqx9Lc1y6wUfcrk8mgUSnqHKiAv0PAvN9PICXdgJwCU7VTNtyMzmcXQimXSbPP16by1O+BszkNWRYRUZPBniqyqrJXw5YrxZyFSiGHu1qBA+dy8fsRPWQyGdRKOZp5qK9eiVgx3YO/p6bW27Q0RSnpBrjX4X2rFHJolXKsO3QREwaGN2BlRERNA0MVWZVdYEK7YK969Rg50oC2gdiQnIHTlwvQOsATpjIzMgzFyLByFaL/dVM++HmooVQ03c7b1Mw8hDWz7cq/SrcHeeFwOm9sTURkC4YqsmrnX5cxpHNw7Q2dTMtmFROVrj+cgbH9WsFLa/3qxcqrEM9esZzI1Ne9sjeroncrwFMDnZvS5a9+yy8pQ2m5QKCN46kq+XtW9FSeupRvMY8YERFVxVBFVRzXV9x0987Wrjc4WaWQI7J9IDYfy8JXu87gmbvDbJ5AVIiKHrrsAhNOZP69vCFPIZrNAnnFZTicnoujF414pFcofG2cRb4u4pIzAAC3BdStp6p1oAe2pAI/H0zH5EFt7V4XEVFTwlBFVfyYeAEA8GCXEFzILayltfNpG+SFDEMxjlw04oudaWjh5477OwVDa2WAthAC5UJABlm1c2JVdwrRU6OEv5e6YjJTTw38vdTwc7d+CrFywPypSwX4ePMJJJ3PxYWcqlM/zPn1OABgxgMdMKJPaJ1mlK/JyayK2e593esW2NzVSmiUcnz951mGKiKiWjBUURUJp68gSKeBt7sKF3IdXU3dKRVyRLYPQtdbfbBy7zmcyy7EZztOA6iYdsHPQw0vrRKHL1Q/VqhdsBeUChk0SgUMRaUoLTfj7JVCBHhp4KZSINBLA5msYu6nQlMZAr20aOXvjou5xUhON8DfU4PwQA8knK55RnIfdxXKygWmD2mHL3emQaWQ4URmPt5efxRvrz9q9b6I9fH70UyobJyf6noD2gUgLiUTRabym3KAPxGRrRiqyEJZuRkp6UZEd27u6FJuWICXBi/e1wbH9Xn4/WjF+TxDUSkMRdVPZqmQy1BuFtIteq536eptcK69qTRQMUFmaubfr7mcX4LL+dZvmXN/p2A8fXcYwgO9LG4BNLJvSwBASVk52r4eB6DivojHZw222stmq+LS8qsD9+t26q9SxViqTKzadw5j+oXVuw4ioqaOoYosbEutmEphRJ9QB1diHzKZDO2b69C+uQ5ms0B2oQleGiXKhZBOrV1/G5aSsnLkFZehpNQMANCo5NAoK07paZQKmMrNuJRXgrJyM3zc1biUV4IQHy2MxWUoLTejZTN3FJvMkMkqbsNTeYpRKf/7tOCOE5ex48RleGmVaOZZ9RTimfeiEbPyADYczkC7N+JwaOYgaXLTulqXdBEA0PVWn3q9XnX1dOZbvxzFU3e2cvlB+0REDYWhiizM+fUYAKB3Kz8HV2J/crlMuprtWteHBI1SAY1n9T1DaqUcnpq//+kEXJ3Ly+ea8Uqe2r8DlEwmg7KaIJJXXIa84jKcufx3z5dcJoOvh6qit1AIbEjWo+tbv+Prsb3R//a6T8b67saKY3pLPWfHFwKYOrgt3o9Lxdvrj2Lmgx3rtR0ioqau6U7KQ3WWlVeMU5cKMLij9UHd1DjMQuBKvgmp+jy0CfRC51u8AQBPfrUPY5btxc8H05F0PhfnswtRXFpe47ZW7z+P3KJSdGiuu6E5x1oHeMJLq8SyXWdwMsv6qVEiopudTPA+Ho3GaDTC29sbBoMBOp3O0eVUEb1wJ45cNCJh+n1o7l3Rq3HkogG/H8ms5ZXU0PKKS/HVrjPS82CdFg91vwVqpVw6hejrroaPuxruagXKygV2n7qEdzZUXE044b42NxSqtCoFojoG4b552wEAT0a0xGvR7aFRuk74LjcLyADkFJrg7aZq0hO9EpF92fr9zdN/dbR48WJ88MEH0Ov16Nq1KxYtWoQ+ffo4uqwbtmrvORy5aMQ9twdIgYqch5dWhef734bEcznYdyYHemMxlmw/BaVchhAfN/i6q+DjXjEzfGpmHpKvmQU94rZmNzwzfnFpOYpKy3FoxiB0fft3fJ1wFl8nnMXQbiEAgA//3dUpQ0puoQnxx7Lw8upDVdZ5aZUYc2crjL8vHGql89VORK6HPVV18N133+HJJ5/E0qVL0bdvX3z88cdYvXo1UlNTERhY+1iXhuypOn0pH4aiUnRv4Vun113KK8Gcjcfw08F0AMCpd4dYzNfEnirnU24W+CszDzv+ugytSo6cwuqvZuwf7l/n34nq+LirMOqOllDIZVi89SQ+/P2E1XZh/h5Iu1yAni19MahDENIuFyBQp0XI1Rte7z+bg2CdFuFBnki7XIDOt3hDq1IgSKeBp0aFkrJy3OrrDiFEvYKasbgU3/x5Fst3n0Gm0fIKzKmD2+H9uONVXtOqmTsm3BeOh7rfAnk185UR0c3L1u9vhqo66Nu3L3r37o1PPvkEAGA2mxEaGooJEyZg2rRptb6+IUNVq2kbAAAh3loM6hiM0Xe2gloph0ohg1ohh1lUTJdQUmaG3liMzccy8cWO0zBfc/RPzr6/ypcYQ5XzKzcLGIsr5tIqLRPwdldBIZfBrQHGxXUN9caAtoHS4P7i0nL8dkSPCzlF2JuWje0nLiFYp4XeWFzLlmyjUshQWi6gVshxi68bArw0OJ9diO4tfHAiMx/9WjdDmVlg87FMtA3WYceJSxav9/NQ48EuzTF9SPsq4wSLS8ux9XgWXvouCaYys8VrBrYLRFFpOQZ1DEa52YxWzTzg466GVlXx70MGGSo7/6QIJj2v+OH69TKZ7JqfLdtK/2flNZXPr3+NTFYxBYjKCXsIiZoahio7M5lMcHd3xw8//IChQ4dKy0ePHo3c3Fz8/PPPVV5TUlKCkpK//1I2GAxo0aIFzp8/b9dQlVtowl3vb63365/q1wrjB7SxOjj9XHYBks7l3kB11NTcHuQFf08NCkxlNrUvMpXBTa1EaZkZBSVlKC41Q62UwVQmYCgulaa0cNcokZ5TiF0nL6N7Cx+sP6xHc28NNEoFhBAoLjPj4LkcuKkUyMozoXWABxRyGWQyGRQyGXw8VNAq5UjPLcbDPW/Bv7rdYvOM9GazQOLZHIyJ3Ye2wV5IrWaeMmfl6rNcuHj5AKpeReyqXPlduKsVeOKOlnjh3tZ2Px5GoxGhoaHIzc2Ft7d39Q0F2SQ9PV0AELt377ZYPmXKFNGnTx+rr5k5c6YAwAcffPDBBx98NIHH+fPna8wKHKjegKZPn47JkydLz81mM7Kzs9GsWbMG/aumMlHbu0eMGhaPm2vicXM9PGauyZHHTQiBvLw8hISE1NiOocpG/v7+UCgUyMy0HF+UmZmJ4OBgq6/RaDTQaCwnm/Tx8WmoEqvQ6XT8D4YL4nFzTTxurofHzDU56rjVeNrvKo5wtJFarUbPnj0RHx8vLTObzYiPj0dERIQDKyMiIiJnwJ6qOpg8eTJGjx6NXr16oU+fPvj4449RUFCAMWPGOLo0IiIicjCGqjp49NFHcenSJcyYMQN6vR7dunVDXFwcgoKCHF2aBY1Gg5kzZ1Y59UjOjcfNNfG4uR4eM9fkCseNUyoQERER2QHHVBERERHZAUMVERERkR0wVBERERHZAUMVERERkR0wVDUxixcvRqtWraDVatG3b1/s3bvX0SXdVHbs2IEHH3wQISEhkMlkWLt2rcV6IQRmzJiB5s2bw83NDZGRkfjrr78s2mRnZ2PkyJHQ6XTw8fHBuHHjkJ+fb9Hm8OHDuPvuu6HVahEaGoq5c+c29FtrsubMmYPevXvDy8sLgYGBGDp0KFJTUy3aFBcXIyYmBs2aNYOnpyeGDx9eZSLgc+fOITo6Gu7u7ggMDMSUKVNQVmZ5f8Rt27ahR48e0Gg0aNOmDWJjYxv67TVZS5YsQZcuXaSJICMiIvDrr79K63nMXMN7770HmUyGiRMnSstc+tjZ5cZ45BRWrVol1Gq1+Oqrr8SRI0fEM888I3x8fERmZqajS7tpbNy4Ubz22mvip59+EgDEmjVrLNa/9957wtvbW6xdu1YcOnRI/POf/xRhYWGiqKhIajN48GDRtWtX8eeff4qdO3eKNm3aiMcee0xabzAYRFBQkBg5cqRISUkR3377rXBzcxOfffZZY73NJiUqKkosW7ZMpKSkiKSkJDFkyBDRokULkZ+fL7V5/vnnRWhoqIiPjxf79+8Xd9xxh7jzzjul9WVlZaJTp04iMjJSHDx4UGzcuFH4+/uL6dOnS21Onz4t3N3dxeTJk8XRo0fFokWLhEKhEHFxcY36fpuKdevWiQ0bNogTJ06I1NRU8eqrrwqVSiVSUlKEEDxmrmDv3r2iVatWokuXLuKll16SlrvysWOoakL69OkjYmJipOfl5eUiJCREzJkzx4FV3byuD1Vms1kEBweLDz74QFqWm5srNBqN+Pbbb4UQQhw9elQAEPv27ZPa/Prrr0Imk4n09HQhhBCffvqp8PX1FSUlJVKbqVOnirZt2zbwO7o5ZGVlCQBi+/btQoiKY6RSqcTq1aulNseOHRMAREJCghCiIkzL5XKh1+ulNkuWLBE6nU46Tq+88oro2LGjxb4effRRERUV1dBv6abh6+srvvzySx4zF5CXlyfCw8PFpk2bxD333COFKlc/djz910SYTCYkJiYiMjJSWiaXyxEZGYmEhAQHVkaV0tLSoNfrLY6Rt7c3+vbtKx2jhIQE+Pj4oFevXlKbyMhIyOVy7NmzR2rTv39/qNVqqU1UVBRSU1ORk5PTSO+m6TIYDAAAPz8/AEBiYiJKS0stjlu7du3QokULi+PWuXNni4mAo6KiYDQaceTIEanNtduobMN/nzeuvLwcq1atQkFBASIiInjMXEBMTAyio6OrfL6ufuw4o3oTcfnyZZSXl1eZ3T0oKAjHjx93UFV0Lb1eDwBWj1HlOr1ej8DAQIv1SqUSfn5+Fm3CwsKqbKNyna+vb4PUfzMwm82YOHEi+vXrh06dOgGo+EzVanWVm6Fff9ysHdfKdTW1MRqNKCoqgpubW0O8pSYtOTkZERERKC4uhqenJ9asWYMOHTogKSmJx8yJrVq1CgcOHMC+ffuqrHP1f28MVUREV8XExCAlJQV//PGHo0shG7Rt2xZJSUkwGAz44YcfMHr0aGzfvt3RZVENzp8/j5deegmbNm2CVqt1dDl2x9N/TYS/vz8UCkWVKyQyMzMRHBzsoKroWpXHoaZjFBwcjKysLIv1ZWVlyM7OtmhjbRvX7oPqbvz48Vi/fj22bt2KW2+9VVoeHBwMk8mE3Nxci/bXH7fajkl1bXQ6HXs86kmtVqNNmzbo2bMn5syZg65du2LBggU8Zk4sMTERWVlZ6NGjB5RKJZRKJbZv346FCxdCqVQiKCjIpY8dQ1UToVar0bNnT8THx0vLzGYz4uPjERER4cDKqFJYWBiCg4MtjpHRaMSePXukYxQREYHc3FwkJiZKbbZs2QKz2Yy+fftKbXbs2IHS0lKpzaZNm9C2bVue+qsHIQTGjx+PNWvWYMuWLVVOrfbs2RMqlcriuKWmpuLcuXMWxy05OdkiEG/atAk6nQ4dOnSQ2ly7jco2/PdpP2azGSUlJTxmTmzgwIFITk5GUlKS9OjVqxdGjhwp/ezSx65Bh8FTo1q1apXQaDQiNjZWHD16VDz77LPCx8fH4goJalh5eXni4MGD4uDBgwKA+Oijj8TBgwfF2bNnhRAVUyr4+PiIn3/+WRw+fFj861//sjqlQvfu3cWePXvEH3/8IcLDwy2mVMjNzRVBQUFi1KhRIiUlRaxatUq4u7tzSoV6euGFF4S3t7fYtm2byMjIkB6FhYVSm+eff160aNFCbNmyRezfv19ERESIiIgIaX3lJd6DBg0SSUlJIi4uTgQEBFi9xHvKlCni2LFjYvHixbw8/wZMmzZNbN++XaSlpYnDhw+LadOmCZlMJn7//XchBI+ZK7n26j8hXPvYMVQ1MYsWLRItWrQQarVa9OnTR/z555+OLummsnXrVgGgymP06NFCiIppFd544w0RFBQkNBqNGDhwoEhNTbXYxpUrV8Rjjz0mPD09hU6nE2PGjBF5eXkWbQ4dOiTuuusuodFoxC233CLee++9xnqLTY614wVALFu2TGpTVFQk/vOf/whfX1/h7u4uHnroIZGRkWGxnTNnzoj7779fuLm5CX9/f/Hyyy+L0tJSizZbt24V3bp1E2q1Wtx2220W+6C6GTt2rGjZsqVQq9UiICBADBw4UApUQvCYuZLrQ5UrHzuZEEI0bF8YERERUdPHMVVEREREdsBQRURERGQHDFVEREREdsBQRURERGQHDFVEREREdsBQRURERGQHDFVEREREdsBQRURERGQHDFVERI3sypUrCAwMxJkzZwAA27Ztg0wmq3ITWXubNm0aJkyY0KD7ILqZMVQRkdN66qmnIJPJqjwGDx7s6NJuyOzZs/Gvf/0LrVq1uuFtZWZmQqVSYdWqVVbXjxs3Dj169AAA/Pe//8Xy5ctx+vTpG94vEVXFUEVETm3w4MHIyMiweHz77bcNuk+TydRg2y4sLMT//d//Ydy4cXbZXlBQEKKjo/HVV19VWVdQUIDvv/9e2pe/vz+ioqKwZMkSu+ybiCwxVBGRU9NoNAgODrZ4+Pr6SutlMhm+/PJLPPTQQ3B3d0d4eDjWrVtnsY2UlBTcf//98PT0RFBQEEaNGoXLly9L6++9916MHz8eEydOlIIHAKxbtw7h4eHQarUYMGAAli9fLp2mKygogE6nww8//GCxr7Vr18LDwwN5eXlW38/GjRuh0Whwxx13VPueCwsLcf/996Nfv37SKcEvv/wS7du3h1arRbt27fDpp59K7ceNG4f4+HicO3fOYjurV69GWVkZRo4cKS178MEHq+3VIqIbw1BFRC7vrbfewiOPPILDhw9jyJAhGDlyJLKzswEAubm5uO+++9C9e3fs378fcXFxyMzMxCOPPGKxjeXLl0OtVmPXrl1YunQp0tLS8PDDD2Po0KE4dOgQnnvuObz22mtSew8PD4wYMQLLli2z2M6yZcvw8MMPw8vLy2qtO3fuRM+ePat9L7m5ufjHP/4Bs9mMTZs2wcfHBytWrMCMGTMwe/ZsHDt2DO+++y7eeOMNLF++HAAwZMgQBAUFITY2tkotw4YNg4+Pj7SsT58+uHDhgjSei4jsSBAROanRo0cLhUIhPDw8LB6zZ8+W2gAQr7/+uvQ8Pz9fABC//vqrEEKIWbNmiUGDBlls9/z58wKASE1NFUIIcc8994ju3btbtJk6daro1KmTxbLXXntNABA5OTlCCCH27NkjFAqFuHjxohBCiMzMTKFUKsW2bduqfU//+te/xNixYy2Wbd26VQAQx44dE126dBHDhw8XJSUl0vrWrVuLlStXWrxm1qxZIiIiQno+bdo0ERYWJsxmsxBCiJMnTwqZTCY2b95s8TqDwSAA1FgjEdUPe6qIyKkNGDAASUlJFo/nn3/eok2XLl2knz08PKDT6ZCVlQUAOHToELZu3QpPT0/p0a5dOwDAqVOnpNdd33uUmpqK3r17Wyzr06dPlecdO3aUeoy++eYbtGzZEv3796/2/RQVFUGr1Vpd949//ANt2rTBd999B7VaDaBiXNSpU6cwbtw4i/fwzjvvWNQ/duxYpKWlYevWrQAqeqlatWqF++67z2Ifbm5uACpOMRKRfSkdXQARUU08PDzQpk2bGtuoVCqL5zKZDGazGQCQn5+PBx98EO+//36V1zVv3txiP/Xx9NNPY/HixZg2bRqWLVuGMWPGQCaTVdve398fOTk5VtdFR0fjxx9/xNGjR9G5c2epfgD44osv0LdvX4v2CoVC+jk8PBx33303li1bhnvvvRdff/01nnnmmSq1VJ4WDQgIqPubJaIaMVQRUZPWo0cP/Pjjj2jVqhWUStv/k9e2bVts3LjRYtm+ffuqtHviiSfwyiuvYOHChTh69ChGjx5d43a7d++Ob775xuq69957D56enhg4cCC2bduGDh06ICgoCCEhITh9+rTFgHNrxo0bhxdeeAH//Oc/kZ6ejqeeeqpKm5SUFKhUKnTs2LHGbRFR3fH0HxE5tZKSEuj1eovHtVfu1SYmJgbZ2dl47LHHsG/fPpw6dQq//fYbxowZg/Ly8mpf99xzz+H48eOYOnUqTpw4ge+//14aCH5t74+vry+GDRuGKVOmYNCgQbj11ltrrCcqKgpHjhyptrfqww8/xMiRI3Hffffh+PHjACoG4s+ZMwcLFy7EiRMnkJycjGXLluGjjz6yeO2///1vqFQqPPfccxg0aBBCQ0OrbH/nzp24++67pdOARGQ/DFVE5NTi4uLQvHlzi8ddd91l8+tDQkKwa9culJeXY9CgQejcuTMmTpwIHx8fyOXV/ycwLCwMP/zwA3766Sd06dIFS5Yska7+02g0Fm3HjRsHk8mEsWPH1lpP586d0aNHD3z//ffVtpk/fz4eeeQR3HfffThx4gSefvppfPnll1i2bBk6d+6Me+65B7GxsQgLC7N4nbu7O0aMGIGcnJxqa1m1ahWeeeaZWuskorqTCSGEo4sgInIFs2fPxtKlS3H+/HmL5f/73/8wadIkXLx4URpgXpMNGzZgypQpSElJqTHY2duvv/6Kl19+GYcPH67TqVAisg3/VRERVePTTz9F79690axZM+zatQsffPABxo8fL60vLCxERkYG3nvvPTz33HM2BSqgYkD6X3/9hfT0dKun6BpKQUEBli1bxkBF1EDYU0VEVI1Jkybhu+++Q3Z2Nlq0aIFRo0Zh+vTpUih58803MXv2bPTv3x8///wzPD09HVwxETkSQxURERGRHXCgOhEREZEdMFQRERER2QFDFREREZEdMFQRERER2QFDFREREZEdMFQRERER2QFDFREREZEdMFQRERER2cH/A0NcbZZebghKAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -473,7 +464,7 @@ "ch_nb = 5\n", "\n", "background_detector = background_meas.detectors[0]\n", - "check_source_detector = all_measurements[\"Co60_3\"].detectors[0]\n", + "check_source_detector = all_measurements[\"Na22_2\"].detectors[1]\n", "\n", "assert background_detector.channel_nb == check_source_detector.channel_nb\n", "assert (\n", @@ -489,12 +480,12 @@ "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", "\n", "parameters, covariance = compass.fit_peak_gauss(\n", - " hist, xvals, all_measurements[\"Co60_3\"].check_source.nuclide.energy, search_width=800\n", + " hist, xvals, all_measurements[\"Na22_2\"].check_source.nuclide.energy, search_width=800\n", ")\n", "\n", "# plotting\n", "\n", - "peak_start = 520\n", + "peak_start = 100\n", "peak_end = 800\n", "plt.fill_between(\n", " xvals[peak_start:peak_end],\n", @@ -513,25 +504,25 @@ "\n", "plt.legend()\n", "plt.xlabel(\"Energy (keV)\")\n", - "plt.ylim(bottom=0, top=1500)\n", + "plt.ylim(bottom=0)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Detection efficiency: [0.02047002 0.01863007]\n" + "Detection efficiency: [0.0511642 0.02258572]\n" ] } ], "source": [ - "efficiency = all_measurements[\"Co60_3\"].compute_detection_efficiency(\n", + "efficiency = all_measurements[\"Na22_2\"].compute_detection_efficiency(\n", " background_measurement=background_meas,\n", " calibration_coeffs=calibration_coeffs[4],\n", " channel_nb=4,\n", @@ -542,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -555,7 +546,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -597,10 +588,8 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", - "Processing niobium_2...\n", - "\n", - "Processing niobium_3...\n" + "\n", + "Processing niobium_2...\n" ] } ], From 1e449064a6f9659c1b31f9c9bc1d74a3725cc5b9 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 16:08:25 -0400 Subject: [PATCH 075/137] removed "double" + exposed search width + same timezone + overlap threshold --- example.ipynb | 226 ++++++++++-------- .../activation_foils/compass.py | 48 +++- test/neutron_detection/test_compass.py | 4 +- 3 files changed, 169 insertions(+), 109 deletions(-) diff --git a/example.ipynb b/example.ipynb index 0ae9427..17df3eb 100644 --- a/example.ipynb +++ b/example.ipynb @@ -7,18 +7,33 @@ "outputs": [], "source": [ "from libra_toolbox.neutron_detection.activation_foils.calibration import (\n", - " CheckSource, co60, cs137, mn54, na22\n", + " CheckSource,\n", + " co60,\n", + " cs137,\n", + " mn54,\n", + " na22,\n", + ")\n", + "from libra_toolbox.neutron_detection.activation_foils.compass import (\n", + " Measurement,\n", + " CheckSourceMeasurement,\n", ")\n", - "from libra_toolbox.neutron_detection.activation_foils.compass import Measurement, CheckSourceMeasurement\n", "from datetime import date\n", "\n", "run_dir = \"250317_BABY_1L_run3/DAQ\"\n", "uCi_to_Bq = 3.7e4\n", "\n", - "co60_checksource = CheckSource(co60, activity_date=date(2014, 3, 19), activity=0.872*uCi_to_Bq)\n", - "cs137_checksource = CheckSource(cs137, activity_date=date(2023, 9, 29), activity=9.38*uCi_to_Bq)\n", - "mn54_checksource = CheckSource(mn54, activity_date=date(2016, 5, 2), activity=6.27*uCi_to_Bq)\n", - "na22_checksource = CheckSource(na22, activity_date=date(2023, 9, 29), activity=9.98*uCi_to_Bq)\n", + "co60_checksource = CheckSource(\n", + " co60, activity_date=date(2014, 3, 19), activity=0.872 * uCi_to_Bq\n", + ")\n", + "cs137_checksource = CheckSource(\n", + " cs137, activity_date=date(2023, 9, 29), activity=9.38 * uCi_to_Bq\n", + ")\n", + "mn54_checksource = CheckSource(\n", + " mn54, activity_date=date(2016, 5, 2), activity=6.27 * uCi_to_Bq\n", + ")\n", + "na22_checksource = CheckSource(\n", + " na22, activity_date=date(2023, 9, 29), activity=9.98 * uCi_to_Bq\n", + ")\n", "\n", "check_source_measurements = {\n", " \"Co60_1\": {\n", @@ -35,15 +50,21 @@ " },\n", " \"Co60_4\": {\n", " \"directory\": f\"{run_dir}/Co60_1_0uCi_Jan2006_250318/UNFILTERED\",\n", - " \"check_source\": CheckSource(co60, activity_date=date(2006, 1, 1), activity=1.0*uCi_to_Bq),\n", + " \"check_source\": CheckSource(\n", + " co60, activity_date=date(2006, 1, 1), activity=1.0 * uCi_to_Bq\n", + " ),\n", " },\n", " \"Co60_5\": {\n", " \"directory\": f\"{run_dir}/Co60_1_0uCi_Feb2006_250320_run1/UNFILTERED\",\n", - " \"check_source\": CheckSource(co60, activity_date=date(2006, 2, 1), activity=1.0*uCi_to_Bq),\n", + " \"check_source\": CheckSource(\n", + " co60, activity_date=date(2006, 2, 1), activity=1.0 * uCi_to_Bq\n", + " ),\n", " },\n", " \"Cs137_1\": {\n", " \"directory\": f\"{run_dir}/Cs137_4_66uCi_19Mar2014_250318/UNFILTERED\",\n", - " \"check_source\": CheckSource(cs137, activity_date=date(2014, 3, 19), activity=4.66*uCi_to_Bq),\n", + " \"check_source\": CheckSource(\n", + " cs137, activity_date=date(2014, 3, 19), activity=4.66 * uCi_to_Bq\n", + " ),\n", " },\n", " \"Cs137_2\": {\n", " \"directory\": f\"{run_dir}/Cs137_9_38uCi_29Sep2023_250318_run2/UNFILTERED\",\n", @@ -69,7 +90,6 @@ " \"directory\": f\"{run_dir}/Mn54_6_27uCi_2May2016_250320_run3/UNFILTERED\",\n", " \"check_source\": mn54_checksource,\n", " },\n", - "\n", " \"Na22_2\": {\n", " \"directory\": f\"{run_dir}/Na22_9_98uCi_29Sep2023_250318_run3/UNFILTERED\",\n", " \"check_source\": na22_checksource,\n", @@ -84,7 +104,7 @@ " },\n", "}\n", "\n", - "background_dir = f\"{run_dir}/Background_250322/UNFILTERED\"\n" + "background_dir = f\"{run_dir}/Background_250322/UNFILTERED\"" ] }, { @@ -97,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -133,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:139: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:137: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -163,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -177,7 +197,7 @@ "from libra_toolbox.neutron_detection.activation_foils import compass\n", "\n", "for detector in all_measurements[\"Co60_3\"].detectors:\n", - " hist, bin_edges = detector.get_energy_hist(bins=\"double\")\n", + " hist, bin_edges = detector.get_energy_hist()\n", "\n", " plt.hist(\n", " bin_edges[:-1],\n", @@ -186,7 +206,9 @@ " histtype=\"step\",\n", " label=f\"Ch {detector.channel_nb}\",\n", " )\n", - " peaks = compass.get_peaks(hist, source=all_measurements[\"Co60_3\"].check_source.nuclide.name)\n", + " peaks = compass.get_peaks(\n", + " hist, source=all_measurements[\"Co60_3\"].check_source.nuclide.name\n", + " )\n", " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", "\n", " from scipy.signal import find_peaks\n", @@ -229,7 +251,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -243,7 +265,7 @@ "\n", "\n", "plt.sca(axs[0])\n", - "hist, bin_edges = background_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "hist, bin_edges = background_meas.detectors[1].get_energy_hist()\n", "\n", "plt.hist(\n", " bin_edges[:-1],\n", @@ -260,7 +282,9 @@ "\n", "\n", "background_time = background_meas.detectors[1].real_count_time\n", - "bg_hist_scale = hist * all_measurements[\"Co60_3\"].detectors[1].real_count_time / background_time \n", + "bg_hist_scale = (\n", + " hist * all_measurements[\"Co60_3\"].detectors[1].real_count_time / background_time\n", + ")\n", "plt.hist(\n", " bin_edges[:-1],\n", " bins=bin_edges,\n", @@ -274,7 +298,7 @@ "\n", "plt.sca(axs[2])\n", "\n", - "hist, bin_edges = all_measurements[\"Co60_3\"].detectors[1].get_energy_hist(bins=\"double\")\n", + "hist, bin_edges = all_measurements[\"Co60_3\"].detectors[1].get_energy_hist()\n", "\n", "plt.hist(\n", " bin_edges[:-1],\n", @@ -287,7 +311,11 @@ "\n", "background_detector = background_meas.detectors[1]\n", "\n", - "hist_background_substracted, bin_edges_bg_sub = all_measurements[\"Co60_3\"].detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", + "hist_background_substracted, bin_edges_bg_sub = (\n", + " all_measurements[\"Co60_3\"]\n", + " .detectors[1]\n", + " .get_energy_hist_background_substract(background_detector)\n", + ")\n", "\n", "plt.hist(\n", " bin_edges_bg_sub[:-1],\n", @@ -300,7 +328,7 @@ "\n", "plt.legend()\n", "# plt.yscale(\"log\")\n", - "plt.ylim(top=600)\n", + "plt.ylim(bottom=0, top=600)\n", "plt.show()" ] }, @@ -313,13 +341,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_964087/2939631474.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_979085/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -333,7 +361,7 @@ "\n", "\n", "plt.sca(axs[0])\n", - "hist, bin_edges = background_meas.detectors[1].get_energy_hist(bins=\"double\")\n", + "hist, bin_edges = background_meas.detectors[1].get_energy_hist()\n", "\n", "plt.hist(\n", " bin_edges[:-1],\n", @@ -350,7 +378,7 @@ "\n", "\n", "# background_time = background_meas.detectors[1].real_count_time\n", - "# bg_hist_scale = hist * all_measurements[\"Mn54_1\"].detectors[1].real_count_time / background_time \n", + "# bg_hist_scale = hist * all_measurements[\"Mn54_1\"].detectors[1].real_count_time / background_time\n", "# plt.hist(\n", "# bin_edges[:-1],\n", "# bins=bin_edges,\n", @@ -364,7 +392,7 @@ "\n", "plt.sca(axs[2])\n", "\n", - "hist, bin_edges = all_measurements[\"Mn54_1\"].detectors[1].get_energy_hist(bins=\"double\")\n", + "hist, bin_edges = all_measurements[\"Mn54_1\"].detectors[1].get_energy_hist()\n", "\n", "plt.hist(\n", " bin_edges[:-1],\n", @@ -377,7 +405,11 @@ "\n", "background_detector = background_meas.detectors[1]\n", "\n", - "hist_background_substracted, bin_edges_bg_sub = all_measurements[\"Mn54_1\"].detectors[1].get_energy_hist_background_substract(background_detector, bins=\"double\")\n", + "hist_background_substracted, bin_edges_bg_sub = (\n", + " all_measurements[\"Mn54_1\"]\n", + " .detectors[1]\n", + " .get_energy_hist_background_substract(background_detector)\n", + ")\n", "\n", "plt.hist(\n", " bin_edges_bg_sub[:-1],\n", @@ -401,7 +433,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -422,7 +454,7 @@ "\n", " coeff = np.polyfit(calibration_channels, calibration_energies, 1)\n", " calibration_coeffs[channel_nb] = coeff\n", - " \n", + "\n", " xs = np.linspace(\n", " calibration_channels[0],\n", " calibration_channels[-1],\n", @@ -441,17 +473,17 @@ "plt.xlabel(\"Channel number\")\n", "plt.ylabel(\"Energy (keV)\")\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -461,10 +493,11 @@ } ], "source": [ - "ch_nb = 5\n", + "ch_nb = 4\n", + "meas_name = \"Na22_2\"\n", "\n", - "background_detector = background_meas.detectors[0]\n", - "check_source_detector = all_measurements[\"Na22_2\"].detectors[1]\n", + "background_detector = background_meas.detectors[1]\n", + "check_source_detector = all_measurements[meas_name].detectors[0]\n", "\n", "assert background_detector.channel_nb == check_source_detector.channel_nb\n", "assert (\n", @@ -472,26 +505,31 @@ "), f\"Channel number mismatch: {background_detector.channel_nb} != {ch_nb}\"\n", "\n", "hist, bin_edges = check_source_detector.get_energy_hist_background_substract(\n", - " background_detector, bins=\"double\"\n", + " # background_detector, bins=\"double\"\n", + " # background_detector, bins=int(np.nanmax(check_source_detector.events[:, 1]))\n", + " background_detector,\n", + " bins=None,\n", ")\n", "\n", "calibrated_bin_bedges = np.polyval(calibration_coeffs[ch_nb], bin_edges)\n", "\n", "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", "\n", - "parameters, covariance = compass.fit_peak_gauss(\n", - " hist, xvals, all_measurements[\"Na22_2\"].check_source.nuclide.energy, search_width=800\n", - ")\n", + "for energy_peak in all_measurements[meas_name].check_source.nuclide.energy:\n", "\n", - "# plotting\n", + " parameters, covariance = compass.fit_peak_gauss(\n", + " hist, xvals, [energy_peak], search_width=400\n", + " )\n", "\n", - "peak_start = 100\n", - "peak_end = 800\n", - "plt.fill_between(\n", - " xvals[peak_start:peak_end],\n", - " compass.gauss(xvals[peak_start:peak_end], *parameters),\n", - " alpha=0.5,\n", - ")\n", + " # plotting\n", + "\n", + " peak_start = 100\n", + " peak_end = 3000\n", + " plt.fill_between(\n", + " xvals[peak_start:peak_end],\n", + " compass.gauss(xvals[peak_start:peak_end], *parameters),\n", + " alpha=0.5,\n", + " )\n", "\n", "plt.hist(\n", " calibrated_bin_bedges[:-1],\n", @@ -501,7 +539,7 @@ " label=f\"Ch {detector.channel_nb} - calibrated\",\n", ")\n", "plt.ylabel(\"Counts\")\n", - "\n", + "plt.title(f\"{meas_name} - background substracted\")\n", "plt.legend()\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylim(bottom=0)\n", @@ -510,22 +548,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Detection efficiency: [0.0511642 0.02258572]\n" + "Detection efficiency: [0.02615625 0.01333559]\n" ] } ], "source": [ "efficiency = all_measurements[\"Na22_2\"].compute_detection_efficiency(\n", " background_measurement=background_meas,\n", - " calibration_coeffs=calibration_coeffs[4],\n", - " channel_nb=4,\n", + " calibration_coeffs=calibration_coeffs[5],\n", + " channel_nb=5,\n", + " search_width=300,\n", ")\n", "\n", "print(f\"Detection efficiency: {efficiency}\")" @@ -533,20 +572,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:271: RuntimeWarning: invalid value encountered in sqrt\n", - " act_meas_err = np.sqrt(np.array(areas)) / (\n" - ] - }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -558,19 +589,17 @@ "source": [ "channel_nb = 4\n", "for name, measurement in all_measurements.items():\n", - " try:\n", - " efficiency = measurement.compute_detection_efficiency(\n", - " background_measurement=background_meas,\n", - " calibration_coeffs=calibration_coeffs[channel_nb],\n", - " channel_nb=channel_nb,\n", - " )\n", - " plt.scatter(\n", - " measurement.check_source.nuclide.energy,\n", - " efficiency * 100,\n", - " label=name,\n", - " )\n", - " except:\n", - " continue\n", + " efficiency = measurement.compute_detection_efficiency(\n", + " background_measurement=background_meas,\n", + " calibration_coeffs=calibration_coeffs[channel_nb],\n", + " channel_nb=channel_nb,\n", + " search_width=300,\n", + " )\n", + " plt.scatter(\n", + " measurement.check_source.nuclide.energy,\n", + " efficiency * 100,\n", + " label=name,\n", + " )\n", "plt.xlabel(\"Energy (keV)\")\n", "plt.ylabel(\"Detection efficiency (%)\")\n", "plt.legend()\n", @@ -580,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -588,8 +617,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", - "Processing niobium_2...\n" + "\n", + "Processing niobium_2...\n", + "\n", + "Processing niobium_3...\n", + "\n", + "Processing zirconium_1...\n", + "\n", + "Processing zirconium_2...\n", + "\n", + "Processing zirconium_3...\n", + "\n" ] } ], @@ -609,7 +647,7 @@ " print(f\"Processing {sample}...\")\n", " meas = Measurement.from_directory(directory, name=sample)\n", " print(meas)\n", - " all_sample_measurements[sample] = meas\n" + " all_sample_measurements[sample] = meas" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 7143011..6dc9de7 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -33,7 +33,7 @@ def __init__(self, channel_nb) -> None: self.real_count_time = None def get_energy_hist( - self, bins: Union[int, str, NDArray[np.float64]] + self, bins: Union[None, NDArray[np.float64]] = None ) -> Tuple[np.ndarray, np.ndarray]: """ Get the energy histogram of the detector events. @@ -53,17 +53,15 @@ def get_energy_hist( energy_values = np.nan_to_num(energy_values, nan=0) - if isinstance(bins, (np.ndarray, int)): - real_bins = bins - elif bins == "double": - real_bins = int(np.nanmax(energy_values) / 2) + if not bins: + bins = int(np.nanmax(energy_values)) - return np.histogram(energy_values, bins=real_bins) + return np.histogram(energy_values, bins=bins) def get_energy_hist_background_substract( self, background_detector: "Detector", - bins: Union[int, str, NDArray[np.float64]], + bins: Union[NDArray[np.float64], None] = None, ) -> Tuple[np.ndarray, np.ndarray]: ps_to_seconds = 1e-12 raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) @@ -207,7 +205,7 @@ def get_expected_activity(self) -> float: self.check_source.activity_date, datetime.time.min ) # add a timezone - activity_datetime = activity_datetime.replace(tzinfo=datetime.timezone.utc) + activity_datetime = activity_datetime.replace(tzinfo=self.start_time.tzinfo) else: activity_datetime = self.check_source.activity_date @@ -221,6 +219,7 @@ def compute_detection_efficiency( background_measurement: Measurement, calibration_coeffs: np.ndarray, channel_nb: int, + search_width: float = 800, ) -> Union[np.ndarray, float]: """ Computes the detection efficiency of a check source given the @@ -252,7 +251,7 @@ def compute_detection_efficiency( ][0] hist, bin_edges = check_source_detector.get_energy_hist_background_substract( - background_detector, bins="double" + background_detector, bins=None ) calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) @@ -261,7 +260,7 @@ def compute_detection_efficiency( hist, calibrated_bin_bedges, self.check_source.nuclide.energy, - search_width=800, + search_width=search_width, ) act_meas = np.array(areas) / ( @@ -381,7 +380,7 @@ def get_calibration_data( sample = measurement.name[:-2] hist, bin_edges = detector.get_energy_hist_background_substract( - background_detector, bins="double" + background_detector, bins=None ) peaks_ind = get_peaks(hist, sample) peaks = bin_edges[peaks_ind] @@ -437,7 +436,13 @@ def gauss(x, b, m, *args): return out -def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): +def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600, threshold_overlap=200): + + if len(peak_ergs) > 1: + if np.max(peak_ergs) - np.min(peak_ergs) > threshold_overlap: + raise ValueError( + f"Peak energies {peak_ergs} are too far away from each to be fitted together." + ) search_start = np.argmin( np.abs((peak_ergs[0] - search_width / (2 * len(peak_ergs))) - xvals) @@ -470,7 +475,24 @@ def fit_peak_gauss(hist, xvals, peak_ergs, search_width=600): return parameters, covariance -def get_multipeak_area(hist, bins, peak_ergs, search_width=600): +def get_multipeak_area( + hist, bins, peak_ergs, search_width=600, threshold_overlap=200 +) -> List[float]: + + if len(peak_ergs) > 1: + if np.max(peak_ergs) - np.min(peak_ergs) > threshold_overlap: + areas = [] + for peak in peak_ergs: + area = get_multipeak_area( + hist, + bins, + [peak], + search_width=search_width, + threshold_overlap=threshold_overlap, + ) + areas += area + return areas + # get midpoints of every bin xvals = np.diff(bins) / 2 + bins[:-1] diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 0bcfabd..d8d96ec 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -300,7 +300,7 @@ def test_measurement_object_from_directory(no_root): assert measurement.detectors[0].events.shape[1] == 2 - measurement.detectors[0].get_energy_hist(bins="double") + measurement.detectors[0].get_energy_hist(bins=None) @pytest.mark.parametrize( @@ -310,7 +310,7 @@ def test_measurement_object_from_directory(no_root): 20, 50, 100, - "double", + None, np.arange(0, 10, 1), np.linspace(0, 10, num=100), ], From 1c969ce1f4183a4ecafd027e2185760d392b5470 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 16:11:36 -0400 Subject: [PATCH 076/137] removed double --- .../neutron_detection/activation_foils/compass.py | 13 ++++++------- test/neutron_detection/test_compass.py | 4 ++-- 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index a5a1650..a186e06 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -178,12 +178,13 @@ def __init__(self, channel_nb) -> None: self.real_count_time = None def get_energy_hist( - self, bins: Union[int, str, NDArray[np.float64]] + self, bins: Union[int, NDArray[np.float64], None] = None ) -> Tuple[np.ndarray, np.ndarray]: """ Get the energy histogram of the detector events. Args: - bins: number of bins or "double" to use half the max energy as bin size + bins: number of bins, can be a numpy array, if None, it will be set to the + maximum energy value in the events (one bin per energy value) Returns: Tuple of histogram values and bin edges """ @@ -198,12 +199,10 @@ def get_energy_hist( energy_values = np.nan_to_num(energy_values, nan=0) - if isinstance(bins, (np.ndarray, int)): - real_bins = bins - elif bins == "double": - real_bins = int(np.nanmax(energy_values) / 2) + if bins is None: + bins = int(np.nanmax(energy_values)) - return np.histogram(energy_values, bins=real_bins) + return np.histogram(energy_values, bins=bins) class Measurement: diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 0bcfabd..d8d96ec 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -300,7 +300,7 @@ def test_measurement_object_from_directory(no_root): assert measurement.detectors[0].events.shape[1] == 2 - measurement.detectors[0].get_energy_hist(bins="double") + measurement.detectors[0].get_energy_hist(bins=None) @pytest.mark.parametrize( @@ -310,7 +310,7 @@ def test_measurement_object_from_directory(no_root): 20, 50, 100, - "double", + None, np.arange(0, 10, 1), np.linspace(0, 10, num=100), ], From 16812259d2ed2fc8ff9861de74134266ff4ac817 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 16:17:55 -0400 Subject: [PATCH 077/137] updated docstrings --- .../neutron_detection/activation_foils/calibration.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 4b2fbed..9f32ed1 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -12,9 +12,9 @@ class Nuclide: name : The name of the nuclide. energy : - The energy of the gamma rays emitted by the nuclide. + The energy of the gamma rays emitted by the nuclide (in keV). intensity : - The intensity of the gamma rays emitted by the nuclide. (must sum to 1) + The intensity of the gamma rays emitted by the nuclide. half_life : The half-life of the nuclide in seconds. """ From df3d04cbcb8b7b6577d8f1f632c4de889deeb038 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 21:52:17 -0400 Subject: [PATCH 078/137] fixed bins is None --- .../activation_foils/compass.py | 137 +----------------- 1 file changed, 2 insertions(+), 135 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index cb90246..ebbb3de 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -53,8 +53,8 @@ def get_energy_hist( energy_values = np.nan_to_num(energy_values, nan=0) - if not bins: - bins = int(np.nanmax(energy_values)) + if bins is None: + bins = int(np.nanmax(energy_values)) + 1 return np.histogram(energy_values, bins=bins) @@ -720,136 +720,3 @@ def get_live_time_from_root(root_filename: str, channel: int) -> Tuple[float, fl live_count_time = root_file[f"LiveTime_{channel}"].members["fMilliSec"] / 1000 real_count_time = root_file[f"RealTime_{channel}"].members["fMilliSec"] / 1000 return live_count_time, real_count_time - - -class Detector: - events: NDArray[Tuple[float, float]] # type: ignore # Array of (time in ps, energy) pairs - channel_nb: int - live_count_time: float - real_count_time: float - - def __init__(self, channel_nb) -> None: - """ - Initialize a Detector object. - Args: - channel_nb: channel number of the detector - """ - self.channel_nb = channel_nb - self.events = np.empty((0, 2)) # Initialize as empty 2D array with 2 columns - self.live_count_time = None - self.real_count_time = None - - def get_energy_hist( - self, bins: Union[int, NDArray[np.float64], None] = None - ) -> Tuple[np.ndarray, np.ndarray]: - """ - Get the energy histogram of the detector events. - Args: - bins: number of bins, can be a numpy array, if None, it will be set to the - maximum energy value in the events (one bin per energy value) - Returns: - Tuple of histogram values and bin edges - """ - - energy_values = self.events[:, 1].copy() - time_values = self.events[:, 0].copy() - - # sort data based on timestamp - inds = np.argsort(time_values) - time_values = time_values[inds] - energy_values = energy_values[inds] - - energy_values = np.nan_to_num(energy_values, nan=0) - - if bins is None: - bins = int(np.nanmax(energy_values)) - - return np.histogram(energy_values, bins=bins) - - -class Measurement: - start_time: datetime.datetime - stop_time: datetime.datetime - name: str - detectors: List[Detector] - - def __init__(self, name: str) -> None: - """ - Initialize a Measurement object. - Args: - name: name of the measurement - """ - self.start_time = None - self.stop_time = None - self.name = name - self.detectors = [] - - @classmethod - def from_directory( - cls, source_dir: str, name: str, info_file_optional: bool = False - ) -> "Measurement": - """ - Create a Measurement object from a directory containing Compass data. - Args: - source_dir: directory containing Compass data - name: name of the measurement - info_file_optional: if True, the function will not raise an error - if the run.info file is not found - Returns: - Measurement object - """ - measurement_object = cls(name=name) - - # Get events - time_values, energy_values = get_events(source_dir) - - # Get start and stop time - try: - start_time, stop_time = get_start_stop_time(source_dir) - measurement_object.start_time = start_time - measurement_object.stop_time = stop_time - except FileNotFoundError as e: - if info_file_optional: - warnings.warn( - "run.info file not found. Assuming start and stop time are not needed." - ) - else: - raise FileNotFoundError(e) - - # Create detectors - detectors = [Detector(channel_nb=nb) for nb in time_values.keys()] - - # Get live and real count times - all_root_filenames = glob.glob(os.path.join(source_dir, "*.root")) - if len(all_root_filenames) == 1: - root_filename = all_root_filenames[0] - else: - root_filename = None - print("No root file found, assuming all counts are live") - - for detector in detectors: - detector.events = np.column_stack( - (time_values[detector.channel_nb], energy_values[detector.channel_nb]) - ) - - if root_filename: - live_count_time, real_count_time = get_live_time_from_root( - root_filename, detector.channel_nb - ) - detector.live_count_time = live_count_time - detector.real_count_time = real_count_time - else: - real_count_time = (stop_time - start_time).total_seconds() - # Assume first and last event correspond to start and stop time of live counts - # and convert from picoseconds to seconds - ps_to_seconds = 1e-12 - live_count_time = ( - time_values[detector.channel_nb][-1] - - time_values[detector.channel_nb][0] - ) * ps_to_seconds - detector.live_count_time = live_count_time - detector.real_count_time = real_count_time - - measurement_object.detectors = detectors - - return measurement_object From 7eec77a0d9f956fa216a7f979d42c2d5680a0e8f Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 8 May 2025 22:12:15 -0400 Subject: [PATCH 079/137] added test for background substraction --- test/neutron_detection/test_compass.py | 102 +++++++++++++++++++++++++ 1 file changed, 102 insertions(+) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index d8d96ec..c1cc489 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -331,3 +331,105 @@ def test_detector_get_energy_hist(bins): ) my_detector.get_energy_hist(bins=bins) + + +@pytest.mark.parametrize( + "counting_time_background", + [ + 0.1, + 1, + 10, + 100, + 1000, + 3000, + ], +) +def test_background_sub(counting_time_background): + """ + Test the background subtraction method of the Detector class. + """ + # BUILD + + def background_spectrum(energies): + return np.ones_like(energies) + + def measured_spectrum(energies): + return np.cos(energies / 10) + 10 + + counting_time_measured = 3600 + counting_time_background = counting_time_measured * 5 + + background_rate = 100000 / (3600) + measurement_rate = 3 * background_rate + + nb_events_background = int(background_rate * counting_time_background) + nb_events_measured = int(measurement_rate * counting_time_measured) + nb_events_measured_bg_contrib = int(background_rate * counting_time_measured) + + # Define energy grid for sampling + energy_grid = np.arange(100) + + # Calculate probability distributions using the spectrum functions + bg_probabilities = background_spectrum(energy_grid) + bg_probabilities = bg_probabilities / np.sum(bg_probabilities) # Normalize + measured_probabilities = measured_spectrum(energy_grid) + measured_probabilities = measured_probabilities / np.sum( + measured_probabilities + ) # Normalize + + # Sample from these distributions + energy_events_bg = np.random.choice( + energy_grid, size=nb_events_background, p=bg_probabilities + ) + energy_events_measured = np.random.choice( + energy_grid, size=nb_events_measured, p=measured_probabilities + ) + energy_events_measured_bg_contrib = np.random.choice( + energy_grid, size=nb_events_measured_bg_contrib, p=bg_probabilities + ) + + energy_events_measured = np.concatenate( + (energy_events_measured, energy_events_measured_bg_contrib) + ) + + # Create the measurement objects + ps_to_seconds = 1e-12 + + measurement = compass.Measurement("test") + detector_meas = compass.Detector(channel_nb=1) + detector_meas.real_count_time = counting_time_measured + measurement.detectors = [detector_meas] + time_events_measured = np.random.uniform( + 0, counting_time_measured, nb_events_measured + nb_events_measured_bg_contrib + ) + time_events_measured *= 1 / ps_to_seconds + time_events_measured.sort() + detector_meas.events = np.column_stack( + (time_events_measured, energy_events_measured) + ) + + background_measurment = compass.Measurement("background") + background_detector = compass.Detector(channel_nb=1) + background_detector.real_count_time = counting_time_background + background_measurment.detectors = [background_detector] + background_time_events = np.random.uniform( + 0, counting_time_background, nb_events_background + ) + background_time_events *= 1 / ps_to_seconds + background_time_events.sort() + background_detector.events = np.column_stack( + (background_time_events, energy_events_bg) + ) + + # RUN + hist_bc_sub, _ = detector_meas.get_energy_hist_background_substract( + background_detector=background_detector + ) + + # TEST + hist_bg, _ = background_detector.get_energy_hist() + hist_raw, _ = detector_meas.get_energy_hist() + expected_hist = ( + hist_raw - hist_bg / counting_time_background * counting_time_measured + ) + assert np.allclose(hist_bc_sub, expected_hist, rtol=1e-1) From 36f9ae00f4e89a9b7fef6fc0015967da95928c0a Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 13:05:31 -0400 Subject: [PATCH 080/137] added some tests --- test/neutron_detection/test_compass.py | 155 +++++++++++++++++++++++-- 1 file changed, 148 insertions(+), 7 deletions(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index c1cc489..eb9a97a 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -2,6 +2,10 @@ import numpy as np import os from libra_toolbox.neutron_detection.activation_foils import compass +from libra_toolbox.neutron_detection.activation_foils.calibration import ( + Nuclide, + CheckSource, +) from pathlib import Path import datetime @@ -336,8 +340,6 @@ def test_detector_get_energy_hist(bins): @pytest.mark.parametrize( "counting_time_background", [ - 0.1, - 1, 10, 100, 1000, @@ -347,6 +349,10 @@ def test_detector_get_energy_hist(bins): def test_background_sub(counting_time_background): """ Test the background subtraction method of the Detector class. + Builds a test case with a background measurement and a measured foil measurement, + then checks that the background is correctly subtracted from the measured spectrum. + + Also checks that the result is independent of the counting time of the background measurement. """ # BUILD @@ -356,10 +362,9 @@ def background_spectrum(energies): def measured_spectrum(energies): return np.cos(energies / 10) + 10 - counting_time_measured = 3600 - counting_time_background = counting_time_measured * 5 + counting_time_measured = 200 - background_rate = 100000 / (3600) + background_rate = 300000 / (3600) measurement_rate = 3 * background_rate nb_events_background = int(background_rate * counting_time_background) @@ -422,7 +427,7 @@ def measured_spectrum(energies): ) # RUN - hist_bc_sub, _ = detector_meas.get_energy_hist_background_substract( + computed_hist, _ = detector_meas.get_energy_hist_background_substract( background_detector=background_detector ) @@ -432,4 +437,140 @@ def measured_spectrum(energies): expected_hist = ( hist_raw - hist_bg / counting_time_background * counting_time_measured ) - assert np.allclose(hist_bc_sub, expected_hist, rtol=1e-1) + assert np.allclose(computed_hist, expected_hist, rtol=1e-1) + + +@pytest.mark.parametrize( + "activity_date", + [ + datetime.datetime(2024, 11, 7), + datetime.date(2024, 11, 7), + ], +) +@pytest.mark.parametrize("n_half_lives", [0, 1, 2, 3, 4, 5]) +def test_check_source_expected_activity(n_half_lives, activity_date): + """ + Test the expected activity of a check source. + """ + # BUILD + half_life = 10 * 24 * 3600 # seconds (10 days) + activity = 500 # Bq + + test_nuclide = Nuclide( + name="TestNuclide", + energy=[100, 200], + intensity=[0.5, 0.5], + half_life=half_life, + ) + + check_source = CheckSource( + nuclide=test_nuclide, + activity_date=activity_date, + activity=activity, + ) + + measurement = compass.CheckSourceMeasurement(name="test measurement") + measurement.check_source = check_source + measurement.start_time = activity_date + datetime.timedelta( + seconds=n_half_lives * half_life + ) + measurement.stop_time = measurement.start_time + datetime.timedelta(hours=1) + + # convert start_time and stop_time to datetime + if isinstance(measurement.start_time, datetime.date): + measurement.start_time = datetime.datetime.combine( + measurement.start_time, datetime.datetime.min.time() + ) + + # RUN + computed_activity = measurement.get_expected_activity() + + # TEST + + expected_activity = activity / (2**n_half_lives) + assert np.isclose(computed_activity, expected_activity, rtol=1e-2) + + +@pytest.mark.parametrize("expected_efficiency", [1e-2, 0.5, 1]) +def test_check_source_detection_efficiency(expected_efficiency): + """ + Test the detection efficiency of a check source measurement. + Generates a test case with a known detection efficiency and checks that the + computed efficiency is close to the expected one. + + Using a Mn54 source with an energy of 834.848 keV and an intensity of 1.0. + We generate some events with a normal distribution centered on the energy of the source. + The number of events is given by the expected efficiency, the activity of the source, + the measurement time, and the number of half-lives passed since the activity date. + """ + # BUILD + + ps_to_seconds = 1e-12 + + n_half_lives = 0 + + activity_date = datetime.datetime(2024, 11, 7) + half_life = 10 * 24 * 3600 # seconds (10 days) + activity = 500e1 # Bq + + test_nuclide = Nuclide( + name="TestNuclide Mn54", + energy=[834.848], + intensity=[1.0], + half_life=half_life, + ) + + check_source = CheckSource( + nuclide=test_nuclide, + activity_date=activity_date, + activity=activity, + ) + + measurement = compass.CheckSourceMeasurement(name="test measurement") + measurement.check_source = check_source + measurement.start_time = activity_date + datetime.timedelta( + seconds=n_half_lives * half_life + ) + measurement.stop_time = measurement.start_time + datetime.timedelta(seconds=100) + measurement_time = (measurement.stop_time - measurement.start_time).total_seconds() + + # generate the spectrum which is a normal centered on energy + nb_events_measured = ( + expected_efficiency * activity / (2**n_half_lives) * measurement_time + ) + energy_events = np.random.normal( + loc=test_nuclide.energy[0], + scale=20, + size=int(nb_events_measured), + ) + # make sure the min and max are in the range of the detector + energy_events[0] = 1 + energy_events[-1] = 3000 + time_events = np.random.uniform( + 0, + measurement_time, + size=int(nb_events_measured), + ) + time_events *= 1 / ps_to_seconds + time_events.sort() + + detector_meas = compass.Detector(channel_nb=1) + detector_meas.events = np.column_stack((time_events, energy_events)) + detector_meas.real_count_time = measurement_time + detector_meas.live_count_time = detector_meas.real_count_time + measurement.detectors = [detector_meas] + + background_measurement = compass.Measurement("background") + bg_detector = compass.Detector(channel_nb=1) + bg_detector.real_count_time = 0.5 + background_measurement.detectors = [bg_detector] + + # RUN + computed_efficiency = measurement.compute_detection_efficiency( + background_measurement, + calibration_coeffs=[1.0, 0.0], # assume perfect calibration + channel_nb=1, + ) + + # TEST + assert np.isclose(computed_efficiency, expected_efficiency, rtol=1e-2) From ed2dbcf8f1c8629001ec339fc450c36a7622585b Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 13:05:39 -0400 Subject: [PATCH 081/137] removed unused function --- .../activation_foils/compass.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index ebbb3de..b571b50 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -279,25 +279,6 @@ def compute_detection_efficiency( return detection_efficiency -def get_peak_inputs(samples): - default_inputs = { - "Na22": {"prom_factor": 0.075, "width": [10, 150], "start_index": 100}, - "Co60": {"prom_factor": 0.2, "width": [10, 150], "start_index": 400}, - "Ba133": {"prom_factor": 0.1, "width": [10, 200], "start_index": 100}, - "Mn54": {"prom_factor": 0.2, "width": [10, 100], "start_index": 100}, - } - - defaults = {"prom_factor": 0.075, "width": [10, 150], "start_index": 100} - - peak_inputs = {} - for sample in samples: - if sample in default_inputs.keys(): - peak_inputs[sample] = default_inputs[sample] - else: - peak_inputs[sample] = defaults - return peak_inputs - - def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: """Returns the peak indices of the histogram From 085de9831d14fb20a9a331daf753b8f460b7b2f5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 15:15:47 -0400 Subject: [PATCH 082/137] list instead of dict --- libra_toolbox/neutron_detection/activation_foils/compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b571b50..3752c8d 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -353,7 +353,7 @@ def get_calibration_data( calibration_energies = [] calibration_channels = [] - for measurement in check_source_measurements.values(): + for measurement in check_source_measurements: for detector in measurement.detectors: if detector.channel_nb != channel_nb: continue From e6c729a740a7f62e89be9c75638e2701da472dee Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 16:59:44 -0400 Subject: [PATCH 083/137] test for multipeak area --- .../activation_foils/compass.py | 1 + test/neutron_detection/test_compass.py | 169 ++++++++++++++++++ 2 files changed, 170 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 3752c8d..d29f673 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -380,6 +380,7 @@ def get_calibration_data( return calibration_channels, calibration_energies +# NOTE is this function really needed? def get_calibration_curve( check_source_measurements: List[CheckSourceMeasurement], background_measurement: Measurement, diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index eb9a97a..b6bfe98 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -574,3 +574,172 @@ def test_check_source_detection_efficiency(expected_efficiency): # TEST assert np.isclose(computed_efficiency, expected_efficiency, rtol=1e-2) + + +@pytest.mark.parametrize( + "a, b", + [ + (1.5, 200), + (1, 0), + (2, 600), + ], +) +def test_get_calibration_data(a, b): + """ + Test the get_calibration_data function from the compass module. + Checks that the calibration data is correctly computed from the measurements. + + The test generates a set of measurements with known energies and intensities, + and checks that the computed calibration data matches the expected values. + The energies counts (channels) are generated using a linear function with parameters a and b. + """ + # BUILD + channel_nb = 1 + nb_events_measured = 60000 + measurements = [] + real_energies = np.array([800, 1300, 1800]) + energy_channels = a * real_energies + b + for real_energy, energy_channel in zip( + real_energies, + energy_channels, + ): + test_nuclide = Nuclide( + name="TestNuclide", + energy=[real_energy], + intensity=[1.0], + half_life=10000, + ) + check_source = CheckSource( + nuclide=test_nuclide, + activity_date=datetime.datetime(2024, 11, 7), + activity=5000, + ) + + # create CheckSourceMeasurement + measurement = compass.CheckSourceMeasurement(name="test measurement") + measurement.check_source = check_source + measurement.start_time = datetime.datetime(2024, 11, 7) + detector = compass.Detector(channel_nb=channel_nb) + energy_events = np.random.normal( + loc=energy_channel, scale=30, size=int(nb_events_measured) + ) + + # make sure the min and max are in the range of the detector + energy_events[0] = 1 + energy_events[-1] = 3000 + + time_events = np.random.uniform(0, 100, size=int(nb_events_measured)) + detector.events = np.column_stack((time_events, energy_events)) + detector.real_count_time = 100 + measurement.detectors = [detector] + + measurements.append(measurement) + + # create background measurement + background_measurement = compass.Measurement("background") + bg_detector = compass.Detector(channel_nb=channel_nb) + bg_detector.real_count_time = 100 + background_measurement.detectors = [bg_detector] + + # RUN + calibration_channels, calibration_energies = compass.get_calibration_data( + measurements, background_measurement, channel_nb=channel_nb + ) + + # TEST + assert np.allclose(calibration_channels, energy_channels, rtol=1e-2) + assert np.allclose(calibration_energies, real_energies, rtol=1e-2) + + +def test_get_multipeak_area_single_peak(): + """ + Test the get_multipeak_area function from the compass module. + Checks that the area under the peaks is correctly computed. + """ + # BUILD + energy = 2000 + nb_events_measured = 60000 + energy_events = np.random.normal(loc=energy, scale=30, size=int(nb_events_measured)) + # make sure the min and max are in the range of the detector + energy_events[0] = 1 + energy_events[-1] = 3000 + + hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000)) + + # RUN + areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy]) + + # TEST + expected_area = np.sum(hist) + assert np.isclose(areas[0], expected_area, rtol=1e-2) + + +def test_get_multipeak_area_two_separated_peaks(): + """ + Test the get_multipeak_area function from the compass module. + Checks that the area under the peaks is correctly computed. + """ + # BUILD + energy1 = 1000 + energy2 = 2000 + energy_events = np.empty((0,)) + nb_events_peak1 = 60000 + nb_events_peak2 = 2 * nb_events_peak1 + sigma_peak = 30 + for energy, nb_events in zip( + [energy1, energy2], [nb_events_peak1, nb_events_peak2] + ): + new_energy_events = np.random.normal( + loc=energy, scale=sigma_peak, size=int(nb_events) + ) + # make sure the min and max are in the range of the detector + new_energy_events[0] = 1 + new_energy_events[-1] = 3000 + energy_events = np.concatenate((energy_events, new_energy_events)) + + hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000)) + + # RUN + areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy1, energy2]) + + # TEST + + expected_area_peak_1 = nb_events_peak1 + expected_area_peak_2 = nb_events_peak2 + for i, expected_area in enumerate([expected_area_peak_1, expected_area_peak_2]): + assert np.isclose(areas[i], expected_area, rtol=1e-2) + + +def test_get_multipeak_area_two_close_peaks(): + """ + Test the get_multipeak_area function from the compass module. + Checks that the area under the peaks is correctly computed. + """ + # BUILD + energy1 = 1000 + energy2 = 1100 + energy_events = np.empty((0,)) + nb_events_peak1 = 100000 + nb_events_peak2 = 0.5 * nb_events_peak1 + sigma_peak = 30 + for energy, nb_events in zip( + [energy1, energy2], [nb_events_peak1, nb_events_peak2] + ): + new_energy_events = np.random.normal( + loc=energy, scale=sigma_peak, size=int(nb_events) + ) + # make sure the min and max are in the range of the detector + new_energy_events[0] = 1 + new_energy_events[-1] = 3000 + energy_events = np.concatenate((energy_events, new_energy_events)) + + hist, bin_edges = np.histogram(energy_events, bins=np.arange(0, 3000)) + + # RUN + areas = compass.get_multipeak_area(hist, bin_edges, peak_ergs=[energy1, energy2]) + + # TEST + expected_area_peak_1 = nb_events_peak1 + expected_area_peak_2 = nb_events_peak2 + for i, expected_area in enumerate([expected_area_peak_1, expected_area_peak_2]): + assert np.isclose(areas[i], expected_area, rtol=1e-2) From 72757b590be47301aabba12504b3a24a9876e38e Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 17:01:15 -0400 Subject: [PATCH 084/137] more robust test --- test/neutron_detection/test_compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index b6bfe98..73f059c 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -720,7 +720,7 @@ def test_get_multipeak_area_two_close_peaks(): energy2 = 1100 energy_events = np.empty((0,)) nb_events_peak1 = 100000 - nb_events_peak2 = 0.5 * nb_events_peak1 + nb_events_peak2 = 0.6 * nb_events_peak1 sigma_peak = 30 for energy, nb_events in zip( [energy1, energy2], [nb_events_peak1, nb_events_peak2] From 246845f73c34ec9522ed35476791809ba5e51eb3 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 20:32:59 -0400 Subject: [PATCH 085/137] removed 2 unused functions --- .../activation_foils/compass.py | 39 ------------------- 1 file changed, 39 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index d29f673..2ac9424 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -516,45 +516,6 @@ def get_multipeak_area( return areas -def group_close_values(data, threshold=200): - # Sort the data to group values sequentially - data.sort() - - # Initialize groups and a temporary group - groups = [] - temp_group = [data[0]] - - for i in range(1, len(data)): - # Check if the current value is within the threshold of the last value in the temp group - if abs(data[i] - temp_group[-1]) < threshold: - temp_group.append(data[i]) - else: - # Commit the temp group to groups and start a new group - groups.append(tuple(temp_group)) - temp_group = [data[i]] - - # Add the last group - groups.append(tuple(temp_group)) - - return groups - - -def get_peak_areas(hist, bins, peak_ergs, overlap_width=200, search_width=400): - - areas = [] - # organize peak energies into tuples, in which peak energies close enough - # to have overlapping peaks will be paired together - erg_groups = group_close_values(peak_ergs, threshold=overlap_width) - # print(erg_groups) - - for erg_group in erg_groups: - areas += get_multipeak_area( - hist, bins, erg_group, search_width=len(erg_group) * search_width - ) - # print(areas) - return areas - - def get_channel(filename): """ Extract the channel number from a given filename string. From 975b4fddabde3c0998c1cb7ee5afe719f9ed08b5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 20:38:23 -0400 Subject: [PATCH 086/137] more robust test --- test/neutron_detection/test_compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 73f059c..6f9db70 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -719,7 +719,7 @@ def test_get_multipeak_area_two_close_peaks(): energy1 = 1000 energy2 = 1100 energy_events = np.empty((0,)) - nb_events_peak1 = 100000 + nb_events_peak1 = 1300000 nb_events_peak2 = 0.6 * nb_events_peak1 sigma_peak = 30 for energy, nb_events in zip( From c3f0edaabddfd9351e4ac3b5de1bac4113d5b7d1 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 21:16:38 -0400 Subject: [PATCH 087/137] get_peaks is now a method --- .../activation_foils/compass.py | 101 +++++++++--------- 1 file changed, 51 insertions(+), 50 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 2ac9424..62f4f1c 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -9,7 +9,13 @@ import glob import warnings -from libra_toolbox.neutron_detection.activation_foils.calibration import CheckSource +from libra_toolbox.neutron_detection.activation_foils.calibration import ( + CheckSource, + na22, + co60, + ba133, + mn54, +) from scipy.signal import find_peaks from scipy.optimize import curve_fit @@ -278,65 +284,60 @@ def compute_detection_efficiency( return detection_efficiency + def get_peaks(self, hist: np.ndarray) -> np.ndarray: + """Returns the peak indices of the histogram -def get_peaks(hist: np.ndarray, source: str) -> np.ndarray: - """Returns the peak indices of the histogram + Args: + hist: a histogram - Args: - hist: a histogram - source: the type of source (eg. "Na22", "Co60", "Ba133", "Mn54") + Returns: + the peak indices in ``hist`` + """ - Returns: - the peak indices in ``hist`` - """ - start_index = 100 - prominence = 0.10 * np.max(hist[start_index:]) - height = 0.10 * np.max(hist[start_index:]) - width = [10, 150] - indices = None - distance = 30 - if "na22" in source.lower(): - # find 511 keV peak first - prominence = 0.01 * np.max(hist[start_index:]) - height = 0.9 * np.max(hist[start_index:]) - width = [10, 200] - elif "co60" in source.lower(): - start_index = 400 - height = 0.60 * np.max(hist[start_index:]) - prominence = None - elif "ba133" in source.lower(): - width = [10, 200] - elif "mn54" in source.lower(): - height = 0.6 * np.max(hist[start_index:]) - peaks, peak_data = find_peaks( - hist[start_index:], - prominence=prominence, - height=height, - width=width, - distance=distance, - ) - peaks = np.array(peaks) + start_index - if "na22" in source.lower(): - # Find 1275 keV peak - peak_511 = peaks[0] - start_index = peak_511 + 100 - prominence = 0.5 * np.max(hist[start_index:]) + start_index = 100 + prominence = 0.10 * np.max(hist[start_index:]) height = 0.10 * np.max(hist[start_index:]) - - high_peaks, peak_data = find_peaks( + width = [10, 150] + distance = 30 + if self.check_source.nuclide == na22: + # find 511 keV peak first + prominence = 0.01 * np.max(hist[start_index:]) + height = 0.9 * np.max(hist[start_index:]) + width = [10, 200] + elif self.check_source.nuclide == co60: + start_index = 400 + height = 0.60 * np.max(hist[start_index:]) + prominence = None + elif self.check_source.nuclide == ba133: + width = [10, 200] + elif self.check_source.nuclide == mn54: + height = 0.6 * np.max(hist[start_index:]) + peaks, peak_data = find_peaks( hist[start_index:], prominence=prominence, height=height, width=width, distance=distance, ) - high_peaks = np.array(high_peaks) + start_index - peaks = [peak_511, high_peaks[0]] - - if indices: - peaks = peaks[[indices]][0] + peaks = np.array(peaks) + start_index + if self.check_source.nuclide == na22: + # Find 1275 keV peak + peak_511 = peaks[0] + start_index = peak_511 + 100 + prominence = 0.5 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + + high_peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + high_peaks = np.array(high_peaks) + start_index + peaks = [peak_511, high_peaks[0]] - return peaks + return peaks def get_calibration_data( @@ -363,7 +364,7 @@ def get_calibration_data( hist, bin_edges = detector.get_energy_hist_background_substract( background_detector, bins=None ) - peaks_ind = get_peaks(hist, sample) + peaks_ind = measurement.get_peaks(hist) peaks = bin_edges[peaks_ind] if len(peaks) != len(measurement.check_source.nuclide.energy): From 9e63635e9cea735fec99686ca8d9f2d658c68cf6 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 21:26:25 -0400 Subject: [PATCH 088/137] simplified peak finding params --- example.ipynb | 140 +++++++++++++----- .../activation_foils/compass.py | 25 +--- 2 files changed, 107 insertions(+), 58 deletions(-) diff --git a/example.ipynb b/example.ipynb index 17df3eb..76c99cf 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:137: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:143: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -183,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -206,9 +206,7 @@ " histtype=\"step\",\n", " label=f\"Ch {detector.channel_nb}\",\n", " )\n", - " peaks = compass.get_peaks(\n", - " hist, source=all_measurements[\"Co60_3\"].check_source.nuclide.name\n", - " )\n", + " peaks = all_measurements[\"Co60_3\"].get_peaks(hist)\n", " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", "\n", " from scipy.signal import find_peaks\n", @@ -251,7 +249,73 @@ "outputs": [ { "data": { - "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from libra_toolbox.neutron_detection.activation_foils import compass\n", + "\n", + "for detector in all_measurements[\"Na22_2\"].detectors:\n", + " hist, bin_edges = detector.get_energy_hist_background_substract(background_meas.detectors[0])\n", + "\n", + " plt.hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb}\",\n", + " )\n", + " peaks = all_measurements[\"Na22_2\"].get_peaks(hist)\n", + " # plt.plot(bin_edges[peaks], hist[peaks], '.', ms=10)\n", + "\n", + " from scipy.signal import find_peaks\n", + " import numpy as np\n", + "\n", + " start_index = 100\n", + " height = 0.1 * np.max(hist[start_index:])\n", + " prominence = 0.1 * np.max(hist[start_index:])\n", + " width = [10, 150]\n", + " distance = 30\n", + " peaks, peak_data = find_peaks(\n", + " hist[start_index:],\n", + " prominence=prominence,\n", + " height=height,\n", + " width=width,\n", + " distance=distance,\n", + " )\n", + " plt.plot(bin_edges[start_index:][peaks], peak_data[\"peak_heights\"], \".\", ms=10)\n", + "\n", + " for i, p in enumerate(peaks):\n", + " width = peak_data[\"widths\"][i]\n", + " plt.axvspan(\n", + " bin_edges[start_index:][p] - width,\n", + " bin_edges[start_index:][p] + width,\n", + " color=\"red\",\n", + " alpha=0.2,\n", + " label=\"Peak range\",\n", + " )\n", + "\n", + "plt.legend()\n", + "# plt.yscale(\"log\")\n", + "# plt.ylim(top=2100)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" ] @@ -334,20 +398,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_979085/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_1136359/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -428,12 +492,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -447,7 +511,7 @@ "\n", "for channel_nb in [4, 5]:\n", " calibration_channels, calibration_energies = compass.get_calibration_data(\n", - " all_measurements,\n", + " all_measurements.values(),\n", " background_measurement=background_meas,\n", " channel_nb=channel_nb,\n", " )\n", @@ -478,12 +542,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -548,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -572,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -609,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -617,17 +681,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n", + "\n", "Processing zirconium_1...\n", - "\n", + "\n", "Processing zirconium_2...\n", - "\n", + "\n", "Processing zirconium_3...\n", - "\n" + "\n" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 62f4f1c..c40ed6a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -300,10 +300,11 @@ def get_peaks(self, hist: np.ndarray) -> np.ndarray: width = [10, 150] distance = 30 if self.check_source.nuclide == na22: - # find 511 keV peak first - prominence = 0.01 * np.max(hist[start_index:]) - height = 0.9 * np.max(hist[start_index:]) - width = [10, 200] + start_index = 100 + height = 0.1 * np.max(hist[start_index:]) + prominence = 0.1 * np.max(hist[start_index:]) + width = [10, 150] + distance = 30 elif self.check_source.nuclide == co60: start_index = 400 height = 0.60 * np.max(hist[start_index:]) @@ -320,22 +321,6 @@ def get_peaks(self, hist: np.ndarray) -> np.ndarray: distance=distance, ) peaks = np.array(peaks) + start_index - if self.check_source.nuclide == na22: - # Find 1275 keV peak - peak_511 = peaks[0] - start_index = peak_511 + 100 - prominence = 0.5 * np.max(hist[start_index:]) - height = 0.10 * np.max(hist[start_index:]) - - high_peaks, peak_data = find_peaks( - hist[start_index:], - prominence=prominence, - height=height, - width=width, - distance=distance, - ) - high_peaks = np.array(high_peaks) + start_index - peaks = [peak_511, high_peaks[0]] return peaks From 8c946f96868abc30d8429d860fa2db22e9a20ec5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 21:34:09 -0400 Subject: [PATCH 089/137] more counts --- test/neutron_detection/test_compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 6f9db70..bd510b8 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -511,7 +511,7 @@ def test_check_source_detection_efficiency(expected_efficiency): activity_date = datetime.datetime(2024, 11, 7) half_life = 10 * 24 * 3600 # seconds (10 days) - activity = 500e1 # Bq + activity = 1000e1 # Bq test_nuclide = Nuclide( name="TestNuclide Mn54", From b3e2f17ede265c70d5305dc1ee2a84a1d99e3774 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 9 May 2025 21:38:30 -0400 Subject: [PATCH 090/137] added possibility to provide custom peak finding parameters --- .../neutron_detection/activation_foils/compass.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index c40ed6a..b0d3495 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -284,16 +284,19 @@ def compute_detection_efficiency( return detection_efficiency - def get_peaks(self, hist: np.ndarray) -> np.ndarray: + def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: """Returns the peak indices of the histogram Args: hist: a histogram + kwargs: optional parameters for the peak finding algorithm + see scipy.signal.find_peaks for more information Returns: the peak indices in ``hist`` """ + # peak finding parameters start_index = 100 prominence = 0.10 * np.max(hist[start_index:]) height = 0.10 * np.max(hist[start_index:]) @@ -313,6 +316,16 @@ def get_peaks(self, hist: np.ndarray) -> np.ndarray: width = [10, 200] elif self.check_source.nuclide == mn54: height = 0.6 * np.max(hist[start_index:]) + + # update the parameters if kwargs are provided + if kwargs: + prominence = kwargs.get("prominence", prominence) + height = kwargs.get("height", height) + width = kwargs.get("width", width) + distance = kwargs.get("distance", distance) + + # run the peak finding algorithm + # NOTE: the start_index is used to ignore the low energy region peaks, peak_data = find_peaks( hist[start_index:], prominence=prominence, From 7b186eaacc9f50f37ef6b2ccd01cdc9dff885e07 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Mon, 12 May 2025 09:56:41 -0400 Subject: [PATCH 091/137] docstrings + removed unused function --- .../activation_foils/compass.py | 61 ++++++++++--------- 1 file changed, 33 insertions(+), 28 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b0d3495..cbf67a3 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -22,10 +22,23 @@ class Detector: - events: NDArray[Tuple[float, float]] # type: ignore # Array of (time in ps, energy) pairs + """ + Represents a detector used in COMPASS measurements. + + This class stores detector events (time and energy pairs), channel number, + and timing information. + + Attributes: + events: Array of (time in ps, energy) pairs + channel_nb: Channel number of the detector + live_count_time: Active measurement time excluding dead time (in seconds) + real_count_time: Total elapsed measurement time (in seconds) + """ + + events: NDArray[Tuple[float, float]] # type: ignore channel_nb: int - live_count_time: float - real_count_time: float + live_count_time: Union[float, None] + real_count_time: Union[float, None] def __init__(self, channel_nb) -> None: """ @@ -98,8 +111,23 @@ def get_energy_hist_background_substract( class Measurement: - start_time: datetime.datetime - stop_time: datetime.datetime + """ + Represents a measurement session from a COMPASS detector system. + + The Measurement class encapsulates data from a complete measurement session, + including timing information and detector events across multiple channels. + It provides functionality to load and process measurement data from files + generated by the COMPASS data acquisition system. + + Attributes: + start_time: Start time of the measurement + stop_time: End time of the measurement + name: Identifier for this measurement + detectors: List of ``Detector`` objects for each channel + """ + + start_time: Union[datetime.datetime, None] + stop_time: Union[datetime.datetime, None] name: str detectors: List[Detector] @@ -379,29 +407,6 @@ def get_calibration_data( return calibration_channels, calibration_energies -# NOTE is this function really needed? -def get_calibration_curve( - check_source_measurements: List[CheckSourceMeasurement], - background_measurement: Measurement, - channel_nb: int, -): - - calibration_channels, calibration_energies = get_calibration_data( - check_source_measurements, - background_measurement, - channel_nb, - ) - - # linear fit for calibration curve - coeff = np.polyfit( - calibration_channels, - calibration_energies, - 1, - ) - - return coeff - - def gauss(x, b, m, *args): """Creates a multipeak gaussian with a linear addition of the form: m * x + b + Sum_i (A_i * exp(-(x - x_i)**2) / (2 * sigma_i**2)""" From 8b2a8fb70752a837dabc8ef141618f2c93edcc10 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 10:43:48 -0400 Subject: [PATCH 092/137] improved bins in default mode --- .../neutron_detection/activation_foils/compass.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index cbf67a3..04ed4c9 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -52,12 +52,15 @@ def __init__(self, channel_nb) -> None: self.real_count_time = None def get_energy_hist( - self, bins: Union[None, NDArray[np.float64]] = None + self, bins: Union[None, NDArray[np.float64], int, str] = None ) -> Tuple[np.ndarray, np.ndarray]: """ Get the energy histogram of the detector events. Args: - bins: number of bins or "double" to use half the max energy as bin size + bins: bins for the histogram. If None, bins are automatically generated + (one bin per energy channel). If int, it specifies the number of bins. + If str, it specifies the binning method (e.g., 'auto', 'fd', etc.) see + ``numpy.histogram_bin_edges`` for more details. Returns: Tuple of histogram values and bin edges """ @@ -73,7 +76,9 @@ def get_energy_hist( energy_values = np.nan_to_num(energy_values, nan=0) if bins is None: - bins = int(np.nanmax(energy_values)) + 1 + bins = np.arange( + int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + 1 + ) return np.histogram(energy_values, bins=bins) From 52ac0193a296146060b1fb7bff0564cc1fc5b2b3 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 14:10:28 -0400 Subject: [PATCH 093/137] improved efficiency calculation --- example.ipynb | 60 +++++++++---------- .../activation_foils/compass.py | 42 ++++++++----- 2 files changed, 57 insertions(+), 45 deletions(-) diff --git a/example.ipynb b/example.ipynb index 76c99cf..22bba52 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:143: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:176: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -183,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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lvPvuu8jKyqq0vpiYGPz444+YOXOm3u1p27Ytdu3ahaSkJCQnJ2P06NF69awAEPeZMmUKLl++jKioKKxevRoAxF6hkJAQ5OTkIDg4GGfOnEF6ejqioqLw9ttvQ6PR6N3u6mKAISKihkWpBIqK6v6lVNZ608PDwzF+/HjMnj0bnp6eGD58OM6cOQMPDw8AwIIFC9CtWzcEBATA398fcrkcw4cPr7Q+T09PHDlyBNu3b8fs2bP1asvatWthb2+P3r17IygoCAEBAejWrZteddjY2ODnn39GUlISfHx88NFHH2HhwoUAII6LcXV1RVxcHDQaDQYNGgQvLy/MnDkTdnZ2MDKqu5jBS0hERNQwmJiUzIxbUFAyK+/zYGVVctxq2rJlS5XbTU1NsWjRIixatKjC7Q4ODtizZ0+VdZSdNwYAOnToUGkvDVDy6AFBKD83WYsWLXDkyBGddSEhITrLFV1SKju/CwD07t0bycnJ4nJERIQ4C3Gp0t6e54kBhoiIGgaptGRa/xf8WUgNzTfffINWrVrBzc0NycnJCA0NxahRo2Bubl6v7WKAISKihkMqZaBoYBQKBRYuXAiFQgEXFxe8+eab+Pjjj+u7WQwwNcYnCRAR0Qtg3rx5mDdvXn03oxwO4q0xJhgiIqL6wgBDREREBocBhoiIiAwOAwwREREZHAYYIiIiMjgMMERERGRweBs1ERE1HCrVCz+Rnb+/P3x8fLBu3br6bkqDxgBDREQNg0oFJCSUPErgebGyKpn9t5ohZuLEidi6dSsAiNPpjx8/Hh9++OFzfRIzMcAQEVFDoVaXhBepFJDJ6v54SmXJ8dRqvXphAgMDER4eDqVSif379yMkJASmpqaYP39+HTa2+gRBgEajafSBSu8xMMeOHUNQUBBcXV0hkUjKPZRKIpFU+Fq1apVYpkWLFuW2L1++XKeelJQU9OnTB2ZmZnB3d8fKlStr9gmJiMiwyGSAmVndv2oYkmQyGeRyOZo3b46pU6di4MCB2Lt3LwBAqVRizpw5cHNzg6WlJXr16qXzcMZ79+4hODgYbm5usLCwgJeXF7Zv317l8SIjI2Fra4uIiIgKt8fGxkIikeCXX36Br68vZDIZTpw4gfT0dAwbNgzOzs6wsrJCjx49cOjQIZ19W7RogaVLl2LSpEmwtraGh4cHvvzyS50yJ0+ehI+PD8zMzNC9e3fs2bMHEolE56GPqampGDx4MKysrODs7Ixx48bh7t27enyr+tM7wBQWFqJLly7YuHFjhdszMzN1Xps3b4ZEIsHIkSN1yi1evFin3IwZM8RteXl5GDRoEJo3b47ExESsWrUKYWFh5b5UIiKi+mZubg7V46dnT58+HfHx8dixYwdSUlLw5ptvIjAwEFevXgUAFBUVwdfXF5GRkUhNTcWUKVMwbtw4JCQkVFj3tm3bEBwcjIiICIwZM6bKdnzwwQdYvnw5Ll++DG9vbxQUFGDIkCE4fPgwzp8/j8DAQAQFBSEjI0NnvzVr1qB79+44f/48pk2bhqlTpyItLQ1Aye/joKAgeHl54dy5c1iyZAlCQ0N19s/NzUX//v3RtWtXnD17FgcOHEBWVhZGjRpVo++zuvTuXxo8eDAGDx5c6Xa5XK6z/NNPP6Ffv35o1aqVznpra+tyZUtFRERApVJh8+bNkEql6NSpE5KSkrB27VpMmTJF3yYTERHVOkEQcPjwYURFRWHGjBnIyMhAeHg4MjIy4OrqCgCYM2cODhw4gPDwcCxduhRubm6YM2eOWMeMGTMQFRWFnTt3omfPnjr1b9y4ER999BF+/vlnvPrqq09tz+LFi/Haa6+Jyw4ODujSpYu4vGTJEuzevRt79+7F9OnTxfVDhgzBtGnTAAChoaH45JNPEBMTA09PT2zbtg0SiQRfffUVzMzM0LFjR9y6dQuTJ08W99+wYQO6du2KpUuXius2b94Md3d3XLlyBe3atavuV6qXOr1AlpWVhcjISHHAU1nLly/HkiVL4OHhgdGjR+P9998Xr9fFx8ejb9++kJa5JhkQEIAVK1bg/v37sLe3L1efUqmEUqkUl/Py8urgExER0Ytu3759sLKyQnFxMbRaLUaPHo2wsDDExsZCo9GU+4WtVCrRpEkTAIBGo8HSpUuxc+dO3Lp1CyqVCkqlEhYWFjr7/PDDD8jOzkZcXBx69OhRrXZ1795dZ7mgoABhYWGIjIxEZmYm1Go1Hj16VK4HxtvbW3wvkUggl8uRnZ0NAEhLS4O3tzfMzMzEMn8OWsnJyYiJiYGVlVW5NqWnpxtmgNm6dSusra3x+uuv66z/xz/+gW7dusHBwQEnT57E/PnzkZmZibVr1wIoeXR3y5YtdfZxdnYWt1UUYJYtW4ZFixbV0SchIiIq0a9fP2zatAlSqRSurq7iH98FBQUwNjZGYmIijI2NdfYp/eW+atUqrF+/HuvWrYOXlxcsLS0xc+ZM8RJUqa5du+LcuXPYvHkzunfvDolE8tR2WVpa6izPmTMH0dHRWL16Ndq0aQNzc3O88cYb5Y5lamqqsyyRSKDVaqv3ZTz+3EFBQVixYkW5bS4uLtWuR191GmA2b96MMWPG6CQ3AJg1a5b43tvbG1KpFO+++y6WLVsGWQ0HVc2fP1+n3ry8PLi7u9es4URERJWwtLREmzZtyq3v2rUrNBoNsrOz0adPnwr3jYuLw7BhwzB27FgAgFarxZUrV9CxY0edcq1bt8aaNWvg7+8PY2NjbNiwQe92xsXFYeLEiRgxYgSAkqBx48YNverw9PTEt99+C6VSKf5+PnPmjE6Zbt264ccff0SLFi2e651PdTYT7/Hjx5GWloa///3vTy3bq1cvqNVq8YuVy+XIysrSKVO6XNm4GZlMBhsbG50XERHR89KuXTuMGTMG48ePx65du3D9+nUkJCRg2bJliIyMBAC0bdsW0dHROHnyJC5fvox333233O+7svXFxMTgxx9/xMyZM/VuT9u2bbFr1y4kJSUhOTkZo0eP1qtnBYC4z5QpU3D58mVERUVh9erVACD2CoWEhCAnJwfBwcE4c+YM0tPTERUVhbfffhsajUbvdldXnQWYr7/+Gr6+vjoDiCqTlJQEIyMjODk5AQD8/Pxw7NgxFBcXi2Wio6Ph6elZ4eUjIiJqRJRKoKio7l9lxk3WlvDwcIwfPx6zZ8+Gp6cnhg8fjjNnzsDDwwMAsGDBAnTr1g0BAQHw9/eHXC7H8OHDK63P09MTR44cwfbt2zF79my92rJ27VrY29ujd+/eCAoKQkBAALp166ZXHTY2Nvj555+RlJQEHx8ffPTRR1i4cCEAiFdXXF1dERcXB41Gg0GDBsHLywszZ86EnZ0djIzq7olFevf1FBQU4Nq1a+Ly9evXkZSUBAcHB/EE5eXl4fvvv8eaNWvK7R8fH4/Tp0+jX79+sLa2Rnx8PN5//32MHTtWDCejR4/GokWL8M477yA0NBSpqalYv349Pvnkk5p+TiIiauhMTEpmxi0oKJmV93mwsio5bjVt2bKlyu2mpqZYtGhRpWMyHRwcys2f9mdl540BgA4dOlTaSwOUPHpAEIRy61u0aIEjR47orAsJCdFZruiSUtn5XQCgd+/eSE5OFpcjIiLEWYhLlfb2PE96B5izZ8+iX79+4nLpuJMJEyaIJ3bHjh0QBAHBwcHl9pfJZNixYwfCwsKgVCrRsmVLvP/++zrjV2xtbXHw4EGEhITA19cXjo6OWLhwIW+hJiJqzKTSkmn9X/BnITU033zzDVq1agU3NzckJycjNDQUo0aNgrm5eb22S+8AU1nSK2vKlCmVho1u3brh1KlTTz2Ot7c3jh8/rm/ziIjIkEmlDBQNjEKhwMKFC6FQKODi4oI333wTH3/8cX03i89CIiIiosrNmzcP8+bNq+9mlFN3o2uIiIiI6ggDDBERERkcBhgiIiIyOAwwNSSg6oHMREREVHcYYIiIiMjgMMAQERGRweFt1ERE1GBkFmTivvL+czuevcweLla1/8RkiUSC3bt3V/mYAHo2DDBERNQgZBZkYthPw/BI/ei5HdPcxBw/DftJrxCjUCjw8ccfIzIyErdu3YKTkxN8fHwwc+ZMDBgwoFba9d577+E///kPPvnkkxo9yPFFwABDREQNwn3lfTxSP8KyPsvQyrZVnR/vtwe/Yf7x+bivvF/tAHPjxg28/PLLsLOzw6pVq+Dl5YXi4mJERUUhJCQEv/766zO3a/fu3Th16hRcXV2fua7GjAGGiIgalFa2rdCxScf6bkaFpk2bBolEgoSEBFhaWorrO3XqhEmTJumUvXv3LkaMGIGoqCi4ublhzZo1+Otf/1pl/bdu3cKMGTMQFRWFoUOH1slnaCw4iJeIiKgacnJycODAAYSEhOiEl1J2dnY6y4sWLcKoUaOQkpKCIUOGYMyYMcjJyam0fq1Wi3HjxmHu3Lno1KlTbTe/0WGAISIiqoZr165BEAS0b9++WuUnTpyI4OBgtGnTBkuXLkVBQQESEhIqLb9ixQqYmJjgH//4R201uVHjJSQiIqJqEAT9JjD19vYW31taWsLGxgbZ2dkVlk1MTMT69etx7tw5SCSSZ2rni4I9MERERNXQtm1bSCSSag/UNTU11VmWSCTQarUVlj1+/Diys7Ph4eEBExMTmJiY4Pfff8fs2bPRokWLZ216o8QAQ0REVA0ODg4ICAjAxo0bUVhYWG57bm5ujeseN24cUlJSkJSUJL5cXV0xd+5cREVFPUOrGy9eQqoxPguJiOhFs3HjRrz88svo2bMnFi9eDG9vb6jVakRHR2PTpk24fPlyjept0qQJmjRporPO1NQUcrkcnp6etdH0RocBhoiIGpTfHvzWYI/TqlUrnDt3Dh9//DFmz56NzMxMNG3aFL6+vti0aVMdtJIqwwBDREQNgr3MHuYm5ph/fP5zO6a5iTnsZfZ67ePi4oINGzZgw4YNlZapaMCvvpeYbty4oVf5Fw0DDBERNQguVi74adhPjeJZSFT3GGCIiKjBcLFyYaCgauFdSERERGRwGGCIiIjI4DDAEBERkcFhgCEiIiKDwwBDREREBocBhoiIiAwOAwwREREZHM4DQ0REDYdKBajVz+94JiaAVPr8jlcN/v7+8PHxwbp16+q7KQ0aAwwRETUMKhWQkAAUFDy/Y1pZAT17VjvETJw4EVu3bgVQ8rBFDw8PjB8/Hh9++CFMTPgr9Xnit01ERA2DWl0SXqRSQCar++MplSXHU6v16oUJDAxEeHg4lEol9u/fj5CQEJiammL+/Of3DKeqCIIAjUbT6AMVx8AQEVHDIpMBZmZ1/6phSJLJZJDL5WjevDmmTp2KgQMHYu/evQAApVKJOXPmwM3NDZaWlujVqxdiY2PFfe/du4fg4GC4ubnBwsICXl5e2L59e5XHi4yMhK2tLSIiIircHhsbC4lEgl9++QW+vr6QyWQ4ceIE0tPTMWzYMDg7O8PKygo9evTAoUOHdPZt0aIFli5dikmTJsHa2hoeHh748ssvdcqcPHkSPj4+MDMzQ/fu3bFnzx5IJBIkJSWJZVJTUzF48GBYWVnB2dkZ48aNw927d/X4VvXHAENERPQMzM3NoVKpAADTp09HfHw8duzYgZSUFLz55psIDAzE1atXAQBFRUXw9fVFZGQkUlNTMWXKFIwbNw4JCQkV1r1t2zYEBwcjIiICY8aMqbIdH3zwAZYvX47Lly/D29sbBQUFGDJkCA4fPozz588jMDAQQUFByMjI0NlvzZo16N69O86fP49p06Zh6tSpSEtLAwDk5eUhKCgIXl5eOHfuHJYsWYLQ0FCd/XNzc9G/f3907doVZ8+exYEDB5CVlYVRo0bV6PusLr0DzLFjxxAUFARXV1dIJBLs2bNHZ/vEiRMhkUh0XoGBgTplcnJyMGbMGNjY2MDOzg7vvPMOCv50zTMlJQV9+vSBmZkZ3N3dsXLlSv0/HRERUR0RBAGHDh1CVFQU+vfvj4yMDISHh+P7779Hnz590Lp1a8yZMwevvPIKwsPDAQBubm6YM2cOfHx80KpVK8yYMQOBgYHYuXNnufo3btyIadOm4eeff8Zf/vKXp7Zn8eLFeO2119C6dWs4ODigS5cuePfdd9G5c2e0bdsWS5YsQevWrcXeolJDhgzBtGnT0KZNG4SGhsLR0RExMTEASgKURCLBV199hY4dO2Lw4MGYO3euzv4bNmxA165dsXTpUrRv3x5du3bF5s2bERMTgytXrtT0630qvS+QFRYWokuXLpg0aRJef/31CsuUXh8sJftTN92YMWOQmZmJ6OhoFBcX4+2338aUKVOwbds2ACWJb9CgQRg4cCC++OILXLhwAZMmTYKdnR2mTJmib5OJiIhqzb59+2BlZYXi4mJotVqMHj0aYWFhiI2NhUajQbt27XTKK5VKNGnSBACg0WiwdOlS7Ny5E7du3YJKpYJSqYSFhYXOPj/88AOys7MRFxeHHj16VKtd3bt311kuKChAWFgYIiMjkZmZCbVajUePHpXrgfH29hbfSyQSyOVyZGdnAwDS0tLg7e0NMzMzsUzPnj119k9OTkZMTAysrKzKtSk9Pb3c91Fb9A4wgwcPxuDBg6ssU3p9sCKXL1/GgQMHcObMGfHL/uyzzzBkyBCsXr0arq6uiIiIgEqlwubNmyGVStGpUyckJSVh7dq1DDBERFSv+vXrh02bNkEqlcLV1VUcLFtQUABjY2MkJibC2NhYZ5/SX+6rVq3C+vXrsW7dOnh5ecHS0hIzZ84UL0GV6tq1K86dO4fNmzeje/fukEgkT22XpaWlzvKcOXMQHR2N1atXo02bNjA3N8cbb7xR7limpqY6yxKJBFqttnpfxuPPHRQUhBUrVpTb5uLiUu169FUnQ5RjY2Ph5OQEe3t79O/fH//+97/F9BkfHw87OzudpDhw4EAYGRnh9OnTGDFiBOLj49G3b19Iy4wKDwgIwIoVK3D//n3Y29uXO6ZSqYRSqRSX8/Ly6uKjERHRC87S0hJt2rQpt75r167QaDTIzs5Gnz59Ktw3Li4Ow4YNw9ixYwEAWq0WV65cQceOHXXKtW7dGmvWrIG/vz+MjY2xYcMGvdsZFxeHiRMnYsSIEQBKgsaNGzf0qsPT0xPffvstlEqleDXlzJkzOmW6deuGH3/8ES1atHiudz7V+iDewMBAfPPNNzh8+DBWrFiBo0ePYvDgwdBoNAAAhUIBJycnnX1MTEzg4OAAhUIhlnF2dtYpU7pcWubPli1bBltbW/Hl7u5e2x+NiIioUu3atcOYMWMwfvx47Nq1C9evX0dCQgKWLVuGyMhIAEDbtm0RHR2NkydP4vLly3j33XeRlZVVaX0xMTH48ccfMXPmTL3b07ZtW+zatQtJSUlITk7G6NGj9epZASDuM2XKFFy+fBlRUVFYvXo1AIi9QiEhIcjJyUFwcDDOnDmD9PR0REVF4e233xZ/99eFWo9Kb731lvjey8sL3t7eaN26NWJjYzFgwIDaPpxo/vz5mDVrlricl5fHEENEZIjK9KYb2nHCw8Px73//G7Nnz8atW7fg6OiIl156SRyEu2DBAvz2228ICAiAhYUFpkyZguHDh+PBgwcV1ufp6YkjR46IPTFr1qypdlvWrl2LSZMmoXfv3nB0dERoaKjeVydsbGzw888/Y+rUqfDx8YGXlxcWLlyI0aNHi+NiXF1dERcXh9DQUAwaNAhKpRLNmzdHYGAgjIzq7mbnOu/radWqFRwdHXHt2jUMGDBAZ3BQKbVajZycHHHcjFwuL5dIS5crG1sjk8nKDRYmIiIDYmJSMjNuQUHJrLzPg5VVyXGracuWLVVuNzU1xaJFi7Bo0aIKtzs4OJS7e/fPys4bAwAdOnSotJcGKHn0gCAI5da3aNECR44c0VkXEhKis1zRJaWy87sAQO/evZGcnCwuR0REiLMQlyrt7Xme6jzA/PHHH7h37544kMfPzw+5ublITEyEr68vAODIkSPQarXo1auXWOajjz5CcXGxOLgoOjoanp6eFY5/qR/l/7EQEdEzkEpLpvV/wZ+F1NB88803aNWqFdzc3JCcnIzQ0FCMGjUK5ubm9douvQNMQUEBrl27Ji5fv34dSUlJcHBwgIODAxYtWoSRI0dCLpcjPT0d8+bNQ5s2bRAQEACgJEkGBgZi8uTJ+OKLL1BcXIzp06fjrbfegqurK4CSa26LFi3CO++8g9DQUKSmpmL9+vX45JNPauljExFRgySVMlA0MAqFAgsXLoRCoYCLiwvefPNNfPzxx/XdLP0DzNmzZ9GvXz9xuXTcyYQJE7Bp0yakpKRg69atyM3NhaurKwYNGoQlS5boXN6JiIjA9OnTMWDAABgZGWHkyJH49NNPxe22trY4ePAgQkJC4OvrC0dHRyxcuJC3UBMRET1n8+bNw7x58+q7GeXoHWAqu9ZWKioq6ql1ODg4iJPWVcbb2xvHjx/Xt3lERET0AuCzkIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDM7ze+oSERHR06hUL/xEdv7+/vDx8cG6devquykNGgMMERE1DCoVkJBQ8iiB58XKqmT232qGmIkTJ2Lr1q0AIE6nP378eHz44YfP9UnMxABDREQNhVpdEl6kUuB5PNtOqSw5nlqtVy9MYGAgwsPDoVQqsX//foSEhMDU1BTz58+vw8ZWnyAI0Gg0jT5QcQxMDfFJSEREdUQmA8zM6v5Vw5Akk8kgl8vRvHlzTJ06FQMHDsTevXsBAEqlEnPmzIGbmxssLS3Rq1cvnYcz3rt3D8HBwXBzc4OFhQW8vLywffv2Ko8XGRkJW1tbREREVLg9NjYWEokEv/zyC3x9fSGTyXDixAmkp6dj2LBhcHZ2hpWVFXr06IFDhw7p7NuiRQssXboUkyZNgrW1NTw8PPDll1/qlDl58iR8fHxgZmaG7t27Y8+ePZBIJDoPfUxNTcXgwYNhZWUFZ2dnjBs3Dnfv3tXjW9UfAwwREdEzMDc3h+rx07OnT5+O+Ph47NixAykpKXjzzTcRGBiIq1evAgCKiorg6+uLyMhIpKamYsqUKRg3bhwSEhIqrHvbtm0IDg5GREQExowZU2U7PvjgAyxfvhyXL1+Gt7c3CgoKMGTIEBw+fBjnz59HYGAggoKCkJGRobPfmjVr0L17d5w/fx7Tpk3D1KlTkZaWBgDIy8tDUFAQvLy8cO7cOSxZsgShoaE6++fm5qJ///7o2rUrzp49iwMHDiArKwujRo2q0fdZXY27f4mIiKiOCIKAw4cPIyoqCjNmzEBGRgbCw8ORkZEhPpx4zpw5OHDgAMLDw7F06VK4ublhzpw5Yh0zZsxAVFQUdu7ciZ49e+rUv3HjRnz00Uf4+eef8eqrrz61PYsXL8Zrr70mLjs4OKBLly7i8pIlS7B7927s3bsX06dPF9cPGTIE06ZNAwCEhobik08+QUxMDDw9PbFt2zZIJBJ89dVXMDMzQ8eOHXHr1i1MnjxZ3H/Dhg3o2rUrli5dKq7bvHkz3N3dceXKFbRr1666X6leGGCIiIj0sG/fPlhZWaG4uBharRajR49GWFgYYmNjodFoyv3CViqVaNKkCQBAo9Fg6dKl2LlzJ27dugWVSgWlUgkLCwudfX744QdkZ2cjLi4OPXr0qFa7unfvrrNcUFCAsLAwREZGIjMzE2q1Go8ePSrXA+Pt7S2+l0gkkMvlyM7OBgCkpaXB29sbZmZmYpk/B63k5GTExMTAysqqXJvS09MZYIiIiBqCfv36YdOmTZBKpXB1dRUHyxYUFMDY2BiJiYkwNjbW2af0l/uqVauwfv16rFu3Dl5eXrC0tMTMmTPFS1ClunbtinPnzmHz5s3o3r07JBLJU9tlaWmpszxnzhxER0dj9erVaNOmDczNzfHGG2+UO5apqanOskQigVarrd6X8fhzBwUFYcWKFeW2ubi4VLsefTHAEBER6cHS0hJt2rQpt75r167QaDTIzs5Gnz59Ktw3Li4Ow4YNw9ixYwEAWq0WV65cQceOHXXKtW7dGmvWrIG/vz+MjY2xYcMGvdsZFxeHiRMnYsSIEQBKgsaNGzf0qsPT0xPffvstlEolZI8HPZ85c0anTLdu3fDjjz+iRYsWz/XOJw7iJSIiqgXt2rXDmDFjMH78eOzatQvXr19HQkICli1bhsjISABA27ZtER0djZMnT+Ly5ct49913kZWVVWl9MTEx+PHHHzFz5ky929O2bVvs2rULSUlJSE5OxujRo/XqWQEg7jNlyhRcvnwZUVFRWL16NQCIvUIhISHIyclBcHAwzpw5g/T0dERFReHtt9+GRqPRu93VxQBDREQNi1IJFBXV/UuprPWmh4eHY/z48Zg9ezY8PT0xfPhwnDlzBh4eHgCABQsWoFu3bggICIC/vz/kcjmGDx9eaX2enp44cuQItm/fjtmzZ+vVlrVr18Le3h69e/dGUFAQAgIC0K1bN73qsLGxwc8//4ykpCT4+Pjgo48+wsKFCwFAHBfj6uqKuLg4aDQaDBo0CF5eXpg5cybs7OxgZFR3MYOXkIiIqGEwMSmZGbegoGRW3ufByqrkuNW0ZcuWKrebmppi0aJFWLRoUYXbHRwcsGfPnirrKDtvDAB06NCh0l4aoOTRA4JQfnayFi1a4MiRIzrrQkJCdJYruqRUdn4XAOjduzeSk5PF5YiICHEW4lKlvT3PEwMMERE1DFJpybT+L/izkBqab775Bq1atYKbmxuSk5MRGhqKUaNGwdzcvF7bxQBDREQNh1TKQNHAKBQKLFy4EAqFAi4uLnjzzTfx8ccf13ezGGCIiIiocvPmzcO8efPquxnlcBBvjfFpSERERPWFAYaIiIgMDgMMERHVG33nJaHGoTbOO8fAEBHRcyeVSmFkZITbt2+jadOmkEql1ZounwybIAhQqVS4c+cOjIyMIH2GAdsMMERE9NwZGRmhZcuWyMzMxO3bt+u7OfScWVhYwMPD45kmumOAISKieiGVSuHh4QG1Wl2nU85Tw2JsbAwTE5Nn7nFjgCEionojkUhgampa7onIRE/DQbxERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAabG+CwkIiKi+qJ3gDl27BiCgoLg6uoKiUSCPXv2iNuKi4sRGhoKLy8vWFpawtXVFePHjy83SVGLFi0gkUh0XsuXL9cpk5KSgj59+sDMzAzu7u5YuXJlzT4hERERNTp6B5jCwkJ06dIFGzduLLft4cOHOHfuHP75z3/i3Llz2LVrF9LS0vDXv/61XNnFixcjMzNTfM2YMUPclpeXh0GDBqF58+ZITEzEqlWrEBYWhi+//FLf5hIREVEjpPdEdoMHD8bgwYMr3GZra4vo6GiddRs2bEDPnj2RkZEBDw8Pcb21tTXkcnmF9UREREClUmHz5s2QSqXo1KkTkpKSsHbtWkyZMkXfJhMREVEjU+djYB48eACJRAI7Ozud9cuXL0eTJk3QtWtXrFq1Cmq1WtwWHx+Pvn376jzkKSAgAGlpabh//36Fx1EqlcjLy9N5ERERUeNUp48SKCoqQmhoKIKDg2FjYyOu/8c//oFu3brBwcEBJ0+exPz585GZmYm1a9cCABQKBVq2bKlTl7Ozs7jN3t6+3LGWLVuGRYsW1eGnISIiooaizgJMcXExRo0aBUEQsGnTJp1ts2bNEt97e3tDKpXi3XffxbJlyyCTyWp0vPnz5+vUm5eXB3d395o1noiIiBq0OgkwpeHl999/x5EjR3R6XyrSq1cvqNVq3LhxA56enpDL5cjKytIpU7pc2bgZmUxW4/BDREREhqXWx8CUhperV6/i0KFDaNKkyVP3SUpKgpGREZycnAAAfn5+OHbsGIqLi8Uy0dHR8PT0rPDyEREREb1Y9O6BKSgowLVr18Tl69evIykpCQ4ODnBxccEbb7yBc+fOYd++fdBoNFAoFAAABwcHSKVSxMfH4/Tp0+jXrx+sra0RHx+P999/H2PHjhXDyejRo7Fo0SK88847CA0NRWpqKtavX49PPvmklj42ERERGTK9A8zZs2fRr18/cbl03MmECRMQFhaGvXv3AgB8fHx09ouJiYG/vz9kMhl27NiBsLAwKJVKtGzZEu+//77O+BVbW1scPHgQISEh8PX1haOjIxYuXMhbqImIiAhADQKMv78/BKHyafSr2gYA3bp1w6lTp556HG9vbxw/flzf5hEREdELgM9CIiIiIoPDAENEREQGhwGGiIiIDA4DDBERERkcBhgiIiIyOAwwREREZHAYYIiIiMjgMMAQERGRwWGAISIiIoPDAFNDVc83TERERHWJAYaIiIgMDgMMERERGRwGmBrjRSQiIqL6wgBDREREBocBhoiIiAwOAwwREREZHAYYIiIiMjgMMERERGRwGGCIiIjI4DDAEBERkcFhgCEiIiKDwwBDREREBocBhoiIiAwOAwwREREZHAaYGuOzkIiIiOoLAwwREREZHAYYIiIiMjgMMERERGRwGGCIiIjI4DDAEBERkcFhgCEiIiKDwwBDREREBocBhoiIiAyO3gHm2LFjCAoKgqurKyQSCfbs2aOzXRAELFy4EC4uLjA3N8fAgQNx9epVnTI5OTkYM2YMbGxsYGdnh3feeQcFBQU6ZVJSUtCnTx+YmZnB3d0dK1eu1P/TERERUaOkd4ApLCxEly5dsHHjxgq3r1y5Ep9++im++OILnD59GpaWlggICEBRUZFYZsyYMbh48SKio6Oxb98+HDt2DFOmTBG35+XlYdCgQWjevDkSExOxatUqhIWF4csvv6zBRyQiIqLGxkTfHQYPHozBgwdXuE0QBKxbtw4LFizAsGHDAADffPMNnJ2dsWfPHrz11lu4fPkyDhw4gDNnzqB79+4AgM8++wxDhgzB6tWr4erqioiICKhUKmzevBlSqRSdOnVCUlIS1q5dqxN0iIiI6MVUq2Ngrl+/DoVCgYEDB4rrbG1t0atXL8THxwMA4uPjYWdnJ4YXABg4cCCMjIxw+vRpsUzfvn0hlUrFMgEBAUhLS8P9+/crPLZSqUReXp7Oi4iIiBqnWg0wCoUCAODs7Kyz3tnZWdymUCjg5OSks93ExAQODg46ZSqqo+wx/mzZsmWwtbUVX+7u7s/+gYiIiKhBajR3Ic2fPx8PHjwQXzdv3qzvJhEREVEdqdUAI5fLAQBZWVk667OyssRtcrkc2dnZOtvVajVycnJ0ylRUR9lj/JlMJoONjY3Oi4iIiBqnWg0wLVu2hFwux+HDh8V1eXl5OH36NPz8/AAAfn5+yM3NRWJioljmyJEj0Gq16NWrl1jm2LFjKC4uFstER0fD09MT9vb2tdlkIiIiMkB6B5iCggIkJSUhKSkJQMnA3aSkJGRkZEAikWDmzJn497//jb179+LChQsYP348XF1dMXz4cABAhw4dEBgYiMmTJyMhIQFxcXGYPn063nrrLbi6ugIARo8eDalUinfeeQcXL17Ed999h/Xr12PWrFm19sGJiIjIcOl9G/XZs2fRr18/cbk0VEyYMAFbtmzBvHnzUFhYiClTpiA3NxevvPIKDhw4ADMzM3GfiIgITJ8+HQMGDICRkRFGjhyJTz/9VNxua2uLgwcPIiQkBL6+vnB0dMTChQt5CzUREREBqEGA8ff3hyAIlW6XSCRYvHgxFi9eXGkZBwcHbNu2rcrjeHt74/jx4/o2j4iIiF4AjeYuJCIiInpxMMAQERGRwWGAISIiIoPDAENEREQGhwGmxiofyExERER1iwGGiIiIDA4DDBERERkcBhgiIiIyOAwwREREZHAYYIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDA4DDBERERkcBhgiIiIyOAwwNcQnIREREdUfBhgiIiIyOAwwREREZHAYYIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDA4DDBERERkcBhgiIiIyOAwwREREZHAYYIiIiMjgMMDUGJ+GREREVF8YYIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGp9YDTIsWLSCRSMq9QkJCAAD+/v7ltr333ns6dWRkZGDo0KGwsLCAk5MT5s6dC7VaXdtNJSIiIgNlUtsVnjlzBhqNRlxOTU3Fa6+9hjfffFNcN3nyZCxevFhctrCwEN9rNBoMHToUcrkcJ0+eRGZmJsaPHw9TU1MsXbq0tptLREREBqjWA0zTpk11lpcvX47WrVvj1VdfFddZWFhALpdXuP/Bgwdx6dIlHDp0CM7OzvDx8cGSJUsQGhqKsLAwSKXS2m4yERERGZg6HQOjUqnw7bffYtKkSZBIJOL6iIgIODo6onPnzpg/fz4ePnwobouPj4eXlxecnZ3FdQEBAcjLy8PFixcrPZZSqUReXp7Oi4iIiBqnWu+BKWvPnj3Izc3FxIkTxXWjR49G8+bN4erqipSUFISGhiItLQ27du0CACgUCp3wAkBcVigUlR5r2bJlWLRoUe1/CCIiImpw6jTAfP311xg8eDBcXV3FdVOmTBHfe3l5wcXFBQMGDEB6ejpat25d42PNnz8fs2bNEpfz8vLg7u5e4/qIiIio4aqzAPP777/j0KFDYs9KZXr16gUAuHbtGlq3bg25XI6EhASdMllZWQBQ6bgZAJDJZJDJZM/YaiIiIjIEdTYGJjw8HE5OThg6dGiV5ZKSkgAALi4uAAA/Pz9cuHAB2dnZYpno6GjY2NigY8eOddXcGuCzkIiIiOpLnfTAaLVahIeHY8KECTAxeXKI9PR0bNu2DUOGDEGTJk2QkpKC999/H3379oW3tzcAYNCgQejYsSPGjRuHlStXQqFQYMGCBQgJCWEPCxEREQGoowBz6NAhZGRkYNKkSTrrpVIpDh06hHXr1qGwsBDu7u4YOXIkFixYIJYxNjbGvn37MHXqVPj5+cHS0hITJkzQmTeGiIiIXmx1EmAGDRoEQSh/icXd3R1Hjx596v7NmzfH/v3766JpRERE1AjwWUhERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAYaIiIgMDgMMERERGRwGGCIiIjI4DDBERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAYaIiIgMDgMMERERGRwGGCIiIjI4DDBERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAU2NCfTeAiIjohcUAQ0RERAaHAYaIiIgMDgMMERERGRwGGCIiIjI4DDBERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAYaIiIgMDgMMERERGRwGmBrik5CIiIjqT60HmLCwMEgkEp1X+/btxe1FRUUICQlBkyZNYGVlhZEjRyIrK0unjoyMDAwdOhQWFhZwcnLC3LlzoVara7upREREZKBM6qLSTp064dChQ08OYvLkMO+//z4iIyPx/fffw9bWFtOnT8frr7+OuLg4AIBGo8HQoUMhl8tx8uRJZGZmYvz48TA1NcXSpUvrorlERERkYOokwJiYmEAul5db/+DBA3z99dfYtm0b+vfvDwAIDw9Hhw4dcOrUKbz00ks4ePAgLl26hEOHDsHZ2Rk+Pj5YsmQJQkNDERYWBqlUWhdNJiIiIgNSJ2Ngrl69CldXV7Rq1QpjxoxBRkYGACAxMRHFxcUYOHCgWLZ9+/bw8PBAfHw8ACA+Ph5eXl5wdnYWywQEBCAvLw8XL16s9JhKpRJ5eXk6LyIiImqcaj3A9OrVC1u2bMGBAwewadMmXL9+HX369EF+fj4UCgWkUins7Ox09nF2doZCoQAAKBQKnfBSur10W2WWLVsGW1tb8eXu7l67H4yIiIgajFq/hDR48GDxvbe3N3r16oXmzZtj586dMDc3r+3DiebPn49Zs2aJy3l5eQwxREREjVSd30ZtZ2eHdu3a4dq1a5DL5VCpVMjNzdUpk5WVJY6Zkcvl5e5KKl2uaFxNKZlMBhsbG50XERERNU51HmAKCgqQnp4OFxcX+Pr6wtTUFIcPHxa3p6WlISMjA35+fgAAPz8/XLhwAdnZ2WKZ6Oho2NjYoGPHjnXdXCIiIjIAtX4Jac6cOQgKCkLz5s1x+/Zt/Otf/4KxsTGCg4Nha2uLd955B7NmzYKDgwNsbGwwY8YM+Pn54aWXXgIADBo0CB07dsS4ceOwcuVKKBQKLFiwACEhIZDJZLXdXCIiIjJAtR5g/vjjDwQHB+PevXto2rQpXnnlFZw6dQpNmzYFAHzyyScwMjLCyJEjoVQqERAQgM8//1zc39jYGPv27cPUqVPh5+cHS0tLTJgwAYsXL67tphIREZGBqvUAs2PHjiq3m5mZYePGjdi4cWOlZZo3b479+/fXdtOIiIiokeCzkGqMT0MiIiKqLwwwREREZHAYYIiIiMjgMMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDA4DDBERERkcBhgiIiIyOAwwREREZHAYYIiIiMjgMMAQERGRwWGAqTE+C4mIiKi+MMAQERGRwWGAISIiIoPDAENEREQGhwGGiIiIDA4DDBERERkck/puAJHBKvgDuPkL8FABWMgB98GAVbP6bhUR0QuBAYaoJjJ+AZLXABKU3FEvAXDtO8BnDuAeWM+NIyJq/HgJiUhfBX+UhBdoAUGr+9+k1UDhrXpuIBFR48cAQ6Svm7+U9LhURAIgY//zbA0R0QuJAYZIXw8VlU/ELDzeTkREdYoBhkhfFvKqe2As5M+zNURELyQGGCJ9uQ+uugfGY8jzbA0R0QuJAYZIX1bNSu42ghHUghE0ggRqwQiAUcl6S7f6biERUaPH26iJasI9EIJ9Z3wZ/QOaSe7gD6Eppg16k+GFiOg5YYAhqiGNhRtWqt8Slzvn26OvZT02iIjoBcIAQ1RDZ+8V6yzPT3yAuKFONauMs/oSEemFAaaGhEpHcdKL4Hq+Gm/F5uisu/VQW7PKOKsvEZHeOIi3hgTmlxfaiWxV7VTEWX2JiGqEAYaoFv1445F+O3BWXyKiGmGAIXpG/+xiLb4vUOt5GYmz+hIR1UitB5hly5ahR48esLa2hpOTE4YPH460tDSdMv7+/pBIJDqv9957T6dMRkYGhg4dCgsLCzg5OWHu3LlQq9W13VyiZ+ZhZSy+z1HqGWA4qy8RUY3UeoA5evQoQkJCcOrUKURHR6O4uBiDBg1CYWGhTrnJkycjMzNTfK1cuVLcptFoMHToUKhUKpw8eRJbt27Fli1bsHDhwtpuLlHNlOk1kRo9SSDrLxVWULgK7oMhCELFY6o4qy8RUaVq/S6kAwcO6Cxv2bIFTk5OSExMRN++fcX1FhYWkMsr/uvy4MGDuHTpEg4dOgRnZ2f4+PhgyZIlCA0NRVhYGKRSaW03m6jGjCvrQakOq2bY32QGAu9+BkGQQCIRYCyRlISX0ll9i4qqXZ1KoUBu7FEU370DU8emsPN/FdJKfs6IiAxZnY+BefDgAQDAwcFBZ31ERAQcHR3RuXNnzJ8/Hw8fPhS3xcfHw8vLC87OzuK6gIAA5OXl4eLFixUeR6lUIi8vT+dFVFf+eb7k31dnOxMYPUOAuVmoRsitl9BftQZfav6CSM1LQOu/Af236n0Lde7Ro0ifMxf3IiORd+o07kVGIn3OXOQePVbzBhIRNVB1Og+MVqvFzJkz8fLLL6Nz587i+tGjR6N58+ZwdXVFSkoKQkNDkZaWhl27dgEAFAqFTngBIC4rFBUPaly2bBkWLVpUR5+EqGK7BjRB1iNNjfcPO58PAPhdkIuz+v61g/49JiqFAplf/bfk/v7S61GP/5v51Vew8GzHnhgialTqNMCEhIQgNTUVJ06c0Fk/ZcoU8b2XlxdcXFwwYMAApKeno3Xr1jU61vz58zFr1ixxOS8vD+7u7jVrOFE1SY0kcLc0gaPMCHf1HcAL4KG6/OCXzIcauFgYV1C6crmxRwGJpOIJiiQS5MYehdNbf9O7fUREDVWdXUKaPn069u3bh5iYGDRrVvWU6L169QIAXLt2DQAgl8uRlZWlU6Z0ubJxMzKZDDY2NjovoudFWsOfJE0FgWP7bw8rKFm14rt3Kp9dURBKthMRNSK1HmAEQcD06dOxe/duHDlyBC1btnzqPklJSQAAFxcXAICfnx8uXLiA7OxssUx0dDRsbGzQsWPH2m4y0TMb4GomvtfqMU2zh1X5TtCU+8UVlKyaqWPTkh6YikgkJdtrSKVQIHvHd7i1YQOyd3wHVSWXcYmInqdaDzAhISH49ttvsW3bNlhbW0OhUEChUODRo5IZStPT07FkyRIkJibixo0b2Lt3L8aPH4++ffvC29sbADBo0CB07NgR48aNQ3JyMqKiorBgwQKEhIRAJpPVdpOJntm/fJ5MZrf2YkG19/OwLLlUZGYMTGprAQC4XYNnKtn5vwoI5Z/QJQCAIJRsrwEODCaihqrWA8ymTZvw4MED+Pv7w8XFRXx99913AACpVIpDhw5h0KBBaN++PWbPno2RI0fi559/FuswNjbGvn37YGxsDD8/P4wdOxbjx4/H4sWLa7u5RHqrqIfFxEiCZo/HrfyaW/0JFzWCACMAX71sL4YPpUb/B21J5XLY/X08BAmgKfMSJIDL5Mk1GsCrMzBYq9X5b+ZXX7EnhojqVa0P4hWe0n3u7u6Oo0ePPrWe5s2bY/9+PgeGGp784pJ/47amupds/nhYcjeSqR5/FpROfNfHWYbDt5UAgN8LNVBqBMj0nGDGsk9vjDP6Fv1TtHDKBbLtgCPeRmjfPBEb0Pdpu5fDgcFE1JDV6V1IjRsfR/2i8ttXMiB2XS+7CrfXoAMFANDa+smP47V8NTrZmeq1/9a7B5DlIMF2f907mLLyk2vUHg4MJqKGjA9zJNLTw8cJxU6q20Ny482SyzTnc/QbhOsoK/kxHNvaXFz3VmyO3u368s7PTy+kh7ocGExE9KwYYIj0UHb8i2klU/DeKareINyNl0sG+87oaAkAkEgkCH/FHsCTy1S1RalV6b1P6cDgP3vWgcFERLWBAYZID6oy2UTfMSp/Vnq30vX8JzP5WpvWrM47xblVbj/38KredUrlcrhMngxIJOUGBm8cIoHpn2bLJiJ6njgGpoYkxsr6bgLVg7ITz1mY1DzAPFILKN17RPMnl4462JX8SJa9nFQdcQUXxPdjmwzCHPlbeKRVon/aTDzSKrHw1teI9lyrdzvtXu0LC892WP3DXJ2BwVkOEhQLakgl+o3TISKqLQwwRHpQl+mBcdNzuv+yNl9XQi0AruZG6OLwJARYmpR0in6b/ghhPjYwqeaTIv9562vx/Tx5MCQSCayMzcW7AhXF+o+pKXXQ7LdyA4MBQCWoIQUDDBHVD15CItJD6R1G/+ltV2W5p00ncOpeyVwxtx9VPl7mQQ3HwUjKDLx9xdqrRnWUNf+PLytc/7tK8dTPSURUVxhgiPSgfvwL+2nDX3KfEj6O36n+ZHfPYo78rTqr+630RUh+lF5n9RMRVYUBhkgPk0/kAgAUlfScTG5X8jgA1VNuRJKblSSgKY/LVyQuS/9xVkNsX9JZNpE821XiR9qq25Bcg8HBRES1gQGGSA+KRyV3DN1+qKlwe7cmUgDAnKSqnyhdqC7poTGroisn7Hye3u37wGWMzrKZkVR8v/u+/s8vmp2xscrtqxXf6V0nEVFtYIAhqia1VkDW4zle/uJuVmGZ1MdPkr74oOKAU6rJ48nr+ruWfzhpRN+SuWByVALU2qePMYnNOy++tzO20tlma2wpvl94a/NT6/qzO+pcneXJTf8CF9MmOus4DoaI6gMDDFE1lZ2UtmMl0/wXPw4cVd089MsdATcKtfBrKoWPg7Tc9pedn4Sa0p6aqszIWF+mjc82N01ZWkGLX4sydNb1t+6GL1rM1ln3SNB/kjwiomfFAENUix5fYcJdpYCzDyoOH5v/KOnF6dbk6bcg10bfhpOJXY32+/ftb3SWL3Tegs4WrdBK5ooD7VaJ6zNVd5+leURENcIAQ1SL2tg8mS+lss6Tx1ePqgwnpT+YT7uEVKx9cjfTUFu/Css0kzpVWUdlvr8fW+k2N2lTrPOYAeDpA32JiOoCA8wz4LX/F0s1hqNgQpsnY04qu5hz4n7Jf6t6anV4n5JxMF+mFVZ5vJj8J+NfljR7p8IyZWfL9UqdWGV9lYlo9c9y65qZljzM8e3ry2tUJxHRs2CAeQZCrXTw629fym1oH/82vZnzEH1XxiCnsPw4hMiUTCjVVQ8mpeqblFB1mPizpwWeqkarlN6d9OWVqu9m+iEnVnxvWskt0wtcx1XdkGrwtmhdbp1SKBmwXMQxMERUDxhgnsHz6IE5fvUObuU+Epd3JGRg+rbzGLz+OAAgPO4GMnIeYux/TwMAbtwtxPdnb+LktbsI2XYOXRYdrPM2vij0nXzuf7erngzGsopnKVX3OZHxhRcBAPbG1pWWaS6T4x9OI6tXYQX+avdyhevzNVWHKyKiusRnIT2D59EDM+7rBPF9l2a2SP7jAQAgLSsfu879gc1x1wEAlzLzMC0iEfsvKAAAPVs6AACKip8yoxpVWz8nE8Rkq7Hl8eWdyvz6ujPa78rC/jtV11fV06ybWT79OUvFwpPetXed/lplWTX064lTap/0qqiFivd9qC0S3y/OjsBC86rbQERUm9gD8wzqMsBotAK+PfW7zrrS8FJq1s5kneXS8AIACdefPLzvoUqN/8XfwImrvFvkWZzJKemB8ZeXn7ulrKompwOA0mwyuFnl9cjNnx5gFOon59gEVZcfYd9XfF82eFTm6zuR4vuxTQZVWKa1mZv4/vu84zhQlPTUeomIagsDzLOoww6Yv289gwV7Umulro4Lo/DPny5i7NenMfa/p3XGz2iqMzKVUKwVUKDHFaRpbWTwqGCuu3ylGoUaYJS7FO6Wz9YB+r/cw9UuKzd1wCtW3gCAv91c9tTym+78JL73smhVYZlWMled5bkPIqrdHiKiZ8UA8wzqsgcmJu0p1x9q6MS1u5jyv7MY8Xkc+qyMwaQtZ3S2Z+cVocUHkfjyWMlD+vKLivG/+BsN+o6rIeuPw3dJNAqVaqjUWoTtvYgWH0SiUFl7D0ws0vNKnIkRUNHzHPttOAUAiL/39LYFtzIHAGRXcHCNoMX2B7Hisr1J5WNgSk1pGgQAuFGc9dSy1RXb/tNaq4uISB8cA/MM6iLAqDVatPnolyrLOFrJcLeg4rk3jGS3YWx5DcU5fWFseRVapTMEtU2ZEhocVeyCiVUajK1exvGMDCRcb4PTv91DUBdXTPnfWQDA0v2/Yun+X8W9/vnTRXwW3BVBXVzRkBRrtLiUWfLMoE7/ioKl1BgvtSqZ6l6l1sKy6qs91ZbxeBz1680rfoTAn+UXC8hUAok3H8DX88kDG+8Wlty5Y1aNPx2yHj8w8qt0JT7y0N1WdkzLaIeBGGTb46n1dbVsK77XCtpK/3opO7fM0zQxefJvS8r/nRDRc8T/4zyDuuiVmLH9fLl1q9/sgjnfl4x3ubF8KARBwOD1x/GrIh8dXGxwOTMPRjIFrJ1PQCO7BInJQxTn9IWFx9cAgCLFMKjzOsNIlgVj8z8gczoAADCxKnmScPD/lIBEg/XxGpi7/w/WDkDh9WkwtTsLpWIESm/4nbl3B048yEJwy/kYvjEOMwe2xdiXmsPRSjclXLz9APlFajFIVObBo2KoNVo0sao6Zdy4W4jf7hagf3tn5BSqMHLTSVy/W4jJfVpie8JNnbKFKg2OXysZ61Osqb0BzEMTS+oqO89LVc7klASMkeHncGP50HLbqxrAWypHWXJM4wqSRtkBvJ3MW1arTWVla/Mgr2RbyqN08f1it4rnlinryxZzMeXGKqhQez1eRERPwwDTwPySqii3bmQ3N7Rqaokfr34Hr61eGNFmBG7Z74a8qTUm+P4D/0n8AffU16CFRpxbJLCTE+Ie//42k/8EyH8qV28pi+Zfl1tn2fJzAIDUvuQuKOVdf5hYX8T+63fw3f6+gNFDrDt0BetjE5Gy4A14hR0EoMXIbu44+Khk3pHXL2xHVH4ICjUlg01/GhKP+w9VaGIpg5WZCbr/+xAAYOUb3hjV3R2XbufBxFiCds5PLoco1Rr4r46tsN1fHb9e4XqV+nHYCD+D79/zg5Ws9v6Ze1Tj7iAA6O9kgtQqHuj4aTeLSreVinjVHp12Z8O6gtuty/bAmEqq1yYAsDG2RJ6mEO/e/wo/oeK7hkpn1v3YbTL+al/xLdRl+Vl1Et8LglDl/DZERLWFAeYZ1PVt1HYWplg7qgt2X9uNgzcOIu52HABg97XdAIBCdT4+Pv1xhfvGaSfValtkjrHie2OLa7Bo/l9xufd3S2HdoeT9wSdT1mB72jeQOT+5U2bY/pKp7vMv687cOu+HFNzJV2JVVFqttdfY/AZ+vWeJSVtMsPPdkuM+eFgMW4unP3/oz8r25NhIq/freZSHDJ9eLQkC83ddwLLXvXAz58m8Ka2snh46LE1Kul5W/VqEqc5G4iWfB6o89LkTJpbraN6iWm0CgI9cxiH0jy/wmya70jJTf18LAOht3bna9ZbKLy6ADarXS0VE9Cw4iPcZ1OXAVidrGRI+HAjnpnfxr5P/EsNLQ1A2vFRF5lzJWB6JCsYWv8HY4hokpiUB58/hxdThGKSO0SULRg8hbRID4M+XhLSApOJZYC1afAGr1muQcD0Hb27/GEm3bqHL4oM4kJopnrc/7udj1fFdUP/pUtO9R/fw7aVvkZaTBq+tXthy+Qdxm0ZQIzBtDgLSZuNkfuV3iTWzePKjtT0hAzfuFqLPyphKyz9Nq6MlbbxdcBuv/PSauD7Gcx2ayyq7GKS/B5onsw3bGukfRF7+aWCttYWIqCrsgWlAWnzweO4NiRJ2HlFIuWuHt6PertVjrOizAq4WLTEualSt1qsP6/YLK1yvVTmgOK8LNA9bwcx5/+N1TWBscQNS+wQU53eGoGoCmfM+SB1OAgA0RS54eP3/YGSWAan9KagL2kOd7y3WaWKbiF9V32PcoR0wa9YBU7fnQ9rkKD4fNhkzT0wAAKgl9/HVoWK81MoBdi7HMbLNW1hxZoVYx6eX14g9TL6XnrT33d9XV/g5LnTeUm7dkE+PV/v7KcvD0hgZhSWXi7LylViYpPvd2VXj7qOyyvbWBB96G9v/+p3O9leuzxbfmxpV/38PUc0/RsDvHwEAfrn+Cwa3HKxXu4iI9CURGvL9sc8gLy8Ptra2ePDgAWxsbJ6+QzWtO7kHX18tebDdqdGnYGlaO93lhUo1Ov0rCoAW1h0+rJU6S33U6yN8fPpjbBywEX2blUxoptaq0fV/XQEAH/b6EAeuH8C57HO1etz6orwzALKm1Z8jBQAEjTkkxo+eXlAPDnkD8fut/vhzR+dENwnCutkCZk+/o6mgWIvOe0ou9xyd/hL+cthf3JbSehMk5uZ6t6vsAx1Pjz4NC1ML4OFDfLb/Q3xZ+OR7qyiIVaqoCF7X3hMXk8Ylwdio+mNziIhKVff3N3tgnkFtZr8Ltx5AJt8Nqf3papUP8wtDb9fesJRaov/O/lBqSsZbLO69GN3l3THt0DSs77cerexKJiF7ve3rkBpLxf1NjExw5M0jcDR3hEQiQXD7YKg0Ksw+OhuxN2PFcBZxOQJaQYuVZ1bW2meta/qGFwC1Hl4AIMfmEKysYyCRaJB/+WPg8Wy5H7SS4DeVAlqYok2Z2Wz/7G/XwpCjyYOxxeuARIu3T6zVbbPk2YfLRv8ejWFthqFYq9YJL/9r9ZHedY216INvH5b0ND1UP4S1VL/eISIifTDAPIPaGsSbeusB3vryFKw7VB1eBnoMRJ9mfZCem46R7Z48nO/s2LM49scxdHfuXvLXNICfR/yss2/Z8FKqqUXTcmU+6/+ZzroxHcYAALo07YLf837HX1r9BXuu7YGfqx9e++E1rHp1FeYenQug5K9un//5AACczJ1wr+geNI9v99Wq7NDc7GWYW97BlfwEvCgkkpLPb93hIxSmz4JW1QSmRkYYlhEGALAwMsPSZpMRX3AR813GwlhihIuPrmPS9RXilP+lY47ulJn65wPrYTVu08HmSzHo95JevgVxC9CpSScsORmmU8bHom0Fe1Yt1PqvTwJMMQMMEdUtXkLSU9lLSHHBcbCRPnvdrf+9ChKJFubNvq1w+6q+qzD32Fx81v8z+Lv7P/Pxatuh3w/BWGKMfh79dNbvvrobC08uRH+XkfjpSHec/nAg0vISEHI4pMbHspHaIE+V96xNfqqCq/NhYn0Bxma3YWp3DtYSM+QLRfjM4//gInXEd/cO4/v7sQAACSTP5cGeZR1x/Cea2rlV6zJUOUVFiL17GjNywyvcnNDxPzA30nMGwKIiID8fXllzxVUXJlzQv21E9MLjJaTnoDayX2xaNizcvym3/u1Ob2PPtT34dsi3kFvKcfRvR+Fg5vDMx6sLA5tXfOfJiLYjMKLtCADAyv4ayEyM4WzTV/zFVqAqQGFxIW4V3IJW0MLEyATF2mL4OPlgYdxC7Pttn1jX373+jomdJsJYYgy/7SW3RbtZueFWwS0AwAc9P4CxxBhvtX8LXlu9xP12/3U3zEzMMHjXk0GlTaUt8HbniVh5Lgyedp1hb9QBF29qcU/1B7TKprAy10BQ26L4/isoBlCU9Rec62MOUxt7MTAsdJuINmbN0NOyA9qYuYnjSk52+BwmEmPcL7wrDmqtC5ZGNQguZVhIyvfIAcD7TUboH14q4bXViyGGiOoMe2D0VLYH5sRbJ2Ars32m+vp/8QnumG8Wl2tzYLCh+y33N+y6ugsuVi7ipaw/+/bSt3i12atwt3EX1yVlJ8HNyq3cJTKgZAp9pUYJc5OqB7/eyn0ERyspPj18FaM6NUXz1DOAtXWlPR6lAUYc+FpUhE8zv8dXhfqPxylla2yJEx026gyQffTHaLS39sVrUjX8XC3hYG2GrEcavOysR+goKsLv93/DX+7pjmuSwQQnW38CqXkNLv087oFJ6miHcTGTxdXD2wzHkpeX6F8fEb2wGkUPzMaNG7Fq1SooFAp06dIFn332GXr27FnfzRI9S/bTaAUE/XeLTngZ1noYw0sZrexaYU6POVWWGdtxbLl1Pk4+lZY3khg9NbwAgJtdSZm5Ae2Bhw+fUhpY5zEDakF35t2R5j3RuUsAzuZeQIhPCFQaFS5nJuHXcwfQ084bjpZOeC1tls4+S90mo525B/5QZaOHZXtxfQsjOdIfaaDO90ZqfjFSAXxyowDWJoXIVwu48aZ+c8E0N2mK5DfiEXPvNE7dPoUPvWZCcvw4INF/or+yfBy90cq2FX578BsAYM+1PbCR2mB299kwknDaKSKqPQ02wHz33XeYNWsWvvjiC/Tq1Qvr1q1DQEAA0tLS4OTkVN/NAwDczL8JOzO7Gu37z31xuCl7clfJuA4TMa/n7Cr2oIZsgI1vuXVuxg5wc+uL/m1LLl9Zmlqit3Mv9LZUAmbWgKkZznX8L0yNTHBDqYCZkRRy05LLhJ5m7jp17WgSgtl37XEABTrr89UlIXpNaj58HaXwl1e/J8ZIYoQBHgMwwGNAtUJadYUHhmPLxS0ITy0ZY/PNpW/wzaVvsOrVVfgt9zcMbD4Q7ezb1drxiOjF1GAvIfXq1Qs9evTAhg0bAABarRbu7u6YMWMGPvjgg6fu/zwuIblYuGJn0HewldkiszATDmYOMDOpfGzCuaxzmH98Pm4X3tZZP7/zFoz2Lf8LkBqIhw+BY8eqvIRUzuNLKujbF7Ao89yjZ6grpY0P1p+4CSNBi+i0u9VuvrWpBEqNANXjCYfdzCUY6AA4tm2Bfl5uSP4jF4X5j1B07Tf8rjbBP7xs4WphjPxiAUYS4E6RFstS8rHQxxoelsa4U6SFs3nJLeGJd0tmQnY3USMrpwCzM8wxqqcHbucWYaivMSZGv1Fpu3wce+Dy/QtQaooQ1CoIP/9WcufcxI7vID7zBIyNjPGG+3zcVJ7C37uMxQVFBnJy7eDXTgpNsSWcrM1wI+8GDmccxpgOYyAzMkPOQxUcrWS4X6hCQXEB0h6cg4WpBexkdrAwtoSdzBGFxYX4/PTPmNorCI+KVTh75zjkFnJYoRWa2Toh8ff7kFidx7cXf8CHPRbA2dINTtZmKFAVwNTYFDJjGQRBwL2ie3A0dwQA5KvyYWJkUq3evVL3C1UQUPIE+lu5jxAedwNJN3NhZX0HN/P+QP59TwBAr5YOeLmNI8xNjdG3XVP8cf8h2jpZw9ZCAkGirPEfUc/LhTsX0MK2Ra3elVasKcadR3fgbOEszjeUWZCJrIdZ6OTYCaczT+POwztoZ98OV+5fgaO5I3q59EJSdhI8HTyhETSwkdrARI/JGun5qe7v7wYZYFQqFSwsLPDDDz9g+PDh4voJEyYgNzcXP/1U/sGESqUSSuWT+0wfPHgADw8P3Lx5s1YDzOen9+Gb9H9XWcZR5oL/vPY5Ru4b8dT65AWzEDHhdVjW4gMHqZY9fAjExQFSKSCrZg+HUgmoVMDLL5cPMM9a18OHSI+Ow8UiIwx2t8C5+2q8k1B7PSi1R4Cp3WlIpHchtT9T342pM+rC5jCx/L3OjyNoTSAxevoTv7XKJjCS3auyTFvbzrj6oORRGB2kb+GyagcAoJNtb1x8cLKCPSSwNrEDBBPka+5Uq71veSzE8ewfcavoIgCgjbUXruWXDOo2kUjh7/gOirT50AgqxOfsBAC4yNqhp8MbuPHwPArU92AqkSHjUQoeah5U65jPysehL5oad4GRBIjK/gx9rD9A66Y2uJWfCVuZDYqQjQKlErZm1rj/qACF6lxIAHhYeKOpjREePASSFWloaecKcxMb3C3OgL2JK3KK/4C1cRMUaQtgZQ6g2B656my42JjBSNMEFlJTXMm5DolEAlOJBGamxshXPTnXTaSuKJZkIU+pe5naTGIJe6krMouuAH+aF0pqZASpiREKytRjZyJHbnEm7EzlyC1WiPt0cGiJi3fSYWMuxY27hXC0kYntljy+9Fuy7220auKANo5NIDOWwdzUHL7Otf/Hd15eHtzd3ZGbmwtb2yrGmQoN0K1btwQAwsmTJ3XWz507V+jZs2eF+/zrX/8SAPDFF1988cUXX43gdfPmzSqzQqP5s3/+/PmYNevJgEitVoucnBw0adKkVmYsLVWaDGu7Z4fqHs+dYeJ5M0w8b4apIZw3QRCQn58PV1fXKss1yADj6OgIY2NjZGVl6azPysqCXF7x3RYymQyyP3XJ29nZ1VUTYWNjwx9KA8VzZ5h43gwTz5thqu/zVuWlo8ca5H2NUqkUvr6+OHz4yRwaWq0Whw8fhp+fXz22jIiIiBqCBtkDAwCzZs3ChAkT0L17d/Ts2RPr1q1DYWEh3n777fpuGhEREdWzBhtg/va3v+HOnTtYuHAhFAoFfHx8cODAATg7O9dru2QyGf71r3+Vu1xFDR/PnWHieTNMPG+GyZDOW4O8jZqIiIioKg1yDAwRERFRVRhgiIiIyOAwwBAREZHBYYAhIiIig8MAo6eNGzeiRYsWMDMzQ69evZCQkFDfTXqhHDt2DEFBQXB1dYVEIsGePXt0tguCgIULF8LFxQXm5uYYOHAgrl69qlMmJycHY8aMgY2NDezs7PDOO++goED3Kc8pKSno06cPzMzM4O7ujpUrV9b1R2u0li1bhh49esDa2hpOTk4YPnw40tLSdMoUFRUhJCQETZo0gZWVFUaOHFluIsuMjAwMHToUFhYWcHJywty5c6FW6z4bKDY2Ft26dYNMJkObNm2wZcuWuv54jdamTZvg7e0tTmjm5+eHX375RdzOc2YYli9fDolEgpkzZ4rrGs25q5WHF70gduzYIUilUmHz5s3CxYsXhcmTJwt2dnZCVlZWfTfthbF//37ho48+Enbt2iUAEHbv3q2zffny5YKtra2wZ88eITk5WfjrX/8qtGzZUnj06JFYJjAwUOjSpYtw6tQp4fjx40KbNm2E4OBgcfuDBw8EZ2dnYcyYMUJqaqqwfft2wdzcXPjPf/7zvD5moxIQECCEh4cLqampQlJSkjBkyBDBw8NDKCgoEMu89957gru7u3D48GHh7NmzwksvvST07t1b3K5Wq4XOnTsLAwcOFM6fPy/s379fcHR0FObPny+W+e233wQLCwth1qxZwqVLl4TPPvtMMDY2Fg4cOPBcP29jsXfvXiEyMlK4cuWKkJaWJnz44YeCqampkJqaKggCz5khSEhIEFq0aCF4e3sL//d//yeubyznjgFGDz179hRCQkLEZY1GI7i6ugrLli2rx1a9uP4cYLRarSCXy4VVq1aJ63JzcwWZTCZs375dEARBuHTpkgBAOHPmjFjml19+ESQSiXDr1i1BEATh888/F+zt7QWlUimWCQ0NFTw9Pev4E70YsrOzBQDC0aNHBUEoOUempqbC999/L5a5fPmyAECIj48XBKEkuBoZGQkKhUIss2nTJsHGxkY8T/PmzRM6deqkc6y//e1vQkBAQF1/pBeGvb298N///pfnzADk5+cLbdu2FaKjo4VXX31VDDCN6dzxElI1qVQqJCYmYuDAgeI6IyMjDBw4EPHx8fXYMip1/fp1KBQKnXNka2uLXr16iecoPj4ednZ26N69u1hm4MCBMDIywunTp8Uyffv2hVQqFcsEBAQgLS0N9+/ff06fpvF68OABAMDBwQEAkJiYiOLiYp3z1r59e3h4eOicNy8vL52JLAMCApCXl4eLFy+KZcrWUVqGP5/PTqPRYMeOHSgsLISfnx/PmQEICQnB0KFDy32/jencNdiZeBuau3fvQqPRlJsJ2NnZGb/++ms9tYrKUigUAFDhOSrdplAo4OTkpLPdxMQEDg4OOmVatmxZro7Sbfb29nXS/heBVqvFzJkz8fLLL6Nz584ASr5TqVRa7uGrfz5vFZ3X0m1VlcnLy8OjR49gbm5eFx+pUbtw4QL8/PxQVFQEKysr7N69Gx07dkRSUhLPWQO2Y8cOnDt3DmfOnCm3rTH9vDHAENFzExISgtTUVJw4caK+m0LV4OnpiaSkJDx48AA//PADJkyYgKNHj9Z3s6gKN2/exP/93/8hOjoaZmZm9d2cOsVLSNXk6OgIY2PjciO1s7KyIJfL66lVVFbpeajqHMnlcmRnZ+tsV6vVyMnJ0SlTUR1lj0H6mz59Ovbt24eYmBg0a9ZMXC+Xy6FSqZCbm6tT/s/n7WnnpLIyNjY2/Eu+hqRSKdq0aQNfX18sW7YMXbp0wfr163nOGrDExERkZ2ejW7duMDExgYmJCY4ePYpPP/0UJiYmcHZ2bjTnjgGmmqRSKXx9fXH48GFxnVarxeHDh+Hn51ePLaNSLVu2hFwu1zlHeXl5OH36tHiO/Pz8kJubi8TERLHMkSNHoNVq0atXL7HMsWPHUFxcLJaJjo6Gp6cnLx/VgCAImD59Onbv3o0jR46Uuzzn6+sLU1NTnfOWlpaGjIwMnfN24cIFnfAZHR0NGxsbdOzYUSxTto7SMvz5rD1arRZKpZLnrAEbMGAALly4gKSkJPHVvXt3jBkzRnzfaM7dcxsu3Ajs2LFDkMlkwpYtW4RLly4JU6ZMEezs7HRGalPdys/PF86fPy+cP39eACCsXbtWOH/+vPD7778LglByG7WdnZ3w008/CSkpKcKwYcMqvI26a9euwunTp4UTJ04Ibdu21bmNOjc3V3B2dhbGjRsnpKamCjt27BAsLCx4G3UNTZ06VbC1tRViY2OFzMxM8fXw4UOxzHvvvSd4eHgIR44cEc6ePSv4+fkJfn5+4vbS2zoHDRokJCUlCQcOHBCaNm1a4W2dc+fOFS5fvixs3LiRt+Q+gw8++EA4evSocP36dSElJUX44IMPBIlEIhw8eFAQBJ4zQ1L2LiRBaDznjgFGT5999png4eEhSKVSoWfPnsKpU6fqu0kvlJiYGAFAudeECRMEQSi5lfqf//yn4OzsLMhkMmHAgAFCWlqaTh337t0TgoODBSsrK8HGxkZ4++23hfz8fJ0yycnJwiuvvCLIZDLBzc1NWL58+fP6iI1ORecLgBAeHi6WefTokTBt2jTB3t5esLCwEEaMGCFkZmbq1HPjxg1h8ODBgrm5ueDo6CjMnj1bKC4u1ikTExMj+Pj4CFKpVGjVqpXOMUg/kyZNEpo3by5IpVKhadOmwoABA8TwIgg8Z4bkzwGmsZw7iSAIwvPr7yEiIiJ6dhwDQ0RERAaHAYaIiIgMDgMMERERGRwGGCIiIjI4DDBERERkcBhgiIiIyOAwwBAREZHBYYAhIiIig8MAQ0RERAaHAYaIiIgMDgMMERERGRwGGCIiIjI4/w8P001ONGFmTgAAAABJRU5ErkJggg==", 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", 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", 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", 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mutT7+zC6/KKS22/uOXac1qPnEOLnw9qXuwGQcbzQZcRLRERERMqvgIAAZs+e7e0wyiXtRihukZ3vOo/5+gZxhPi7nnfQIP7sc1qz8ovo9eEi5/XHC3a5N0ARERERkUtMyZa4xcH0v7dxrxsXwmu9ripR5/V/XH3ONtYdyHS+Hv37FvcFJyIiIiLiBUq2xC1O3dLypyFtiQ7xK1HHaDTQq/GFr8X6a+dRN0QmIiIicuHs9pLLHuTK464N27VmS9zi1NVV/maTW9q8879L2T26h9ZuiYiIiMf5+vpiNBo5dOgQ0dHR+Pr66meQK5TD4eDIkSMYDAbMZvP5HzgHJVviFhsPWT3S7i9rD3FL48oeaVtERETkJKPRSFJSEocPH+bQoUPeDke8zGAwUKVKFUymixtEULIlFy0rr/CMOw2eyb+aJ/DTmkP0v6YaE//ac976R7MvzZkWIiIiIr6+viQmJlJUVITNdmkOMZbyyWw2X3SiBUq2xA2KbBc+p7VtzSh2j+7B9tTsC0q2RERERC6lk1PHLnb6mAhogwxxg1d+3Viq+gZD8QHGvqazf/x6NqwEQFiAvtGJiIiISMWkZEsuSlpOAT+vKf285shgPzaM6M5Vlc989taImxsAEKpkS0REREQqKCVbclEKTlurFRtacsv3s/H1MfLNoDYlyq+rH+t8nX5ca7ZEREREpGLSmi1xm/976BoSIwJL9Uyw398fwed61OXu1tUI8DVxLDsfgKe/X0fH2tGEB/liPse0QxERERGR8kY/vYpbfH5vC5omhhMVfOEjW6e7p01xogUQEeTrLJ+9OZWrXp7B3mM5Fx2niIiIiMilopEtcYuLOfLvz2c6ERXs53IY8qmHCP618yj5RXZ2HcmhamTQRfQkIiIiInLpKNkSr6sSXrqphyIiIiIiFYGmEUq599u6w94OQURERESk1JRsyUVJtuYBuEwBdJcHOlR3e5siIiIiIpeKki0ps9mbUug1fhEA9ePPfF7Wxbi9eYLb2xQRERERuVSUbEmZjZu73fk61N/9hw9Xjw52e5siIiIiIpeKki0REREREREPULIlIiIiIiLiAV5NthYsWMBNN91EfHw8BoOBn376yeV+//79MRgMLl/XX3+9S520tDT69u1LaGgoYWFhDBgwgOzsbJc669ato3379vj7+5OQkMBbb73l6bcmbtKwisXbIYiIiIiIlIlXk62cnBwaNWrE+PHjz1rn+uuv5/Dhw86vr7/+2uV+37592bhxI7NmzeK3335jwYIFDBo0yHnfarXSrVs3qlatysqVK3n77bd55ZVX+Pjjjz32vq4Uwf6eP6ZtVK+rPd6HiIiIiIgnePVQ4xtuuIEbbrjhnHX8/PyIi4s7473Nmzczffp0li9fTvPmzQEYN24cPXr04J133iE+Pp5JkyZRUFDAZ599hq+vLw0aNGDNmjWMGTPGJSmT0qtfKZRFO455tA+D4e/XRqPh7BVFRERERMqZcr9m648//iAmJoY6deowePBgjh37+4f7xYsXExYW5ky0ALp27YrRaGTp0qXOOtdeey2+vr7OOt27d2fr1q2kp6efsc/8/HysVqvLl5RkCTAT6Gti6XNdPNrHSZ8s3MW/Z28/R20RERERkfKjXCdb119/PV9++SVz5szhzTffZP78+dxwww3YbDYAkpOTiYmJcXnGx8eHiIgIkpOTnXViY2Nd6py8PlnndKNHj8ZisTi/EhJ03tPZBPr6EBvq77H2EyICna8Xbj/K2NnbPNaXiIiIiIg7eXUa4fn06dPH+frqq6+mYcOG1KhRgz/++IMuXTw3mjJ8+HCGDRvmvLZarUq4vCgq2Jej2QXeDkNEREREpFTK9cjW6apXr05UVBQ7duwAIC4ujtTUVJc6RUVFpKWlOdd5xcXFkZKS4lLn5PXZ1oL5+fkRGhrq8iXeM/WR9i7XBzNy2ZKsqZ0iIiIiUr5VqGTrwIEDHDt2jEqVKgHQpk0bMjIyWLlypbPO3LlzsdvttGrVyllnwYIFFBYWOuvMmjWLOnXqEB4efmnfwGXmWE4BhTa7x/s5fZpi2zfmcv17Cz3er4iIiIjIxfBqspWdnc2aNWtYs2YNALt372bNmjXs27eP7OxsnnrqKZYsWcKePXuYM2cOt9xyCzVr1qR79+4A1KtXj+uvv56BAweybNkyFi1axNChQ+nTpw/x8fEA3Hnnnfj6+jJgwAA2btzIt99+y7///W+XaYJSNinWPEIuwfbvIiIiIiIVkVd/Ul6xYgWdOnVyXp9MgPr168dHH33EunXr+OKLL8jIyCA+Pp5u3brx2muv4efn53xm0qRJDB06lC5dumA0Gunduzfvv/++877FYmHmzJkMGTKEZs2aERUVxUsvvaRt391g2vozbzAiIiIiIiJeTrY6duyIw+E46/0ZM2act42IiAgmT558zjoNGzZk4UJNOxMRERERkUunQq3ZkvKnW/3Y81cSEREREbkCKdmSi9K5bsz5K3nYyU06Nh+2Mnra5nOOloqIiIiIXCpKtqTCKrLZ+WXtIZq+Novs/CIe/no1/1mwi7xCz++QKCIiIiJyPtpKTspk77EcACwBZq/F0GzkbDJzi7f0t+YWkltg81osIiIiIiKn08iWlIk1twiAWIv/eWq6x/8GtCpRdjLREhEREREpj5RsSZlMmL8TgCrhAZekv3a1oni+Rz3gzJtyHM7Mdb4uKNI0QhERERHxPiVbUiapWXkARAf7naem+wy8tjpLhnehamRgiXu9P1rs3Bhj6NerLllMIiIiIiJno2RLyiQs0JfOdWMwGAyXtN84iz8Pd6l1xnuHMosTwCW7jl3KkEREREREzkjJllQ4viZ9bEVERESk/NNPrVImszalYLOXz/OsCm3lMy4RERERubIo2arADqTneuUA37X7MwCYv+3IJe8bwHgBUxetedqpUERERES8q0zJ1qpVq1i/fr3z+ueff6ZXr14899xzFBQUuC04ObuYkOIt14974WypApt3d/vz9TEye1gHnr2h7lnr/POjv5i8dJ9XklEREREREShjsvXAAw+wbds2AHbt2kWfPn0IDAxkypQpPP30024NUM4sPMh7hwmXBzVjgvExnn2Ea1tKNs/9uJ4D6blnrSMiIiIi4kllSra2bdtG48aNAZgyZQrXXnstkydPZuLEifzwww/ujE/kvBIizn7Wlwa2RERERMRbypRsORwO7PbiqWSzZ8+mR48eACQkJHD06FH3RSflkvnEboAv3FjPq3F0rBNN3bgQvh7YmrG3N/JqLCIiIiIip/Mpy0PNmzdn5MiRdO3alfnz5/PRRx8BsHv3bmJjY90aoJQ/J2fvtakR6dU4asaEMP2xawGoEh5IfqGdZ/9vfYl6Dofjkp8HJiIiIiJSppGtsWPHsmrVKoYOHcrzzz9PzZo1Afj++++55ppr3BqgyIXq0zKRz/o3dylbfzCTRiNmsmJPGiv3pjF+3g4vRSciIiIiV5oyjWw1atTIZTfCk95++218fMrUpIhbdK7rOrI6ZPIqAH5bd5hJS/dSaHMwpFNNb4QmIiIiIleYMo1sVa9enWPHjpUoz8vLo3bt2hcdlJRvKdZ8b4dwTg3iQ0uUTfxrj/Ow48zjhczbmnqpwxIRERGRK0yZhqH27NmDzVbyfKf8/HwOHDhw0UFJ+TbwyxUA2Ozlc6u/YL9zf6wbvToTgB2jbsDHpHO9RURERMQzSpVs/fLLL87XM2bMwGKxOK9tNhtz5swhKSnJfdFJueRrMlJgsxN0nqTGW6pHB7F0d9p5693x3yV8fm/L8yZnIiIiIiJlUaqfMnv16gWAwWCgX79+LvfMZjPVqlXj3XffdVtwUj79s3kVJi/dR43oYG+Hckav3nIVZpORLxfvPWe95XvSeWfGVl65ucElikxEREREriSlmkNlt9ux2+0kJiaSmprqvLbb7eTn57N161Z69uzpqVilnDiQnkv16CBvh3FWZpORoSc2wXjg2urnrLt0dxq5BSWnxIqIiIiIXKwyLVjZvXs3UVFR7o5FKgiHw1FuR7VOign1Z88bNzK8x7kPXt582Mp9E5dfoqhERERE5EpS5sUqc+bMYc6cOc4RrlN99tlnFx2YlF/bU7Jpkhjm7TAu2As31mPk1M1nvb94V8mdNUVERERELlaZRrZGjBhBt27dmDNnDkePHiU9Pd3lSy5vydY8qoQHeDuMC3Z/+3NPJQT4eMHOSxCJiIiIiFxJyjSyNWHCBCZOnMjdd9/t7niknMvKKwSgcljFSbYuxOvTtnBzo8rEWfy9HYqIiIiIXCbKNLJVUFDANddc4+5YpAI4ebRWbOjll5S0Hj2HvEJtliEiIiIi7lGmZOv+++9n8uTJ7o5FKoDDmbkAFNjs56lZvvRpkcBXA1pyT5uq56w3a1PKJYpIRERERC53ZZpGmJeXx8cff8zs2bNp2LAhZrPZ5f6YMWPcEpyUP+/M2AYUb/9ekbzRuyEA7WpG8WiXWny8cBf/mb+rRL2Hv17NTY3iL3V4IiIiInIZKlOytW7dOho3bgzAhg0bXO4ZDIaLDkrKL9OJsdAgX5N3Aykjg8FAZLAfw2+od8ZkC6CgyI6vT5kGfUVEREREnMqUbM2bN8/dcUgFEX9iY4y7Wp97Ol5FcVOjeH5de8ilrPnIWSx7viv+5oqZUIqIiIhI+eDVX98vWLCAm266ifj4eAwGAz/99JPLfYfDwUsvvUSlSpUICAiga9eubN++3aVOWloaffv2JTQ0lLCwMAYMGEB2drZLnXXr1tG+fXv8/f1JSEjgrbfe8vRbu2zZ7A4SIwLxMVX8kZ+FT3di7G2NSpRb84q0UYaIiIiIXLQyjWx16tTpnNMF586de0Ht5OTk0KhRI+677z5uvfXWEvffeust3n//fb744guSkpJ48cUX6d69O5s2bcLfv3g3vL59+3L48GFmzZpFYWEh9957L4MGDXJu4GG1WunWrRtdu3ZlwoQJrF+/nvvuu4+wsDAGDRpUhncvwX5lPgu7XEmICPR2CCIiIiJyGSvTT80n12udVFhYyJo1a9iwYQP9+vW74HZuuOEGbrjhhjPeczgcvPfee7zwwgvccsstAHz55ZfExsby008/0adPHzZv3sz06dNZvnw5zZs3B2DcuHH06NGDd955h/j4eCZNmkRBQQGfffYZvr6+NGjQgDVr1jBmzBglW2WQfrwQu8Ph7TBERERERMq9MiVbY8eOPWP5K6+8UmIKX1nt3r2b5ORkunbt6iyzWCy0atWKxYsX06dPHxYvXkxYWJgz0QLo2rUrRqORpUuX8o9//IPFixdz7bXX4uvr66zTvXt33nzzTdLT0wkPDy/Rd35+Pvn5+c5rq9Xqlvd0OUix5mG+DKYQnuqDO5swdPJqb4chIiIiIpcZt/7UfNddd/HZZ5+5pa3k5GQAYmNjXcpjY2Od95KTk4mJiXG57+PjQ0REhEudM7Vxah+nGz16NBaLxfmVkJBw8W/oMmEAasYEezsMt+rZMJ5xdzShYRWLs2zS0n38ctrGGSIiIiIipeHWZGvx4sXOtVQV2fDhw8nMzHR+7d+/39shiYfd1Cie8Xc2dV6/PWMrj3y9mj+2pnoxKhERERGpyMo0jfD0zSwcDgeHDx9mxYoVvPjii24JLC4uDoCUlBQqVarkLE9JSXGuGYuLiyM11fWH4aKiItLS0pzPx8XFkZKS4lLn5PXJOqfz8/PDz8/PLe9DKo6EiED+e09zBn65wlk2dtY2OtaJOcdTIiIiIiJnVqaRrVOn2FksFiIiIujYsSPTpk3j5ZdfdktgSUlJxMXFMWfOHGeZ1Wpl6dKltGnTBoA2bdqQkZHBypUrnXXmzp2L3W6nVatWzjoLFiygsLDQWWfWrFnUqVPnjOu15NyOZOefv1IFdl191ymnmbmF2O3aEERERERESq9MI1uff/65WzrPzs5mx44dzuvdu3ezZs0aIiIiSExM5LHHHmPkyJHUqlXLufV7fHw8vXr1AqBevXpcf/31DBw4kAkTJlBYWMjQoUPp06cP8fHxANx5552MGDGCAQMG8Mwzz7Bhwwb+/e9/n3WTDzm7DQcz2XUkh7jQij9V9FwqhwVwMCMXgD3HjnP1KzPY+Or1HumroMjOY9+upnfTKlSLCqJG9OW1Hk5ERETkSnZRByatXLmSzZs3A9CgQQOaNGlSqudXrFhBp06dnNfDhg0DoF+/fkycOJGnn36anJwcBg0aREZGBu3atWP69Oku68ImTZrE0KFD6dKlC0ajkd69e/P+++8771ssFmbOnMmQIUNo1qwZUVFRvPTSS9r2vQwyjhePDr5ycwMvR+JZQzrV5Lkf1zuvcwpsOByOc54tV1aHMnKZtj6ZaeuLN2vZ88aNbu9DRERERLyjTMlWamoqffr04Y8//iAsLAyAjIwMOnXqxDfffEN0dPQFtdOxY0cc5zizyWAw8Oqrr/Lqq6+etU5ERITzAOOzadiwIQsXLrygmOT8An1N3g7hknt35jae6Fbb7QlXx3f+cGt7IiIiIlJ+lGnN1sMPP0xWVhYbN24kLS2NtLQ0NmzYgNVq5ZFHHnF3jFJOFNhs3g7Baz6Yt4NGI2by0R87cTgcvDNjK/O3HeHjBcXXRTZ7qdscNXVTibKc/CJ3hCsiIiIi5UCZkq3p06fz4YcfUq9ePWdZ/fr1GT9+PL///rvbgpPyZdam4p0fo4Iv750aO9T5e2T2H00qO19b84p4c/oWZm1K4YN5O+j32TJen7aFw5l5tHljLj+sPHDG9hwOB+sOZJQo/+/C3SXKGrw8g+9W6KgBERERkctBmZItu92O2WwuUW42m7HbS/8bfqkYbHY7NWOC8Tdf3tMIK4cFsPDpTtzRMpG3/tmQpKggl/uDvlrpcn0su4AjWfk8MWUt/7fq74Tru+X7Sc8p4IdVB7n5g0Ws3Jt+Qf0//f06cguu3FFEERERkctFmZKtzp078+ijj3Lo0CFn2cGDB3n88cfp0qWL24KT8ifE/6L2VKkwEiICGX3r1ZhNRn4a0vacdf854S/n62HfrWXmxmQ2H7by9A/rGPbdGg6f2Nlw9b7iZOv9Odt5+vu152zzQhMzERERESm/ypRsffDBB1itVqpVq0aNGjWoUaMGSUlJWK1Wxo0b5+4YpZzYl3acc+xnctmyBJiZ/lj7s97PL3IdzR301Upu/89iALYkZznLR07dzJ3/XcKYWdv4bsWZpxyedNenSy8iYhEREREpD8o0TJGQkMCqVauYPXs2W7ZsAYrPvOratatbg5Py5XiBjcggX2+H4RVBvqX7q2LNK97oIv14gUv5XzuPXXAb87am0qlOTKn6FREREZHyo1QjW3PnzqV+/fpYrVYMBgPXXXcdDz/8MA8//DAtWrSgQYMG2mL9MmYwGIi9zA80dre8QjuLdh4t07P3fr6cRTuO8tCkldjtV+CQooiIiEgFV6pk67333mPgwIGEhoaWuGexWHjggQcYM2aM24KT8iW34MrdljzO4k+/NlXL9OySXWlnvffvPo3P+WzfT5YybX0yhdp4RkRERKTCKVWytXbtWq6//vqz3u/WrRsrV648632p2PYeO46bz/StMMwmIyNuuYrlz3flr2c788vQc2+acaG61IulTfVIWiVFUDkswC1tioiIiEj5UKqFKCkpKWfc8t3ZmI8PR44cueigpHyy2R3UjAnxdhheFR1SfMZYvBsToy8HtMTucND5nflua1NEREREvK9UI1uVK1dmw4YNZ72/bt06KlWqdNFBSflkNhm5Qge2zuvz/i2cr7vWi3W5d1392NOrO5kMBswmI34+f59dNntYB/a8caNLPZvWbImIiIhUOKVKtnr06MGLL75IXl5eiXu5ubm8/PLL9OzZ023BiVQEK17oyrW1o53X3Ru4JlePda3F74+eeev4AN+/k6yTUzRPTieMO2UzkvovzcBxJe67LyIiIlKBlWoa4QsvvMD//d//Ubt2bYYOHUqdOnUA2LJlC+PHj8dms/H88897JFDxLofDQW6hzdthlEtRwcVTCyuHBXAwI5f2taJZ9nwX9qfl8t3y/TSItwDw28PtSD9ewN2fLjtjOyNubsAnC3fj61P8O5BYiz/J1r9/sdFwxEymPNiGKuGBBPtdGYdLi4iIiFRkpfqJLTY2lr/++ovBgwczfPhw52/aDQYD3bt3Z/z48cTGnn3KlFRcJ3fUiwy+Ms/ZOpPEiEAiznDumCXATICviZgQf5pVDXeWX1W5OOn648mO3P3ZUvan5bo816VeLF3qnf3vT1ZeET3+vRC7A1omRdCoioW7W1cjMTLQTe9IRERERNyp1L8er1q1KtOmTSM9PZ0dO3bgcDioVasW4eHh539YKqynf1jr7RDKnd8eaYfZWKqZuABUiwri90evxZpbeM56hUUlt3s/uXRr2e40lu1O478Ld7us70rLKSA5M4/68SWPZxARERGRS6v0PymeEB4eTosWLWjZsqUSrStAXqHOeTpdqL/ZZc1VafKuYD+f8+5ouC/t+AW19fGCnQAcycqn6Wuz6PH+QtJzCpi2/vCFByQiIiIiblfmZEuuLOGBxVv+h/qffev/K92oXlfTqU60c83VxRpzWyO61os5b73Xp22h2rNTaTFqtrOsyWuzeGjSKv67YBeFtuJEuchmp8impFlERETkUlGyJRfklsaVAehU9/w//F+prq0dzef3tsRkdM8G+d0axDHhrmYX1caoaZup9fzvzNqUQuvRc3jgKx06LiIiInKpKNmSC3IoI/f8lcTtTEYD/dpU5asBLS+qnYFfruBodgFztqS6KTIREREROR8lW3JBJi3d5+0QrkgGg4ERt1xF+1rR5698gW754E+3tSUiIiIiZ6dkS6SCiAnxK1HWtV4sr93SoFTtrD2Qif3EtoY2uw5KFhEREfEUJVtyQerGhdCvTVVvh3FFm/HYtfzxZEfn9fLnu/JJv+b0blbFpV6I//lPdLj948X8b8le2r4xlzwdVi0iIiLiEaU+Z0tEvCM8yJfwIF/mPdmRjOMFRJ8Y6Qr09WHx8M4cTM/lqsoWDmXksiU5i+rRQTgccLygiI8X7GLGxhRnW8v3pLN8TzoAny/aw+CONbzynkREREQuZ0q25IJsSc6iZVKEt8MQICkqCAhyKatkCaCSpfjcrurRwVSPDna5/5+7I6j27NQztvfm9C3c0jieiCBf/M2mM9YRERERkdLTNEI5r11HsgFIjAj0ciTiKbmFNuq+OJ1PFu7ydigiIiIilw0lW3JedkfxJgqNE8K8G4hclE51zr6jYZd35wPw/pztTF66j+MFReQW2Phrx1F2nki2cwu0tktERESkNDSNUM4rOTMfAD8fTTGryCbc3YxUaz4HM3Lp8/GSM9ax5hXx3I/rGT1tM1n5RQAYDLDmpW40GjGT6+rH8t7tjQny07cO8Z6DGblUDiueNptXaGPWphRuahTv5ahERERK0siWnFdmbiEAVaM0jbAi8/MxkRARSKukiPNuiHEy0QJwOKDHvxcCMGtTCk9/v+6cz+ac8qyIu703extt35jLF3/tAeCt6Vt5+OvV/LbukHcDExEROQMlW3JeRXa7t0MQNzIYDPRrU61UzxzMyHW+PnDi9ap96VR7dirfrzzg3D5+8c5jNHh5BluTs8jOL+KJ79aSYs0DwOFw8OXiPRzLznfPG5Er0nuztwPw8i8bWbj9COsOZAAwdPJqL0YlIiJyZkq25Lx2HskBwF/TCC8bcRZ/vhnUukzPrt2fQfu35nLrh38B8OSUtdR9cTozNiaz9sQPvusOZPDiTxv4YdUBvlq8lw0HM/lo/k5e+nkjzUbO5sWfNgAwbf1h9qcdd8t7kivP3Z8uY8XedOd1tWenMnbWNi9GJCIi4krJllyQ2FA/fH30cbmctK4eyZwnOpTp2f1puSXKHvhqJW/8vgWAp75fx4+rDwLwwbwd9Bz3J29N3+qs+9WSvQA8NGkV/5qwmK3JWczcmMyeozllikeuDHuPnf/z8e852y9BJCIiIhdGPz3LBTEaDN4OQTygRnQwtWODiQjyZd0r3S5p3+Pn7QAg2ZpH9/cWMOirlXR8549St+NwOBg/bwcH0jVCdjmbtzWVDm//cUF19x7Lca41FRER8SYlW3JODoeD9+ds53BmnrdDEQ/5YfA1zHuiI6H+Zl64sd5Z6xndnG/P3JRyxvK9x3IoKLIzetpmHv92zVmfX7k3HbvdgTW3iLdnbD1r3We+X8esTSnY7Q6OZGm9WEV17+fLL7huh7f/oNGImVofKCIiXleuk61XXnkFg8Hg8lW3bl3n/by8PIYMGUJkZCTBwcH07t2blBTXH+D27dvHjTfeSGBgIDExMTz11FMUFWm3tAtVZHd4OwTxsBB/M5ZAMwB9W1Xlqe512DbyBta+3I1nrq/LnCc6sOKFruwafaPLcx/2bXpR/a7dn3HG8g5v/8G/JvzFfxbs4sfVB/lz+1HGz9vBij1pzjr7047T+6O/uO0/i3FQ/BnNyvv77/XJDTt2pGbz7Yr9DPxyBdWfm0aLUbP535K9dH7nD9buz3BZL7Zg2xHsp33eD6QfL1Eml96a0z4rj3SuCUCAuXgdqc9ZfhNgzdP3ehER8a5yf1hOgwYNmD17tvPax+fvkB9//HGmTp3KlClTsFgsDB06lFtvvZVFixYBYLPZuPHGG4mLi+Ovv/7i8OHD3HPPPZjNZl5//fVL/l4qMs0ivDIE+JoY0qn4B1lfH2OJLeJrRAc5N0y54ao4nutRl9enFa/TuqNlIl8v2+eWONYeyHS+vuvTpc7Xe94oTvhOJlMr9qY7N+rYkpxFn48X8997mnP1KzMZc1sjaseGlGj7hRObc9wyvvj7RLOq4djsDtbsz+Dp6+vQp0UiEUG+5BfZaPfmPPpfU41Xbm7glvclpfe/JXud/89Oalcrml5NKlMlPBBfHyOv/LKRiSe2ghcRESlPyn2y5ePjQ1xcXInyzMxMPv30UyZPnkznzp0B+Pzzz6lXrx5LliyhdevWzJw5k02bNjF79mxiY2Np3Lgxr732Gs888wyvvPIKvr6+Z+wzPz+f/Py/p59YrVbPvLkKwHHil/rv/LORdwORcmHqI+2ZuSmFlMw8DAYDg66tQYDZRH6RnfvaJhEeaObDP3Z6rP+nv19LTIg/accLnGW7TtlUY8muNK5+ZSYAw75by69D2523zZWn7Gb31vStvDV9Ky/2rM8dLROA4rVCfZITqBoRRICvduS81OZvO1KiLCrYl+rRwc7rWxrH8+eOo7xwYz1+XXuYH1YduJQhioiInFW5nkYIsH37duLj46levTp9+/Zl377i35yvXLmSwsJCunbt6qxbt25dEhMTWbx4MQCLFy/m6quvJjY21lmne/fuWK1WNm7ceNY+R48ejcVicX4lJCR46N2VfztSswGICfXzciRSHvibTdzcKJ6B11Z3lt3dphr3t6+O0Wjg6ev/nuZ7Y8NKhPq79/c53604wAfzdjB56YWNoN30wZ9l6ue13zaxM7U4iYsM8uX69xZyz2dL2XUku0ztSdll5f290UX3BrF8fHczl0QLoEliOLOHdaBjnRiqRwc5yw9nlNw1U0RE5FIq18lWq1atmDhxItOnT+ejjz5i9+7dtG/fnqysLJKTk/H19SUsLMzlmdjYWJKTkwFITk52SbRO3j9572yGDx9OZmam82v//v3ufWMViP3E0FZ44JlHAUXOZvydTXn91qud11tHXl+iToibkzF3+seHxdMMI4OLf9GwfE86nd+d782Qrjg5+UUs2fX3Wr2u9WLp1qDkTIdTNaxicb7uP3E5aTkF56gtIiLiWeU62brhhhv417/+RcOGDenevTvTpk0jIyOD7777zqP9+vn5ERoa6vIlIhcuIqg4Ob/+xA/G4YFm/HxMLHuuCwuf7sSW14oTrztaJjqfmfdkR25tUrlU/cwedq2bIi7p5OYwMSGuo7oZxwuYdcpOiid37NThzO5zJCufA+nHafzqzFI/275WtPN1QZGdEb+efRaDiIiIp5XfXyufQVhYGLVr12bHjh1cd911FBQUkJGR4TK6lZKS4lzjFRcXx7Jly1zaOLlb4ZnWgUlJB09Mwwnyq1AfFfGiETc3oHX1SKD4fLYa0UEMbF887TAm1N9Zb90r3Qjx8+FA+nGurRVNUlQQ8WEBANzduip2h4NJS/fRrX4sNzasxO6jOQSYTYw+cXAygOECd27548mOfPjHDr5b4bqWp8fVcUxbf/ZRboBJp01ZbPzqLKB4ndBdratSOyaEMbO2MWtTCk0SwxjSqSaxp7xPKb3rxs4n47h7zsn6ec0h/t2niVvaEhERKa0K9RN0dnY2O3fu5O6776ZZs2aYzWbmzJlD7969Adi6dSv79u2jTZs2ALRp04ZRo0aRmppKTEwMALNmzSI0NJT69et77X1UJCnW4vO1Kp/4IVjkfPpdU8352mg0MOvxDhjPsDV3qH/xdvMf9m3mLOtaP5bPF+3m3rbViA8LIMBsYmjnmoSdmMbqcDiICPKlQ51oDBhwOP7elr1RQhh3tEjg+5UHGHFLA+rFhVL9uWkAxIcF8FT3us5kq36lUDYdttI4IYwP7mjKH9tSefGnjc5fLlyIn9cc4uc1h/hhcPH3mw2HMll/MBOb3cGof1x9nqdd5RXa8Ddr842TLjbRigv1J9n699mAS3Ydc/4CQERE5FIq19MIn3zySebPn8+ePXv466+/+Mc//oHJZOKOO+7AYrEwYMAAhg0bxrx581i5ciX33nsvbdq0oXXr1gB069aN+vXrc/fdd7N27VpmzJjBCy+8wJAhQ/Dz04YPF+JgevEPn2aT9n6XsjlTonU2jRPCWPNyN6pHB+NvNvFCz/rORAuKR7L+1TyBmBB/ok+b3vfzkLb0aZnI94OvoUG8xaVfXx8jfubib3cPdqjBVwNaclOjeP7ZLAGj0UDnurHMeaIDiRGBDGiXVKr31/uj4g15TuZ9P685REGR/YKfn7ruMHVfnK61RW4054kOLtd9Pl5Csg5mFxERLyjXI1sHDhzgjjvu4NixY0RHR9OuXTuWLFlCdHTxnPyxY8diNBrp3bs3+fn5dO/enQ8//ND5vMlk4rfffmPw4MG0adOGoKAg+vXrx6uvvuqtt1ThHEjPJSEi4IKna4lcLLPpwn8HFBXsxyNdap11rdfA9knUiik+ayvU38yfz3Sicljx53ncHa5Ty/zNJuY/1RGAmZuS2Z+Wy/g7mzJk8qpSxZ+dX0T9l6Zze4sErq0dzXX1Yhkzaxv/WbCTz/q3oHnVCAJ8TUzfcJi3Zmxl14lzy9JyCrA7HEQFX9m/CLKd5RDpDrWjubZ29BnvnS7Iz4eIIF+XBHbtgQziLJo+LiIil5bBceo8HDkjq9WKxWIhMzPT65tlZOYW0mjETOdak40junt0PVWHt+cREeTLjw+19VgfIuXN4cxc1h3IpHuDOKo9O9Xt7b93e2Me+3bNGe99/2AbCorsXFMzyqU8NSuPZ75fx8h/XH1ZT+t99ddNfLZot0vZ9MfaUzeudN97bXYHRgMkDS+eSvr+HU24uVG82+IUEZErV2lyg3I9jVC8L7fAxlXxlvNXFLmMVLIE0P3ETooT721B86rhdKxzYaMqF+JsiRbAPycs5s5PlpJqdZ32tmRXGvO2HuH39YfdFkd5dGqiteeNG9k28oZSJ1oAJqPBZUT+ka9Xa8dIERG55JRsyVk5HA5Ss/IJCzR7OxQRr+lYJ4bvB1/DxHtbOsv+cWLaYoAHN7XoOe5PjhcUseFgJsey85m+oTjJOnnQeF6h7bJbhzTxlERr5QvFB9b7+rjvn6mP5u90W1siIiIXolyv2RLvmrKyeOe2xTuPeTkSkfJhzhMdyCu0MXNj8RESH93VlNwCG4MnraJFtXAe61qbfp8tc57RdTFSs/Kp/9KMEuWzN6ewJdnKQ/9bxa6jOex540bnvae/X0uXerHOUbmKwuFwsPZAJq/8ugkoXp8V6aa1a74mIwW24g1LkiKD3NKmiIjIhdLIlpzVyR3VRtzSwMuRiJQPNaKDaRBvoW+rRHo3rUKzquH4nNjQo2OdGNrWjGLH6z14qnsd5zO/P9qeGtFB1IoJdmkryLdso2JHswu4/r2F7DpavLFGy1Gz2ZJsZffRHL5bcYAHvlrJpkPWMr7DS8vhcJBbYOPTP3fTa/wiZ3kli/vOKbuz1d8HZ29JzuL1aZvd1raIiMj5aGRLzspoMGAwQAOt2RJxERPqz7u3NQIgyK84aYoI+nuL+vvaJvH2jK30aZFAvUqh/DSkLT5GI70/+ov2taJ4qnsdfExGFu04St9Pll5ULKlZ+Vz/3kKXsvK+jfzW5CyGTF5Fg/hQfl5zyOXebc2r8GJP952D+FLP+tzTpiqd353PD6uKR+uvvyqOponhbutDRETkbJRsiYhchGtqRDHp/lZcU+PvQ3MDfE2sfbkbloDi9Y4hJw5w/mlIW8ymvzduqB0b4tLWztd7UOPEQcwXY2tKFiajgTY1Lu1BvkU2O/lFdjYdtlItMqjEWWipWXmk5xTS/b0FwN/rz07Vp2WiW3dYNRoNVI92HVW84+MlbB15g9v6EBERORslW3JWc7ekooMBRM6v7WnbtAPOROtUp2/2EB3ix7/7NKZxQhibD1sxnXIQ83X1Yxl969U8+8N6Zm8uXiN2e/MEhveoS+NXZ50zntd+K1779MPga7DZHbRMimDGxmS++GsPsaH+9GxYiS71YoHi4ySC/Xxc+i6Lb5fvY+ys7RwvKMKaV0RCRAALn+7svP/5ot2MOLEm61w8uenISfmlOHRaRETkYmjNlpzV0t3HqHnaOhMRca9bGlemamQQ119VCYBvB7UG4IFrqxMV7OecrtiyWgRv/rMhYYG+zHmiA/9sVsXZRs+GlRh7e6MSbff+6C9u+89ixs/bwQNfreSvncf4cfVBBnyxgiW7jrF2fwYd3p7Hk1PWsnJvGgO/XMHGQ5lsT8k6Z8wH0o8zYOJy5pxIAgEmzN9FsjUPa14RAPvTcvl9/WG2JFuZuyWFvy5go52YED/qnDba5y5v/7Ohy/WeE2veREREPEmHGl+A8nyo8ZLhXYhz42LyUzV5dSb3t6/OkE41PdK+iJxZek4B4aesAbPbHRjPMPJU7dmpNE4I46chxYeOT1y027mj38VaMrwLk5fuJT4sgObVwqkZE8Luozm8+fsWpm9Mdtb7dlBrlu9J452Z2y66z1N3VvQEu91B9VOmae56vccZ/1xFRETOpTS5gaYRVlA1T6xBOJSZ65Fk68/tR0k/XkhOfpHb2xaRczs10QLOmhBMe6Q9iZGBzuv+bZOYvTmVP3ccvegYWo+e43J98hc8p7v94yUX1c/S57rw0s8bePy62hfVzoU4/c+x/8TlfHlfy7PUFhERuXiaRlhBefq3sXd9WrxD2ncrDni0HxEpu/rxoQSftpnEQx1r4OtjpGVSBPe1TXJbX2dKtNwhNtSf/9zdnLpxl2bWwA+D2zhfL9h25JL0KSIiVy6NbMk59T3ljBoRKf+uqRnFtlN22vtl7UGOZhfwUs/6vPrbJhIjAtmXdtwrsXWsE82DHWrQJDGMh/63CktgyU1EPO2qyq5HWXz0x04Gd6xxyeMQEZErg5ItOafeTaucv5KIlFsLn+6Mj8mA2WQk0NdE9wZxWPMK6fD2HyXqdq4bw9wtqaVqv1v9WGZuKt4oI8TPhztaJfLxgl1nrPtm74bEhhZPe/7P3c0wGi79eik/H9fdDt+cvoVgPxN3t6l2yWMREZHLn5ItOSd/X800FanIAnz/Ti76tCweqQ4P8uWL+1rStkYkaccLeOK7tbz1z4ZUsgSw80g2mbmFvDFtC8v2pLm0tW3kDSzaeZT8Qjvvzd7GT0Pa4m82cd2Y+WxPzWbmsGupZAmgZnQwT/+w7pxx+Zi8973lt4fb8c8Jf5FXWLwF/Is/b2TVvgxG33o1/pdg63kREblyKNmSs+pcN4aYEM/sdCgi3tWhdjQAMSH+fDWglbO8xonNd74c0JK6L07n1iaVaVszCn+zCV8fI53qxABw/VVxzmd+fbgd+UV259lit7VIoGOdaI4X2ADIzi9i4fajxJx2yLG3XFXZwvpXulPr+d+dZT+uPsiPqw+ybeQNJc5DExERKSslW1JCWk4BANfWKnlQq4hcGfzNJmY8di3Vo4Mwn2cUyt9sKjEiFBPq+oua09dKeZvZZGTBU5249u15LuVjZ2/j8a61lXCJiIhb6F8TKWHt/gwA2tWK9m4gIuJVdeJCzptoVWSJkYHc1CjepeyjP3Yy+5TDmkVERC7G5fuvqJTZ1pQsAEL8NfApIpe3cXc0YcJdTV3KjuUU8Pi3a1jkhvPKRETkyqZkS0p44/ctAIR5YVtmEZFL7fqrKrHgqU48fX0dAF78aQM/rj5I30+WejkyERGp6JRsiYv9p5y/c/oWySIil6vEyEAe6lizRHm1Z6fy5/ajFBTZcTgcXohMREQqMs0TExcfzd/p7RBERLxm1D+u4vkfN7iU3fXpUnyMBh7qWINgfx96N61CZHD52FlRRETKN41siYtlu4vP1Vn14nVejkRE5NLrWCeGJolhJcqL7A7en7uD16dtodnI2RQU2S99cCIiUuEo2RKno9n57EjNpmW1CCKCfL0djojIJVc5LIAfH2rLr0PbnbNe7Rd+Z+bGZPIKbRTZNMVQRETOTNMIxen/Vh0A/t6NUETkSnV1FQubX72egxnH6TpmwRnrDPpq5d+vr63Ocz3qse5ABg2rhF2iKEVEpLzTyJY4nZwW81n/5l6ORETE+wJ8TdSMCWHRs53p1fjv87gqhwWUqPvxgl1Ue3YqN3+wiO5j/07ObHYHOflFZOUVUmjzzNTDnPwibHaNrImIlEca2aqg/M3FOwWmZOa5pb2svELembkNgAbxFre0KSJyOagcFsB7fZrQvFoE9SqFsHpfBiOnbqZ2bDDbUrJL1N+aksWb07fQNDGc39Yd4uc1h5z3PuzblLemb2HM7Y1pmhgOFCdkJqMBgLScAsICzBiNBjYczGRHajbWvEI6140hIsiXH1cfJDrYj1ZJkRzMyGX30RzemrGFShZ/nr2hHo0TwtiRms3AL1fwwZ1N9P1cRMTLDA5NND8vq9WKxWIhMzOT0NBQr8aSmVtIoxEz+ahvUwZPWsUbt15Nn5aJF93uh3/s4K3pWwHY88aNF92eiMjlLCe/iCA/H75bsZ+nv19Xpjb+2awK368snr7duW4Mz/WoR9cx86lk8WfqI+1p+tqsUrdpMhrOOMr1ef8WNKgcyvaUbJpVDcffbGLzYSt1YkMwnkj0RMqjBduOUK9SKNEh2gFUyo/S5AYa2RKW70lzJlq+Js0sFRE5nyC/4n8+b2ueQKukCIL9fHAAP646yKhpmy+ojZOJFsDcLanM3ZIKwOHMvDIlWsBZpxPeO3G5y/X/BrTirk+LD21e+3I3rLmFBPv5MGH+Tv7VvApxlgC2HLbSvFpEmeIQuVB5hTbyi+wE+ZrYc+w48WH+zNqUwhd/7WFIp5oM+GIFALtH98Bmd+Bz4ueUnPwijmTlUy0qyJvhi5yXkq0rXMbxAv41YbHzeuoj596BS0REXFWN/PuHvYHXVqdNjUi+X3mAiX/tAeDWppX5v1UHy9T2B3c24Vh2ARsOZjLllOQM4F/NqjjLqkUGsufY8TM1cUYnEy2ARiNmutz7z4JdztdPXFebd2dt4+rKFjrXjSEtp4B+11TF4SieLnnj1ZUotDnw9fn7F3UOh4PdR3OICvEj1N8MQKo1D7sD4iz+Ln2tO5BB5bAAIoP9cDgczN92hA61owEwGM484rbpkJV9aTlcf1UlZ5nd7mDB9iM0TggjLNB1N931BzKJDvGj9eg5+BgNTB7YmpZJETgcDvan5XIwI5foEF8ig/wI8vPh80W76d+2Gn4+Juf7OTWeIpud44U2Qv3NOByOs8Yp5+ZwOHjuxw18vWzfWeucTLQAkoZPA6BmTDD3t0vi2f9bD0D7WlG0rxVFoyphtEyK0P8PKXc0jfAClOdphA92qMGzN9Sl0GbHXMpRKbvdQfXnpjmv17x0XYl/pEREpGz2px3naHY+TRLDWbzzGL9vOMyImxswbX0y21OzGNKpJkey8rnmjbnOZybd34qBX65g/J1NaZoYjiXQ7LyXV2ijx/sLebFnfZpXDSfE38yhjFymrT9MTKg/j3y9msYJYbSoFo6PychHf+wkKtiPo9n5APj6GIkJ8eNAeq5H3u/97ZKICvFjzMxtFJzYDOTL+1qyYm8678/ZDsCOUTdQaHMwft4OPlu0m+MFNgC+GtCSVGs+T0xZ69LmI51rEh7kS7uaUXy/8gBRwX7OkcOvBrTEaDDQ95OlLs8836Mef+08yrytR1ymapaWn4+R/NPOU6saGUjG8UIycwtdyn8YfA2/rDnIzY3jWbs/k+vqx2I2GV2Sy6emrKVXk8q0rRlVpngqqiKbnV1Hc/hh5QEe7FCD2ZtTWLUv45xJVlldWzuaL+9rCcC2lCxqx4a4vQ8RKF1uoGTrApTXZOvp79eRlV/kvPefu5vRrX7sBf1W56sle3nxpw3O6380qczY2xt7ImQRETmP/WnHCfbzITzIF7vdUaZ1VPvTjpMQEehSZrc7sOYVYnfgPD8xr9DGtpQsgv18+HrZPv67cLez/o1XV2Lq+sMX92akhKGdavLBvB0uZSc3WHn/jibkFdjYn36cr5ft42h2AS2rRXBfu2oA1IgO5lBmHr+vP0zXerF0rR/Lqn3pJ9oIYeG2I7SqHsnxgqITCXVxgpdXaMNsMjo3XzmXrLxCfH2MztG8k1Kz8pi67jD9r6mGwWBg4fYjJEUFUSU8kPScAkIDin8Z8M3yfSzcdpTbWySw+2gONruDzNxCPvxjB6XZKNPXx3jGA8NjQ/1IseZfeEMn+JuN9GtTjSe61XEZfRW5WEq23Ky8JluDJ606Y51bm1bm2Rvq4udjwhJgLnH/QPpx2r05z6Vs3SvdnNM9RETkyvHuzK1Ujw6iU50YwgKLk71jOQVk5xexdn8G/mYjD/7vzP/eVASVLP4czszjkS61uKdNVZqPnO1y/5HONXl/7o6zPF0x+RgNFJ3IcowGaFszisOZeeQW2GiZFMGSXce4qrKFqytb+Gn1QXYdzQHgk3uaUy0qiPgwf1Ks+XR65w8AqkcFcWvTys5di5c914WWr8+56Di/HdSaEH8zE//azXcrDjDx3hYcL7BxfYO4Er9wyMkvIrfQxuPfrqFX48qEBphpXT2Cf01YzJbk858POuGuZvj6GAj1N9O8WgR5hTZ8jAbnGjCR0lCydRbjx4/n7bffJjk5mUaNGjFu3Dhatmx53ufKa7J1NDufF3/eeM76D3SozsaDVjrXjSEqxI/f1x/m9w3JzvsJEQH8+FBbooK1y4+IiJzZybVJ+UXF0/5OjoBkHi/EEmgmJ794VMXHaOCBr1ZyNDufQdfWYPmeNO5rl8SqvekkRQXx0s8biAnxp3O9GHpcXYkv/trD/e2TMGCg9gu/A8UHRH+8YBe/PdyOiCBfPvtzN5/8WTz61rFONHXiQpi3JZVbGldm7f4MBl5bnUBfE7VjQ06sAQskNrT43zRrbhGhAT7nnfGRX2SjzgvTuat1Il3qxpKWU0BEkC8mo4Fgfx/W7c+gdY1IfE1GOr87nye71ebmRpU5kH6cZGsedeJCqBsXym/rDnEsu4DZm1M4kpXP9tRsbm1amanrDvNQx5qMnb3NU/+LyrVpj7Rn3tZU7HYHgzvWIP14ISH+Ps5jbPIKbfxvyV7ubZt0QSNxZ+JwOHjll418sXgvULyWa+H2o+d97vGutWmUYOFgRi7takZxNLuAtJwCWlWPIMTv/J8duTIp2TqDb7/9lnvuuYcJEybQqlUr3nvvPaZMmcLWrVuJiYk557PlNdm64epKZB4v5GBGLoU2OwU2u8tmF+cz5cE2tNBOUyIiUg5k5xfhYzTgbzaRW2AjwPfvKW02u4P04wVEBvl67IffY9n5hAf6nncK5+mxnY3d7mB/+nGXDVQycwsJMJvw9TEybf1hQvx92H00h3hLAEV2B/UqhbDraI7zjLYdqdnc0TIRX5ORtQcyqBoZxPytR2hYxeLcYfLffRrz+LdrqGQJoGu9GGeycdL97ZKcyaq79W5ahR9WFa+J69mwEqlZ+TxzfR2aVY1gzuYUOtaJKXPyVFaZxwvZkmylVfVIJi3dyzU1ohg1dROzN6deVLtd68Uye3MKAPe2rYbN7mDDwUw6143Bz8eE0Wgg8MTnosWJkbOYED98TEb8zUbSjxdyOCOXBvEWtqZksXpfOne0TMRggLxCO6nWPGJC/Anx9yEr7+9fEqRa84gK9sNoNJBXaHMmqCedvknLmTZtOVOdzNzCEuv0z/TsqX2evH+hG8OUZgOZ/CIbGccLiQ31P+t7cDgoN0dVKNk6g1atWtGiRQs++OADAOx2OwkJCTz88MM8++yzLnXz8/PJz/97bnBmZiaJiYns37+/XCRbbd+Yy9jbGnFdg7gS93cdzSbY14d+ny9jf1rxImh/s5G8wuI50NWjg7j3mmr0bBRf6g01REREpHxwOBxk5xcR4m/meEERZpPR5d/1IpudvWk51IgOYV9aDg4H2B0OkqKCL7iPUw/c3nssh+gQP7Jyiyi02QkP8iXIzwdrXiG/rT1EnxaJ5eYH4bPJK7Rx238W06Z6JN8s33/WoxIqiqqRgewtxS6k7taoioW1BzKd19Wjg2hQKZT5245gzSs64zNhAT5k5J753tmc+nPsoPbVeaRrrbIH7SZWq5WEhAQyMjKwWM59ePwVkWwVFBQQGBjI999/T69evZzl/fr1IyMjg59//tml/iuvvMKIESMucZQiIiIiIlJR7N+/nypVqpyzzhVxztbRo0ex2WzExsa6lMfGxrJly5YS9YcPH86wYcOc13a7nbS0NCIjI8vF3N2T2XR5GGkTOR99XqUi0edVKhJ9XqUiuZw+rw6Hg6ysLOLj489b94pItkrLz88PPz/XDSPCwsK8E8w5hIaGVvgPq1w59HmVikSfV6lI9HmViuRy+byeb/rgSVfEop2oqChMJhMpKSku5SkpKcTFlVz3JCIiIiIicrGuiGTL19eXZs2aMWfO32dC2O125syZQ5s2bbwYmYiIiIiIXK6umGmEw4YNo1+/fjRv3pyWLVvy3nvvkZOTw7333uvt0ErNz8+Pl19+ucRUR5HySJ9XqUj0eZWKRJ9XqUiu1M/rFbEb4UkffPCB81Djxo0b8/7779OqVStvhyUiIiIiIpehKyrZEhERERERuVSuiDVbIiIiIiIil5qSLREREREREQ9QsiUiIiIiIuIBSrZEREREREQ8QMmWiIiIiIiIByjZEhERERER8QAlWyIiIiIiIh6gZEtERERERMQDlGyJiIiIiIh4gJItERERERERD1CyJSIiIiIi4gFKtkRERERERDxAyZaIiIiIiIgHKNkSERERERHxACVbIiIiIiIiHqBkS0RERERExAOUbImIiIiIiHiAki0REREREREPULIlIiIiIiLiAUq2REREREREPEDJloiIiIiIiAco2RIREREREfEAJVsiIiIiIiIeoGRLRERERETEA5RsiYiIiIiIeEC5T7YWLFjATTfdRHx8PAaDgZ9++snlvsPh4KWXXqJSpUoEBATQtWtXtm/f7lInLS2Nvn37EhoaSlhYGAMGDCA7O/sSvgsREREREbnSlPtkKycnh0aNGjF+/Pgz3n/rrbd4//33mTBhAkuXLiUoKIju3buTl5fnrNO3b182btzIrFmz+O2331iwYAGDBg26VG9BRERERESuQAaHw+HwdhAXymAw8OOPP9KrVy+geFQrPj6eJ554gieffBKAzMxMYmNjmThxIn369GHz5s3Ur1+f5cuX07x5cwCmT59Ojx49OHDgAPHx8d56OyIiIiIichnz8XYAF2P37t0kJyfTtWtXZ5nFYqFVq1YsXryYPn36sHjxYsLCwpyJFkDXrl0xGo0sXbqUf/zjHyXazc/PJz8/33ltt9tJS0sjMjISg8Hg2TclIiIiIiLllsPhICsri/j4eIzGc08UrNDJVnJyMgCxsbEu5bGxsc57ycnJxMTEuNz38fEhIiLCWed0o0ePZsSIER6IWERERERELgf79++nSpUq56xToZMtTxk+fDjDhg1zXmdmZpKYmMj+/fsJDQ31YmSQmVtI2zfmAtChdhTj+zbzajwiIiIiIlcSq9VKQkICISEh561boZOtuLg4AFJSUqhUqZKzPCUlhcaNGzvrpKamujxXVFREWlqa8/nT+fn54efnV6I8NDTU68mWw1yI0S8QAN+AYK/HIyIiIiJyJbqQ5UXlfjfCc0lKSiIuLo45c+Y4y6xWK0uXLqVNmzYAtGnThoyMDFauXOmsM3fuXOx2O61atbrkMYuIiIiIyJWh3I9sZWdns2PHDuf17t27WbNmDRERESQmJvLYY48xcuRIatWqRVJSEi+++CLx8fHOHQvr1avH9ddfz8CBA5kwYQKFhYUMHTqUPn36aCdCERERERHxmHKfbK1YsYJOnTo5r0+uperXrx8TJ07k6aefJicnh0GDBpGRkUG7du2YPn06/v7+zmcmTZrE0KFD6dKlC0ajkd69e/P+++9f8vciIiIiIiJXjgp1zpa3WK1WLBYLmZmZXl8jlZlbSKMRMwHoUjeGT/u38Go8IiIiIpcTh8NBUVERNpvN26GIF5nNZkwm0xnvlSY3KPcjWyIiIiIil0JBQQGHDx/m+PHj3g5FvMxgMFClShWCg4Mvqh0lWyIiIiJyxbPb7ezevRuTyUR8fDy+vr4XtNucXH4cDgdHjhzhwIED1KpV66wjXBdCyZaIiIiIXPEKCgqw2+0kJCQQGBjo7XDEy6Kjo9mzZw+FhYUXlWxV6K3fRURERETcyWjUj8dyYWdoXQh9mkRERERERDxA0whFRERERM7hYEYu6TkFl6Sv8CBfKocFXJK+xPOUbImIiIiInMXBjFy6vjuf3MJLsxV8gNnE7Cc6lCrh6tixI40bN+a9997zSEz9+/cnIyODn376ySPte8OePXtISkpi9erVNG7c2GP9KNkSERERETmL9JwCcgttvHd7Y2rGXNw24OezIzWbx75dQ3pOgUa3LhNKtkREREREzqNmTDBXVbZ4O4zLRkFBAb6+vt4Ow+O0QYaIiIiISAVXVFTE0KFDsVgsREVF8eKLL+JwOAD46quvaN68OSEhIcTFxXHnnXeSmprq8vzGjRvp2bMnoaGhhISE0L59e3bu3HnGvpYvX050dDRvvvmms2zkyJHExMQQEhLC/fffz7PPPusyPa9///706tWLUaNGER8fT506dQBYv349nTt3JiAggMjISAYNGkR2drbzuY4dO/LYY4+59N+rVy/69+/vvK5WrRqvv/469913HyEhISQmJvLxxx+7PLNs2TKaNGmCv78/zZs3Z/Xq1Rf8Z3sxlGyJiIiIiFRwX3zxBT4+Pixbtox///vfjBkzhk8++QSAwsJCXnvtNdauXctPP/3Enj17XJKVgwcPcu211+Ln58fcuXNZuXIl9913H0VFRSX6mTt3Ltdddx2jRo3imWeeAWDSpEmMGjWKN998k5UrV5KYmMhHH31U4tk5c+awdetWZs2axW+//UZOTg7du3cnPDyc5cuXM2XKFGbPns3QoUNL/f7fffddZxL10EMPMXjwYLZu3QpAdnY2PXv2pH79+qxcuZJXXnmFJ598stR9lIWmEYqIiIiIVHAJCQmMHTsWg8FAnTp1WL9+PWPHjmXgwIHcd999znrVq1fn/fffp0WLFmRnZxMcHMz48eOxWCx88803mM1mAGrXrl2ijx9//JF77rmHTz75hNtvv91ZPm7cOAYMGMC9994LwEsvvcTMmTNdRqgAgoKC+OSTT5zTB//73/+Sl5fHl19+SVBQEAAffPABN910E2+++SaxsbEX/P579OjBQw89BMAzzzzD2LFjmTdvHnXq1GHy5MnY7XY+/fRT/P39adCgAQcOHGDw4MEX3H5ZaWRLRERERKSCa926tctBvG3atGH79u3YbDZWrlzJTTfdRGJiIiEhIXTo0AGAffv2AbBmzRrat2/vTLTOZOnSpfzrX//iq6++ckm0ALZu3UrLli1dyk6/Brj66qtd1mlt3ryZRo0aORMtgLZt22K3252jUheqYcOGztcGg4G4uDjnVMnNmzfTsGFD/P39nXXatGlTqvbLSsmWiIiIiMhlKi8vj+7duxMaGsqkSZNYvnw5P/74I1C8SQVAQMD5dz6sUaMGdevW5bPPPqOwsLBMsZyaVF0oo9HoXHt20pn6Pz1RNBgM2O32Uvfnbkq2REREREQquKVLl7pcL1myhFq1arFlyxaOHTvGG2+8Qfv27albt26JzTEaNmzIwoULz5lERUVFMXfuXHbs2MFtt93mUrdOnTosX77cpf7p12dSr1491q5dS05OjrNs0aJFGI1G5wYa0dHRHD582HnfZrOxYcOG87Z9ej/r1q0jLy/PWbZkyZJStVFWWrMlIiIiInIeO1Kzz1/Ji33s27ePYcOG8cADD7Bq1SrGjRvHu+++S2JiIr6+vowbN44HH3yQDRs28Nprr7k8O3ToUMaNG0efPn0YPnw4FouFJUuW0LJlS2fSAxATE8PcuXPp1KkTd9xxB9988w0+Pj48/PDDDBw4kObNm3PNNdfw7bffsm7dOqpXr37OmPv27cvLL79Mv379eOWVVzhy5AgPP/wwd999t3O9VufOnRk2bBhTp06lRo0ajBkzhoyMjFL92dx55508//zzDBw4kOHDh7Nnzx7eeeedUrVRVkq2RERERETOIjzIlwCzice+XXNJ+gswmwgPKv35U/fccw+5ubm0bNkSk8nEo48+yqBBgzAYDEycOJHnnnuO999/n6ZNm/LOO+9w8803O5+NjIxk7ty5PPXUU3To0AGTyUTjxo1p27ZtiX7i4uKYO3cuHTt2pG/fvkyePJm+ffuya9cunnzySfLy8rjtttvo378/y5YtO2fMgYGBzJgxg0cffZQWLVoQGBhI7969GTNmjLPOfffdx9q1a7nnnnvw8fHh8ccfp1OnTqX6swkODubXX3/lwQcfpEmTJtSvX58333yT3r17l6qdsjA4Tp8EKSVYrVYsFguZmZmEhoZ6NZbM3EIajZgJQJe6MXzav4VX4xERERG5HOTl5bF7926SkpJcNlIAOJiRS3pOwSWJIzzIl8ph519DVd5dd911xMXF8dVXX3k7lDI51+ehNLmBRrZERERERM6hcljAZZEAecrx48eZMGEC3bt3x2Qy8fXXXzN79mxmzZrl7dC8TsmWiIiIiIiUmcFgYNq0aYwaNYq8vDzq1KnDDz/8QNeuXb0dmtcp2RIRERERkTILCAhg9uzZ3g6jXNLW7yIiIiIiIh6gZEtERERE5ATtHSfgvs+Bki0RERERueKZzWageLMHkYKC4t0nTSbTRbXj8TVb+fn5+Pn5ebobEREREZEyM5lMhIWFkZqaChSfAWUwGLwclXiD3W7nyJEjBAYG4uNzcemS25Ot33//nW+++YaFCxeyf/9+7HY7QUFBNGnShG7dunHvvfcSHx/vtv5sNhuvvPIK//vf/0hOTiY+Pp7+/fvzwgsvOP+COBwOXn75Zf773/+SkZFB27Zt+eijj6hVq5bb4hARERGRii0uLg7AmXDJlctoNJKYmHjRCbfbkq0ff/yRZ555hqysLHr06MEzzzxDfHw8AQEBpKWlsWHDBmbPns1rr71G//79ee2114iOjr7oft98800++ugjvvjiCxo0aMCKFSu49957sVgsPPLIIwC89dZbvP/++3zxxRckJSXx4osv0r17dzZt2lTikDIRERERuTIZDAYqVapETEwMhYWF3g5HvMjX1xej8eJXXBkcblr91aZNG1544QVuuOGGcwZ28OBBxo0bR2xsLI8//vhF99uzZ09iY2P59NNPnWW9e/cmICCA//3vfzgcDuLj43niiSd48sknAcjMzCQ2NpaJEyfSp0+f8/ZRmlOiPS0zt5BGI2YC0KVuDJ/2b+HVeEREREREriSlyQ3cNrK1ePHiC6pXuXJl3njjDXd1yzXXXMPHH3/Mtm3bqF27NmvXruXPP/9kzJgxAOzevZvk5GSXQ9UsFgutWrVi8eLFZ0y28vPzyc/Pd15brVa3xSsiIiIiIleGCn+o8bPPPovVaqVu3bqYTCZsNhujRo2ib9++ACQnJwMQGxvr8lxsbKzz3ulGjx7NiBEjPBu4iIiIiIhc1tyWbA0bNuyC654cdXKH7777jkmTJjF58mQaNGjAmjVreOyxx4iPj6dfv35lanP48OEu78dqtZKQkOCukEVERERE5ArgtmRr9erVLterVq2iqKiIOnXqALBt2zZMJhPNmjVzV5cAPPXUUzz77LPO6YBXX301e/fuZfTo0fTr18+5q0xKSgqVKlVyPpeSkkLjxo3P2Kafn5+2qxcRERERkYvitmRr3rx5ztdjxowhJCSEL774gvDwcADS09O59957ad++vbu6BIoPnjt9Qw6TyYTdbgcgKSmJuLg45syZ40yurFYrS5cuZfDgwW6NRURERERE5CSPrNl69913mTlzpjPRAggPD2fkyJF069aNJ554wm193XTTTYwaNYrExEQaNGjA6tWrGTNmDPfddx9QvIXnY489xsiRI6lVq5Zz6/f4+Hh69erltjhERERERERO5ZFky2q1cuTIkRLlR44cISsry619jRs3jhdffJGHHnqI1NRU4uPjeeCBB3jppZecdZ5++mlycnIYNGgQGRkZtGvXjunTp+uMLRERERER8Ri3nbN1qnvuuYeFCxfy7rvv0rJlSwCWLl3KU089Rfv27fniiy/c3aVH6ZwtEREREREBL52zdaoJEybw5JNPcueddzpP3/bx8WHAgAG8/fbbnuhSRERERESkXPFIshUYGMiHH37I22+/zc6dOwGoUaMGQUFBnuhORERERESk3DGev0rZHT58mMOHD1OrVi2CgoLwwIxFERERERGRcskjydaxY8fo0qULtWvXpkePHhw+fBiAAQMGuHUnQhERERERkfLKI8nW448/jtlsZt++fQQGBjrLb7/9dqZPn+6JLkVERERERMoVj6zZmjlzJjNmzKBKlSou5bVq1WLv3r2e6FJERERERKRc8cjIVk5OjsuI1klpaWn4+fl5oksREREREZFyxSPJVvv27fnyyy+d1waDAbvdzltvvUWnTp080aWIiIiIiEi54pFphG+99RZdunRhxYoVFBQU8PTTT7Nx40bS0tJYtGiRJ7oUEREREREpVzwysnXVVVexbds22rVrxy233EJOTg633norq1evpkaNGp7oUkREREREpFzxyMgWgMVi4fnnn/dU8yIiIiIiIuWa25KtdevWXXDdhg0buqtbERERERGRcsltyVbjxo0xGAw4HI5z1jMYDNhsNnd1KyIiIiIiUi65LdnavXu3u5oSERERERGp8NyWbFWtWtVdTYmIiIiIiFR4HtsgA2DTpk3s27ePgoICl/Kbb77Zk92KiIiIiIh4nUeSrV27dvGPf/yD9evXu6zjMhgMAFqzJSIiIiIilz2PnLP16KOPkpSURGpqKoGBgWzcuJEFCxbQvHlz/vjjD090KSIiIiIiUq54ZGRr8eLFzJ07l6ioKIxGI0ajkXbt2jF69GgeeeQRVq9e7YluRUREREREyg2PjGzZbDZCQkIAiIqK4tChQ0DxJhpbt271RJciIiIiIiLlikdGtq666irWrl1LUlISrVq14q233sLX15ePP/6Y6tWre6JLERERERGRcsUjydYLL7xATk4OAK+++io9e/akffv2REZG8u2333qiSxERERERkXLFI8lW9+7dna9r1qzJli1bSEtLIzw83LkjoYiIiIiIyOXMI2u2MjMzSUtLcymLiIggPT0dq9XqiS5FRERERETKFY8kW3369OGbb74pUf7dd9/Rp08fT3QpIiIiIiJSrngk2Vq6dCmdOnUqUd6xY0eWLl3q9v4OHjzIXXfdRWRkJAEBAVx99dWsWLHCed/hcPDSSy9RqVIlAgIC6Nq1K9u3b3d7HCIiIiIiIid5JNnKz8+nqKioRHlhYSG5ublu7Ss9PZ22bdtiNpv5/fff2bRpE++++y7h4eHOOm+99Rbvv/8+EyZMYOnSpQQFBdG9e3fy8vLcGouIiIiIiMhJHtkgo2XLlnz88ceMGzfOpXzChAk0a9bMrX29+eabJCQk8PnnnzvLkpKSnK8dDgfvvfceL7zwArfccgsAX375JbGxsfz000+a1igiIiIiIh7hkWRr5MiRdO3albVr19KlSxcA5syZw/Lly5k5c6Zb+/rll1/o3r07//rXv5g/fz6VK1fmoYceYuDAgQDs3r2b5ORkunbt6nzGYrHQqlUrFi9efMZkKz8/n/z8fOe1NvUQEREREZHS8sg0wrZt27J48WISEhL47rvv+PXXX6lZsybr1q2jffv2bu1r165dfPTRR9SqVYsZM2YwePBgHnnkEb744gsAkpOTAYiNjXV5LjY21nnvdKNHj8ZisTi/EhIS3BqziIiIiIhc/jwysgXQuHFjJk2a5Knmnex2O82bN+f1118HoEmTJmzYsIEJEybQr1+/MrU5fPhwhg0b5ry2Wq1KuEREREREpFQ8MrK1atUq1q9f77z++eef6dWrF8899xwFBQVu7atSpUrUr1/fpaxevXrs27cPgLi4OABSUlJc6qSkpDjvnc7Pz4/Q0FCXLxERERERkdLwSLL1wAMPsG3bNqB4mt/tt99OYGAgU6ZM4emnn3ZrX23btmXr1q0uZdu2baNq1apA8WYZcXFxzJkzx3nfarWydOlS2rRp49ZYRERERERETvJIsrVt2zYaN24MwJQpU+jQoQOTJ09m4sSJ/PDDD27t6/HHH2fJkiW8/vrr7Nixg8mTJ/Pxxx8zZMgQAAwGA4899hgjR47kl19+Yf369dxzzz3Ex8fTq1cvt8YiIiIiIiJykkfWbDkcDux2OwCzZ8+mZ8+eACQkJHD06FG39tWiRQt+/PFHhg8fzquvvkpSUhLvvfceffv2ddZ5+umnycnJYdCgQWRkZNCuXTumT5+Ov7+/W2MRERERERE5yeBwOBzubrRz584kJCTQtWtXBgwYwKZNm6hZsybz58+nX79+7Nmzx91depTVasVisZCZmen19VuZuYU0GlG8fX6XujF82r+FV+MREREREbmSlCY38Mg0wvfee49Vq1YxdOhQnn/+eWrWrAnA999/zzXXXOOJLkVERERERMoVj0wjbNiwoctuhCe9/fbbmEwmT3QpIiIiIiJSrnhkZAsgIyODTz75hOHDh5OWlgbApk2bSE1N9VSXIiIiIiIi5YZHRrbWrVtHly5dCAsLY8+ePQwcOJCIiAj+7//+j3379vHll196olsREREREZFywyMjW8OGDePee+9l+/btLjv+9ejRgwULFniiSxERERERkXLFI8nW8uXLeeCBB0qUV65cmeTkZE90KSIiIiIiUq54JNny8/PDarWWKN+2bRvR0dGe6FJERERERKRc8UiydfPNN/Pqq69SWFgIgMFgYN++fTzzzDP07t3bE12KiIiIiIiUKx5Jtt59912ys7OJiYkhNzeXDh06ULNmTUJCQhg1apQnuhQRERERESlXPLIbocViYdasWSxatIi1a9eSnZ1N06ZN6dq1qye6ExERERERKXfcnmwVFhYSEBDAmjVraNu2LW3btnV3FyIiIiIiIuWe26cRms1mEhMTsdls7m5aRERERESkwvDImq3nn3+e5557jrS0NE80LyIiIiIiUu55ZM3WBx98wI4dO4iPj6dq1aoEBQW53F+1apUnuhURERERESk3PJJs9erVyxPNioiIiIiIVBgeSbZefvllTzQrIiIiIiJSYbhtzZbD4XBXUyIiIiIiIhWe25KtBg0a8M0331BQUHDOetu3b2fw4MG88cYb7upaRERERESk3HHbNMJx48bxzDPP8NBDD3HdddfRvHlz4uPj8ff3Jz09nU2bNvHnn3+yceNGhg4dyuDBg93VtYiIiIiISLnjtmSrS5curFixgj///JNvv/2WSZMmsXfvXnJzc4mKiqJJkybcc8899O3bl/DwcHd1KyIiIiIiUi65fYOMdu3a0a5dO3c3KyIiIiIiUqF45FBjERERERGRK52SLREREREREQ9QsiUiIiIiIuIBSrZEREREREQ8QMmWiIiIiIiIB3gk2Vq1ahXr1693Xv/888/06tWL55577ryHHl+sN954A4PBwGOPPeYsy8vLY8iQIURGRhIcHEzv3r1JSUnxaBwiIiIiInJl80iy9cADD7Bt2zYAdu3aRZ8+fQgMDGTKlCk8/fTTnugSgOXLl/Of//yHhg0bupQ//vjj/Prrr0yZMoX58+dz6NAhbr31Vo/FISIiIiIi4pFka9u2bTRu3BiAKVOmcO211zJ58mQmTpzIDz/84Ikuyc7Opm/fvvz3v/91OTQ5MzOTTz/9lDFjxtC5c2eaNWvG559/zl9//cWSJUvO2FZ+fj5Wq9XlS0REREREpDQ8kmw5HA7sdjsAs2fPpkePHgAkJCRw9OhRT3TJkCFDuPHGG+natatL+cqVKyksLHQpr1u3LomJiSxevPiMbY0ePRqLxeL8SkhI8EjMIiIiIiJy+fJIstW8eXNGjhzJV199xfz587nxxhsB2L17N7GxsW7v75tvvmHVqlWMHj26xL3k5GR8fX0JCwtzKY+NjSU5OfmM7Q0fPpzMzEzn1/79+90es4iIiIiIXN58PNHo2LFjueuuu/jpp594/vnnqVmzJgDff/8911xzjVv72r9/P48++iizZs3C39/fLW36+fnh5+fnlrZEREREROTK5JFkq1GjRi67EZ709ttv4+Pj3i5XrlxJamoqTZs2dZbZbDYWLFjABx98wIwZMygoKCAjI8NldCslJYW4uDi3xiIiIiIiInKSR6YRVq9enWPHjpUoz8vLo3bt2m7tq0uXLqxfv541a9Y4v5o3b07fvn2dr81mM3PmzHE+s3XrVvbt20ebNm3cGouIiIiIiMhJHhnZ2rNnDzabrUR5fn4+Bw4ccGtfISEhXHXVVS5lQUFBREZGOssHDBjAsGHDiIiIIDQ0lIcffpg2bdrQunVrt8YiIiIiIiJykluTrV9++cX5esaMGVgsFue1zWZjzpw5JCUlubPLCzJ27FiMRiO9e/cmPz+f7t278+GHH17yOERERERE5MphcDgcDnc1ZjQWz0o0GAyc3qzZbKZatWq8++679OzZ011dXhJWqxWLxUJmZiahoaFejSUzt5BGI2YC0KVuDJ/2b+HVeEREREREriSlyQ3cOrJ18mytpKQkli9fTlRUlDubFxERERERqTA8smZr9+7dnmhWRERERESkwvBIsgUwZ84c5syZQ2pqqnPE66TPPvvMU92KiIiIiIiUCx5JtkaMGMGrr75K8+bNqVSpEgaDwRPdiIiIiIiIlFseSbYmTJjAxIkTufvuuz3RvIiIiIiISLnnkUONCwoKuOaaazzRtIiIiIiISIXgkWTr/vvvZ/LkyZ5oWkREREREpELwyDTCvLw8Pv74Y2bPnk3Dhg0xm80u98eMGeOJbkVERERERMoNjyRb69ato3HjxgBs2LDB5Z42yxARERERkSuBR5KtefPmeaJZERERERGRCsMja7ZERERERESudB4Z2erUqdM5pwvOnTvXE92KiIiIiIiUGx5Jtk6u1zqpsLCQNWvWsGHDBvr16+eJLkVERERERMoVjyRbY8eOPWP5K6+8QnZ2tie6FBERERERKVcu6Zqtu+66i88+++xSdnlZO5SZ5+0QRERERETkLC5psrV48WL8/f0vZZeXrUBfE+k5Bd4OQ0REREREzsIj0whvvfVWl2uHw8Hhw4dZsWIFL774oie6vOJUsviTX2T3dhgiIiIiInIWHkm2LBaLy7XRaKROnTq8+uqrdOvWzRNdioiIiIiIlCseSbY+//xzTzQrIiIiIiJSYXgk2Tpp5cqVbN68GYAGDRrQpEkTT3YnIiIiIiJSbngk2UpNTaVPnz788ccfhIWFAZCRkUGnTp345ptviI6O9kS3IiIiIiIi5YZHdiN8+OGHycrKYuPGjaSlpZGWlsaGDRuwWq088sgjnuhSRERERESkXPHIyNb06dOZPXs29erVc5bVr1+f8ePHa4MMERERERG5InhkZMtut2M2m0uUm81m7HZtVy4iIiIiIpc/jyRbnTt35tFHH+XQoUPOsoMHD/L444/TpUsXT3QpIiIiIiJSrngk2frggw+wWq1Uq1aNGjVqUKNGDZKSkrBarYwbN86tfY0ePZoWLVoQEhJCTEwMvXr1YuvWrS518vLyGDJkCJGRkQQHB9O7d29SUlLcGoeIiIiIiMipPLJmKyEhgVWrVjF79my2bNkCQL169ejatavb+5o/fz5DhgyhRYsWFBUV8dxzz9GtWzc2bdpEUFAQAI8//jhTp05lypQpWCwWhg4dyq233sqiRYvcHo+IiIiIiAiAweFwOLwdhDsdOXKEmJgY5s+fz7XXXktmZibR0dFMnjyZf/7znwBs2bKFevXqsXjxYlq3bn3eNq1WKxaLhczMTEJDQz39Fs4pM7eQRiNmUiM6iPwiO38+09mr8YiIiIiIXElKkxu4dRrh3LlzqV+/PlartcS9zMxMGjRowMKFC93Z5Rn7AYiIiACKD1YuLCx0GVWrW7cuiYmJLF68+Ixt5OfnY7VaXb5ERERERERKw63J1nvvvcfAgQPPmOFZLBYeeOABxowZ484uXdjtdh577DHatm3LVVddBUBycjK+vr7Ow5VPio2NJTk5+YztjB49GovF4vxKSEjwWMwiIiIiInJ5cmuytXbtWq6//vqz3u/WrRsrV650Z5cuhgwZwoYNG/jmm28uqp3hw4eTmZnp/Nq/f7+bIhQRERERkSuFWzfISElJOeP5Ws7OfHw4cuSIO7t0Gjp0KL/99hsLFiygSpUqzvK4uDgKCgrIyMhwGd1KSUkhLi7ujG35+fnh5+fnkThFREREROTK4NaRrcqVK7Nhw4az3l+3bh2VKlVyZ5c4HA6GDh3Kjz/+yNy5c0lKSnK536xZM8xmM3PmzHGWbd26lX379tGmTRu3xiIiIiIiInKSW0e2evTowYsvvsj111+Pv7+/y73c3Fxefvllevbs6c4uGTJkCJMnT+bnn38mJCTEuQ7LYrEQEBCAxWJhwIABDBs2jIiICEJDQ3n44Ydp06bNBe1EKCIiIiIiUhZu3fo9JSWFpk2bYjKZGDp0KHXq1AGKt1ofP348NpuNVatWERsb664uMRgMZyz//PPP6d+/P1B8qPETTzzB119/TX5+Pt27d+fDDz886zTC02nrdxERERERgdLlBm4d2YqNjeWvv/5i8ODBDB8+nJN5nMFgoHv37owfP96tiRbAheSK/v7+jB8/nvHjx7u1bxERERERkbNxa7IFULVqVaZNm0Z6ejo7duzA4XBQq1YtwsPD3d2ViIiIiIhIueX2ZOuk8PBwWrRo4anmRUREREREyjW37kYoIiIiIiIixZRsiYiIiIiIeICSLREREREREQ9QsiUiIiIiIuIBSrZEREREREQ8QMmWiIiIiIiIByjZEhERERER8QAlWyIiIiIiIh6gZEtERERERMQDfLwdgFye9h07zoGM42w+nEWD+FBSs/KpER1EJUsARgOEBfp6O0QREREREY9SsiVusemQlR7vL7zg+hFBvrx6SwOqRgSRGBEIQLC/D9tSsqgcHkCov9lToYqIiIiIXBJKtuSi5BXamLUphYe/Xl2q59JyChg6+fzPrHihK1HBfmUNT0RERETEa5RsSZnlFtgY9NUKFm4/6lL+5X0taVjFQligL3a7A6PRAIDD4eCLv/bwyq+baFktgmV70s7bR/ORswGoGhnI1wNbEx8W4P43IiIiIiLiAUq2pMzu/nQpK/amA/Bm76vpWi+WEH8zvj5/77tyMtECMBgM9G+bRP+2Sc4ym93B2gMZHM+30ap6BLmFNowGA/4+Rkb/voVP/9wNwN5jx7nmjbn0bZVIxzoxJEUFUTMm+BK9UxERERGR0lOyJWUybf1hZ6L19cDWtKkRWaZ2TEYDTRPDnddm09+J2os96/Ncj3r0/ugv1uzPAGDS0n1MWroPgEYJYWw5bKXAZue7B9qwdn8G78/ZzqJnOxOiNV8iIiIi4mVKtiqwA+m5FNns+Jgu7Q7+KdY8Hpq0CoBP+zUvc6J1IUxGAz8Nacu3y/fx27rDLlMW155IwAD+NWGx8/XVr8wEwGiA7wdfQ1p2AS2rR2A2GjmYkUtSVBB5hTaC/PTxFxERERHP0U+bFVS1yCB2Hskhv+jSJ1td350PwNv/bEiXerGXpM/bWyRye4tE5/WqfenY7A5G/raJgxl5gIOj2QUuz9gdcOuHf5237X80qcytTSsTHeJHtcgg/M0md4cvIiIiIlcgJVsVlJ/ZO+dRj/h1I1n5RSRFBfGv5gleiQFwTj38eWi7EvfyCm0s35PGDysPcDgzj6W7z70Rx4+rD/Lj6oMuZZ/3b0GIvw/NqoZjMBjO8qSIiIiIyNkp2ZILNn7eDj5ftAeAuU908G4w5+BvNtG+VjTta0U7y3Lyi/A3m7DZHdgdDrLyitiSbCU5M4+F24/yy9pDLm3cO3H5GduOt/jzz+YJTF66l/rxFqpHBXFL43i+WbafxolhXFs7mrTsAiqHB7D+YCZNE8MIMJv4Ze0hLAFmgv18MBkNpOUUsGjHUb5YvLdEHwPaJfFIl1r4mowE+JooKLK7bDoiIiIiIhWDweFwOLwdRHlntVqxWCxkZmYSGhrq1VgycwtpNGImPa6OY9r6ZDaO6H5J1h7Z7A5qPDcNgAVPdSIxMtDjfXpDfpGNn1cf4ukf1nk7FBeNE8K4qVE8LaqFs+5AJg3iQ8kvslM5LIBAXxN+ZhNmk4HM3EKMBgMh/j7k5NsIDzQ7R+ZO/lXXSJ2IiIhI2ZUmN9DIllyQhq/MAIq3eL9cEy0APx8Tt7VI4LYWJadIbkvJIiuviJ2p2RgM8NT36wjyNdG0aniJs8ZO1aiKhYMZeRzNzncpT4wIZOK9LUg/XsjR7Hz2px1n/Lwd1IwJpsDmcNkAZM3+DOeOjBfL32wkItCXplXDCfL1oXm1cHpcXYmNh6w0iA+lyO7AEmAmr9CGwwG+PkZMRgMFRXZMRgMmo5I18a5Tz++D4l8G6XMpIiLlkZItOa/XfttEToGNqyqHumxScaWpHRsCQLOqxevFTl+z5nA4SM3KJzbUv8x93N++usv1oYxc9hzLYfW+DPYczWHZnjT2HjtOJYs/hTY70SH+GIBNh63OZywBZjJzC8/aR16hnUOZeRxadxiAb1fs56nv3TuSZzYZKLQVj6RFBPkSE+LHluQs5/1qkYE83LkWT0xZS+WwAPx8jOw6msOdrRK5rn4s6TkFxIT4cyD9OIU2O6EBZurEhRAX6k+BzY7JYGDtgQyKbA6qhAeyPTWLqpFB2B0OwgLMHM0uICbEj7BAM9+t2E9iRBBRwb5k5xdRyRLAziPZBPn54OdjJMTfhxA/M9a8QuLDAii02Qn0NRHib2ZLspVaMSHkF9kIMJtwOFzPjhPPs9sdTNtwmG+X72dbShYp1vxz1g8wm/jvPc1JsebRu1mVSxSliIjImWka4QW4kqcRvjNjKx/M2wHAnjdu9Fg/4nl2e/FatfwiGztSs5m9OZU9x3JYtS+djONnT87k7Px8jMU7ghoNWALM5BfZaVszkkqWAK6qbOFYdnHyPXtzCgFmE1UjAymwOQj0NeFjNJBfZKdNjUh+X3+Y3s2qsCM1mxB/MyH+PviajESH+J2oa8TmcLBqbzrX1IjEZDTgcECBze6ye2ZBkR2zyVAhp4ra7A4KiuxsTrby2DdrSD9eQFZekdva/6x/czrXvTS7p4qIyOVN0wjPYvz48bz99tskJyfTqFEjxo0bR8uWLb0d1kXxVKaclVfoPK8KYMeoGzzUk1wqRqMBS6AZMBMT6s81NaNK3cbJ380U2Oz4+RT/kG/NK8Tfx4Svj5HcAhuLdhzlqsoW7A4HEUG+fDhvBw0qW6gbF8LaA5l0qB3N7E0p7DmWwzU1oli1L52svCKuqRHJ9ysPkF9ko9DmwNdkJCUrj/BAX4L8fEjLyadZYjjvz93BVZVDsQSYGdi+Ov0/L97MpGZMMB1rR/Pj6oMcyylg0LXVWbj9KMcLimhbM4oQPx9+XXuIGjHB7E87TkyoPwfTc2leLZyf1xwiJsSP1Kxzj5qcLr/IDkCR3cGxnOKjB2ZsTCn1nyvAfxfuLtNzZxPka6JV9Uh2pGZzLDufzvViWbU3nYMZuXStF0tYoBmzycDvG5IJCzDT/5pq/LruMG2qRxLga+JYdgG5hUWEBphZvTeDO1slklNQxPLdadSIDubTRbvpVj+WOEsAi3cepUp4IKlZeRzJymdbSjYAVSMDqRMbgtlkZOeRbOwOB0ey8kkvY3I/78mOhPj7EBXsV+KeNa8Qs9FIgc3OuDnb+eRP1z/P+yaucLm+q3UiCeGB9LumGn4+xgqZoIqISPl3xYxsffvtt9xzzz1MmDCBVq1a8d577zFlyhS2bt1KTEzMOZ8tjyNb97SpypeL9/LzkLY0SghzW/sH0o/z8NerWb0vw1m2e3QP/SAiVzSbvfjbZKHNjs3uwMdkYM/R41SNDCxOMHcepWHlMAyG4sRrz7EcNh7MJMDXh1oxwQT5+XAsO5/s/CJC/c3sOppNzZhg9hw9zroDGeQU2IgL9Wf30RyC/EzM2JhCoK+JznVjSD9ewKIdx4DiKXJBfiYignxJyylwni1XNTKQvceOe+3Px10CzCYGtEuib+tE4kL93fJ9JyuvkJ9WH2TGxhT+3HH2tZUnWQLMNEoIIzLIF5PRQLXIQJpWDSe3wEb9+FCOZRcQEeRLVLCfcw2jw+HQ90gRkStIaXKDKybZatWqFS1atOCDDz4AwG63k5CQwMMPP8yzzz57zmfLY7L1eNfajJ29jfvaJlElPIB6lUIpsNmJCPQlPMhMaIAZH6OBQF8fHA4HdgfObc+PF9g4kpXPsex8tqdms/fYcTYczGTZHtfzqO5vl8QLPet76Z2KSFnZ7A4MFI9m5hbYKCiy42c2Oo9AyCko4mB6LlC8KczR7Hx8TAZqRgez80gOWXmF+PoYWbUvg4TwABwUJ0I1Y4JJzymgadVwNp9YJ2izOwjwNbHv2HHa144mK6+QjOOFbDiYSb1KoRzOzCMmxA9rXiHNqoY7p0ACGA0GrxxrkJZTQE5+Ed+vPMAnC3eRU2BzS7uVwwI4mFH859q+VhSRQb4cysxj2Sln/cWF+pNszaN2bDCxof7sOZZDoLl4kkluoY2qkYFYcwupFRvCxkNWgv2Kj3/Iyi8ixM+HtQcyCQs007luDAVFdo5lF7B4V3EyHhZoJsjXh+gQP9bsz6BFtXBSrPlEBPlSyeJP1cgg/tp5lEoWfwLMJgrtDnYfyaFBfCi5hTaqhAdizSvkUEYu7WpGkVtgw+ZwkJlbyJr9GdSMDsbfbKJyeAABZhNmU/Gax2PZ+QT4mogI8sNmd7AtJQujAeLDAgAwGOBQRh5VwgOICPIlr9BOek4Bwf4+HM3O52hWPs2rRWA2GVm04ygJEQEU2oo3PQkPLP73rNDmYGdqNr4+RowGA4czc6l/4t89Px8T+UU2Vu/LoGpkINUig5yjzkYDFNocFNrsrNqXTlSwHz4mA3Gh/uw5moOf2UR8mD+bDlmpG1f873t2fhGHMnMxGQxUjQzkeIGNQpud2FB/An19WLb7GLViQkix5hHga8JgMFAjOsg5ghse6Euwvw8H0o5Tt1IoNruDIpuD/enHqR0bTKi/GR+TEZPBwLGcfOyO4jjtDgcOR/GMFbPJ6JxJ4KB4yrLRYMBoKE7wrXmF2OwOQv3N5Bed+DvuYyI8yOycWpxbYCOv0Ob8O1Zkd+DnYyzuwwEO/u7P4XCc+G9xj3+XF/8djw4p/n97MCOX4BPrXX1MhhP/fw2kZRdgMho4kp1PJUvxWmKDwUD68QIMQGpWPnaHo/hzZ7NTZHeQk1/Eyr3pXF3ZQvrxQpbvSSPE3we7Hcw+RhZuP0LfVonsTM0hNMCHnAIbdWND2H00h8aJYWTnF7H36HGqRgXicECIvw+1YkKc/88DfE34moqnYEcE+mL2MWA2GTGbjPibjRgwYDDgjNVw4rN68rPjazJqja6clZKt0xQUFBAYGMj3339Pr169nOX9+vUjIyODn3/+2aV+fn4++fl/TyfKzMwkMTGR/fv3ez3Zysor5J8T/uL5G+vz6NdrKLTZ3dp+YkQAH97VjMTwQH2TEZErUl6hjfScApKtuRQWQWZuActP/DLqWHYB7WpFcSA9lwXbj5AUGcSsLam0rxmJr8nIH9uOEBfqT0SQLylZeWQeLyQ7/+9k7qr4UDYcsp6tawJ9jRwvKP6+HnEi0TiZPJ3cdMZggKYJ4ThwsCXZ6qx/PqH+PlgvcB2cr4+RgiL3/vsiUtEE+5s4309ChvPWKP47eyEupNqFjKJfWDsXUOkCW7uQttwRU6CvD3e2SuTOVlUvoDXPslqtJCQkkJGRgcViOWfdK2LN1tGjR7HZbMTGui6Ojo2NZcuWLSXqjx49mhEjRpQoT0gouR24t9z0smfa3Q80eskzbYuIXA4+OUPZrlNebz/Hs/tL0c+56u4rRTsiIpeLhcBgbwdxiqysLCVbZTF8+HCGDRvmvLbb7aSlpREZGVku5uWfzKbLw0ibyPno8yoViT6vUpHo8yoVyeX0eXU4HGRlZREfH3/euldEsvX/7d13eFRF28fx726S3RRIAoEk9CK9IzWiAoKEIkWw8aKAYg9NrCiiooKCStEA+qiAPgKPgFioQigihE7ohCIQWhIgpAGpe94/IgtLQs+ShPw+17WXe2ZmZ+6THEPuzDkzJUqUwMXFhZgYx1XCYmJiCAwMzNbearVitTquduXr6+vMEG+Kt7d3gb9YpfDQ9SoFia5XKUh0vUpBcqdcr9ea0brg9j+ZnAcsFguNGjUiLCzMXmaz2QgLCyMoKCgPIxMRERERkTtVoZjZAhgyZAh9+vShcePGNG3alHHjxnH27FmefvrpvA5NRERERETuQIUm2Xr88cc5efIkw4cPJzo6mgYNGrBo0aJsi2YUBFarlffeey/brY4i+ZGuVylIdL1KQaLrVQqSwnq9Foql30VERERERG63QvHMloiIiIiIyO2mZEtERERERMQJlGyJiIiIiIg4gZItERERERERJ1CyJSIiIiIi4gRKtkRERERERJxAyZaIiIiIiIgTKNkSERERERFxAiVbIiIiIiIiTqBkS0RERERExAmUbImIiIiIiDiBki0REREREREnULIlIiIiIiLiBEq2REREREREnEDJloiIiIiIiBMo2RIREREREXECJVsiIiIiIiJOoGRLRERERETECZRsiYiIiIiIOIGSLRERERERESdQsiUiIiIiIuIESrZEREREREScQMmWiIiIiIiIEyjZEhERERERcQIlWyIiIiIiIk6Q75OtY8eO8eSTT+Ln54eHhwd169Zl48aN9nrDMBg+fDilSpXCw8ODtm3bsm/fPoc+4uLi6NWrF97e3vj6+tKvXz+Sk5Nv96mIiIiIiEghkq+TrTNnztCiRQvc3NxYuHAhu3bt4vPPP6dYsWL2NqNHj2bChAlMnjyZdevW4eXlRXBwMCkpKfY2vXr1YufOnSxZsoR58+bx119/8fzzz+fFKYmIiIiISCFhMgzDyOsgruStt95i9erVrFq1Ksd6wzAoXbo0r776Kq+99hoACQkJBAQEMHXqVJ544gl2795NrVq12LBhA40bNwZg0aJFdOzYkaNHj1K6dOnbdj4iIiIiIlJ4uOZ1AFfz+++/ExwczKOPPsrKlSspU6YML7/8Ms899xwABw8eJDo6mrZt29o/4+PjQ7NmzQgPD+eJJ54gPDwcX19fe6IF0LZtW8xmM+vWrePhhx/ONm5qaiqpqan2Y5vNRlxcHH5+fphMJieesYiIiIiI5GeGYZCUlETp0qUxm69+o2C+Trb++ecfJk2axJAhQ3j77bfZsGEDAwcOxGKx0KdPH6KjowEICAhw+FxAQIC9Ljo6Gn9/f4d6V1dXihcvbm9zuVGjRvHBBx844YxEREREROROcOTIEcqWLXvVNvk62bLZbDRu3JiRI0cC0LBhQ3bs2MHkyZPp06eP08YdOnQoQ4YMsR8nJCRQvnx5jhw5gre3t9PGvR4J59PxGVcJgHGNlzD4wZp5Go+IiIiISGGSmJhIuXLlKFq06DXb5utkq1SpUtSqVcuhrGbNmsyZMweAwMBAAGJiYihVqpS9TUxMDA0aNLC3iY2NdegjIyODuLg4++cvZ7VasVqt2cq9vb3zPNky3NLxtmbdyujuWSTP4xERERERKYyu5/GifL0aYYsWLYiMjHQo27t3LxUqVACgUqVKBAYGEhYWZq9PTExk3bp1BAUFARAUFER8fDybNm2yt1m2bBk2m41mzZrdhrMQEREREZHCKF/PbL3yyivcc889jBw5kscee4z169fzzTff8M033wBZ2eTgwYP56KOPqFq1KpUqVeLdd9+ldOnSdOvWDciaCWvfvj3PPfcckydPJj09nf79+/PEE09oJUIREREREXGafJ1sNWnShLlz5zJ06FBGjBhBpUqVGDduHL169bK3eeONNzh79izPP/888fHx3HvvvSxatAh3d3d7m59++on+/fvTpk0bzGYzPXr0YMKECXlxSiIiIiIiUkjk63228ovExER8fHxISEjI82ekEs6n4/NpCQBGN1vDGx1q52k8IiIid4LMzEzS09PzOgwRyScsFssVl3W/kdwgX89sydW52PSPgoiIyK0wDIPo6Gji4+PzOhQRyUfMZjOVKlXCYrHcUj9KtgqgFMMNd1M6xVKigAZ5HY6IiEiBdSHR8vf3x9PT87pWFxORO5vNZuP48eOcOHGC8uXL39LPBSVbBdA5rLijWS0REZFbkZmZaU+0/Pz88jocEclHSpYsyfHjx8nIyMDNze2m+8nXS7+LiIiIOMuFZ7Q8PT3zOBIRyW8u3D6YmZl5S/0o2RIREZFCTbcOisjlcuvngpItERERERERJ9AzWyIiIiKXORZ/njNn027LWMW8LJTx9XBK3yaTiblz59KtWzen9C8iV6dkS0REROQSx+LP0/bzlZxPv7VnNa6Xh5sLS19tecMJV3R0NB9//DHz58/n2LFj+Pv706BBAwYPHkybNm1uOp6+ffsybdo0h7Lg4GAWLVp0032KFFZKtkREREQuceZsGufTMxn3eAOq+Bdx6lj7Y5MZ/L8IzpxNu6Fk69ChQ7Ro0QJfX1/GjBlD3bp1SU9PZ/HixYSEhLBnz55biqt9+/ZMmTLFfmy1Wm+pv5ykpaXd8h5GIvmdki0RERGRHFTxL0KdMj55HUaOXn75ZUwmE+vXr8fLy8teXrt2bZ555hmHtqdOneLhhx9m8eLFlClThs8//5wuXbpctX+r1UpgYGCuxvz+++/z66+/0r9/fz7++GMOHz6MzWZj0aJFfPTRR+zYsQMXFxeCgoIYP348d911FwCPPPIIgYGBfPXVVwAMHjyY8ePHs3v3bmrUqEFaWhrFihXjt99+o23btrkas8it0gIZIiIiIgVIXFwcixYtIiQkxCHRusDX19fh+IMPPuCxxx5j27ZtdOzYkV69ehEXF3fVMVasWIG/vz/Vq1fnpZde4vTp07kS+/79+5kzZw6//PILERERAJw9e5YhQ4awceNGwsLCMJvNPPzww9hsNgBatmzJihUr7H2sXLmSEiVK2Ms2bNhAeno699xzT67EKJKblGyJiIiIFCD79+/HMAxq1KhxXe379u1Lz549qVKlCiNHjiQ5OZn169dfsX379u354YcfCAsL49NPP2XlypV06NDhlvcbgqxbB3/44QcaNmxIvXr1AOjRowfdu3enSpUqNGjQgO+//57t27eza9cuAFq1asWuXbs4efIkZ86cYdeuXQwaNMiebK1YsYImTZpovzTJl5RsiYiIiBQghmHcUPsLSQ2Al5cX3t7exMbGXrH9E088QZcuXahbty7dunVj3rx5bNiwwWF26VI//fQTRYoUsb9WrVp1xb4rVKhAyZIlHcr27dtHz549qVy5Mt7e3lSsWBGAqKgoAOrUqUPx4sVZuXIlq1atomHDhjz00EOsXLkSyJrpatWq1XV8JURuPz2zJSIiIlKAVK1aFZPJdN2LYLi5uTkcm0wm+y1616Ny5cqUKFGC/fv357jKYZcuXWjWrJn9uEyZMlfsK6fbHjt37kyFChX4z3/+Q+nSpbHZbNSpU4e0tDR7vPfffz8rVqzAarXSqlUr6tWrR2pqKjt27GDNmjW89tpr130+IreTZrZERERECpDixYsTHBxMaGgoZ8+ezVYfHx+fq+MdPXqU06dPU6pUqRzrixYtSpUqVewvD4/rX1Xx9OnTREZGMmzYMNq0aUPNmjU5c+ZMtnYXnttasWIFrVq1wmw2c//99zNmzBhSU1Np0aLFTZ+fiDMp2RIREREpYEJDQ8nMzKRp06bMmTOHffv2sXv3biZMmEBQUNBN95ucnMzrr7/O2rVrOXToEGFhYXTt2pUqVaoQHByci2eQpVixYvj5+fHNN9+wf/9+li1bxpAhQ7K1u/Dc1s6dO7n33nvtZT/99BONGzfOccZMJD/QbYQFUHFTcl6HICIicsfbH+v8f29vdozKlSuzefNmPv74Y1599VVOnDhByZIladSoEZMmTbrpeFxcXNi2bRvTpk0jPj6e0qVL065dOz788EOn7LVlNpuZOXMmAwcOpE6dOlSvXp0JEyZkewarbt26+Pr6Uq1aNYoUydr7rFWrVmRmZup5LcnXTMaNPmVZCCUmJuLj40NCQgLe3t55GkvC+XR8Pi0BwPd1f+SZHlffJ0NERERylpKSwsGDB6lUqRLu7u728mPx52n7+UrOp9/66nvXw8PNhaWvtryhTY1FxLmu9PMBbiw30MyWiIiIyCXK+Hqw9NWWnDmbdlvGK+ZlUaIlcodSsiUiIiJymTK+HkqAROSW5esFMt5//31MJpPD69IN/FJSUggJCcHPz48iRYrQo0cPYmJiHPqIioqiU6dOeHp64u/vz+uvv05GRsbtPhURERERESlk8v3MVu3atVm6dKn92NX1YsivvPIK8+fPZ9asWfj4+NC/f3+6d+/O6tWrAcjMzKRTp04EBgayZs0aTpw4Qe/evXFzc2PkyJG3/VxERERERKTwyPfJlqurK4GBgdnKExIS+O6775g+fToPPPAAAFOmTKFmzZqsXbuW5s2b8+eff7Jr1y6WLl1KQEAADRo04MMPP+TNN9/k/fffx2Kx5Dhmamoqqamp9uPExETnnJyIiIiIiNyx8vVthAD79u2jdOnSVK5cmV69ehEVFQXApk2bSE9Pp23btva2NWrUoHz58oSHhwMQHh5O3bp1CQgIsLcJDg4mMTGRnTt3XnHMUaNG4ePjY3+VK1fOSWcnIiIiIiJ3qnydbDVr1oypU6eyaNEiJk2axMGDB7nvvvtISkoiOjoai8WCr6+vw2cCAgKIjo4GIDo62iHRulB/oe5Khg4dSkJCgv115MiR3D0xERERERG54+Xr2wg7dOhgf1+vXj2aNWtGhQoV+Pnnn/HwcN4KQVar1Skb94mIiIiISOGRr2e2Lndh5/D9+/cTGBhIWloa8fHxDm1iYmLsz3gFBgZmW53wwnFOz4GJiIiIiIjklgKVbCUnJ3PgwAFKlSpFo0aNcHNzIywszF4fGRlJVFQUQUFBAAQFBbF9+3ZiY2PtbZYsWYK3tze1atW67fGLiIhIARF/BI5H3J5XvPMeVzCZTPz6669O6/9GvP/++zRo0MCpY0ydOjXbIyZ3gooVKzJu3Din9d+qVSsGDx7stP7zqxUrVmAymbJN3uSmfH0b4WuvvUbnzp2pUKECx48f57333sPFxYWePXvi4+NDv379GDJkCMWLF8fb25sBAwYQFBRE8+bNAWjXrh21atXiqaeeYvTo0URHRzNs2DBCQkJ0m6CIiIjkLP4IhDaF9HO3Zzw3TwhZD743tiBXdHQ0H3/8MfPnz+fYsWP4+/vToEEDBg8eTJs2bW46nL59+zJt2jSHsuDgYBYtWnTTfcqdrVWrVjRo0MCpCeEFK1asoHXr1pw5c6ZAJNb5Otk6evQoPXv25PTp05QsWZJ7772XtWvXUrJkSQDGjh2L2WymR48epKamEhwczMSJE+2fd3FxYd68ebz00ksEBQXh5eVFnz59GDFiRF6dkoiIiOR3505nJVrd/wMlqjl3rFN74Zfnssa8gWTr0KFDtGjRAl9fX8aMGUPdunVJT09n8eLFhISEsGfPnlsKq3379kyZMsV+XJj/SJ2ZmYnJZMJsLlA3hOU7hmGQmZnpsGduYZCvr5qZM2dy/PhxUlNTOXr0KDNnzuSuu+6y17u7uxMaGkpcXBxnz57ll19+yfYsVoUKFViwYAHnzp3j5MmTfPbZZ4XumywiIiI3oUQ1KN3Aua+bTOZefvllTCYT69evp0ePHlSrVo3atWszZMgQ1q5d69D21KlTPPzww3h6elK1alV+//33a/ZvtVoJDAy0v4oVK3ZTcebk66+/ply5cnh6evLYY4+RkJBgr9uwYQMPPvggJUqUwMfHh5YtW7J582aHz8fHx/PCCy8QEBCAu7s7derUYd68eTmOdfLkSRo3bszDDz9s30P1999/p2rVqri7u9O6dWumTZvmcCvZhVsRf//9d2rVqoXVaiUqKoozZ87Qu3dvihUrhqenJx06dGDfvn32sXK6TXLcuHFUrFjRfty3b1+6devGZ599RqlSpfDz8yMkJIT09HR7m9jYWDp37oyHhweVKlXip59+uubXdMWKFTRt2hQvLy98fX1p0aIFhw8fdhjzUoMHD6ZVq1YOZRkZGfTv3x8fHx9KlCjBu+++i2EY9vqJEyfav24BAQE88sgj9v5XrlzJ+PHjMZlMmEwmDh06ZL9Fb+HChTRq1Air1crff//NgQMH6Nq1KwEBARQpUoQmTZqwdOlSh1hSU1N58803KVeuHFarlSpVqvDdd99x6NAhWrduDUCxYsUwmUz07dsXAJvNxqhRo6hUqRIeHh7Ur1+f2bNnO/S7YMECqlWrhoeHB61bt+bQoUPX/NreqnydbImIiIiIo7i4OBYtWkRISAheXl7Z6i+/teqDDz7gscceY9u2bXTs2JFevXoRFxd31TFWrFiBv78/1atX56WXXuL06dO5Evv+/fv5+eef+eOPP1i0aBFbtmzh5ZdfttcnJSXRp08f/v77b9auXUvVqlXp2LEjSUlJQNYv1B06dGD16tX897//ZdeuXXzyySe4uLhkG+vIkSPcd9991KlTh9mzZ2O1Wjl48CCPPPII3bp1Y+vWrbzwwgu888472T577tw5Pv30U7799lt27tyJv78/ffv2ZePGjfz++++Eh4djGAYdO3Z0SJSux/Llyzlw4ADLly9n2rRpTJ06lalTp9rr+/bty5EjR1i+fDmzZ89m4sSJDusPXC4jI4Nu3brRsmVLtm3bRnh4OM8//zwmk+mG4po2bRqurq6sX7+e8ePH88UXX/Dtt98CsHHjRgYOHMiIESOIjIxk0aJF3H///QCMHz+eoKAgnnvuOU6cOMGJEycc9qh96623+OSTT9i9ezf16tUjOTmZjh07EhYWxpYtW2jfvj2dO3e276UL0Lt3b2bMmMGECRPYvXs3X3/9NUWKFKFcuXLMmTMHyFqr4cSJE4wfPx7I2if3hx9+YPLkyezcuZNXXnmFJ598kpUrVwJZ10P37t3p3LkzERERPPvss7z11ls39DW6KYZcU0JCggEYCQkJeR2KEX8uzTDe8zaM97yN72b/ltfhiIiIFFjnz583du3aZZw/f96x4tiWrH9rj21xfhA3Mda6desMwPjll1+u2RYwhg0bZj9OTk42AGPhwoVX/MyMGTOM3377zdi2bZsxd+5co2bNmkaTJk2MjIyM644xJ++9957h4uJiHD161F62cOFCw2w2GydOnMjxM5mZmUbRokWNP/74wzAMw1i8eLFhNpuNyMjIHNtPmTLF8PHxMfbs2WOUK1fOGDhwoGGz2ez1b775plGnTh2Hz7zzzjsGYJw5c8beB2BERETY2+zdu9cAjNWrV9vLTp06ZXh4eBg///yz/fzq16/v0PfYsWONChUq2I/79OljVKhQweFr+eijjxqPP/64YRiGERkZaQDG+vXr7fW7d+82AGPs2LE5nvPp06cNwFixYkWO9X369DG6du3qUDZo0CCjZcuW9uOWLVsaNWvWzPa1qlmzpmEYhjFnzhzD29vbSExMzHGMli1bGoMGDXIoW758uQEYv/76a46fuVTt2rWNL7/80jCMi1+DJUuW5Nj2Qr8Xvl+GYRgpKSmGp6ensWbNGoe2/fr1M3r27GkYhmEMHTrUqFWrlkP9m2++ma2vC67488G4sdxAM1siIiIiBYhxya1d16NevXr2915eXnh7e191puSJJ56gS5cu1K1bl27dujFv3jw2bNjAihUrcmz/008/UaRIEftr1apVV+y7fPnylClTxn4cFBSEzWYjMjISyNqi57nnnqNq1ar4+Pjg7e1NcnKyfdYjIiKCsmXLUq3alW+/PH/+PPfddx/du3e339p2QWRkJE2aNHFo37Rp02x9WCwWh6/b7t27cXV1pVmzZvYyPz8/qlevzu7du68YS05q167tMBNXqlQp+/fjwjiNGjWy19eoUeOqC0EUL16cvn37EhwcTOfOnRk/fjwnTpy4oZgAmjdv7vC1CgoKYt++fWRmZvLggw9SoUIFKleuzFNPPcVPP/3EuXPXt4BM48aNHY6Tk5N57bXXqFmzJr6+vhQpUoTdu3c7fI9dXFxo2bLldce+f/9+zp07x4MPPuhwLf7www8cOHAAyPraXvr9u3COzqZkS0RERKQAqVq1KiaT6boXwXBzc3M4NplM2Gy26x6vcuXKlChRgv379+dY36VLFyIiIuyvy3+5vhF9+vQhIiKC8ePHs2bNGiIiIvDz8yMtLQ0ADw+Pa/ZhtVpp27Yt8+bN49ixYzcVh4eHxw3fhmc2m7MlwjndYnir34+cTJkyhfDwcO655x7+97//Ua1aNfuze9cb19UULVqUzZs3M2PGDEqVKsXw4cOpX7/+dS2Zfvmtrq+99hpz585l5MiRrFq1ioiICOrWrXtD3+PLJScnAzB//nyHa3HXrl3Zntu63ZRsiYiIiBQgxYsXJzg4mNDQUM6ePZutPrf3DDp69CinT5+mVKlSOdYXLVqUKlWq2F9X+2U5KiqK48eP24/Xrl2L2WymevXqAKxevZqBAwfSsWNHateujdVq5dSpU/b29erV4+jRo+zdu/eKY5jNZn788UcaNWpE69atHcarXr06GzdudGi/YcOGq38BgJo1a5KRkcG6devsZadPnyYyMtK+d2vJkiWJjo52SGwiIiKu2felatSoQUZGBps2bbKXRUZGXtf3tGHDhgwdOpQ1a9ZQp04dpk+fbo/r8pmunOK69NwA+zNzF2bhXF1dadu2LaNHj2bbtm0cOnSIZcuWAVkzgZmZmdd1jqtXr6Zv3748/PDD1K1bl8DAQIeFKurWrYvNZrM/a3U5i8UC4DDepQuZXHotVqlSxf78WM2aNVm/fn22c3Q2JVsiIiIiBUxoaCiZmZk0bdqUOXPmsG/fPnbv3s2ECRNu6dao5ORkXn/9ddauXcuhQ4cICwuja9euVKlSheDg4FuO293dnT59+rB161ZWrVrFwIEDeeyxx+yrSVetWpUff/yR3bt3s27dOnr16uWQvLVs2ZL777+fHj16sGTJEg4ePMjChQuz7QHm4uLCTz/9RP369XnggQeIjo4G4IUXXmDPnj28+eab7N27l59//tm+OMXVZrKqVq1K165dee655/j777/ZunUrTz75JGXKlKFr165A1l5TJ0+eZPTo0Rw4cIDQ0FAWLlx4Q1+f6tWr0759e1544QXWrVvHpk2bePbZZ6+awB48eJChQ4cSHh7O4cOH+fPPP9m3bx81a9YE4IEHHmDjxo388MMP7Nu3j/fee48dO3Zk6ycqKoohQ4YQGRnJjBkz+PLLLxk0aBAA8+bNY8KECURERHD48GF++OEHbDabPUmuWLEi69at49ChQ5w6deqqM3VVq1bll19+ISIigq1bt/J///d/Du0rVqxInz59eOaZZ/j11185ePAgK1as4OeffwayVho3mUzMmzePkydPkpycTNGiRXnttdd45ZVXmDZtGgcOHGDz5s18+eWX9j3jXnzxRfbt28frr79OZGQk06dPd1iYxFmUbImIiIjk5NReOB7h3NepK8/QXE3lypXZvHkzrVu35tVXX6VOnTo8+OCDhIWFMWnSpJs84awkZdu2bXTp0oVq1arRr18/GjVqxKpVq3Jlr60qVarQvXt3OnbsSLt27ahXr57DHqnfffcdZ86c4e677+app55i4MCB+Pv7O/QxZ84cmjRpQs+ePalVqxZvvPFGjrMqrq6uzJgxg9q1a/PAAw8QGxtLpUqVmD17Nr/88gv16tVj0qRJ9tUIr3V+U6ZMoVGjRjz00EMEBQVhGAYLFiyw3xZYs2ZNJk6cSGhoKPXr12f9+vW89tprN/w1mjJlCqVLl6Zly5Z0796d559/PtvX4FKenp7s2bPHvgXA888/T0hICC+88AKQtSH1u+++yxtvvEGTJk1ISkqid+/e2frp3bs358+fp2nTpoSEhDBo0CCef/55IGuFy19++YUHHniAmjVrMnnyZPvXFrJuDXRxcaFWrVqULFnSYWXBy33xxRcUK1aMe+65h86dOxMcHMzdd9/t0GbSpEk88sgjvPzyy9SoUYPnnnvOPotbpkwZPvjgA9566y0CAgLo378/AB9++CHvvvsuo0aNombNmrRv35758+dTqVIlIOt5wTlz5vDrr79Sv359Jk+ezMiRI6/323LTTMaNPmVZCCUmJuLj40NCQgLe3t55GkvC+XR8Pi0BwPd1f+SZHl3yNB4REZGCKiUlhYMHD1KpUiXc3d0vVsQfgdCmWRsb3w5unhCy/oY2NZbc8/HHHzN58mSOHDmS16FIPnLFnw/cWG6g3X1FRERELuVbLiv5OZc7e0tdk6efEq3baOLEiTRp0gQ/Pz9Wr17NmDFj7LMjIrlNyZaIiIjI5XzLKQG6Q+3bt4+PPvqIuLg4ypcvz6uvvsrQoUPzOiy5QynZEhEREZFCY+zYsYwdOzavw5BCQgtkiIiIiIiIOIGSLRERESnUtFaYiFwut34uKNkSERGRQunCkt3nzt2mVQdFpMBIS0sDsG/qfLP0zJaIiIgUSi4uLvj6+hIbGwtk7Vd0tY1tRaRwsNlsnDx5Ek9PT1xdby1dUrIlIiIihVZgYCCAPeESEQEwm82UL1/+lv8Ao2RLRERECi2TyUSpUqXw9/cnPT09r8MRkXzCYrFgNt/6E1dKtkRERKTQc3FxueVnM0RELqcFMkRERERERJygQCVbn3zyCSaTicGDB9vLUlJSCAkJwc/PjyJFitCjRw9iYmIcPhcVFUWnTp3w9PTE39+f119/nYyMjNscvYiIiIiIFCYFJtnasGEDX3/9NfXq1XMof+WVV/jjjz+YNWsWK1eu5Pjx43Tv3t1en5mZSadOnUhLS2PNmjVMmzaNqVOnMnz48Nt9CiIiIiIiUogUiGQrOTmZXr168Z///IdixYrZyxMSEvjuu+/44osveOCBB2jUqBFTpkxhzZo1rF27FoA///yTXbt28d///pcGDRrQoUMHPvzwQ0JDQ+3r518uNTWVxMREh5eIiIiIiMiNKBDJVkhICJ06daJt27YO5Zs2bSI9Pd2hvEaNGpQvX57w8HAAwsPDqVu3LgEBAfY2wcHBJCYmsnPnzhzHGzVqFD4+PvZXuXLlnHBWIiIiIiJyJ8v3ydbMmTPZvHkzo0aNylYXHR2NxWLB19fXoTwgIIDo6Gh7m0sTrQv1F+pyMnToUBISEuyvI0eO5MKZiIiIiIhIYZKvl34/cuQIgwYNYsmSJbi7u9+2ca1WK1ar9baNJyIiIiIid558PbO1adMmYmNjufvuu3F1dcXV1ZWVK1cyYcIEXF1dCQgIIC0tjfj4eIfPxcTE2HeEDwwMzLY64YXjC21ERERERERyW75Ottq0acP27duJiIiwvxo3bkyvXr3s793c3AgLC7N/JjIykqioKIKCggAICgpi+/btxMbG2tssWbIEb29vatWqddvPSURERERECod8fRth0aJFqVOnjkOZl5cXfn5+9vJ+/foxZMgQihcvjre3NwMGDCAoKIjmzZsD0K5dO2rVqsVTTz3F6NGjiY6OZtiwYYSEhOhWQRERERERcZp8nWxdj7Fjx2I2m+nRowepqakEBwczceJEe72Liwvz5s3jpZdeIigoCC8vL/r06cOIESPyMGoREREREbnTmQzDMPI6iPwuMTERHx8fEhIS8Pb2ztNYEs6n4/NpCQC+r/sjz/TokqfxiIiIiIgUJjeSG+TrZ7ZEREREREQKKiVbIiIiIiIiTuCUZGvz5s1s377dfvzbb7/RrVs33n77bdLS0pwxpIiIiIiISL7ilGTrhRdeYO/evQD8888/PPHEE3h6ejJr1izeeOMNZwwpIiIiIiKSrzgl2dq7dy8NGjQAYNasWdx///1Mnz6dqVOnMmfOHGcMKSIiIiIikq84JdkyDAObzQbA0qVL6dixIwDlypXj1KlTzhhSREREREQkX3FKstW4cWM++ugjfvzxR1auXEmnTp0AOHjwIAEBAc4YUkREREREJF9xSrI1duxYNm/eTP/+/XnnnXeoUqUKALNnz+aee+5xxpAiIiIiIiL5iqszOq1fv77DaoQXjBkzBldXpwwpIiIiIiKSrzhlZqty5cqcPn06W3lKSgrVqlVzxpAiIiIiIiL5ilOSrUOHDpGZmZmtPDU1laNHjzpjSBERERERkXwlV+/p+/333+3vFy9ejI+Pj/04MzOTsLAwKlWqlJtDioiIiIiI5Eu5mmx169YNAJPJRJ8+fRzq3NzcqFixIp9//nluDikiIiIiIpIv5WqydWFvrUqVKrFhwwZKlCiRm92LiIiIiIgUGE5ZGvDgwYPO6FZERERERKTAcNo67GFhYYSFhREbG2uf8brg+++/d9awIiIiIiIi+YJTkq0PPviAESNG0LhxY0qVKoXJZHLGMCIiIiIiIvmWU5KtyZMnM3XqVJ566ilndC8iIiIiIpLvOWWfrbS0NO655x5ndC0iIiIiIlIgOCXZevbZZ5k+ffot9zNp0iTq1auHt7c33t7eBAUFsXDhQnt9SkoKISEh+Pn5UaRIEXr06EFMTIxDH1FRUXTq1AlPT0/8/f15/fXXycjIuOXYRERERERErsYptxGmpKTwzTffsHTpUurVq4ebm5tD/RdffHFd/ZQtW5ZPPvmEqlWrYhgG06ZNo2vXrmzZsoXatWvzyiuvMH/+fGbNmoWPjw/9+/ene/furF69GsjaSLlTp04EBgayZs0aTpw4Qe/evXFzc2PkyJG5ft4iIiIiIiIXmAzDMHK709atW195QJOJZcuW3XTfxYsXZ8yYMTzyyCOULFmS6dOn88gjjwCwZ88eatasSXh4OM2bN2fhwoU89NBDHD9+nICAACDrebI333yTkydPYrFYrmvMxMREfHx8SEhIwNvb+6Zjzw0J59Px+TRr/7Lv6/7IMz265Gk8IiIiIiKFyY3kBk6Z2Vq+fHmu95mZmcmsWbM4e/YsQUFBbNq0ifT0dNq2bWtvU6NGDcqXL29PtsLDw6lbt6490QIIDg7mpZdeYufOnTRs2DDHsVJTU0lNTbUfJyYm5vr5iIiIiIjInc0pz2zlpu3bt1OkSBGsVisvvvgic+fOpVatWkRHR2OxWPD19XVoHxAQQHR0NADR0dEOidaF+gt1VzJq1Ch8fHzsr3LlyuXuSYmIiIiIyB3PKTNbrVu3vureWjdyG2H16tWJiIggISGB2bNn06dPH1auXJkbYV7R0KFDGTJkiP04MTFRCZeIiIiIiNwQpyRbDRo0cDhOT08nIiKCHTt20KdPnxvqy2KxUKVKFQAaNWrEhg0bGD9+PI8//jhpaWnEx8c7zG7FxMQQGBgIQGBgIOvXr3fo78JqhRfa5MRqtWK1Wm8oThERERERkUs5JdkaO3ZsjuXvv/8+ycnJt9S3zWYjNTWVRo0a4ebmRlhYGD169AAgMjKSqKgogoKCAAgKCuLjjz8mNjYWf39/AJYsWYK3tze1atW6pThERERERESuxinJ1pU8+eSTNG3alM8+++y62g8dOpQOHTpQvnx5kpKSmD59OitWrGDx4sX4+PjQr18/hgwZQvHixfH29mbAgAEEBQXRvHlzANq1a0etWrV46qmnGD16NNHR0QwbNoyQkBDNXImIiIiIiFPd1mQrPDwcd3f3624fGxtL7969OXHiBD4+PtSrV4/Fixfz4IMPAlkzaGazmR49epCamkpwcDATJ060f97FxYV58+bx0ksvERQUhJeXF3369GHEiBG5fm4iIiIiIiKXckqy1b17d4djwzA4ceIEGzdu5N13373ufr777rur1ru7uxMaGkpoaOgV21SoUIEFCxZc95giIiIiIiK5wSnJlo+Pj8Ox2WymevXqjBgxgnbt2jljSBERERERkXzFKcnWlClTnNGtiIiIiIhIgeHUZ7Y2bdrE7t27AahduzYNGzZ05nCFjgkjr0MQEREREZErcEqyFRsbyxNPPMGKFSvse2DFx8fTunVrZs6cScmSJZ0xbKGRaZhwMRmUOH8wr0MREREREZErMDuj0wEDBpCUlMTOnTuJi4sjLi6OHTt2kJiYyMCBA50xZKGSgFdehyAiIiIiItfglJmtRYsWsXTpUmrWrGkvq1WrFqGhoVogQ0RERERECgWnzGzZbDbc3Nyylbu5uWGz2ZwxpIiIiIiISL7ilGTrgQceYNCgQRw/ftxeduzYMV555RXatGnjjCFFRERERETyFackW1999RWJiYlUrFiRu+66i7vuuotKlSqRmJjIl19+6YwhRURERERE8hWnPLNVrlw5Nm/ezNKlS9mzZw8ANWvWpG3bts4YTkREgFNJ55n38394uOeL+Hha8jocERGRQi9XZ7aWLVtGrVq1SExMxGQy8eCDDzJgwAAGDBhAkyZNqF27NqtWrcrNIUVE5F+75n1F3yPvsvfvX/I6FBERESGXk61x48bx3HPP4e3tna3Ox8eHF154gS+++CI3hxQRkX9ZMpMBcMk8z5TQj/l1/h95HJGIiEjhlqvJ1tatW2nfvv0V69u1a8emTZtyc0gREbnMmaRzPH1yNN02PJnXoYiIiBRquZpsxcTE5Ljk+wWurq6cPHkyN4cUESn0DJuN9XPGEpeQBECtnZ/b684mxedRVCIiIpKryVaZMmXYsWPHFeu3bdtGqVKlcnNIEZFCL3LTMppuf5+OJ78DoJTptL0u5nBkXoUlIiJS6OVqstWxY0feffddUlJSstWdP3+e9957j4ceeig3hxQRKfROJp7L6xBEREQkB7m69PuwYcP45ZdfqFatGv3796d69eoA7Nmzh9DQUDIzM3nnnXdyc0gRkULvxPJvnbSRh4iIiNyKXP3nOSAggDVr1vDSSy8xdOhQDMMAwGQyERwcTGhoKAEBAbk5pIhIofeY68or1sX9s4VKtZpgMjtlD3sRERG5ilz/W2iFChVYsGABZ86cYf/+/RiGQdWqVSlWrFhuDyUiItfQePObrD17muY9nX9XQUamjRk/T6dt+66UKlbU6eOJiIjkd077U2exYsVo0qQJTZs2velEa9SoUTRp0oSiRYvi7+9Pt27diIx0fNg7JSWFkJAQ/Pz8KFKkCD169CAmJsahTVRUFJ06dcLT0xN/f39ef/11MjIybvrcREQKkqIHF9+WcaKP7OepyBB2/vDqbRlPREQkv8vX95WsXLmSkJAQ1q5dy5IlS0hPT6ddu3acPXvW3uaVV17hjz/+YNasWaxcuZLjx4/TvXt3e31mZiadOnUiLS2NNWvWMG3aNKZOncrw4cPz4pRERHLViB8XXrNNRnrabYgETLZ0AIqnnWB/bNJtGVNERCQ/MxkXHqwqAE6ePIm/vz8rV67k/vvvJyEhgZIlSzJ9+nQeeeQRIGsxjpo1axIeHk7z5s1ZuHAhDz30EMePH7c/LzZ58mTefPNNTp48icViuea4iYmJ+Pj4kJCQgLe3t1PP8VoSzqeT+UlFipuS+aPKB3R+cnCexiMieex9n+tsl+DcOIBj/+ykzA/3sDqzNgeM0rjW6UL3Hr1wd3Nx+tgiIiK3y43kBvl6ZutyCQlZvywUL14cgE2bNpGenk7btm3tbWrUqEH58uUJDw8HIDw8nLp16zoszBEcHExiYiI7d+7McZzU1FQSExMdXiIicn1auOykt+sS/m/PAIZ8NJqMTFtehyQiIpInCkyyZbPZGDx4MC1atKBOnToAREdHY7FY8PX1dWgbEBBAdHS0vc3lKyBeOL7Q5nKjRo3Cx8fH/ipXrlwun42ISOEw0fQJGWnn8zoMERGRPFFgkq2QkBB27NjBzJkznT7W0KFDSUhIsL+OHDni9DFFRJxp564deTZ2UnJyno0tIiKSlwpEstW/f3/mzZvH8uXLKVu2rL08MDCQtLQ04uPjHdrHxMQQGBhob3P56oQXji+0uZzVasXb29vhJSKS3/wWcSzH8tRB2W+RTj+2zdnhXFHJ0Gqkv1c8z8YXERHJK/k62TIMg/79+zN37lyWLVtGpUqVHOobNWqEm5sbYWFh9rLIyEiioqIICgoCICgoiO3btxMbG2tvs2TJEry9valVq9btOREnSc/QcxAihVnCX5McjtOKV4eBW7B6/bvdxgPvct7IWgQo5eQ/tzs8B26mzDwdX0REJC/k+qbGuSkkJITp06fz22+/UbRoUfszVj4+Pnh4eODj40O/fv0YMmQIxYsXx9vbmwEDBhAUFETz5s0BaNeuHbVq1eKpp55i9OjRREdHM2zYMEJCQrBarXl5erfsVPLtWc5ZRPKfs6kZdDg9DUwXyywD1188eP0f8PKDsE8BaL53DDtXN6R2i063OVIREZHCK18nW5MmZf3VtlWrVg7lU6ZMoW/fvgCMHTsWs9lMjx49SE1NJTg4mIkTJ9rburi4MG/ePF566SWCgoLw8vKiT58+jBgx4nadhohIrouPPUIZ08WVUjMemuD4A93LL+u/lyRj504ecmpMZ1O1WbyIiMil8nWydT1bgLm7uxMaGkpoaOgV21SoUIEFCxbkZmgiInnKlJFqf5/mWhRLo945tjvhUprKmYecHs+cubO4a8vIfH5zuoiIyO2lfxZFRAq4LVUHgMmUY53J4mV/n5qe7rQYmmwbTgPz1Z8L+yc2kWTNfomISCGiZEtEpADacuTMdbUr8/RU+3vX03udFA2YufaCPZUnlmNk6DdOi0FERCS/UbIlIlIAbV7843W1s/hXc3IkN2Zk4tC8DkFEROS2UbIlIlIAvev2U16H4MDMtZ+xFRERKWyUbImIFHB1atS4rnYVov90yvg7V8+ntBGTY11y2fuzlR3Zt9UpcYiIiOQ3SrZERAo4r/pdr6tdKU46ZfzkLbNzrrj3FYo882u24tSZTzslDhERkfxGyZaISCGycO5/c73P8ylX2GC9zXtgdslWbEo/l+sxiIiI5EdKtkRECpiw1eH29xvqXXuD9sNe9e3vK+6bmuvxlEw7nHPFFZajv8t8ItdjEBERyY+UbImIFDBBf168bdDk5n7N9mWfn2l/f9bFO9fj8cpIuGr9Or9uuT6miIhIQeCa1wGIiMiN8TSl3lB7F5/S9veNk8JyNZaUc8lUtEU5lB2t8DDWGu0o+e9xswHTMFISMH1SPlfHFhERye+UbImIyE05dOosn4WG8tVl5WUv2Uj5ApO7j8PxyaRUSha1Oi84ERGRfEC3EYqIFCDRZ5Idjn083G64j7jYY7kSy/GNv/OVMdKh7ECjd6/YPs2vpv39mUNa/l1ERO58SrZERAqQXbMcF8So2rT9DfdxZOcabJmZtxyLJS0+W5m1XP3sDS+0f/lvotpMAsCUeWO3QoqIiBRESrZERAqQzKSLe2UdqfQoeJe+Suuc1V/5LBtD++RmWHZl67e9cqWLK2neFQDIsBlOGV9ERCQ/UbIlIlKAeCfssr8v12PkVVpeXeW4VbkRjoOYB8Zdcbn3y6385RsOnz6b6zGIiIjkJ0q2REQKkGbmPRcPivhf9+cyMXPGUsp+bMaWm2EBEHD/09fd9kXXPzi4YVGuxyAiIpKfKNkSESkEXF7fR2ybcfbjZBffPIvlAnP8wbwOQURExKmUbImIFAZeJTBMLvbDZHMub24csv66mmVmZtjfu5tzf3ZNREQkP1GyJSJSQBzZd3G5dKPfkjyMJAclq19XMxMXF8aIiolzVjQiIiL5Qr5Ptv766y86d+5M6dKlMZlM/Prrrw71hmEwfPhwSpUqhYeHB23btmXfvn0ObeLi4ujVqxfe3t74+vrSr18/kpMd96oREcnPDMNg3Q/D7Memck1vuI8i1tzbx95mM9gbnXRLfTxyehI/Lo/InYBERETyoXyfbJ09e5b69esTGhqaY/3o0aOZMGECkydPZt26dXh5eREcHExKSoq9Ta9evdi5cydLlixh3rx5/PXXXzz//PO36xRERG7Z6eQUHnH565b68KtQJ5eigdC5Yfzf8Y9vuZ+qGz/gfNqt7/klIiKSH+X7ZKtDhw589NFHPPzww9nqDMNg3LhxDBs2jK5du1KvXj1++OEHjh8/bp8B2717N4sWLeLbb7+lWbNm3HvvvXz55ZfMnDmT48eP3+azERG5OaZzp2+5Dw/fkpyo/RwAZq/it9RXyz0jrt0oB5Wr1nY4Ppt4hoc+mXtLsYiIiORX+T7ZupqDBw8SHR1N27YXN9H08fGhWbNmhIeHAxAeHo6vry+NGze2t2nbti1ms5l169bl2G9qaiqJiYkOLxGRvJRbWwCXevQzUg1XzsadYHPkza8GWCdtm/19Stvr3+/LtWhJh+M2LlsIs/W76ThERETyswKdbEVHRwMQEBDgUB4QEGCvi46Oxt/fcS8aV1dXihcvbm9zuVGjRuHj42N/lStXzgnRi4hcn6W7Yug5aWWu9Wc1ZdDIvI8y01vddB9m08X0z/3ekFyISkRE5M5ToJMtZxk6dCgJCQn215EjR/I6JBEpxPYunswSXs71fgNM8Tf1ud2Hjt3SuMd6ryXu5V231IeIiEhBUKCTrcDAQABiYmIcymNiYux1gYGBxMbGOtRnZGQQFxdnb3M5q9WKt7e3w0tEJK+0PH9xmfdDvkEcqfncLfVntPvolj6f+FOfW/p8mco1Ke5f5pb6EBERKQgKdLJVqVIlAgMDCQsLs5clJiaybt06goKCAAgKCiI+Pp5NmzbZ2yxbtgybzUazZs1ue8xy834MP8Su4xefnzsSd47YpJSrfAK2H01g65F4Wo1ZzvH48wCkZeS8kWrc2TT2xdzaUtYizmDGZH9/oMKjlHv8s1vqz9Q8a5Ys1XAlNvHcDX++SdrGWxpfRESksMj3yVZycjIRERFEREQAWYtiREREEBUVhclkYvDgwXz00Uf8/vvvbN++nd69e1O6dGm6desGQM2aNWnfvj3PPfcc69evZ/Xq1fTv358nnniC0qVL592JyXVJTs0gIzMrOXr3t5089nW4ve6+0ctpN/YvElPSc/zs+bRMOn/1N11DVxN3+iSR0UmELt9P689WZGtrGAZ3f7iEB8f+xdEzWb98pmXYrrok9e4TiRw+ffYWzk7k+iSYL86ue50/cesdml3Y7d4AqymDiCmv3PjHTbm1XMdFtkwt/y4iIneefJ9sbdy4kYYNG9KwYUMAhgwZQsOGDRk+fDgAb7zxBgMGDOD555+nSZMmJCcns2jRItzd3e19/PTTT9SoUYM2bdrQsWNH7r33Xr755ps8OR/JbumuGKJO5/zX9Y7jVzFw5hYA2pk34JN68RdNX5JIP5fI/aOXc/TMOY7EZfVhGAZHz5wj08j6hfA3yzC2uT9HVOQmNiyZyZCznwNw5mwasYlZM2OfLNqDO6nUMx3gSFzWDFi/aRvoMWkNAMfjz9uTvgt6jl9AxzELb+qcf996nFPJqTnWLdpxghMJ52+qX7kzmVzccr3P8+5ZCwv5JP9zS/0YflVu/sOthtrfbpg95pbiEBERyY9c8zqAa2nVqhWGceW/oppMJkaMGMGIEVfe86V48eJMnz7dGeHJTQiZvhl3VxdGda+LxdXMsz9sxNVsYv/IjpxLy+CzxXvp/0AVintZOBaXRFRc1uzRN5axHDeKk5HZG1cXM0usb3DS8KXjuVHc++lyAJpWKk6zCj4sW7mc2g2b85tlGPXNWb9M7lofxlTLf+xxDBw7lZLnDvDy4HdZ9Nca9rgPAWBzXGO46x4yDqykLOeA++jz6Q+Uu6sWje4qxUst78JsNrHE+ganDB/gEQAyMm24mE2YTBdv+bpURqYNVxczqRmZDJyxhQblfPk1pAUAWw7H8e2suXz88lOsmjGa6d4N+eHN3s748ksBZHHJ+Zq6FWaLBwDN0teTmZGBi+uN/3NgDNmNyd3n5oNo9RZb1y2j/vl12M6dufl+RERE8ql8P7Mld575204wZ/NRqg1bSN8p63nZ5TfuNnaRlJLOX3tP8v3qg3w8fzdr9p/iJ8tIhrn+l+G/7QCgtCmOKu8s5LPFkZQ0JVDLfBg/Ehju+gPPuCxk18GjFPv7A+Zb32brlvX2RAvgU7eLidbvW4/zY8brfGGZzC/jh7DSOsRed/z3D9gXncgMy8d8YxnL5qgzLLG+wdtRz+MZ9jYfz9vGX3tPUtKUQE1zlP1zfb5ewYe/byPubBpbos5wIuE8H81ZR2pGJnO3HKXhh0s4l5ZB8vl03nSdQerRrfYZrOOLPiM0eQgnD+/iY7fv+fzcMM6lZfDO3O1XnAGTwiP2TO7v9Vf7qc/t7zd8/eJ1f+7CH79SXLwweZcGi1fuBGTk/CyliIhIQZbvZ7ak4Pst4hi7TyTRoU4gl0/6rIg8yVT3/wFQ8f2afNClNoGcZs5mmLP5KIfcd9PcvJuK4U8x4t87Q8ubYoj+ayX8e2fVJveX7P0Nd/vR/n6x9a0rxjR8xl90+be/N9z+51D3kMtaBn/3LeP+PR4z+T/MsEAV83GqmI+zYeNBflzbjvstFz+Tnmnj05jniDhRhbG7W1M5eTPWSs0ZduQDRqTO5eDuDUzkd35aU4Exi/aw1/0PXnL9gyGL7uGLxxvgFp+1ueyeqGiqAiVNCYTvPUzLzYP41fVdnu3c+rq+1nnlRMJ5Svl45HUYd6QNh+IIdrm4IEWVMv5XaX393IqWsL/3jt993Z87GBNPZWBnhT40ypVIsgRFfQPoVkIREbmzKNkSpxs0MwKAySsPAPCQORxXMilqOkeMUcyh7fm4Y6x1H8CfmY3YaKtmL3cjw/7+L+uNP9B/uQj3F65aX+v8Rvv/HTMsHzvUNTHvpYllr/2459uj8SKFby2nKOtyiodS1oIrrE9yAaDu7s/ob95KcVMyy5d8xe+WFfbPNj81i57fpPBi8lFwgV9Xrqfzv0mcW+xW2rlsYm3MH8DNJ1v7YpKo4OeFxdU5E9nbok6y6Ou3afT4O7SpV9EpY1wq05Y1s+JivvKtdYtXrqbyXVWpWjZ3EpO8cPhkIuczDPZ9248mrvCPS0Uqd3mbEnUfyfWxTO7Xt72FYRi4TWwCZvCuGpQrY5sv+QvMkbhzlCvumSv9ioiI5Ae6jVCcau0/pzFhw52Lt8J9ZfmScZaJfOg2lW8sY+3lHqSwcU3WMv7tXDbxttsMe90+99v7/NLzrvOvu+0My8d8a/k8W/nR01m3fj3sspripmQAXnGdTQ3zxU2yvaLX43noT1q6bANgkts4e92UpVuy9Xk+LZOzqRnZyq8k02bQZeyfvPK/CMeY10exPDI25w/doMzIP3nD7Wc8Ir63l0XFJnAo5trP4KRn2lhz4NQ12/1zMpkjcedIy7DRf9h7/G94dw7HnLbX22wGf+/L6ic1I5Pg5R1J+E9nMm0GKelZq9ydS04g6Xwamw5nxbVj1W9kZlz/1/J2i/7yQWp8XZ7/c12WdVypB9R/HMwuuT7WWdP1JTjHTpygnPkkACbP4rkydonAcvb3g8Z8nSt9ioiI5Bea2RKnOJuagQE88c1axrh+Q33zAdqlXf0Wod3uz/Buet/bEt/t0N3l72xlRU2Oqwx2cllPJ5f19mOL6eLy16GWCQBExcZzYPgjVL6vJ69s8KV00jb6uS5gc9VBtG7elBLe7pyIT6FiCS92n0ikfe1AzpxLw8fDDQwbm6wvMWv3/azeN5EWVbNmeo79NoL1Rglaj/zEPl7CuXTC/zlNyuENuFrcSYqLoXTFGrRs1vjqJ/rvMzwm42LsB77sjIUMbAP/5PTZNJpUzPrF3GYzOH02jZJFrRiGwagpv1D/8HfEDvkf/sWuPLsy+IvvSTdb+PnNJ5hkGQ/A1C9f5L6B35Nhg4U7TvDT0g082qoROw8eZRrQ2LSHj4f35x/Xyjz3SBcqzXqQcFtNfstsQecHWtL9797MWNyaRz74BTeX/PV3pxNnkmhm3uNQ5u/hvGeaGicuJSM9DVc3yxXbTP17P9ELx/BWLi+MWOrRMWSOnouLkcEv1veZs7YTPZpXu/YHRURECgAlW5Kr/rv2MMN+zVrMoqp/EUzYeNT1LwCCzDsxuPqqah+6TXV2iAXOY2lzsuagVy/hvcwmdLBuAKDTP72Ys+9e3svoSgYuHDYCMWGjV9PyTN9wlGaV/Fj/TywH3FPp47qE2f99nm3PTeXxr9ey220WAEvnVCb0RE3G92xEyPTNcHwLf1iH2ceO3+XFkqJbeLBW1jLhCyIOM3dZOK+1q8pd1evi6mZhf2wSDf9tbxgG3/19kGddtgLQ+YspBJjO0OijdzGbTbw7ZzOrN2/htVZlWPPXEka6fQcucCh6L2vnz6Npz3cxu7iQnmlj1PjxPNztcWpXLMXv1ncBSDIes8fW1/VPRs36ib3HTlHKFMcG9+/44e8HecN1ib3NO67/zXozewSYoJvLGrq5rGHNyvngAj1dl3M64Qx+xf04cDKZCsU9cc0HiVefLxfw52VlVYK6OnVM149LsueFw9Qo5ZutzjAMGvz5KA3cLi44E1DUmjsDexTDuP8NWDkSgIoL/g+aa9NkERG5M5iMq62rLgAkJibi4+NDQkIC3t7X92yDsyScTyfzk4oUNyXzTYmhPN//yotA3E6GYTA+bB/jlu5zKD/k/n+5NkZG2ea4Hl17fY3NbtBzJszqC/0Ww6R7cmxm1H4Y0865uRZjXuqaOoLfrFn7zz2c+gEHjUD6u/7Ks65Ze4FlGGaGV5lFg8gJPOa60v65eMOLx9KGE2iK4wfLp9n6rZYyjfXDOzJ19lwG//O8vXyDTzuavDKL1PdKYDWl848tkJ9L9CcgdhVPuy526KNDsXl0rl+Ku8JecFjs4YLVbs1pkb6WA90Xcle9e1i19Dfu+zvr1tEn0oYx0/IRAPv77abKdzXtn9tpq0Bt8+Gb/ZIB0M/vR3q3uZtxP86mWv17+PSJprfUX27YPLwRd5v3Xyx4eR3418j1cc6OKIOXLdl+vLr+KFo8/HK2dieOH6HUN3UcyjLfjMLF4xaWfb+ULRNGXHJb4vsJudOviIiIE9xIbqCZLckVP62LYtzSfdxlOkZxkthhVMz1MYyeM2FM9n4N3wqYqgXDnvmQeCyr8PkVEFgH3jwELq7Q/lNY9GZWnacfVGgBpephqt0drpJsJVbrTpEiRTFvnpbr55PbLiRaAHOt7+XYpn7klw6JFoCv6Sx/Wt+8Yr8/W0Yw+KMdTLU43gbaID6Mu95ewAFLOgCVzdG8FTcsx58q1WMW8O2ieqy1Zn8ODaBFelYSHXc2hWkffcChc27c9+8dbRcSLYABE39l4SUTKreaaAF8d/op+BlaWmH7rop8P20Az/R5lrnrD5B49hx9Wte95TFuxMmkVMdEC5ySaAEc9qhNrbPr7McWU85/ezPSHDcdt7l54WItmnuBmF0wTGZM/y7/viAsjI5t2uRe/yIiInlEM1vXQTNbVxabmELTkUsxY/Ct22c84BKR+4NYvSE1Ed6KgvTz8Hl1x/pHp0Lth+HgKtjwLTwyBcyX3QqWngLj60NyNPRdABVbONb/MRg2Tck+dvBIqPIghDbJVpXRfACua7+0H59uMRy/1VfeXFuuLcJWmQbmf0g3XHC75Pm12y1h8GH2fBFMSeIp+fYOirrn/KBSaso5TkcfoXTF6jnWX8naic9DsQo0e3wopn+v1dikFGZvOsrqP2fzk2WUve2B8o9y1zPf3vzJXEVK+H9wX/ya/XinpR61317l0OZQ9GkqTq7sUGb034SpRJXcDebsKRhz18VjzW6JiEg+dSO5Qd4/nCAF2n++n8wyy6t0Nq+5+USr83i47+IvfPSc6VhftNQl7wOh7QeO9f61sv5b6T54bFr2RAvAzR1ei4Q3D2dPtABqPOR43PDJrP+Wa36xrPTdELLBfujadjg0etp+7PfAQPv7+IYvsavFePtxRotXobnj7VmRpR8GIMU9+/LkNlPhnHRu8O8m1HmZaAH8vHQ1zcx7qGyOZsPXL3MqOirHdpHjulB6alMMw2DZjiP2DX9TM3KOPzXlHJmZmTSP/R/NI0djGlGMT955gX4ffkmfkd9TdOmbDokWwF2PfpxjX7nBPeg5jIER9uPaadtYtePgxXgzMjm65CuHzxwp1yX3Ey0ArxLXbiMiIlLAKNkqwJJvYAlwZ7nv9Gwqm6MZb5l4kx28BvWegDbvXiyr3gEqtYSmL0D7T6DIhWTk38U17h0MbxyEAZth2EkoeQOzCh6+OZdXbZvVl/nfJKdraNZx2UZQNAD8qkC7j6BkNbj/daj3OLhaoPO4rATupTVgurgkt2/Ll/H08bMfm1q+Ae1HcX7Qxf25KtzTI6vusanZwjG/cyJr/GEniXpsSbZ6ubJkjzK33EftrRdvXXzgzM+4TM5K0JdH7GPviXiOnjlHVPRJ6qVkJd8jx4/ngdl1WL5hG79u2M/AD0ZxLN5x5cklu2KI/qQhLh86Lpn+lttMvsscxkLrUJ5yXepQFxE0Puv6cyJT8Uqce+biraVlfm5P4rkUjsSdo/qwRfy957i9bru1IeX6/ZhTN7ki+pJ998K/DnHaOCIiIrdL4fzz+R0i4Vxano5/+PRZyptubK+mjGodcd27IOug2UuOSVavOeD97yxWn98vltd9FPYuhks3XvUsnvXKTa6WrKQp7uDFYwB3Hxiw6WK7B4Y5fs7D92IS1/0/UPFe8C5NxYoXn3O5sAGvh7cf5yq1w/2+gbhXvg9Kb8Fa3PEWLYexAS9Pj1s8sTvL4Qf/Q2KmK3WXPZ29cmAERVwsMLYW1OpKRIInDY7NyN7uX2fL3o/X0b+yld/jssvhuBiJ1Hx3EX+bn2WVrS4fpz/JBveLM5XvxGc9I7fut0k09jrJ1y5L2fLFXE73/YMzMVGkHdnMg7uG3vC52m7Tj2j3wIvPhFU2R/Pbfz9hU5Ivf1u/pKzp4j5o1QY4dzGZQNPFvdmCTvwXCHXqeCIiIs6mZKsAM5uvvoy6s8zZdJRyxT157OtwDrnH3NBnXdt/DHsXQN3HoMMnjpVV2+b8Ia8S0LDXTUZ7g0pWv7GZssvVu7g0Of414Mk5sPdPcPk3eXJxxbPPrIttLk+0Kt4H3o4zM35lqpJa5wlcrF4Y22bhakvBlJk7ibbNvRjmx3+ETVNhx+zr+sz5yu3x+GfRFeuTeodR1L8CfHZrt5qdrv5/+EVOZ1+LL6jqnZm14EnJGlSwJ9kHYNlH0Hc+rJsM5e+B4pWyqnrOhMqtqIML0R8vwa1KS/zqtCXl5EGSNs6kZGrWbYFeD4/DNqUT5uQTWd+LuH9yDgbY7fI4cHH5+JwMdZsB/35rGpr3ww81c2x3vc7FHbulz18vs8Xd4bjr8bF0BS7dqeGUZxVKFCmGMx0pWp9ySVvtx9+NeZ1nXhuNyZQ3P+tERERulZItuWGvztp67UaXem45/DEIoreBpQg8uwwCb+8Kb3mmStus1zUYJapBwjFMT80F02V397q5Y33k66z3nb+AuS/C1hlZs2i/PJdV3mIQ3PcqfFI+67jhU9BhNIwsxeUi3Wri2qI/lWs1xnxhlbtK93HUVApid+Lf8S0si96AExHZA/Xyx6PLGDj8cNbzcz847v106sEJlKj87ybIQf0hPOt5n43+j9Dw5G+4GOkYDXphivjJ/hlb42fJqNIOS+V74fgWmNqJjKod8LuvH0ROp2r9e8A/h6Tl/tchaEDW83gV73Wsq94ByPoBF/j+AXuxOxCx7xAlY6PY12EmVf3uwjx4a1YybDLB+7m0lHkuqeqbf5KMEoOzzwDmNs8eoTD14vey39lviE0agb+3ZndFRKRgUrIlNyQpJZ0FlqHUMh/mvfQ+1/5AqfpQ5u6sZ6/+Gp11S17Z7AtCFHamZ5dmrZjokvOqdw66fAUt38iaiTm9P+s5s5ZvZNU9OQe2zYLgj8HimXVr5k9Zz4YldfmOor/3w+zqxl2tnszWbdnuH/4bjClrZmhOPzi8Oqts0FaY+xL0+A/4lAXfrKTOKBKIKTka44F3Sd3+GyUadbvYYbuPsNXshvn7ttQv54PL/V9jbJ2JqWto1jNxx7dA6YaYTSbsN01WvBcGbcW1aOmsWynfi8+K50rc3K9cdw02l3/XkHe9ZC35J3/hTPwZis3rd9P9Xo+oHvMoV7sFphFXnikyKtxDQNMeTo3jhli8nD6EX+nKpFuL4ZZ68XbCiZ++wbsfTrDfiisiIlKQKNmS62YYBvXeX8RB96y9jT5wy2HvqTqPwJF1kHAEenwHtbpllVdsARV/u33BFjTuPlmv6+HievH2w9ZvO9ZdPpNWtW3W92FOP4qWrcPRCt0p++DgnPu9NKnxLgVPL4CVY6BEFShWEZ5ZmP0jL6yEk3swVW6F+/2vXVZpwuxeBAA337JQpwemOpckD2XuzjmOYhVzjimXXLXHKm3wSnC8NXZXyQ7UOpl17me8a1AscQ8AKSZ33I2U6x43pnQbzsf+Q8WMgxiunllLvnuXhXqPQtv3L86qDdkDp/ZiqtzyBs7q1qW4l8Q95WS28kTvanj3GJ/DJ5zA4oXbgA0Ot6C+7/YDM8eaeOLVCbcnBhERkVykZEuuy9nUDEZ+/ikH3Udnr3T3zfrvoAjwKAbnz0D8EShV73aGKFdS9xGo3hEsnpR9Ooe9xK6m5etXry8amPW6Ev+a0Gde1ibS+UTteo1h6c9UKpv9FksAi08Aaf/3C4aXP2lTu1IueCAU+xhcLBQrWgpsGaR8dQ/mewdBWgIseTfHfgCOuZanNLGYMlIIeP4Xzs57GzaGUtr/35UqB2wE18tm57xLXVwo5jZy778Gzp2GEtWI/fEZitZqi0eDR/F2seS8nYKzFClJwgOj8Vn2hr3oiaRppGWMw+KqBXRFRKRg0abG1yG/bmr8gdtg3nvng2t/KBcEfzCDxcaLOVe+sgt8bn25bZHbJnZP1gIm12Kz5ZxoXFp+eA2knydlzWTc4g/g0n8jRloypk/KcTboNbxaDc76A4RveTAMOBmZ49ipn1QhrVhVir6QfQaxsDFSkzGNcvyZ8nKR8RQpHsArPVpRykfPcImISN65kdxAM1sFWPz59Nsyjs1mcE/aasjpcaL731CiJQXP9SRacOUZnUvLK9wDgHuVNvYik7s3vBqJV5GArFshrUX/rTBdcWzrwPVY3TyvL647nMlaBF6N5NTsIZQ4PB+AicmDIBle+nQQX374Pq4uWd8Dw2bDMAzMLi5X61JERCRPKNkqwGw2g7OpGXhZc/fbaBgGKWmZnD+XxLz5v7Fv9xY+dLtsI9P7X4cWg2/LQ/MiBdLVbq/MSW7vG1fQFQ3E+8E34Nv5DsWTLONZ+P4afs28ly9HDGfhJ49T2ppG7UFz8LRc/Wfhun9O06C8L1bXi4nZubSMa35ORETkZhWqf2FCQ0MZM2YM0dHR1K9fny+//JKmTZvmdVg37UGXzRzYuop6TVvnWp8nk1JZ/MOnPHnyczyA3pDzjFa9x8FaJNfGFRG5nKVIzgloB5cNdHDZwMj3onnb7U/IgL0f1cOVTBYFTWfnGROWcydpXr8m23Zsp5bHGR56qAdHp/RmoymAsvf3pkublpw7n8LiUY9guW8QD7VrZ+9//rYTtK5RMlsSZsvMJO7UCQzPkgCULGol7mwa3u6u9pk2Z8m0GZw5l0aJItYc6/esX4JXMX/KVa3v1DicKTYpBf+iN7/CqBRMEdu3gbUonslRuLl7UqlWE/43awb+Ze+idVDB/R1N5IJC88zW//73P3r37s3kyZNp1qwZ48aNY9asWURGRuLvf/WlyPPrM1uXWuXWgnOdv6ayvw+nl3+JX437OL17Jc2eeCdr1bNLGDbbxc99PYDkdDPWGg+ydd8hhpy88sP+AJSsASf3ZC0FfumqcSIizpBwDHb/AYvevKVu/i/tbaZbRtqPV5ia0cpYB8B6W3VMvWYTNX80PRJ/JNMw8Z/Mh2hviWB92aepWKsxRc1p1FzwCADzMptT1XSU/cXuo+6ZpYS730uHQRNZuGIV3YPb4mo2sXbXAY789V+aPtCNc2eiqdqkHTO++4Iux8dx0Ks+mSlJrE2vSvFyNbjLByrf9xin8SZzald80qJJfvwXLEX8iFj4HRZXM/cf/oovM7rxZO8XOL76J4pWvZcyNZpyYlsY7qWqUfnnBwGYZTyA130h1K0YSGDFGmxZt5K0td9yMsVElU6DsAbWoFIJL7ZF7ufUmh85V7w2de4qT8XaTXF1dcOwGfy2dBn3Bd1D4tGdmE0myle7G5Mp65FDw7BxdP82Ek8e4eyhTWB2pVnPYRyO3AIYJJ08htnFlZTEWHzK1sBWshZVA7yJiz3G6WMHKHVXXeKio7BlplO0eCAmkwnvYiXZvj2C+b9Mo2zDdnRp2xrfIl6YTCZMZvO//2aZOLIvgrJV6nHsnx2kp6bgW7EBNsOgpLcH51Mz+CNsGU0qlaBclboc278NS6mapCXEEL32Zxo/8joxR/ZxdMtimnTtj81mY8ui76nduifbt23C19uHKjXqcTo5lRP/bKdOvSZA1q9HJpOJ44ciyUhPpWSZSrh7FCEu5ihnYg5TrvrdWN0v3v67ad7XFClVjRLlqpEYd4LYLQsp26wHXj5+eBcriYurKxnpaRz7ZyflqtTHwOBccgKrZozmgadHcDT+PB5nj5OZkUr5ag0wbDZMZjOH92wmZudKAPyqBZFwbA8N2/XGZrNhNps5cmA7e/bspsz2r7jrlcVYrR6YzGZOHT+Mq8WKT3F/++8Ch3dvokLNRkDWnSyXbhp+YTzDMDBsBiazCZPJZC+3ZWYCJgzDhslk5qvZC6ibGkGVe7ri4mrB6lmEfWHTKFq5CenpGfiWCCSjWGUqFvfAbHZhx19zqbhiIP81deJlfibTMOFicvw1dEyx93j9TNbz6ElvnSJ27wbu+qUDu9r/j8T96zBZvbAln6RZ71GYTNivkQvxmUwm+3ldWnfhv9fjRtrK7XPh99f88L25kdyg0CRbzZo1o0mTJnz1VdYmqzabjXLlyjFgwADeeusth7apqamkpqbajxMSEihfvjxHjhzJH8nW2DoUN529qc+fMnwoYUq44c9lVLgf18d/yDo4tAruyr3ZNBGRa4pcCGZXUswexP71PeWj/8zriEQKtB2lelD1+G9YTRkstDWjg3ldXoeU62JNJfE3HLe0SDLcOefiQ4bN4FjZjnie3Mo5/4Z4mdMA8I9aTEnjJNsq9SPD7I7nqa2kp54npey9WI6txZqRRHKRCvidO8jpItXxKOLNOVdfyh6YSbxbAEbp+pyz+ON2dA1pRStg8i2LkXySKtHzOVDxCfsKtK7H1pHhXgL87gLAlpqEz7FVAKSXqElK0YrXt/3JyT2YM85hBNTDmpFAisUvx2a2c3HUOPo/9vrcBwF1bujr6JEay3lr1sSEkZmB+9G/MXmXIsWw4Hb2OG7+VThnDXD4jPnEFmyuHll/pL88ltRkikSvw93Llzi/u+3n6Z52mrT441nP4gbWxyM1lvST/5DuGYApM4XGZxayzb0J9V6Ze0PxO0NiYiLlypUjPj4eH5+rb91TKJKttLQ0PD09mT17Nt26dbOX9+nTh/j4eH77zXH/p/fff58PPrg9q/yJiIiIiEjBc+TIEcqWLXvVNoXima1Tp06RmZlJQIBj1h0QEMCePXuytR86dChDhgyxH9tsNuLi4vDz83OYbs8rF7Lp/DDTJnItul6lINH1KgWJrlcpSO6k69UwDJKSkihduvQ12xaKZOtGWa1WrFbHh5B9fX3zJpir8Pb2LvAXqxQeul6lINH1KgWJrlcpSO6U6/Vatw9ekPdPmN0GJUqUwMXFhZiYGIfymJgYAgNvcHlmERERERGR61Aoki2LxUKjRo0ICwuzl9lsNsLCwggKCsrDyERERERE5E5VaG4jHDJkCH369KFx48Y0bdqUcePGcfbsWZ5++um8Du2GWa1W3nvvvWy3OorkR7pepSDR9SoFia5XKUgK6/VaKFYjvOCrr76yb2rcoEEDJkyYQLNmzfI6LBERERERuQMVqmRLRERERETkdikUz2yJiIiIiIjcbkq2REREREREnEDJloiIiIiIiBMo2RIREREREXECJVsFTGhoKBUrVsTd3Z1mzZqxfv36vA5JCoG//vqLzp07U7p0aUwmE7/++qtDvWEYDB8+nFKlSuHh4UHbtm3Zt2+fQ5u4uDh69eqFt7c3vr6+9OvXj+TkZIc227Zt47777sPd3Z1y5coxevRoZ5+a3GFGjRpFkyZNKFq0KP7+/nTr1o3IyEiHNikpKYSEhODn50eRIkXo0aNHtk3vo6Ki6NSpE56envj7+/P666+TkZHh0GbFihXcfffdWK1WqlSpwtSpU519enKHmTRpEvXq1cPb2xtvb2+CgoJYuHChvV7XquRnn3zyCSaTicGDB9vLdM3mwJACY+bMmYbFYjG+//57Y+fOncZzzz1n+Pr6GjExMXkdmtzhFixYYLzzzjvGL7/8YgDG3LlzHeo/+eQTw8fHx/j111+NrVu3Gl26dDEqVapknD9/3t6mffv2Rv369Y21a9caq1atMqpUqWL07NnTXp+QkGAEBAQYvXr1Mnbs2GHMmDHD8PDwML7++uvbdZpyBwgODjamTJli7Nixw4iIiDA6duxolC9f3khOTra3efHFF41y5coZYWFhxsaNG43mzZsb99xzj70+IyPDqFOnjtG2bVtjy5YtxoIFC4wSJUoYQ4cOtbf5559/DE9PT2PIkCHGrl27jC+//NJwcXExFi1adFvPVwq233//3Zg/f76xd+9eIzIy0nj77bcNNzc3Y8eOHYZh6FqV/Gv9+vVGxYoVjXr16hmDBg2yl+uazU7JVgHStGlTIyQkxH6cmZlplC5d2hg1alQeRiWFzeXJls1mMwIDA40xY8bYy+Lj4w2r1WrMmDHDMAzD2LVrlwEYGzZssLdZuHChYTKZjGPHjhmGYRgTJ040ihUrZqSmptrbvPnmm0b16tWdfEZyJ4uNjTUAY+XKlYZhZF2bbm5uxqxZs+xtdu/ebQBGeHi4YRhZf1wwm81GdHS0vc2kSZMMb29v+/X5xhtvGLVr13YY6/HHHzeCg4OdfUpyhytWrJjx7bff6lqVfCspKcmoWrWqsWTJEqNly5b2ZEvXbM50G2EBkZaWxqZNm2jbtq29zGw207ZtW8LDw/MwMinsDh48SHR0tMO16ePjQ7NmzezXZnh4OL6+vjRu3Njepm3btpjNZtatW2dvc//992OxWOxtgoODiYyM5MyZM7fpbOROk5CQAEDx4sUB2LRpE+np6Q7Xa40aNShfvrzD9Vq3bl0CAgLsbYKDg0lMTGTnzp32Npf2caGNfh7LzcrMzGTmzJmcPXuWoKAgXauSb4WEhNCpU6ds15Wu2Zy55nUAcn1OnTpFZmamw8UJEBAQwJ49e/IoKhGIjo4GyPHavFAXHR2Nv7+/Q72rqyvFixd3aFOpUqVsfVyoK1asmFPilzuXzWZj8ODBtGjRgjp16gBZ15LFYsHX19eh7eXXa07X84W6q7VJTEzk/PnzeHh4OOOU5A60fft2goKCSElJoUiRIsydO5datWoRERGha1XynZkzZ7J582Y2bNiQrU4/X3OmZEtERO5IISEh7Nixg7///juvQxG5ourVqxMREUFCQgKzZ8+mT58+rFy5Mq/DEsnmyJEjDBo0iCVLluDu7p7X4RQYuo2wgChRogQuLi7ZVnSJiYkhMDAwj6ISwX79Xe3aDAwMJDY21qE+IyODuLg4hzY59XHpGCLXq3///sybN4/ly5dTtmxZe3lgYCBpaWnEx8c7tL/8er3WtXilNt7e3gXur66StywWC1WqVKFRo0aMGjWK+vXrM378eF2rku9s2rSJ2NhY7r77blxdXXF1dWXlypVMmDABV1dXAgICdM3mQMlWAWGxWGjUqBFhYWH2MpvNRlhYGEFBQXkYmRR2lSpVIjAw0OHaTExMZN26dfZrMygoiPj4eDZt2mRvs2zZMmw2G82aNbO3+euvv0hPT7e3WbJkCdWrV9cthHLdDMOgf//+zJ07l2XLlmW7NbVRo0a4ubk5XK+RkZFERUU5XK/bt293+APBkiVL8Pb2platWvY2l/ZxoY1+HsutstlspKam6lqVfKdNmzZs376diIgI+6tx48b06tXL/l7XbA7yeoUOuX4zZ840rFarMXXqVGPXrl3G888/b/j6+jqs6CLiDElJScaWLVuMLVu2GIDxxRdfGFu2bDEOHz5sGEbW0u++vr7Gb7/9Zmzbts3o2rVrjku/N2zY0Fi3bp3x999/G1WrVnVY+j0+Pt4ICAgwnnrqKWPHjh3GzJkzDU9PTy39LjfkpZdeMnx8fIwVK1YYJ06csL/OnTtnb/Piiy8a5cuXN5YtW2Zs3LjRCAoKMoKCguz1F5YmbteunREREWEsWrTIKFmyZI5LE7/++uvG7t27jdDQ0AK9NLHkjbfeestYuXKlcfDgQWPbtm3GW2+9ZZhMJuPPP/80DEPXquR/l65GaBi6ZnOiZKuA+fLLL43y5csbFovFaNq0qbF27dq8DkkKgeXLlxtAtlefPn0Mw8ha/v3dd981AgICDKvVarRp08aIjIx06OP06dNGz549jSJFihje3t7G008/bSQlJTm02bp1q3HvvfcaVqvVKFOmjPHJJ5/crlOUO0RO1ylgTJkyxd7m/Pnzxssvv2wUK1bM8PT0NB5++GHjxIkTDv0cOnTI6NChg+Hh4WGUKFHCePXVV4309HSHNsuXLzcaNGhgWCwWo3Llyg5jiFyPZ555xqhQoYJhsViMkiVLGm3atLEnWoaha1Xyv8uTLV2z2ZkMwzDyZk5NRERERETkzqVntkRERERERJxAyZaIiIiIiIgTKNkSERERERFxAiVbIiIiIiIiTqBkS0RERERExAmUbImIiIiIiDiBki0REREREREnULIlIiIiIiLiBEq2REREREREnEDJloiIiIiIiBMo2RIREREREXGC/wdYLtAogT2RywAAAABJRU5ErkJggg==", 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" ] @@ -405,13 +405,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1136359/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_1394234/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -497,7 +497,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -619,7 +619,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Detection efficiency: [0.02615625 0.01333559]\n" + "Detection efficiency: [0.02615627 0.0133356 ]\n" ] } ], @@ -681,17 +681,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n", + "\n", "Processing zirconium_1...\n", - "\n", + "\n", "Processing zirconium_2...\n", - "\n", + "\n", "Processing zirconium_3...\n", - "\n" + "\n" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 04ed4c9..12cd482 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -223,7 +223,8 @@ class CheckSourceMeasurement(Measurement): def get_expected_activity(self) -> float: """ - Calculates the expected activity of a check source given the + Calculates the expected activity of a check source at the + beginning of the measurement given the half-life and the date of the measurement. The expected activity is calculated using the formula: .. math:: A(t) = A_0 e^{-\\lambda t} @@ -252,7 +253,6 @@ def get_expected_activity(self) -> float: act_expec = self.check_source.activity * np.exp(-decay_constant * time) return act_expec - # should be a method of a class called CheckSourceMeasurement def compute_detection_efficiency( self, background_measurement: Measurement, @@ -264,18 +264,19 @@ def compute_detection_efficiency( Computes the detection efficiency of a check source given the check source data and the calibration coefficients. The detection efficiency is calculated using the formula: - .. math:: \\eta = \\frac{A_{meas}}{A_{expec}} + .. math:: \\eta = \\frac{N_{meas}}{N_{expec}} - where :math:`A_{meas}` is the measured activity and :math:`A_{expec}` is the expected activity. - The measured activity is calculated using the formula: - .. math:: A_{meas} = \\frac{A_{peak}}{I \\cdot t_{live}} + where :math:`N_{meas}` is the total number of counts measured under the energy peak + and :math:`N_{expec}` is the total number of emitted gamma-rays from the check source. - where :math:`A_{peak}` is the area of the peak, :math:`I` is the intensity of the check source - and :math:`t_{live}` is the live count time of the detector. + The expected number of counts :math:`N_{expec}` is calculated according to Equation 3 + in https://doi.org/10.2172/1524045. Args: - background_measurement: _description_ - calibration_coeffs: _description_ + background_measurement: background measurement + calibration_coeffs: the calibration polynomial coefficients for the detector + channel_nb: the channel number of the detector + search_width: the search width for the peak fitting Returns: the detection efficiency @@ -302,18 +303,29 @@ def compute_detection_efficiency( search_width=search_width, ) - act_meas = np.array(areas) / ( + nb_counts_measured = np.array(areas) / ( np.array(self.check_source.nuclide.intensity) - * check_source_detector.live_count_time ) - act_meas_err = np.sqrt(np.array(areas)) / ( + nb_counts_measured_err = np.sqrt(np.array(areas)) / ( np.array(self.check_source.nuclide.intensity) - * check_source_detector.live_count_time ) act_expec = self.get_expected_activity() + decay_constant = np.log(2) / self.check_source.nuclide.half_life + + expected_nb_counts = act_expec / decay_constant + live_count_time_correction_factor = ( + check_source_detector.live_count_time + / check_source_detector.real_count_time + ) + decay_counting_correction_factor = 1 - np.exp( + -decay_constant * check_source_detector.real_count_time + ) + expected_nb_counts *= ( + live_count_time_correction_factor * decay_counting_correction_factor + ) - detection_efficiency = act_meas / act_expec + detection_efficiency = nb_counts_measured / expected_nb_counts return detection_efficiency From b08ccfb0ce42907f0adf8f3dae48f29a1b58f338 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 14:43:14 -0400 Subject: [PATCH 094/137] moved get expected activity to checksource --- .../activation_foils/calibration.py | 24 ++++++++++++- .../activation_foils/compass.py | 34 +------------------ test/neutron_detection/test_compass.py | 16 +++------ 3 files changed, 28 insertions(+), 46 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index feacb79..ab77adc 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -1,6 +1,7 @@ from dataclasses import dataclass from typing import List -from datetime import datetime +import datetime +import numpy as np @dataclass @@ -64,6 +65,27 @@ class CheckSource: activity_date: datetime.date activity: float + def get_expected_activity(self, date: datetime.date) -> float: + + decay_constant = np.log(2) / self.nuclide.half_life + + # Convert date to datetime if needed + if isinstance(self.activity_date, datetime.date) and not isinstance( + self.activity_date, datetime.datetime + ): + + activity_datetime = datetime.datetime.combine( + self.activity_date, datetime.datetime.min.time() + ) + # add a timezone + activity_datetime = activity_datetime.replace(tzinfo=date.tzinfo) + else: + activity_datetime = self.activity_date + + time = (date - activity_datetime).total_seconds() + act_expec = self.activity * np.exp(-decay_constant * time) + return act_expec + class ActivationFoil: nuclide: Nuclide diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 12cd482..8fa5fef 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -221,38 +221,6 @@ def from_directory( class CheckSourceMeasurement(Measurement): check_source: CheckSource - def get_expected_activity(self) -> float: - """ - Calculates the expected activity of a check source at the - beginning of the measurement given the - half-life and the date of the measurement. - The expected activity is calculated using the formula: - .. math:: A(t) = A_0 e^{-\\lambda t} - - where :math:`A_0` is the initial activity, :math:`\\lambda` is the decay constant - and :math:`t` is the time since the measurement date. - - Returns: - the expected activity of the check source in Bq - """ - decay_constant = np.log(2) / self.check_source.nuclide.half_life - - # Convert date to datetime if needed - if isinstance( - self.check_source.activity_date, datetime.date - ) and not isinstance(self.check_source.activity_date, datetime.datetime): - activity_datetime = datetime.datetime.combine( - self.check_source.activity_date, datetime.time.min - ) - # add a timezone - activity_datetime = activity_datetime.replace(tzinfo=self.start_time.tzinfo) - else: - activity_datetime = self.check_source.activity_date - - time = (self.start_time - activity_datetime).total_seconds() - act_expec = self.check_source.activity * np.exp(-decay_constant * time) - return act_expec - def compute_detection_efficiency( self, background_measurement: Measurement, @@ -310,7 +278,7 @@ def compute_detection_efficiency( np.array(self.check_source.nuclide.intensity) ) - act_expec = self.get_expected_activity() + act_expec = self.check_source.get_expected_activity(self.start_time) decay_constant = np.log(2) / self.check_source.nuclide.half_life expected_nb_counts = act_expec / decay_constant diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index bd510b8..0d2c73a 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -469,21 +469,13 @@ def test_check_source_expected_activity(n_half_lives, activity_date): activity=activity, ) - measurement = compass.CheckSourceMeasurement(name="test measurement") - measurement.check_source = check_source - measurement.start_time = activity_date + datetime.timedelta( - seconds=n_half_lives * half_life - ) - measurement.stop_time = measurement.start_time + datetime.timedelta(hours=1) - + start_time = activity_date + datetime.timedelta(seconds=n_half_lives * half_life) # convert start_time and stop_time to datetime - if isinstance(measurement.start_time, datetime.date): - measurement.start_time = datetime.datetime.combine( - measurement.start_time, datetime.datetime.min.time() - ) + if isinstance(start_time, datetime.date): + start_time = datetime.datetime.combine(start_time, datetime.datetime.min.time()) # RUN - computed_activity = measurement.get_expected_activity() + computed_activity = check_source.get_expected_activity(start_time) # TEST From 225329b1f8ade2c1796fdc8b5dd2acbbd1954f29 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 15:24:29 -0400 Subject: [PATCH 095/137] intensity multiplication moved to expected counts instead of measured counts --- .../neutron_detection/activation_foils/compass.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 8fa5fef..66de212 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -271,17 +271,16 @@ def compute_detection_efficiency( search_width=search_width, ) - nb_counts_measured = np.array(areas) / ( - np.array(self.check_source.nuclide.intensity) - ) - nb_counts_measured_err = np.sqrt(np.array(areas)) / ( - np.array(self.check_source.nuclide.intensity) - ) + nb_counts_measured = np.array(areas) + nb_counts_measured_err = np.sqrt(np.array(areas)) act_expec = self.check_source.get_expected_activity(self.start_time) + gamma_rays_expected = act_expec * ( + np.array(self.check_source.nuclide.intensity) + ) decay_constant = np.log(2) / self.check_source.nuclide.half_life - expected_nb_counts = act_expec / decay_constant + expected_nb_counts = gamma_rays_expected / decay_constant live_count_time_correction_factor = ( check_source_detector.live_count_time / check_source_detector.real_count_time From c61e420fdd8859fb4d7d8869b24a26bdbadf9588 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 15:31:06 -0400 Subject: [PATCH 096/137] var naming --- libra_toolbox/neutron_detection/activation_foils/compass.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 66de212..f6c1c5e 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -264,15 +264,15 @@ def compute_detection_efficiency( calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) - areas = get_multipeak_area( + nb_counts_measured = get_multipeak_area( hist, calibrated_bin_bedges, self.check_source.nuclide.energy, search_width=search_width, ) - nb_counts_measured = np.array(areas) - nb_counts_measured_err = np.sqrt(np.array(areas)) + nb_counts_measured = np.array(nb_counts_measured) + nb_counts_measured_err = np.sqrt(nb_counts_measured) act_expec = self.check_source.get_expected_activity(self.start_time) gamma_rays_expected = act_expec * ( From 63272f860a940448f9ce5e110994921b03c36e63 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 16 May 2025 21:09:19 -0400 Subject: [PATCH 097/137] compute gamma emmissions for SampleMeasurement --- example.ipynb | 151 ++++++++++++++---- .../activation_foils/calibration.py | 9 ++ .../activation_foils/compass.py | 97 +++++++++++ 3 files changed, 225 insertions(+), 32 deletions(-) diff --git a/example.ipynb b/example.ipynb index 22bba52..412beff 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:176: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:177: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -405,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1394234/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_1423543/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -673,7 +673,62 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# fit efficiency curve with polynomial\n", + "\n", + "def efficiency_curve(channel_nb, measurements):\n", + " efficiencies = []\n", + " energies = []\n", + " for measurement in measurements:\n", + " efficiency = measurement.compute_detection_efficiency(\n", + " background_measurement=background_meas,\n", + " calibration_coeffs=calibration_coeffs[channel_nb],\n", + " channel_nb=channel_nb,\n", + " search_width=300,\n", + " )\n", + " efficiencies.append(efficiency)\n", + " energies.append(measurement.check_source.nuclide.energy)\n", + "\n", + " # flatten the lists\n", + " energies = [energy for sublist in energies for energy in sublist]\n", + " efficiencies = [efficiency for sublist in efficiencies for efficiency in sublist]\n", + " return energies, efficiencies\n", + "energies, efficiencies = efficiency_curve(4, list(all_measurements.values()))\n", + "\n", + "detection_efficiency_coeffs = np.polyfit(energies, efficiencies, 2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(energies, efficiencies, label=\"Data\", alpha=0.5)\n", + "xs = np.linspace(450, 1400, 100)\n", + "plt.plot(xs, np.polyval(detection_efficiency_coeffs, xs), label=\"Fit\")\n", + "plt.xlabel(\"Energy (keV)\")\n", + "plt.ylabel(\"Detection efficiency\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -681,37 +736,69 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n", - "Processing zirconium_1...\n", - "\n", - "Processing zirconium_2...\n", - "\n", - "Processing zirconium_3...\n", - "\n" + "\n" ] } ], "source": [ + "from libra_toolbox.neutron_detection.activation_foils.compass import SampleMeasurement\n", + "from libra_toolbox.neutron_detection.activation_foils.calibration import nb92m, ActivationFoil\n", + "\n", + "nb_foil = ActivationFoil(\n", + " nuclide=nb92m,\n", + " mass=0.5678, # in grams\n", + " name=\"Nb foil\",\n", + ")\n", + "\n", "sample_measurements_directories = {\n", " \"niobium_1\": f\"{run_dir}/Niobium_250318_1253_count1/UNFILTERED\",\n", " \"niobium_2\": f\"{run_dir}/Niobium_250319_1124_count2/UNFILTERED\",\n", " \"niobium_3\": f\"{run_dir}/Niobium_250321_0935_count3/UNFILTERED\",\n", - " \"zirconium_1\": f\"{run_dir}/Zirconium_1L_3_240317_2312/UNFILTERED\",\n", - " \"zirconium_2\": f\"{run_dir}/Zirconium_250318_2219_count2/UNFILTERED\",\n", - " \"zirconium_3\": f\"{run_dir}/Zirconium_250320_1042_count3/UNFILTERED\",\n", + " # \"zirconium_1\": f\"{run_dir}/Zirconium_1L_3_240317_2312/UNFILTERED\",\n", + " # \"zirconium_2\": f\"{run_dir}/Zirconium_250318_2219_count2/UNFILTERED\",\n", + " # \"zirconium_3\": f\"{run_dir}/Zirconium_250320_1042_count3/UNFILTERED\",\n", "}\n", "\n", "all_sample_measurements = {}\n", "\n", "for sample, directory in sample_measurements_directories.items():\n", " print(f\"Processing {sample}...\")\n", - " meas = Measurement.from_directory(directory, name=sample)\n", + " meas = SampleMeasurement.from_directory(directory, name=sample)\n", " print(meas)\n", - " all_sample_measurements[sample] = meas" + " all_sample_measurements[sample] = meas\n", + " if \"niobium\" in sample:\n", + " meas.foil = nb_foil" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "niobium_1: [449600.30282758] ± [5727.34454993] emmited gamma rays\n", + "niobium_2: [1137301.58500562] ± [9109.14961268] emmited gamma rays\n", + "niobium_3: [890498.3337663] ± [8060.39952153] emmited gamma rays\n" + ] + } + ], + "source": [ + "for sample, measurement in all_sample_measurements.items():\n", + " activity, activity_err = measurement.get_gamma_emmited(\n", + " background_measurement=background_meas,\n", + " efficiency_coeffs=detection_efficiency_coeffs,\n", + " calibration_coeffs=calibration_coeffs[4],\n", + " channel_nb=4,\n", + " search_width=300,\n", + " )\n", + " print(f\"{sample}: {activity} ± {activity_err} emmited gamma rays\")" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index ab77adc..d360175 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -58,6 +58,13 @@ class Nuclide: half_life=312.20 * 24 * 3600, ) +nb92m = Nuclide( + name="Nb92m", + energy=[934.44], + intensity=[0.9915], + half_life=10.25 * 24 * 3600, +) + @dataclass class CheckSource: @@ -87,6 +94,8 @@ def get_expected_activity(self, date: datetime.date) -> float: return act_expec +@dataclass class ActivationFoil: nuclide: Nuclide mass: float + name: str diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index f6c1c5e..b8aaeb7 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -11,6 +11,7 @@ import warnings from libra_toolbox.neutron_detection.activation_foils.calibration import ( CheckSource, + ActivationFoil, na22, co60, ba133, @@ -350,6 +351,102 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: return peaks +class SampleMeasurement(Measurement): + foil: ActivationFoil + + def get_gamma_emmited( + self, + background_measurement: Measurement, + efficiency_coeffs, + calibration_coeffs, + channel_nb: int, + search_width: float = 800, + ): + # find right background detector + + background_detector = [ + d for d in background_measurement.detectors if d.channel_nb == channel_nb + ][0] + check_source_detector = [ + d for d in self.detectors if d.channel_nb == channel_nb + ][0] + + hist, bin_edges = check_source_detector.get_energy_hist_background_substract( + background_detector, bins=None + ) + + calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + + nb_counts_measured = get_multipeak_area( + hist, + calibrated_bin_bedges, + self.foil.nuclide.energy, + search_width=search_width, + ) + + nb_counts_measured = np.array(nb_counts_measured) + nb_counts_measured_err = np.sqrt(nb_counts_measured) + + detection_efficiency = np.polyval(efficiency_coeffs, self.foil.nuclide.energy) + + gamma_emmitted = nb_counts_measured / detection_efficiency + gamma_emmitted_err = nb_counts_measured_err / detection_efficiency + return gamma_emmitted, gamma_emmitted_err + + def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: + """Returns the peak indices of the histogram + + Args: + hist: a histogram + kwargs: optional parameters for the peak finding algorithm + see scipy.signal.find_peaks for more information + + Returns: + the peak indices in ``hist`` + """ + + # peak finding parameters + start_index = 100 + prominence = 0.10 * np.max(hist[start_index:]) + height = 0.10 * np.max(hist[start_index:]) + width = [10, 150] + distance = 30 + if self.foil.nuclide == na22: + start_index = 100 + height = 0.1 * np.max(hist[start_index:]) + prominence = 0.1 * np.max(hist[start_index:]) + width = [10, 150] + distance = 30 + elif self.foil.nuclide == co60: + start_index = 400 + height = 0.60 * np.max(hist[start_index:]) + prominence = None + elif self.foil.nuclide == ba133: + width = [10, 200] + elif self.foil.nuclide == mn54: + height = 0.6 * np.max(hist[start_index:]) + + # update the parameters if kwargs are provided + if kwargs: + prominence = kwargs.get("prominence", prominence) + height = kwargs.get("height", height) + width = kwargs.get("width", width) + distance = kwargs.get("distance", distance) + + # run the peak finding algorithm + # NOTE: the start_index is used to ignore the low energy region + peaks, peak_data = find_peaks( + hist[start_index:], + prominence=prominence, + height=height, + width=width, + distance=distance, + ) + peaks = np.array(peaks) + start_index + + return peaks + + def get_calibration_data( check_source_measurements: List[CheckSourceMeasurement], background_measurement: Measurement, From 9c78b9a1efc44aba055e82db10533b9ba037c023 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 20 May 2025 13:46:39 -0400 Subject: [PATCH 098/137] I've got _a_ neutron flux --- example.ipynb | 88 +++++++++++++------ .../activation_foils/calibration.py | 30 ++++++- .../activation_foils/compass.py | 70 ++++++++++++++- .../activation_foils/explicit.py | 4 +- 4 files changed, 158 insertions(+), 34 deletions(-) diff --git a/example.ipynb b/example.ipynb index 412beff..12bbde2 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:177: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:181: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -405,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1423543/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_1577835/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -673,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -736,21 +736,37 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], "source": [ "from libra_toolbox.neutron_detection.activation_foils.compass import SampleMeasurement\n", - "from libra_toolbox.neutron_detection.activation_foils.calibration import nb92m, ActivationFoil\n", + "from libra_toolbox.neutron_detection.activation_foils.calibration import (\n", + " nb93,\n", + " nb92m,\n", + " ActivationFoil,\n", + " Reaction,\n", + ")\n", + "\n", + "\n", + "# source: https://scipub.euro-fusion.org/wp-content/uploads/eurofusion/WPJET3PR17_16948_submitted.pdf\n", + "# See Figure 3\n", + "Nb93_n_2n_Nb92m_cross_section_at_14Mev = 0.46 # barn\n", + "barn_to_cm2 = 1e-24\n", + "Nb93_n_2n_Nb92m_cross_section_at_14Mev *= barn_to_cm2 # cm^2\n", + "\n", + "nb_reaction = Reaction(\n", + " reactant=nb93, product=nb92m, cross_section=Nb93_n_2n_Nb92m_cross_section_at_14Mev\n", + ")\n", "\n", "nb_foil = ActivationFoil(\n", - " nuclide=nb92m,\n", - " mass=0.5678, # in grams\n", + " reaction=nb_reaction,\n", + " mass=0.5678, # in grams\n", " name=\"Nb foil\",\n", ")\n", "\n", @@ -776,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -784,21 +800,35 @@ "output_type": "stream", "text": [ "niobium_1: [449600.30282758] ± [5727.34454993] emmited gamma rays\n", + "niobium_1: [7.04569697e+31] neutrons/s\n", "niobium_2: [1137301.58500562] ± [9109.14961268] emmited gamma rays\n", - "niobium_3: [890498.3337663] ± [8060.39952153] emmited gamma rays\n" + "niobium_2: [7.30598486e+31] neutrons/s\n", + "niobium_3: [890498.3337663] ± [8060.39952153] emmited gamma rays\n", + "niobium_3: [5.29043868e+31] neutrons/s\n" ] } ], "source": [ + "irradiations = [\n", + " {\"t_on\": 0, \"t_off\": 8*3600}\n", + "]\n", + "\n", "for sample, measurement in all_sample_measurements.items():\n", - " activity, activity_err = measurement.get_gamma_emmited(\n", + " emmited_gammas, err = measurement.get_gamma_emmited(\n", " background_measurement=background_meas,\n", " efficiency_coeffs=detection_efficiency_coeffs,\n", " calibration_coeffs=calibration_coeffs[4],\n", " channel_nb=4,\n", " search_width=300,\n", " )\n", - " print(f\"{sample}: {activity} ± {activity_err} emmited gamma rays\")" + " print(f\"{sample}: {emmited_gammas} ± {err} emmited gamma rays\")\n", + " neutron_flux = measurement.get_neutron_flux(\n", + " number_of_decays_measured=emmited_gammas,\n", + " irradiations=irradiations,\n", + " distance=5, # cm\n", + " time_generator_off=measurement.start_time,\n", + " )\n", + " print(f\"{sample}: {neutron_flux} neutrons/s\")" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index d360175..cc1e662 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -25,6 +25,14 @@ class Nuclide: energy: List[float] intensity: List[float] half_life: float + atomic_mass: float = None + + @property + def decay_constant(self): + """ + Returns the decay constant of the nuclide in 1/s. + """ + return np.log(2) / self.half_life ba133 = Nuclide( @@ -65,6 +73,21 @@ class Nuclide: half_life=10.25 * 24 * 3600, ) +nb93 = Nuclide( + name="Nb93", + energy=[0.0], + intensity=[1.0], + half_life=16.13 * 365.25 * 24 * 3600, + atomic_mass=92.90637, +) + + +@dataclass +class Reaction: + reactant: Nuclide + product: Nuclide + cross_section: float + @dataclass class CheckSource: @@ -96,6 +119,11 @@ def get_expected_activity(self, date: datetime.date) -> float: @dataclass class ActivationFoil: - nuclide: Nuclide + reaction: Reaction mass: float name: str + abundance: float = 1.0 + + @property + def nb_atoms(self): + return self.abundance * (self.mass / self.reaction.reactant.atomic_mass) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b8aaeb7..dbc2ccb 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -17,6 +17,10 @@ ba133, mn54, ) +from libra_toolbox.neutron_detection.activation_foils.explicit import ( + get_chain, + delay_time, +) from scipy.signal import find_peaks from scipy.optimize import curve_fit @@ -377,17 +381,19 @@ def get_gamma_emmited( calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + energy = self.foil.reaction.product.energy + nb_counts_measured = get_multipeak_area( hist, calibrated_bin_bedges, - self.foil.nuclide.energy, + energy, search_width=search_width, ) nb_counts_measured = np.array(nb_counts_measured) nb_counts_measured_err = np.sqrt(nb_counts_measured) - detection_efficiency = np.polyval(efficiency_coeffs, self.foil.nuclide.energy) + detection_efficiency = np.polyval(efficiency_coeffs, energy) gamma_emmitted = nb_counts_measured / detection_efficiency gamma_emmitted_err = nb_counts_measured_err / detection_efficiency @@ -446,6 +452,66 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: return peaks + def get_neutron_flux( + self, + number_of_decays_measured: float, + irradiations: list, + distance: float, + time_generator_off: datetime.datetime, + ): + """calculates the neutron flux during the irradiation + + Args: + number_of_decays_measured: number of decays measured + irradiations: list of dictionaries with keys "t_on" and "t_off" for irradiations + distance: distance from the target plane to the foil in cm + + Returns: + pint.Quantity: neutron flux + """ + + flux = ( + number_of_decays_measured + / self.foil.nb_atoms + / self.foil.reaction.cross_section + ) + + flux *= ( + get_chain( + irradiations, decay_constant=self.foil.reaction.product.decay_constant + ) + ** -1 + ) + time_between_generator_off_and_start_of_counting = ( + time_generator_off - self.start_time + ).total_seconds() + + flux *= ( + -1 + / self.foil.reaction.product.decay_constant + * ( + np.exp( + -self.foil.reaction.product.decay_constant + * ( + time_between_generator_off_and_start_of_counting + + (self.stop_time - self.start_time).total_seconds() + ) + ) + - np.exp( + -self.foil.reaction.product.decay_constant + * time_between_generator_off_and_start_of_counting + ) + ) + ) ** -1 + + # convert n/cm2/s to n/s + distance_from_target_plane = distance + area_of_sphere = 4 * np.pi * distance_from_target_plane**2 + + flux *= area_of_sphere + + return flux + def get_calibration_data( check_source_measurements: List[CheckSourceMeasurement], diff --git a/libra_toolbox/neutron_detection/activation_foils/explicit.py b/libra_toolbox/neutron_detection/activation_foils/explicit.py index 974f071..6e2a1bc 100644 --- a/libra_toolbox/neutron_detection/activation_foils/explicit.py +++ b/libra_toolbox/neutron_detection/activation_foils/explicit.py @@ -3,7 +3,7 @@ import numpy as np -def get_chain(irradiations): +def get_chain(irradiations, decay_constant=Nb92m_decay_constant): """ Returns the value of (1 - exp(-\lambda * \Delta t_1)) * (1 - exp(-\lambda * \Delta t_2)) * ... * (1 - exp(-\lambda * \Delta t_n)) @@ -23,7 +23,7 @@ def get_chain(irradiations): for period in periods: delta_t = period["end"] - period["start"] - result = 1 - result * np.exp(-Nb92m_decay_constant * delta_t) + result = 1 - result * np.exp(-decay_constant * delta_t) return result From 8111af9c6672d1eb5b66c2426f8ffc42ab9f120f Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 20 May 2025 14:57:00 -0400 Subject: [PATCH 099/137] divide by avogadro --- .../neutron_detection/activation_foils/calibration.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index cc1e662..9a1b97e 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -126,4 +126,7 @@ class ActivationFoil: @property def nb_atoms(self): - return self.abundance * (self.mass / self.reaction.reactant.atomic_mass) + avogadro = 6.022e23 # part/mol + return self.abundance * ( + self.mass / self.reaction.reactant.atomic_mass * avogadro + ) From 6d25a4957fa73bfc043b3a8cef55a08be24e7d5d Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 20 May 2025 15:47:05 -0400 Subject: [PATCH 100/137] added get_detector method --- .../neutron_detection/activation_foils/compass.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index dbc2ccb..931864a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -222,6 +222,19 @@ def from_directory( return measurement_object + def get_detector(self, channel_nb: int) -> Detector: + """ + Get the detector object for a given channel number. + Args: + channel_nb: channel number of the detector + Returns: + Detector object for the specified channel + """ + for detector in self.detectors: + if detector.channel_nb == channel_nb: + return detector + raise ValueError(f"Detector with channel number {channel_nb} not found.") + class CheckSourceMeasurement(Measurement): check_source: CheckSource From 104878219877099de1d2e68035699aae21d266bd Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 20 May 2025 17:48:56 -0400 Subject: [PATCH 101/137] new get_neutron_flux method --- example.ipynb | 161 +++++++++++++----- .../activation_foils/calibration.py | 1 + .../activation_foils/compass.py | 88 ++++++---- 3 files changed, 171 insertions(+), 79 deletions(-) diff --git a/example.ipynb b/example.ipynb index 12bbde2..ecc9d93 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -183,7 +183,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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", 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", 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" ] @@ -405,13 +405,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1577835/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_1625259/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -547,7 +547,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -736,11 +736,11 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], @@ -792,43 +792,114 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Processing channel 4...\n", "niobium_1: [449600.30282758] ± [5727.34454993] emmited gamma rays\n", - "niobium_1: [7.04569697e+31] neutrons/s\n", + "niobium_1: 9.68e+07 neutrons/s\n", "niobium_2: [1137301.58500562] ± [9109.14961268] emmited gamma rays\n", - "niobium_2: [7.30598486e+31] neutrons/s\n", + "niobium_2: 1.03e+08 neutrons/s\n", "niobium_3: [890498.3337663] ± [8060.39952153] emmited gamma rays\n", - "niobium_3: [5.29043868e+31] neutrons/s\n" + "niobium_3: 8.28e+07 neutrons/s\n", + "Processing channel 5...\n", + "niobium_1: [599611.90796695] ± [6614.16671655] emmited gamma rays\n", + "niobium_1: 1.13e+08 neutrons/s\n", + "niobium_2: [1394301.65985281] ± [10085.9865613] emmited gamma rays\n", + "niobium_2: 1.14e+08 neutrons/s\n", + "niobium_3: [1390828.53068015] ± [10073.41690841] emmited gamma rays\n", + "niobium_3: 1.20e+08 neutrons/s\n" ] } ], "source": [ + "from datetime import datetime\n", "irradiations = [\n", - " {\"t_on\": 0, \"t_off\": 8*3600}\n", + " {\"t_on\": 0, \"t_off\": 12*3600}\n", "]\n", + "time_generator_off = datetime.strptime(\"3/17/2025 22:10\", \"%m/%d/%Y %H:%M\")\n", "\n", - "for sample, measurement in all_sample_measurements.items():\n", - " emmited_gammas, err = measurement.get_gamma_emmited(\n", - " background_measurement=background_meas,\n", - " efficiency_coeffs=detection_efficiency_coeffs,\n", - " calibration_coeffs=calibration_coeffs[4],\n", - " channel_nb=4,\n", - " search_width=300,\n", - " )\n", - " print(f\"{sample}: {emmited_gammas} ± {err} emmited gamma rays\")\n", - " neutron_flux = measurement.get_neutron_flux(\n", - " number_of_decays_measured=emmited_gammas,\n", - " irradiations=irradiations,\n", - " distance=5, # cm\n", - " time_generator_off=measurement.start_time,\n", - " )\n", - " print(f\"{sample}: {neutron_flux} neutrons/s\")" + "for ch_nb in [4, 5]:\n", + " print(f\"Processing channel {ch_nb}...\")\n", + " for sample, measurement in all_sample_measurements.items():\n", + " emmited_gammas, err = measurement.get_gamma_emmited(\n", + " background_measurement=background_meas,\n", + " efficiency_coeffs=detection_efficiency_coeffs,\n", + " calibration_coeffs=calibration_coeffs[ch_nb],\n", + " channel_nb=ch_nb,\n", + " search_width=300,\n", + " )\n", + " # add timezone\n", + " time_generator_off = time_generator_off.replace(tzinfo=measurement.start_time.tzinfo)\n", + "\n", + " print(f\"{sample}: {emmited_gammas} ± {err} emmited gamma rays\")\n", + " neutron_flux = measurement.get_neutron_flux(\n", + " photon_counts=emmited_gammas, # NOTE should account for intensity\n", + " irradiations=irradiations,\n", + " distance=5.08, # cm\n", + " time_generator_off=time_generator_off,\n", + " channel_nb=ch_nb,\n", + " )\n", + " print(f\"{sample}: {neutron_flux[0]:.2e} neutrons/s\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing channel 4...\n", + "[ 0 1 2 ... 4092 4093 4094]\n", + "[ 0 1 2 ... 4092 4093 4094]\n", + "[ 0 1 2 ... 4092 4093 4094]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for ch_nb in [4]:\n", + " print(f\"Processing channel {ch_nb}...\")\n", + " for sample, measurement in all_sample_measurements.items():\n", + " detector = measurement.get_detector(ch_nb)\n", + " background_detector = background_meas.get_detector(ch_nb)\n", + " hist, bin_edges = detector.get_energy_hist_background_substract(background_detector)\n", + " # hist, bin_edges = detector.get_energy_hist()\n", + " print(bin_edges)\n", + " plt.stairs(\n", + " hist, bin_edges, alpha=0.5, label=sample\n", + " )\n", + "\n", + "plt.xlim(0, 2000)\n", + "plt.ylim(-1e3, 1e3)\n", + "# plt.yscale(\"log\")\n", + "plt.legend()" ] } ], diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 9a1b97e..8507269 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -123,6 +123,7 @@ class ActivationFoil: mass: float name: str abundance: float = 1.0 + thickness: float = None @property def nb_atoms(self): diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 931864a..f627a6b 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -82,7 +82,7 @@ def get_energy_hist( if bins is None: bins = np.arange( - int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + 1 + int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) ) return np.histogram(energy_values, bins=bins) @@ -381,12 +381,8 @@ def get_gamma_emmited( ): # find right background detector - background_detector = [ - d for d in background_measurement.detectors if d.channel_nb == channel_nb - ][0] - check_source_detector = [ - d for d in self.detectors if d.channel_nb == channel_nb - ][0] + background_detector = background_measurement.get_detector(channel_nb) + check_source_detector = self.get_detector(channel_nb) hist, bin_edges = check_source_detector.get_energy_hist_background_substract( background_detector, bins=None @@ -467,59 +463,83 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: def get_neutron_flux( self, - number_of_decays_measured: float, + channel_nb: int, + photon_counts: float, irradiations: list, distance: float, time_generator_off: datetime.datetime, - ): + total_efficiency=1, + branching_ratio=1, + ) -> float: """calculates the neutron flux during the irradiation + Based on Equation 1 from: + Lee, Dongwon, et al. "Determination of the Deuterium-Tritium (D-T) Generator + Neutron Flux using Multi-foil Neutron Activation Analysis Method." , + May. 2019. https://doi.org/10.2172/1524045 Args: - number_of_decays_measured: number of decays measured irradiations: list of dictionaries with keys "t_on" and "t_off" for irradiations - distance: distance from the target plane to the foil in cm Returns: - pint.Quantity: neutron flux + neutron flux """ + time_between_generator_off_and_start_of_counting = ( + self.start_time - time_generator_off + ).total_seconds() + + # Spectroscopic Factor to account for the branching ratio and the + # total detection efficiency + + f_spec = total_efficiency * branching_ratio + + number_of_decays_measured = photon_counts / f_spec flux = ( number_of_decays_measured / self.foil.nb_atoms / self.foil.reaction.cross_section ) - flux *= ( - get_chain( - irradiations, decay_constant=self.foil.reaction.product.decay_constant - ) - ** -1 - ) - time_between_generator_off_and_start_of_counting = ( - time_generator_off - self.start_time - ).total_seconds() + detector = self.get_detector(channel_nb) - flux *= ( - -1 - / self.foil.reaction.product.decay_constant + f_time = ( + get_chain(irradiations, self.foil.reaction.product.decay_constant) + * np.exp( + -self.foil.reaction.product.decay_constant + * time_between_generator_off_and_start_of_counting + ) * ( - np.exp( + 1 + - np.exp( -self.foil.reaction.product.decay_constant - * ( - time_between_generator_off_and_start_of_counting - + (self.stop_time - self.start_time).total_seconds() - ) + * detector.real_count_time ) + ) + * (detector.live_count_time / detector.real_count_time) + / self.foil.reaction.product.decay_constant + ) + + # Correction factor of gamma-ray self-attenuation in the foil + if self.foil.thickness is None: + f_self = 1 + else: + f_self = ( + 1 - np.exp( - -self.foil.reaction.product.decay_constant - * time_between_generator_off_and_start_of_counting + -self.foil.mass_attenuation_coefficient + * self.foil.density + * self.foil.thickness ) + ) / ( + self.foil.mass_attenuation_coefficient + * self.foil.density + * self.foil.thickness ) - ) ** -1 + + flux /= f_time * f_self # convert n/cm2/s to n/s - distance_from_target_plane = distance - area_of_sphere = 4 * np.pi * distance_from_target_plane**2 + area_of_sphere = 4 * np.pi * distance**2 flux *= area_of_sphere From d14d2cda83700dbd47f85d24ae884e58ddcdedd1 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 09:33:46 -0400 Subject: [PATCH 102/137] energy, intensity, half life optional in Nuclide --- .../neutron_detection/activation_foils/calibration.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 8507269..ce9e38d 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -22,9 +22,9 @@ class Nuclide: """ name: str - energy: List[float] - intensity: List[float] - half_life: float + energy: List[float] = None + intensity: List[float] = None + half_life: float = None atomic_mass: float = None @property @@ -75,9 +75,6 @@ def decay_constant(self): nb93 = Nuclide( name="Nb93", - energy=[0.0], - intensity=[1.0], - half_life=16.13 * 365.25 * 24 * 3600, atomic_mass=92.90637, ) From e52f751cf041b3e73ccc73e5c60d6e49e63ba5e9 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 13:15:28 -0400 Subject: [PATCH 103/137] added tests + renamed method --- .../activation_foils/compass.py | 9 +- test/neutron_detection/test_compass.py | 209 ++++++++++++++++++ 2 files changed, 212 insertions(+), 6 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index f627a6b..9252d37 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -17,10 +17,7 @@ ba133, mn54, ) -from libra_toolbox.neutron_detection.activation_foils.explicit import ( - get_chain, - delay_time, -) +from libra_toolbox.neutron_detection.activation_foils.explicit import get_chain from scipy.signal import find_peaks from scipy.optimize import curve_fit @@ -371,7 +368,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: class SampleMeasurement(Measurement): foil: ActivationFoil - def get_gamma_emmited( + def get_gamma_emitted( self, background_measurement: Measurement, efficiency_coeffs, @@ -461,7 +458,7 @@ def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: return peaks - def get_neutron_flux( + def get_neutron_rate( self, channel_nb: int, photon_counts: float, diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 0d2c73a..6064ee0 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -5,6 +5,8 @@ from libra_toolbox.neutron_detection.activation_foils.calibration import ( Nuclide, CheckSource, + ActivationFoil, + Reaction, ) from pathlib import Path import datetime @@ -735,3 +737,210 @@ def test_get_multipeak_area_two_close_peaks(): expected_area_peak_2 = nb_events_peak2 for i, expected_area in enumerate([expected_area_peak_1, expected_area_peak_2]): assert np.isclose(areas[i], expected_area, rtol=1e-2) + + +@pytest.mark.parametrize("efficiency", [1e-2, 0.1, 0.5, 1.0]) +def test_get_gamma_emitted(efficiency: float): + # BUILD + nuclide_reactant = Nuclide(name="TestNuclide", atomic_mass=200) + activated_nuclide = Nuclide( + name="ActivatedNuclide", + energy=[1000], + intensity=[1.0], + half_life=10 * 24 * 3600, + ) + + reaction = Reaction( + reactant=nuclide_reactant, + product=activated_nuclide, + cross_section=20.0, + ) + + foil = ActivationFoil(reaction=reaction, mass=0.1, name="TestFoil") + + measurement = compass.SampleMeasurement("sample") + measurement.foil = foil + + count_time_hr = 1 # hr + measurement.start_time = datetime.datetime(2024, 11, 7) + measurement.stop_time = datetime.datetime(2024, 11, 7, count_time_hr) + + measurement.detectors = [ + compass.Detector(channel_nb=4), + compass.Detector(channel_nb=3), + ] + measurement.get_detector(3).real_count_time = count_time_hr * 3600 + measurement.get_detector(3).live_count_time = count_time_hr * 3600 + + nb_counts = 50000 + energy_events = np.random.normal( + loc=activated_nuclide.energy[0], scale=30, size=int(nb_counts) + ) + time_events = np.random.uniform(0, 100, size=energy_events.size) + measurement.get_detector(3).events = np.column_stack((time_events, energy_events)) + + background_measurement = compass.Measurement("background") + background_measurement.detectors = [compass.Detector(channel_nb=3)] + background_measurement.get_detector(3).events = np.array([(0, 0), (1, 4000)]) + background_measurement.get_detector(3).real_count_time = count_time_hr * 3600 + background_measurement.get_detector(3).live_count_time = count_time_hr * 3600 + + # RUN + gammas_emmitted = measurement.get_gamma_emitted( + background_measurement=background_measurement, + efficiency_coeffs=np.array([0.0, efficiency]), # assume perfect efficiency + calibration_coeffs=np.array([1.0, 0.0]), # assume perfect calibration + channel_nb=3, + search_width=300, + ) + computed_value = gammas_emmitted[0] + + # TEST + expected_value = nb_counts / efficiency + assert np.isclose(computed_value, expected_value, rtol=1e-2) + + +@pytest.mark.parametrize("distance", [1.0, 5.0, 10.0]) +@pytest.mark.parametrize("photon_counts", [1e6, 1e7, 1e8, 0.0]) +def test_get_neutron_rate_very_long_half_life(photon_counts, distance): + # BUILD + + half_life = 100 * 24 * 3600 # seconds (100 days) + + nuclide_reactant = Nuclide(name="TestNuclide", atomic_mass=200) + activated_nuclide = Nuclide( + name="ActivatedNuclide", + energy=[1000], + intensity=[1.0], + half_life=half_life, + ) + + reaction = Reaction( + reactant=nuclide_reactant, + product=activated_nuclide, + cross_section=20.0, + ) + + foil = ActivationFoil( + reaction=reaction, + mass=0.1, + name="TestFoil", + thickness=None, + ) + + measurement = compass.SampleMeasurement("sample") + measurement.foil = foil + + count_time_hr = 2 # hr + measurement.start_time = datetime.datetime(2024, 11, 7) + measurement.stop_time = datetime.datetime(2024, 11, 7, count_time_hr) + + measurement.detectors = [ + compass.Detector(channel_nb=4), + compass.Detector(channel_nb=3), + ] + measurement.get_detector(3).real_count_time = count_time_hr * 3600 + measurement.get_detector(3).live_count_time = measurement.get_detector( + 3 + ).real_count_time + + irradiation_time = 3600 # seconds + irradiations = [{"t_on": 0, "t_off": irradiation_time}] + + # RUN + computed_rate = measurement.get_neutron_rate( + channel_nb=3, + photon_counts=photon_counts, + irradiations=irradiations, + distance=distance, # cm + time_generator_off=measurement.start_time, + ) + + # TEST + expected_nb_decays = photon_counts / activated_nuclide.intensity[0] # decay events + expected_activity = expected_nb_decays / (count_time_hr * 3600) # Bq + # ignoring decays then: + # irradiation_time * cross_section * nb_atoms * neutron_flux * decay_constant = activity + expected_neutron_flux = expected_activity / ( + irradiation_time + * foil.reaction.cross_section + * foil.nb_atoms + * activated_nuclide.decay_constant + ) + area_of_sphere = 4 * np.pi * distance**2 + expected_neutron_rate = expected_neutron_flux * area_of_sphere + assert np.isclose(computed_rate, expected_neutron_rate) + + +@pytest.mark.parametrize("distance", [1.0, 5.0, 10.0]) +@pytest.mark.parametrize("photon_counts", [1e15, 1e15, 1e15, 0.0]) +def test_get_neutron_rate_very_moderate_life(photon_counts, distance): + # BUILD + + half_life = 10 * 24 * 3600 # seconds (10 day) + + nuclide_reactant = Nuclide(name="TestNuclide", atomic_mass=200) + activated_nuclide = Nuclide( + name="ActivatedNuclide", + energy=[1000], + intensity=[1.0], + half_life=half_life, + ) + + reaction = Reaction( + reactant=nuclide_reactant, + product=activated_nuclide, + cross_section=20.0, + ) + + foil = ActivationFoil( + reaction=reaction, + mass=0.1, + name="TestFoil", + thickness=None, + ) + + measurement = compass.SampleMeasurement("sample") + measurement.foil = foil + + count_time_hr = 1 # hr + measurement.start_time = datetime.datetime(2024, 11, 7) + measurement.stop_time = datetime.datetime(2024, 11, 7, count_time_hr) + + measurement.detectors = [ + compass.Detector(channel_nb=4), + compass.Detector(channel_nb=3), + ] + measurement.get_detector(3).real_count_time = count_time_hr * 3600 + measurement.get_detector(3).live_count_time = measurement.get_detector( + 3 + ).real_count_time + + irradiation_time = 0.5 * half_life + irradiations = [{"t_on": 0, "t_off": irradiation_time}] + + # RUN + computed_rate = measurement.get_neutron_rate( + channel_nb=3, + photon_counts=photon_counts, + irradiations=irradiations, + distance=distance, # cm + time_generator_off=measurement.start_time, + ) + + # TEST + expected_nb_decays = photon_counts / activated_nuclide.intensity[0] # decay events + expected_neutron_flux = ( + expected_nb_decays + * activated_nuclide.decay_constant + / ( + (1 - np.exp(-foil.reaction.product.decay_constant * irradiation_time)) + * (1 - np.exp(-foil.reaction.product.decay_constant * count_time_hr * 3600)) + * foil.reaction.cross_section + * foil.nb_atoms + ) + ) + + area_of_sphere = 4 * np.pi * distance**2 + expected_neutron_rate = expected_neutron_flux * area_of_sphere + assert np.isclose(computed_rate, expected_neutron_rate) From 3436e7be73591fa1d302c32685c0de4be0f7ef99 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 13:19:22 -0400 Subject: [PATCH 104/137] re-ran notebook --- example.ipynb | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/example.ipynb b/example.ipynb index ecc9d93..3388787 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:181: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:178: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -405,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_1625259/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_42236/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -736,11 +736,11 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], @@ -826,7 +826,7 @@ "for ch_nb in [4, 5]:\n", " print(f\"Processing channel {ch_nb}...\")\n", " for sample, measurement in all_sample_measurements.items():\n", - " emmited_gammas, err = measurement.get_gamma_emmited(\n", + " emmited_gammas, err = measurement.get_gamma_emitted(\n", " background_measurement=background_meas,\n", " efficiency_coeffs=detection_efficiency_coeffs,\n", " calibration_coeffs=calibration_coeffs[ch_nb],\n", @@ -837,7 +837,7 @@ " time_generator_off = time_generator_off.replace(tzinfo=measurement.start_time.tzinfo)\n", "\n", " print(f\"{sample}: {emmited_gammas} ± {err} emmited gamma rays\")\n", - " neutron_flux = measurement.get_neutron_flux(\n", + " neutron_flux = measurement.get_neutron_rate(\n", " photon_counts=emmited_gammas, # NOTE should account for intensity\n", " irradiations=irradiations,\n", " distance=5.08, # cm\n", @@ -865,7 +865,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, From ccc091f0f818f9cf94268af7a3970729250ce584 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 13:26:33 -0400 Subject: [PATCH 105/137] more events to reduce chance of failing test --- test/neutron_detection/test_compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 6064ee0..97de579 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -505,7 +505,7 @@ def test_check_source_detection_efficiency(expected_efficiency): activity_date = datetime.datetime(2024, 11, 7) half_life = 10 * 24 * 3600 # seconds (10 days) - activity = 1000e1 # Bq + activity = 5000e1 # Bq test_nuclide = Nuclide( name="TestNuclide Mn54", From 1ea29096ca75a90dbafb28a24620a8ee26d85930 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 14:06:16 -0400 Subject: [PATCH 106/137] removed unused method --- .../activation_foils/compass.py | 53 ------------------- 1 file changed, 53 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 9252d37..e3440fb 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -405,59 +405,6 @@ def get_gamma_emitted( gamma_emmitted_err = nb_counts_measured_err / detection_efficiency return gamma_emmitted, gamma_emmitted_err - def get_peaks(self, hist: np.ndarray, **kwargs) -> np.ndarray: - """Returns the peak indices of the histogram - - Args: - hist: a histogram - kwargs: optional parameters for the peak finding algorithm - see scipy.signal.find_peaks for more information - - Returns: - the peak indices in ``hist`` - """ - - # peak finding parameters - start_index = 100 - prominence = 0.10 * np.max(hist[start_index:]) - height = 0.10 * np.max(hist[start_index:]) - width = [10, 150] - distance = 30 - if self.foil.nuclide == na22: - start_index = 100 - height = 0.1 * np.max(hist[start_index:]) - prominence = 0.1 * np.max(hist[start_index:]) - width = [10, 150] - distance = 30 - elif self.foil.nuclide == co60: - start_index = 400 - height = 0.60 * np.max(hist[start_index:]) - prominence = None - elif self.foil.nuclide == ba133: - width = [10, 200] - elif self.foil.nuclide == mn54: - height = 0.6 * np.max(hist[start_index:]) - - # update the parameters if kwargs are provided - if kwargs: - prominence = kwargs.get("prominence", prominence) - height = kwargs.get("height", height) - width = kwargs.get("width", width) - distance = kwargs.get("distance", distance) - - # run the peak finding algorithm - # NOTE: the start_index is used to ignore the low energy region - peaks, peak_data = find_peaks( - hist[start_index:], - prominence=prominence, - height=height, - width=width, - distance=distance, - ) - peaks = np.array(peaks) + start_index - - return peaks - def get_neutron_rate( self, channel_nb: int, From e9f704d52af13f58800f9948831821e6e0e7ef32 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 14:34:11 -0400 Subject: [PATCH 107/137] split in two methods (flux and rate) --- example.ipynb | 40 +++++++------- .../activation_foils/compass.py | 54 +++++++++++++++++-- 2 files changed, 69 insertions(+), 25 deletions(-) diff --git a/example.ipynb b/example.ipynb index 3388787..98cd857 100644 --- a/example.ipynb +++ b/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -405,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_42236/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_58431/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -736,11 +736,11 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], @@ -865,7 +865,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index e3440fb..6e41b6d 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -405,16 +405,15 @@ def get_gamma_emitted( gamma_emmitted_err = nb_counts_measured_err / detection_efficiency return gamma_emmitted, gamma_emmitted_err - def get_neutron_rate( + def get_neutron_flux( self, channel_nb: int, photon_counts: float, irradiations: list, - distance: float, time_generator_off: datetime.datetime, total_efficiency=1, branching_ratio=1, - ) -> float: + ): """calculates the neutron flux during the irradiation Based on Equation 1 from: Lee, Dongwon, et al. "Determination of the Deuterium-Tritium (D-T) Generator @@ -422,12 +421,16 @@ def get_neutron_rate( May. 2019. https://doi.org/10.2172/1524045 Args: + channel_nb: channel number of the detector irradiations: list of dictionaries with keys "t_on" and "t_off" for irradiations + time_generator_off: time when the generator was turned off + photon_counts: number of gamma rays measured + total_efficiency: total efficiency of the detector + branching_ratio: branching ratio of the reaction Returns: - neutron flux + neutron flux in n/cm2/s """ - time_between_generator_off_and_start_of_counting = ( self.start_time - time_generator_off ).total_seconds() @@ -482,6 +485,47 @@ def get_neutron_rate( flux /= f_time * f_self + return flux + + def get_neutron_rate( + self, + channel_nb: int, + photon_counts: float, + irradiations: list, + distance: float, + time_generator_off: datetime.datetime, + total_efficiency=1, + branching_ratio=1, + ) -> float: + """ + Calculates the neutron rate during the irradiation. + It assumes that the neutron flux is isotropic. + + Based on Equation 1 from: + Lee, Dongwon, et al. "Determination of the Deuterium-Tritium (D-T) Generator + Neutron Flux using Multi-foil Neutron Activation Analysis Method." , + May. 2019. https://doi.org/10.2172/1524045 + + Args: + channel_nb: channel number of the detector + irradiations: list of dictionaries with keys "t_on" and "t_off" for irradiations + time_generator_off: time when the generator was turned off + photon_counts: number of gamma rays measured + total_efficiency: total efficiency of the detector + branching_ratio: branching ratio of the reaction + + Returns: + neutron rate in n/s + """ + + flux = self.get_neutron_flux( + channel_nb=channel_nb, + photon_counts=photon_counts, + irradiations=irradiations, + time_generator_off=time_generator_off, + total_efficiency=total_efficiency, + branching_ratio=branching_ratio, + ) # convert n/cm2/s to n/s area_of_sphere = 4 * np.pi * distance**2 From ba85c87afa690287e8c25bf112747779d33a4103 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 14:52:17 -0400 Subject: [PATCH 108/137] documentation --- .../activation_foils/calibration.py | 56 +++++++++++++++++-- 1 file changed, 51 insertions(+), 5 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index ce9e38d..5117a30 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -84,6 +84,17 @@ class Reaction: reactant: Nuclide product: Nuclide cross_section: float + """ + Class to hold the information of a reaction. + Attributes + ---------- + reactant : + The reactant of the reaction. + product : + The product of the reaction. + cross_section : + The cross section of the reaction in cm2. + """ @dataclass @@ -92,7 +103,28 @@ class CheckSource: activity_date: datetime.date activity: float + """ + Class to hold the information of a check source. + Attributes + ---------- + nuclide : + The nuclide of the check source. + activity_date : + The date of the calibrated activity of the check source. + activity : + The activity of the check source in Bq. + """ + def get_expected_activity(self, date: datetime.date) -> float: + """ + Returns the expected activity of the check source at a given date. + + Args: + date: the date to calculate the expected activity for. + + Returns: + the expected activity of the check source in Bq + """ decay_constant = np.log(2) / self.nuclide.half_life @@ -119,12 +151,26 @@ class ActivationFoil: reaction: Reaction mass: float name: str - abundance: float = 1.0 thickness: float = None + """Class to hold the information of an activation foil. + Attributes + ---------- + reaction : + The reaction that produces the nuclide. + mass : + The mass of the foil in grams. + name : + The name of the foil. + thickness : + The thickness of the foil in cm. + """ + @property - def nb_atoms(self): + def nb_atoms(self) -> float: + """ + Returns the number of atoms in the foil. + """ avogadro = 6.022e23 # part/mol - return self.abundance * ( - self.mass / self.reaction.reactant.atomic_mass * avogadro - ) + abundance = 1 + return abundance * (self.mass / self.reaction.reactant.atomic_mass * avogadro) From 21ba6d8c1ee5c46df3f030bf319899a922a51ae3 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 21 May 2025 14:52:44 -0400 Subject: [PATCH 109/137] unused variable --- libra_toolbox/neutron_detection/activation_foils/compass.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 6e41b6d..d9f2fcd 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -553,8 +553,6 @@ def get_calibration_data( if detector.channel_nb != channel_nb: continue - sample = measurement.name[:-2] - hist, bin_edges = detector.get_energy_hist_background_substract( background_detector, bins=None ) From 98bb71105cccb7d1021c2627f04aec8f58765fe8 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 22 May 2025 09:02:15 -0400 Subject: [PATCH 110/137] moved example --- example.ipynb => docs/examples/example.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename example.ipynb => docs/examples/example.ipynb (99%) diff --git a/example.ipynb b/docs/examples/example.ipynb similarity index 99% rename from example.ipynb rename to docs/examples/example.ipynb index 98cd857..606341b 100644 --- a/example.ipynb +++ b/docs/examples/example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -19,7 +19,7 @@ ")\n", "from datetime import date\n", "\n", - "run_dir = \"250317_BABY_1L_run3/DAQ\"\n", + "run_dir = \"../../250317_BABY_1L_run3/DAQ\"\n", "uCi_to_Bq = 3.7e4\n", "\n", "co60_checksource = CheckSource(\n", From 4f004b05a1dcb97929bab940de2078aafaa3b735 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 22 May 2025 09:50:20 -0400 Subject: [PATCH 111/137] use get_detector --- .../neutron_detection/activation_foils/compass.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index d9f2fcd..e3d2df5 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -266,12 +266,8 @@ def compute_detection_efficiency( """ # find right background detector - background_detector = [ - d for d in background_measurement.detectors if d.channel_nb == channel_nb - ][0] - check_source_detector = [ - d for d in self.detectors if d.channel_nb == channel_nb - ][0] + background_detector = background_measurement.get_detector(channel_nb) + check_source_detector = self.get_detector(channel_nb) hist, bin_edges = check_source_detector.get_energy_hist_background_substract( background_detector, bins=None From d5a1fee2e992160ab0860b89576c26935c44e3ba Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 23 May 2025 11:11:26 -0400 Subject: [PATCH 112/137] more readable --- .../activation_foils/compass.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index e3d2df5..5419b1f 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -431,18 +431,6 @@ def get_neutron_flux( self.start_time - time_generator_off ).total_seconds() - # Spectroscopic Factor to account for the branching ratio and the - # total detection efficiency - - f_spec = total_efficiency * branching_ratio - - number_of_decays_measured = photon_counts / f_spec - flux = ( - number_of_decays_measured - / self.foil.nb_atoms - / self.foil.reaction.cross_section - ) - detector = self.get_detector(channel_nb) f_time = ( @@ -479,6 +467,18 @@ def get_neutron_flux( * self.foil.thickness ) + # Spectroscopic Factor to account for the branching ratio and the + # total detection efficiency + f_spec = total_efficiency * branching_ratio + + number_of_decays_measured = photon_counts / f_spec + + flux = ( + number_of_decays_measured + / self.foil.nb_atoms + / self.foil.reaction.cross_section + ) + flux /= f_time * f_self return flux From f1f8385e06d4dd5e059f9ce28ce35598a201d639 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Fri, 23 May 2025 16:35:12 -0400 Subject: [PATCH 113/137] add assertion for negative counts --- libra_toolbox/neutron_detection/activation_foils/compass.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 5419b1f..12f6a05 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -285,6 +285,11 @@ def compute_detection_efficiency( nb_counts_measured = np.array(nb_counts_measured) nb_counts_measured_err = np.sqrt(nb_counts_measured) + # assert that all numbers in nb_counts_measured are > 0 + assert np.all( + nb_counts_measured > 0 + ), f"Some counts measured are <= 0: {nb_counts_measured}" + act_expec = self.check_source.get_expected_activity(self.start_time) gamma_rays_expected = act_expec * ( np.array(self.check_source.nuclide.intensity) From 14f95915c1f3518b7b2750c2b6092606ca82cdee Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 3 Jun 2025 10:58:26 -0700 Subject: [PATCH 114/137] fixed typo --- docs/examples/example.ipynb | 52 +++++++++---------- .../activation_foils/compass.py | 8 +-- 2 files changed, 30 insertions(+), 30 deletions(-) diff --git a/docs/examples/example.ipynb b/docs/examples/example.ipynb index 606341b..e0089be 100644 --- a/docs/examples/example.ipynb +++ b/docs/examples/example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -405,7 +405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_58431/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_243325/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -575,9 +575,9 @@ " bins=None,\n", ")\n", "\n", - "calibrated_bin_bedges = np.polyval(calibration_coeffs[ch_nb], bin_edges)\n", + "calibrated_bin_edges = np.polyval(calibration_coeffs[ch_nb], bin_edges)\n", "\n", - "xvals = np.diff(calibrated_bin_bedges) / 2 + calibrated_bin_bedges[:-1]\n", + "xvals = np.diff(calibrated_bin_edges) / 2 + calibrated_bin_edges[:-1]\n", "\n", "for energy_peak in all_measurements[meas_name].check_source.nuclide.energy:\n", "\n", @@ -596,8 +596,8 @@ " )\n", "\n", "plt.hist(\n", - " calibrated_bin_bedges[:-1],\n", - " bins=calibrated_bin_bedges,\n", + " calibrated_bin_edges[:-1],\n", + " bins=calibrated_bin_edges,\n", " weights=hist,\n", " histtype=\"step\",\n", " label=f\"Ch {detector.channel_nb} - calibrated\",\n", @@ -736,11 +736,11 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], @@ -865,7 +865,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 12f6a05..ecfa5fd 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -273,11 +273,11 @@ def compute_detection_efficiency( background_detector, bins=None ) - calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges) nb_counts_measured = get_multipeak_area( hist, - calibrated_bin_bedges, + calibrated_bin_edges, self.check_source.nuclide.energy, search_width=search_width, ) @@ -386,13 +386,13 @@ def get_gamma_emitted( background_detector, bins=None ) - calibrated_bin_bedges = np.polyval(calibration_coeffs, bin_edges) + calibrated_bin_edges = np.polyval(calibration_coeffs, bin_edges) energy = self.foil.reaction.product.energy nb_counts_measured = get_multipeak_area( hist, - calibrated_bin_bedges, + calibrated_bin_edges, energy, search_width=search_width, ) From 4a7c4ab8153b3ca4932f156836b545de9ebbdfaf Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 3 Jun 2025 11:00:37 -0700 Subject: [PATCH 115/137] moved example --- docs/{examples => non_tested_examples}/example.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/{examples => non_tested_examples}/example.ipynb (100%) diff --git a/docs/examples/example.ipynb b/docs/non_tested_examples/example.ipynb similarity index 100% rename from docs/examples/example.ipynb rename to docs/non_tested_examples/example.ipynb From c97469fa7ebf77b1cf76f2ba20151b53b1002aea Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Mon, 30 Jun 2025 17:23:05 -0400 Subject: [PATCH 116/137] Added zirconium reactions and updated foil data and usage --- .../activation_foils/__init__.py | 2 +- .../activation_foils/calibration.py | 31 +++++++++++++++++-- .../activation_foils/compass.py | 4 +-- 3 files changed, 32 insertions(+), 5 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/__init__.py b/libra_toolbox/neutron_detection/activation_foils/__init__.py index 049aafc..7850cab 100644 --- a/libra_toolbox/neutron_detection/activation_foils/__init__.py +++ b/libra_toolbox/neutron_detection/activation_foils/__init__.py @@ -1 +1 @@ -from . import explicit, settings, calculations +from . import explicit, settings, calculations, calibration, compass diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 5117a30..98daff1 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -26,6 +26,7 @@ class Nuclide: intensity: List[float] = None half_life: float = None atomic_mass: float = None + abundance: float = None @property def decay_constant(self): @@ -76,6 +77,20 @@ def decay_constant(self): nb93 = Nuclide( name="Nb93", atomic_mass=92.90637, + abundance=1.00 +) + +zr89 = Nuclide( + name="Zr89", + energy=[909.15], + intensity = [0.9904], + half_life=78.41 * 3600 +) + +zr90 = Nuclide( + name="Zr90", + atomic_mass=89.90469876, + abundance=0.515 ) @@ -96,6 +111,18 @@ class Reaction: The cross section of the reaction in cm2. """ +nb93_n2n = Reaction( + reactant=nb93, + product=nb92m, + cross_section=0.45966e-24 # cm2 at 14.1 MeV from IRDF-II 2020 +) + +zr90_n2n = Reaction( + reactant=zr90, + product=zr89, + cross_section=0.62389e-24 # cm2 at 14.1 MeV from IRDF-II 2020 +) + @dataclass class CheckSource: @@ -151,6 +178,7 @@ class ActivationFoil: reaction: Reaction mass: float name: str + density: float thickness: float = None """Class to hold the information of an activation foil. @@ -172,5 +200,4 @@ def nb_atoms(self) -> float: Returns the number of atoms in the foil. """ avogadro = 6.022e23 # part/mol - abundance = 1 - return abundance * (self.mass / self.reaction.reactant.atomic_mass * avogadro) + return self.reaction.reactant.abundance * (self.mass / self.reaction.reactant.atomic_mass * avogadro) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index ecfa5fd..f279ff3 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -671,13 +671,13 @@ def get_multipeak_area( # Use unimodal gaussian to estimate counts from just one peak peak_params = [parameters[0], parameters[1], parameters[2 + 3 * i], mean, sigma] all_peak_params += [peak_params] - gross_area = np.trapezoid( + gross_area = np.trapz( gauss(xvals[peak_start:peak_end], *peak_params), x=xvals[peak_start:peak_end], ) # Cut off trapezoidal area due to compton scattering and noise - trap_cutoff_area = np.trapezoid( + trap_cutoff_area = np.trapz( parameters[0] + parameters[1] * xvals[peak_start:peak_end], x=xvals[peak_start:peak_end], ) From 437fb1fcdcca3557828a83a58b4759c7857bb16a Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Tue, 1 Jul 2025 17:27:38 -0400 Subject: [PATCH 117/137] Added density argument to tests (not actually tested) --- test/neutron_detection/test_compass.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 97de579..cf43ec7 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -756,7 +756,7 @@ def test_get_gamma_emitted(efficiency: float): cross_section=20.0, ) - foil = ActivationFoil(reaction=reaction, mass=0.1, name="TestFoil") + foil = ActivationFoil(reaction=reaction, mass=0.1, name="TestFoil", density=1.0) measurement = compass.SampleMeasurement("sample") measurement.foil = foil @@ -825,6 +825,7 @@ def test_get_neutron_rate_very_long_half_life(photon_counts, distance): reaction=reaction, mass=0.1, name="TestFoil", + density=1.0, thickness=None, ) @@ -897,6 +898,7 @@ def test_get_neutron_rate_very_moderate_life(photon_counts, distance): reaction=reaction, mass=0.1, name="TestFoil", + density=1.0, thickness=None, ) From e7f254cc878cda2f57a7cc3d5398e35dbe648ed5 Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Wed, 2 Jul 2025 13:00:48 -0400 Subject: [PATCH 118/137] Removed density argument from tests and added default abundance --- .../neutron_detection/activation_foils/calibration.py | 4 ++-- test/neutron_detection/test_compass.py | 4 +--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 98daff1..8de56ec 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -26,7 +26,7 @@ class Nuclide: intensity: List[float] = None half_life: float = None atomic_mass: float = None - abundance: float = None + abundance: float = 1.00 @property def decay_constant(self): @@ -178,7 +178,7 @@ class ActivationFoil: reaction: Reaction mass: float name: str - density: float + density: float = 1.0 thickness: float = None """Class to hold the information of an activation foil. diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index cf43ec7..97de579 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -756,7 +756,7 @@ def test_get_gamma_emitted(efficiency: float): cross_section=20.0, ) - foil = ActivationFoil(reaction=reaction, mass=0.1, name="TestFoil", density=1.0) + foil = ActivationFoil(reaction=reaction, mass=0.1, name="TestFoil") measurement = compass.SampleMeasurement("sample") measurement.foil = foil @@ -825,7 +825,6 @@ def test_get_neutron_rate_very_long_half_life(photon_counts, distance): reaction=reaction, mass=0.1, name="TestFoil", - density=1.0, thickness=None, ) @@ -898,7 +897,6 @@ def test_get_neutron_rate_very_moderate_life(photon_counts, distance): reaction=reaction, mass=0.1, name="TestFoil", - density=1.0, thickness=None, ) From 3001797810052b2d23384bb43da8e8e6fbca6398 Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Wed, 2 Jul 2025 14:54:53 -0400 Subject: [PATCH 119/137] Added density, thickness check and test of check --- .../activation_foils/calibration.py | 8 ++++++- test/neutron_detection/test_compass.py | 23 +++++++++++++++++++ 2 files changed, 30 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 8de56ec..7f133f9 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -178,7 +178,7 @@ class ActivationFoil: reaction: Reaction mass: float name: str - density: float = 1.0 + density: float = None thickness: float = None """Class to hold the information of an activation foil. @@ -190,10 +190,16 @@ class ActivationFoil: The mass of the foil in grams. name : The name of the foil. + density : + The density of the foil in g/cm3. Default is 1.0 g/cm3. thickness : The thickness of the foil in cm. """ + def __post_init__(self): + if (self.thickness is None) != (self.density is None): + raise ValueError("Thickness and density must either both be floats or both be None.") + @property def nb_atoms(self) -> float: """ diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 97de579..761eb5c 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -944,3 +944,26 @@ def test_get_neutron_rate_very_moderate_life(photon_counts, distance): area_of_sphere = 4 * np.pi * distance**2 expected_neutron_rate = expected_neutron_flux * area_of_sphere assert np.isclose(computed_rate, expected_neutron_rate) + + +def test_activationfoil_density_thickness_validation(): + + nuclide_reactant = Nuclide(name="TestNuclide", atomic_mass=200) + activated_nuclide = Nuclide( + name="ActivatedNuclide", + energy=[1000], + intensity=[1.0], + half_life=10 * 24 * 3600, # 10 days + ) + + reaction = Reaction( + reactant=nuclide_reactant, + product=activated_nuclide, + cross_section=20.0, + ) + + with pytest.raises(ValueError, match="Thickness and density must either both be floats or both be None."): + ActivationFoil(reaction=reaction, mass=1.0, name="foil", density=1.0) + + with pytest.raises(ValueError, match="Thickness and density must either both be floats or both be None."): + ActivationFoil(reaction=reaction, mass=1.0, name="foil", thickness=0.1) From bb031f19aab14cd5210a03a2ad509bf45f7f5287 Mon Sep 17 00:00:00 2001 From: Collin Dunn <94483121+cdunn314@users.noreply.github.com> Date: Wed, 2 Jul 2025 15:22:13 -0400 Subject: [PATCH 120/137] Update libra_toolbox/neutron_detection/activation_foils/calibration.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Rémi Delaporte-Mathurin <40028739+RemDelaporteMathurin@users.noreply.github.com> --- libra_toolbox/neutron_detection/activation_foils/calibration.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 7f133f9..73212fa 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -191,7 +191,7 @@ class ActivationFoil: name : The name of the foil. density : - The density of the foil in g/cm3. Default is 1.0 g/cm3. + The density of the foil in g/cm3. thickness : The thickness of the foil in cm. """ From 0fa9dd3c68eb8e1f0fc7389391c057194915ac1e Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Wed, 2 Jul 2025 15:26:30 -0400 Subject: [PATCH 121/137] Added documentation for abundance and atomic_mass --- .../neutron_detection/activation_foils/calibration.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/libra_toolbox/neutron_detection/activation_foils/calibration.py b/libra_toolbox/neutron_detection/activation_foils/calibration.py index 7f133f9..5bb0867 100644 --- a/libra_toolbox/neutron_detection/activation_foils/calibration.py +++ b/libra_toolbox/neutron_detection/activation_foils/calibration.py @@ -19,6 +19,10 @@ class Nuclide: The intensity of the gamma rays emitted by the nuclide. half_life : The half-life of the nuclide in seconds. + atomic_mass : + The atomic mass of the nuclide in atomic mass units (amu). + abundance : + The natural abundance of the nuclide as a fraction (default is 1.00). """ name: str From 1c3717195e4893c2073a2846aeb0074f99fb7a80 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 2 Jul 2025 20:21:21 -0400 Subject: [PATCH 122/137] added to_h5 and from_h5 methods --- .../activation_foils/compass.py | 110 +++++++ pyproject.toml | 2 +- test/neutron_detection/test_compass.py | 293 ++++++++++++++++++ 3 files changed, 404 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index f279ff3..fd83441 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -7,6 +7,7 @@ import datetime import uproot import glob +import h5py import warnings from libra_toolbox.neutron_detection.activation_foils.calibration import ( @@ -219,6 +220,115 @@ def from_directory( return measurement_object + def to_h5(self, filename: str, mode: str = "w") -> None: + """ + Save the measurement data to an HDF5 file. + Args: + filename: name of the output HDF5 file + mode: file opening mode ('w' for write/overwrite, 'a' for append) + """ + with h5py.File(filename, mode) as f: + # Create a group for the measurement (or get existing one) + if self.name in f: + # If group already exists, we could either raise an error or overwrite + # For now, let's overwrite the existing group + del f[self.name] + measurement_group = f.create_group(self.name) + + # Store start and stop time + if self.start_time: + measurement_group.attrs["start_time"] = self.start_time.isoformat() + if self.stop_time: + measurement_group.attrs["stop_time"] = self.stop_time.isoformat() + + # Store detectors + for detector in self.detectors: + detector_group = measurement_group.create_group(f"detector_{detector.channel_nb}") + detector_group.create_dataset("events", data=detector.events) + detector_group.attrs["live_count_time"] = detector.live_count_time + detector_group.attrs["real_count_time"] = detector.real_count_time + + @classmethod + def from_h5(cls, filename: str, measurement_name: str = None) -> Union["Measurement", List["Measurement"]]: + """ + Load measurement data from an HDF5 file. + Args: + filename: name of the HDF5 file + measurement_name: specific measurement name to load. If None, loads all measurements. + Returns: + Single Measurement object if measurement_name is specified, + or list of Measurement objects if loading all measurements. + """ + measurements = [] + + with h5py.File(filename, "r") as f: + # Get all measurement group names + measurement_names = [name for name in f.keys() if isinstance(f[name], h5py.Group)] + + if measurement_name is not None: + if measurement_name not in measurement_names: + raise ValueError(f"Measurement '{measurement_name}' not found in file. Available: {measurement_names}") + measurement_names = [measurement_name] + + for name in measurement_names: + measurement = cls(name=name) + measurement_group = f[name] + + # Load start and stop time + if "start_time" in measurement_group.attrs: + measurement.start_time = datetime.datetime.fromisoformat( + measurement_group.attrs["start_time"] + ) + if "stop_time" in measurement_group.attrs: + measurement.stop_time = datetime.datetime.fromisoformat( + measurement_group.attrs["stop_time"] + ) + + # Load detectors + detectors = [] + for detector_name in measurement_group.keys(): + if detector_name.startswith("detector_"): + channel_nb = int(detector_name.replace("detector_", "")) + detector = Detector(channel_nb=channel_nb) + + detector_group = measurement_group[detector_name] + detector.events = detector_group["events"][:] + detector.live_count_time = detector_group.attrs["live_count_time"] + detector.real_count_time = detector_group.attrs["real_count_time"] + + detectors.append(detector) + + measurement.detectors = detectors + measurements.append(measurement) + + return measurements[0] if measurement_name is not None else measurements + + @classmethod + def write_multiple_to_h5(cls, measurements: List["Measurement"], filename: str) -> None: + """ + Save multiple measurement objects to a single HDF5 file. + Args: + measurements: list of Measurement objects to save + filename: name of the output HDF5 file + """ + with h5py.File(filename, "w") as f: + for measurement in measurements: + # Create a group for each measurement + measurement_group = f.create_group(measurement.name) + + # Store start and stop time + if measurement.start_time: + measurement_group.attrs["start_time"] = measurement.start_time.isoformat() + if measurement.stop_time: + measurement_group.attrs["stop_time"] = measurement.stop_time.isoformat() + + # Store detectors + for detector in measurement.detectors: + detector_group = measurement_group.create_group(f"detector_{detector.channel_nb}") + detector_group.create_dataset("events", data=detector.events) + detector_group.attrs["live_count_time"] = detector.live_count_time + detector_group.attrs["real_count_time"] = detector.real_count_time + def get_detector(self, channel_nb: int) -> Detector: """ Get the detector object for a given channel number. diff --git a/pyproject.toml b/pyproject.toml index 1a9a939..0c88cb2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,7 +15,7 @@ description = "Design and analysis tools for LIBRA project" license = {file = "LICENSE"} requires-python = ">=3.10" dynamic = ["version"] -dependencies = ["numpy", "pint", "scipy", "matplotlib", "sympy", "pandas", "h5py", "uproot"] +dependencies = ["numpy", "pint", "scipy", "matplotlib", "sympy", "pandas", "h5py", "uproot", "h5py"] [project.optional-dependencies] neutronics = ["openmc-data-downloader"] diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 761eb5c..001b519 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -10,6 +10,7 @@ ) from pathlib import Path import datetime +import h5py @pytest.mark.parametrize( @@ -967,3 +968,295 @@ def test_activationfoil_density_thickness_validation(): with pytest.raises(ValueError, match="Thickness and density must either both be floats or both be None."): ActivationFoil(reaction=reaction, mass=1.0, name="foil", thickness=0.1) + + +def create_test_measurement(name: str, num_detectors: int = 2, num_events: int = 100) -> compass.Measurement: + """ + Helper function to create a test measurement with synthetic data. + """ + measurement = compass.Measurement(name) + + # Set start and stop times + measurement.start_time = datetime.datetime(2025, 1, 1, 10, 0, 0) + measurement.stop_time = datetime.datetime(2025, 1, 1, 10, 15, 0) + + # Create detectors with synthetic events + for channel_nb in range(num_detectors): + detector = compass.Detector(channel_nb) + + # Generate synthetic events (time in ps, energy) + times = np.random.uniform(0, 1e12, num_events) # Random times in ps + energies = np.random.uniform(100, 1000, num_events) # Random energies + detector.events = np.column_stack((times, energies)) + + # Set timing information + detector.live_count_time = 900.0 # 15 minutes + detector.real_count_time = 900.0 + + measurement.detectors.append(detector) + + return measurement + + +def test_measurement_to_h5_single(tmpdir): + """ + Test the Measurement.to_h5 method for a single measurement. + """ + # Create test measurement + measurement = create_test_measurement("test_measurement", num_detectors=2, num_events=50) + + # Save to HDF5 + h5_file = os.path.join(tmpdir, "test_single.h5") + measurement.to_h5(h5_file, mode="w") + + # Verify file exists and has correct structure + assert os.path.exists(h5_file) + + with h5py.File(h5_file, "r") as f: + # Check measurement group exists + assert "test_measurement" in f + measurement_group = f["test_measurement"] + + # Check attributes + assert "start_time" in measurement_group.attrs + assert "stop_time" in measurement_group.attrs + assert measurement_group.attrs["start_time"] == "2025-01-01T10:00:00" + assert measurement_group.attrs["stop_time"] == "2025-01-01T10:15:00" + + # Check detectors + assert "detector_0" in measurement_group + assert "detector_1" in measurement_group + + # Check detector data + detector_group = measurement_group["detector_0"] + assert "events" in detector_group + assert detector_group["events"].shape[1] == 2 # time, energy columns + assert detector_group["events"].shape[0] == 50 # number of events + assert detector_group.attrs["live_count_time"] == 900.0 + assert detector_group.attrs["real_count_time"] == 900.0 + + +def test_measurement_to_h5_append_mode(tmpdir): + """ + Test the Measurement.to_h5 method with append mode for multiple measurements. + """ + # Create test measurements + measurement1 = create_test_measurement("measurement_1", num_detectors=1, num_events=30) + measurement2 = create_test_measurement("measurement_2", num_detectors=2, num_events=40) + + h5_file = os.path.join(tmpdir, "test_append.h5") + + # Save first measurement + measurement1.to_h5(h5_file, mode="w") + + # Append second measurement + measurement2.to_h5(h5_file, mode="a") + + # Verify both measurements are in the file + with h5py.File(h5_file, "r") as f: + assert "measurement_1" in f + assert "measurement_2" in f + + # Check first measurement + assert "detector_0" in f["measurement_1"] + assert f["measurement_1"]["detector_0"]["events"].shape[0] == 30 + + # Check second measurement + assert "detector_0" in f["measurement_2"] + assert "detector_1" in f["measurement_2"] + assert f["measurement_2"]["detector_0"]["events"].shape[0] == 40 + + +def test_measurement_to_h5_overwrite_existing(tmpdir): + """ + Test that writing a measurement with the same name overwrites the existing one. + """ + # Create initial measurement + measurement1 = create_test_measurement("same_name", num_detectors=1, num_events=30) + measurement1.detectors[0].live_count_time = 100.0 + + # Create updated measurement with same name + measurement2 = create_test_measurement("same_name", num_detectors=1, num_events=50) + measurement2.detectors[0].live_count_time = 200.0 + + h5_file = os.path.join(tmpdir, "test_overwrite.h5") + + # Save first measurement + measurement1.to_h5(h5_file, mode="w") + + # Overwrite with second measurement + measurement2.to_h5(h5_file, mode="a") + + # Verify only the second measurement data remains + with h5py.File(h5_file, "r") as f: + assert "same_name" in f + detector_group = f["same_name"]["detector_0"] + assert detector_group["events"].shape[0] == 50 # New data + assert detector_group.attrs["live_count_time"] == 200.0 # New timing + + +def test_measurement_write_multiple_to_h5(tmpdir): + """ + Test the Measurement.write_multiple_to_h5 class method. + """ + # Create multiple test measurements + measurements = [ + create_test_measurement("exp_1", num_detectors=1, num_events=20), + create_test_measurement("exp_2", num_detectors=2, num_events=30), + create_test_measurement("exp_3", num_detectors=3, num_events=40), + ] + + h5_file = os.path.join(tmpdir, "test_multiple.h5") + + # Write all measurements to file + compass.Measurement.write_multiple_to_h5(measurements, h5_file) + + # Verify all measurements are in the file + with h5py.File(h5_file, "r") as f: + assert "exp_1" in f + assert "exp_2" in f + assert "exp_3" in f + + # Check each measurement has correct number of detectors + assert len([k for k in f["exp_1"].keys() if k.startswith("detector_")]) == 1 + assert len([k for k in f["exp_2"].keys() if k.startswith("detector_")]) == 2 + assert len([k for k in f["exp_3"].keys() if k.startswith("detector_")]) == 3 + + # Check event counts + assert f["exp_1"]["detector_0"]["events"].shape[0] == 20 + assert f["exp_2"]["detector_0"]["events"].shape[0] == 30 + assert f["exp_3"]["detector_0"]["events"].shape[0] == 40 + + +def test_measurement_from_h5_single(tmpdir): + """ + Test the Measurement.from_h5 method for loading a single measurement. + """ + # Create and save a test measurement + original_measurement = create_test_measurement("test_load", num_detectors=2, num_events=35) + h5_file = os.path.join(tmpdir, "test_load_single.h5") + original_measurement.to_h5(h5_file) + + # Load the measurement back + loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="test_load") + + # Verify loaded measurement matches original + assert loaded_measurement.name == "test_load" + assert loaded_measurement.start_time == original_measurement.start_time + assert loaded_measurement.stop_time == original_measurement.stop_time + assert len(loaded_measurement.detectors) == 2 + + # Check detector data + for i, detector in enumerate(loaded_measurement.detectors): + original_detector = original_measurement.detectors[i] + assert detector.channel_nb == original_detector.channel_nb + assert detector.live_count_time == original_detector.live_count_time + assert detector.real_count_time == original_detector.real_count_time + np.testing.assert_array_equal(detector.events, original_detector.events) + + +def test_measurement_from_h5_all_measurements(tmpdir): + """ + Test the Measurement.from_h5 method for loading all measurements from a file. + """ + # Create and save multiple measurements + measurements = [ + create_test_measurement("load_1", num_detectors=1, num_events=25), + create_test_measurement("load_2", num_detectors=2, num_events=35), + ] + + h5_file = os.path.join(tmpdir, "test_load_all.h5") + compass.Measurement.write_multiple_to_h5(measurements, h5_file) + + # Load all measurements + loaded_measurements = compass.Measurement.from_h5(h5_file) + + # Verify we got all measurements + assert len(loaded_measurements) == 2 + loaded_names = [m.name for m in loaded_measurements] + assert "load_1" in loaded_names + assert "load_2" in loaded_names + + # Find corresponding measurements + load_1 = next(m for m in loaded_measurements if m.name == "load_1") + load_2 = next(m for m in loaded_measurements if m.name == "load_2") + + assert len(load_1.detectors) == 1 + assert len(load_2.detectors) == 2 + assert load_1.detectors[0].events.shape[0] == 25 + assert load_2.detectors[0].events.shape[0] == 35 + + +def test_measurement_from_h5_nonexistent_measurement(tmpdir): + """ + Test that loading a non-existent measurement raises appropriate error. + """ + # Create a measurement and save it + measurement = create_test_measurement("existing", num_detectors=1, num_events=10) + h5_file = os.path.join(tmpdir, "test_nonexistent.h5") + measurement.to_h5(h5_file) + + # Try to load a non-existent measurement + with pytest.raises(ValueError, match="Measurement 'nonexistent' not found in file"): + compass.Measurement.from_h5(h5_file, measurement_name="nonexistent") + + +def test_measurement_h5_roundtrip(tmpdir): + """ + Test complete roundtrip: create -> save -> load -> verify data integrity. + """ + # Create measurement with specific, verifiable data + measurement = compass.Measurement("roundtrip_test") + measurement.start_time = datetime.datetime(2025, 7, 2, 14, 30, 0) + measurement.stop_time = datetime.datetime(2025, 7, 2, 15, 0, 0) + + # Create detector with specific events + detector = compass.Detector(channel_nb=5) + detector.events = np.array([ + [1000000000, 150.5], # time in ps, energy + [2000000000, 250.7], + [3000000000, 350.9], + ]) + detector.live_count_time = 1800.0 + detector.real_count_time = 1800.0 + measurement.detectors = [detector] + + # Save and load + h5_file = os.path.join(tmpdir, "roundtrip.h5") + measurement.to_h5(h5_file) + loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="roundtrip_test") + + # Verify exact data integrity + assert loaded_measurement.name == "roundtrip_test" + assert loaded_measurement.start_time == measurement.start_time + assert loaded_measurement.stop_time == measurement.stop_time + assert len(loaded_measurement.detectors) == 1 + + loaded_detector = loaded_measurement.detectors[0] + assert loaded_detector.channel_nb == 5 + assert loaded_detector.live_count_time == 1800.0 + assert loaded_detector.real_count_time == 1800.0 + np.testing.assert_array_equal(loaded_detector.events, detector.events) + + +def test_measurement_h5_empty_measurement(tmpdir): + """ + Test saving and loading a measurement with no detectors. + """ + # Create empty measurement + measurement = compass.Measurement("empty_test") + measurement.start_time = datetime.datetime(2025, 1, 1, 12, 0, 0) + measurement.stop_time = datetime.datetime(2025, 1, 1, 12, 30, 0) + measurement.detectors = [] # No detectors + + # Save and load + h5_file = os.path.join(tmpdir, "empty.h5") + measurement.to_h5(h5_file) + loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="empty_test") + + # Verify empty measurement + assert loaded_measurement.name == "empty_test" + assert loaded_measurement.start_time == measurement.start_time + assert loaded_measurement.stop_time == measurement.stop_time + assert len(loaded_measurement.detectors) == 0 + From 3de320ada04eafdb6c3f9d07f3dd3595ceebc392 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Wed, 2 Jul 2025 21:12:35 -0400 Subject: [PATCH 123/137] added way to only store the spectrum and energy bins --- .../activation_foils/compass.py | 109 ++++++++--- test/neutron_detection/test_compass.py | 185 ++++++++++++++++++ 2 files changed, 271 insertions(+), 23 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index fd83441..953be37 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -36,12 +36,16 @@ class Detector: channel_nb: Channel number of the detector live_count_time: Active measurement time excluding dead time (in seconds) real_count_time: Total elapsed measurement time (in seconds) + spectrum: Cached energy spectrum (accessed via property) + bin_edges: Cached bin edges for the energy spectrum (accessed via property) """ events: NDArray[Tuple[float, float]] # type: ignore channel_nb: int live_count_time: Union[float, None] real_count_time: Union[float, None] + _spectrum: Union[NDArray[np.float64], None] = None + _bin_edges: Union[NDArray[np.float64], None] = None def __init__(self, channel_nb) -> None: """ @@ -54,6 +58,16 @@ def __init__(self, channel_nb) -> None: self.live_count_time = None self.real_count_time = None + @property + def spectrum(self) -> Union[NDArray[np.float64], None]: + """Get the cached energy spectrum. Read-only property.""" + return getattr(self, "_spectrum", None) + + @property + def bin_edges(self) -> Union[NDArray[np.float64], None]: + """Get the cached bin edges for the energy spectrum. Read-only property.""" + return getattr(self, "_bin_edges", None) + def get_energy_hist( self, bins: Union[None, NDArray[np.float64], int, str] = None ) -> Tuple[np.ndarray, np.ndarray]: @@ -67,6 +81,9 @@ def get_energy_hist( Returns: Tuple of histogram values and bin edges """ + if self._spectrum is not None and self._bin_edges is not None: + # If spectrum and bin edges are already calculated, return them + return self._spectrum, self._bin_edges energy_values = self.events[:, 1].copy() time_values = self.events[:, 0].copy() @@ -80,7 +97,7 @@ def get_energy_hist( if bins is None: bins = np.arange( - int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + 1 ) return np.histogram(energy_values, bins=bins) @@ -92,6 +109,20 @@ def get_energy_hist_background_substract( ) -> Tuple[np.ndarray, np.ndarray]: ps_to_seconds = 1e-12 raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) + + # If background spectrum and bin edges are already calculated, return them + if ( + background_detector._spectrum is not None + and background_detector._bin_edges is not None + ): + assert ( + raw_bin_edges == background_detector._bin_edges + ).all(), "Background detector bin edges do not match" + return ( + raw_hist - background_detector._spectrum, + raw_bin_edges, + ) + background_times = background_detector.events[:, 0].copy() background_energies = background_detector.events[:, 1].copy() @@ -220,7 +251,7 @@ def from_directory( return measurement_object - def to_h5(self, filename: str, mode: str = "w") -> None: + def to_h5(self, filename: str, mode: str = "w", spectrum_only=False) -> None: """ Save the measurement data to an HDF5 file. Args: @@ -243,37 +274,52 @@ def to_h5(self, filename: str, mode: str = "w") -> None: # Store detectors for detector in self.detectors: - detector_group = measurement_group.create_group(f"detector_{detector.channel_nb}") - detector_group.create_dataset("events", data=detector.events) + detector_group = measurement_group.create_group( + f"detector_{detector.channel_nb}" + ) + if spectrum_only: + hist, bin_edges = detector.get_energy_hist(bins=None) + detector_group.create_dataset("spectrum", data=hist) + detector_group.create_dataset("bin_edges", data=bin_edges) + detector_group.create_dataset("events", data=[]) + else: + detector_group.create_dataset("events", data=detector.events) + detector_group.attrs["live_count_time"] = detector.live_count_time detector_group.attrs["real_count_time"] = detector.real_count_time @classmethod - def from_h5(cls, filename: str, measurement_name: str = None) -> Union["Measurement", List["Measurement"]]: + def from_h5( + cls, filename: str, measurement_name: str = None + ) -> Union["Measurement", List["Measurement"]]: """ Load measurement data from an HDF5 file. Args: filename: name of the HDF5 file measurement_name: specific measurement name to load. If None, loads all measurements. Returns: - Single Measurement object if measurement_name is specified, + Single Measurement object if measurement_name is specified, or list of Measurement objects if loading all measurements. """ measurements = [] - + with h5py.File(filename, "r") as f: # Get all measurement group names - measurement_names = [name for name in f.keys() if isinstance(f[name], h5py.Group)] - + measurement_names = [ + name for name in f.keys() if isinstance(f[name], h5py.Group) + ] + if measurement_name is not None: if measurement_name not in measurement_names: - raise ValueError(f"Measurement '{measurement_name}' not found in file. Available: {measurement_names}") + raise ValueError( + f"Measurement '{measurement_name}' not found in file. Available: {measurement_names}" + ) measurement_names = [measurement_name] - + for name in measurement_names: measurement = cls(name=name) measurement_group = f[name] - + # Load start and stop time if "start_time" in measurement_group.attrs: measurement.start_time = datetime.datetime.fromisoformat( @@ -283,28 +329,39 @@ def from_h5(cls, filename: str, measurement_name: str = None) -> Union["Measurem measurement.stop_time = datetime.datetime.fromisoformat( measurement_group.attrs["stop_time"] ) - + # Load detectors detectors = [] for detector_name in measurement_group.keys(): if detector_name.startswith("detector_"): channel_nb = int(detector_name.replace("detector_", "")) detector = Detector(channel_nb=channel_nb) - + detector_group = measurement_group[detector_name] detector.events = detector_group["events"][:] - detector.live_count_time = detector_group.attrs["live_count_time"] - detector.real_count_time = detector_group.attrs["real_count_time"] - + detector.live_count_time = detector_group.attrs[ + "live_count_time" + ] + detector.real_count_time = detector_group.attrs[ + "real_count_time" + ] + + if "spectrum" in detector_group: + detector._spectrum = detector_group["spectrum"][:] + if "bin_edges" in detector_group: + detector._bin_edges = detector_group["bin_edges"][:] + detectors.append(detector) - + measurement.detectors = detectors measurements.append(measurement) - + return measurements[0] if measurement_name is not None else measurements @classmethod - def write_multiple_to_h5(cls, measurements: List["Measurement"], filename: str) -> None: + def write_multiple_to_h5( + cls, measurements: List["Measurement"], filename: str + ) -> None: """ Save multiple measurement objects to a single HDF5 file. Args: @@ -318,13 +375,19 @@ def write_multiple_to_h5(cls, measurements: List["Measurement"], filename: str) # Store start and stop time if measurement.start_time: - measurement_group.attrs["start_time"] = measurement.start_time.isoformat() + measurement_group.attrs["start_time"] = ( + measurement.start_time.isoformat() + ) if measurement.stop_time: - measurement_group.attrs["stop_time"] = measurement.stop_time.isoformat() + measurement_group.attrs["stop_time"] = ( + measurement.stop_time.isoformat() + ) # Store detectors for detector in measurement.detectors: - detector_group = measurement_group.create_group(f"detector_{detector.channel_nb}") + detector_group = measurement_group.create_group( + f"detector_{detector.channel_nb}" + ) detector_group.create_dataset("events", data=detector.events) detector_group.attrs["live_count_time"] = detector.live_count_time detector_group.attrs["real_count_time"] = detector.real_count_time diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 001b519..505c042 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -1260,3 +1260,188 @@ def test_measurement_h5_empty_measurement(tmpdir): assert loaded_measurement.stop_time == measurement.stop_time assert len(loaded_measurement.detectors) == 0 + +def test_measurement_h5_roundtrip_spectrum_only(tmpdir): + """ + Test complete roundtrip with spectrum_only flag: create -> save -> load -> verify spectrum data integrity. + """ + # Create measurement with specific, verifiable data + measurement = compass.Measurement("roundtrip_spectrum_test") + measurement.start_time = datetime.datetime(2025, 7, 2, 14, 30, 0) + measurement.stop_time = datetime.datetime(2025, 7, 2, 15, 0, 0) + + # Create detector with specific events that will create a predictable spectrum + detector = compass.Detector(channel_nb=5) + # Create events with integer energies for predictable histogram + detector.events = np.array([ + [1000000000, 100.0], # time in ps, energy + [2000000000, 100.0], # Same energy -> 2 counts in bin 100 + [3000000000, 200.0], # Different energy -> 1 count in bin 200 + [4000000000, 200.0], # Same energy -> 2 counts in bin 200 + [5000000000, 300.0], # Different energy -> 1 count in bin 300 + [5000000000, 300.0], # Same energy -> 2 counts in bin 300 + [5000000000, 400.0], # Different energy -> 1 count in bin 400 + ]) + detector.live_count_time = 1800.0 + detector.real_count_time = 1800.0 + measurement.detectors = [detector] + + # Get the expected spectrum before saving + expected_hist, expected_bin_edges = detector.get_energy_hist(bins=None) + + # Save with spectrum_only=True and load + h5_file = os.path.join(tmpdir, "roundtrip_spectrum.h5") + measurement.to_h5(h5_file, spectrum_only=True) + loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="roundtrip_spectrum_test") + + # Verify basic measurement data integrity + assert loaded_measurement.name == "roundtrip_spectrum_test" + assert loaded_measurement.start_time == measurement.start_time + assert loaded_measurement.stop_time == measurement.stop_time + assert len(loaded_measurement.detectors) == 1 + + loaded_detector = loaded_measurement.detectors[0] + assert loaded_detector.channel_nb == 5 + assert loaded_detector.live_count_time == 1800.0 + assert loaded_detector.real_count_time == 1800.0 + + # Verify events array is empty (spectrum_only mode) + assert loaded_detector.events.shape[0] == 0 + + # Verify spectrum data is present and correct + assert hasattr(loaded_detector, 'spectrum') + assert hasattr(loaded_detector, 'bin_edges') + np.testing.assert_array_equal(loaded_detector.spectrum, expected_hist) + np.testing.assert_array_equal(loaded_detector.bin_edges, expected_bin_edges) + + # Verify the spectrum contains expected counts + # The exact bin positions depend on the histogram implementation + print(f"Spectrum: {loaded_detector.spectrum}") + print(f"Bin edges: {loaded_detector.bin_edges}") + assert np.sum(loaded_detector.spectrum) == 7 # Total number of events + + +def test_measurement_h5_spectrum_only_file_structure(tmpdir): + """ + Test that spectrum_only mode creates the correct HDF5 file structure. + """ + # Create measurement with events + measurement = create_test_measurement("spectrum_structure_test", num_detectors=1, num_events=100) + + # Save with spectrum_only=True + h5_file = os.path.join(tmpdir, "spectrum_structure.h5") + measurement.to_h5(h5_file, spectrum_only=True) + + # Verify file structure + with h5py.File(h5_file, "r") as f: + assert "spectrum_structure_test" in f + measurement_group = f["spectrum_structure_test"] + + # Check measurement attributes + assert "start_time" in measurement_group.attrs + assert "stop_time" in measurement_group.attrs + + # Check detector group + assert "detector_0" in measurement_group + detector_group = measurement_group["detector_0"] + + # In spectrum_only mode, should have spectrum and bin_edges, but empty events + assert "spectrum" in detector_group + assert "bin_edges" in detector_group + assert "events" in detector_group + + # Events should be empty array + assert detector_group["events"].shape[0] == 0 + + # Spectrum should have data + assert detector_group["spectrum"].shape[0] > 0 + assert detector_group["bin_edges"].shape[0] > 0 + + # Timing attributes should still be present + assert "live_count_time" in detector_group.attrs + assert "real_count_time" in detector_group.attrs + + +def test_measurement_h5_spectrum_only_vs_full_size_comparison(tmpdir): + """ + Test that spectrum_only mode produces smaller files than full event storage. + """ + # Create measurement with many events to see file size difference + measurement = create_test_measurement("size_test", num_detectors=1, num_events=1000) + + # Save in both modes + h5_file_full = os.path.join(tmpdir, "full_events.h5") + h5_file_spectrum = os.path.join(tmpdir, "spectrum_only.h5") + + measurement.to_h5(h5_file_full, spectrum_only=False) + measurement.to_h5(h5_file_spectrum, spectrum_only=True) + + # Compare file sizes + full_size = os.path.getsize(h5_file_full) + spectrum_size = os.path.getsize(h5_file_spectrum) + + # Spectrum-only file should be smaller (unless histogram has more bins than events) + # At minimum, both files should exist and have reasonable sizes + assert full_size > 0 + assert spectrum_size > 0 + + # For 1000 events, the full file should typically be larger + # (though this could depend on the specific data and compression) + print(f"Full events file size: {full_size} bytes") + print(f"Spectrum only file size: {spectrum_size} bytes") + + +def test_measurement_h5_spectrum_only_analysis_capability(tmpdir): + """ + Test that spectrum_only data can still be used for basic analysis. + """ + # Create measurement with well-defined energy distribution + measurement = compass.Measurement("analysis_test") + measurement.start_time = datetime.datetime(2025, 7, 2, 10, 0, 0) + measurement.stop_time = datetime.datetime(2025, 7, 2, 10, 30, 0) + + detector = compass.Detector(channel_nb=1) + # Create events with known energy distribution + energies = np.concatenate([ + np.full(50, 500.0), # 50 events at 500 keV + np.full(30, 600.0), # 30 events at 600 keV + np.full(20, 700.0), # 20 events at 700 keV + ]) + times = np.random.uniform(0, 1e12, len(energies)) + detector.events = np.column_stack((times, energies)) + detector.live_count_time = 1800.0 + detector.real_count_time = 1800.0 + measurement.detectors = [detector] + + # Save with spectrum_only=True + h5_file = os.path.join(tmpdir, "analysis_spectrum.h5") + measurement.to_h5(h5_file, spectrum_only=True) + + # Load and analyze spectrum + loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="analysis_test") + loaded_detector = loaded_measurement.detectors[0] + + # Verify we can analyze the spectrum + assert hasattr(loaded_detector, 'spectrum') + assert hasattr(loaded_detector, 'bin_edges') + + # Check total counts + total_counts = np.sum(loaded_detector.spectrum) + assert total_counts == 100 # 50 + 30 + 20 + + # Check that peak energies are preserved in the spectrum + # Find bin centers + bin_centers = (loaded_detector.bin_edges[:-1] + loaded_detector.bin_edges[1:]) / 2 + + # Find peaks in the spectrum (simple approach) + peak_indices = np.where(loaded_detector.spectrum > 15)[0] # Bins with significant counts + peak_energies = bin_centers[peak_indices] + + # Should have peaks near our input energies (500, 600, 700) + assert len(peak_energies) >= 3, "Should find at least 3 energy peaks" + + # Verify the spectrum structure makes sense + assert loaded_detector.spectrum.dtype in [np.int32, np.int64, np.uint32, np.uint64] + assert loaded_detector.bin_edges.dtype in [np.int32, np.int64, np.uint32, np.uint64] + assert len(loaded_detector.bin_edges) == len(loaded_detector.spectrum) + 1 + From c172223950fa01b0e38620a144c4ef749fc702be Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 3 Jul 2025 08:06:18 -0400 Subject: [PATCH 124/137] fix channel nb comparison --- libra_toolbox/neutron_detection/activation_foils/compass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 953be37..b307424 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -716,7 +716,7 @@ def get_calibration_data( background_detector = [ detector for detector in background_measurement.detectors - if detector.channel_nb == detector.channel_nb + if detector.channel_nb == channel_nb ][0] calibration_energies = [] From 01bf29d46e7628c8ed7d7cfb23856a26aa4641b0 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 3 Jul 2025 08:33:13 -0400 Subject: [PATCH 125/137] additional fixes --- .../neutron_detection/activation_foils/compass.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index b307424..9dfa7f2 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -107,6 +107,11 @@ def get_energy_hist_background_substract( background_detector: "Detector", bins: Union[NDArray[np.float64], None] = None, ) -> Tuple[np.ndarray, np.ndarray]: + + assert ( + self.channel_nb == background_detector.channel_nb + ), f"Channel number mismatch: {self.channel_nb} != {background_detector.channel_nb}" + ps_to_seconds = 1e-12 raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) @@ -115,13 +120,7 @@ def get_energy_hist_background_substract( background_detector._spectrum is not None and background_detector._bin_edges is not None ): - assert ( - raw_bin_edges == background_detector._bin_edges - ).all(), "Background detector bin edges do not match" - return ( - raw_hist - background_detector._spectrum, - raw_bin_edges, - ) + raise ValueError("Background spectrum and bin edges must be calculated.") background_times = background_detector.events[:, 0].copy() background_energies = background_detector.events[:, 1].copy() From e0535af66e539d41e6a343464a678a650cf6cdb5 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 3 Jul 2025 08:33:24 -0400 Subject: [PATCH 126/137] adapted example --- docs/non_tested_examples/example.ipynb | 360 ++++++++++++++++++++----- 1 file changed, 297 insertions(+), 63 deletions(-) diff --git a/docs/non_tested_examples/example.ipynb b/docs/non_tested_examples/example.ipynb index e0089be..30988e6 100644 --- a/docs/non_tested_examples/example.ipynb +++ b/docs/non_tested_examples/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:178: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:232: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -180,10 +180,77 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [], + "source": [ + "mode = \"w\"\n", + "for meas in all_measurements.values():\n", + " meas.to_h5(\"data.h5\", mode=mode, spectrum_only=True)\n", + " mode = \"a\" # Change to append mode after the first measurement\n", + "\n", + "background_meas.to_h5(\"data.h5\", mode=\"a\", spectrum_only=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Co60_1...\n", + "\n", + "Processing Co60_2...\n", + "\n", + "Processing Co60_3...\n", + "\n", + "Processing Co60_4...\n", + "\n", + "Processing Co60_5...\n", + "\n", + "Processing Cs137_1...\n", + "\n", + "Processing Cs137_2...\n", + "\n", + "Processing Cs137_3...\n", + "\n", + "Processing Cs137_4...\n", + "\n", + "Processing Mn54_1...\n", + "\n", + "Processing Mn54_2...\n", + "\n", + "Processing Mn54_3...\n", + "\n", + "Processing Na22_2...\n", + "\n", + "Processing Na22_3...\n", + "\n", + "Processing Na22_4...\n", + "\n" + ] + } + ], + "source": [ + "for name, values in check_source_measurements.items():\n", + " print(f\"Processing {name}...\")\n", + " meas = CheckSourceMeasurement.from_h5(\"data.h5\", measurement_name=name)\n", + " meas.check_source = values[\"check_source\"]\n", + " print(meas)\n", + " all_measurements[name] = meas\n", + "\n", + "background_meas = Measurement.from_h5(\"data.h5\", measurement_name=\"Background\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -244,12 +311,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -263,7 +330,13 @@ "from libra_toolbox.neutron_detection.activation_foils import compass\n", "\n", "for detector in all_measurements[\"Na22_2\"].detectors:\n", - " hist, bin_edges = detector.get_energy_hist_background_substract(background_meas.detectors[0])\n", + " # raw_hist, raw_bin_edges = detector.get_energy_hist(bins=None)\n", + " # common_bins = np.intersect1d(\n", + " # raw_bin_edges, background_meas.detectors[0]._bin_edges\n", + " # )\n", + " # find correct bg detector\n", + " bg_detector = [d for d in background_meas.detectors if d.channel_nb == detector.channel_nb][0]\n", + " hist, bin_edges = detector.get_energy_hist_background_substract(bg_detector)\n", "\n", " plt.hist(\n", " bin_edges[:-1],\n", @@ -310,12 +383,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -398,20 +471,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_243325/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_241423/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -492,12 +565,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -542,12 +615,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -560,7 +633,7 @@ "ch_nb = 4\n", "meas_name = \"Na22_2\"\n", "\n", - "background_detector = background_meas.detectors[1]\n", + "background_detector = background_meas.detectors[0]\n", "check_source_detector = all_measurements[meas_name].detectors[0]\n", "\n", "assert background_detector.channel_nb == check_source_detector.channel_nb\n", @@ -612,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -636,12 +709,121 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing Co60_1...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4064 4065 4066]\n", + "Counts measured: [np.float64(47036.03830551509), np.float64(44205.39387658654)]\n", + "Processing Co60_2...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4026 4027 4028]\n", + "Counts measured: [np.float64(22368.399686140438), np.float64(22576.072847491916)]\n", + "Processing Co60_3...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", + "Counts measured: [np.float64(16154.528046739626), np.float64(13558.504178212668)]\n", + "Processing Co60_4...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4090 4091 4092]\n", + "Counts measured: [np.float64(29827.171495947478), np.float64(30058.52544959307)]\n", + "Processing Co60_5...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4038 4039 4040]\n", + "Counts measured: [np.float64(10901.980284982346), np.float64(9338.42098327135)]\n", + "Processing Cs137_1...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4084 4085 4086]\n", + "Counts measured: [np.float64(663460.7071836735)]\n", + "Processing Cs137_2...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", + "Counts measured: [np.float64(1082378.6051444719)]\n", + "Processing Cs137_3...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4087 4088 4089]\n", + "Counts measured: [np.float64(1133356.7423398674)]\n", + "Processing Cs137_4...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4057 4058 4059]\n", + "Counts measured: [np.float64(782959.8477999661)]\n", + "Processing Mn54_1...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", + "Counts measured: [np.float64(8319.977883210617)]\n", + "Processing Mn54_2...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", + "Counts measured: [np.float64(8913.022518906384)]\n", + "Processing Mn54_3...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", + "Counts measured: [np.float64(3577.3883280784066)]\n", + "Processing Na22_2...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", + "Counts measured: [np.float64(2218849.9724823623), np.float64(502280.9213963469)]\n", + "Processing Na22_3...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4067 4068 4069]\n", + "Counts measured: [np.float64(1351072.877852661), np.float64(371085.7465765631)]\n", + "Processing Na22_4...\n", + "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4091 4092 4093]\n", + "Counts measured: [np.float64(2131183.0539309), np.float64(484562.51706595556)]\n" + ] + } + ], + "source": [ + "channel_nb = 4\n", + "calibration_coeffs_ = calibration_coeffs[channel_nb]\n", + "for name, measurement in all_measurements.items():\n", + " \n", + " background_detector = background_meas.get_detector(channel_nb)\n", + " check_source_detector = measurement.get_detector(channel_nb)\n", + "\n", + " hist, bin_edges = check_source_detector.get_energy_hist_background_substract(\n", + " background_detector, bins=None\n", + " )\n", + " print(f\"Processing {name}...\")\n", + " print(calibration_coeffs_, bin_edges)\n", + " calibrated_bin_edges = np.polyval(calibration_coeffs_, bin_edges)\n", + "\n", + " nb_counts_measured = compass.get_multipeak_area(\n", + " hist,\n", + " calibrated_bin_edges,\n", + " measurement.check_source.nuclide.energy,\n", + " search_width=200,\n", + " )\n", + "\n", + " print(f\"Counts measured: {nb_counts_measured}\")\n", + "\n", + " if not np.all(\n", + " np.array(nb_counts_measured) > 0\n", + " ):\n", + " plt.hist(\n", + " calibrated_bin_edges[:-1],\n", + " bins=calibrated_bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {check_source_detector.channel_nb} - calibrated\",\n", + " )\n", + " for energy_peak in measurement.check_source.nuclide.energy:\n", + " parameters, covariance = compass.fit_peak_gauss(\n", + " hist, xvals, [energy_peak], search_width=200\n", + " )\n", + " search_start = np.argmin(\n", + " np.abs((energy_peak - 200 / (2 * len([energy_peak]))) - xvals)\n", + " )\n", + " search_end = np.argmin(\n", + " np.abs((energy_peak + 200 / (2 * len([energy_peak]))) - xvals)\n", + " )\n", + " plt.fill_between(\n", + " xvals[search_start:search_end],\n", + " compass.gauss(xvals[search_start:search_end], *parameters),\n", + " alpha=0.5,\n", + " )\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -657,7 +839,7 @@ " background_measurement=background_meas,\n", " calibration_coeffs=calibration_coeffs[channel_nb],\n", " channel_nb=channel_nb,\n", - " search_width=300,\n", + " search_width=200,\n", " )\n", " plt.scatter(\n", " measurement.check_source.nuclide.energy,\n", @@ -673,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -687,7 +869,7 @@ " background_measurement=background_meas,\n", " calibration_coeffs=calibration_coeffs[channel_nb],\n", " channel_nb=channel_nb,\n", - " search_width=300,\n", + " search_width=200,\n", " )\n", " efficiencies.append(efficiency)\n", " energies.append(measurement.check_source.nuclide.energy)\n", @@ -703,12 +885,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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QRXYhSRLevKc7gjzVOFGgx0vfH3R0SURERI2KIYrsxsddiUX39oBMAr5JO4e1aeccXRIREVGjYYgiu+rbzhfTbu0AAHjhuwwcLyh1cEVERESNgyGK7G7qwA7o284HZQYznlixFxVGs6NLIiIisjuGKLI7uUzCO/dGw9ddiUM5xXh13V+OLomIiMjuGKKoUQR6qvGf0T0AAF/uOIN1+7MdWxAREZGdMURRo4nv6I/HBrQHAMz+5gBOX9A7uCIiIiL7YYiiRjXjto7o3dYbpZUmPL4iDZUm9o8iIqLmgSGKGpVCLsO7Y6Lh5eaCjKxizP/psKNLIiIisguGKGp0rbxc8Z9R3QEAn247hZ8O5Di4IiIiomvnFCHqvffeQ1hYGNRqNWJjY7Fz584rtl+zZg0iIyOhVqsRFRWFn376ybrNaDRi1qxZiIqKgru7O1q1aoXx48cjO/vvjs2nTp3CpEmTEB4eDldXV7Rv3x5z586FwWCwaSNJUrVl+/bt9r8BLcDAyEBMiW8HAJj19X72jyIioibP4SFq1apVmDFjBubOnYu0tDR0794dCQkJyM/Pr7H9tm3bMGbMGEyaNAl79+5FYmIiEhMTkZGRAQAoKytDWloaXnzxRaSlpWHt2rU4cuQIhg8fbj3G4cOHYbFY8MEHH+DgwYN4++23sWzZMjz33HPVzvfrr78iJyfHuvTq1atxbkQL8MzgTujd1hsllSY89mUax48iIqImTRJCCEcWEBsbiz59+mDJkiUAAIvFgtDQUEydOhWzZ8+u1n706NHQ6/VYt26ddV3fvn3Ro0cPLFu2rMZz7Nq1CzExMTh9+jTatGlTY5s333wTS5cuxYkTJwBUPYkKDw/H3r170aNHjwZdW3FxMbRaLXQ6HTw9PRt0jOYmR1eOYe9uQaHegPv7tsG/E6McXRIREZGNuv5+O/RJlMFgwJ49ezBo0CDrOplMhkGDBiE1NbXGfVJTU23aA0BCQkKt7QFAp9NBkiR4eXldsY2Pj0+19cOHD0dAQAD69euHH3744YrXU1lZieLiYpuFbAVr/+4f9cX2M/hxH8ePIiKipsmhIer8+fMwm80IDAy0WR8YGIjc3Nwa98nNza1X+4qKCsyaNQtjxoypNU1mZmZi8eLFmDJlinWdRqPBwoULsWbNGqxfvx79+vVDYmLiFYPU/PnzodVqrUtoaGitbVuyAZ0C8MQtEQCA2d/s5/x6RETUJDm8T1RjMhqNGDVqFIQQWLp0aY1tsrKyMGTIENxzzz2YPHmydb2fnx9mzJhhfd34+uuv4/7778ebb75Z6/nmzJkDnU5nXc6ePWv3a2ounhrUAbHhPtAbzHjsizSUG9g/ioiImhaHhig/Pz/I5XLk5eXZrM/Ly0NQUFCN+wQFBdWp/aUAdfr0aSQnJ9f4FCo7Oxu33HILbrzxRnz44YdXrTc2NhaZmZm1blepVPD09LRZqGYKuQyLx0TDT6PCkbwSvPBdBhzcPY+IiKheHBqilEolevXqhZSUFOs6i8WClJQUxMXF1bhPXFycTXsASE5Otml/KUAdO3YMv/76K3x9fasdJysrCwMGDECvXr2QlJQEmezqtyI9PR3BwcF1vTy6igBPNRaPiYZMAr5JO4fVu/nkjoiImg6FowuYMWMGJkyYgN69eyMmJgaLFi2CXq/HxIkTAQDjx49HSEgI5s+fDwB48sknER8fj4ULF2LYsGFYuXIldu/ebX2SZDQaMXLkSKSlpWHdunUwm83W/lI+Pj5QKpXWANW2bVu89dZbKCgosNZz6YnW8uXLoVQqER0dDQBYu3YtPvnkE3z00UfX7d60BHHtffH04E54c+MRvPT9QXQN0aJLK62jyyIiIroqh4eo0aNHo6CgAC+99BJyc3PRo0cPbNiwwdp5/MyZMzZPiW688UasWLECL7zwAp577jl06NAB3333Hbp27Qqg6gnTpc7f/xyaYNOmTRgwYACSk5ORmZmJzMxMtG7d2qbN5a+UXn31VZw+fRoKhQKRkZFYtWoVRo4c2Ri3oUV7NL49dp8qxKYjBXjsyzT8OLUfPNUuji6LiIjoihw+TlRzxnGi6u6i3oB/Ld6CrKJyDOkShKX394QkSY4ui4iIWqAmMU4U0SXe7kq8N7YnXOQSNhzMxUd/nnR0SURERFfEEEVOo0eoF166owsA4PUNh7HjxAUHV0RERFQ7hihyKvfHtkFij1YwWwSe+Gov8osrHF0SERFRjRiiyKlIkoTX7o5Cx0ANCkoq8cSKvTCaLY4ui4iIqBqGKHI6bkoFlt7fCxqVAjtPFeLNjUccXRIREVE1DFHklNr7a/DmyG4AgA83n8DPB3IcXBEREZEthihyWkOjgjH55nAAwDNr9iEznxMVExGR82CIIqc2a0ikdaLiR77Yg9JKk6NLIiIiAsAQRU5OIZdhyX09EeipQmZ+KWZ9vZ8TFRMRkVNgiCKn5++hwvtje8FFLmH9gRwOxElERE6BIYqahF5tvfHivzoDqBqIM/U4B+IkIiLHYoiiJmNc37a4OzoEZovA1K/SkKMrd3RJRETUgjFEUZMhSRLm3RWFG4I9cb7UgEe+SEOlyezosoiIqIViiKImxVUpxwf394LW1QX7zhbh5R8OOrokIiJqoRiiqMlp4+uGd8dEQ5KAr3aexVc7zzi6JCIiaoEYoqhJiu/oj2cGdwIAzP3+IPaeuejgioiIqKVhiKIm67EB7ZHQJRAGswWPfpGGgpJKR5dEREQtCEMUNVmSJOGte7qjvb87cosr8PiXaTCYLI4ui4iIWgiGKLoqi0XgbGEZDucW42xhGSwW5xkx3EPtgg/H94aHSoGdpwrx7/V/ObokIiJqIRSOLoCcW2Z+CTZm5OF4QSkqTGaoFXK099cgoWsgIgI8HF0eAKC9vwaL7u2Bhz7bjc9ST6NrKy1G9Ql1dFlERNTM8UkU1SozvwRJW08hI1sHLzcXtPPTwMvNBRnZOiRtPYXM/BJHl2h16w2BmD6oIwDghe8y2NGciIgaXb1D1KZNmxqjDnIyFovAxow8FOoN6BCggYfaBXKZBA+1CzoEaFCoN+CXg3lO9WrviVsirB3NH/liD/KLKxxdEhERNWP1DlFDhgxB+/bt8e9//xtnz55tjJrICWQVleN4QSmCtWpIkmSzTZIkBGvVyMwvRVaR80y9IpNJWDiqBzoEaJBXXIlHv+SI5kRE1HjqHaKysrLwxBNP4Ouvv0a7du2QkJCA1atXw2AwNEZ95CB6gwkVJjPclDV3m3NVylFpMkNvMF3nyq5Mo1JUdTRXK7Dn9EW89N1BCOE8T8uIiKj5qHeI8vPzw/Tp05Geno4dO3agY8eOeOyxx9CqVStMmzYN+/bta4w66TpzVyqgVshRVktIKjeYoVLI4V5LyHKkcD93LB4TDZkErNp9Fp9vP+3okoiIqBm6po7lPXv2xJw5c/DEE0+gtLQUn3zyCXr16oWbb74ZBw9yTrOmLMTLFe39NcjRVVR7kiOEQI6uAhEBGoR4uTqowisb0CkAs4ZEAgD+78e/kHr8goMrIiKi5qZBIcpoNOLrr7/G7bffjrZt22Ljxo1YsmQJ8vLykJmZibZt2+Kee+6xd610HclkEhK6BsLHXYlj+aUoqTDCZLGgpMKIY/ml8HFXYnCXQMhk0tUP5iAP92+HO3u0gtki8NiXe3C2sMzRJRERUTMiiXp2GJk6dSq++uorCCEwbtw4PPTQQ+jatatNm9zcXLRq1QoWS8sePbq4uBharRY6nQ6enp6OLqdBLh8nqtJU9QovIkCDwV2cZ5yoK6kwmjFy2TZkZBUjMsgDax+7sdZ+XkREREDdf7/rHaJuvfVWPPTQQ7j77ruhUqlqbGMymbB161bEx8fXr+pmpjmEKKBquIOsonLoDSa4KxUI8XJ16idQ/5RdVI7hS7bgfKkBQ7sG4b37ejap+omI6PpqtBBFdddcQlRzsPtUIcb8dzuMZoEnb+2A6bd1dHRJRETkpOr6+13vPlHz58/HJ598Um39J598ggULFtT3cETXRe8wH8xLjAIAvJNyDD8dyLHbsZ15bkEiImo89e4c8sEHH2DFihXV1nfp0gX33nsvZs2aZZfCiOxtVJ9QHM4twSdbT+Lp1fvQ1tcNXVppr+mYTWFuQSIiahz1fhKVm5uL4ODgauv9/f2Rk2O/v90TNYbnbo9E/47+KDeaMXn5bhSUVDb4WE1pbkEiIrK/eoeo0NBQbN26tdr6rVu3olWrVnYpiqixKOQyLB4TjXZ+7sjWVeCRL/Y0aGqYpji3IBER2Ve9Q9TkyZPx1FNPISkpCadPn8bp06fxySefYPr06Zg8eXJj1EhkV1pXF/x3wt9Twzy3NqPeU8M0xbkFiYjIvurdJ2rmzJm4cOECHnvsMet8eWq1GrNmzcKcOXPsXiBRY2jvr8F79/XExE934Zu0c+gYqMGU+PZ13v/vuQVrHrHdVSlHXnGF080tSERE9lPvJ1GSJGHBggUoKCjA9u3bsW/fPhQWFuKll15qjPqIGk3/jv546V+dAQCvbziM5L/y6rxvU55bkIiI7KPBc+dpNBr06dMHXbt2rXXQTSJnNz6uLcbGtoEQwJMr9+JQTnGd9mvqcwsSEdG1q/dfk/V6PV5//XWkpKQgPz+/2tQuJ06csFtxRI1NkiS8PLwLTp7XY9vxC3ho+W589/hN8Pe48l8MLs0tmK0rx7H8qr5Rrko5yg1m5OgqmsTcgkREdG3qHaIeeugh/PHHHxg3bhyCg4Ordaolampc5DK8P7YnEt/bilMXyjDl891YMbkv1C7yK+4XEeCBiTeFWceJyiuugEohR1SItsnMLUhERNdA1JNWqxVbtmyp725XtGTJEtG2bVuhUqlETEyM2LFjxxXbr169WnTq1EmoVCrRtWtXsX79eus2g8Egnn32WdG1a1fh5uYmgoODxbhx40RWVpbNMS5cuCDuu+8+4eHhIbRarXjwwQdFSUmJTZt9+/aJfv36CZVKJVq3bi0WLFhQr+vS6XQCgNDpdPXajxwjM79ERM3dINrOWiemrkgTFoulTvuZzRZx5oJeHMrRiTMX9MJsrtt+RETknOr6+13vPlHe3t7w8fGxW4hbtWoVZsyYgblz5yItLQ3du3dHQkIC8vPza2y/bds2jBkzBpMmTcLevXuRmJiIxMREZGRkAADKysqQlpaGF198EWlpaVi7di2OHDmC4cOH2xxn7NixOHjwIJKTk7Fu3Tps3rwZDz/8sHV7cXExBg8ejLZt22LPnj1488038fLLL+PDDz+027WTc2nvr8HS+3tBIZPww75svJNyrE77yWQSQn3cEBnkiVAfN77CIyJqKeqbzj7//HMxcuRIodfrG5zwLhcTEyMef/xx65/NZrNo1aqVmD9/fo3tR40aJYYNG2azLjY2VkyZMqXWc+zcuVMAEKdPnxZCCPHXX38JAGLXrl3WNj///LOQJMn6xOr9998X3t7eorKy0tpm1qxZolOnTnW+Nj6JappW7Dgt2s5aJ9rOWie+23vO0eUQEdF11mhPohYuXIiNGzciMDAQUVFR6Nmzp81SHwaDAXv27MGgQYOs62QyGQYNGoTU1NQa90lNTbVpDwAJCQm1tgcAnU4HSZLg5eVlPYaXlxd69+5tbTNo0CDIZDLs2LHD2qZ///5QKpU25zly5AguXrxY43kqKytRXFxss1DTMyamDSbfHA4AmPn1fuw5XfP/vYmIqGWrd8fyxMREu538/PnzMJvNCAwMtFkfGBiIw4cP17hPbm5uje1zc3NrbF9RUYFZs2ZhzJgx8PT0tB4jICDApp1CoYCPj4/1OLm5uQgPD692nkvbvL29q51r/vz5+L//+7/aLpeakNlDb8DJ82X49VAeHv6s6ou9UB83R5dFREROpN4hau7cuY1RR6MwGo0YNWoUhBBYunRpo59vzpw5mDFjhvXPxcXFCA0NbfTzkv3JZRLeubcH7lmWir9yivHgp7vw9aM3Quvq4ujSiIjISTRosM2ioiJ89NFHmDNnDgoLCwEAaWlpyMrKqtdx/Pz8IJfLkZdnO1J0Xl4egoKCatwnKCioTu0vBajTp08jOTnZ+hTq0jH+2XHdZDKhsLDQepzaznNpW01UKhU8PT1tFmq63FUKfPxAbwR6qnAsvxSPf5kGo9ly9R2JiKhFqHeI2r9/Pzp27IgFCxbgrbfeQlFREQBg7dq19Z47T6lUolevXkhJSbGus1gsSElJQVxcXI37xMXF2bQHgOTkZJv2lwLUsWPH8Ouvv8LX17faMYqKirBnzx7rut9++w0WiwWxsbHWNps3b4bRaLQ5T6dOnWp8lUfNU7DWFR9P6AM3pRxbMs/jxe/qP1kxERE1U/XtsX7rrbeKmTNnCiGE0Gg04vjx40IIIbZu3Sratm1b38OJlStXCpVKJT799FPx119/iYcfflh4eXmJ3NxcIYQQ48aNE7Nnz7a237p1q1AoFOKtt94Shw4dEnPnzhUuLi7iwIEDQoiqcaKGDx8uWrduLdLT00VOTo51ufxLuyFDhojo6GixY8cOsWXLFtGhQwcxZswY6/aioiIRGBgoxo0bJzIyMsTKlSuFm5ub+OCDD+p8bfw6r/n49a9cET676ou9pb9nOrocIiJqRHX9/a53iPL09BSZmVU/IpeHqFOnTgmVStWAUoVYvHixaNOmjVAqlSImJkZs377dui0+Pl5MmDDBpv3q1atFx44dhVKpFF26dLEZbPPkyZMCQI3Lpk2brO0uXLggxowZIzQajfD09BQTJ0684mCbISEh4vXXX6/XdTFENS9JW05Yhz5Yvz/b0eUQEVEjqevvtyRE/d5NBAQEYOPGjYiOjoaHhwf27duHdu3aITk5GQ8++CDOnj1r30dlTVhxcTG0Wi10Oh37RzUTL/9wEJ9uOwWVQoYVk/uiV1u+2iUiam7q+vtd7z5Rw4cPxyuvvGLtKyRJEs6cOYNZs2ZhxIgRDa+YqAl48V+dMeiGAFSaLJj82W6cvqB3dElEROQgDRpss7S0FAEBASgvL0d8fDwiIiLg4eGBefPmNUaNRE5DLpPw7phoRIVoUag34IGkXbioNzi6LCIicoB6v867ZMuWLdi/fz9KS0vRs2fPaqOIE1/nNWf5JRW4671tyCoqR++23nh9RBRMFgF3pQIhXq6cP4+IqAmr6+93g0MUXR1DVPN2LK8Eie9vhb7SjNZerohq7QlXFwXa+2uQ0DUQEQEeji6RiIgaoK6/33Uasfzdd9/Fww8/DLVajXffffeKbadNm1a/SomaKEkC+ob74rcj+ThXVI4ATxV6hLoiI1uHbF05Jt4UxiBFRNSM1elJVHh4OHbv3g1fX99q88nZHEyScOLECbsW2JTxSVTzZbEILP39ODKydTCZLUg+VDUC/oCO/ujWWotj+aWICtHikfj2fLVHRNTE2PVJ1MmTJ2v8d6KWKquoHMcLShGsVcND7YJSgxmpxy/g96MFcFcpEKxVIzO/FFlF5Zy4mIiomWrQ3HlELZ3eYEKFyQw3ZdXfQ/q09UbXVlV/W9lwMBe6ciMqTWboDSZHlklERI2o3iFqxIgRWLBgQbX1b7zxBu655x67FEXk7NyVCqgVcpT9LyRJkoRbOgUgzNcNZovAuv05MJgscFfW6WEvERE1QfUOUZs3b8btt99ebf3QoUOxefNmuxRF5OxCvFzR3l+DHF2FdUJimUzC7VHBCPBQodJkwc5TF6GU82EvEVFzVe//wpeWlkKpVFZb7+LiguLiYrsUReTsZDIJCV0D4eOuxLH8UpRUGGGyWFBhNKNriCc0KgV05UZM+mwXSiv5So+IqDmqd4iKiorCqlWrqq1fuXIlOnfubJeiiJqCiAAPTLwpDF1baVFUZsSp83oUlRnRu60PltwXDR93JTKyivHYl2kwmi2OLpeIiOys3h02XnzxRdx99904fvw4Bg4cCABISUnBV199hTVr1ti9QCJnFhHggXYDNMgqKofeYLIZsfyTB/pgzIfbsfloAWZ9sx8L7+kOSeJwB0REzUWDRixfv349XnvtNaSnp8PV1RXdunXD3LlzER8f3xg1NlkcJ4o2Hc7HQ5/thtki8OiA9pg1JNLRJRER0VVw2hcnwBDVMlgsosYnUZes3n0Wz369HwAw947OmHhT7QPWEhGR49l1sE0iqllmfgk2ZuTheEEpKkxmqBXyanPnjeodijxdBRYmH8Ur6/6Cr0aF4d1bObhyIiK6VnUKUT4+Pjh69Cj8/Pzg7e19xX4dhYWFdiuOyJll5pcgaespFOoNCNaq4aZ0RZnBVOPceU8MjEBBaSU+Sz2Np1enw8dNiX4d/Bx8BUREdC3qFKLefvtteHhU/RgsWrSoMeshahIsFoGNGXko1BvQIUBj/YuFh9oFGpUCx/JL8cvBPLTz00AmkyBJEube0QUXSg1YfyAHUz7fjZUPxyGqtdbBV0JERA1VpxC1b98+jBw5EiqVCuHh4bjxxhuhUPBNILVcl8+d988ns5Ik1Th3nlwm4T+ju+NimQHbjl/AA0k78fWjNyLcz90Rl0BERNeoTuNELV68GKWlpQCAW265ha/sqMX759x5/+SqlNc4d55KIccH43qhSytPXNAbMP6THcgvrrgeJRMRkZ3V6XFSWFgY3n33XQwePBhCCKSmpsLb27vGtv3797drgUTO6PK58zzULtW2lxvMUCnkNc6d56F2wacTYzBy2TacvlCG8Z/sxKqH46B1q34cIiJyXnUa4uC7777DI488gvz8fEiShNp2kSQJZrPZ7kU2VRzioPmyWASW/n4cGdk6mz5RACCEwLH8UkSFaPFIfHub4Q4ud7awDCOWbkN+SSV6t/XG55Ni4aqUX69LICKiWtT197tOr/MSExORm5uL4uJiCCFw5MgRXLx4sdrC13zUUtQ2d15JhRHH8kvh467E4C6BtQYoAAj1ccNnk2LgqVZg9+mLeOzLPZwehoioCalTiJoxYwb0ej00Gg02bdqE8PBwaLXaGheilqK2ufOiQrQ2wxtcSWSQJz55oA/ULjJsOlKAmWv2wWLh+LdERE1BnV7nubi44Ny5cwgMDIRcLkdOTg4CAgKuR31NGl/ntQxXG7G8LjYdzsfkz3bDZBF44MYwzL2jM+fZIyJyELuOWM6O5US1k8kk6zAGDXVLZADeuqc7nlqVjk+3nYKnqwtm3NbRThUSEVFjYMfyRsQnUVRfn6WewkvfHwQAvDDsBjx0czsHV0RE1PI0ygTEpaWl8PT0xJEjR2p9ncd+UX9jiKKGWJxyDAuTjwIA3hjRDaP6hDq4IiKilqVRJiC+vGM5RywnqmKPPlGXe2JgBHTlRny05SRmr90PD7UCQ6OC7VgxERHZQ72TUHx8PI4fP46kpCQcP34c77zzDgICAvDzzz+jTZs26NKlS2PUSeSUMvNLsDEjD8cLSlFhMkOtkKO9vwYJXQPr9HVeTSRJwvPDbkBxhRGrd5/DtJV78ZFKgfiO/naunoiIrkWdhji43B9//IGoqCjs2LEDa9eutU4Hs2/fPsydO9fuBRI5q8z8EiRtPYWMbB283FzQzk8DLzcXZGTrkLT1FDLzSxp8bEmSMP/ubrg9KghGs8CUz3dj50mOw0ZE5EzqHaJmz56Nf//730hOToZSqbSuHzhwILZv327X4oiclcUisDEjD4V6AzoEaOChdoFcJsFD7YIOARoU6g345WDeNY35JJdJWDQ6GgM6+aPCaMGDn+7C/nNF9rsIIiK6JvUOUQcOHMBdd91VbX1AQADOnz9vl6KInF1WUTmOF5QiWKuuNp6TJEkI1qqRmV+KrKLyazqPUiHDsvt7ITbcB6WVJkz4ZCeO5jX8CRcREdlPvUOUl5cXcnJyqq3fu3cvQkJC7FIUkbPTG0yoMJnhVsMEwwDgqpSj0mSG3mC65nOpXeT4+IE+6B7qhYtlRoz9aAdOnddf83GJiOja1DtE3XvvvZg1axZyc3MhSRIsFgu2bt2KZ555BuPHj2+MGomcjrtSAbVCjrJaQlK5wQyVQg73WkJWfWlUCiyf2AeRQR4oKKnE2I924NzFMrscm4iIGqbeIeq1115DZGQkQkNDUVpais6dO6N///648cYb8cILLzRGjUROJ8TLFe39NcjRVVQbfFYIgRxdBSICNAjxcrXbOb3clPh8Uiza+bsjq6gcYz/agbziCrsdn4iI6qdeg21e7uzZszhw4ABKS0sRHR2NDh062Lu2Jo+DbTZvl77OK9QbEKxVw1UpR7nBjBxdBXzclXWehLi+cnTlGPVBKs4WlqO9vztWTYmDn0Zl9/MQEbVUjTJiOdUPQ1Tzd/k4UZWmqld4EQEaDO7S8HGi6uJsYRlGfZCKHF0FIoM8sPLhvvByU159RyIiuiqGKCfAENUy2HvE8ro6eV6PUR+koqCkEt1aa/HFQ7HwVLs0+nmJiJq7uv5+17tPFBHZkskkhPq4ITLIE6E+btclQAFAuJ87vnwoFj7uSuw/p8OET3aitPLavwYkIqK6cXiIeu+99xAWFga1Wo3Y2Fjs3Lnziu3XrFmDyMhIqNVqREVF4aeffrLZvnbtWgwePBi+vr6QJAnp6ek220+dOgVJkmpc1qxZY21X0/aVK1fa7bqJ7KFjoAc+nxQDrasL9p4pwsSknbV+MUhERPbl0BC1atUqzJgxA3PnzkVaWhq6d++OhIQE5Ofn19h+27ZtGDNmDCZNmoS9e/ciMTERiYmJyMjIsLbR6/Xo168fFixYUOMxQkNDkZOTY7P83//9HzQaDYYOHWrTNikpyaZdYmKi3a6dyF66tNLii0mx8FArsOvURTz46S6UG8yOLouIqNlrUJ+ooqIi7Ny5E/n5+bBYLDbb6jNWVGxsLPr06YMlS5YAACwWC0JDQzF16lTMnj27WvvRo0dDr9dj3bp11nV9+/ZFjx49sGzZMpu2p06dQnh4OPbu3YsePXpcsY7o6Gj07NkTH3/8sXWdJEn49ttv6xWcKisrUVlZaf1zcXExQkND2SeKrou9Zy5i3MdVr/T6Rfjhowm9oXaRO7osIqImp9H6RP34449o06YNhgwZgieeeAJPPvmkdXnqqafqfByDwYA9e/Zg0KBBfxcjk2HQoEFITU2tcZ/U1FSb9gCQkJBQa/u62LNnD9LT0zFp0qRq2x5//HH4+fkhJiYGn3zySbXxgP5p/vz50Gq11iU0NLTBdRHVV3QbbyRN7AM3pRxbMs9jyud7UGHkEykiosZS7xD19NNP48EHH0RpaSmKiopw8eJF61JYWPdZ5s+fPw+z2YzAwECb9YGBgcjNza1xn9zc3Hq1r4uPP/4YN9xwA2688Uab9a+88gpWr16N5ORkjBgxAo899hgWL158xWPNmTMHOp3Oupw9e7bBdRE1RJ8wH3zyQB+oXWT442gBHv1iDypNDFJERI2h3nNSZGVlYdq0aXBzc2uMeq6r8vJyrFixAi+++GK1bZevi46Ohl6vx5tvvolp06bVejyVSgWVioMekmP1beeLTyb0wYPLd2HTkQI8+kUalt7fEyoFX+0REdlTvZ9EJSQkYPfu3dd8Yj8/P8jlcuTl5dmsz8vLQ1BQUI37BAUF1av91Xz99dcoKyurUz+u2NhYnDt3zqbPE5GzujHCDx9P6AOVQobfDufjsS/S+ESKiMjO6h2ihg0bhpkzZ+Lll1/GN998gx9++MFmqSulUolevXohJSXFus5isSAlJQVxcXE17hMXF2fTHgCSk5NrbX81H3/8MYYPHw5/f/+rtk1PT4e3tzefNFGTcdNlQSrlcD4e/zINBpPl6jsSEVGd1Pt13uTJkwFU9Rn6J0mSYDbX/W+7M2bMwIQJE9C7d2/ExMRg0aJF0Ov1mDhxIoCqL/1CQkIwf/58AMCTTz6J+Ph4LFy4EMOGDcPKlSuxe/dufPjhh9ZjFhYW4syZM8jOzgYAHDlyBEDVU6zLn1hlZmZi8+bN1caZAqo6z+fl5aFv375Qq9VITk7Ga6+9hmeeeabO10bkDPp1qPpK76Hlu/HroXw89uUevDeWr/aIiOxCONjixYtFmzZthFKpFDExMWL79u3WbfHx8WLChAk27VevXi06duwolEql6NKli1i/fr3N9qSkJAGg2jJ37lybdnPmzBGhoaHCbDZXq+nnn38WPXr0EBqNRri7u4vu3buLZcuW1dj2SnQ6nQAgdDpdvfYjsrfNR/NFx+d/Em1nrRMPJu0UFUaTo0siInJadf395tx5jYhz55Ez2XLsPCYt34VKkwW3dPLH0vt7cRwpIqIaNOrceX/88QfuuOMOREREICIiAsOHD8eff/7Z4GKJqPH16+CHpP8Nf7DpSAHHkSIiukb1DlFffPEFBg0aBDc3N0ybNg3Tpk2Dq6srbr31VqxYsaIxaiQiO7kxwg9JD8TA1UWOP44WYPJnuxmkiIgaqN6v82644QY8/PDDmD59us36//znP/jvf/+LQ4cO2bXApoyv88hZbT9xAROTdqHcaEZcO198/EBvuCnr/Z0JEVGz1Giv806cOIE77rij2vrhw4fj5MmT9T0cETlA33a++GxSDDQqBVJPXMADn+xCaaXJ0WURETUp9Q5RoaGh1cZqAoBff/2Vc8URNSF9wnzw2aQYeKgV2HmqEOM+3gFdudHRZRERNRn1fn7/9NNPY9q0aUhPT7fON7d161Z8+umneOedd+xeIBE1np5tvLHiob64/+Md2HumCPd/tAOfT4qBl5vS0aURETm9Bg1x8O2332LhwoXW/k833HADZs6ciTvvvNPuBTZl7BNFTcVf2cW4/+MdKNQbEBnkgS8eioWfhqPzE1HLVNffb44T1YgYoqgpOZpXgrEf7UBBSSXa+7vjy4f6IkirdnRZRETXXaOOE0VEzU/HQA+snhKHVlo1jhfoMeqDVJwtLHN0WURETqtOIcrHxwfnz58HAHh7e8PHx6fWhYiarnA/d6x+JA5tfNxwprAMoz9IxcnzekeXRUTklOrUsfztt9+Gh4eH9d8lSWrUoojIcVp7u2H1lDiM/Wg7jhfocc+yVHzxUAwig/hKmojocuwT1YjYJ4qasvOllRj38U4cyimG1tUFyx+MQY9QL0eXRUTU6BqtT5RcLkd+fn619RcuXIBczslMiZoLP40KKyf3Rc82XtCVGzH2v9uRevyCo8siInIa9Q5RtT24qqyshFLJsWWImhOtmws+nxSLmyJ8oTeYMSFpJ1IO5Tm6LCIip1DnwTbfffddAIAkSfjoo4+g0Wis28xmMzZv3ozIyEj7V0hEDuWuUuDjCX3wxIq9+PVQHqZ8vgcLR3XHnT1C7Hoei0Ugq6gceoMJ7koFQrxcIZOx/yUROa8694kKDw8HAJw+fRqtW7e2eXWnVCoRFhaGV155BbGxsY1TaRPEPlHUnBjNFsxcsw/fpWdDkoBX7uyKcX3b2uXYmfkl2JiRh+MFpagwmaFWyNHeX4OEroGICPCwyzmIiOqqrr/fdX4SdWly4VtuuQVr166Ft7f3tVdJRE2Gi1yG/4zqAQ+1Cz7ffhovfpcBXZkBj98ScU1f7GbmlyBp6ykU6g0I1qrhpnRFmcGEjGwdsnXlmHhTGIMUETmleveJ2rRpEwMUUQslk0l45c4umDYwAgDw1i9H8e/1h2CxNOwjX4tFYGNGHgr1BnQI0MBD7QK5TIKH2gUdAjQo1Bvwy8G8Bh+fiKgx1TtEjRgxAgsWLKi2/o033sA999xjl6KIyHlJkoQZgzvhxX91BgB8vOUkZn69Hyazpd7Hyioqx/GCUgRr1dWeZkmShGCtGpn5pcgqKrdL7URE9lTvELV582bcfvvt1dYPHToUmzdvtktRROT8JvULx8J7ukMuk/BN2jlM+XwPyg3meh1DbzChwmSGm7LmngWuSjkqTWboDSZ7lExEZFf1DlGlpaU1DmXg4uKC4uJiuxRFRE3DiF6t8cH9vaBSyJByOB/jPt4BXZmxzvu7KxVQK+QoqyUklRvMUCnkcK8lZBEROVK9Q1RUVBRWrVpVbf3KlSvRuXNnuxRFRE3HoM6B+OKhWHiqFdh9+iLu+WAbcnUVddo3xMsV7f01yNFVVBuDTgiBHF0FIgI0CPFybYzSiYiuSb3/evfiiy/i7rvvxvHjxzFw4EAAQEpKCr766iusWbPG7gUSkfPrE+aD1Y/EYcInO3E0rxQjlm7DZ5Ni0N5fc8X9ZDIJCV0Dka0rx7H8qr5Rrko5yg1m5Ogq4OOuxOAugRwvioicUoPmzlu/fj1ee+01pKenw9XVFd26dcPcuXMRHx/fGDU2WRwnilqas4VlmPDJTpw4r4e3mws+fqAPera5+te8l48TVWmqeoUXEaDB4C4cJ4qIrr+6/n5zAuJGxBBFLdGF0ko8+Oku7Dung9pFhvfu64lbbwi86n4csZyInEWjTUAMAEVFRfjoo4/w3HPPobCwEACQlpaGrKyshlVLRM2Gr0aFrx7ui1s6+aPCaMHkz3Zj5c4zV91PJpMQ6uOGyCBPhPq4MUARkdOrd4jav38/OnbsiAULFuDNN99EUVERAGDt2rWYM2eOvesjoibITanAh+N7455erWERwOy1B/DOr8dqncCciKgpqneImjFjBh544AEcO3YMarXauv7222/nOFFEZOUil+GNkd3wxC1Vo5u//etRzFl7AMYGDMpJROSM6h2idu3ahSlTplRbHxISgtzcXLsURUTNgyRJeCahE15N7AqZBKzcdRYPLd8NfSUHzySipq/eIUqlUtU4qObRo0fh7+9vl6KIqHkZ17ctPhjXG2oXGf44WoDRH6Yiv6RuY0kRETmreoeo4cOH45VXXoHRWDUqsSRJOHPmDGbNmoURI0bYvUAiah5u6xyIlQ/HwdddiYysYtz13jZk5pc4uiwiogard4hauHAhSktLERAQgPLycsTHxyMiIgIeHh6YN29eY9RIRM1Ej1AvrH3sRoT7uSOrqBx3v78N246fd3RZREQN0uBxorZu3Yp9+/ahtLQUPXv2xKBBg+xdW5PHcaKIalaoN2DyZ7ux5/RFuMglzL+7G0b2au3osoiIADTiYJufffYZRo8eDZVKZbPeYDBg5cqVGD9+fMMqboYYoohqV2E045k1+7Bufw4AYNqtHTB9UAdIEseHIiLHarQQJZfLkZOTg4CAAJv1Fy5cQEBAAMxmc8MqboYYooiuzGIReOuXI3j/9+MAgLuiQ/D6iCioFHIHV0ZELVmjjVguhKjxb4rnzp2DVqut7+GIqAWTySQ8OyQSr98dBblMwrd7s3D/RztQqDc4ujQioqtS1LVhdHQ0JEmCJEm49dZboVD8vavZbMbJkycxZMiQRimSiJq3e2PaIFirxuMr9mLXqYv41+I/sfyBGHQI4uTDROS86hyiEhMTAQDp6elISEiARqOxblMqlQgLC+MQB0TUIJn5JTiQpUP31lrsOX0R2UUV+NeSLXjlzi4Y3aeNo8sjIqpRnUPU3LlzAQBhYWEYPXq0zZQvREQNlZlfgkW/HsPR3BKYhUCQVoVcXSUqTBbM/uYAcnTleGpQJ0eXSURUTb37RE2YMAEVFRX46KOPMGfOHBQWFgIA0tLSkJWVZfcCiaj5slgEVuw4g31ni2C2WOChVsDfQ42OQRpoVHIIAIt+zcTc7zNg4px7RORk6h2i9u/fj44dO2LBggV46623UFRUBABYu3Yt5syZU+8C3nvvPYSFhUGtViM2NhY7d+68Yvs1a9YgMjISarUaUVFR+Omnn2y2r127FoMHD4avry8kSUJ6enq1YwwYMMDav+vS8sgjj9i0OXPmDIYNGwY3NzcEBARg5syZMJk43xeRPZ27WIbtJy5ALgG+GhVUCjlkkgRXFwVuCPKAl2vVw/Llqacx8dNd0JUbHVwxEdHf6h2ipk+fjgceeADHjh2zeaV3++23Y/PmzfU61qpVqzBjxgzMnTsXaWlp6N69OxISEpCfn19j+23btmHMmDGYNGkS9u7di8TERCQmJiIjI8PaRq/Xo1+/fliwYMEVzz158mTk5ORYlzfeeMO6zWw2Y9iwYTAYDNi2bRuWL1+OTz/9FC+99FK9ro+IruzEeT10ZUZ4urlU++pXJpOhja8bfNxcoFTI8Oex87jr/a04eV7voGqJiGzVe5worVaLtLQ0tG/fHh4eHti3bx/atWuH06dPo1OnTqioqPukorGxsejTpw+WLFkCALBYLAgNDcXUqVMxe/bsau1Hjx4NvV6PdevWWdf17dsXPXr0wLJly2zanjp1CuHh4di7dy969Ohhs23AgAHo0aMHFi1aVGNdP//8M/71r38hOzsbgYGBAIBly5Zh1qxZKCgogFKprHG/yspKVFZWWv9cXFyM0NBQjhNFVIvfj+TjuW8PwF+jgtql+thQ5UYzLpRW4uH+7fHBH8eRrauA1tUF793XE/06+DmgYiJqCRptnCiVSoXi4uJq648ePQp/f/86H8dgMGDPnj0208XIZDIMGjQIqampNe6TmppabXqZhISEWttfyZdffgk/Pz907doVc+bMQVlZmc15oqKirAHq0nmKi4tx8ODBWo85f/58aLVa6xIaGlrvuohaknA/d3i5KlFUZsQ//z4nhICuzAitqxIDOvnjuyduQnQbL+jKjZiQtBOfbDlZbR8iouup3iFq+PDheOWVV2A0VvVNkCQJZ86cwaxZs+o1xMH58+dhNpttggoABAYGIjc3t8Z9cnNz69W+Nvfddx+++OILbNq0CXPmzMHnn3+O+++//6rnubStNnPmzIFOp7MuZ8+erVddRC1NqLcb+ob7wCIELugNqDSZYREClSYzLugNsAiBuHY+CPV2Q4CHGl9N7osRPVvDbBF4Zd1fePbr/ag0cZYEInKMOg9xcMnChQsxcuRIBAQEoLy8HPHx8cjNzUVcXBzmzZvXGDXa3cMPP2z996ioKAQHB+PWW2/F8ePH0b59+wYfV6VSVZtTkIhqJ5NJuK9vG+SXVuJoXglKKv7+eEMuk9A91AtjYttAJqvqL6V2keOte7rhhmAPvPbTIazZcw6ZBaX44P5eCPDksCtEdH3VO0RptVokJydjy5Yt2L9/P0pLS9GzZ89qr9muxs/PD3K5HHl5eTbr8/LyEBQUVOM+QUFB9WpfV7GxsQCAzMxMtG/fHkFBQdW+Erx03ms9FxHZigjwwFODOmDDgVwcyNKhzGiCm4sC3VprkdA1CBEBtqOWS5KEh25uh46BHnhiRRr2ninCHUu2YNn9vRDdxttBV0FELVG9Q9Ql/fr1Q79+/Rp8YqVSiV69eiElJcU6GrrFYkFKSgqeeOKJGveJi4tDSkoKnnrqKeu65ORkxMXFNbgOANZhEIKDg63nmTdvHvLz860TLScnJ8PT0xOdO3e+pnMRUXURAR547BYNsorKoTeY4K5UIMTL1foEqib9O/rjhyf64aHPdiMzvxSjP9iOVxM5wjkRXT/1ClEWiwWffvop1q5di1OnTkGSJISHh2PkyJEYN25cjRMTX8mMGTMwYcIE9O7dGzExMVi0aBH0ej0mTpwIABg/fjxCQkIwf/58AMCTTz6J+Ph4LFy4EMOGDcPKlSuxe/dufPjhh9ZjFhYW4syZM8jOzgYAHDlyBEDVE6SgoCAcP34cK1aswO233w5fX1/s378f06dPR//+/dGtWzcAwODBg9G5c2eMGzcOb7zxBnJzc/HCCy/g8ccf5+s6okYik0kI9XGr1z5hfu747vGb8PTqdGw8mIdZ3xzA/nM6zL2jC5SKenf5JCKqH1FHFotFDBs2TEiSJHr06CHuvfdeMXr0aNGtWzchSZK4884763ooG4sXLxZt2rQRSqVSxMTEiO3bt1u3xcfHiwkTJti0X716tejYsaNQKpWiS5cuYv369Tbbk5KSBIBqy9y5c4UQQpw5c0b0799f+Pj4CJVKJSIiIsTMmTOFTqezOc6pU6fE0KFDhaurq/Dz8xNPP/20MBqN9bo2nU4nAFQ7NhHZl9lsEYtTjoqw2etE21nrxN3vbxV5unJHl0VETVRdf7/rPE5UUlISnnzySXz//fe45ZZbbLb99ttvSExMxJIlSzB+/Hj7prwmrK7jTBCRffx2OA9PrkxHSYUJ/h4qvD+2J/qE+Ti6LCJqYuw+TtRXX32F5557rlqAAoCBAwdi9uzZ+PLLLxtWLRGRHQyMDMQPT/RDp0APFJRUYsyH25G0leNJEVHjqHOI2r9/P4YMGVLr9qFDh2Lfvn12KYqIqKHC/dzx7eM34o7urWCyCPzfj3/hqVXpKDNw7ksisq86h6jCwsJqA1BeLjAwEBcvXrRLUURE18JNqcC79/bAS//qDIVMwvfp2bjrvW04UVDq6NKIqBmpc4gym81QKGr/mE8ul8Nk4t/0iMg5SJKEB/uFY8XkvvD3UOFIXgmGL9mKnw7kOLo0Imom6tyxXCaTYejQobV+4l9ZWYkNGzbAbOYUDJewYzmRc8gvrsATX+3FzpOFAIBJ/cIxe2gkXOQcBoGIqqvr73edQ9SlsZuuJikpqW4VtgAMUUTOw2S24M1fjuCDP04AAHq19caS+6IRrHV1cGVE5GzsHqKo/hiiiJzPLwdz8fSafSipMMHHXYn/jOqOAZ0CHF0WETkRuw9xQETUHAzuEoR1U/uhSytPFOoNeCBpF97YcBgms8XRpRFRE8MQRUQtTltfd3zz6I0YH9cWAPD+78cx5r/bkaurcHBlRNSUMEQRUYukdpHjlTu7Ysl90dCoFNh16iJuf/dPbDqS7+jSiKiJYIgiohbtX91a2bzem5i0C/PW/wWDia/3iOjKGKKIqMUL86t6vffAjWEAgP/+eRIjl23D6Qt6xxZGRE6NIYqICFWv914e3gUfjusFrasL9p/TYdi7W/DDvmxHl0ZEToohiojoMoO7BOGnJ29G77beKK00YdpXe/HMmn0oreSMDERkiyGKiOgfQrxcsfLhvpg2MAIyCfh6zzn8690/se9skaNLIyInwhBFRFQDhVyGGYM74avJfdFKq8apC2UYsXQblv5+HGYLxygmIoYoIqIrim3ni5+f7I9hUcEwWQQWbDiMsR9tR1ZRuaNLIyIHY4giIroKrZsLltwXjTdGdIObUo7tJwoxZNFmfJ+e5ejSiMiBGKKIiOpAkiSM6hOKn5+8GdFtvFBSYcKTK9Px5Mq90JUbHV0eETkAQxQRUT209XXHmilxeGpQB8hlEr5Pz8bQRZuxNfO8o0sjouuMIYqIqJ4UchmeGtQRax6JQ1tfN2TrKjD2ox14+YeDKDeYHV0eEV0nDFFERA3Us403fpp2M+7v2wYA8Om2Uxi2mEMhELUUDFFERNfAXaXAvxOj8OnEPgjwUOFEgR53L92Ghb8c4fx7RM0cQxQRkR0M6BSAX6b3xx3dW8FsEVj8WyaGL9mCjCydo0sjokbCEEVEZCdebkosHhON98f2hI+7EodzS5D43lYs+vUojGY+lSJqbhiiiIjs7PaoYPwyvT+Gdg2CySKw6NdjGL5kK59KETUzDFFERI3AT6PC+2N74t0x0fByc8GhnGLc+d5WvLXxCCpN/IKPqDlgiCIiaiSSJGF491ZInh6P26OCYLYILNmUiX+9uwV7z1x0dHlEdI0YooiIGpm/hwrvj+2FpWN7wk+jxLH8UoxYug2vrvsLZQaTo8sjogZiiCIiuk6GRgUjeXo87ooOgUUAH285icFvb8YfRwscXRoRNQBDFBHRdeTtrsTbo3sgaWIfhHi54tzFckz4ZCemr0pHod7g6PKIqB4YooiIHOCW/40r9eBN4ZAk4Nu9Wbh14e/4es85CCEcXR4R1QFDFBGRg7irFHjpjs749rGbEBnkgYtlRjyzZh/G/Hc7jheUOro8IroKhigiIgfrEeqFH6f2w+yhkVC7yLD9RCGGLvoTbycfRYWRwyEQOSuGKCIiJ+Ail+GR+PZInh6PAZ38YTBb8E7KMQxZxI7nRM6KIYqIyImE+rgh6YE+eO++ngjwUOHUhTJM+GQnHvtyD3J05Y4uj4guwxBFRORkJEnCsG7BSHk6Hg/eFA65TMJPB3Jx68I/8OHm45yHj8hJSIKfgTSa4uJiaLVa6HQ6eHp6OrocImqi/souxovfZ2DP6apRziMCNHj5ji7o18HPwZURNU91/f1miGpEDFFEZC8Wi8DXe85hwYbDuPC/8aSGdg3C88NuQGtvNwdXR9S8MEQ5AYYoIrI3XbkRbycfxefbT8NsEVC7yPBofAQe7t8Orkq5o8sjahbq+vvt8D5R7733HsLCwqBWqxEbG4udO3desf2aNWsQGRkJtVqNqKgo/PTTTzbb165di8GDB8PX1xeSJCE9Pd1me2FhIaZOnYpOnTrB1dUVbdq0wbRp06DT6WzaSZJUbVm5cqVdrpmIqKG0ri54eXgXrJ/WD33b+aDCaMHbvx7FoP/8gR/3ZXOgTqLryKEhatWqVZgxYwbmzp2LtLQ0dO/eHQkJCcjPz6+x/bZt2zBmzBhMmjQJe/fuRWJiIhITE5GRkWFto9fr0a9fPyxYsKDGY2RnZyM7OxtvvfUWMjIy8Omnn2LDhg2YNGlStbZJSUnIycmxLomJiXa5biKiaxUZ5ImvJvfFkvuiEeLliqyickz9ai9GfZCKA+d0Vz8AEV0zh77Oi42NRZ8+fbBkyRIAgMViQWhoKKZOnYrZs2dXaz969Gjo9XqsW7fOuq5v377o0aMHli1bZtP21KlTCA8Px969e9GjR48r1rFmzRrcf//90Ov1UCgUAKqeRH377bf1Ck6VlZWorKy0/rm4uBihoaF8nUdEjarCaMaHm09g6e/HUW40Q5KAu6NbY2ZCJwRp1Y4uj6jJcfrXeQaDAXv27MGgQYP+LkYmw6BBg5CamlrjPqmpqTbtASAhIaHW9nV16SZdClCXPP744/Dz80NMTAw++eSTqz4mnz9/PrRarXUJDQ29prqIiOpC7SLHtFs74Ldn4pHYoxWEAL5JO4cBb23Cf345An2lydElEjVLDgtR58+fh9lsRmBgoM36wMBA5Obm1rhPbm5uvdrXtY5XX30VDz/8sM36V155BatXr0ZycjJGjBiBxx57DIsXL77isebMmQOdTmddzp492+C6iIjqK1jrikX3RuO7x29CnzBvVBgtePe3TAx463es3HkGJo4vRWRXiqs3ab6Ki4sxbNgwdO7cGS+//LLNthdffNH679HR0dDr9XjzzTcxbdq0Wo+nUqmgUqkaq1wiojrpEeqF1VPisCEjF69vOIzTF8owe+0BfLzlJGYNicStNwRAkiRHl0nU5DnsSZSfnx/kcjny8vJs1ufl5SEoKKjGfYKCgurV/kpKSkowZMgQeHh44Ntvv4WLi8sV28fGxuLcuXM2fZ6IiJyVJEkYGhWMX6b3xwvDboCXmwuO5Zfioc92Y/QH27H3zEVHl0jU5DksRCmVSvTq1QspKSnWdRaLBSkpKYiLi6txn7i4OJv2AJCcnFxr+9oUFxdj8ODBUCqV+OGHH6BWX73jZXp6Ory9vfmkiYiaFJVCjoduboc/Zt6CRwe0h0ohw85Thbjr/W2Y8vluHMsrcXSJRE2WQ1/nzZgxAxMmTEDv3r0RExODRYsWQa/XY+LEiQCA8ePHIyQkBPPnzwcAPPnkk4iPj8fChQsxbNgwrFy5Ert378aHH35oPWZhYSHOnDmD7OxsAMCRI0cAVD3FCgoKsgaosrIyfPHFFyguLkZxcTEAwN/fH3K5HD/++CPy8vLQt29fqNVqJCcn47XXXsMzzzxzPW8PEZHdaF1dMGtIJMb1bYu3k4/im7Rz2HgwD8l/5eGu6NZ4alAHhPpw5HOiehEOtnjxYtGmTRuhVCpFTEyM2L59u3VbfHy8mDBhgk371atXi44dOwqlUim6dOki1q9fb7M9KSlJAKi2zJ07VwghxKZNm2rcDkCcPHlSCCHEzz//LHr06CE0Go1wd3cX3bt3F8uWLRNms7le16bT6QQAodPp6n1fiIga09HcYvHwZ7tE21nrRNtZ60TEc+vF3O8zRJ6u3NGlETlcXX+/Oe1LI+K0L0Tk7NLPFuHNjYexNfMCAEDtIsO4vm3xSHx7+GrYfYFaJs6d5wQYooioqdiaeR4LfzmCtDNFAAA3pRwP3BiGyTe3g7e70rHFEV1nDFFOgCGKiJoSIQR+P1qA//xyFAeyqqaOcVfKMeHGMDx0czv4MExRC8EQ5QQYooioKRJC4Je/8rDo12M4lFP14Y2bUo7xcWGYfHM4X/NRs8cQ5QQYooioKbNYBJIP5eHdlGM4mF0Vplxd5Lgvtg0e7t8OgZ6cl4+aJ4YoJ8AQRUTNgRACvx7Kx7spx6yv+ZRyGUb2bo1H49tzaARqdhiinABDFBE1J0II/HG0AO9tysSuU1UjnstlEu7oFowp8e1xQzD/O0fNA0OUE2CIIqLmaufJQizZlInNRwus6+I7+uOR+Pbo286Hc/NRk8YQ5QQYooioucvI0mHpH8fx84EcWP73a9K9tRaT+7fDkC5BUMgdNrsYUYMxRDkBhigiailOX9Djw80nsGbPORhMFgBAiJcrHuwXjtF9QqFROXSWMaJ6YYhyAgxRRNTSnC+txGepp/HF9tMo1BsAAB4qBe6NCcX4uDB2QqcmgSHKCTBEEVFLVWE045u0c/h4y0mcKNADAGQSMOiGQDxwUxji2vk6Vb8pi0Ugq6gceoMJ7koFQrxcIZM5T33OprnfL4YoJ8AQRUQtncUi8PvRfCRtPYU/j523ro8M8sC4uLZI7BECdwe/6svML8GGA7k4kKWD3miCu4sCUSFaDIkKQkSAh0Nrc0aZ+SXYmJGH4wWlqDCZoVbI0d5fg4Sugc3mfjFEOQGGKCKivx3LK8Hy1FP4Zk8Wyo1mAFWv+kb0ao37+7ZFRIDmuteUmV+CRb8ew9G8Epgtf/8cymUSOgZ64KlBHZpNMLCHzPwSJG09hUK9AcFaNdyUCpQZTMjRVcDHXYmJN4U1i/vFEOUEGKKIiKrTlRmxZs9ZfLH9NE5dKLOuj2vnizGxbZDQJRAqhbzR67BYBP697i8kH8qDUiGDh9oFLnIJRrNASYURBpMFgzsH4vlhnZvVq6qGslgElv5+HBnZOnQI0Ni8jhVC4Fh+KaJCtHgkvn2Tv191/f3m5xJERHRdad1c8NDN7fDgTeHYknken6Wexm+H85B64gJST1yAj7sS9/RqjXtj2iDcz73R6jh7sQzbTxZCJknwcXOB0SxQYbRA/r8/55cYkHqiEGcvlqGtb+PV0VRkFZXjeEEpgrXqav3ZJElCsFaNzPxSZBWVt5gPCBiiiIjIIWQyCf07+qN/R39kFZVj1a6zWLXrDPKKK/HB5hP4YPMJxIb7YFTvUNweFQxXpX2fTp08r0dRuQEeKgVydBUoN1pgEQIySYKriwxuKgV05QacPK9niAKgN5hQYTLDTela43ZXpRx5xRXQG0zXuTLHYYgiIiKHC/FyxYzbOmLawAhsOlKAFTtO44+jBdhxshA7ThZi7g8HcUf3Vrind2tEh3rZ7cs+k8mCAmMlhABUChlkkgSLAPQGM8qMZiia+Gspe3JXKqBWyFFmMMFD7VJte7nBDJVCDndly4kWLedKiYjI6SnkMtzWORC3dQ5EdlE51qadw+rd53CmsAxf7TyDr3aeQTs/d9zdMwSJ0SFo7d3w10Zhvm4QACoMZmhdXWARgEkISJCgVshQVG6Eh1qBMN+W8WrqakK8XNHeX4OMbB00KkW1PlE5ugpEhWgR4lXzk6rmiCGKiIicUisvVzwxsAMeGxCBHScLsXr3WWzIyMWJ83q89ctRvPXLUcS180VidCsM6RoMrWv1pyNXIpMkeLq6oLTShItlBgASAGH9pyRJ8FC7QOZE41k5kkwmIaFrILJ15TiWX9U3ylUpR7nBbP06b3CXwCbfqbw++HVeI+LXeURE9lVaacLPB3KwNi0LqScuWNcr5TLcEumPO3uEYGBkANQuV+8/dTi3GC98ewBHcktQYbJABlhzlAWAq0KGjkGe+PddXREZxP+GX3L5OFGVpqpXeBEBGgzu0vLGieKTKCIiajI0KgXu6R2Ke3qH4tzFMnyfno3v07NwNK8UGw/mYePBPGhUCgy6IQDDurVC/45+tQ6X4OYiR5nBDA+1CzwloKzSDLMQkEsS3FVyWIRAucEEtzoEspYkIsAD7QZomvWI5XXFEEVERE1Sa283PH5LBB6/JQKHc4vxfXo2fkjPRlZROb5Lz8Z36dnwUClwW+dADI0Kxs0d/GyeUFW9hpHgIpchwEMJo1lYQ5SLXEJ+iQECEvi6pjqZTGoxwxhcCUMUERE1eZFBnogc4omZgzth79kirN+fg58O5CC3uAJr92Zh7d4suCnluKVTABK6BmFgZADKjWb4aZSQJOBimREaddXXZ0azxfpnX3eldXR1on9in6hGxD5RRESOY7EIpJ25iHX7c/DLwVxk6yqs25RyGXq08UKl0YxgrRq68qrO5SaLBQqZDD7uSgR5qgBImH5bRz51aWE47YsTYIgiInIOQggcyNJhQ0au9Qu/y/lrlGjl5YpATzWCtWp4qhXILNA3m2lMqH7YsZyIiOh/JElCt9Ze6NbaCzMTOuF4gR6/HsrDj/uycTC7GAWlBhSUGgDooFJUPYkK93NHnzBvBiiqFZ9ENSI+iSIicn67T13Ax3+ewv4sHfKKK2Cy2P4sdg72xM0d/HBzB3/0DvOu0/AJ15vFIvi1nB3xdZ4TYIgiImoaLoWQ4gojThbokZGtw5/HzuNgdrFNO6VChl5tvBHX3hc3tvdFt9ZeUCpkDqq6yuXjNlWYzFAr5Gjvr0FC1+YzbtP1xhDlBBiiiIiatvOlldiaeR6bj57Hn8cKkF9SabPd1UWOXm290SfMBzHhPohu43Vdn1Rl5pcgaespFOoNCNaq4aZUoMxgso4gPvGmMAapBmCIcgIMUUREzYcQAifO67Ht+AWkHj+P1OMXcLHMaNPGRS4hKkSLnm280autN3q29Uagp7pR6rFYBJb+fhwZ2Tp0CNBUm8vuWH4pO8Y3EEOUE2CIIiJqviwWgaP5Jdh1shA7ThZi58nCak+qgKqJe3uEeqF7qBbdWnshKkQLd9W1f9d1trAMbycfhZebCzQqBUoqTDCYLVDKZfBQK1BaaUJRmZFDNDQAv84jIiJqRDKZVDXIZ5AnxsWFQQiB0xfKsOf0RaSduYg9py/iaF4JsorKkVVUjvUHcqr2k4D2/hp0aeWJriFadG7liS7BWmjd6jeBst5gQoXJjAqjHIdyLlaNc2W2QCGXwdtNiTA/N1SazNAbTHa/dnZkr8IQRUREZAeSJCHMzx1hfu4Y0as1AKCkwoj953TYd64I+89W/TNHV4Fj+aU4ll+K79KzrfuHeLmiU5AHOgV5IPJ//wz3c6917j93pQIGkwV7ThfCbBHQqF3golbAaBYoKKlAob4SoT5ucFfa96eeHdn/xhBFRETUSDzULrgpwg83RfhZ1+UXVyAjW4eDWcVV/8wuxrmL5dYnVr8dzre2lUlAW193tPfXICJAg3b/C2lhfm4I8lCh0mhBUbkRoV5qmCwCFUYL5JIEL1cFzhZVINBkQbAd+2RV78juijKDCRnZOmTryltcR3aGKCIiousowFONgZ5qDIwMtK7TlRlxJK8Eh3OLcTi3BEdyS3A0rwQlFSacPK/HyfNVg4Nezk0ph1ySYBYCGdklkCRALpMgkyTIJUDrpoRSIUNOcYVd+kRZLAIbM/JQqDfYdGT3UFf1yTqWX4pfDuahnZ+mxbzaY4giIiJyMK2bC2LCq4ZJuEQIgYKSShzLL0Xm/5ZTF6oCVVZROcoMV54YWVdhQn5JJaavSke4nzv8PVQI8FAhwFMNP40KPu5K+LoroXV1qVPoySoqx/GCUgRr1TZfAgJVrzKDtWpk5pciq6i8UTuyV5rMKCozolBvQHG5EbHtfBvtXFfDEEVEROSEJElCgKcaAZ5qm9eBAFBhNGPHiQt44bsMlFaaoJTLUG40w2CywGi2wCwAiwDKDGbsPn0Ru09frPU8MgnwdqsKUx6uLtD+b/FQK+CulMNdpYBGpUBxhfF/IcoVLopKKGSy/z35AmSSBAGB86WVOJRbjEqTGcCloCVgtgAmiwVmi4DJImAwWWAwWVBpsqDSZEaF0YIygwn6SrP1n6WVRhSXm1BcYURxhRG6ciMu6o0orfy7o7wkAZnzbofcQU++GKKIiIiaGLWLHOF+7vBQKyCXql4RXv50yGS2IEdXDhe5DGNi2sAsqvpi5ZdUIr+kEhdKK6ue5FSYYBHABb0BF/SGOp173zndFbdvPnb+mq6tLuQyCd5uLvB2U0JvMMFTXb8vG+2FIYqIiKgJKjOa4adR4YIEFOoN0KgVcJHLYDRbUFphgre7Er7uKgyIDEBkUM1jHRnNFlzUG1BYZoCuzIjiChN05VVPfUorTNAbTNBXVi2llSZk5pdCV26EUi6DySJgtghYhIBFVB1LJklQu8jwzwEoFTIJcpkEhUwGmQxQymVQKeRQucigUlT9u7tKDjdl1dMvV6UCHmoFPF1d4Gn9pwt83JXwcVPCQ61win5XDFFERERNkLtSAT+NCn4aJXJ1lSgsM0BfaYJcJkOApxpBnioA0hWHOHCRy6yvDOvi0td5F0orq56CySSYLQIlFSb4alQt7us8x86aCOC9995DWFgY1Go1YmNjsXPnziu2X7NmDSIjI6FWqxEVFYWffvrJZvvatWsxePBg+Pr6QpIkpKenVztGRUUFHn/8cfj6+kKj0WDEiBHIy7P96uHMmTMYNmwY3NzcEBAQgJkzZ8Jksv+AZURE5HgWi8DZwjIczi3G2cIyWCzOP5lHiJcr2vtrUG60oFdbL8S180VMuC/i2vmiVxsvlBstiAjQIMTL1W7njAjwwMDIAOgrzdh+ohC/HynA9hOF0BvMGBgZ0KICFODgELVq1SrMmDEDc+fORVpaGrp3746EhATk5+fX2H7btm0YM2YMJk2ahL179yIxMRGJiYnIyMiwttHr9ejXrx8WLFhQ63mnT5+OH3/8EWvWrMEff/yB7Oxs3H333dbtZrMZw4YNg8FgwLZt27B8+XJ8+umneOmll+x38URE5BQy80uw9PfjeDv5KN5NOYa3k49i6e/HkZlf4ujSrkgmk5DQNRA+7kpkFughSYCXm0tVZ+sCPXzclRjcJdCur70y80vw2+F8uKsUiGvniwGdAhDXzhfuSgV+O5zv9PfM3hw6d15sbCz69OmDJUuWAAAsFgtCQ0MxdepUzJ49u1r70aNHQ6/XY926ddZ1ffv2RY8ePbBs2TKbtqdOnUJ4eDj27t2LHj16WNfrdDr4+/tjxYoVGDlyJADg8OHDuOGGG5Camoq+ffvi559/xr/+9S9kZ2cjMLBqHI9ly5Zh1qxZKCgogFKprPF6KisrUVn597xJxcXFCA0N5dx5REROqvrgkQqUGUzI0VXAx13ZJF5PXT6CeKXJDJVCjogADQZ3se8I4i1pwuO6zp3nsCdRBoMBe/bswaBBg/4uRibDoEGDkJqaWuM+qampNu0BICEhodb2NdmzZw+MRqPNcSIjI9GmTRvrcVJTUxEVFWUNUJfOU1xcjIMHD9Z67Pnz50Or1VqX0NDQOtdFRETX1z8Hj/RQu0Auk+ChdkGHAA0K9Qb8cjDP6V/tRQR44NEB7TH9to6YemsHTL+tIx6Jb2/38FefcaJaCoeFqPPnz8NsNtsEFQAIDAxEbm5ujfvk5ubWq31tx1AqlfDy8qr1OLWd59K22syZMwc6nc66nD17ts51ERHR9dWcQoFMJiHUxw2RQZ4I9XFrlCdBlyY8dqulo7qrUt5oEx47K36dZ0cqlQoqlcrRZRARUR38HQpq7njtqpQjr7iiRYWCK3FXKqBWyFFmMMGjhnGZyg1VrxLtPeGxM3PYkyg/Pz/I5fJqX8Xl5eUhKCioxn2CgoLq1b62YxgMBhQVFdV6nNrOc2kbERE1fZeHgpq0xFBwJZe+BszRVeCf3amFEMjRVdj9a0Bn57AQpVQq0atXL6SkpFjXWSwWpKSkIC4ursZ94uLibNoDQHJycq3ta9KrVy+4uLjYHOfIkSM4c+aM9ThxcXE4cOCAzVeCycnJ8PT0ROfOnet8LiIicl4MBfVz+deAR/NKkF1UhrzicmQXleFoXkmjfA3o7Bwar2fMmIEJEyagd+/eiImJwaJFi6DX6zFx4kQAwPjx4xESEoL58+cDAJ588knEx8dj4cKFGDZsGFauXIndu3fjww8/tB6zsLAQZ86cQXZ2NoCqgARUPUEKCgqCVqvFpEmTMGPGDPj4+MDT0xNTp05FXFwc+vbtCwAYPHgwOnfujHHjxuGNN95Abm4uXnjhBTz++ON8XUdE1ExcCgXZunIcy6/qG+WqlKPcYLZ+ndfSQsHVXBonKmnLSew7WwSD2QKlXIaIAA3u6R3q9F8y2ptDQ9To0aNRUFCAl156Cbm5uejRowc2bNhg7cR95swZyGR/Pyy78cYbsWLFCrzwwgt47rnn0KFDB3z33Xfo2rWrtc0PP/xgDWEAcO+99wIA5s6di5dffhkA8Pbbb0Mmk2HEiBGorKxEQkIC3n//fes+crkc69atw6OPPoq4uDi4u7tjwoQJeOWVVxrzdhAR0XUWEeCBiTeFWYcIyCuugEohR1SI1u5DBDQHmfkl+HZvFvJKKqFUyKCQS5BJEnKLK/Ht3iy09XVrUffMoeNENXd1HWeCiIgcy2IRyCoqh95ggrtSgRAvVz6B+geLReDf6/9C8l95UMoleLi6WOfqKyk3wmAWuK1zIF4Y1rnJ37u6/n6ztxwREbV4l4YIoNqdu1iG7ScuQC4BvhqVdVgIlUIOpUaGvOIK7DhxAeculqGNr7uDq70+HD53HhERETm/E+f10JUZ4enmUuO4Wlo3FxSVG3HivN5BFV5/DFFERERUJ0ICJNT2qq5pv8JrCIYoIiIiuqpwP3d4uSpRVGascUgIXZkRWlclwv1axqs8gCGKiIiI6iDU2w19w31gEQIX9AZUmsywCIFKkxkX9AZYhEBcOx+EerecvmXsWE5ERERXJZNJuK9vG+SXVuJoXglKKv4e6V0uk9A91AtjYts0+S/z6oMhioiIiOokIsADTw3qgA0HcnEgS4cyowluLgp0a61FQtegFjVGFMAQRURERPUQEeCBx27RcFwtMEQRERFRPXFcrSrsWE5ERETUAAxRRERERA3AEEVERETUAAxRRERERA3AEEVERETUAAxRRERERA3AEEVERETUAAxRRERERA3AEEVERETUAByxvBEJIQAAxcXFDq6EiIiI6urS7/al3/HaMEQ1opKSEgBAaGiogyshIiKi+iopKYFWq611uySuFrOowSwWC7Kzs+Hh4QFJankTMzpCcXExQkNDcfbsWXh6ejq6nBaF995xeO8dh/fecRrz3gshUFJSglatWkEmq73nE59ENSKZTIbWrVs7uowWydPTk/9BcxDee8fhvXcc3nvHaax7f6UnUJewYzkRERFRAzBEERERETUAQxQ1KyqVCnPnzoVKpXJ0KS0O773j8N47Du+94zjDvWfHciIiIqIG4JMoIiIiogZgiCIiIiJqAIYoIiIiogZgiCIiIiJqAIYoahKysrJw//33w9fXF66uroiKisLu3but24UQeOmllxAcHAxXV1cMGjQIx44dszlGYWEhxo4dC09PT3h5eWHSpEkoLS293pfSpJjNZrz44osIDw+Hq6sr2rdvj1dffdVmPinee/vYvHkz7rjjDrRq1QqSJOG7776z2W6v+7x//37cfPPNUKvVCA0NxRtvvNHYl+b0rnTvjUYjZs2ahaioKLi7u6NVq1YYP348srOzbY7Be98wV/vf/eUeeeQRSJKERYsW2ax36L0XRE6usLBQtG3bVjzwwANix44d4sSJE2Ljxo0iMzPT2ub1118XWq1WfPfdd2Lfvn1i+PDhIjw8XJSXl1vbDBkyRHTv3l1s375d/PnnnyIiIkKMGTPGEZfUZMybN0/4+vqKdevWiZMnT4o1a9YIjUYj3nnnHWsb3nv7+Omnn8Tzzz8v1q5dKwCIb7/91ma7Pe6zTqcTgYGBYuzYsSIjI0N89dVXwtXVVXzwwQfX6zKd0pXufVFRkRg0aJBYtWqVOHz4sEhNTRUxMTGiV69eNsfgvW+Yq/3v/pK1a9eK7t27i1atWom3337bZpsj7z1DFDm9WbNmiX79+tW63WKxiKCgIPHmm29a1xUVFQmVSiW++uorIYQQf/31lwAgdu3aZW3z888/C0mSRFZWVuMV38QNGzZMPPjggzbr7r77bjF27FghBO99Y/nnj4m97vP7778vvL29RWVlpbXNrFmzRKdOnRr5ipqOK/2QX7Jz504BQJw+fVoIwXtvL7Xd+3PnzomQkBCRkZEh2rZtaxOiHH3v+TqPnN4PP/yA3r1745577kFAQACio6Px3//+17r95MmTyM3NxaBBg6zrtFotYmNjkZqaCgBITU2Fl5cXevfubW0zaNAgyGQy7Nix4/pdTBNz4403IiUlBUePHgUA7Nu3D1u2bMHQoUMB8N5fL/a6z6mpqejfvz+USqW1TUJCAo4cOYKLFy9ep6tp+nQ6HSRJgpeXFwDe+8ZksVgwbtw4zJw5E126dKm23dH3niGKnN6JEyewdOlSdOjQARs3bsSjjz6KadOmYfny5QCA3NxcAEBgYKDNfoGBgdZtubm5CAgIsNmuUCjg4+NjbUPVzZ49G/feey8iIyPh4uKC6OhoPPXUUxg7diwA3vvrxV73OTc3t8ZjXH4OurKKigrMmjULY8aMsU56y3vfeBYsWACFQoFp06bVuN3R915xTXsTXQcWiwW9e/fGa6+9BgCIjo5GRkYGli1bhgkTJji4uuZt9erV+PLLL7FixQp06dIF6enpeOqpp9CqVSvee2pxjEYjRo0aBSEEli5d6uhymr09e/bgnXfeQVpaGiRJcnQ5NeKTKHJ6wcHB6Ny5s826G264AWfOnAEABAUFAQDy8vJs2uTl5Vm3BQUFIT8/32a7yWRCYWGhtQ1VN3PmTOvTqKioKIwbNw7Tp0/H/PnzAfDeXy/2us9BQUE1HuPyc1DNLgWo06dPIzk52foUCuC9byx//vkn8vPz0aZNGygUCigUCpw+fRpPP/00wsLCADj+3jNEkdO76aabcOTIEZt1R48eRdu2bQEA4eHhCAoKQkpKinV7cXExduzYgbi4OABAXFwcioqKsGfPHmub3377DRaLBbGxsdfhKpqmsrIyyGS2/5mQy+WwWCwAeO+vF3vd57i4OGzevBlGo9HaJjk5GZ06dYK3t/d1upqm51KAOnbsGH799Vf4+vrabOe9bxzjxo3D/v37kZ6ebl1atWqFmTNnYuPGjQCc4N5fc9d0oka2c+dOoVAoxLx588SxY8fEl19+Kdzc3MQXX3xhbfP6668LLy8v8f3334v9+/eLO++8s8bPv6Ojo8WOHTvEli1bRIcOHfiZ/VVMmDBBhISEWIc4WLt2rfDz8xPPPvustQ3vvX2UlJSIvXv3ir179woA4j//+Y/Yu3ev9Qswe9znoqIiERgYKMaNGycyMjLEypUrhZubW4v/zP5K995gMIjhw4eL1q1bi/T0dJGTk2NdLv/ai/e+Ya72v/t/+ufXeUI49t4zRFGT8OOPP4quXbsKlUolIiMjxYcffmiz3WKxiBdffFEEBgYKlUolbr31VnHkyBGbNhcuXBBjxowRGo1GeHp6iokTJ4qSkpLreRlNTnFxsXjyySdFmzZthFqtFu3atRPPP/+8zY8H7719bNq0SQCotkyYMEEIYb/7vG/fPtGvXz+hUqlESEiIeP3116/XJTqtK937kydP1rgNgNi0aZP1GLz3DXO1/93/U00hypH3XhLisqGHiYiIiKhO2CeKiIiIqAEYooiIiIgagCGKiIiIqAEYooiIiIgagCGKiIiIqAEYooiIiIgagCGKiIiIqAEYooiIiIgagCGKiKiRXbhwAQEBATh16hQA4Pfff4ckSSgqKmrU886ePRtTp05t1HMQtWQMUUTkNB544AFIklRtGTJkiKNLuybz5s3DnXfeaZ15/lrk5eXBxcUFK1eurHH7pEmT0LNnTwDAM888g+XLl+PEiRPXfF4iqo4hioicypAhQ5CTk2OzfPXVV416ToPB0GjHLisrw8cff4xJkybZ5XiBgYEYNmwYPvnkk2rb9Ho9Vq9ebT2Xn58fEhISsHTpUrucm4hsMUQRkVNRqVQICgqyWby9va3bJUnCRx99hLvuugtubm7o0KEDfvjhB5tjZGRkYOjQodBoNAgMDMS4ceNw/vx56/YBAwbgiSeewFNPPWUNGgDwww8/oEOHDlCr1bjllluwfPly62s3vV4PT09PfP311zbn+u677+Du7o6SkpIar+enn36CSqVC3759a73msrIyDB06FDfddJP1Fd9HH32EG264AWq1GpGRkXj//fet7SdNmoSUlBScOXPG5jhr1qyByWTC2LFjrevuuOOOWp9aEdG1YYgioibn//7v/zBq1Cjs378ft99+O8aOHYvCwkIAQFFREQYOHIjo6Gjs3r0bGzZsQF5eHkaNGmVzjOXLl0OpVGLr1q1YtmwZTp48iZEjRyIxMRH79u3DlClT8Pzzz1vbu7u7495770VSUpLNcZKSkjBy5Eh4eHjUWOuff/6JXr161XotRUVFuO2222CxWJCcnAwvLy98+eWXeOmllzBv3jwcOnQIr732Gl588UUsX74cAHD77bcjMDAQn376abVa7r77bnh5eVnXxcTE4Ny5c9b+WERkR4KIyElMmDBByOVy4e7ubrPMmzfP2gaAeOGFF6x/Li0tFQDEzz//LIQQ4tVXXxWDBw+2Oe7Zs2cFAHHkyBEhhBDx8fEiOjraps2sWbNE165dbdY9//zzAoC4ePGiEEKIHTt2CLlcLrKzs4UQQuTl5QmFQiF+//33Wq/pzjvvFA8++KDNuk2bNgkA4tChQ6Jbt25ixIgRorKy0rq9ffv2YsWKFTb7vPrqqyIuLs7659mzZ4vw8HBhsViEEEJkZmYKSZLEr7/+arOfTqcTAK5YIxE1DJ9EEZFTueWWW5Cenm6zPPLIIzZtunXrZv13d3d3eHp6Ij8/HwCwb98+bNq0CRqNxrpERkYCAI4fP27d759Ph44cOYI+ffrYrIuJian25y5dulifCH3xxRdo27Yt+vfvX+v1lJeXQ61W17jttttuQ0REBFatWgWlUgmgql/T8ePHMWnSJJtr+Pe//21T/4MPPoiTJ09i06ZNAKqeQoWFhWHgwIE253B1dQVQ9cqQiOxL4egCiIgu5+7ujoiIiCu2cXFxsfmzJEmwWCwAgNLSUtxxxx1YsGBBtf2Cg4NtztMQDz30EN577z3Mnj0bSUlJmDhxIiRJqrW9n58fLl68WOO2YcOG4ZtvvsFff/2FqKgoa/0A8N///hexsbE27eVyufXfO3TogJtvvhlJSUkYMGAAPvvsM0yePLlaLZdec/r7+9f/YonoihiiiKhZ6dmzJ7755huEhYVBoaj7f+I6deqEn376yWbdrl27qrW7//778eyzz+Ldd9/FX3/9hQkTJlzxuNHR0fjiiy9q3Pb6669Do9Hg1ltvxe+//47OnTsjMDAQrVq1wokTJ2w6iNdk0qRJePTRRzF8+HBkZWXhgQceqNYmIyMDLi4u6NKlyxWPRUT1x9d5RORUKisrkZuba7Nc/mXd1Tz++OMoLCzEmDFjsGvXLhw/fhwbN27ExIkTYTaba91vypQpOHz4MGbNmoWjR49i9erV1o7blz/d8fb2xt13342ZM2di8ODBaN269RXrSUhIwMGDB2t9GvXWW29h7NixGDhwIA4fPgygquP8/Pnz8e677+Lo0aM4cOAAkpKS8J///Mdm33vuuQcuLi6YMmUKBg8ejNDQ0GrH//PPP3HzzTdbX+sRkf0wRBGRU9mwYQOCg4Ntln79+tV5/1atWmHr1q0wm80YPHgwoqKi8NRTT8HLywsyWe3/yQsPD8fXX3+NtWvXolu3bli6dKn16zyVSmXTdtKkSTAYDHjwwQevWk9UVBR69uyJ1atX19rm7bffxqhRozBw4EAcPXoUDz30ED766CMkJSUhKioK8fHx+PTTTxEeHm6zn5ubG+69915cvHix1lpWrlyJyZMnX7VOIqo/SQghHF0EEZEzmjdvHpYtW4azZ8/arP/8888xffp0ZGdnWzuEX8n69esxc+ZMZGRkXDHI2dvPP/+Mp59+Gvv376/Xq00iqhv+fxUR0f+8//776NOnD3x9fbF161a8+eabeOKJJ6zby8rKkJOTg9dffx1TpkypU4ACqjqQHzt2DFlZWTW+cmsser0eSUlJDFBEjYRPooiI/mf69OlYtWoVCgsL0aZNG4wbNw5z5syxhpCXX34Z8+bNQ//+/fH9999Do9E4uGIiciSGKCIiIqIGYMdyIiIiogZgiCIiIiJqAIYoIiIiogZgiCIiIiJqAIYoIiIiogZgiCIiIiJqAIYoIiIiogZgiCIiIiJqgP8Hb/JxIK2FSwEAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -728,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -736,11 +918,11 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n" ] } ], @@ -792,7 +974,45 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "for meas in all_sample_measurements.values():\n", + " meas.to_h5(\"data.h5\", mode=\"a\", spectrum_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing niobium_1...\n", + "\n", + "Processing niobium_2...\n", + "\n", + "Processing niobium_3...\n", + "\n" + ] + } + ], + "source": [ + "for sample, directory in sample_measurements_directories.items():\n", + " print(f\"Processing {sample}...\")\n", + " meas = SampleMeasurement.from_h5(\"data.h5\", measurement_name=sample)\n", + " print(meas)\n", + " all_sample_measurements[sample] = meas\n", + " if \"niobium\" in sample:\n", + " meas.foil = nb_foil" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -800,19 +1020,33 @@ "output_type": "stream", "text": [ "Processing channel 4...\n", - "niobium_1: [449600.30282758] ± [5727.34454993] emmited gamma rays\n", - "niobium_1: 9.68e+07 neutrons/s\n", - "niobium_2: [1137301.58500562] ± [9109.14961268] emmited gamma rays\n", - "niobium_2: 1.03e+08 neutrons/s\n", - "niobium_3: [890498.3337663] ± [8060.39952153] emmited gamma rays\n", - "niobium_3: 8.28e+07 neutrons/s\n", + "niobium_1: [451336.84117134] ± [5764.89885571] emmited gamma rays\n", + "niobium_1: 9.72e+07 neutrons/s\n", + "niobium_2: [1155986.73572747] ± [9226.09081067] emmited gamma rays\n", + "niobium_2: 1.05e+08 neutrons/s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:596: RuntimeWarning: invalid value encountered in sqrt\n", + " nb_counts_measured_err = np.sqrt(nb_counts_measured)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "niobium_3: [-2353796.0508161] ± [nan] emmited gamma rays\n", + "niobium_3: -2.19e+08 neutrons/s\n", "Processing channel 5...\n", - "niobium_1: [599611.90796695] ± [6614.16671655] emmited gamma rays\n", - "niobium_1: 1.13e+08 neutrons/s\n", - "niobium_2: [1394301.65985281] ± [10085.9865613] emmited gamma rays\n", - "niobium_2: 1.14e+08 neutrons/s\n", - "niobium_3: [1390828.53068015] ± [10073.41690841] emmited gamma rays\n", - "niobium_3: 1.20e+08 neutrons/s\n" + "niobium_1: [605163.6341688] ± [6675.40639871] emmited gamma rays\n", + "niobium_1: 1.14e+08 neutrons/s\n", + "niobium_2: [1407211.31183843] ± [10179.37135757] emmited gamma rays\n", + "niobium_2: 1.16e+08 neutrons/s\n", + "niobium_3: [1403706.02542878] ± [10166.68532395] emmited gamma rays\n", + "niobium_3: 1.21e+08 neutrons/s\n" ] } ], @@ -849,7 +1083,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -857,24 +1091,24 @@ "output_type": "stream", "text": [ "Processing channel 4...\n", - "[ 0 1 2 ... 4092 4093 4094]\n", - "[ 0 1 2 ... 4092 4093 4094]\n", - "[ 0 1 2 ... 4092 4093 4094]\n" + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -919,7 +1153,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.2" + "version": "3.13.5" } }, "nbformat": 4, From 7740c4be7b5c2464746e3e850a28c62bb3e9c95c Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Thu, 3 Jul 2025 11:53:09 -0400 Subject: [PATCH 127/137] updated example --- docs/non_tested_examples/example.ipynb | 225 ++++++++++++++----------- 1 file changed, 126 insertions(+), 99 deletions(-) diff --git a/docs/non_tested_examples/example.ipynb b/docs/non_tested_examples/example.ipynb index 30988e6..2fba8ab 100644 --- a/docs/non_tested_examples/example.ipynb +++ b/docs/non_tested_examples/example.ipynb @@ -117,35 +117,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:232: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:209: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -200,35 +200,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n" + "\n" ] } ], @@ -478,7 +478,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_241423/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_257427/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, @@ -615,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -642,8 +642,6 @@ "), f\"Channel number mismatch: {background_detector.channel_nb} != {ch_nb}\"\n", "\n", "hist, bin_edges = check_source_detector.get_energy_hist_background_substract(\n", - " # background_detector, bins=\"double\"\n", - " # background_detector, bins=int(np.nanmax(check_source_detector.events[:, 1]))\n", " background_detector,\n", " bins=None,\n", ")\n", @@ -918,47 +916,52 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n", + "Processing zirconium_1...\n", + "\n", + "Processing zirconium_2...\n", + "\n", + "Processing zirconium_3...\n", + "\n" ] } ], "source": [ "from libra_toolbox.neutron_detection.activation_foils.compass import SampleMeasurement\n", "from libra_toolbox.neutron_detection.activation_foils.calibration import (\n", - " nb93,\n", - " nb92m,\n", " ActivationFoil,\n", - " Reaction,\n", - ")\n", - "\n", - "\n", - "# source: https://scipub.euro-fusion.org/wp-content/uploads/eurofusion/WPJET3PR17_16948_submitted.pdf\n", - "# See Figure 3\n", - "Nb93_n_2n_Nb92m_cross_section_at_14Mev = 0.46 # barn\n", - "barn_to_cm2 = 1e-24\n", - "Nb93_n_2n_Nb92m_cross_section_at_14Mev *= barn_to_cm2 # cm^2\n", - "\n", - "nb_reaction = Reaction(\n", - " reactant=nb93, product=nb92m, cross_section=Nb93_n_2n_Nb92m_cross_section_at_14Mev\n", + " zr90_n2n, nb93_n2n,\n", ")\n", "\n", "nb_foil = ActivationFoil(\n", - " reaction=nb_reaction,\n", - " mass=0.5678, # in grams\n", - " name=\"Nb foil\",\n", + " reaction=nb93_n2n,\n", + " mass=0.5359,\n", + " name=\"Niobium #3\",\n", + " density=8.582,\n", + " thickness=0.01 * 2 * 2.54, # 0.01 inch in cm\n", + ")\n", + "nb_foil.mass_attenuation_coefficient = 0.06120 # cm^2/g at 1 MeV\n", + "\n", + "zirconium1 = ActivationFoil(\n", + " reaction=zr90_n2n,\n", + " mass=0.9036,\n", + " name=\"Zirconium #1\",\n", + " density=6.505,\n", + " thickness=0.005 * 8 * 2.54, # 0.01 inch in cm\n", ")\n", + "zirconium1.mass_attenuation_coefficient = 0.08590 # cm^2/g\n", "\n", "sample_measurements_directories = {\n", " \"niobium_1\": f\"{run_dir}/Niobium_250318_1253_count1/UNFILTERED\",\n", " \"niobium_2\": f\"{run_dir}/Niobium_250319_1124_count2/UNFILTERED\",\n", " \"niobium_3\": f\"{run_dir}/Niobium_250321_0935_count3/UNFILTERED\",\n", - " # \"zirconium_1\": f\"{run_dir}/Zirconium_1L_3_240317_2312/UNFILTERED\",\n", - " # \"zirconium_2\": f\"{run_dir}/Zirconium_250318_2219_count2/UNFILTERED\",\n", - " # \"zirconium_3\": f\"{run_dir}/Zirconium_250320_1042_count3/UNFILTERED\",\n", + " \"zirconium_1\": f\"{run_dir}/Zirconium_1L_3_240317_2312/UNFILTERED\",\n", + " \"zirconium_2\": f\"{run_dir}/Zirconium_250318_2219_count2/UNFILTERED\",\n", + " \"zirconium_3\": f\"{run_dir}/Zirconium_250320_1042_count3/UNFILTERED\",\n", "}\n", "\n", "all_sample_measurements = {}\n", @@ -969,7 +972,9 @@ " print(meas)\n", " all_sample_measurements[sample] = meas\n", " if \"niobium\" in sample:\n", - " meas.foil = nb_foil" + " meas.foil = nb_foil\n", + " elif \"zirconium\" in sample:\n", + " meas.foil = zirconium1" ] }, { @@ -992,11 +997,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n" + "\n", + "Processing zirconium_1...\n", + "\n", + "Processing zirconium_2...\n", + "\n", + "Processing zirconium_3...\n", + "\n" ] } ], @@ -1007,7 +1018,9 @@ " print(meas)\n", " all_sample_measurements[sample] = meas\n", " if \"niobium\" in sample:\n", - " meas.foil = nb_foil" + " meas.foil = nb_foil\n", + " elif \"zirconium\" in sample:\n", + " meas.foil = zirconium1" ] }, { @@ -1020,33 +1033,31 @@ "output_type": "stream", "text": [ "Processing channel 4...\n", - "niobium_1: [451336.84117134] ± [5764.89885571] emmited gamma rays\n", - "niobium_1: 9.72e+07 neutrons/s\n", - "niobium_2: [1155986.73572747] ± [9226.09081067] emmited gamma rays\n", - "niobium_2: 1.05e+08 neutrons/s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:596: RuntimeWarning: invalid value encountered in sqrt\n", - " nb_counts_measured_err = np.sqrt(nb_counts_measured)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "niobium_3: [-2353796.0508161] ± [nan] emmited gamma rays\n", - "niobium_3: -2.19e+08 neutrons/s\n", + "niobium_1: [421177.56667845] ± [5568.95764857] emmited gamma rays\n", + "niobium_1: 9.75e+07 neutrons/s\n", + "niobium_2: [1095916.4761057] ± [8983.17846382] emmited gamma rays\n", + "niobium_2: 1.07e+08 neutrons/s\n", + "niobium_3: [899693.81776096] ± [8139.33020337] emmited gamma rays\n", + "niobium_3: 8.99e+07 neutrons/s\n", + "zirconium_1: [1678703.37830234] ± [10951.01206566] emmited gamma rays\n", + "zirconium_1: 8.79e+07 neutrons/s\n", + "zirconium_2: [1366007.96450431] ± [9878.565664] emmited gamma rays\n", + "zirconium_2: 8.48e+07 neutrons/s\n", + "zirconium_3: [1968107.97886165] ± [11857.461856] emmited gamma rays\n", + "zirconium_3: 8.40e+07 neutrons/s\n", "Processing channel 5...\n", "niobium_1: [605163.6341688] ± [6675.40639871] emmited gamma rays\n", - "niobium_1: 1.14e+08 neutrons/s\n", + "niobium_1: 1.22e+08 neutrons/s\n", "niobium_2: [1407211.31183843] ± [10179.37135757] emmited gamma rays\n", - "niobium_2: 1.16e+08 neutrons/s\n", + "niobium_2: 1.24e+08 neutrons/s\n", "niobium_3: [1403706.02542878] ± [10166.68532395] emmited gamma rays\n", - "niobium_3: 1.21e+08 neutrons/s\n" + "niobium_3: 1.30e+08 neutrons/s\n", + "zirconium_1: [2290779.86918969] ± [12792.60393814] emmited gamma rays\n", + "zirconium_1: 1.14e+08 neutrons/s\n", + "zirconium_2: [2200967.12008692] ± [12539.32193514] emmited gamma rays\n", + "zirconium_2: 1.23e+08 neutrons/s\n", + "zirconium_3: [3136271.66467644] ± [14968.35630193] emmited gamma rays\n", + "zirconium_3: 1.25e+08 neutrons/s\n" ] } ], @@ -1057,7 +1068,7 @@ "]\n", "time_generator_off = datetime.strptime(\"3/17/2025 22:10\", \"%m/%d/%Y %H:%M\")\n", "\n", - "for ch_nb in [4, 5]:\n", + "for ch_nb, search_width in zip([4, 5], [200, 300]):\n", " print(f\"Processing channel {ch_nb}...\")\n", " for sample, measurement in all_sample_measurements.items():\n", " emmited_gammas, err = measurement.get_gamma_emitted(\n", @@ -1065,7 +1076,7 @@ " efficiency_coeffs=detection_efficiency_coeffs,\n", " calibration_coeffs=calibration_coeffs[ch_nb],\n", " channel_nb=ch_nb,\n", - " search_width=300,\n", + " search_width=search_width,\n", " )\n", " # add timezone\n", " time_generator_off = time_generator_off.replace(tzinfo=measurement.start_time.tzinfo)\n", @@ -1083,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1093,22 +1104,38 @@ "Processing channel 4...\n", "[ 0 1 2 ... 4093 4094 4095]\n", "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", "[ 0 1 2 ... 4093 4094 4095]\n" ] }, { "data": { + "image/png": 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", "text/plain": [ - "" + "
" ] }, - "execution_count": 20, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing channel 5...\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n", + "[ 0 1 2 ... 4093 4094 4095]\n" + ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1118,22 +1145,22 @@ } ], "source": [ - "for ch_nb in [4]:\n", + "for ch_nb in [4, 5]:\n", " print(f\"Processing channel {ch_nb}...\")\n", " for sample, measurement in all_sample_measurements.items():\n", " detector = measurement.get_detector(ch_nb)\n", " background_detector = background_meas.get_detector(ch_nb)\n", " hist, bin_edges = detector.get_energy_hist_background_substract(background_detector)\n", " # hist, bin_edges = detector.get_energy_hist()\n", - " print(bin_edges)\n", " plt.stairs(\n", " hist, bin_edges, alpha=0.5, label=sample\n", " )\n", "\n", - "plt.xlim(0, 2000)\n", - "plt.ylim(-1e3, 1e3)\n", - "# plt.yscale(\"log\")\n", - "plt.legend()" + " plt.xlim(0, 2000)\n", + " plt.ylim(-1e3, 1e3)\n", + " # plt.yscale(\"log\")\n", + " plt.legend()\n", + " plt.show()" ] } ], From 67a8f04919c7de4f3621303a82cf4337d724e2d9 Mon Sep 17 00:00:00 2001 From: RemDelaporteMathurin Date: Tue, 15 Jul 2025 16:00:03 -0400 Subject: [PATCH 128/137] removed the events cutoff and scale bg everytime + use live time by default + fixed tests --- docs/non_tested_examples/example.ipynb | 292 +++++++++--------- .../activation_foils/compass.py | 57 ++-- test/neutron_detection/test_compass.py | 269 +++++++++------- 3 files changed, 336 insertions(+), 282 deletions(-) diff --git a/docs/non_tested_examples/example.ipynb b/docs/non_tested_examples/example.ipynb index 2fba8ab..bdf8b97 100644 --- a/docs/non_tested_examples/example.ipynb +++ b/docs/non_tested_examples/example.ipynb @@ -111,41 +111,67 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Energy;1', 'Energy/EnergyCH4@V1725_292;1', 'Energy/Calibration_4;1', 'Energy/EnergyCH5@V1725_292;1', 'Energy/Calibration_5;1', 'Time;1', 'Time/TimeCH4@V1725_292;1', 'Time/TimeCH5@V1725_292;1', 'PSD;1', 'PSD/PSDCH4@V1725_292;1', 'PSD/PSDCH5@V1725_292;1', 'PSD_E;1', 'PSD_E/PSDvsECH4@V1725_292;1', 'PSD_E/PSDvsECH5@V1725_292;1', 'RealTime_4;1', 'LiveTime_4;1', 'RealTime_5;1', 'LiveTime_5;1']\n", + "\n", + "{'_cursor': Cursor(6, origin=-52), '_file': , '_parent': None, '_concrete': , '_members': {'fNcells': 4097, 'fXaxis': , 'fYaxis': , 'fZaxis': , 'fBarOffset': 0, 'fBarWidth': 1000, 'fEntries': 417265.0, 'fTsumw': 417265.0, 'fTsumw2': 417265.0, 'fTsumwx': 202513872.0, 'fTsumwx2': 285424114272.0, 'fMaximum': -1111.0, 'fMinimum': -1111.0, 'fNormFactor': 0.0, 'fContour': , 'fSumw2': , 'fOption': , 'fFunctions': , 'fBufferSize': 0, 'fBuffer': array([], dtype='>f8'), 'fBinStatErrOpt': 0, 'fStatOverflows': 2}, '_bases': [, , , ], '_num_bytes': 550, '_instance_version': 8, '_is_memberwise': np.uint16(0), '_speedbump1': array([0], dtype=uint8)}\n", + "4097\n" + ] + } + ], + "source": [ + "import uproot\n", + "root_filename = f\"{run_dir}/Co60_0_872uCi_19Mar2014_250319_run3/UNFILTERED/Hcompass_Co60_0_872uCi_19Marc2014_250319_run3_20250319_095305.root\"\n", + "with uproot.open(root_filename) as root_file:\n", + " print(root_file.keys())\n", + " print(root_file[\"Energy/EnergyCH4@V1725_292;1\"]._bases[0])\n", + " print(root_file[\"Energy/EnergyCH4@V1725_292;1\"]._bases[0].__dict__)\n", + " print(root_file[\"Energy/EnergyCH4@V1725_292;1\"]._bases[0]._members['fNcells'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n", + "\n", "Processing background...\n" ] }, @@ -153,7 +179,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:209: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", + "/home/remidm/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:201: UserWarning: run.info file not found. Assuming start and stop time are not needed.\n", " warnings.warn(\n" ] } @@ -178,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -187,12 +213,12 @@ " meas.to_h5(\"data.h5\", mode=mode, spectrum_only=True)\n", " mode = \"a\" # Change to append mode after the first measurement\n", "\n", - "background_meas.to_h5(\"data.h5\", mode=\"a\", spectrum_only=False)" + "background_meas.to_h5(\"data.h5\", mode=\"a\", spectrum_only=True)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -200,35 +226,35 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "\n", + "\n", "Processing Co60_2...\n", - "\n", + "\n", "Processing Co60_3...\n", - "\n", + "\n", "Processing Co60_4...\n", - "\n", + "\n", "Processing Co60_5...\n", - "\n", + "\n", "Processing Cs137_1...\n", - "\n", + "\n", "Processing Cs137_2...\n", - "\n", + "\n", "Processing Cs137_3...\n", - "\n", + "\n", "Processing Cs137_4...\n", - "\n", + "\n", "Processing Mn54_1...\n", - "\n", + "\n", "Processing Mn54_2...\n", - "\n", + "\n", "Processing Mn54_3...\n", - "\n", + "\n", "Processing Na22_2...\n", - "\n", + "\n", "Processing Na22_3...\n", - "\n", + "\n", "Processing Na22_4...\n", - "\n" + "\n" ] } ], @@ -245,12 +271,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -311,12 +337,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -383,12 +409,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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y/rT3FD2mrSR+Qt8KtS0iIiJSUXWCfKtcAnQpGQwGfvjhByZPnkxOTg5Nmzblyy+/pF+/fu4Oze2UbInbBft7YTIaKLQ6zmg9qW3gRURERCo9X19fli1b5u4wKiXN0ZJK4YrooBLLrVYbBYXuPyNBRERERKSslGxJpfD+3Z359L6uxcqf+t82hr9/8XMaREREREQqG00jlEqhhrcHV0QHFyv/fOMxN0QjIiIilyurVTNqxHnnvirZknKzWm3kFzrv5AAvDyPRIX4cTclyWpsiIiIipeHl5YXRaOTEiRPUqlULLy+vix7KK9WTzWbj1KlTGAwGPD2L75pdFkq2pNymLy06VTzE39tpbQ6+oi4zl+1zWnsiIiIipWE0GomJieHkyZOcOHHC3eGImxkMBurWrYvJZKpQO0q2pNze/eUwAB4m1//W57MNR7mpfV28PLTMUERERFzDy8uL6OhoCgoKKCy8NIcYS+Xk6elZ4UQLlGxJBdQP9WdPYjq1Apw3snU+T365nV8OnGH2be1d3peIiIhcvs5NHavo9DER0G6EUgHXtKoNUOLGFq7w3dYT2gZeRERERKoMJVtSbgdPZRDi7+XUNhvU8r/g/Yc+/c2p/YmIiIiIuIqSLSm3QquNusG+Tm3z+raRPH99i/Pe/2F7IvWf+t6+XkxEREREpLJSsiUVYvZ1/nzmu7vFcHuX6AvWeWnhLqf3KyIiIiLiTEq2pFKaPKiVu0MQEREREakQJVtSKekQQRERERGp6pRsSbmtPXQGm821fQT7adtVEREREamalGxJuRxPzeZMZh6/HDjtsj7m3d2Jb8d0d1n7IiIiIiKupEONpVxSs/Jc3kevpmEu70NERERExFU0siVVxptDr2BQu0h3hyEiIiIiUipKtqTKGNi6NhOvb2m/Lii0ujEaEREREZELU7IlVUqIvxcP920MQKNnfiQnv9DNEYmIiIiIlExrtqRC6gT5uryPa9vUpnFYDft14F8OUt5+PI1O9UNcHoOIiIiISFm5dWRr9erVXH/99URGRmIwGPj6668d7g8fPhyDweDwM2DAAIc6KSkpDB06lMDAQIKCghgxYgQZGRkOdbZt20aPHj3w8fEhKiqKadOmufqtVXvGP87B+veQdi7va87tV/BIvyYu70dERERExJncmmxlZmbStm1b5syZc946AwYM4OTJk/afTz75xOH+0KFD2blzJ0uXLmXhwoWsXr2a++67z37fYrHQv39/6tWrx6ZNm3jttdeYNGkSb7/9tsve1+XgjRUHAPAwaSaqiIiIiEhJ3DqN8JprruGaa665YB1vb28iIiJKvLd7924WLVrEhg0b6NixIwCzZ89m4MCB/Otf/yIyMpKPP/6YvLw83nvvPby8vGjZsiVbtmxhxowZDkmZlM3320+6re96IX72115K9kRERESkkqr031R/+uknwsLCaNq0KaNGjeLMmTP2e/Hx8QQFBdkTLYB+/fphNBpZt26dvU7Pnj3x8vKy14mLi2Pv3r2cPXu2xD5zc3OxWCwOP1J5+Hia7K8Tzma5MRIRERERkfOr1MnWgAED+PDDD1m+fDlTp05l1apVXHPNNRQWFu1Al5iYSFiY48G3Hh4ehISEkJiYaK8THh7uUOfc9bk6fzdlyhTMZrP9JyoqytlvrdootF767df9vP9Mtn47msr329w3yiYiIiIicj6VOtkaMmQIN9xwA61bt2bQoEEsXLiQDRs28NNPP7m03wkTJpCWlmb/SUhIcGl/Vdm5jTIupSuig/m/EV0AePeXw4yev/mSxyAiIiIicjGVOtn6uwYNGlCzZk0OHCjanCEiIoLk5GSHOgUFBaSkpNjXeUVERJCUlORQ59z1+daCeXt7ExgY6PAjjvo0C8PDaKB9dLBb+o9tGOqWfkVERERESqtKJVvHjh3jzJkz1K5dG4DY2FhSU1PZtGmTvc6KFSuwWq106dLFXmf16tXk5+fb6yxdupSmTZsSHOyeRKGqK7TaWLEnmUKbzd2h2OlwYxERERGpbNyabGVkZLBlyxa2bNkCwOHDh9myZQtHjx4lIyODxx9/nLVr13LkyBGWL1/OjTfeSKNGjYiLiwOgefPmDBgwgJEjR7J+/Xp+/fVXxowZw5AhQ4iMjATg9ttvx8vLixEjRrBz504+++wz/v3vfzN+/Hh3ve0qL6+gaJ1WJcq1+MGNuyOKiIiIiJTErcnWxo0bad++Pe3btwdg/PjxtG/fnokTJ2Iymdi2bRs33HADTZo0YcSIEXTo0IGff/4Zb29vexsff/wxzZo1o2/fvgwcOJDu3bs7nKFlNptZsmQJhw8fpkOHDjz66KNMnDhR275XcX9fKeaOtWMiIiIiIhfi1nO2evXqhe0CwyOLFy++aBshISHMnz//gnXatGnDzz//XOb4pGS/p2S6OwSMRgOf3x/LLf+JByC0htdFnhARERERubSq1JotqRxy8i/9du8lUYIlIiIiIpWZki2psrxMf3581xw8Q2pWnhujERERERFxpGRLqqyoED8+GtEZgLd+Osjgt9a4OSIRERERkT8p2ZJyG9WrobtDoEfjWvbXB0+5fy2ZiIiIiMg5Srak3G5oG+nuEEREREREKi0lW1KtJFty3B2CiIiIiAigZEuqmTHzf3NaWztPpJGWne+09kRERETk8qJkS6qV9UdSKLSe/+y2srj29V+49Y9zvEREREREykrJllR5y8Zf5XA9d9VBp7W9JzHdaW2JiIiIyOVFyZZUeWZfT4drrdsSERERkcpAyZZUeQE+Hg7XG38/y9JdSaw7dMZpfWw8ksIn6486rT0RERERqf7KlWxt3ryZ7du326+/+eYbBg0axNNPP01eXp7TghMpDR9PE0devZZIsw8AO09YGPnhRm59e61T2t+bmM4/5sYz4X/bL15ZREREROQP5Uq27r//fvbt2wfAoUOHGDJkCH5+fixYsIAnnnjCqQGKlFaf5mEuaff/1v7uknZFREREpHorV7K1b98+2rVrB8CCBQvo2bMn8+fPZ968eXz55ZfOjE8qodSsyjl6GRbgU6Hns/IKmPfrYaxWG0l/Wff1kZItERERESmHciVbNpsNq9UKwLJlyxg4cCAAUVFRnD592nnRSaU0/P0N7g6hRPf1bFCsrKDQyrJdSaV6/vXlB5j03S52nrDQ5ZXlJdZJy9K5WyIiIiJSOuVKtjp27MjLL7/MRx99xKpVq7j22msBOHz4MOHh4U4NUCqvUH8vd4fgwGQ0FCubtngv9364kfnrjnLoVAYJKVnnfT4nvxCAhz87/8HIv6dkVjxQEREREbkseFy8SnEzZ87kjjvu4Ouvv+aZZ56hUaNGAHzxxRdceeWVTg1QKp8rG4bi5WEkLLBi0/aczdNk5P3hnXj5+10cPFWUFL29+hAAT3/15+YWR169ttizVqvNfhjyoVPnT6gs2QXODFlEREREqrFyJVtt27Z12I3wnNdeew0Pj3I1KVWIyWjA37ty/nfu3SyMAB8P/jE3/rx1th1LpU3dIIeyJ77cxhebjl20/TveXce4fk14uF/jioYqIiIiItVcuaYRNmjQgDNnip9hlJOTQ5MmTSoclEhFdKwfQsd6wee9f8MbvxYru1ii1bVBiP31zGX7yh+ciIiIiFw2ypVsHTlyhMLCwmLlubm5HDt28dEBqdoOJme4O4SL8rvIyFtuwZ+f34ttoDH9n235zx0dnRKXiIiIiFw+yjQX7Ntvv7W/Xrx4MWaz2X5dWFjI8uXLiYmJcV50Uill5xdSu5Kt1yqrps8uYs9LA/h43VFeWrjrvPV2vRiHn1fxvyZnMnIJreHtyhBFREREpIorU7I1aNAgAAwGA8OGDXO45+npSf369Zk+fbrTgpPKydNkJNDX091hXFDdYN+L1mn23KLz3osO8WPV470wGIrvcAiw6fezBPt70al+SIn3AQ4kp9MoLODiwYqIiIhItVSmaYRWqxWr1Up0dDTJycn2a6vVSm5uLnv37uW6665zVawipfb89S14/+5O5X7+P3d2KJZofTnqz502x3zyG/+cG8+eREuJz6/ad4p+M1aX+owvEREREal+yrVm6/Dhw9SsWdPZsUgVUGi1kZye6+4wLsrbw0TvpmHlfj6mpn+xsg5/2XQjr6DoUO+sPMe1i1l5BRQUWu1ndv16UId8i4iIiFyuyr1/9/Lly1m+fLl9hOuv3nvvvQoHJhd35MylP2B3+pK9AOcd0ams5t3dieHvbyhV3YVju+PjaSpVXZut6DDkc/UHzfmVfUkZ9GisX0aIiIiIXO7KNbL1wgsv0L9/f5YvX87p06c5e/asw4+4XpCfJ5m5xXeEdLVz52uF+Htd8r4rolfTMLZP6l+qus0iSr/OatK3O7nm3z9z7GwWs5btY19S0U6NP+8vGtHyNJXrr5iIiIiIVAPlGtmaO3cu8+bN484773R2PFJKof5e5BZYL17RyQJ9ij4yL9zQ6pL3XR7fjO5GWnY+AAE+nhcc4fIwGiiw2i7Y3rR/tOGJL7bZr7cfTwOg+9SVJdZfuSeZEH8vHriqYXnCFxEREZEqrFy/ds/Ly+PKK6+8eEWplrxMRkzGknfpq2zaRgXRs0kt+3VGbsF565pLscPiDW0jy9T//uQMXv1xT5meEREREZHqoVzJ1r333sv8+fOdHYuIy3n9Ma3vlo517WWLH+nJwrHdS/W8j6eJ+Al9ytzv6YzKv6mIiIiIiDhXuaYR5uTk8Pbbb7Ns2TLatGmDp6fjiMCMGTOcEpyIs/VrHs70f7blxnaRdKwXwg3tIu2bWzxydRP+s+ogxvOcrXVObfPFz/D6u9cW7WXqP9qUK2YRERERqZrKlWxt27aNdu3aAbBjxw6He+c7BFaqjwLrpV8r5ixGo4HBHYpGtW7pFOVw786u9bitUxTGUkyRfHpgM175ofTTAz/bmMAN7SLp1ki7FIqIiIhcLsqVbK1cWfJmAFL9HTyVyYW3kKjaPEq5e2D3RrWAsq3FGvrOOva8NKDU28qLiIiISNXm1n2pV69ezfXXX09kZCQGg4Gvv/7a4b7NZmPixInUrl0bX19f+vXrx/79+x3qpKSkMHToUAIDAwkKCmLEiBFkZGQ41Nm2bRs9evTAx8eHqKgopk2b5uq3Vm0VWK3UC/Fzdxhu1yIykHeHdSxWXifozymGN7Wvw83t6zjcb/bcIuasPODy+ERERETE/cqVbPXu3Zs+ffqc96e0MjMzadu2LXPmzCnx/rRp03j99deZO3cu69atw9/fn7i4OHJycux1hg4dys6dO1m6dCkLFy5k9erV3Hffffb7FouF/v37U69ePTZt2sRrr73GpEmTePvtt8vz1gWo4VPus7Crlb7Nw3nxxpYOZb8+VfT59/cyMfPWdrx8U/Et8l9bvPeSxCciIiIi7lWub83n1mudk5+fz5YtW9ixYwfDhg0rdTvXXHMN11xzTYn3bDYbs2bN4tlnn+XGG28E4MMPPyQ8PJyvv/6aIUOGsHv3bhYtWsSGDRvo2LFolGH27NkMHDiQf/3rX0RGRvLxxx+Tl5fHe++9h5eXFy1btmTLli3MmDHDISkTKY+7YutzW+do3vn5MAs2JgDQsV4wQ7tGA+Dn5UFcy3AW70xyZ5giIiIi4gblSrZmzpxZYvmkSZOKTeErr8OHD5OYmEi/fv3sZWazmS5duhAfH8+QIUOIj48nKCjInmgB9OvXD6PRyLp167jpppuIj4+nZ8+eeHl52evExcUxdepUzp49S3BwcLG+c3Nzyc39c6tui8XilPck1ZOnycioXg0Z2SMGgC9GOZ5B1z46WMmWiIiIyGXIqWu27rjjDt577z2ntJWYmAhAeHi4Q3l4eLj9XmJiImFhYQ73PTw8CAkJcahTUht/7ePvpkyZgtlstv9ERUWVWO9ylJZdQBXejNClzre5xtnMPABiG4QWKxMRERGR6supyVZ8fDw+Pj7ObNItJkyYQFpamv0nISHB3SFVGolp2Rjduq1K1RPoW3QOXW3zn383bn9nnbvCEREREZFLpFzTCG+++WaHa5vNxsmTJ9m4cSPPPfecUwKLiIgAICkpidq1a9vLk5KS7GvGIiIiSE5OdniuoKCAlJQU+/MREREkJTlO4Tp3fa7O33l7e+Pt7e2U91HdGDDQJDzA3WFUKff3bEC9UD/OZubxv9+OA7D7pIUtCam0iwpyb3AiIiIi4jLlGqP46xQ7s9lMSEgIvXr14ocffuD55593SmAxMTFERESwfPlye5nFYmHdunXExsYCEBsbS2pqKps2bbLXWbFiBVarlS5dutjrrF69mvz8fHudpUuX0rRp0xLXa4k4m4fJyHVtIuFvB34PmvOrmyISERERkUuhXCNb77//vlM6z8jI4MCBP88cOnz4MFu2bCEkJITo6GgeeeQRXn75ZRo3bkxMTAzPPfcckZGRDBo0CIDmzZszYMAARo4cydy5c8nPz2fMmDEMGTKEyMhIAG6//XZeeOEFRowYwZNPPsmOHTv497//fd5NPuTC1h9JoW5InYtXlFJJz8knwMfT3WGIiIiIiAtU6MCkTZs2sXv3bgBatmxJ+/bty/T8xo0b6d27t/16/PjxAAwbNox58+bxxBNPkJmZyX333Udqairdu3dn0aJFDuvCPv74Y8aMGUPfvn0xGo0MHjyY119/3X7fbDazZMkSRo8eTYcOHahZsyYTJ07Utu/lsPbQGQAOncp0cyTVx97EdDrWD3F3GCIiIiLiAuVKtpKTkxkyZAg//fQTQUFBAKSmptK7d28+/fRTatWqVap2evXqhc1mO+99g8HAiy++yIsvvnjeOiEhIcyfP/+C/bRp04aff/65VDHJ+SWkZAGQmqWd9MqjfqhfsbJ/zI3n0aubMLZvYzdEJCIiIiKuVK41W2PHjiU9PZ2dO3eSkpJCSkoKO3bswGKx8NBDDzk7RpFqoUfjWqx5qk+x8ulL97khGhERERFxtXKNbC1atIhly5bRvHlze1mLFi2YM2cO/fv3d1pwUrn4epkA6N64ppsjqboig3zdHYKIiIiIXCLlGtmyWq14ehZf1O/p6YlVJ95WW4F/bOQwqlcjN0dStS1/9Cq+f6i7Q9ngt9bw1W/HeOar7SSn57gpMhERERFxpnKNbPXp04eHH36YTz75xL7r3/Hjxxk3bhx9+/Z1aoBSeWTnFwJguEg9ubCGtWoUK9v0+1k2/X4WgBo+Hky4pnmxOiIiIiJStZRrZOuNN97AYrFQv359GjZsSMOGDYmJicFisTB79mxnxyiVxLkNMmrW0IHPzhDkd54t38+/Z4yIiIiIVCHlGtmKiopi8+bNLFu2jD179gBFZ17169fPqcFJ5RPg7YGXR7lydPmbT0Z25Zp/F98lU7mWiIiISPVQpm/NK1asoEWLFlgsFgwGA1dffTVjx45l7NixdOrUiZYtW2qLdZFSal47kMWP9CxWvmJPsv31hY5GEBEREZHKrUzJ1qxZsxg5ciSBgYHF7pnNZu6//35mzJjhtOCkcsnMLSS3QBugOFPTiAA6xzgeanwgOYOWExdR/6nviZnwAw98tAmA9YdTKLSWnHzZbDbmrjrIidRsl8csIiIiIqVTpmRr69atDBgw4Lz3+/fvz6ZNmyoclFROG39PwVtTCJ1u9m3ti5Vl5hXaXy/amUjnycu45T/xNHz6B57/Zkex+mnZ+bz64x6GvbfepbGKiIiISOmV6ZtzUlJSiVu+n+Ph4cGpU6cqHJRUTqlZ+bSLDnJ3GNVOaXZ3TE7Ptb/+IP53AI7/MYqV95fRxkOnM50am1QepzNyOfWXz4GIiIhUfmVKturUqcOOHcV/q37Otm3bqF27doWDksrJaIC6wX7uDqPaqRXgzUuDWpXpmVnL9tHt1RV0eWUZTZ79kVnL9tvvpWblcSA5nay8Ao6nZpP9l1GyirDZbGw8kgLAu78cZs7KA05pVy7OZrPR8eVldJq8zN2hiIiISBmUaTfCgQMH8txzzzFgwAB8fHwc7mVnZ/P8889z3XXXOTVAkerOYDBwZ9d6zFiyl7NZ+aV65lxylWQpGumYt+YIAIVWG+1eXOpQt3UdM9+NdTxEuTwWbjvJ2E9+Y97dnXhp4S4ARl3VEEtOPkajwX7otTjf6Yw8++v0nHwC9GctIiJSJZRpZOvZZ58lJSWFJk2aMG3aNL755hu++eYbpk6dStOmTUlJSeGZZ55xVaziZsdTc9wdQrU25ebW9tdt6pqd1u7242lk5BaQX1ixzU1y/jjU+vEvttnLbMC1r//CoDd+JTO3AOt5NvCQirn1P/H214VWG2sPndFOlSIiIlVAmUa2wsPDWbNmDaNGjWLChAn2f+wNBgNxcXHMmTOH8PBwlwQq7mWz2TidkUuIv36j7ioDWtVmys2tOZCcQVp2PtuOpTmt7VbPLybIz5MHezXkvp4NS/1cTn4hPp4mAIL9vAAc1g19GH/Evnas/8zVXNkwlNf+2dZpcUuRv67F+2LTMV7+fjc3ta/DzFvbuS8oERERuagyH2pcr149fvjhB86ePcuBAwew2Ww0btyY4OBgV8QnlcSukxYAWkU6b8RFirutczRQlORc0yqCvs3DOZCczqxl+1m47WSF2k7NyueVH/aUKtmy2Wx8sekYj3+xjddva88NbSNLrPfCd7vsr4+nZrNg0zF+T8liys2tSUzL4cqGoRgMpdkCRErr5e93A/DVb8eVbImIiFRyZU62zgkODqZTp07OjEUqseNni0YvOtRTUn0p+Hia6Nu8aJS4UViAU9vOL7TiYTRwJjOPmjW8gaLk6t1fDnN920jCA314bfFe3vzpIAAPffIb4z/bQkEppwiuP5xC3+mrAPjvXR3xMBpITs/h1k7RTn0fl4Ptx9LIK7RSN9iXY2d1hpqIiEhVU+5kSy5PJqNGKdztHx3q8uKNLUlMy6HPH0nN30WF+JKQUvKX84c++Y3wQB/mrTnCwVcGYjIaSM3K5+Xvd7N8dzKPxTWxJ1rnlDbR+jtLdj6PLtgKoGSrHK5/45cL3v/9TCb1Qv0vUTQiIiJSVjqhVqQKGH91E/vrbo1C8fPyoEGtGrw/vBMNaxV92T70ykB7nQ/u7swbtxc/LBngxx2J9t0LD57KYPL3u2j/UtEOhvGHzjD4rfgSnyuPaYv3OK0tKW74+xvcHYKIiIhcgEa2RKqABrVq8OrNrXnqf9vxMP75O5LezcLo1bQWuQVWjEYDcS3D+f1MFg1q1bCvs7uQ/jNXuzJs+9b0AB+t/Z1rW9cmxN/LpX1Wdw1q+XPoVNGGGYdPZ5JXYMXLQ783ExERqYz0L7SUyg/bK7Y5g1Tc4D+mD17TKsKh3GAw2HcMnHtHBxb+caZWh3rBRIe4/hDqx/o3YfiV9S9a77mvd3DFS0uZ8uNul8dUHZxva/cVj/ZyuP7H3DUV3tZfREREXEPJlpTK11tOuDuEy56nychdsfXxMJ3/r63BYLDfr232ZfUTvQkL8HZqHEO7RDP7tvaM7BFDj8Y1GdOnMZNuaFnq5/+z6pBT46muXlu81+G6TpAvH97TGXCcVrrtWBrZf5yBJiIiIpWLki2Ram5IpygANj7bj8fjmjKmdyOg/AcnT76pNde3jeTpgc2Zd3dne3m3RqEAvDX0Cp6/vkUFo5a/b1Ly+QOx9GxSC4CH+jZmxi1/nmf2jX4ZIiIiUikp2RKp5sZd3YStz/enZg1vRvduxGNxTdn6fH96/fHFHaBzTEiJzwb7FR1ifUV0EAA3t69jv2cwGBx2p3z7zo589eCVXNO6Nnd3i6FOkK8L3s3l4+9r2/7+5/nXWYbPfb3jUoQkIiIiZaRkS0rF+48F+MF+2tygqjEYDJh9PR3KzL6e9G/559qv4VfWZ9Oz/Rzq/KNDXX6b2J/3hnfkiweu5NArA5n+l9GUv/P39qB99J/nsJ1bOyZlV1BopUO9YDyMBgJ8St7HqEEtxy3fkyw5lyI0ERERKQMlW1Iq9UP9GX5lfYw6Z6vaaFXHzLPXNrdfh/5xwHGwnyfrn+nL1MFtAOjTLByj0YDRaMBgKP1//2B/Lw69MpDbOkcVu1f/qe/5fEOCNnY4j3s/3MjSXUkUWG0sG38VKx/rVayO8W//LdYeOnOJohMREZHSUrIlpZKcrt+aXw5+eKgHix7pSViAj1MOsDYaDUy5uQ1HXr2Wpwc2c7j3xJfbaPzMj/x+JrPC/VQ3P+09ZX8dHuhDTM3iBxe3rmO2r8cDCNKos4iISKWjZEtK5WxWPoF/m4omVd8/O0QxLLYeV/2xfqtFZCDhgT4u6eu+ng1LLL/lP/FsO5ZKWla+S/qtan7Zf7pU9YxGg8MukB4adRYREal0lGxJqdU2u+ZLuLiP2c+TF25shb/3pTnfvGl4QLGyJEsuN7zxK7f8J95ediA547znTFV3+5PTS1333PlqAB+sOeKCaERERKQilGyJyCXzw8M9OPTKQG76y66G5+xNSufY2SwOncqg34xV/Pdnx/O4bDZbsQSsOiZkmbkF9tcf39vlovV/fLgHAEt2JbHzRJrL4hIREZGyU7IlIpeM6Y+NNmacZ1fD7lNX0mf6KgB+O5rqcO/hT7fwwne77Ndf/XaMfjNWUVDNNtnYduzPhKlbo5oXrd+8dqD99Yh5G10Sk4iIiJSPki0RueQMBgNRIRc+h+u3o6n8fiaTfUnp2Gw2vt16gnlrjtDuxSWkZeUz9ce9HDyVScYfI0FWa9Uf5fppbzJLdiUB8P7wTqV+btuk/gAkavt3ERGRSkXJloi4RYB30YYrt3SsW+L9REsOV732E/1nrualhbvt5alZ+bR9cYk9sRj+/gZy8gvpMW0lC7edcH3gLjRm/m8AeHkY6d0srNTPBfp40qCmP22jglwUmYiIiJSHki25qLyC6jVNSyqHN4deweNxTZl8U2tiG4ResO57vx4+770tCak0e24Rx1Ozeefn89er7H49cNo+SlfrjzPPyqJBLX+2JqQ6OSoRERGpiEqdbE2aNAmDweDw06zZn2f15OTkMHr0aEJDQ6lRowaDBw8mKSnJoY2jR49y7bXX4ufnR1hYGI8//jgFBQV/70ouYODrPwNcsh3r5PJQv6Y/o3s3wtNk5P27Sz9l7mLWH05xWluX0tB31tlffze2e5mfv7pFOACr9p26SE0RERG5VCp1sgXQsmVLTp48af/55Zdf7PfGjRvHd999x4IFC1i1ahUnTpzg5ptvtt8vLCzk2muvJS8vjzVr1vDBBx8wb948Jk6c6I63UmUdSM4AoE0ds5sjkerKx9PE/snX2NceldeWhFRu+U88E/63nZNp2U6KzvU2/e6YIIb4l/2A4vqhRQcfD3tvPa2eX0xhNVjDJiIiUtVV+qEKDw8PIiIiipWnpaXx7rvvMn/+fPr06QPA+++/T/PmzVm7di1du3ZlyZIl7Nq1i2XLlhEeHk67du146aWXePLJJ5k0aRJeXiV/ocnNzSU3N9d+bbFYXPPmqoirW4STkJJF/Zr+7g5FqjFPkxFPk5H/G9GFO95dd/EHLuCT9UfJL7RyR9d6tKvk65h+3n+KO99db7+edWu7crWT8Zct4zNyC8jJL9RotIiIiJtV+pGt/fv3ExkZSYMGDRg6dChHjx4FYNOmTeTn59OvXz973WbNmhEdHU18fNHhqPHx8bRu3Zrw8HB7nbi4OCwWCzt37jxvn1OmTMFsNtt/oqKiXPTuqgYDOtBYLp3ujWuy44U4lozryXVtavP9Q91pWKvsif4Xm44xaM6vDH5rDSfTsjl2NssF0VbcoVOZDtc3tot0SrsHT2U4pR0REREpv0qdbHXp0oV58+axaNEi3nrrLQ4fPkyPHj1IT08nMTERLy8vgoKCHJ4JDw8nMTERgMTERIdE69z9c/fOZ8KECaSlpdl/EhISnPvGROSCanh70CQ8gDduv4KWkWY+GdnVfq99dFCx+i/d2PK8bW36/SyxU1bQfepKV4TqVLXNPhgMhnI92zLScZrv26sPnaemiIiIXCqVeo7JNddcY3/dpk0bunTpQr169fj888/x9b3wGT0V4e3tjbd32XcDExHXCAv0+WOEqwY+niZmL99Pem4Bj/RrTFZeITVrePPcN+cfrT6n/lPfc1P7OjwxoCm1za77f0h53dG1XrmfjTD70KJ2ILtOXt7TnkVERCqTSj2y9XdBQUE0adKEAwcOEBERQV5eHqmpqQ51kpKS7Gu8IiIiiu1OeO66pHVgUlxBoZUlu5JIz9EOjuJeLSPN+HiaABjbtzFPD2yOn5cHNcu4TfpXvx2n+9SVJFtysNncu4lESmYen6w/ar++u1v9CrU3/Za29tcLt52koFDHNoiIiLhTlUq2MjIyOHjwILVr16ZDhw54enqyfPly+/29e/dy9OhRYmNjAYiNjWX79u0kJyfb6yxdupTAwEBatGhxyeOvirYeSwNg4+9n3RyJyIW9N7xjqesWWm10fmU5g95cwz3zNnAmI/fiD7nA2E82sycx3X7t51WxyQbNawcypncj+/Wxs1VnR0YREZHqqFInW4899hirVq3iyJEjrFmzhptuugmTycRtt92G2WxmxIgRjB8/npUrV7Jp0ybuvvtuYmNj6dq1aH1H//79adGiBXfeeSdbt25l8eLFPPvss4wePVrTBEvJy1SpPyIidn2ahV+80t9sTUhlxZ5kOry8jBV7HEfBC602l2+fvjUhzeltRoX8OT3yZFqO09sXERGR0qvUa7aOHTvGbbfdxpkzZ6hVqxbdu3dn7dq11KpVC4CZM2diNBoZPHgwubm5xMXF8eabb9qfN5lMLFy4kFGjRhEbG4u/vz/Dhg3jxRdfdNdbqrKm3Nza3SGIlNrhKQPJLbDi42niQHI6cbN+vmjidM+8jTSLCGBsn8Z4eRiZ+M0O2kUF8dYdHZwe364TFvth4ecMv7K+U9q+pWMUT3653SltiYiISMVU6mTr008/veB9Hx8f5syZw5w5c85bp169evzwww/ODu2y01oHGksVUMPbg1Z1AjEYDPb1XY3CAjj4ykBW7EnCaoV7P9x43uf3JKYzev5m+/XJtEQycwsqdF6V1Wqj0GbD02QkLSsfP29TsUQL4PnrnTO1+a+7GT786W+se7pvuXc4FBERkYrRHDG5oAKrFthL1bHq8V7Mu7tziff6NAunX4uyTzW84Y1fWLAxgfSc/HLF9NgXWxn23nqSLDm0fXEJ0xbtKbGeMxOiiMCic/GS03N5aeFuEjWdUERExC2UbMkFxR86A0CdoMq3TbbI34XW8LaPaJ3Pd2O606l+cKnbPHgqk8e/2EbrSUv4MP4I1jKu4/rf5uOsOXiGLq8Ubebz358PO9zvWC+Y1/7RpkxtXsy8ezrZX7/362HGf77Fqe2LiIhI6VTqaYTifqlZ+fh7mQj293J3KCJO0bqumQUPXMnZzDye/3YnI3s04Po3finVsxO/2cnpjDzu6VafrcfSuKpJLYf72XmF7Em00LZuED/sOInnRTaYGd27IY/HNSv3ezmfkL/9fT10KtPpfYiIiMjFKdmSC8rKK6BmgHZulOon2N+L129rD8DQLtF8vO7oRZ4osvukhWHvrWfrsTS2Pt8fs68nt/93Lde0irAfrHxb5yg+WZ9wwXZWPd6LeqH+FXsT5xEW4MPPT/Smx7SVACRachj1f5uYeWu7i478iYiIiPNoGqFc0LZjaZi0uF6quTF9GhUrq232KbHu0l1J9vPn2r6whP+uPsSag2fsiRZw0UTrUogK8XO4/nFHIk98sc1N0YiIiFyelGzJBZ1IzaF57UB3hyHiUrXNf65J/PHhHtzdrT6/PNmHO7vWu+izk3/YXep+nruuBX2ahTH8yvrUDfa7+AMVNLRLtMP1t1tPuLxPERER+ZOmEcp5rdiTxOmMXBqF1XB3KCKXTPPagTx/fUsAXhrUint7xHDNv38mK6+wQu3WCfJlRPcYRnSPcUaYpTL5ptZYbfDJ+j+nSObkF2oqoYiIyCWikS05r9Ef/+buEEQumRvbRfLKTcUP764X6s+uFwfwzMDmxe6N6tXwou22jQri+raRvDOso1PiLKspN7fm7m717dcvfLeTDUdS3BKLiIjI5UYjW3Je+YVFZ2xtP57m5khEXO/fQ9pf8P7Ing04mZbDe78Wbd3+5ahYOtQL4a2fDharGxXiy/x7u3LwVAa9moa5JN6yGHxFXd7/9QhQtJ7sk/UJLB3Xk8bhAe4NTEREpJpTsiXn1ax2ADuOWzSNUOQPE69vwZg+jfhk/VHaRxWd1fXlqCs5djaLzNxCnv5qOw/1acT4/k2B4ptUVCafbkjguetauDsMERGRak3JlpxXWnY+ABOucf45QCJVVYi/F6N7/7l7YYd6wXSoV5R4tY0y0zLS7K7QzqvWH8c3RJp9OJGWA8C7vxxm1b5TLB3XE4N2HBUREXEJrdmS80pIyQbQFzGRUqqMiRZAeKAPu18cwJoJfZlz+xX28gPJGbz36xG+/u04ALkFhexNTHdXmCIiItWORrZERC4Dvl5FOxBe26Y2ry8PYG9SUVL10sJdABw6lcHepHQW70zi4CsDMRn1SxYREZGK0siWlGj+uqKtosf1a+LmSETE2b4Z063Y9ODXVxxg8c4kAKw2mzvCEhERqXaUbEmJ5qw8AEBWfoGbIxERZ/PxNHFvjwbnvd958jLWHDh9CSMSERGpnpRsSYl6NK6Jl4eRkRf4QiYiVZfJaGDrxP68flt7rmkV4XDvbFY+t7+zjl/2K+ESERGpCCVbUqJPNySADWrW8HZ3KCLiImY/T25oG8lbd3Qo8f4d767jnZ8PMfLDjZxIzSYtKx+bphiKiIiUmjbIkGKSLEVbQ+f9caixiFR/9UP9OHImq1j5y9/vBmDprqL1XFc2DGX+yK7k5BeSm2/F7Od5SeMUERGpSjSyJcXkFRQlWS/d2NLNkYjIpfLT47059MpAIgJ9ABh+Zf0S6605eIb6T31P73/9RNys1ZcwQhERkapHyZYUU2AtmiYUU7OGmyMRkUvJaDTw0+O92DLxavq3CLeX/3tIu2J1T6blkGjJ4X+bjwF//pJGRERE/qRkS4rp/a+fAGhQy9+9gYjIJefjaSLIz4srG9Vky8SrOTxlINe0qn3e+uM/30r9p76nybM/subAafL/mH5caLUx5YfdJKQ4Tk0stGrNl4iIXD6UbMl5RQb5ujsEEXGjID8vDAYDXh5GFo7tzs9P9KZf87Dz1r/9nXVcP/sX8gqsvPXTAf6z+hA9pq2k/lPfs+1YKi8t3EWTZ39k7qqDnM3M47XFezhyOhOAnPxCvtt64lK9NRERkUtCG2RUYcmWXJe13SRcUwhF5E+t6pgBuLVTNMt2J5+33p7EdJo8+2Ox8hve+NX++tUf9/Dqj3sAmLPyIADXtIrgxx2JtK0bRHSoHwCZuQX4e+ufKXF09EwWYYHeeBgNmIwGAD7fmMC1bSKpoc+LiFQy+r9SFRXs58XBU5lk5xXi62VyWrvxB88AMKJ7jNPaFJHqo1/zMN4d1pH9yRn2hKltVBBbE1Ir1O6POxKL2p+5iqubh/P99pMAfDemO63rmivUtlQvPV9bCUCL2oHsOmlh4nUteHHhLp78cjtHXr3WzdGJiDjSNMIqKiyw6PwrG85b/1BotXHbf9cC0C4q2Gntikj1YTAY6Ns8nAeuasj2Sf1ZOq4nXz94JW/f6XhWV3hg+c7oyyuw2hMtgLGfbMaSk8+B5AzyC608/82OYuvAqoKCQqvOKCunrLwCmjzzI+/9cpjEtBx7+a6TFgBeXLjLXvbKD7up/9T3LN+dRHZe4SWPVUTk7ww2/d//oiwWC2azmbS0NAIDA90aS1p2Pm1fWMLA1hH8sD2RXS/G4eflnAHKMxm5dHh5GYB+OygiZWaz2TAYDKRm5RHk50VOfiFnMvNIy8rHaARfTxMfrPmd389kMu0fbThyJpPBb8WXuv3aZh9O/vFl+6lrmjGyRwOOpmQRU/PPzXx+2X+aeqF+1AnyZdPRszSsVYMQfy+nv1egTDMLbnt7LQ3D/BnRvQH1Q/0wGIqmv2XmFuDjabJPh4OiP8eUzDxC/zhUPie/EC+TEeNf6lwuTqZlEztlRbmf3/3iAKfO/hARgbLlBkq2SuFySLZsNhvTl+zjjZUHiG0Qyif3dXVCtCIiF2az2dh6LI3wQG9sNrjy1fJ/sX6wV0Pe/OmgQ1mjsBr834guzFq2j+evb2n/4p1bUMjpjDzqXGAjoJ/3n8Lbw0TrOmYMBsjILaDmHwlQfqGV1pMWM+qqRozq1ZDEtByW7k6iU/1gagV4s3DrSYL8PJny4x5SMvOKtR3g7UH3xjXt0ydvbl+HcLMPV0QHM/LDjQC8P7wTjy7Yan/+p8d6Ub9m9d8lNtmSw6Kdiew4nsbnG49VuL33hnckN99K/Zr+NK/t3n/DRaR6ULLlZJdDsvXCdzt5/9cjABx6ZeBl+RtUEXG/ZEsOq/efZsL/tjGmd2Nqm3144sttTmv/69HdGPfZFg7/sQsiwAf3dCYlM5dnvtrB+8M7ERbow8HkDO79I+n5qwEtIwgL9KZF7UCe+t92p8VVWq3rmCm02niobyMs2QX8e/l+Hu7XmLpBvlzZqKZD3aNnsgg3e+NhNHLPvA3c3a0+vZqefzfJyiA5PYfOk5ef9/5LN7ZkzsqDPNKvMUM6RwNFCfsvB06z6fezzFq2/4Lta9aGiDiDki0nq+7JliUnnzaTltiv9Y+RiLhbodWGyWhwmEZ2S8e6TLy+JRuOpHD3+xvcHGHVUC/Uj7F9GvPYgq0E+3my/NFebEk4S4foEAJ9Pdhx3FLuDUjiD56hXVRQuabpJaRk8b/Nxwnx9+S5b3bSoKY/6bkFnEoveZfd+3o2YGiXaOqFnn9kz2azsTcpnbdXH+J/m4/z5IBmTF20p1i99+/uRK8mtQDs0zlFRMpCyZaTVfdk6+oZq9ifnGG/VrIlIpVJfqGVpbuSuKZVhP3L8eHTmRw5nUl4oA/hgd58tPZ3PE1G2tYNYuK3O+jZuBbhgT6sP3yGlXtPXZI4x/Vrwsxl+xzKnr22OXWDffH2NHHPvA3YbNCveTjLdicB8MNDPXjqf9vYdiztvO22jAxk5wmL0+MdFluPD+J/586u9bimVQTfbj1BoK8nY/s04o2VB2geEciAVhGcSM3mlR92M6pXQzb9fpYtCak8NaA5PV9bSVzLcO7oWo9WkWaC/7Y27uiZLKx/fMXw9TKx64SFnPxCMnILePyLi49Wrp3QlxB/L7w8KraX1/6kdK6eubpYucloYM9LA/hl/2my8gqpF+pHVIgfZl/PCvUnItWfki0nq8zJ1toJfZm76iA9m9SkT7Nw9iWls/ukhevbRLL9eBpto4LO25bNZmNLQio3vbkGgOeua0GbumY61Q+5RO9GRMT1zmbmYTQaMBpg2e4kujYIJcjXi5Np2Xy5+RhdYkIpsFo5m5nPqYxcYmr6s3hHIglns9hw5CxDOkVxU/s65BfaMPt60rx2AF9sOsZ7vx6mYa0a3N4lGm8PE51jQsjJL2TjkbPc8e46lo2/ikZhjmcW5uQX4uNZ8khQQkoWk7/fTYTZhweuasg3W45zT/cYPE1GElKy2H48jQ/jjzD9lnacycjlsw0JPHttC/778yH+u/oQ6bkFl+KPs1Ru6xzNJ+uPluvZZhEBzB/Z1ekbm6zed4q73ltfqv77NAvjiQHNnNq/iFQfSracrDInWxcz5ebWtK5jpmVkIGcy8ziRmk10iB9ZeYX839rfHRaT7598DZ4mnQYgInJOeQ9WdseBzGlZ+ZzNyqPQZqNhrRq8+dMBpi3ay7vDOtKraRjD3ltPem4BLWoH8Mn6hEsa24Vc16Y217aujafJSO9mYQ47Mzrb3sR0Dp/OYPHOJL767fgF6zao6c/QrvW4unk4aw6eJt9qIze/kLti62O12Ui25BLs70mAjyd5BdYKj8CJSNWhZOs85syZw2uvvUZiYiJt27Zl9uzZdO7c+aLPVcZka2yfRsxeccBp7f723NXFpoCIiEjVdm47/r+buXQfi3cm4mEy4GE0siUhlYVju7PhSAovfPfnuVVv39mBHScshAV488oPu6kb7Mu+pD+nnT91TTNe/XEPdYN9CfbzYvvx80+HvKFtJIt2JJJXaOWFG1rSOKwGHeoH4+3hnq3Zz2bmMXPZPj6M/71C7dzQNpJvt54AiqaFFm3b78XZrDxOZ+QxfcleHo9rSv8WEeQWFJKTb8Xs68l/Vh1kRI8Ypx3fIiKXjpKtEnz22WfcddddzJ07ly5dujBr1iwWLFjA3r17CQu78O5MlTHZmvaPNjzxx5z378Z0Z87KAyzaWTTSdf9VDfjPqkOlbnPKza257Y9dnURE5PJzOiPXvq09FP1bcyo9h0ZhAcXqpmblEX/wDImWHO7uFuNw7+CpDEL8vEjJyqPv9FW8PKgVA1pFEOrvVWk3o8gtKMRkMGADUrPyWXPwNH5eHvYt+N3Jy8OIyWAgO7/ogGY/LxODr6hLek4+X2854VC3Y71gxl/dhEBfT0L8vUjLzmfdoTPE1KpBdl4B4YE++Hl5EGH2Ib/QSoCPB6l/jIZuOJzClY1qkpqVT7uoIExGAxm5BSRbcjAaDKRl5xPs50VuQSE1a3hzKiMXs68nh05lcjw1m+6NarI/OR0fTxP+Xh7UCvBmb2I6TcJrYDQa+P1MFl9sSqBlpJkrooMJ8PEgLTufVnXMnM3M49eDp+naIBRPk5G0rHwycguw5OTj52XCaDBQJ8gXXy8TJ1KzCQ/0AYrW3BVabRw6lYmft4ka3h6EB/rwyfqj9GseTqi/FwdOZWAyGmhYqwY2m41jZ7MJ8vPEaDAUG3n+6xTfnD/+vE1GAymZeYQFeJNbYKXQanN47txX6LTsfMy+nuQWWLHZKHHTmPNNIT73C5G//2LEZrORlp2Pp8mIh6novZaUmJ/vFyplrVNWJbV57s+jpL5KG8OF6mXkFuDtYawUs7CUbJWgS5cudOrUiTfeeAMAq9VKVFQUY8eO5amnnnKom5ubS27unzsipaWlER0dTUJCQqVItrq9uoKZt7Tl6pYRDvfyC614moxYrTbeXn2IwVfUoYaPJwYDLNqRiNnXkyuig7HabAT5eXI6PRcvDxNmPy0GFhERKUlegRWrzUZaVj5Z+QWs3neK+jX98TQZeeqLbaRk5bs7RJFS8fY00rl+MJm5hWw+mlrh9kL9Pakd5MeOC4xot6lrLrYBUNu6Zrb+UebjacRkNJCZW2i/37x2AJ4mo/25phEB7E1MB4qOv6gMZ8FaLBaioqJITU3FbL7wjq6XRbKVl5eHn58fX3zxBYMGDbKXDxs2jNTUVL755huH+pMmTeKFF164xFGKiIiIiEhVkZCQQN26dS9Y57KYKHz69GkKCwsJDw93KA8PD2fPnuJncEyYMIHx48fbr61WKykpKYSGhlaKaRDnsunKMNImcjH6vEpVos+rVCX6vEpVUp0+rzabjfT0dCIjIy9a97JItsrK29sbb29vh7KgoCD3BHMBgYGBVf7DKpcPfV6lKtHnVaoSfV6lKqkun9eLTR88x/0rzC6BmjVrYjKZSEpKcihPSkoiIiLiPE+JiIiIiIiU32WRbHl5edGhQweWL19uL7NarSxfvpzY2Fg3RiYiIiIiItXVZTONcPz48QwbNoyOHTvSuXNnZs2aRWZmJnfffbe7Qyszb29vnn/++WJTHUUqI31epSrR51WqEn1epSq5XD+vl8VuhOe88cYb9kON27Vrx+uvv06XLl3cHZaIiIiIiFRDl1WyJSIiIiIicqlcFmu2RERERERELjUlWyIiIiIiIi6gZEtERERERMQFlGyJiIiIiIi4gJItERERERERF1CyJSIiIiIi4gJKtkRERERERFxAyZaIiIiIiIgLKNkSERERERFxASVbIiIiIiIiLqBkS0RERERExAWUbImIiIiIiLiAki0REREREREXULIlIiIiIiLiAkq2REREREREXEDJloiIiIiIiAso2RIREREREXEBJVsiIiIiIiIuoGRLRERERETEBZRsiYiIiIiIuICSLRERERERERdQsiUiIiIiIuICSrZERERERERcQMmWiIiIiIiICyjZEhERERERcYFKn2ytXr2a66+/nsjISAwGA19//bXDfZvNxsSJE6lduza+vr7069eP/fv3O9RJSUlh6NChBAYGEhQUxIgRI8jIyLiE70JERERERC43lT7ZyszMpG3btsyZM6fE+9OmTeP1119n7ty5rFu3Dn9/f+Li4sjJybHXGTp0KDt37mTp0qUsXLiQ1atXc999912qtyAiIiIiIpchg81ms7k7iNIyGAx89dVXDBo0CCga1YqMjOTRRx/lscceAyAtLY3w8HDmzZvHkCFD2L17Ny1atGDDhg107NgRgEWLFjFw4ECOHTtGZGSku96OiIiIiIhUYx7uDqAiDh8+TGJiIv369bOXmc1munTpQnx8PEOGDCE+Pp6goCB7ogXQr18/jEYj69at46abbirWbm5uLrm5ufZrq9VKSkoKoaGhGAwG174pERERERGptGw2G+np6URGRmI0XniiYJVOthITEwEIDw93KA8PD7ffS0xMJCwszOG+h4cHISEh9jp/N2XKFF544QUXRCwiIiIiItVBQkICdevWvWCdKp1sucqECRMYP368/TotLY3o6GgSEhIIDAx0Y2SQlp1Pt1dXADCiewzjrm7i1nhERERERC4nFouFqKgoAgICLlq3SidbERERACQlJVG7dm17eVJSEu3atbPXSU5OdniuoKCAlJQU+/N/5+3tjbe3d7HywMBAtydbNs98jN5+APj413B7PCIiIiIil6PSLC+q9LsRXkhMTAwREREsX77cXmaxWFi3bh2xsbEAxMbGkpqayqZNm+x1VqxYgdVqpUuXLpc8ZhERERERuTxU+pGtjIwMDhw4YL8+fPgwW7ZsISQkhOjoaB555BFefvllGjduTExMDM899xyRkZH2HQubN2/OgAEDGDlyJHPnziU/P58xY8YwZMgQ7UQoIiIiIiIuU+mTrY0bN9K7d2/79bm1VMOGDWPevHk88cQTZGZmct9995Gamkr37t1ZtGgRPj4+9mc+/vhjxowZQ9++fTEajQwePJjXX3/9kr8XERERERG5fFSpc7bcxWKxYDabSUtLc/saqbTsfNq+sASAUb0a8uSAZm6NR0RERKQ6sdlsFBQUUFhY6O5QxI08PT0xmUwl3itLblDpR7ZERERERC6FvLw8Tp48SVZWlrtDETczGAzUrVuXGjVqVKgdJVsiIiIictmzWq0cPnwYk8lEZGQkXl5epdptTqofm83GqVOnOHbsGI0bNz7vCFdpKNkSERERkcteXl4eVquVqKgo/Pz83B2OuFmtWrU4cuQI+fn5FUq2qvTW7yIiIiIizmQ06uuxlO4MrdLQp0lERERERMQFNI1QREREROQCjqdmczYz75L0FezvRZ0g30vSl7ieki0RERERkfM4nppNv+mryM6/NFvB+3qaWPboVWVKuHr16kW7du2YNWuWS2IaPnw4qampfP311y5p3x2OHDlCTEwMv/32G+3atXNZP0q2RERERETO42xmHtn5hcy6tR2Nwiq2DfjFHEjO4JHPtnA2M0+jW9WEki0RERERkYtoFFaDVnXM7g6j2sjLy8PLy8vdYbicNsgQEREREaniCgoKGDNmDGazmZo1a/Lcc89hs9kA+Oijj+jYsSMBAQFERERw++23k5yc7PD8zp07ue666wgMDCQgIIAePXpw8ODBEvvasGEDtWrVYurUqfayl19+mbCwMAICArj33nt56qmnHKbnDR8+nEGDBjF58mQiIyNp2rQpANu3b6dPnz74+voSGhrKfffdR0ZGhv25Xr168cgjjzj0P2jQIIYPH26/rl+/Pq+88gr33HMPAQEBREdH8/bbbzs8s379etq3b4+Pjw8dO3bkt99+K/WfbUUo2RIRERERqeI++OADPDw8WL9+Pf/+97+ZMWMG77zzDgD5+fm89NJLbN26la+//pojR444JCvHjx+nZ8+eeHt7s2LFCjZt2sQ999xDQUFBsX5WrFjB1VdfzeTJk3nyyScB+Pjjj5k8eTJTp05l06ZNREdH89ZbbxV7dvny5ezdu5elS5eycOFCMjMziYuLIzg4mA0bNrBgwQKWLVvGmDFjyvz+p0+fbk+iHnzwQUaNGsXevXsByMjI4LrrrqNFixZs2rSJSZMm8dhjj5W5j/LQNEIRERERkSouKiqKmTNnYjAYaNq0Kdu3b2fmzJmMHDmSe+65x16vQYMGvP7663Tq1ImMjAxq1KjBnDlzMJvNfPrpp3h6egLQpEmTYn189dVX3HXXXbzzzjvceuut9vLZs2czYsQI7r77bgAmTpzIkiVLHEaoAPz9/XnnnXfs0wf/+9//kpOTw4cffoi/vz8Ab7zxBtdffz1Tp04lPDy81O9/4MCBPPjggwA8+eSTzJw5k5UrV9K0aVPmz5+P1Wrl3XffxcfHh5YtW3Ls2DFGjRpV6vbLSyNbIiIiIiJVXNeuXR0O4o2NjWX//v0UFhayadMmrr/+eqKjowkICOCqq64C4OjRowBs2bKFHj162BOtkqxbt45//vOffPTRRw6JFsDevXvp3LmzQ9nfrwFat27tsE5r9+7dtG3b1p5oAXTr1g2r1WoflSqtNm3a2F8bDAYiIiLsUyV3795NmzZt8PHxsdeJjY0tU/vlpWRLRERERKSaysnJIS4ujsDAQD7++GM2bNjAV199BRRtUgHg63vxnQ8bNmxIs2bNeO+998jPzy9XLH9NqkrLaDTa156dU1L/f08UDQYDVqu1zP05m5ItEREREZEqbt26dQ7Xa9eupXHjxuzZs4czZ87w6quv0qNHD5o1a1Zsc4w2bdrw888/XzCJqlmzJitWrODAgQPccsstDnWbNm3Khg0bHOr//bokzZs3Z+vWrWRmZtrLfv31V4xGo30DjVq1anHy5En7/cLCQnbs2HHRtv/ez7Zt28jJybGXrV27tkxtlJfWbImIiIiIXMSB5IyLV3JjH0ePHmX8+PHcf//9bN68mdmzZzN9+nSio6Px8vJi9uzZPPDAA+zYsYOXXnrJ4dkxY8Ywe/ZshgwZwoQJEzCbzaxdu5bOnTvbkx6AsLAwVqxYQe/evbntttv49NNP8fDwYOzYsYwcOZKOHTty5ZVX8tlnn7Ft2zYaNGhwwZiHDh3K888/z7Bhw5g0aRKnTp1i7Nix3Hnnnfb1Wn369GH8+PF8//33NGzYkBkzZpCamlqmP5vbb7+dZ555hpEjRzJhwgSOHDnCv/71rzK1UV5KtkREREREziPY3wtfTxOPfLblkvTn62ki2L/s50/dddddZGdn07lzZ0wmEw8//DD33XcfBoOBefPm8fTTT/P6669zxRVX8K9//YsbbrjB/mxoaCgrVqzg8ccf56qrrsJkMtGuXTu6detWrJ+IiAhWrFhBr169GDp0KPPnz2fo0KEcOnSIxx57jJycHG655RaGDx/O+vXrLxizn58fixcv5uGHH6ZTp074+fkxePBgZsyYYa9zzz33sHXrVu666y48PDwYN24cvXv3LtOfTY0aNfjuu+944IEHaN++PS1atGDq1KkMHjy4TO2Uh8H290mQUozFYsFsNpOWlkZgYKBbY0nLzqftC0sAGNWrIU8OaObWeERERESqg5ycHA4fPkxMTIzDRgoAx1OzOZuZd0niCPb3ok7QxddQVXZXX301ERERfPTRR+4OpVwu9HkoS26gkS0RERERkQuoE+RbLRIgV8nKymLu3LnExcVhMpn45JNPWLZsGUuXLnV3aG6nZEtERERERMrNYDDwww8/MHnyZHJycmjatClffvkl/fr1c3dobqdkS0REREREys3X15dly5a5O4xKSVu/i4iIiIiIuICSLRERERGRP2jvOAHnfQ6UbImIiIjIZc/T0xMo2uxBJC+vaPdJk8lUoXZcvmYrNzcXb29vV3cjIiIiIlJuJpOJoKAgkpOTgaIzoAwGg5ujEnewWq2cOnUKPz8/PDwqli45Pdn68ccf+fTTT/n5559JSEjAarXi7+9P+/bt6d+/P3fffTeRkZFO66+wsJBJkybxf//3fyQmJhIZGcnw4cN59tln7X9BbDYbzz//PP/9739JTU2lW7duvPXWWzRu3NhpcYiIiIhI1RYREQFgT7jk8mU0GomOjq5wwu20ZOurr77iySefJD09nYEDB/Lkk08SGRmJr68vKSkp7Nixg2XLlvHSSy8xfPhwXnrpJWrVqlXhfqdOncpbb73FBx98QMuWLdm4cSN33303ZrOZhx56CIBp06bx+uuv88EHHxATE8Nzzz1HXFwcu3btKnZImYiIiIhcngwGA7Vr1yYsLIz8/Hx3hyNu5OXlhdFY8RVXTku2pk2bxsyZM7nmmmtKDOyWW24B4Pjx48yePZv/+7//Y9y4cRXud82aNdx4441ce+21ANSvX59PPvmE9evXA0WjWrNmzeLZZ5/lxhtvBODDDz8kPDycr7/+miFDhlQ4BhERERGpPkwmU4XX6oiAE5Ot+Pj4UtWrU6cOr776qrO65corr+Ttt99m3759NGnShK1bt/LLL78wY8YMAA4fPkxiYqLDoWpms5kuXboQHx9fYrKVm5tLbm6u/dpisTgtXhERERERuTxU+UONn3rqKSwWC82aNcNkMlFYWMjkyZMZOnQoAImJiQCEh4c7PBceHm6/93dTpkzhhRdecG3gIiIiIiJSrTkt2Ro/fnyp654bdXKGzz//nI8//pj58+fTsmVLtmzZwiOPPEJkZCTDhg0rV5sTJkxweD8Wi4WoqChnhSwiIiIiIpcBpyVbv/32m8P15s2bKSgooGnTpgDs27cPk8lEhw4dnNUlAI8//jhPPfWUfTpg69at+f3335kyZQrDhg2z7yqTlJRE7dq17c8lJSXRrl27Etv09vbWdvUiIiIiIlIhTku2Vq5caX89Y8YMAgIC+OCDDwgODgbg7Nmz3H333fTo0cNZXQJFB8/9fUMOk8mE1WoFICYmhoiICJYvX25PriwWC+vWrWPUqFFOjUVEREREROQcl6zZmj59OkuWLLEnWgDBwcG8/PLL9O/fn0cffdRpfV1//fVMnjyZ6OhoWrZsyW+//caMGTO45557gKItPB955BFefvllGjdubN/6PTIykkGDBjktDhERERERkb9ySbJlsVg4depUsfJTp06Rnp7u1L5mz57Nc889x4MPPkhycjKRkZHcf//9TJw40V7niSeeIDMzk/vuu4/U1FS6d+/OokWLdMaWiIiIiIi4jMFms9mc3ehdd93Fzz//zPTp0+ncuTMA69at4/HHH6dHjx588MEHzu7SpSwWC2azmbS0NAIDA90aS1p2Pm1fWALAqF4NeXJAM7fGIyIiIiJyOSlLbuCSka25c+fy2GOPcfvtt9tP3/bw8GDEiBG89tprruhSRERERESkUnFJsuXn58ebb77Ja6+9xsGDBwFo2LAh/v7+ruhORERERESk0jFevEr5nTx5kpMnT9K4cWP8/f1xwYxFERERERGRSsklydaZM2fo27cvTZo0YeDAgZw8eRKAESNGOHUnQhERERERkcrKJcnWuHHj8PT05OjRo/j5+dnLb731VhYtWuSKLkVERERERCoVl6zZWrJkCYsXL6Zu3boO5Y0bN+b33393RZciIiIiIiKViktGtjIzMx1GtM5JSUnB29vbFV2KiIiIiIhUKi5Jtnr06MGHH35ovzYYDFitVqZNm0bv3r1d0aWIiIiIiEil4pJphNOmTaNv375s3LiRvLw8nnjiCXbu3ElKSgq//vqrK7oUERERERGpVFwystWqVSv27dtH9+7dufHGG8nMzOTmm2/mt99+o2HDhq7oUkREREREpFJxycgWgNls5plnnnFV8yIiIiIiIpWa05Ktbdu2lbpumzZtnNWtiIiIiIhIpeS0ZKtdu3YYDAZsNtsF6xkMBgoLC53VrYiIiIiISKXktGTr8OHDzmpKRERERESkynNaslWvXj1nNSUiIiIiIlLluWyDDIBdu3Zx9OhR8vLyHMpvuOEGV3YrIiIiIiLidi5Jtg4dOsRNN93E9u3bHdZxGQwGAK3ZEhERERGRas8l52w9/PDDxMTEkJycjJ+fHzt37mT16tV07NiRn376yRVdioiIiIiIVCouGdmKj49nxYoV1KxZE6PRiNFopHv37kyZMoWHHnqI3377zRXdioiIiIiIVBouGdkqLCwkICAAgJo1a3LixAmgaBONvXv3uqJLERERERGRSsUlI1utWrVi69atxMTE0KVLF6ZNm4aXlxdvv/02DRo0cEWXIiIiIiIilYpLkq1nn32WzMxMAF588UWuu+46evToQWhoKJ999pkruhQREREREalUXJJsxcXF2V83atSIPXv2kJKSQnBwsH1HQhERERERkerMJWu20tLSSElJcSgLCQnh7NmzWCwWV3QpIiIiIiJSqbgk2RoyZAiffvppsfLPP/+cIUOGuKJLERERERGRSsUlyda6devo3bt3sfJevXqxbt06p/d3/Phx7rjjDkJDQ/H19aV169Zs3LjRft9mszFx4kRq166Nr68v/fr1Y//+/U6PQ0RERERE5ByXJFu5ubkUFBQUK8/Pzyc7O9upfZ09e5Zu3brh6enJjz/+yK5du5g+fTrBwcH2OtOmTeP1119n7ty5rFu3Dn9/f+Li4sjJyXFqLCIiIiIiIue4ZIOMzp078/bbbzN79myH8rlz59KhQwen9jV16lSioqJ4//337WUxMTH21zabjVmzZvHss89y4403AvDhhx8SHh7O119/rWmNIiIiIiLiEi5Jtl5++WX69evH1q1b6du3LwDLly9nw4YNLFmyxKl9ffvtt8TFxfHPf/6TVatWUadOHR588EFGjhwJwOHDh0lMTKRfv372Z8xmM126dCE+Pr7EZCs3N5fc3Fz7tTb1EBERERGRsnLJNMJu3boRHx9PVFQUn3/+Od999x2NGjVi27Zt9OjRw6l9HTp0iLfeeovGjRuzePFiRo0axUMPPcQHH3wAQGJiIgDh4eEOz4WHh9vv/d2UKVMwm832n6ioKKfGLCIiIiIi1Z9LRrYA2rVrx8cff+yq5u2sVisdO3bklVdeAaB9+/bs2LGDuXPnMmzYsHK1OWHCBMaPH2+/tlgsSrhERERERKRMXDKytXnzZrZv326//uabbxg0aBBPP/00eXl5Tu2rdu3atGjRwqGsefPmHD16FICIiAgAkpKSHOokJSXZ7/2dt7c3gYGBDj8iIiIiIiJl4ZJk6/7772ffvn1A0TS/W2+9FT8/PxYsWMATTzzh1L66devG3r17Hcr27dtHvXr1gKLNMiIiIli+fLn9vsViYd26dcTGxjo1FhERERERkXNckmzt27ePdu3aAbBgwQKuuuoq5s+fz7x58/jyyy+d2te4ceNYu3Ytr7zyCgcOHGD+/Pm8/fbbjB49GgCDwcAjjzzCyy+/zLfffsv27du56667iIyMZNCgQU6NRURERERE5ByXrNmy2WxYrVYAli1bxnXXXQdAVFQUp0+fdmpfnTp14quvvmLChAm8+OKLxMTEMGvWLIYOHWqv88QTT5CZmcl9991Hamoq3bt3Z9GiRfj4+Dg1FhERERERkXNckmx17NjRvv37qlWreOutt4Cibdj/viugM1x33XX2hK4kBoOBF198kRdffNHpfYuIiIiIiJTEJdMIZ82axebNmxkzZgzPPPMMjRo1AuCLL77gyiuvdEWXIiIiIiIilYpLRrbatGnjsBvhOa+99homk8kVXYqIiIiIiFQqLhnZAkhNTeWdd95hwoQJpKSkALBr1y6Sk5Nd1aWIiIiIiEil4ZKRrW3bttG3b1+CgoI4cuQII0eOJCQkhP/9738cPXqUDz/80BXdioiIiIiIVBouGdkaP348d999N/v373fY8W/gwIGsXr3aFV2KiIiIiIhUKi5JtjZs2MD9999frLxOnTokJia6oksREREREZFKxSXJlre3NxaLpVj5vn37qFWrliu6FBERERERqVRckmzdcMMNvPjii+Tn5wNF51wdPXqUJ598ksGDB7uiSxERERERkUrFJcnW9OnTycjIICwsjOzsbK666ioaNWpEQEAAkydPdkWXIiIiIiIilYpLdiM0m80sXbqUX3/9la1bt5KRkcEVV1xBv379XNGdiIiIiIhIpeP0ZCs/Px9fX1+2bNlCt27d6Natm7O7EBERERERqfScPo3Q09OT6OhoCgsLnd20iIiIiIhIleGSNVvPPPMMTz/9NCkpKa5oXkREREREpNJzyZqtN954gwMHDhAZGUm9evXw9/d3uL9582ZXdCsiIiIiIlJpuCTZGjRokCuaFRERERERqTJckmw9//zzrmhWRERERESkynDami2bzeaspkRERERERKo8pyVbLVu25NNPPyUvL++C9fbv38+oUaN49dVXndW1iIiIiIhIpeO0aYSzZ8/mySef5MEHH+Tqq6+mY8eOREZG4uPjw9mzZ9m1axe//PILO3fuZMyYMYwaNcpZXYuIiIiIiFQ6Tku2+vbty8aNG/nll1/47LPP+Pjjj/n999/Jzs6mZs2atG/fnrvuuouhQ4cSHBzsrG5FREREREQqJadvkNG9e3e6d+/u7GZFRERERESqFJccaiwiIiIiInK5U7IlIiIiIiLiAkq2REREREREXEDJloiIiIiIiAso2RIREREREXEBlyRbmzdvZvv27fbrb775hkGDBvH0009f9NDjinr11VcxGAw88sgj9rKcnBxGjx5NaGgoNWrUYPDgwSQlJbk0DhERERERuby5JNm6//772bdvHwCHDh1iyJAh+Pn5sWDBAp544glXdAnAhg0b+M9//kObNm0cyseNG8d3333HggULWLVqFSdOnODmm292WRwiIiIiIiIuSbb27dtHu3btAFiwYAE9e/Zk/vz5zJs3jy+//NIVXZKRkcHQoUP573//63BoclpaGu+++y4zZsygT58+dOjQgffff581a9awdu3aEtvKzc3FYrE4/IiIiIiIiJSFS5Itm82G1WoFYNmyZQwcOBCAqKgoTp8+7YouGT16NNdeey39+vVzKN+0aRP5+fkO5c2aNSM6Opr4+PgS25oyZQpms9n+ExUV5ZKYRURERESk+nJJstWxY0defvllPvroI1atWsW1114LwOHDhwkPD3d6f59++imbN29mypQpxe4lJibi5eVFUFCQQ3l4eDiJiYkltjdhwgTS0tLsPwkJCU6PWUREREREqjcPVzQ6c+ZM7rjjDr7++mueeeYZGjVqBMAXX3zBlVde6dS+EhISePjhh1m6dCk+Pj5OadPb2xtvb2+ntOVKqVmu3WxERERERETKzyXJVtu2bR12Izzntddew8PDuV1u2rSJ5ORkrrjiCntZYWEhq1ev5o033mDx4sXk5eWRmprqMLqVlJRERESEU2O51JIsue4OQUREREREzsMl0wgbNGjAmTNnipXn5OTQpEkTp/bVt29ftm/fzpYtW+w/HTt2ZOjQofbXnp6eLF++3P7M3r17OXr0KLGxsU6N5VIzuDsAERERERE5L5eMbB05coTCwsJi5bm5uRw7dsypfQUEBNCqVSuHMn9/f0JDQ+3lI0aMYPz48YSEhBAYGMjYsWOJjY2la9euTo1FRERERETkHKcmW99++6399eLFizGbzfbrwsJCli9fTkxMjDO7LJWZM2diNBoZPHgwubm5xMXF8eabb17yOERERERE5PJhsNlsNmc1ZjQWzUo0GAz8vVlPT0/q16/P9OnTue6665zV5SVhsVgwm82kpaURGBjo1ljSsvNp+8ISAPo2C+Pd4Z3cGo+IiIiIyOWkLLmBU0e2zp2tFRMTw4YNG6hZs6YzmxcREREREakyXLJm6/Dhw65oVkREREREpMpwSbIFsHz5cpYvX05ycrJ9xOuc9957z1XdioiIiIiIVAouSbZeeOEFXnzxRTp27Ejt2rUxGLRJuYiIiIiIXF5ckmzNnTuXefPmceedd7qieRERERERkUrPJYca5+XlceWVV7qiaRERERERkSrBJcnWvffey/z5813RtIiIiIiISJXgkmmEOTk5vP322yxbtow2bdrg6enpcH/GjBmu6FZERERERKTScEmytW3bNtq1awfAjh07HO5pswwREREREbkcuCTZWrlypSuaFRERERERqTJcsmZLRERERETkcueSka3evXtfcLrgihUrXNGtiIiIiIhIpeGSZOvceq1z8vPz2bJlCzt27GDYsGGu6FJERERERKRScUmyNXPmzBLLJ02aREZGhiu6FBERERERqVQu6ZqtO+64g/fee+9SdikiIiIiIuIWlzTZio+Px8fH51J2KSIiIiIi4hYumUZ48803O1zbbDZOnjzJxo0bee6551zRpYiIiIiISKXikmTLbDY7XBuNRpo2bcqLL75I//79XdGliIiIiIhIpeKSZOv99993RbMiIiIiIiJVhkuSrXM2bdrE7t27AWjZsiXt27d3ZXciIiIiIiKVhkuSreTkZIYMGcJPP/1EUFAQAKmpqfTu3ZtPP/2UWrVquaJbERERERGRSsMluxGOHTuW9PR0du7cSUpKCikpKezYsQOLxcJDDz3kii5FREREREQqFZeMbC1atIhly5bRvHlze1mLFi2YM2eONsgQEREREZHLgktGtqxWK56ensXKPT09sVqtruhSRERERESkUnFJstWnTx8efvhhTpw4YS87fvw448aNo2/fvq7oUkREREREpFJxSbL1xhtvYLFYqF+/Pg0bNqRhw4bExMRgsViYPXu2U/uaMmUKnTp1IiAggLCwMAYNGsTevXsd6uTk5DB69GhCQ0OpUaMGgwcPJikpyalxiIiIiIiI/JVL1mxFRUWxefNmli1bxp49ewBo3rw5/fr1c3pfq1atYvTo0XTq1ImCggKefvpp+vfvz65du/D39wdg3LhxfP/99yxYsACz2cyYMWO4+eab+fXXX50ej4iIiIiICIDBZrPZ3B2EM506dYqwsDBWrVpFz549SUtLo1atWsyfP59//OMfAOzZs4fmzZsTHx9P165dL9qmxWLBbDaTlpZGYGCgq9/CBaVl59P2hSUA9G0WxrvDO7k1HhERERGRy0lZcgOnTiNcsWIFLVq0wGKxFLuXlpZGy5Yt+fnnn53ZZYn9AISEhABFByvn5+c7jKo1a9aM6Oho4uPjS2wjNzcXi8Xi8CMiIiIiIlIWTk22Zs2axciRI0vM8MxmM/fffz8zZsxwZpcOrFYrjzzyCN26daNVq1YAJCYm4uXlZT9c+Zzw8HASExNLbGfKlCmYzWb7T1RUlMtiFhERERGR6smpydbWrVsZMGDAee/379+fTZs2ObNLB6NHj2bHjh18+umnFWpnwoQJpKWl2X8SEhKcFKGIiIiIiFwunLpBRlJSUonna9k78/Dg1KlTzuzSbsyYMSxcuJDVq1dTt25de3lERAR5eXmkpqY6jG4lJSURERFRYlve3t54e3u7JE4REREREbk8OHVkq06dOuzYseO897dt20bt2rWd2SU2m40xY8bw1VdfsWLFCmJiYhzud+jQAU9PT5YvX24v27t3L0ePHiU2NtapsYiIiIiIiJzj1JGtgQMH8txzzzFgwAB8fHwc7mVnZ/P8889z3XXXObNLRo8ezfz58/nmm28ICAiwr8Mym834+vpiNpsZMWIE48ePJyQkhMDAQMaOHUtsbGypdiIUEREREREpD6du/Z6UlMQVV1yByWRizJgxNG3aFCjaan3OnDkUFhayefNmwsPDndUlBoOhxPL333+f4cOHA0WHGj/66KN88skn5ObmEhcXx5tvvnneaYR/p63fRUREREQEypYbOHVkKzw8nDVr1jBq1CgmTJjAuTzOYDAQFxfHnDlznJpoAZQmV/Tx8WHOnDnMmTPHqX2LiIiIiIicj1OTLYB69erxww8/cPbsWQ4cOIDNZqNx48YEBwc7uysREREREZFKy+nJ1jnBwcF06qQpbiIiIiIicnly6m6EIiIiIiIiUkTJlriMzWYjLTvf3WGIiIiIiLiFy6YRyuXtTEYuHV5eBsATA5ryQM+GGI0l7xwpIiIiIlIdaWRLnO6r347ZEy2AaYv20uDpH9wYkYiIiIjIpadkS5wqJ7+QcZ9ttV97mv4czdqflO6OkERERERE3ELJljhV/5mr7a+3T+rP/skD2fhsPwCunrmaOSsPkJFb4K7wREREREQuGSVb4jTrDp3haEoWAL882ZsAH08AatbwZuJ1LQB4bfFeNhxOKdVh1CIiIiIiVZmSLXGKb7ee4Na31wLw+f2x1A32c7h/T/cYRvVqCMDd8zYQM+EHsvI0wiUiIiIi1ZeSLamwo2eyeOiT3wC4vUs0nWNCSqw3pncjh+sWExfTb8YqCq0a5RIRERGR6kfJllSI1Wqj52sr7deTB7U6b11/bw+OvHotXzwQay87kJxBw6d/YO6qgyRbclwaq4iIiIjIpaRkSyqk0TNFW7q3jQpixaNXYTBc/CytjvVDeH94J7o3qmkve/XHPXR+ZTn1n/qeZ77aztpDZ0jL0oHIIiIiIlJ16VBjKbf9SemcmwH49YNXlirROqd3szB6NwvjQHIGY+ZvZk/in9vCf7zuKB+vO2q/bhxWg6gQP/7ZoS6dY0LwMBmp4e2BSYcki4iIiEglpmRLyiW/0MrVf2zz/tNjvcqUaP1Vo7AaLHqkJ9l5hRw+ncl/fz7EV78dd6izPzmD/ckZrNiT7FC+bHxPGoUFlO8NiIiIiIi4mJItKZfe//oJgHqhftSv6V/h9ny9TLSIDGTmre14dXBrDiRnsGxXMm3qmrl73oYSn+k3oyjZG9IpilcHt6lwDCIiIiIizqRkS8osISWLY2ezAVg4trvT2/f2MNEy0kzLSDMAu16Mw8tkxMNUtMQwJ7+Qq15bSZIlF4BPNyTw6YYErm4RzkN9GtO6rtkpcRw9k0VYoDc+niantCciIiIilxclW1Jmaw+dAWDKza3tBxe7kp+X48fUx9PEuqf7kZaVz4JNCbz8/W4Alu5KYumuJIe6bw29ggPJGfRpHoaXyYjVBpFBPg5xF1ptZOQWcCo9hwWbjhHbIJRlu5P4v7VH+bv3hneke6NaeHlobxkRERERuTCDzWbTIUcXYbFYMJvNpKWlERgY6NZY0rLzafvCEgBa1Qlk4dgel7T/jNwCWj2/GIAdL8RRw9v9+Xp+oZVf9p8m0ZLDhP9tv6R9/3tIO/IKrJxMy6FtVBD/WXWQPs3C2HE8jW3H07i9c9G5Y60izRi1oYeIiIhIlVeW3MD935SlXLw9jJxOz7vk/U75oWgU6cN7OleKRAvA02Skd7MwAG7rHA1AZm4B/7f2d6b8uAeA6BA/jqZklao9Py8T469uQruoIIL9vajh7UGgjye7TloY/NYah7oPf7ql2PNrDp6xvz436vZXzSIC+PyBWLxMRk1RFBEREanGNLJVCpVxZKthLX9yC6z88mSfS9Z3QkoWPaatxGCAw1OuvWT9OltiWg4RZh9OpmWTnlNA47Aa5BZY8TIZSUrPobbZ94LP5xdaOZORx+wV+/l43VE8TQbyC210axSKAQN7Ei2Agbti62EyGnht8d4LtufraeK+ng3wNBmIDPLlxnZ1tK29iIiISCVVltxAyVYpKNlynD74yciuxDYMvST9Vhc5+YWYjAY+WX+UT9YnsPukxSntNosIIDk9F19PE8dTs2lVJ5Adxy1cER3E0ZQsPE1G2kUF8c+OdalVw4cAHw/77pE5+YUOI2uWnHwC/1jLlpNfyOmMXE5n5BFp9sHTZCQjt4C5qw4S4OPJybRsDiRnMKpXQzJyCki05NCraRj+XiZsFCWQXh5GwgN9yC+0kl9oLbb2Ti4ur8CK1WZj1b5T3P/RJnw8jUwd3IbUrHy6NAihWYR7/38kIiJyOVKy5WRKtuC+DzeyZFcSDWv5s/zRXpekz+rOarWRkpXHV5uP81vCWX7YnujukC6Jq1uE079FOC0jzfh6mQgLKNrxUaN5f7Lk5PPOz4d5ffn+i9YN8PFgdO9GPHBVw0sQmYiIiGjNljjV6Yxclvyxy9+PD/d0czTVh9FooGYNb0b2bFDifZvNZj8sOiEli/3J6ZxIzaFLTAgBPp5YbTbyCqz8cuA0dYJ9iQ7xY+WeZPy8POjRuCYn03L49cBpft5/is1HU+nZpBar952qUMxxLcNJsuTSLCKABrX8mb/uKKE1vLHZbBw5k0VKZh5Bfp4E+3lx+HRmiW2UtGtkSeoE+VIv1I9dJy30bRbOl5uP2e81CqthH1Hr3bQWwX5eYIC4lhH4eJpoUNOfCLMPWbmFBPp6/PHnSaXdpCQxLYe07Hye/Xo7G46cLbHOs9c2x8vDSEGhjZNp2Xy79QRJllzScwp49cc9vPrjHtrWNfP8DS0J9vMixgnn34mIiEjFaGSrFC7nka1T6bl0mrwMKDpTq1Ud55xhJZVHcnoOYQE+QNG0NQ+jwalJic1mw2qD3IJCDiRncDQli0U7EjmQnMGexHS8PYzkFlid1l95XNemNoG+nsSE+hMZ5Et2fiFHz2RSO8iX3k3DOHw6kxaRgRgM2KdaFlptJY7GnSsvtNowGor+zuYVWvE0Gtl5wkKwvycbDqdwKiOXrQlp/HLgdIkxjegew309GxAe6HPeuHPyCzl4KoMFG48xb82RYveHdIriHx3q0qFesD1xFxERkYrRNMLzmDNnDq+99hqJiYm0bduW2bNn07lz54s+d7kmW2sOnub2/64D4IGrGvLUNc1c1pfIOTabjdSsfHy9TJzOyKWGtweeJiM/7T1F4/AanM3MY/vxNMIDfcjOKyQyyJeaAV4s2ZnEjKX7LmmsIf5epGQ6b1fQt4ZeQe9mYeXepfKj+COsOXiGH3ecf0pq6zpmrm4Rjp+Xiaua1CIswAeTyYDvH1M5s/IKtL5ORETkApRsleCzzz7jrrvuYu7cuXTp0oVZs2axYMEC9u7dS1hY2AWfrazJVnZeIWsm9HV6H6v3nWLa4j3sOF60icNdsfWYdH3LSjsFS6QsbDYbWXmFGAxw7Gw2Qb6eeJqM3Pp2PP/sEEV6bgGWP0ajVu5JpmuDUBJSstj4u+P0vlB/L3z+2JjkfOoG++JhNJBwNptaNby5tVMUBVYrAT6e9GpalOh4exjx8zI5deTJZrOx84SFXw6c5tUf9xDg7UF6bkGZ2ji32Yqvp4kb2kZyKiOXs1l5ZOUW0i4qCE8PAx7GomMXokP8OJmWTV6BlSbhAdQK8CYxLYec/KI/51oBf26Ukl9go2aAF0aDgcw/YvLyMFLD24OM3AKHA8f/OpVW5Byr1YbRaHD4fBQUWvEw6bB5Ebk0lGyVoEuXLnTq1Ik33ngDAKvVSlRUFGPHjuWpp5664LOVNdk6eCqTpeN6YqPo3K0AH08ycwvILbBiMEBBoY3QGl5YbTZstqI1K1abzeHakpPPqfRcVuxJ5qO1vzv0FejjwfJHe1ErwNs9b1ZEnKag0IrJaKDAaiMjp4BfDpxmf1I689cn0C7KzLLdyfh6msjOL3R3qOfl5WEkr8BKzRpeZOcV0iisBgE+nvxy4DQ1vD3o2iCU389kcjYrnwa1/Fl/OIXujWpitdlYc/AMd3ath5+XCU+TkbxCK+k5+Xy+8Rj392yAh9HAybQcsvMLaVDTn9OZeRw/m01iWg4FVivNagcS6OPJ0ZRMOtcPZfPRs2TlFeDr5UGInyd7EtNpFFaDZbuT6N6oFiv2JGEyGmgXFUSE2ZejKVlsTUgFinYR3ZOYTqi/F+k5BdiwERXix6FTf65zrBXgzan0XADqhfqRlVfIPzvU5WhKFrkFVowGiAr2Y/vxNOqH+uPnbSIjp4B6oX5k5xfy64Ez1AnypU6wL7VqeHMiLZvaZh9qeHtSaLViMBjwNBk4dCqTlnXMnM3Mw2g0EOznSUSgDweSM6hZwxt/bw/2JaUT6OtBTM0anMnIZUtCKo3CapCVV0jLyEBSMvPsI9DnvlDYbDYOn87E28OEj6cRD5ORPSct+P9xPmN6TgFmX08SLTkUFFr5z+pDDGpXhzrBvpxKz2VPooWGtWpgyc63rxn28TTSpm4Q6w+nlOrzUrOGF6H+3nRrVJPU7DxOpediNBjo3zLc/svEKxuGYrXZ8PU0YTQYCPLzJCUzj0BfTyLNvpj9ipL/czn/udT/XJJ37tpkNJBbYKWGtwe5BUW7z3oYjX+UF+JpNBb7hWV+oRVPJYgiVZKSrb/Jy8vDz8+PL774gkGDBtnLhw0bRmpqKt98841D/dzcXHJzc+3XaWlpREdHk5CQ4PZkKz0nn3/MXUPfZuF8GP/7xR8oh9pmH6YObs0V9UJc0r6IVD05+YV4exjJzi9k53ELyek5eBiLzqZrGhbA3uR01h8+Q4d6wQT7ebMvKZ3wQG+On81m27E0bu8STU6BlQ2HU6gZ4I3pjy/2/xf/OxFmX4wG8PP2INmSQ2SQDz/vP3PeWOoG+3LsbNGI4rlz7s6npPueHkZq+ntxMi3HXmY0gLWU/xrWquHFqYzSTx+NDvElI6eAlKz8Uj9TGj6eRnLy3bve0ZVa1zFjNMDWY2lAUfJUP9Sf5PQcUrLyaB0ZRPyhM/Y/h9gGocQf+vNzUyfYBx8PE1abjcOnS3eovSv5eBoptIHNaqPgLx82P6+ihOuvo7gOaZmhxJcY/nJlOF+d87RpOE8HBsOfI4d+XkUJqNVmw8afv7A9963x3HpcsJGTbyW30IrpXFJaUr8lxFjSeygp/pJj/2tpUVBeHibSsvPILwQPExRYwdvDQE3/yvdL48r85buypgb+3h7c2jGKf3SMcncoWCwWoqKiSE1NxWy+8H4Gl0WydeLECerUqcOaNWuIjY21lz/xxBOsWrWKdevWOdSfNGkSL7zwwqUOU0REREREqoiEhATq1q17wTpaBV2CCRMmMH78ePu11WolJSWF0NDQSrF+4Fw2XRlG2kQuRp9XqUr0eZWqRJ9XqUqq0+fVZrORnp5OZGTkReteFslWzZo1MZlMJCU5nu2TlJREREREsfre3t54ezsOOQcFBbkyxHIJDAys8h9WuXzo8ypViT6vUpXo8ypVSXX5vF5s+uA5l8XKTC8vLzp06MDy5cvtZVarleXLlztMKxQREREREXGWy2JkC2D8+PEMGzaMjh070rlzZ2bNmkVmZiZ33323u0MTEREREZFq6LJJtm699VZOnTrFxIkTSUxMpF27dixatIjw8HB3h1Zm3t7ePP/888WmOopURvq8SlWiz6tUJfq8SlVyuX5eL4vdCEVERERERC61y2LNloiIiIiIyKWmZEtERERERMQFlGyJiIiIiIi4gJItERERERERF1CyJSIiIiIi4gJKtkRERERERFxAyZaIiIiIiIgLKNkSERERERFxASVbIiIiIiIiLqBkS0RERERExAWUbImIiIiIiLiAki0REREREREXULIlIiIiIiLiAkq2REREREREXEDJloiIiIiIiAso2RIREREREXEBJVsiIiIiIiIuoGRLRERERETEBZRsiYiIiIiIuICSLRERERERERdQsiUiIiIiIuICSrZERERERERcQMmWiIiIiIiICyjZEhERERERcQElWyIiIiIiIi5Q6ZOt48ePc8cddxAaGoqvry+tW7dm48aN9vs2m42JEydSu3ZtfH196devH/v373doIyUlhaFDhxIYGEhQUBAjRowgIyPjUr8VERERERG5jFTqZOvs2bN069YNT09PfvzxR3bt2sX06dMJDg6215k2bRqvv/46c+fOZd26dfj7+xMXF0dOTo69ztChQ9m5cydLly5l4cKFrF69mvvuu88db0lERERERC4TBpvNZnN3EOfz1FNP8euvv/Lzzz+XeN9msxEZGcmjjz7KY489BkBaWhrh4eHMmzePIUOGsHv3blq0aMGGDRvo2LEjAIsWLWLgwIEcO3aMyMjIS/Z+RERERETk8uHh7gAu5NtvvyUuLo5//vOfrFq1ijp16vDggw8ycuRIAA4fPkxiYiL9+vWzP2M2m+nSpQvx8fEMGTKE+Ph4goKC7IkWQL9+/TAajaxbt46bbrqpWL+5ubnk5ubar61WKykpKYSGhmIwGFz4jkVEREREpDKz2Wykp6cTGRmJ0XjhiYKVOtk6dOgQb731FuPHj+fpp59mw4YNPPTQQ3h5eTFs2DASExMBCA8Pd3guPDzcfi8xMZGwsDCH+x4eHoSEhNjr/N2UKVN44YUXXPCORERERESkOkhISKBu3boXrFOpky2r1UrHjh155ZVXAGjfvj07duxg7ty5DBs2zGX9TpgwgfHjx9uv09LSiI6OJiEhgcDAQJf1Wxpp2fl0e3UFAK/e3Jrr2moapIiIiIjIpWKxWIiKiiIgIOCidSt1slW7dm1atGjhUNa8eXO+/PJLACIiIgBISkqidu3a9jpJSUm0a9fOXic5OdmhjYKCAlJSUuzP/523tzfe3t7FygMDA92ebNk88zF6+wHgVyPA7fGIiIiIiFyOSrO8qFLvRtitWzf27t3rULZv3z7q1asHQExMDBERESxfvtx+32KxsG7dOmJjYwGIjY0lNTWVTZs22eusWLECq9VKly5dLsG7EBERERGRy1GlHtkaN24cV155Ja+88gq33HIL69ev5+233+btt98GirLJRx55hJdffpnGjRsTExPDc889R2RkJIMGDQKKRsIGDBjAyJEjmTt3Lvn5+YwZM4YhQ4ZoJ0IREREREXGZSp1sderUia+++or/b+++w6I62jaA31vYBYRdihQLKHZRsWDDGLti718SX2NJjDEJGo0laqKxpJhX81qDplsSS2I3Fiwo2FARRUUR0YigNAXpbdk93x8bjq4ggrAUuX/XtRd7ZubMzFmOyMPMmZk7dy4WL14MFxcXrFy5EqNHjxbLfPrpp0hPT8f777+PpKQkdO7cGT4+PjA1NRXLbN68GZMnT0bPnj0hlUoxYsQIrF69ujwuiYiIiIiIqogKvc9WRZGSkgK1Wo3k5ORyf0YqOVODlouOAABWvtkKQ1vXKtf+EBERvQq0Wi00Gk15d4OIKgiFQvHcZd2LExtU6JEtIiIiImMSBAGxsbFISkoq764QUQUilUrh4uIChUJRonoYbBEREVGVlRdo2dvbw9zcvEirixHRq02n0yE6OhoxMTFwdnYu0c8FBltERERUJWm1WjHQsrW1Le/uEFEFYmdnh+joaOTm5sLExOSl66nQS78TERERGUveM1rm5ubl3BMiqmjypg9qtdoS1cNgi4iIiKo0Th0komeV1s8FBltERERERERGwGe2iIiIiJ7xICkTj9NzyqQt62oK1LIyM0rdEokEu3fvxtChQ41SPxEVjsEWERER0VMeJGWi1//8kakp2bMaRWVmIsOxGV2LHXDFxsbi66+/xoEDB/DgwQPY29ujVatWmDZtGnr27PnS/Rk/fjw2btxokObp6QkfH5+XrpOoqmKwRURERPSUx+k5yNRosfLNVmhgb2HUtm7Hp2Han8F4nJ5TrGArIiICr732GqysrLBs2TK0aNECGo0Ghw8fhpeXF27evFmifvXt2xfr168Xj5VKZYnqK0hOTk6J9zAiqugYbBEREREVoIG9BZrXUpd3Nwr00UcfQSKR4MKFC6hWrZqY3qxZM7z77rsGZR89eoRhw4bh8OHDqFWrFv73v/9h8ODBhdavVCrh6OhYqn1euHAh9uzZg8mTJ+Prr7/GvXv3oNPp4OPjg6+++gohISGQyWTw8PDAqlWrUL9+fQDAyJEj4ejoiO+//x4AMG3aNKxatQqhoaFo0qQJcnJyYG1tjb1796JXr16l2meikuICGURERESVSGJiInx8fODl5WUQaOWxsrIyOF60aBHeeOMNXL16Ff3798fo0aORmJhYaBt+fn6wt7dH48aN8eGHHyIhIaFU+n779m3s3LkTu3btQnBwMAAgPT0d06dPx8WLF+Hr6wupVIphw4ZBp9MBALp27Qo/Pz+xDn9/f1SvXl1MCwwMhEajQadOnUqlj0SlicEWERERUSVy+/ZtCIKAJk2aFKn8+PHjMWrUKDRo0ADffPMN0tLScOHCheeW79u3LzZt2gRfX1/897//hb+/P/r161fi/YYA/dTBTZs2oXXr1nBzcwMAjBgxAsOHD0eDBg3QqlUr/Pbbb7h27Rpu3LgBAOjWrRtu3LiBhw8f4vHjx7hx4wamTp0qBlt+fn5o164d90ujConBFhEREVElIghCscrnBTUAUK1aNahUKsTHxz+3/FtvvYXBgwejRYsWGDp0KPbv34/AwECD0aWnbd68GRYWFuLr1KlTz627Tp06sLOzM0gLDw/HqFGjUK9ePahUKtStWxcAEBkZCQBo3rw5bGxs4O/vj1OnTqF169YYOHAg/P39AehHurp161aET4Ko7PGZLSIiIqJKpGHDhpBIJEVeBMPExMTgWCKRiFP0iqJevXqoXr06bt++XeAqh4MHD0aHDh3E41q1aj23roKmPQ4aNAh16tTBzz//jJo1a0Kn06F58+bIyckR+9ulSxf4+flBqVSiW7ducHNzQ3Z2NkJCQnD27FnMnDmzyNdDVJY4skVERERUidjY2MDT0xPe3t5IT0/Pl5+UlFSq7d2/fx8JCQmoUaNGgfmWlpZo0KCB+DIzK/qqigkJCQgLC8O8efPQs2dPNG3aFI8fP85XLu+5LT8/P3Tr1g1SqRRdunTBsmXLkJ2djddee+2lr4/ImBhsEREREVUy3t7e0Gq1aN++PXbu3Inw8HCEhoZi9erV8PDweOl609LSMGvWLJw7dw4RERHw9fXFkCFD0KBBA3h6epbiFehZW1vD1tYWP/30E27fvo3jx49j+vTp+crlPbd1/fp1dO7cWUzbvHkz2rZtW+CIGVFFwGmERERERAW4HZ9WYduoV68eLl26hK+//hozZsxATEwM7Ozs4O7ujnXr1r10f2QyGa5evYqNGzciKSkJNWvWRJ8+ffDll18aZa8tqVSKbdu24eOPP0bz5s3RuHFjrF69Ot8zWC1atICVlRUaNWoECwv93mfdunWDVqvl81pUoUmE4j5lWQWlpKRArVYjOTkZKpWqXPuSnKlBy0VHAAAr32yFoa2fPy+aiIiIni8rKwt3796Fi4sLTE1NxfQHSZno9T9/ZGpKvvpeUZiZyHBsRtdibWpMRMb1vJ8PQPFiA45sERERET2llpUZjs3oisfpOWXSnnU1BQMtolcUgy0iIiKiZ9SyMmMAREQlVqEXyFi4cCEkEonB6+kN/LKysuDl5QVbW1tYWFhgxIgRiIuLM6gjMjISAwYMgLm5Oezt7TFr1izk5uaW9aUQEREREVEVU+FHtpo1a4Zjx46Jx3L5ky5/8sknOHDgALZv3w61Wo3Jkydj+PDhOHPmDABAq9ViwIABcHR0xNmzZxETE4OxY8fCxMQE33zzTZlfCxERERERVR0VPtiSy+VwdHTMl56cnIxff/0VW7ZsQY8ePQAA69evR9OmTXHu3Dl07NgRR44cwY0bN3Ds2DE4ODigVatW+PLLLzF79mwsXLgQCoWiwDazs7ORnZ0tHqekpBjn4oiIiIiI6JVVoacRAkB4eDhq1qyJevXqYfTo0YiMjAQABAUFQaPRoFevXmLZJk2awNnZGQEBAQCAgIAAtGjRAg4ODmIZT09PpKSk4Pr1689tc8mSJVCr1eLLycnJSFdHRERERESvqgodbHXo0AEbNmyAj48P1q1bh7t37+L1119HamoqYmNjoVAoYGVlZXCOg4MDYmNjAQCxsbEGgVZefl7e88ydOxfJycniKyoqqnQvjIiIiIiIXnkVehphv379xPdubm7o0KED6tSpg7/++gtmZsZbIUipVBpl4z4iIiIiIqo6KvTI1rPydg6/ffs2HB0dkZOTg6SkJIMycXFx4jNejo6O+VYnzDsu6DkwIiIiIiKi0lKpgq20tDTcuXMHNWrUgLu7O0xMTODr6yvmh4WFITIyEh4eHgAADw8PXLt2DfHx8WKZo0ePQqVSwdXVtcz7T0RERJVEUhQQHVw2ryTjPa4gkUiwZ88eo9VfHAsXLkSrVq2M2saGDRvyPWLyKqhbty5WrlxptPq7deuGadOmGa3+isrPzw8SiSTf4E1pqtDTCGfOnIlBgwahTp06iI6OxoIFCyCTyTBq1Cio1WpMmDAB06dPh42NDVQqFaZMmQIPDw907NgRANCnTx+4urpizJgxWLp0KWJjYzFv3jx4eXlxmiAREREVLCkK8G4PaDLKpj0Tc8DrAmBVvAW5YmNj8fXXX+PAgQN48OAB7O3t0apVK0ybNg09e/Z86e6MHz8eGzduNEjz9PSEj4/PS9dJr7Zu3bqhVatWRg0I8/j5+aF79+54/PhxpQisK3Swdf/+fYwaNQoJCQmws7ND586dce7cOdjZ2QEAVqxYAalUihEjRiA7Oxuenp5Yu3ateL5MJsP+/fvx4YcfwsPDA9WqVcO4ceOwePHi8rokIiIiqugyEvSB1vCfgeqNjNvWo1vAron6NosRbEVEROC1116DlZUVli1bhhYtWkCj0eDw4cPw8vLCzZs3S9Stvn37Yv369eJxVf4jtVarhUQigVRaqSaEVTiCIECr1RrsmVsVVOi7Ztu2bYiOjkZ2djbu37+Pbdu2oX79+mK+qakpvL29kZiYiPT0dOzatSvfs1h16tTBwYMHkZGRgYcPH+K7776rct9kIiIiegnVGwE1Wxn39ZLB3EcffQSJRIILFy5gxIgRaNSoEZo1a4bp06fj3LlzBmUfPXqEYcOGwdzcHA0bNsS+ffteWL9SqYSjo6P4sra2fql+FuTHH3+Ek5MTzM3N8cYbbyA5OVnMCwwMRO/evVG9enWo1Wp07doVly5dMjg/KSkJkyZNgoODA0xNTdG8eXPs37+/wLYePnyItm3bYtiwYeIeqvv27UPDhg1hamqK7t27Y+PGjQZTyfKmIu7btw+urq5QKpWIjIzE48ePMXbsWFhbW8Pc3Bz9+vVDeHi42FZB0yRXrlyJunXrisfjx4/H0KFD8d1336FGjRqwtbWFl5cXNBqNWCY+Ph6DBg2CmZkZXFxcsHnz5hd+pn5+fmjfvj2qVasGKysrvPbaa7h3755Bm0+bNm0aunXrZpCWm5uLyZMnQ61Wo3r16pg/fz4EQRDz165dK35uDg4OGDlypFi/v78/Vq1aBYlEAolEgoiICHGK3qFDh+Du7g6lUonTp0/jzp07GDJkCBwcHGBhYYF27drh2LFjBn3Jzs7G7Nmz4eTkBKVSiQYNGuDXX39FREQEunfvDgCwtraGRCLB+PHjAQA6nQ5LliyBi4sLzMzM0LJlS+zYscOg3oMHD6JRo0YwMzND9+7dERER8cLPtqQqdLBFRERERIYSExPh4+MDLy8vVKtWLV/+s1OrFi1ahDfeeANXr15F//79MXr0aCQmJhbahp+fH+zt7dG4cWN8+OGHSEhIKJW+3759G3/99Rf+/vtv+Pj44PLly/joo4/E/NTUVIwbNw6nT5/GuXPn0LBhQ/Tv3x+pqakA9L9Q9+vXD2fOnMEff/yBGzdu4Ntvv4VMJsvXVlRUFF5//XU0b94cO3bsgFKpxN27dzFy5EgMHToUV65cwaRJk/D555/nOzcjIwP//e9/8csvv+D69euwt7fH+PHjcfHiRezbtw8BAQEQBAH9+/c3CJSK4sSJE7hz5w5OnDiBjRs3YsOGDdiwYYOYP378eERFReHEiRPYsWMH1q5da7D+wLNyc3MxdOhQdO3aFVevXkVAQADef/99SCSSYvVr48aNkMvluHDhAlatWoXly5fjl19+AQBcvHgRH3/8MRYvXoywsDD4+PigS5cuAIBVq1bBw8MDEydORExMDGJiYgz2qJ0zZw6+/fZbhIaGws3NDWlpaejfvz98fX1x+fJl9O3bF4MGDRL30gWAsWPHYuvWrVi9ejVCQ0Px448/wsLCAk5OTti5cycA/VoNMTExWLVqFQD9PrmbNm3CDz/8gOvXr+OTTz7B22+/DX9/fwD6+2H48OEYNGgQgoOD8d5772HOnDnF+oxeikAvlJycLAAQkpOTy7srQlJGjlBn9n6hzuz9wu5L98u7O0RERJVWZmamcOPGDSEzM9Mw48FlQVig0n81tpdo6/z58wIAYdeuXS8sC0CYN2+eeJyWliYAEA4dOvTcc7Zu3Srs3btXuHr1qrB7926hadOmQrt27YTc3Nwi97EgCxYsEGQymXD//pPfXw4dOiRIpVIhJiamwHO0Wq1gaWkp/P3334IgCMLhw4cFqVQqhIWFFVh+/fr1glqtFm7evCk4OTkJH3/8saDT6cT82bNnC82bNzc45/PPPxcACI8fPxbrACAEBweLZW7duiUAEM6cOSOmPXr0SDAzMxP++usv8fpatmxpUPeKFSuEOnXqiMfjxo0T6tSpY/BZ/t///Z/w5ptvCoIgCGFhYQIA4cKFC2J+aGioAEBYsWJFgdeckJAgABD8/PwKzB83bpwwZMgQg7SpU6cKXbt2FY+7du0qNG3aNN9n1bRpU0EQBGHnzp2CSqUSUlJSCmyja9euwtSpUw3STpw4IQAQ9uzZU+A5T2vWrJmwZs0aQRCefAZHjx4tsGxevXnfL0EQhKysLMHc3Fw4e/asQdkJEyYIo0aNEgRBEObOnSu4uroa5M+ePTtfXXme+/NBKF5swJEtIiIiokpEeGpqV1G4ubmJ76tVqwaVSlXoSMlbb72FwYMHo0WLFhg6dCj279+PwMBA+Pn5FVh+8+bNsLCwEF+nTp16bt3Ozs6oVauWeOzh4QGdToewsDAA+i16Jk6ciIYNG0KtVkOlUiEtLU0c9QgODkbt2rXRqNHzp19mZmbi9ddfx/Dhw8WpbXnCwsLQrl07g/Lt27fPV4dCoTD43EJDQyGXy9GhQwcxzdbWFo0bN0ZoaOhz+1KQZs2aGYzE1ahRQ/x+5LXj7u4u5jdp0qTQhSBsbGwwfvx4eHp6YtCgQVi1ahViYmKK1ScA6Nixo8Fn5eHhgfDwcGi1WvTu3Rt16tRBvXr1MGbMGGzevBkZGUVbQKZt27YGx2lpaZg5cyaaNm0KKysrWFhYIDQ01OB7LJPJ0LVr1yL3/fbt28jIyEDv3r0N7sVNmzbhzp07APSf7dPfv7xrNDYGW0RERESVSMOGDSGRSIq8CIaJiYnBsUQigU6nK3J79erVQ/Xq1XH79u0C8wcPHozg4GDx9ewv18Uxbtw4BAcHY9WqVTh79iyCg4Nha2uLnJwcAICZmdkL61AqlejVqxf279+PBw8evFQ/zMzMij0NTyqV5guEC5piWNLvR0HWr1+PgIAAdOrUCX/++ScaNWokPrtX1H4VxtLSEpcuXcLWrVtRo0YNfPHFF2jZsmWRlkx/dqrrzJkzsXv3bnzzzTc4deoUgoOD0aJFi2J9j5+VlpYGADhw4IDBvXjjxo18z22VNQZbRERERJWIjY0NPD094e3tjfT09Hz5pb1n0P3795GQkIAaNWoUmG9paYkGDRqIr8J+WY6MjER0dLR4fO7cOUilUjRu3BgAcObMGXz88cfo378/mjVrBqVSiUePHonl3dzccP/+fdy6deu5bUilUvz+++9wd3dH9+7dDdpr3LgxLl68aFA+MDCw8A8AQNOmTZGbm4vz58+LaQkJCQgLCxP3brWzs0NsbKxBYBMcHPzCup/WpEkT5ObmIigoSEwLCwsr0ve0devWmDt3Ls6ePYvmzZtjy5YtYr+eHekqqF9PXxsA8Zm5vFE4uVyOXr16YenSpbh69SoiIiJw/PhxAPqRQK1WW6RrPHPmDMaPH49hw4ahRYsWcHR0NFiookWLFtDpdOKzVs9SKBQAYNDe0wuZPH0vNmjQQHx+rGnTprhw4UK+azQ2BltERERElYy3tze0Wi3at2+PnTt3Ijw8HKGhoVi9enWJpkalpaVh1qxZOHfuHCIiIuDr64shQ4agQYMG8PT0LHG/TU1NMW7cOFy5cgWnTp3Cxx9/jDfeeENcTbphw4b4/fffERoaivPnz2P06NEGwVvXrl3RpUsXjBgxAkePHsXdu3dx6NChfHuAyWQybN68GS1btkSPHj0QGxsLAJg0aRJu3ryJ2bNn49atW/jrr7/ExSkKG8lq2LAhhgwZgokTJ+L06dO4cuUK3n77bdSqVQtDhgwBoN9r6uHDh1i6dCnu3LkDb29vHDp0qFifT+PGjdG3b19MmjQJ58+fR1BQEN57771CA9i7d+9i7ty5CAgIwL1793DkyBGEh4ejadOmAIAePXrg4sWL2LRpE8LDw7FgwQKEhITkqycyMhLTp09HWFgYtm7dijVr1mDq1KkAgP3792P16tUIDg7GvXv3sGnTJuh0OjFIrlu3Ls6fP4+IiAg8evSo0JG6hg0bYteuXQgODsaVK1fwn//8x6B83bp1MW7cOLz77rvYs2cP7t69Cz8/P/z1118A9CuNSyQS7N+/Hw8fPkRaWhosLS0xc+ZMfPLJJ9i4cSPu3LmDS5cuYc2aNeKecR988AHCw8Mxa9YshIWFYcuWLQYLkxgLgy0iIiKigjy6BUQHG/f16PkjNIWpV68eLl26hO7du2PGjBlo3rw5evfuDV9fX6xbt+4lL1gfpFy9ehWDBw9Go0aNMGHCBLi7u+PUqVOlstdWgwYNMHz4cPTv3x99+vSBm5ubwR6pv/76Kx4/fow2bdpgzJgx+Pjjj2Fvb29Qx86dO9GuXTuMGjUKrq6u+PTTTwscVZHL5di6dSuaNWuGHj16ID4+Hi4uLtixYwd27doFNzc3rFu3TlyN8EXXt379eri7u2PgwIHw8PCAIAg4ePCgOC2wadOmWLt2Lby9vdGyZUtcuHABM2fOLPZntH79etSsWRNdu3bF8OHD8f777+f7DJ5mbm6OmzdvilsAvP/++/Dy8sKkSZMA6Deknj9/Pj799FO0a9cOqampGDt2bL56xo4di8zMTLRv3x5eXl6YOnUq3n//fQD6FS537dqFHj16oGnTpvjhhx/EzxbQTw2UyWRwdXWFnZ2dwcqCz1q+fDmsra3RqVMnDBo0CJ6enmjTpo1BmXXr1mHkyJH46KOP0KRJE0ycOFEcxa1VqxYWLVqEOXPmwMHBAZMnTwYAfPnll5g/fz6WLFmCpk2bom/fvjhw4ABcXFwA6J8X3LlzJ/bs2YOWLVvihx9+wDfffFPUb8tLkwjFfcqyCkpJSYFarUZycjJUKlW59iU5U4OWi44AAFa+2QpDW9d6wRlERERUkKysLNy9excuLi4wNTV9kpEUBXi3129sXBZMzAGvC8Xa1JhKz9dff40ffvgBUVFR5d0VqkCe+/MBxYsNuLsvERER0dOsnPTBT0bp7C31Qua2DLTK0Nq1a9GuXTvY2trizJkzWLZsmTg6QlTaGGwRERERPcvKiQHQKyo8PBxfffUVEhMT4ezsjBkzZmDu3Lnl3S16RTHYIiIiIqIqY8WKFVixYkV5d4OqCC6QQUREREREZAQMtoiIiKhK41phRPSs0vq5wGCLiIiIqqS8JbszMspo1UEiqjRycnIAQNzU+WXxmS0iIiKqkmQyGaysrBAfHw9Av19RYRvbElHVoNPp8PDhQ5ibm0MuL1m4xGCLiIiIqixHR0cAEAMuIiIAkEqlcHZ2LvEfYBhsERERUZUlkUhQo0YN2NvbQ6PRlHd3iKiCUCgUkEpL/sQVgy0iIiKq8mQyWYmfzSAiehYXyCAiIiIiIjKCShVsffvtt5BIJJg2bZqYlpWVBS8vL9ja2sLCwgIjRoxAXFycwXmRkZEYMGAAzM3NYW9vj1mzZiE3N7eMe09ERERERFVJpQm2AgMD8eOPP8LNzc0g/ZNPPsHff/+N7du3w9/fH9HR0Rg+fLiYr9VqMWDAAOTk5ODs2bPYuHEjNmzYgC+++KKsL4GIiIiIiKqQShFspaWlYfTo0fj5559hbW0tpicnJ+PXX3/F8uXL0aNHD7i7u2P9+vU4e/Yszp07BwA4cuQIbty4gT/++AOtWrVCv3798OWXX8Lb21tcP/9Z2dnZSElJMXgREREREREVR6UItry8vDBgwAD06tXLID0oKAgajcYgvUmTJnB2dkZAQAAAICAgAC1atICDg4NYxtPTEykpKbh+/XqB7S1ZsgRqtVp8OTk5GeGqiIiIiIjoVVbhg61t27bh0qVLWLJkSb682NhYKBQKWFlZGaQ7ODggNjZWLPN0oJWXn5dXkLlz5yI5OVl8RUVFlcKVEBERERFRVVKhl36PiorC1KlTcfToUZiampZZu0qlEkqlsszaIyIiIiKiV0+FHtkKCgpCfHw82rRpA7lcDrlcDn9/f6xevRpyuRwODg7IyclBUlKSwXlxcXHijvCOjo75VifMO84rQ0REREREVNoqdLDVs2dPXLt2DcHBweKrbdu2GD16tPjexMQEvr6+4jlhYWGIjIyEh4cHAMDDwwPXrl1DfHy8WObo0aNQqVRwdXUt82siIiIiIqKqoUJPI7S0tETz5s0N0qpVqwZbW1sxfcKECZg+fTpsbGygUqkwZcoUeHh4oGPHjgCAPn36wNXVFWPGjMHSpUsRGxuLefPmwcvLi1MFiYiIiIjIaCp0sFUUK1asgFQqxYgRI5CdnQ1PT0+sXbtWzJfJZNi/fz8+/PBDeHh4oFq1ahg3bhwWL15cjr0mIiIiIqJXnUQQBKG8O1HRpaSkQK1WIzk5GSqVqlz7kpypQctFRwAAK99shaGta5Vrf4iIiIiIqpLixAYV+pktIiIiIiKiyorBFhERERERkREYJdi6dOkSrl27Jh7v3bsXQ4cOxWeffYacnBxjNElERERERFShGCXYmjRpEm7dugUA+Oeff/DWW2/B3Nwc27dvx6effmqMJomIiIiIiCoUowRbt27dQqtWrQAA27dvR5cuXbBlyxZs2LABO3fuNEaTREREREREFYpRgi1BEKDT6QAAx44dQ//+/QEATk5OePTokTGaJCIiIiIiqlCMEmy1bdsWX331FX7//Xf4+/tjwIABAIC7d+/CwcHBGE0SERERERFVKEYJtlasWIFLly5h8uTJ+Pzzz9GgQQMAwI4dO9CpUydjNElERERERFShyI1RacuWLQ1WI8yzbNkyyOVGaZKIiIiIiKhCMcrIVr169ZCQkJAvPSsrC40aNTJGk0RERERERBWKUYKtiIgIaLXafOnZ2dm4f/++MZokIiIiIiKqUEp1Tt++ffvE94cPH4ZarRaPtVotfH194eLiUppNEhG90KO0bJgrZDBXcBozERERlZ1S/c1j6NChAACJRIJx48YZ5JmYmKBu3br43//+V5pNEhG90OwlS/FIp8KfiyfD1ERW3t0hIiKiKqJUg628vbVcXFwQGBiI6tWrl2b1REQv5VeT7wAAMxZEo++oKejdonY594iIiIiqAqM8s3X37l0GWkRU4fxP8QPgu6i8u0FERERVhNEeYPD19YWvry/i4+PFEa88v/32m7GaJSIqlK0mtry7QERERFWEUYKtRYsWYfHixWjbti1q1KgBiURijGaIiIiIiIgqLKMEWz/88AM2bNiAMWPGGKN6IqKXlpqlKe8uEBERURVhlGe2cnJy0KlTJ2NUTURUIl21AeXdBSIiIqoijBJsvffee9iyZUuJ61m3bh3c3NygUqmgUqng4eGBQ4cOiflZWVnw8vKCra0tLCwsMGLECMTFxRnUERkZiQEDBsDc3Bz29vaYNWsWcnNzS9w3IiIiIiKiwhhlGmFWVhZ++uknHDt2DG5ubjAxMTHIX758eZHqqV27Nr799ls0bNgQgiBg48aNGDJkCC5fvoxmzZrhk08+wYEDB7B9+3ao1WpMnjwZw4cPx5kzZwDoN1IeMGAAHB0dcfbsWcTExGDs2LEwMTHBN998U+rXTUSVQ3xqFuwtTcu7G0RERPSKkwiCIJR2pd27d39+gxIJjh8//tJ129jYYNmyZRg5ciTs7OywZcsWjBw5EgBw8+ZNNG3aFAEBAejYsSMOHTqEgQMHIjo6Gg4ODgD0z5PNnj0bDx8+hEKhKFKbKSkpUKvVSE5Ohkqleum+l4bkTA1aLjoCAFj5ZisMbV2rXPtDVCksVBscfpzjhdX8gwsRERG9hOLEBkYZ2Tpx4kSp16nVarF9+3akp6fDw8MDQUFB0Gg06NWrl1imSZMmcHZ2FoOtgIAAtGjRQgy0AMDT0xMffvghrl+/jtatWxfYVnZ2NrKzs8XjlJSUUr8eIiob8alZsH8mbbXCGwCDLSIiIjIuozyzVZquXbsGCwsLKJVKfPDBB9i9ezdcXV0RGxsLhUIBKysrg/IODg6IjdXvoxMbG2sQaOXl5+U9z5IlS6BWq8WXk5NT6V4UEZWZeUu+Le8uEBERURVllJGt7t27F7q3VnGmETZu3BjBwcFITk7Gjh07MG7cOPj7+5dGN59r7ty5mD59unickpLCgIuoElq4PQA/KVYUmBeVmAEnG/My7hERERFVJUYJtlq1amVwrNFoEBwcjJCQEIwbN65YdSkUCjRo0AAA4O7ujsDAQKxatQpvvvkmcnJykJSUZDC6FRcXB0dHRwCAo6MjLly4YFBf3mqFeWUKolQqoVQqi9XP8hCVmFHeXSCq0CaHvAE85+8+1/22wWn4u2XbISIiIqpSjBJsrVhR8F+SFy5ciLS0tBLVrdPpkJ2dDXd3d5iYmMDX1xcjRowAAISFhSEyMhIeHh4AAA8PD3z99deIj4+Hvb3+qY2jR49CpVLB1dW1RP2oCDI02vLuAlGFVl3y/OctkyNDy7AnREREVBWV6TNbb7/9Nn777bcil587dy5OnjyJiIgIXLt2DXPnzoWfnx9Gjx4NtVqNCRMmYPr06Thx4gSCgoLwzjvvwMPDAx07dgQA9OnTB66urhgzZgyuXLmCw4cPY968efDy8qoUI1dEVHqSHDoYHL+Z9BMEna6cekNERERVQZkGWwEBATA1LfreNvHx8Rg7diwaN26Mnj17IjAwEIcPH0bv3r0B6EfQBg4ciBEjRqBLly5wdHTErl27xPNlMhn2798PmUwGDw8PvP322xg7diwWL15c6tdGRBWbxeg/yrS9LI0Wa30uIYsj0ERERFWWUaYRDh8+3OBYEATExMTg4sWLmD9/fpHr+fXXXwvNNzU1hbe3N7y9vZ9bpk6dOjh48GCR2ySiV5SZFZL7rIT6yLQyae6s/2F8dG4UjuB39Ok7uEzaJCIioorFKMGWWm24gahUKkXjxo2xePFi9OnTxxhNEhEZSExJh81TxxKJBNl1upVZ+xapdwAA6pSwMmuTiIiIKhajBFvr1683RrVEREWmy0gwOJbJjPLjjoiIiOi5jPrbR1BQEEJD9St+NWvWDK1btzZmc0REBQqy7AH3Avb+23nhLkZ2rF+qbQmCgDO3ExD1MA3tS7VmIiIiqmyMEmzFx8fjrbfegp+fn7gHVlJSErp3745t27bBzs7OGM0SERXI1ERWYLr10SlAx9J9pvPAER+0OfsROksSS7VeIiIiqnyMshrhlClTkJqaiuvXryMxMRGJiYkICQlBSkoKPv74Y2M0SUT0XGmW9QpMb6oLL/W2bO/7oiYDLSIiIoKRRrZ8fHxw7NgxNG3aVExzdXWFt7c3F8ggojJXu7qqvLtAREREVZBRRrZ0Oh1MTEzypZuYmEDHTUSJqAysPRIivq/Ve0qZtHnsRhw0MJyyqNHyZx4REVFVZZRgq0ePHpg6dSqio6PFtAcPHuCTTz5Bz549jdEkEZGBL/4Z9eTAVP38gqUkLSMTrn92RJeoHwzStUn3jd42ERERVUxGCba+//57pKSkoG7duqhfvz7q168PFxcXpKSkYM2aNcZokoioQPFvHhDf21hWM8jLvz7hy9PlZBT4rFZqUkIBpYmIiKgqMMozW05OTrh06RKOHTuGmzdvAgCaNm2KXr16GaM5IqLn0po/Wf1UrrIH3j0M/OYJAMg2Mf6I18DsA4h++Ag17aobvS0iIiKqWEp1ZOv48eNwdXVFSkoKJBIJevfujSlTpmDKlClo164dmjVrhlOnTpVmk0RExePcUXybJLMtkyYzHnIqIRERUVVUqsHWypUrMXHiRKhU+Vf+UqvVmDRpEpYvX16aTRIRVUyjd4pvd+zYXI4dISIiovJSqsHWlStX0Ldv3+fm9+nTB0FBQaXZJBFRodRmivJpuOGTadNzdD+VTx+IiIioXJVqsBUXF1fgku955HI5Hj58WJpNEhEVyrx6nTJpRxDKpBkiIiKqREo12KpVqxZCQkKem3/16lXUqFGjNJskIiqc9Pk/5szS7+Ong+dLpZmoe3fypd3s+Zv4XmA0RkREVOWUarDVv39/zJ8/H1lZWfnyMjMzsWDBAgwcOLA0myQiKr6Z4QCAxtL7GHr+jVKpUp6dJL7PlumXmNfJzcU0gRu6ExERVTmluvT7vHnzsGvXLjRq1AiTJ09G48aNAQA3b96Et7c3tFotPv/889Jskoio+CzsES2xR00hHvaSpFKt+lG7maj++nulWicRERFVTqUabDk4OODs2bP48MMPMXfuXHHajEQigaenJ7y9veHg4FCaTRIRVQiHjvigCYD0+gNQXaWfLv30xMHUrFyoq8nKpW9ERERUPkp9U+M6derg4MGDePz4MW7fvg1BENCwYUNYW1uXdlNERAV6mJwBuxcXK1WfaH/Nl5aZkSa+P+Lvh//r71mWXSIiIqJyVqrPbD3N2toa7dq1Q/v27V860FqyZAnatWsHS0tL2NvbY+jQoQgLCzMok5WVBS8vL9ja2sLCwgIjRoxAXFycQZnIyEgMGDAA5ubmsLe3x6xZs5Cbm/vS10ZEFVeuVoeNh/Sbp8e6ffTccroyeIZKps0W379+fpLR2yMiIqKKxWjBVmnw9/eHl5cXzp07h6NHj0Kj0aBPnz5IT08Xy3zyySf4+++/sX37dvj7+yM6OhrDhw8X87VaLQYMGICcnBycPXsWGzduxIYNG/DFF1+UxyURkZHtPHYKM2/qF71QtBn13HK1JY+M3hc7lan43lHyGGnZ/CMPERFRVVKhgy0fHx+MHz8ezZo1Q8uWLbFhwwZERkaKGyMnJyfj119/xfLly9GjRw+4u7tj/fr1OHv2LM6dOwcAOHLkCG7cuIE//vgDrVq1Qr9+/fDll1/C29sbOTk55Xl5RGQEzg8OiO91ps8fVc/utxwAkCi1LdX2He2fTGCs3W4I0jvOEI8PbM8/1ZCIiIheXRU62HpWcnIyAMDGxgYAEBQUBI1Gg169eollmjRpAmdnZwQEBAAAAgIC0KJFC4OFOTw9PZGSkoLr168X2E52djZSUlIMXkRUOShkEvG9RPL8csoOE3DetDNsdAnwCb5bojZ1OgEJgiVCaoyA0sbpSYbMBOa95oiHdR6dLFE7REREVLlUmmBLp9Nh2rRpeO2119C8eXMAQGxsLBQKBaysrAzKOjg4IDY2Vizz7AqIecd5ZZ61ZMkSqNVq8eXk5FRgufIjQI20FxcjqoISpU9Gs2zV6kLL2mqiAQDmfgtL1Oba3zfDVpKKVFPHfHkSuQJX7fT7C8oEjqYTERFVJZUm2PLy8kJISAi2bdtm9Lbmzp2L5ORk8RUVFWX0Notjjnwrrpi+DwjcJJXoWdVu7X1yYKoqtKz0339D5trUErU5+a5XofnpJjYlqp+IiIgqp1Jf+t0YJk+ejP379+PkyZOoXbu2mO7o6IicnBwkJSUZjG7FxcXB0dFRLHPhwgWD+vJWK8wr8yylUgmlUlnKV1F63pD5AQAkBrv4EBEAdJLdKHJZmVQClOLfLB4lccSZiIiInqjQI1uCIGDy5MnYvXs3jh8/DhcXF4N8d3d3mJiYwNfXV0wLCwtDZGQkPDw8AAAeHh64du0a4uPjxTJHjx6FSqWCq6tr2VwIEVVISaalO0W4naLgZ7/sVeYAADMTbmpMRERUlVTokS0vLy9s2bIFe/fuhaWlpfiMlVqthpmZGdRqNSZMmIDp06fDxsYGKpUKU6ZMgYeHBzp27AgA6NOnD1xdXTFmzBgsXboUsbGxmDdvHry8vCr06BURGZ9GqijV+hzHbyowvd6gT4GbPyDbwb1U2yMiIqKKrUKPbK1btw7Jycno1q0batSoIb7+/PNPscyKFSswcOBAjBgxAl26dIGjoyN27dol5stkMuzfvx8ymQweHh54++23MXbsWCxevLg8LomIXiE5uTr8708fpAmmONdoJmD2nKXm/00/GHwXGi2ftSQiIqoqKvTIliC8+JkkU1NTeHt7w9vb+7ll6tSpg4MHD5Zm14iogsupVhOlO26VX3TUHcwIfRMoZIn5p8032YxfdvXBe/831Kj9IiIiooqhQo9sEREVV3C115AusYBiyrkXlpX+GyRl5OS+VFtSTbr4Xqst2oI1rSM3vFRbREREVPkw2CKiV4oOUtxRNgVMC99jCwCkFvYAgC7Z/iVu1zwxpEjl5NIiDoMRERFRpcdgi4heGYIgIC4lC7oiTEEGgFp1G5WovfCoGPF9qxGzi3SOYNe0RG0SERFR5cFgi4heGTciYtBPFgjLzAdFKm/XY3KJ2jO/d1x8L6ldtJUGY3LMStQmERERVR4MtojolXHj4gkAQH1pzAtK/ktmAgBIE0xfapXAIg6gGWh177fin0RERESVEoMtInplOCecKvY558y6wkKShR93HCr2udrkIgZ1T6khSSz2OURERFQ5MdgiolfCo7Rs/HM/ttjnmeUkAABej1hV7HPNtclFKieRSJDe8h3xuCjbWhAREVHlx2CrEkoVzAEA6uzocu4JUcVxN/QSRsn10whz5eZFPs8+V//vSGZuVew2TbIeAQByRu8pvKBEgmrDVoqHn636udhtERERUeXDYKsSioEtAMBEm1nOPSGqOEwznkzpk08JLPJ5edP6ciydi9Ve5KN0WGoScF3VGYqG3Yt17pKkWcUqT0RERJUTgy0ievWoaxf7lEe5ZsjSaItcfv+qj+AijUOqmVORz7nXvGSrHxIREVHlwmCLiAhAj6jv8f3aoj+39ZFsDwDArJplkc8xbTemuN0iIiKiSozBFhERALlEh5mPFxX7vOxiTD8UpPJi109ERESVF4MtIqJ/ZQhKo9ZvbsJgi4iIqCphsEVEVVrG00uyQ2LUtlR2taGV6AMurY7LvxMREb3qGGwRUZVmPnRFic6XFic+k8kR2GgGAGDxL3+WqF0iIiKq+BhsUYWTnKFBenZueXeDKhGNVod1J8Je7mTJk2hJiZwinXL66pO21NVrvlSzi6InvdR5REREVHkw2KJycynyMW7Hp+VL91x5Ei0XHSn0XK1OgPeJ20jJ0hire1SJpKSkYC2+LXE9comuaOVC/hLf55pYFKsNUwt1scoTERFR5cVgi4wmV6vD6fBHBebpdAKGrz2LXsv98+XFpmQhVycgOdMwkErPzoUg6J9zuRT5GMsOh6Htl8dKv+NU6Ug06eXdhSLTykzLuwtERERURhhskdEsP3oLb/96HpEJGQbpZ28/gv+th8897z3ZAbwhOwGN9skoQ0qWBs0WHMa3h24CALw2X0IfaSC0Wo5skaFsefFGmp71MCWrlHpSsdoiIiKislfhg62TJ09i0KBBqFmzJiQSCfbs2WOQLwgCvvjiC9SoUQNmZmbo1asXwsPDDcokJiZi9OjRUKlUsLKywoQJE5CWln/6GpWu6KRMAECGJhffHw9H3TkHEJOcif/8ch7vbAjEeJkP+kgDcSnyMXQ6AfcfZyAlS4N5Jpux1ORnTN9yHn8GRuJ2fBqmbQsGAPx48h8IggCbtHD8pFiBvxXzyvEKqSJSvPV78U/65Ib4Nj36RiEF9eQmLz861bCO05O2Hlx/6XqIiIio4qvwm76kp6ejZcuWePfddzF8+PB8+UuXLsXq1auxceNGuLi4YP78+fD09MSNGzdgaqr/hWj06NGIiYnB0aNHodFo8M477+D999/Hli1byvpyqgyNVgfNv0tb9115Skw/ezsBACCFDgtNNgEA6q5tZ3BuxL+/x17/5z5O/pMqps+Sb8MhbXv8evouvjL5DQDgKr1ntGugyudeo/Go49yx+Ceqa4lvr4XdRt0mbQotrntqc2JzhaxYTama9QG25x1x+XciIqJXWYUf2erXrx+++uorDBs2LF+eIAhYuXIl5s2bhyFDhsDNzQ2bNm1CdHS0OAIWGhoKHx8f/PLLL+jQoQM6d+6MNWvWYNu2bYiOji7jq6kaQmNS0PDzQzh19TYmyvZDgifTAWdsv4Lakni0ktx+YT39ZBdQX/IAADBRth9e8n3YpViAE2HxaCu9JZZ7lJZd4FeqOvIWWnls1wFQmL9UHRqJAgBg9/Bckc9JdeqOOk3avlR7RERE9Oqr8MFWYe7evYvY2Fj06tVLTFOr1ejQoQMCAgIAAAEBAbCyskLbtk9+IerVqxekUinOnz9fYL3Z2dlISUkxeFHRLdinnxo1R74Fn5tsQT1JDOTIxRjZEUigw2nlNOxSLhTLvy07iupIxhDpaUyQHRTTvzJZjwOKz7BTsQCfm+hHIRUSLVT/HDRor+1Xx7D5/D2Dr39djBIX06BX37GtJdsrCwCko/UrDGakp76gJBARmwgAsJywBzCzKnHbRERE9Gqq8NMICxMbGwsAcHBwMEh3cHAQ82JjY2Fvb2+QL5fLYWNjI5Z51pIlS7Bo0SIj9LhqSM3S75FlJtHvWeQgeQxf5SwAQKxgk6/8Vybr8ZXJ+gLrMpVo4C4xfAavv8wwSDZFNs7cfoR6kmj8ddEK7SWhmL1DhytRSfh6WAuxXK5Whyv3k+Fex7rQ/l+MSEQbZ2tIi7Vb7avtn4dpsDQ1gZ2l0iBdqxOw/GgYxnnUhb2q/FbZ+8xka4nrkDXojodSO1jnFD7inZqRiTceri5RW9lyCyhz+dwoERHRq65Sj2wZy9y5c5GcnCy+oqKiyrtLlYZWJyA0JgW1JQ9RU6J/PmuL4hsx/2fF8hK3MUhmOM3rpuk7UN/YjOPKmegcvQF/Kb/EDPl2bD9/B98cDEWWRosZf13BpN+DMGLdWSQUMs3wXkI6Rv4QAO8T+ac5Pk4v2oa3r6IPlv+BcSt350uPSkhHzslVWPF3waPEZU0uK1mAHKuo88Iym3f/XaI2AOBez58AAGGxLx5FIyIiosqrUgdbjo6OAIC4uDiD9Li4ODHP0dER8fHxBvm5ublITEwUyzxLqVRCpVIZvOjFTt56iPqfHUQfaSBOK6eig/RmmbU9QKoPwDxlgQCAyfK9WG+yFJtOhuLuo3Tsv/QPwsKuY458KwLvPn/vr4m/ncU8+e9Iio/E/ccZGPvbBSSm5+D74+Fo/eVRhMel4l5C5dnTqbQcUc7Gn7nTAOgXP7n/WL+cvzw5Ap+bbMGQ2O/LsXdPONeq9eJChdBBgowcbaFlxt/6qERtAIBOrh8hdD4xBTodp7sSERG9qip1sOXi4gJHR0f4+vqKaSkpKTh//jw8PDwAAB4eHkhKSkJQUJBY5vjx49DpdOjQoUOZ97k0pApmAAB1dkw59+SJ2/FpGPvbBQDARPmBMm+/s0z/nJib9K6Y9prsOk4pp0GW8RBhpuNxWjkVH8j/xsLNvrgc+RgA4BMSA/9bD5GTq4NGp4Nl4jW8Jz+E+tfXoPN/T+DkrYcY4n0at2OT0EZyC71XnETXZX44diOuwH5Udtm5WoQ8SC4wz1KSia/230CTz/fjjf9uN8hT6Mpvv6in+6tyKdliFZqcbLymCcBfJ688t4yppPT2dmsqjYJGwwVdiIiIXlUVPthKS0tDcHAwgoODAegXxQgODkZkZCQkEgmmTZuGr776Cvv27cO1a9cwduxY1KxZE0OHDgUANG3aFH379sXEiRNx4cIFnDlzBpMnT8Zbb72FmjVrlt+FlUC0YAsAUOgyy7knT4z99ck0MhtUnKlRdpJkfP7HcYO0KfI9GLb2LCITMvDBH0EY99t5vPbf47ifkIqdSv2zegqJFoOlZxBh+h9UexyGkfGrsUu5EEOlp/GO7BCiHhkGJFGJGZi76xoycnKRlp37wn4JgoDkzPLbkLmgtlOzNJi5LRCffv8HsnO1SM7UIDlDg7N3nowEZgX8hDumY3DW9OOy7G6hNq79CgBwruH0l16JME8D7R0AQHffQcjJ1b2gdCkRyqgdIiIiKnMVfoGMixcvonv37uLx9OnTAQDjxo3Dhg0b8OmnnyI9PR3vv/8+kpKS0LlzZ/j4+Ih7bAHA5s2bMXnyZPTs2RNSqRQjRozA6tUle8CdDEUnZ8FZEodp8p2oLy3iiFu794DAXwzTzKyBTP2oU6pgBktJyQPKjKwc4Kl1HUbLfaGUaLA1oDa+M/kRCmiwNb0H+q1Iw61/b5uRspMYKTsJANipWIAjCW0BGbBSsRYAMO+wHBkdv4O5Qv9PaMXRWzh3+Qou3XuMrFwt/Gc9uWd1OgExKVmoZWUmpn13JAzeJ+7gzjf9IXuJhTjuP85ALSszSCTFPzdLo0XnRXvwgWdreHVvAEA/NbDTwj1YYvIzBirP49Md7XEwOAJ17VTAo1vY/+/n9/RCJqt9w2GSHI0Pi92D0rXMRP/8k61V4QufFIWVRD9F1E6SjNT0JCjU+Rd0ISIiIiqqCh9sdevWrdAlvCUSCRYvXozFixc/t4yNjQ03MDaixPQcuEhicEI5o2gnjN4BxF4D2r8P2DYAfOY8yavbGQjVL0BQGoEWABxQfpYvbaTsJPqfOY3ZSn1ANVgWgEu6BgWeX02SDXMYTvWqL4nGqmPhaO1sjW6N7dA4ciuWm/6A9x7OgBxaJKR1gq2FPkJZsO86fj93D042ZpjwmgvGetTFzQtH8V/5MSRn9EJSpgb17Cyw8tgtRCdlYunIlmI7dx+lo7qFApamJmJaVGIGPJcewqjOTTF/oOsLrz9Xq4Nc9mQQOzszHddM38O3x97C64FvwlKuQ8f6drhm+p5YplXIN1hqehzByfXQSvlPgfXe9N0EJ8lDwAQQKsDmvNb1Whm9DUEQUBprVDZuUPC9RkRERK+WCj+NkCo+7xO38btiSeGF3vj9yfuGvYHXpwNKC6Djh8D/bXySN2Qt8OYfT469AoEJx0q3w/9aY7LG4LiN9PkbLXvKLhocvyM/jH0nA/HBH0FoMt8H9inXAAC/KP6HHxQr4f6Vvs8arQ5BgWcwS74NTZJO4eu/r+K/h2/ia+1KvCn3w9s/nUKv/50AAFw5/heEy3/gRFg8UrP00/wGfncIQ1b74+A1/WhhapYGQ5fuwQ3Td2EetA6f79ZPXQSAlCwN5u8JgfeJ24h4lI5Ff1/HvivRaPz5fgRG6PeFuhiRiGGr9e3NMdmGA+n/we7kt9Ao8AuD6/uPXD/1spW04EALANYqVmPuv0uux6Vko97cAzhzu+DFR8qCda3GJa7jgetE8f26/QHQPrN4RVz8k+0iYobnX52xqCTWdeHXZAEA4Nzthy9dDxEREVVsDLYqMRNtxXhm69fT/6C2pJBfshckAa6Dn5/fZCDQbynw6V3AVAU0HaRPr2YH2DUCnNoB7Sfp02q5A/PiAZlCPD3arjMw4xZgZgMoi75yZJGnOz5HgOkURJj+B/8n88Mw2RmDvN5SfXDWatERrJGtgJd8H35WLEe46VgEnvSBHPpgakDiRmxTfIltFyKxXrEMy0x+wmfrffDmutP4/dw9+Cpn4uPU/2Hn1p/RYs52tFzogyBT/cS9N3UHYXZxHebtuoKMnFy4LTwMq8AVeHRsJYZ9tw84tw5b/tyMMOU4rN7wO3Q6AUt//A1/aiaL/VRJMqGUaPCW3K9En0V/2QX4mMzC+e3flaiel3FT1ggAIFM5vKDkizkMnCe+/zT8P0hKSjTI9wsKEd9rzQ337yuumrn3AQD3ts3IF9QRERHRq6HCTyOk/DT/ftvU2YVvvlpWCl0QQyID8p4rajIQuLk/fxmZHOgwyTBt2E9A7adWluu/FPD85kn5aSFAWizwYxfUdKwJWDoA064Bmkzg2l/A4fxTB40l75mhp/2sWI4v9o5AXc0d1FcaBnWfyHdAEABIAC/5PgDA3N0+eOvf56ICTKdgT2In7NvXCWMUjzFUdhZDZWehEWQwkTxZlry25BHmmWzGTyFJmJD0ASJMR4t5c+TboHxq1bwfhK/R7qumCFJ+WYpXbqiR9AGGZu6GVrdEfA5NEARsOBuBfs1rwFFd8KbHWp3wUs+tAUBschZ0mmxcs/RAixcXfyG5mdowQfPkDxqXIh/jrQsjS6EVvXp9PgBu/4qx8qO4FhmHFnUL3oqCiIiIKi+ObFVCugr0bcvO1WKm/K/nF5jx1F5b/7dBPwJVFC3fBGzrG6bJ5PoXoA+uLP4dyXDuqP+qtAAs7AAPL2BhMvDuYaC64dSyyDojgeZF/4U5Q92wyGWftSsgtMDnxV6XhcBOYriaoa9ylsHxUNlZ/KYwHCV6OtB62vvyAxj14BuDNOUzy5NXk2Rje+6UIvf9ZdWTxqLBZ/vhGxqnX5kxS4OHB5dg0e+HkJ2rxbl/EgzK77l0HwO/3Y0sTeF7WxUkPC4Vc/77HVyl9yBNLaU/PDyz4EiWRr9SYPQ/NyD5pVfptPEvqY2L+P7GL5OQnFF+q1MSERGRcVSc39qp2CrCZqhtFh8Vn+8RNRmo/1rLHbB4aqqVzEQfJJUWS0dgRhjQbkLB+c4dgckXDJLkDo2Akb8CH1/OX96uCfDM8gfKHp++dPdCnlpwwtgGywJeWKaeNPaFZV5EJ7x4BOqu6dvY/PtPcPviAEIjovGpyZ/4+PE3cJ+3C5t+WSk+PwYAKUeX4FDOO8hMSwEA+ITEYlNAxAuXXc/O1eLLVWuwQbEMANBMeq8EV2XowZA/xfePHuif4zsRcg+tC3mm72VI5U8WPXlT7oela9eWav1ERERU/hhsVWJJ5bhPU570HMMRiQTLJsAbm4BJp4Axe4zfAcsiTL1qM058W9Pl39X7bOrppyo+7T1f4PNYoEFvMUlW/ZnRtfkJyKzm9LK9rRQEde1ilQ+06JYv7TfFd7htOhbfbtIvIiHXpCPE9D2sVazGsh9/E8t1ytEHiUJuFjKycxCz7WPUOTgGP/no920r6A8K3x+5jj7zN2CT4r/F6mdR5Zo9+QOBTJOG+KR0DA56xyhtZb42W3z/ddoXhZQkIiKiyojBFr20v69Eo7nEcLU6G7f+gFQG1HDTL3ZREQxcAYzaBji6ATVbP0lv+Sbw0Xn9lMMFSfppiCam+rKjdwBj9+pH58bue3KOTI7b7Z+/zUBllDBsq2HC1Gu40WahQZKg1geYOhMzPCtXk/Pcuncp9fU0lD4Q0/5SfonZO67iB/87kGvSAADj1hyAz+W7eEd+GF1lV9Hk/Fys87uD3gv+wIK9IeL0wyyNFm1OvQd/5fRiXuXL+fOQL+4t71bgNgQO9iVbIAMAzHqX3bOFREREVPa4QAa9tKjHGdivnGeQJpFVwFtKKgMa99O/nmXfRP/16Wd1ZHL98vR56nXVr5SYFgcAUJmZ4FmCqRUkWUml2On8Uq1dYfn4Rv6MRv2AW4eKVVd2jXZQxgQCAHTVmzzJ6LkAEokEro0aA5cA2DcD3jkAiakV8M8JyFNigL0fGdTlbJICZAO59s0hjw9BUbxz9T+wkGSitlS/iuXf0hn4bX9f8SdSL9llOB8fhA9lD5B9yQSrLgzDrYGfY+O+I/BVFvAZlKLaNWuK77802VBwoVn/wKSabam0l6huBpvk66VSFxEREVUsHNmilyIIAmLuhubPUFqUfWfKgrkNYN8UAFDHxlyf1uVToJ/+mSHJ2L2G5Vv8n355+sHfAx+dA+Y/AhQWQI/5wCc3AOWTVe+Ep0fbCmE54DkrCf5nG3KbPLO0fvfPgdn3gCHeBZ6S4Omtn+75ni/sVPrr0TQdqt//TCIBmvQHJhwFPjwDmFnr0+r3AFqPBj6LBqZcgmCiP69mUw8AgHzMLgiqWkW6libSqHzbBbwr9zE4bvTvaJhSosGnJn+h7aHB+RYSyZNVt3uR2i0KmcoRsW/5FF6olAItAAirObTU6iIiIqKKpQIOQ1BFl6vVocHnh/ChbD+QN8gjNwUGrQKajyjXvpWJWm0A1yFAm7GAlZN+OqLpU0uGD1kLtBwFDFhuOJXysydT6TA3EngcASTehaRGSyDiFPDXWKD128Bl/abOKQpHqAYsAnb/uyx+g17A1KvAKjf98fiDwOO7AAD54FXATf10x0S5PWy6/ruwR+u3AZeuQGYi8GMXw+twHfLk/bCfYPLsyJ9T+4KvX1ENsK0PybQQ4M5xSJqPANzH6xc/mXoV+LL0AhGD7j5nEQzda9Nh2m12gXkvS17NplTrK4xUePLcY3auFkq5rMzaJiIiIuPiyBYV281Y/b5aakn6k8TJF4GWb+lXHHzVmVnrR4Ws/l0oIy/QmhEGTDyhH/2RSl/8zJp1XaB+d/2omesQ/WbNg78Xsy3dBug/0wlH9e0BgHUdCEpLCK5Dgbqv6YMpQF/HNP0UPqs6bobtWDkBNVoCb2xCpFUH/SWYPPNPv+WbxX/Grpot4PZ/+mt1bA4AkEhlgMdkwLaBPhg1MsFzCaS9F+iftStF1e1rIUtdD7reT40mfnIdWSbWSGj1Yam29fQeY2//d0up1k1ERETliyNbVGyD15xEuHKcuO+TTmkFqWWNcu5VBWDpWLTVEZ9HrjQ4lHh+rX/zzAiTZOpVQFlAYGTlBIzdB2kt94Lrdx0Cp4Q7gO95WFtZv3w/CyORAJ5f618AcGmTcdrJa87joxcXehlKC5h+8u/2AEfn67+qa8N0ZghMFdVKtalWzZsD/z6Gtl0zGRk5o2Cu4I9mIiKiVwFHtqhYNFod/k/mb7DBrrRW6yebDVOpECxrAAWs/AdAP4r1vM+7XtdCn5uTdP4EmHYNqFa9FHpZBJNOQhi5Pn/6jDAIIzcUuZob1j1Kr0/FlFLzdaR2mKE/UFrk2/i4pOSuAw2Ow//55zkliYiIqLLhb8hULG0WH8U1k58NE1uMLJ/OvKqmhUDyvECrpCQSwMrZOHUXpEZLSBzdgPsXAbc3IEScgnDzIKSWjpA0H6ZfDVLtBARtANpPhHaJE2TabPH0TAtnmKVFwsXtdcD/+PPbMSLVe/v0UyWNKPHtI7D5ow8A4OdNm/D9N18ZtT0iIiIqGwy2qFhG5D61KAYAqJ2BFm+UW39eSVav2KbJEgnQ9xv925qtIPGY/CTv3xUe0UmfJpscqF84JOMRhIizMOvkBZhawUxRDULiDUg6foicoD+guPQbdL0Wl83QvJEDLQCwrtVIfP+9Yg2uRk6Dm3MZjT4SERGR0TDYomJZaPLMMzgfX6oai2JQ6SlsGp51Hf0L0K9y+PRpI34BAChqtAbajIa0dlujdbGsScyscavVZ2gUrA9K1/zgjXvVu2LC6/XwZjtn5Gp1kMv0Qd/T74mIiKhi4//YlZhVbmKZtJOZo8XZO4/gFxYvpiWrmwJz7zPQorInlQKvUKCVx9rpyebSPyuW40jKEGDvFCz1uQm3RYchCAKuRyejzeLDCPt3RVAiIiKq2BhsVWIWuuQyaefL3/fj7G9zsGDD32Ka2sYeUFqWSftEVYGda1cIz0zJfVPuh+qnF+CE5AME3HmEu9cvwA/v4W5oUDn1koiIiIqD0wgrMQGluypaQbQ6AaPufYEWJhGYie1PMob//PyTiKj4zKwgGfEzcO0vg+R35T4AgHG/7oC79BYGmqQhK+ICHqd3QoZGC0eVfo8xmVQCQRAgCIBUavyfDURERPRiDLYqsfTsXKPWn5Orw88Lx8FLHpE/09LBqG0TkSEf5RzxfeydK+j+5S6YIxuPYQEdpPjjg67YezYEKSnJWP3BIGTk5ObbrysvTRAEZGq03M+LiIjIyKrU/7Te3t5YtmwZYmNj0bJlS6xZswbt27d/8YkVlKVgvOc2wmJS8MePS/ClfF/+zDeMu1EtUZX2jg8glQH2rsCSWgUW+UC+Hx/I9xukbf+lC76SnwQAuM9Zh96yIJzUtcSvHw+FhVKO3f4XkBS0A00GfIyom0GQ3zmC5m8sQONatrjyzwMMaNcEsUmZCLkXjZzsbHRv1RiZGi1sqikQfOsfXDl9EGPGe+UbNYtPyUDI1cvo1L4DTEsYvAVfuYy7gQcwdMLnkEgkiE/JgL3KvER1llRmVg5ytbmwrFa+/aiMEh7Fwdq6OqQymUF6YsIjqK1skJmZjqCfvZBdox3a9x+HSyd2w63LYAASBPlsQmqWBqqaDSGRyfF6l95ISErGvbBgdHq9V/lcEBHRS5AIgiCUdyfKwp9//omxY8fihx9+QIcOHbBy5Ups374dYWFhsLe3L/TclJQUqNVqJCcnQ6VSlVGPC5acqcGfX4/D+/IDAIDbo05DJwBmpmao5VwfgiAg8l446ro8WUpa8tTqbzqdIP6yJAgCJBL91CMAuHbjOjSpjxDtuxaDNIcL7sDki0D1hka6OiJ6mu6v8ZDe2G3UNh4KKthJUrAttxvcpHfhKr0n5qUJptiu7Yp35PqfB1vbbYdvUCiGaA7ijr0nWsXvQTfZlXx1bsvtBrUkHdFCdfSVXcAGs7Hw6D4YweeOQ2Jug9zHUZiVsRz3dPa4pHCHsttMPPD/FRM1WwAAW6wm4T9JPwIA/nb/DVozW2hib8LswRlUy4yBTiKDxkQFmUyKHC1gkp2IXItakDm3h8zGGZnX9sMxKQjxHeejb99BuB14CPFnNsN20GLUdLBDZPg13Lt+Hn3/MxXh4WFQq1S4cno/cO8MZLVao2HnEYi89w8eh5+DY9RB2Okeoe6skzgXFISky3vRZMhMZD24hoxqToi9uBdtBk9ByKUzyAr3gzI3BaaNesCyZiPY2DkiK0eL64d/gWO7oajp4IA6tWsD0E/7DAu9htCQIChTo1DdpQWad+qPf25dh8WOt3C/x0qk3LsGrcwU9dp6IjMjHRHndsG2yetIC/FBjXZDkPPwH7QJmo3gfnsBbQ7qHHkXN5VuUHb/FI/P/Aq718ZA6zMfj62bo/sHq/DgQSRcXBogMvIudIKAenXrITjwNOo1dsP1gEOo2aQt6tSpb/D/gkQigVYnQCox/L/kaekZmQgNPovWHXtAAuBW6BXERd5E1/OTcEtXG4kmDrAYtATxwT6wbzcMzbe/XqJ79hezd/Fe5m+4oGuMm87/QeOO/dCknguu3gzD621aIDktA6Hh4WjX0g0S6KfVRsXEITU1CU0bNoJEIjH4v/B5nv4MXlTuRWWMobzaNTZBp4OkDLbcICqJ4sQGVSbY6tChA9q1a4fvv/8eAKDT6eDk5IQpU6Zgzpw5BmWzs7ORnf1kY9Xk5GQ4OzsjKiqqQgRbu777QPzlpyiyBDkkAJSSEkw7fHMLIOiA+t1evg4iKh5tLpAWB9wPBE4uA7p/DtRyB8KPAofnvPh8olJ0UeqGJtowWEiykSKY4SGsUV8SXd7demmPhWpIgiVcJLG4ixqwQjriYIOExm/D7PbfaKN98oeEbEGOQLuRkNvVh+T+BdTU3MODOsPR4uZyVJPk4KKpB9pmBQAAIlAL1YR0mCAHN216QmrvKtaj0+mguH8KWbbNIdFkwOpREBS6TDy2aAhdzTZPymWlwvLBKTTLvY4Y2CGq4Rj9iPe/JNGX0S71GB7ADtGNxxlcly7pAZTJ/0AmaKDMTUOiYydILez050F4pmw0TFLuItfptcK35SgJnQBl1ClkWjWAVF2z8KJJD9AxbguCzDvDPCcBWokJ0p1eh9TETJ+flQrz2EBoJSawyYpCTI3ukKpqANAHacqo08hS1YNZ8m1kOrSG1ExtWH9yNNSPLkMnkSG1dldIFWYQcnNhev80Mu1aQGpuC0XkKWRZ1Yfs33qfJb9/FgpNCjLMnQBdjj54d2xd5I9DSLgNeXYitDX1M6sErQbV/9mDXKkSSQ4dYJpwA1m1X4dE9u9MAQkgJEZAmX4fmtqvGdaV9+x+Qjik2cnQ1SydlXqF5CgoUiKgqd35pe4LITcH5g9OIcOuNaTm1pA+CIRdejhibdoC1RvnK//sfQnoPxfT+6eRWd0N0mq2AACt1ASNer0La8vyn2mQkpICJycnJCUlQa1WF1q2SgRbOTk5MDc3x44dOzB06FAxfdy4cUhKSsLevXsNyi9cuBCLFi0q414SEREREVFlERUVhdr/zlh4nirxzNajR4+g1Wrh4GC4qIODgwNu3ryZr/zcuXMxffp08Vin0yExMRG2trYVYsg+L5quCCNtRC/C+5UqE96vVJnwfqXK5FW6XwVBQGpqKmrWLHy0FqgiwVZxKZVKKJVKgzQrK6vy6UwhVCpVpb9Zqerg/UqVCe9Xqkx4v1Jl8qrcry+aPpinSjyBWL16dchkMsTFxRmkx8XFwdHRsZx6RUREREREr7IqEWwpFAq4u7vD19dXTNPpdPD19YWHh0c59oyIiIiIiF5VVWYa4fTp0zFu3Di0bdsW7du3x8qVK5Geno533nmnvLtWbEqlEgsWLMg31ZGoIuL9SpUJ71eqTHi/UmVSVe/XKrEaYZ7vv/9e3NS4VatWWL16NTp06FDe3SIiIiIioldQlQq2iIiIiIiIykqVeGaLiIiIiIiorDHYIiIiIiIiMgIGW0REREREREbAYIuIiIiIiMgIGGxVMt7e3qhbty5MTU3RoUMHXLhwoby7RFXAyZMnMWjQINSsWRMSiQR79uwxyBcEAV988QVq1KgBMzMz9OrVC+Hh4QZlEhMTMXr0aKhUKlhZWWHChAlIS0szKHP16lW8/vrrMDU1hZOTE5YuXWrsS6NXzJIlS9CuXTtYWlrC3t4eQ4cORVhYmEGZrKwseHl5wdbWFhYWFhgxYkS+Te8jIyMxYMAAmJubw97eHrNmzUJubq5BGT8/P7Rp0wZKpRINGjTAhg0bjH159IpZt24d3NzcoFKpoFKp4OHhgUOHDon5vFepIvv2228hkUgwbdo0MY33bAEEqjS2bdsmKBQK4bfffhOuX78uTJw4UbCyshLi4uLKu2v0ijt48KDw+eefC7t27RIACLt37zbI//bbbwW1Wi3s2bNHuHLlijB48GDBxcVFyMzMFMv07dtXaNmypXDu3Dnh1KlTQoMGDYRRo0aJ+cnJyYKDg4MwevRoISQkRNi6datgZmYm/Pjjj2V1mfQK8PT0FNavXy+EhIQIwcHBQv/+/QVnZ2chLS1NLPPBBx8ITk5Ogq+vr3Dx4kWhY8eOQqdOncT83NxcoXnz5kKvXr2Ey5cvCwcPHhSqV68uzJ07Vyzzzz//CObm5sL06dOFGzduCGvWrBFkMpng4+NTptdLldu+ffuEAwcOCLdu3RLCwsKEzz77TDAxMRFCQkIEQeC9ShXXhQsXhLp16wpubm7C1KlTxXTes/kx2KpE2rdvL3h5eYnHWq1WqFmzprBkyZJy7BVVNc8GWzqdTnB0dBSWLVsmpiUlJQlKpVLYunWrIAiCcOPGDQGAEBgYKJY5dOiQIJFIhAcPHgiCIAhr164VrK2thezsbLHM7NmzhcaNGxv5iuhVFh8fLwAQ/P39BUHQ35smJibC9u3bxTKhoaECACEgIEAQBP0fF6RSqRAbGyuWWbdunaBSqcT789NPPxWaNWtm0Nabb74peHp6GvuS6BVnbW0t/PLLL7xXqcJKTU0VGjZsKBw9elTo2rWrGGzxni0YpxFWEjk5OQgKCkKvXr3ENKlUil69eiEgIKAce0ZV3d27dxEbG2twb6rVanTo0EG8NwMCAmBlZYW2bduKZXr16gWpVIrz58+LZbp06QKFQiGW8fT0RFhYGB4/flxGV0OvmuTkZACAjY0NACAoKAgajcbgfm3SpAmcnZ0N7tcWLVrAwcFBLOPp6YmUlBRcv35dLPN0HXll+POYXpZWq8W2bduQnp4ODw8P3qtUYXl5eWHAgAH57iveswWTl3cHqGgePXoErVZrcHMCgIODA27evFlOvSICYmNjAaDAezMvLzY2Fvb29gb5crkcNjY2BmVcXFzy1ZGXZ21tbZT+06tLp9Nh2rRpeO2119C8eXMA+ntJoVDAysrKoOyz92tB93NeXmFlUlJSkJmZCTMzM2NcEr2Crl27Bg8PD2RlZcHCwgK7d++Gq6srgoODea9ShbNt2zZcunQJgYGB+fL487VgDLaIiOiV5OXlhZCQEJw+fbq8u0L0XI0bN0ZwcDCSk5OxY8cOjBs3Dv7+/uXdLaJ8oqKiMHXqVBw9ehSmpqbl3Z1Kg9MIK4nq1atDJpPlW9ElLi4Ojo6O5dQrIoj3X2H3pqOjI+Lj4w3yc3NzkZiYaFCmoDqeboOoqCZPnoz9+/fjxIkTqF27tpju6OiInJwcJCUlGZR/9n590b34vDIqlarS/dWVypdCoUCDBg3g7u6OJUuWoGXLlli1ahXvVapwgoKCEB8fjzZt2kAul0Mul8Pf3x+rV6+GXC6Hg4MD79kCMNiqJBQKBdzd3eHr6yum6XQ6+Pr6wsPDoxx7RlWdi4sLHB0dDe7NlJQUnD9/Xrw3PTw8kJSUhKCgILHM8ePHodPp0KFDB7HMyZMnodFoxDJHjx5F48aNOYWQikwQBEyePBm7d+/G8ePH801NdXd3h4mJicH9GhYWhsjISIP79dq1awZ/IDh69ChUKhVcXV3FMk/XkVeGP4+ppHQ6HbKzs3mvUoXTs2dPXLt2DcHBweKrbdu2GD16tPie92wBynuFDiq6bdu2CUqlUtiwYYNw48YN4f333xesrKwMVnQhMobU1FTh8uXLwuXLlwUAwvLly4XLly8L9+7dEwRBv/S7lZWVsHfvXuHq1avCkCFDClz6vXXr1sL58+eF06dPCw0bNjRY+j0pKUlwcHAQxowZI4SEhAjbtm0TzM3NufQ7FcuHH34oqNVqwc/PT4iJiRFfGRkZYpkPPvhAcHZ2Fo4fPy5cvHhR8PDwEDw8PMT8vKWJ+/TpIwQHBws+Pj6CnZ1dgUsTz5o1SwgNDRW8vb0r9dLEVD7mzJkj+Pv7C3fv3hWuXr0qzJkzR5BIJMKRI0cEQeC9ShXf06sRCgLv2YIw2Kpk1qxZIzg7OwsKhUJo3769cO7cufLuElUBJ06cEADke40bN04QBP3y7/PnzxccHBwEpVIp9OzZUwgLCzOoIyEhQRg1apRgYWEhqFQq4Z133hFSU1MNyly5ckXo3LmzoFQqhVq1agnffvttWV0ivSIKuk8BCOvXrxfLZGZmCh999JFgbW0tmJubC8OGDRNiYmIM6omIiBD69esnmJmZCdWrVxdmzJghaDQagzInTpwQWrVqJSgUCqFevXoGbRAVxbvvvivUqVNHUCgUgp2dndCzZ08x0BIE3qtU8T0bbPGezU8iCIJQPmNqREREREREry4+s0VERERERGQEDLaIiIiIiIiMgMEWERERERGRETDYIiIiIiIiMgIGW0REREREREbAYIuIiIiIiMgIGGwREREREREZAYMtIiIiIiIiI2CwRUREREREZAQMtoiIiIiIiIyAwRYREREREZER/D/qyA3HEcrjkgAAAABJRU5ErkJggg==", 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" ] @@ -471,20 +497,20 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_257427/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_269325/1088032263.py:32: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend()\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -565,12 +591,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+AftXwb6VcHYPXDwJtmxYOhjO7QKvIBi2EvwrX/8WcXUul8rplGwOJaRz4Gwagz/awsItJ1GAx26pw+v3NcNslDJCLu3pzOVSWbcvkZQsG/VDfPKHi/p6mPGxmjiSlMkP+xOpE+xTopf5jh49iqqqNGrUqFDHR0VFMWjQIAAmT57MzJkz2bZtG7169bri8W+99RYmk4knnniiUM+fmZnJvHnzWLRoEd27a7MnL1iwgOrVqxc4bsSIEfnbderUYebMmbRr147MzEx8fHwIDNT+mg4JCSlQDPbvX3AywU8++YSqVaty4MABmjVrVqiMQpQ7sWth6xw4H6u1PqlObb28qg0hOxnOHwKLDwxdDsH19U4r3MjRpAzW7Uvk2PlMzlzMZtfpVGxOFYAqXmZ+PXyePIeLwR1qUC/EV+e0+pJSUmfxqTkcO59JuL/HZXNuKIpCuL8HR5MyiU/NKdHXVVW1SMe3aNEif9vb2xs/P7/8S2r/FBMTw4wZM5g/f36h5xE5duwYNpuNDh065N8XGBhIw4YFF0eNiYmhd+/e1KhRA19fX2655RYATp06dc3nP3LkCIMGDaJOnTr4+flRq1atQj1OiHIrdi2sfwkS9oEjF4xmMHmCLQvObNOKKMUAd74FEa31TivcyN+vkqRk5RFz8q8iys/DRFVfC9l5DtYfSGT6j0dKtQtKeSCFlM6ybA5yHU68LFduHPS0GMlzOMmyOUr0devXr4+iKIXuUP7PPk6Kolz1stimTZtISkqiRo0amEwmTCYTJ0+e5KmnnsovYIojKyuLnj174ufnx+LFi4mOjmblypUA2Gy2az62d+/epKSk8NFHH7F161a2bt1aqMcJUS45HVpLVG46mD21+8xeYPECgxGcf/6/j7gJMhNBLnGLP/39Kgmqyq9HknGqKkYFQnwsGA0KWTYXIX4eWIwKhxO0liuXq2h/nFckUkjpzNtiwsNkJPsqhVKOzYnVZMT7KoVWcQUGBtKzZ09mzZpFVlbWZftvZPqAYcOGsWfPHnbt2pX/FRERwcSJE1m3bt0VH1O3bl3MZnN+gQNw8eJFDh8+nH/70KFDXLhwgSlTptC1a1caNWp0WauYxaIN2XY6nfn3XbhwgdjYWF544QW6d+9O48aNuXjxYrHfnxBu78w2SDkOHv5gzwGTVZtYMy8DbH+2Hpg8wb86nD8Maaf1zSvcRnxqDkeTMkjKyOX7/YmoKpgNCkE+FswmAxaTgRybE5vDha+nGaeqsic+tcSvmpQn0kdKZ9UCPKlb1Yd9Z9PwsZoKXApTVZVzabk0r+ZPtQDPEn/tWbNm0blzZ9q3b8+rr75KixYtcDgcrF+/ntmzZ3Pw4MFiPW9QUBBBQUEF7jObzYSFhV12qe4SHx8fRo4cycSJEwkKCiIkJITnn38ew98mBKxRowYWi4X33nuP0aNHs2/fPl577bUCz1OzZk0URWHNmjXcddddeHp6UqVKFYKCgpg7dy7h4eGcOnWKZ555pljvTYhyISsZnHaw+oPqAsWoXdLLTdX2W/20+10u7bKfLVPXuMJ9pObY2HbyIvEXtcLIx2rEZFAw/dlH16go2FUXTlXFw2QEVLJtJX/VpDyRFimdGQwKPZuFEuht4UhSJhm5dhwuFxm5do4kZRLobeGOpqGlMp9UnTp12LFjB7feeitPPfUUzZo14/bbb2fDhg3Mnj27xF/veqZNm0bXrl3p3bs3PXr0oEuXLrRp0yZ/f9WqVZk/fz7Lli2jSZMmTJkyhbfffrvAc1SrVo1XXnmFZ555htDQUMaNG4fBYGDp0qXExMTQrFkzJkyYwLRp08r67QlRdryDtT5RTpvWD8qeBTl/jrK1+IDRqhVXBgOYPLT7RKWXmm3jxVX7ib+YgwL8q3YgIb5a/91LV+6cqoqiKFpB5XQBCl6Wkr9qUp4oalF7HVdC6enp+Pv7k5aWhp+fX4F9ubm5nDhxgtq1a+Ph4VHs1/j7CIk8h3Y5r16ID3c0Da30IyL0UFLfVyF04XRoy70kHdJG6mX9eQnc7AUeVSAvTZvyILwlRLSCzhNkOZhK7kRyFiPnR3M8OQuL0UCL6v50rBNIdNxFDidmoAIeJgO5DhfeVhMR/h5cyMzDaDBwX6tqPNatbqlOIF1c1/r8LimVt4R0M/VCfKnTzadMZzYXQlRQRhN0GA3fP60t/wJgMIPRQ7u8ZzSDb5jWctXoHimiKrltJ1IYtXA7qdl2qgV48nLvJvx6+DxHz2cREeBBcmYeCel55NrteJqNeFkMJKbn4lKhZYQvPZuVzlWT8kIKKTdiMChEBnrpHUMIURFUbQi5aYAKRgsYLODM1ZZ/qdoI6t6qFVFVr9xvUVQOK3acYdLyPdidKi2r+/PR8LaE+HpQp6p3/lWSiABPVBUycu2gQFaekwBPMx3qBMk8UkghJYQQFU9GorZ+Xm4qhDSBHq+APRtUFQLrgqe/tiSMtERVWqqqakuS/XQUgLuah/HOA63wtBiBy6+SeJqNqKpK3IVsAOoEe1O9ilelbom6RAopIYSoSHJStfXzLp6AgJra0i++YXqnEm4k1+5k4ld7+Gb3WQAe61aXiXc0vKwoutJVklrBMjDhn6SQEkKIisKeo62fl7gXvEOkiBKXSc7MY9Rn29lxKhWTQWFy3+YMaBepd6xyTQopIYSoCJwO+GoEnPxdmydq6HIIqqt3KuFGjiRmMGJBNKdTcvDzMDFnWBs61Q3WO1a5J4WUEEKUd6oK3/wHYr/T5oga9DmEt7j+40Sl8duRZB5bHENGroOaQV58EtWOulXlMl1JkEJKCCHKu/Uvwa5F2uSbD3wKtbronUi4kc+3neKFVftwulTa1arCh8PaEuht0TtWhSGFlBBClGe/z4A/Zmrb974Hje7WN49wG06XyltrDzF343EA+rSK4K37W2A1GXVOVrHI2FdxXYqisGrVqlJ/HVVVGTVqFIGBgSiKwq5du+jWrRvjx48v9dcWolzauUhrjQK4/VVoPVTfPMJtZNscPLYoJr+ImtCjAf97sJUUUaVACqlKLiEhgccff5w6depgtVqJjIykd+/ebNiw4YaeNyoqCkVRCnz16tXrmo9Zu3Yt8+fPZ82aNZw7d45mzZqxYsWKAgsT16pVi+nTp99QNiEqhEPfwurHte3O/9G+hAAS03MZ8OFmfjiQiMVoYMbAVvynR30UReZ8Kg1yaa8Si4uLo3PnzgQEBDBt2jSaN2+O3W5n3bp1jB07lkOHDt3Q8/fq1YtPP/00/7bVar3m8ceOHSM8PJxOnTrl3xcYGHhDGYSokOJ+g2UPg+rSWqF6vKJ3IuEm9p9NY+T87SSk5xLobWHusDa0rSW/R0uTtEhVYmPGjEFRFLZt20b//v1p0KABTZs25cknn2TLli0Fjk1OTqZv3754eXlRv359Vq9efd3nt1qthIWF5X9VqVLlqsdGRUXx+OOPc+rUKRRFoVatWgAFLu1169aNkydPMmHChPxWLiEqnXO74fNB4MyDhnfDPTNAfhYE8NOhRB6Ys5mE9FzqhfiwakxnKaLKgBRSpUFVwZalz5eqFipiSkoKa9euZezYsXh7e1+2PyAgoMDtV155hQEDBrBnzx7uuusuhgwZQkpKyjVf45dffiEkJISGDRvy2GOPceHChaseO2PGDF599VWqV6/OuXPniI6OvuyYFStWUL16dV599VXOnTvHuXPnCvVehagwLhzTZi3PS4eaXeD+T7QFikWlpqoqn/5+gn8v2E62zUnnekEsf6wTNYJk7dayID+BpcGeDZMj9Hnt585qi5Jex9GjR1FVlUaNGhXqaaOiohg0aBAAkydPZubMmWzbtu2q/Z569epFv379qF27NseOHeO5557jzjvvZPPmzRiNl3d29Pf3x9fXF6PRSFjYlWdiDgwMxGg04uvre9VjhKiwMhK09fOyzkNYcxi0BMweeqcSOnM4Xby65gCfbT4JwKD2kbx6XzPMRmknKStSSFVSaiFbri5p0eKvyf28vb3x8/MjKSnpqscPHDgwf7t58+a0aNGCunXr8ssvv9C9e/eiBxaiMsu5CAv7QepJCKwDQ1eAh7/eqYTOMnLtjFuyk18Pn0dR4Nk7G/FI1zrS7aGMSSFVGsxeWsuQXq9dCPXrayM4Ctuh3Gw2F7itKAoul6vQserUqUNwcDBHjx6VQkqIorBlw5KBkLQffMK09fN8QvROJXR25mI2I+dvJzYxAw+zgekPtqZXM2mp14MUUqVBUQp1eU1PgYGB9OzZk1mzZvHEE09c1k8qNTX1sn5SN+LMmTNcuHCB8PDwG3oei8WC0+ksoVRCuDmnHZZFwektWgvUsBVQpZbeqYTOdp1O5d8LtpOcmUeIr5V5w9vRvLq0UOpFLqJWYrNmzcLpdNK+fXuWL1/OkSNHOHjwIDNnzqRjx47Fft7MzEwmTpzIli1biIuLY8OGDdx3333Uq1ePnj173lDmWrVqsXHjRuLj40lOTr6h5xLCrblc8PU4OLIOTJ4w+EsIbap3KqGz7/ae48EPN5OcmUfjcD9Wje0sRZTOpJCqxOrUqcOOHTu49dZbeeqpp2jWrBm33347GzZsYPbs2cV+XqPRyJ49e7j33ntp0KABI0eOpE2bNmzatOm6c0ldz6uvvkpcXBx169alatWqN/RcQrgtVYUfXoA9S0ExwoAFUONfeqcSOlJVlVk/H2XM4h3kOVzc1iiEZaM7EhHgqXe0Sk9Ri9rruBJKT0/H39+ftLQ0/Pz8CuzLzc3lxIkT1K5dGw8PGUFTUcj3Vehq0zuw4VVtu+9caPmgvnmErmwOF8+v3MuymDMAPNy5Fi/c3QSjQTqVX8+1Pr9LivSREkIIdxIz/68iquebUkRVcqnZNkYvimHL8RQMCvz33qY81LGW3rHE30ghJYQQ7uLA17Bmgrbd9SnoOEbfPEJXcclZjJgfzfHkLLwtRt4fchO3NpQRm+5GCikhhHAHx3+F5f/W1s9rEwW3vah3IqGjbSdSGLVwO6nZdiL8PZgX1Y7G4aVzaUrcGF07m2/cuJHevXsTERGBoiisWrUqf5/dbmfSpEk0b94cb29vIiIieOihhzh7tuD8TCkpKQwZMgQ/Pz8CAgIYOXIkmZmZBY7Zs2cPXbt2xcPDg8jISKZOnVoWb08IIQonfgcsHQxOGzS+F+5+V9bPq8RW7jzD0I+3kpptp2V1f1aN6yxFlBvTtZDKysqiZcuWzJo167J92dnZ7NixgxdffJEdO3awYsUKYmNjuffeewscN2TIEPbv38/69etZs2YNGzduZNSoUfn709PTueOOO6hZsyYxMTFMmzaN//73v8ydO7fU358QQlxX8hFYfD/YMqH2zdD/YzBcvoySqPhUVeXdH2KZ8MVubE4XdzYLY+mojoT4yoAXd+Y2o/YURWHlypX06dPnqsdER0fTvn17Tp48SY0aNTh48CBNmjQhOjqatm3bArB27Vruuusuzpw5Q0REBLNnz+b5558nISEBi8UCwDPPPMOqVauuOqt3Xl4eeXl5+bfT09OJjIyUUXuViHxfRZlIi4dPekLaaQhvBVFrwOqrdyqhg1y7k6e/2sPq3dpVl8e61WXiHQ0xyMi8G1IWo/bK1TxSaWlpKIqSP+P25s2bCQgIyC+iAHr06IHBYGDr1q35x9x88835RRRAz549iY2N5eLFi1d8nTfffBN/f//8r8jIyNJ7U0KIyik7BRb104qooHowdLkUUZXUhcw8hny8ldW7z2IyKEzt34JJvRpJEVVOlJtCKjc3l0mTJjFo0KD8qjIhIYGQkIIjGEwmE4GBgSQkJOQfExoaWuCYS7cvHfNPzz77LGlpaflfp0+fLum3I4SozGxZsGQAnD8EvhHa+nnewXqnEjo4kphBnw9+J+bkRfw8THw2oj0D2skf7+VJuRi1Z7fbGTBgAKqq3tCM24VltVpveAZuIYS4IocNvhgGZ6LBI0BbPy+ght6phA5+O5LMY4tjyMh1UDPIi3nD21EvxEfvWKKI3L5F6lIRdfLkSdavX1/gGmdYWBhJSUkFjnc4HKSkpBAWFpZ/TGJiYoFjLt2+dIy4tn+OqCxL3bp1Y/z48bq8thAlzuWCVY/BsQ1g9oIhX0FIY71TCR18vu0Uwz/dRkaug7Y1q7ByTGcposopty6kLhVRR44c4ccffyQoKKjA/o4dO5KamkpMTEz+fT/99BMul4sOHTrkH7Nx40bsdnv+MevXr6dhw4ZUqVKlbN6IG0tISODxxx+nTp06WK1WIiMj6d27Nxs2bLih542KikJRlAJfvXr1KqHUV/fLL7+gKAqpqaml/lpCFImqwtpJsO8rMJhgwEKIbKd3KlHGnC6Vyd8d5NkVe3G6VPq0imDxIx0I9LZc/8HCLel6aS8zM5OjR4/m3z5x4gS7du0iMDCQ8PBw7r//fnbs2MGaNWtwOp35fZoCAwOxWCw0btyYXr168cgjjzBnzhzsdjvjxo1j4MCBREREADB48GBeeeUVRo4cyaRJk9i3bx8zZszgf//7ny7v+ZpcLq3jqS0TLD7gHwmG0qt14+Li6Ny5MwEBAUybNo3mzZtjt9tZt24dY8eOveqoxsLq1asXn376af5tuVwqKrVfp8K2uYACfT+E+j30TiTKWLbNwfilu/jhgHZVZEKPBjzRvR6KzBlWrulaSG3fvp1bb701//aTTz4JwPDhw/nvf//L6tWrAWjVqlWBx/38889069YNgMWLFzNu3Di6d++OwWCgf//+zJw5M/9Yf39/fvjhB8aOHUubNm0IDg7mpZdeKjDXlFs4HwsHv9HmlHHkgskDgutD495QtWGpvOSYMWNQFIVt27bh7e2df3/Tpk0ZMWJEgWOTk5Pp27cv69ato1q1arzzzjuXzen1T1artUiXT7OysnjsscdYsWIFvr6+/N///d9lxyxcuJAZM2YQGxuLt7c3t912G9OnTyckJIS4uLj8/0+XWhuHDx/O/PnzWbt2La+//jr79u3DaDTSsWNHZsyYQd26dQudT4jrcrkg9SRcOAYuO9hytPuP/ww7P9O2e02B5vfrl1GUKJdLJT41h4w8O5m5DrwsRrJtTnysJnw9zIT6WNkVn8qx85nM3XiCE8lZWIwGpj3QgvtaVdM7vigBuhZS3bp141rTWBVmiqvAwECWLFlyzWNatGjBpk2bipyvzJyPhS1zIPsC+FcDszfYs+DcHm2emX+NLvFiKiUlhbVr1/LGG28UKKIuuTTFxCWvvPIKU6dOZdq0abz33nsMGTKEkydPEhgYeNXX+OWXXwgJCaFKlSrcdtttvP7665ddnv27iRMn8uuvv/L1118TEhLCc889x44dOwoU0na7nddee42GDRuSlJTEk08+SVRUFN999x2RkZEsX76c/v37Exsbi5+fH56enoBWpD355JO0aNGCzMxMXnrpJfr27cuuXbswlGKrn6hEzsfC9k8g7g9IPwu2DHA5ARVUp3aMfw2txfl8bKn9gSTKztGkDNbtS2Tn6YucSskmPceO3aliNir4eZjxtBhJzbaTmWfnYrYdlwomg8KTd9SXIqoCKRej9io0l0tricq+AFUb/bUshNUPqvpqw6MPrYGg+iV6me/o0aOoqkqjRo0KdXxUVBSDBg0CYPLkycycOZNt27Zdtd9Tr1696NevH7Vr1+bYsWM899xz3HnnnWzevBmj8fJZmzMzM5k3bx6LFi2ie/fuACxYsIDq1asXOO7vLWV16tRh5syZtGvXjszMTHx8fPILu5CQkALFYP/+/Qs8zyeffELVqlU5cOAAzZo1K9Q5EOKqzsfCz1Pg7A5w5IA9Uyui1D8LKQDFCLlpcOSHUvsDSZSdo0kZfPp7HKcuZJOUkUtOnpM8hwubw4XTpeBwuki/4MCpqjhd2mMsRgO+nia+iomnfogv3RuHXvtFRLkgf4rrLe20djnPv9rla2spCvhVg/OHteNKUFEntG/RokX+tre3N35+fpeNmPy7gQMHcu+999K8eXP69OnDmjVriI6O5pdffrni8ceOHcNms+UPEgCttbFhw4IfNDExMfTu3ZsaNWrg6+vLLbfcAsCpU6eumf/IkSMMGjSIOnXq4OfnR61atQr1OCGuy+WCA6vh/AEwmMGeC07Hnzsv/ZwpYPLSCquMBMhO1v5Acrn0Si1ugMulsm5fIhcy83C4XDicLtQ/v9d+HiZUVc1vnbpURFlNBppF+FInyIuMXDsL/ojD4ZDvf0UghZTebJlanyjz5ZfXALB4afttmVfeX0z169dHUZRCdyg3m80FbiuKgqsIHwJ16tQhODi4wOCCosrKyqJnz574+fmxePFioqOjWblyJQA2m+2aj+3duzcpKSl89NFHbN26NX/m++s9TojrSjsN53aB6tJajR252r/qpWJK0b5UOxitkJMKiqFU/kASZSM+NYdj5zPx9TBxMduOxWwkx+7CajJgMGijlG2uv8poq8mAt8WISwWDwUCQt4UTyVnsOH3l1TVE+SKFlN4sPlrHcnvWlffbsrX9lpKdXyQwMJCePXsya9YssrIuf+2Snj7gzJkzXLhwgfDw8Cvur1u3LmazOb/AAbh48SKHDx/Ov33o0CEuXLjAlClT6Nq1K40aNbqsVezSUkBOpzP/vgsXLhAbG8sLL7xA9+7dady48VWXBxKiyGyZ2kzlKlox5XKC89J0KwooJq11WVW1xYhVp9YSVQp/IImykWVzkOtwYjQYcDhdGBQFl6piUMClqmTm/fX7x2RQ8LVq3Rmcf14J8LQYsTtdXMiSP+QqAimk9OYfqY3OS4vXftH+napCejxUbaAdV8JmzZqF0+mkffv2LF++nCNHjnDw4EFmzpxJx44di/28mZmZTJw4kS1bthAXF8eGDRu47777qFevHj179rziY3x8fBg5ciQTJ07kp59+Yt++fURFRRXoCF6jRg0sFgvvvfcex48fZ/Xq1bz22msFnqdmzZooisKaNWs4f/48mZmZVKlShaCgIObOncvRo0f56aef8keICnHDLD5g8dYanlTX5f2iLl2xVxStyFKMWotVKfyBJMqGt8WEh8mI0+XCZDT8WUQp2J0q5zNsOFza999kALNRgT/n0jP+2X0jx+bEbNRapkT5J4WU3gwGbYoDryCtY3luOrgc2r/nD4F3EDS6p1Tmk6pTpw47duzg1ltv5amnnqJZs2bcfvvtbNiw4YaW4jEajezZs4d7772XBg0aMHLkSNq0acOmTZuuOZfUtGnT6Nq1K71796ZHjx506dKFNm3a5O+vWrUq8+fPZ9myZTRp0oQpU6bw9ttvF3iOatWq8corr/DMM88QGhrKuHHjMBgMLF26lJiYGJo1a8aECROYNm1asd+fEAX4R0J4K+0Pn7Qz/FVE/Xk53OUEFDBYwJkHngFawVVKfyCJ0lctwJO6VX3IyHVQxcuMze7EqEByplZEKYDZoP2XMCraJJyeFiMWkwGXS2uJqh3szU2RMil0RaCoRe11XAmlp6fj7+9PWlpagSVqQFtM+cSJE9SuXRsPD4/iv8iV5pGq2kAromRkT5krse+rqBwS9sLCvpB1HlDAaNYu36kuQNU6oRuMYLJqP8+hTaGDjNorz/4+au/Y+UzOpeWiAgYFfP4smtJzHaiAt8VIRIA2FcuFLBt+HmaeubORjNorA9f6/C4pMv2Bu6jaUJvioAxnNhdClACXU5u1POu8tvRLQC2tQ7ktA1xol/QMRrD6aFOc1L1V/kCqAOqF+BLVqSavrTnI2bRc4K9O5RaTAX9PM9WqeJGaYyfP7iQly4bZaKBhqC/DO9WSIqoCkULKnRgMUKWm3imEEIWlqvDtk3BwNRgtMOgLCKx9+czmigKBdcHTX/5AqiBsDhcfbTrBxiPJANzbKpxB7Wrg62G64szmF7JsBHlbuCmyCiaTfP8rEimkhBCiuH56HWLmAwr0+wjq3abdH1hbz1SilKVl2xm9KIbNxy9gUODl3k0Z3qnWVY9vX/vqKzqI8k8KKSGEKI4ts2HTn4Md7nkXmvbRNY4oG3HJWYyYH83x5Cy8LUbeH3wTtzYK0TuW0JEUUiVE+uxXLPL9FNe0+wtY+4y2fdsL0HbEtY8XFUJ0XAqjPtvOxWw7Ef4ezItqR+Pw0unALMoPKaRu0KUZv7Ozs/MXyBXlX3Z2NnD5jO5CcPgH+HqMtt3hMej6f/rmEWVi1c54nv5qDzanixbV/fn4obaE+MmIXiGF1A0zGo0EBATkz7Dt5eWF8s8180S5oaoq2dnZJCUlERAQcMUFlkUldmoLfPmQNtdbiweh5+TL18gUFYqqqvzvxyPM3HAEgF5Nw/jfg63wtMjvBqGRQqoEhIWFAVxzEV9RvgQEBOR/X4UAIHE/LBkAjhyofwfcN0tG31VwuXYnT3+1h9W7zwLw6C11mNSzEQaDFM/iL1JIlQBFUQgPDyckJAS73X79Bwi3ZjabpSVKFHQxDhb2g9w0iOwADyzQJt0UFdaFzDxGLYwh5uRFTAaFN/o248F2NfSOJdyQFFIlyGg0ygewEBVNZpI2a3lmAoQ0gcFfgMVL71SiFB1NyuDh+dGcTsnBz8PEnKFt6FQvWO9Ywk1JISWEEFeTmw6L+kPKcQioAUNXgKesj1aR/XYkmccWx5CR66BGoBefRLWjXogsLi2uTgopIYS4EnsufD4IEvaAVzAMWwV+4XqnEqXo822neGHVPpwulbY1qzD3obYEelv0jiXcnBRSQgjxT04HLB8JJ38Diy8MWwFBdfVOJUqJy6UyZe0h5m48DkCfVhG8dX8LrCbpqiGuTwopIYT4O1WFNePh0BowWmHQ5xDeUu9UopRk2xyMX7qLHw4kAjChRwOe6F5PprERhSaFlBBC/N2P/4WdC0ExwP2fQO2ueicSpSQxPZd/L9jO3vg0LEYD0x5owX2tqukdS5QzUkgJIcQlf7wHv0/XtnvPgMb36BpHlJ4DZ9MZuSCac2m5BHpbmDusDW1rBeodS5RDUkgJIQTAriXwwwvado//wk0P6RpHlJ6fDiXy+JKdZNmc1K3qzSdR7agZ5K13LFFOSSElhBCx38PX47TtjuOg83hd44jSM//3E7y65gAuFTrVDWL2kDb4e8nkqqL4pJASQlRucb/DsihQndByMNz+mqyfVwE5nC5eW3OABZtPAvBg20he79sMs1GW+RE3RgopIUTllbAXPh8IjlxocCfc+56sn1cBZeY5eHzJDn6OPY+iwDO9GjHq5joyMk+UCCmkhBCVU8pxbf28vHSo0Qke+BSM8iuxoolPzWHk/GgOJWTgYTYw/cFW9GomE6uKkiO/NYQQlU9GgrZ+XlYShDbX5ooye+qdSpSw3adTGblgO8mZeVT1tfLxQ21pGRmgdyxRwUghJYSoXHJStfXzLsZBlVowdDl4BuibSZS4tfvOMf6LXeTaXTQK82VeVDuqBUixLEqeFFJCiMrDnqOtn5e4D7xDYNhK8A3VO5UoQaqq8uHG40z5/hAA3RpW5f3BN+FjlY87UTrkf5YQonJwOmDZw3DqD7D6a+vnBdbRO5UoQXanixdW7uOL7acBiOpUixfuboxJRuaJUiSFlBCi4nO5YPXjcPh7MHnA4KUQ1lzvVKIEpWXbeWxxDH8cu4BBgZfuaUJU59p6xxKVgBRSQoiKTVVh/YuwewkoRnhgAdTspHcqUYJOXshixPxojp3Pwtti5P3BN3FroxC9Y4lKQgopIUTF9vt02Py+tn3fLGjYS9c4omRtj0th1MIYUrJshPt7MG94O5pE+OkdS1QiUkgJISqumAXw43+17TvegFaDdI0jStbXu+KZuGwPNqeLFtX9+fihtoT4eegdS1QyUkgJISqmA6thzXhtu8sE6DRO1zii5KiqyowNR5j+4xEAejUN438PtsLTYtQ5maiMpJASQlQ8JzbC8pGguuCmh6D7y3onEiUk1+5k0vI9fL3rLACP3lKHST0bYTDIci9CH1JICSEqlrO74PPB4LRBo3vg7v/JIsQVxIXMPB5dGMP2kxcxGRRe79OMge1r6B1LVHJSSAkhKo7ko9qs5bYMqNUV+s+T9fMqiKNJmYyYH82plGx8PUzMGdqGzvWC9Y4lhBRSQogKIv2stn5edjKEt4SBS8AsHY8rgt+PJjN6UQwZuQ5qBHrxSVRb6oX46h1LCEAKKSFERZCdAgv7QdopCKwLQ5aDhwyBrwiWbjvFC6v24XCptKlZhbnD2hDkY9U7lhD5pJASQpRvtixY8iCcPwi+4dr6eT5V9U4lbpDLpfLWukN8+OtxAO5rFcFb/VvgYZaRecK9SCElhCi/nHb4cjic2QYeATB0BVSpqXcqcYNybE4mfLGLtfsTAPhP9/qM71EfRQYNCDckhZQQonxyuWDVY3B0PZg8YfCXENpE71TiBiWl5/Lvz7az50waFqOBqfe3oE/ranrHEuKqpJASQpQ/qgrrnoW9y8BgggcXQo0OeqcSN+jguXRGzo/mbFouVbzMzH2oLe1qBeodS4hrkkJKCFH+bHwbts7RtvvMgfq365tH3LCfDyUxbskOsmxO6lT15tOodtQM8tY7lhDXJYWUEKJ8iZ4HP7+ubd85FVo8oG8eccMW/BHHK9/sx6VCxzpBzBnaBn8vs96xhCgUKaSEEOXHvhXw7VPa9s1PQ4dH9c0jbojD6eL1bw8y/484AAa0rc7rfZpjMRn0DSZEEUghJYQoH479BCtGASq0HQG3Pqd3InEDMvMcPL5kBz/HngdgUq9GjL6ljozME+WOFFJCCPd3JgaWDgWXHZr2hbvelvXzyrH41BxGzo/mUEIGVpOB6Q+24s7m4XrHEqJYpJASQri384dh8f1gz4I63aDvh2CQSRnLqz1nUhm5YDvnM/II9rHy8fC2tIoM0DuWEMUmhZQQwn2lnYGFfSAnBSJuggcXg0mWBymv1u5LYPwXO8m1u2gU5su8qHZUC/DUO5YQN0QKKSGEe8q6oC1CnB4PQfVhyFdg9dE7lSgGVVWZu/E4U9YeQlXhlgZVeX9wa3w9ZGSeKP+kkBJCuJ+8DO1yXvJh8KumrZ/nHaR3KlEMdqeLF1ftY2n0aQAe6liTl+5pgskoI/NExSCFlBDCvTjy4IuhcHYHeAZqRVRApN6pRDGkZdt5bHEMfxy7gEGBF+5uwogutfWOJUSJkkJKCOE+XE5tioPjv4DZW7ucV7Wh3qlEMZy8kMWI+dEcO5+Fl8XIe4Na071xqN6xhChxUkgJIdyDqsJ3/wcHVoHBDAMXQfU2eqcSxbA9LoVRC2NIybIR7u/BvOHtaBLhp3csIUqFFFJCCPfw82TY/gmgQL+5UPc2vROJYvh6VzwTl+3B5nTRvJo/84a3JcTPQ+9YQpQaKaSEEPrbMgc2TtW2734bmvXTN48oMlVVmbHhCNN/PALAHU1CmT6wFV4W+ZgRFZv8DxdC6GvPMlg7Sdu+9Xlo929984giy7U7eWb5HlbtOgvAo7fUYVLPRhgMMvu8qPikkBJC6OfIelg1Wttu/yjcPFHfPKLILmTm8ejCGLafvIjJoPBan2YMal9D71hClJliFVKnTp3i5MmTZGdnU7VqVZo2bYrVKrMNCyGK4PQ2+GIYuBzQ/AHoNUXWzytnjiZlMmJ+NKdSsvH1MDFnaBs61wvWO5YQZarQhVRcXByzZ89m6dKlnDlzBlVV8/dZLBa6du3KqFGj6N+/PwaDTLQmhLiGpIOw+AFw5EC9HnDfByC/N8qVP44mM3pRDOm5DiIDPfk0qh31Qnz1jiVEmSvUb64nnniCli1bcuLECV5//XUOHDhAWloaNpuNhIQEvvvuO7p06cJLL71EixYtiI6OLu3cQojyKvUULOwHualQvT0M+AxMFr1TiSL4IvoUD32yjfRcB21qVmHVmM5SRIlKq1AtUt7e3hw/fpygoMuXaAgJCeG2227jtttu4+WXX2bt2rWcPn2adu3alXhYIUQ5l5WsrZ+XcRaqNobBX4DFW+9UopBcLpWp62KZ8+sxAO5tGcHU+1vgYTbqnEwI/Sjq36/RiStKT0/H39+ftLQ0/PxkUjkhiiUvA+bfA+d2gX8kjPwB/CL0TiUKKcfmZMIXu1i7PwGAJ7rXZ0KP+ijSr024sbL4/C50p4S2bdsyZ84c0tPTSyWIEKICs+fC0sFaEeUVDMNWSRFVjiSl5zJw7mbW7k/AYjTwvwdb8uTtDaSIEoIiFFItW7bk6aefJjw8nGHDhvHLL7+UYiwhRIXhcsKKf8OJjWDxhaFfQXA9vVOJQjp4Lp0+s35n95k0qniZWfTvDvRtXV3vWEK4jUIXUvPmzSMhIYFZs2Zx+vRpunfvTr169Zg8eTLx8fHFevGNGzfSu3dvIiIiUBSFVatWFdivqiovvfQS4eHheHp60qNHD44cOVLgmJSUFIYMGYKfnx8BAQGMHDmSzMzMAsfs2bOHrl274uHhQWRkJFOnTi1WXiFEEakqrBkPB78BowUGLYGI1nqnEoX086Ek7p/9B2fTcqkT7M3KMZ1pXztQ71hCuJUijTf28vIiKiqKX375hcOHDzNw4EA+/PBDatWqxd13382KFSuK9OJZWVm0bNmSWbNmXXH/1KlTmTlzJnPmzGHr1q14e3vTs2dPcnNz848ZMmQI+/fvZ/369axZs4aNGzcyatSo/P3p6enccccd1KxZk5iYGKZNm8Z///tf5s6dW6SsQohi2PAq7PgMFAP0nwe1b9Y7kSikzzbHMXJBNFk2Jx3rBLFyTGdqBcvAACEuo94gl8ulLlu2TA0MDFQNBkOxnwdQV65cWeB5w8LC1GnTpuXfl5qaqlqtVvXzzz9XVVVVDxw4oAJqdHR0/jHff/+9qiiKGh8fr6qqqn7wwQdqlSpV1Ly8vPxjJk2apDZs2LDQ2dLS0lRATUtLK+7bE6Ly+eN9VX3ZT/va/qneaUQhOZwu9eWv96k1J61Ra05ao/7fl7vUPLtT71hCFEtZfH7f0Ax4v/zyC1FRUURFReF0OnnkkUdKorYD4MSJEyQkJNCjR4/8+/z9/enQoQObN28GYPPmzQQEBNC2bdv8Y3r06IHBYGDr1q35x9x8881YLH/NU9OzZ09iY2O5ePHiFV87Ly+P9PT0Al9CiCLYvRTWPadtd38J2kTpGkcUTmaeg0c+2878P+IAeLpXQ6be3wKLSSZLFeJqivzTcebMGV5//XXq1avHbbfdRlxcHB988AHnzp1jzpw5JRYsIUEbYhsaGlrg/tDQ0Px9CQkJhISEFNhvMpkIDAwscMyVnuPvr/FPb775Jv7+/vlfkZGRN/6GhKgsYtfCqjHa9r/GQpcn9c0jCuVsag73z/6Dnw4lYTUZ+GDITYzpVk9G5glxHYVeIubLL7/kk08+YcOGDYSEhDB8+HBGjBhBvXoVb/TNs88+y5NP/vXLPz09XYopIQrj5GZYNhxUJ7QYCHe8LuvnlQN7zqQycsF2zmfkEexj5ePhbWkVGaB3LCHKhUIXUkOHDuXuu+9m5cqV3HXXXaW+nl5YWBgAiYmJhIeH59+fmJhIq1at8o9JSkoq8DiHw0FKSkr+48PCwkhMTCxwzKXbl475J6vVKoswC1FUCftgyYPgyIX6PeG+92X9vHJg7b4Exn+xk1y7i4ahvsyLakv1Kl56xxKi3Cj0b7kzZ86wcuVK7rnnnjJZlLh27dqEhYWxYcOG/PvS09PZunUrHTt2BKBjx46kpqYSExOTf8xPP/2Ey+WiQ4cO+cds3LgRu92ef8z69etp2LAhVapUKfX3IUSlkHICFvWDvDSo0REemA9Gs96pxDWoqsqHvx7jscUx5Npd3NKgKl891lGKKCGKqNAV0d/7Ii1cuJDOnTsTERHByZMnAZg+fTpff/11kV48MzOTXbt2sWvXLkDrYL5r1y5OnTqFoiiMHz+e119/ndWrV7N3714eeughIiIi6NOnDwCNGzemV69ePPLII2zbto3ff/+dcePGMXDgQCIitFmTBw8ejMViYeTIkezfv58vvviCGTNmFLh0J4S4ARmJ2vp5mYkQ0hQGLQWLfBi7M7vTxbMr9vLm94dQVRj2r5rMG94WXw8pfoUosqIO8/vggw/U4OBg9fXXX1c9PT3VY8eOqaqqqp9++qnarVu3Ij3Xzz//rAKXfQ0fPlxVVW0KhBdffFENDQ1VrVar2r17dzU2NrbAc1y4cEEdNGiQ6uPjo/r5+akPP/ywmpGRUeCY3bt3q126dFGtVqtarVo1dcqUKUXKKdMfCHEVOamq+kFnbYqD/zVX1fRzeicS15GaZVMHf7RZrTlpjVr7mTXqJ78dV10ul96xhCgVZfH5XeRFi5s0acLkyZPp06cPvr6+7N69mzp16rBv3z66detGcnJyiRd7epNFi4W4AnsOLOoPJ38H7xAYsRaC6uqdSlzDqQvZPDx/G8fOZ+FlMfLeoNZ0bxx6/QcKUU6Vxed3oTubX3LixAlat758iQer1UpWVlaJhBJCuDmnA74aoRVRVj8YulyKKDcXczKFRz6LISXLRri/B/OGt6NJhPxhKMSNKnKv8dq1a+f3afq7tWvX0rhx45LIJIRwZ6oK3/wHYr8Dk4fWJyq8hd6pxDV8vSueQXO3kpJlo3k1f1aN7SxFlBAlpMgtUk8++SRjx44lNzcXVVXZtm0bn3/+OW+++SYff/xxaWQUQriT9S/BrkWgGOH+T6FWZ70TiatQVZUZG44w/Udtsfc7moQyfWArvCxF/tUvhLiKIv80/fvf/8bT05MXXniB7OxsBg8eTEREBDNmzGDgwIGlkVEI4S5+mw5/zNS2730PGt2laxxxdXkOJ5O+2sOqXWcBePTmOkzq1QiDQSZIFaIkFbmzeXp6en6HrezsbDIzM/OnRjh69GiFnOlcOpsLAexYCKvHadu3vwadn9A3j7iqlCwbjy7cTnTcRYwGhdf7NGNQ+xp6xxKizJXF53eR+0jdfffd5OXlAeDl5ZVfRMXGxtKtW7cSDSeEcBMH18A3fxZOnf8jRZQbO5qUSZ9ZvxMddxFfDxMLHm4vRZQQpajIhZSPjw99+/bF4XDk33fw4EG6detG//79SzScEMINxP2mjdBTXdB6KPR4Re9E4ir+OJpMvw9+51RKNpGBnqwc04ku9YP1jiVEhVbkQmrFihWkpaUxZMgQVFXNnz9q0KBBzJgxozQyCiH0cm43LBkIzjxoeDfcM0MWIXZTX0af5qFPtpGe6+CmGgGsHNOZeiG+escSosIrcmdzT09Pvv32W7p168aAAQPYuHEjDz30ENOmTSuNfEIIvVw4pk24acuAml3g/k/AKKO93I3LpTJ1XSxzfj0GQO+WEUy7vwUeZqPOyYSoHAr1WzE9Pb3AbYPBwBdffMHtt99O//79efHFF/OPkc7YQlQA6edgYR/IOg9hzWHQEjB76J1K/EOOzcmTX+7i+30JADzRvT4TetRHkVZDIcpMoUbtGQyGK/5gXnqooiioqoqiKDidzpJPqTMZtScqlZyL8OldkHQAAuvAiHXgE3L9x4kylZSRyyMLtrP7TBoWo4Ep/ZvT76bqescSwq24zRIxP//8c6m8uBDCzdiytT5RSQfAJwyGrZQiyg0dSkhn5PztxKfmUMXLzIfD2tK+dqDesYSolApVSN1yyy2lnUMIoTenHZZFwekt4OEPw1ZAlVp6pxL/8HNsEo8v2UlmnoM6wd58EtWOWsHeescSotIq1Ki9U6dOFelJ4+PjixVGCKETlwu+HgtH1oHJEwZ/CaFN9U4l/uGzzXGMnB9NZp6Df9UJZMWYTlJECaGzQhVS7dq149FHHyU6Ovqqx6SlpfHRRx/RrFkzli9fXmIBhRClTFXhh+dhzxdgMMGAz6DGv/ROJf7G6VJ55Zv9vPT1flwqPNCmOp+N6ECAl0XvaEJUeoW6tHfgwAHeeOMNbr/9djw8PGjTpg0RERF4eHhw8eJFDhw4wP79+7npppuYOnUqd90l628JUW5sege2fKBt95kNDe7QN48oIDPPwROf7+SnQ0kATOzZkDHd6srIPCHcRJHW2svJyeHbb7/lt99+4+TJk+Tk5BAcHEzr1q3p2bMnzZo1K82supFRe6LC2v4JrJmgbfeaAv96TN88ooCzqTmMXLCdg+fSsZoM/O/BVtzVPFzvWEKUG2Xx+V3kRYsrIymkRIW0f5XWuRwVuv4fdH9R50Di7/aeSWPkgmiSMvII9rHy8fC2tIoM0DuWEOWK20x/IISoYI7/AiseAVRo8zDc9oLeicTfrN2XwPgvdpJrd9Ew1Jd5UW2pXsVL71hCiCuQQkqIyiZ+BywdAk4bNLkP7n5H1s9zE6qqMnfjcaasPYSqwi0NqvL+4Nb4epj1jiaEuAoppISoTM4fhsX3gy0Tat8C/T4Cg6zJ5g7sThcvrtrH0ujTAAz7V01e7t0Ek7HIa8sLIcqQFFJCVBZp8bCwL2RfgIjWMHAxmKx6pxJAWo6dMYtj+P3oBQwKvHhPE6I61ZKReUKUA0UupLKysvD2lgnghChXslO0Iir9DATVhyFfgdVX71QCOHUhm4fnb+PY+Sy8LEbeG9Sa7o1D9Y4lhCikIrcZh4aGMmLECH777bfSyCOEKGl5mbD4AUiOBd8Ibf0872C9Uwkg5mQKfT74nWPnswj392DZ6I5SRAlRzhS5kFq0aBEpKSncdtttNGjQgClTpnD27NnSyCaEuFEOG3w5DOK3g2cVrYgKiNQ7lQC+3hXPoI+2kpJlo1k1P1aN7UzTCH+9YwkhiqjIhVSfPn1YtWoV8fHxjB49miVLllCzZk3uueceVqxYgcPhKI2cQoiicrlg1Wg49hOYvWDwMghppHeqSk9VVWb8eIT/LN2FzeHi9iahfPloR0L9PPSOJoQohhKZkPO9995j4sSJ2Gw2goODGT16NM888wxeXhVj3hOZkFOUO6oK302E6I/AYIbBS6FeD71TVXp5DifPLN/Lyp3awu6jbq7DpF6NMBqkU7kQpcGtJ+RMTExkwYIFzJ8/n5MnT3L//fczcuRIzpw5w1tvvcWWLVv44YcfSjKrEKKwfn1LK6JQoO8cKaLcQEqWjUcXbic67iJGg8Jr9zVjcIcaescSQtygIhdSK1as4NNPP2XdunU0adKEMWPGMHToUAICAvKP6dSpE40bNy7JnEKIwtr2EfzyprZ91zRofr++eQTHzmcyYn40Jy9k4+th4oMhN9G1flW9YwkhSkCRC6mHH36YgQMH8vvvv9OuXbsrHhMREcHzzz9/w+GEEEW09yvtkh5At2eh/SP65hH8cSyZ0QtjSM91UL2KJ59GtaN+qEw9IURFUeQ+UtnZ2RWm71NhSR8pUS4c3QBLHgSXHdo9orVGyYSOuvpy+2meW7EXh0vlphoBzH2oLcE+MgmqEGXFLftIORwO0tPTL7tfURSsVisWi6VEggkhiuDMdvhiqFZENe0Hd06VIkpHLpfKtB9imf3LMQB6t4xg2v0t8DDLcjxCVDRFLqQCAgKuuWxB9erViYqK4uWXX8ZgkDWihCh1SYe09fPs2VD3Nuj7IcjPnm5ybE6e/HIX3+9LAOCJ2+oxvkcDDDIyT4gKqciF1Pz583n++eeJioqiffv2AGzbto0FCxbwwgsvcP78ed5++22sVivPPfdciQcWQvxN6mlY1A9yLkK1tjBgIZikVVgvSRm5PPJZDLtPp2I2Kkzp14L+barrHUsIUYqKXEgtWLCAd955hwEDBuTf17t3b5o3b86HH37Ihg0bqFGjBm+88YYUUkKUpqzkP9fPi4fghjBkGVh99E5VaR1KSGfk/O3Ep+YQ4GXmw6Ft6FAnSO9YQohSVuT2/z/++IPWrVtfdn/r1q3ZvHkzAF26dOHUqVM3nk4IcWV5GdrlvAtHwK86DFsBXoF6p6q0folN4v7Zm4lPzaFOsDcrx3SWIkqISqLIhVRkZCTz5s277P558+YRGamt4XXhwgWqVKly4+mEEJdz5MHSIXB2J3gGauvn+cvlI70s3BzHiPnRZOY56FA7kBVjOlE72FvvWEKIMlLkS3tvv/02DzzwAN9//33+PFLbt2/n0KFDfPXVVwBER0fz4IMPlmxSIQS4nLD833DiVzB7w9CvoGoDvVNVSk6XyuvfHuDT3+MAuL9NdSb3bY7FJB39hahMirXWXlxcHB9++CGxsbEANGzYkEcffZRatWqVdD63IPNICbegqrBmPMTMB6MFBn8JdW/VO1WllJnn4D+f72TDoSQAnu7VkMduqXvNEc1CiLLndvNI2e12evXqxZw5c3jzzTdLJZAQ4ip+el0rolCg30dSROnkXFoOI+Zv5+C5dKwmA+8OaMXdLcL1jiWE0EmRCimz2cyePXtKK4sQ4mq2zIZNb2vb97wLTfvoGqey2nsmjZELoknKyCPYx8pHD7WhdQ3pDypEZVbki/lDhw69YmdzIUQp2f0FrH1G277tBWg7Qt88ldQP+xMY8OFmkjLyaBDqw6qxnaSIEkIUb4mYTz75hB9//JE2bdrg7V1wdMq7775bYuGEqPQO/wBfj9G2OzwGXf9P3zyVkKqqfLzpBJO/P4iqws0NqvL+4Nb4eZj1jiaEcANFLqT27dvHTTfdBMDhw4cL7JOOlkKUoFNb4MuHwOWAFg9Cz8myfl4ZsztdvPT1fj7fps2LN/RfNfhv76aYjDIyTwihKXIh9fPPP5dGDiHE3yXuhyUDwJED9e+A+2bJ+nllLC3HztjFO/jtaDKKAi/e3YSHO9eSPxiFEAUUuZC65OjRoxw7doybb74ZT09PVFWVXzBClISLcbCwH+SmQWQHeGABGOUyUlk6nZLNw/OjOZqUiZfFyMyBrenRJFTvWEIIN1TkP3EvXLhA9+7dadCgAXfddRfnzp0DYOTIkTz11FMlHlCISiUzSVs/LzMBQprA4C/A4qV3qkol5uRF+sz6naNJmYT5ebBsdEcpooQQV1XkQmrChAmYzWZOnTqFl9dfv+AffPBB1q5dW6LhhKhUctNgUX9IOQ4BNWDoCvCUUWFlafXuswz6aAsXsmw0q+bH1+M60zTCX+9YQgg3VuRLez/88APr1q2jevWCa3vVr1+fkydPllgwISoVey58PhgS9oB3VRi2Cvxkkseyoqoq7/90lHfWawNobm8SyoyBrfCyFLv3gxCikijyb4msrKwCLVGXpKSkYLVaSySUEJWK0wHLR8LJ38DqB0OXQ1BdvVNVGnkOJ88u38uKnfEAPNK1Ns/c2RijQfp8CiGur8iX9rp27cpnn32Wf1tRFFwuF1OnTuXWW2XJCiGKRFVhzX/g0BowWmHQ5xDeUu9UlcbFLBvDPt7Gip3xGA0Kb/RtxvN3N5EiSghRaEVukZo6dSrdu3dn+/bt2Gw2nn76afbv309KSgq///57aWQUouL68WXYuQgUA9z/CdTqoneiSuPY+UxGzo8m7kI2vlYTHwy9ia71q+odSwhRzhS5RapZs2YcPnyYLl26cN9995GVlUW/fv3YuXMndevK5QghCu33mfD7DG2790xofI++eSqRzccu0O+DP4i7kE31Kp6sGNNJiighRLEoqqqqeodwd+np6fj7+5OWloafn5/ecURFsHPxX0u/9HgFuozXNU5l8uX20zy3Yi8Ol0rrGgF89FBbgn2kf6cQFVFZfH4Xa0hKamoq27ZtIykpCZfLVWDfQw89VCLBhKiwDn0Hqx/Xtjs9LkVUGXG5VN7+IZYPfjkGwD0twnn7gZZ4mI06JxNClGdFLqS++eYbhgwZQmZmJn5+fgVmM1cURQopIf7O5YK005CbCheOw/lY+O1tUJ3Qagjc/preCSskl0vl9MVsTiRnARDuZ+XN72P55fB5AMbdWo8nb2+AQTqVCyFuUJELqaeeeooRI0YwefLkK06DIIT40/lYOPgNHPsFzh8CWwY4crV9XsHQ8G5ZhLgUHE3KYMmWU2w5kUJqjo08m5O0XAcOl4oC3FSjCp5mI8eTM6kX4qt3XCFEOVfkQio+Pp4nnnhCiighruV8LGyZoy0+nHwYHHnaF4Dy54/dhlfAYISGvfTLWcEcTcpg+o9H2H06FYOiYDEqJOTYcf3ZEzTC34OaQZ7sO5vG2bQcHu5cS4opIcQNKfKovZ49e7J9+/bSyCJExeByaS1RmechMxGcedoXKhjMYPUFkyfkpcO2D7UJOcUNc7lU1u5N4HBiBhaTAatJ4eSFHFwqGA0KflYjOXYnCWl51KvqTUqWjR/2J+JyyXgbIUTxFblF6u6772bixIkcOHCA5s2bYzYXXJX+3nvvLbFwQpRLaach+YjW2pSdAk6b1ifKYNKWf0HVLvF5BcKFY3BmG9TspHfqci8+NYe98Wk4XSp5dhdnUnMAMBsVgr0tuACbw0ViRi6ZeU7C/T04mpRJfGoOkYHSwi6EKJ4iF1KPPPIIAK+++upl+xRFwel03ngqIcozW6ZWKLkcYM8E1aVNuOldVSuuVBVUGxgtkJcBWcl6J64QsmwOMm12kjPzSMvRWvksRoUgbzMGg4KiAqjYnS5sThcBXmYS03PJskmLoBCi+IpcSP1zugMhxD9YfLQi6dRmrYhC+bOI+vPHTXVqhZXTBkYzeAfrGreiUIDDCZn5RVS4n5U8hwutizk4VRVQMBsNWIwGcmxOrCYj3rIwsRDiBhS5j5QQ4jr8qmmj9DITAQVMHn91MFcBh027Ly9dW5y4ens901YI59JymPDFbs5n2lCAUF8r1at44mUxkudw4XKp2OzOP/d54GM1ci4tl3ohPlQL8NQ7vhCiHCt0IXXXXXeRlpaWf3vKlCmkpqbm375w4QJNmjQp0XBClDuqCutfgoQ9gAJB9bWiKTcVbNnaZT/VBc5c8PCD9o+CUVpEbsTeM2n0mfU7B86lE+BlplPdICwmA0kZNrysJhQFUnPs2F0qVbwthPlbOXo+i0BvC3c0DZW5pIQQN6TQS8QYjUbOnTtHSEgIAH5+fuzatYs6deoAkJiYSERERIXsIyVLxIhC2/QObPiz/2CP/4LL+dc8UvYsUIzaqL2QRloRJVMf3JAf9ifwn6W7yLE7aRDqw7zh7chzOPPnkUrLsWF3ukAFX08z1QI8CfaxUi/EhzuahsrUB0JUcG61RMw/6y1Zok+If4iZ/1cR1XMydByrTYXQ/IG/ZjZHAd8Q7XKetEQVm6qqfLzpBJO/P4iqQtf6wcwachN+Htoo4hfuaVJgZvNaQV4YFIVsuxNvi4lqAZ7SEiWEKBHym1yIknBgNayZoG13eVIrogAMBqhSE6gJ4S11i1eR2J0uXl69nyVbTwEwpEMNXrm3KSbjXz0VDAaFmkHe1Azy1iumEKKSKHQhpShKgXX1Lt0nRKV3/FdYPlLr+3TTcOj+kt6JKqz0XDtjF+9g05FkFAVeuLsJIzrXkt9FQgjdFOnSXlRUFFarFYDc3FxGjx6Nt7f2F19eXl7pJBTCnZ3dCUsHa1MZNO4N9/xP1s8rJadTshkxP5ojSZl4mo3MHNSa25uE6h1LCFHJFbqQGj58eIHbQ4cOveyYhx566MYTCVFeJB+FRfdrI/FqdYV+H2sTbooSF3PyIqM+286FLBuhflbmDW9Hs2r+escSQojCF1Kffvppaea4IqfTyX//+18WLVpEQkICERERREVF8cILL+Q35auqyssvv8xHH31EamoqnTt3Zvbs2dSvXz//eVJSUnj88cf55ptvMBgM9O/fnxkzZuDj41Pm70lUEOlnYWEfyE7W+j4NXAJmD71TVUjf7D7LU8t2Y3O4aBrhx7zh7Qjzl3MthHAPbj0h51tvvcXs2bN5//33OXjwIG+99RZTp07lvffeyz9m6tSpzJw5kzlz5rB161a8vb3p2bMnubm5+ccMGTKE/fv3s379etasWcPGjRsZNWqUHm9JVATZKbCwr7amXlA9GLJcmxNKlChVVXlvwxEe/3wnNoeLHo1D+fLRjlJECSHcSqHnkdLDPffcQ2hoKPPmzcu/r3///nh6erJo0SJUVSUiIoKnnnqK//u//wMgLS2N0NBQ5s+fz8CBAzl48CBNmjQhOjqatm3bArB27Vruuusuzpw5Q0RExHVzyDxSIp8tCz67D85Eg284jPwBAmronarCyXM4eXb5XlbsjAfg311q8+xdjTHKlAVCiCIoi89vt26R6tSpExs2bODw4cMA7N69m99++40777wTgBMnTpCQkECPHj3yH+Pv70+HDh3YvHkzAJs3byYgICC/iALo0aMHBoOBrVu3XvF18/LySE9PL/AlBA4bfPmQVkR5BMCwlVJElYKLWTaGfbyNFTvjMRoUXu/TjBfuaSJFlBDCLbn1PFLPPPMM6enpNGrUCKPRiNPp5I033mDIkCEAJCQkABAaWnDkTmhoaP6+hISE/NnYLzGZTAQGBuYf809vvvkmr7zySkm/HVGeuVzw9Rg4+iOYvWDIMghprHeqCufY+UxGzo8m7kI2vlYTs4bcxM0NquodSwghrsqtW6S+/PJLFi9ezJIlS9ixYwcLFizg7bffZsGCBaX6us8++yxpaWn5X6dPny7V1xNuTlVh7TOwdxkYTDBgIUTKQsMlbfOxC/T74A/iLmRTLcCT5WM6SRElhHB7bt0iNXHiRJ555hkGDhwIQPPmzTl58iRvvvkmw4cPJywsDNDW+QsPD89/XGJiIq1atQIgLCyMpKSkAs/rcDhISUnJf/w/Wa3W/PmyhGDjNNj2IaBA3w+hfo/rPkQUzbLtp3lu5V7sTpVWkQF89FBbqvrKz6AQwv25dYtUdnY2BkPBiEajEZfLBUDt2rUJCwtjw4YN+fvT09PZunUrHTt2BKBjx46kpqYSExOTf8xPP/2Ey+WiQ4cOZfAuRLkW/TH8/Ia2fedb0Px+ffNUMC6XyrR1h5j41R7sTpW7W4SzdNS/pIgSQpQbbt0i1bt3b9544w1q1KhB06ZN2blzJ++++y4jRowAtCVqxo8fz+uvv079+vWpXbs2L774IhEREfTp0weAxo0b06tXLx555BHmzJmD3W5n3LhxDBw4sFAj9kQltm8FfKuNBuXmp6HDo/rmqWBy7U6e+nI33+49B8Djt9VjQo8GspiwEKJccetC6r333uPFF19kzJgxJCUlERERwaOPPspLL/21ltnTTz9NVlYWo0aNIjU1lS5durB27Vo8PP6aa2bx4sWMGzeO7t2750/IOXPmTD3ekigvjv0EK0YBKrQdAbc+p3eiCuV8Rh6PfLadXadTMRsV3uzXgvvbVNc7lhBCFJlbzyPlLmQeqUrmTAws6A32LGjaF/rPk6VfSlBsQgYj5kcTn5pDgJeZOUPb8K86QXrHEkJUQGXx+e3WLVJClLnzh2Hx/VoRVaeb1rlciqgS8+vh84xdvIPMPAe1g735JKodtYO99Y4lhBDFJoWUEJekndHWz8tJgYib4MFFYJJOzyVl0ZaTvLx6P06XSvvagXw4tA1VvC16xxJCiBsihZQQAFkXtPXz0uMhuAEM+QqsvnqnqhCcLpU3vj3IJ7+fAKDfTdWY0q8FFpNbDxoWQohCkUJKiLwM7XJe8mHwqwZDV4C39NkpCVl5Dv6zdCc/HtTmcvu/Oxow9tZ6KIqMzBNCVAxSSInKzZEHXwyFszvAM/DP9fMi9U5VIZxLy2Hk/O0cOJeOxWTg3QEtuaeFTDkihKhYpJASlZfLqU1xcPwXMHtrl/OqNtQ7VYWwLz6NkQuiSUzPI8jbwkfD23JTjSp6xxJCiBInhZSonFQVvvs/OLAKDGYYuAiqt9E7VYXww/4E/rN0Fzl2J/VDfPgkqh2RgV56xxJCiFIhhZSonH6eDNs/ARToNxfq3qZ3onJPVVXm/XaCN747iKpC1/rBzBpyE34eZr2jCSFEqZFCSlQ+W+bAxqna9t1vQ7N++uapAOxOFy+v3s+SracAGNyhBq/c2xSzUUbmCSEqNimkROWyZxmsnaRt3/o8tPu3vnkqgPRcO2MX72DTkWQUBZ6/qzEju9SWkXlCiEpBCilReRz5EVaN1rbbPwo3T9Q3TwVwOiWbEfOjOZKUiafZyMxBrbm9SajesYQQosxIISUqh9PbtGkOXA5o/gD0mgLSYnJDdpy6yKjPtpOcaSPUz8q84e1oVs1f71hCCFGmpJASFV/SQVj8ADhyoF4PuO8DMEjfnRvxze6zPLVsNzaHi6YRfswb3o4wfw+9YwkhRJmTQkpUbBdPaku/5KZC9fYw4DMwyfpuxaWqKu//dJR31h8GoEfjUGYMbIW3VX6VCCEqJ/ntJyquzPNaEZVxDqo2hsFfgMVb71TlVp7DybMr9rJiRzwA/+5Sm2fvaozRIJdIhRCVlxRSomLKTYfF/SHlGPjXgGErwCtQ71Tl1sUsG48uimHbiRSMBoVX7m3K0H/V1DuWEELoTgopUfHYc2HpYDi3G7yCtfXz/GSNt+I6fj6TEfOjibuQja/VxPtDbuKWBlX1jiWEEG5BCilRsbicsOLfELcJLL4w9CsIrqd3qnJry/ELjF4UQ2q2nWoBnnz6cDsahPrqHUsIIdyGFFKi4lBVWDMeDn4DRgsMWgIRrfVOVW59FXOGZ1fswe5UaRUZwEcPtaWqr1XvWEII4VakkBIVx4ZXYcdnoBjg/k+g9s16JyqXXC6Vd9cf5v2fjwJwd4tw3nmgJR5mo87JhBDC/UghJSqGP96H397Vtu+ZDo176xqnvMq1O3lq2W6+3XMOgHG31uPJ2xtgkJF5QghxRVJIifJv1+fww/PadveXoc1wffOUU+cz8njks+3sOp2K2ajwZr8W3N+mut6xhBDCrUkhJcq32O/h67Hadsdx0GWCvnnKqcOJGTz8aTTxqTn4e5r5cFgb/lUnSO9YQgjh9qSQEuXXyT9gWRSoTmg5CG5/TdbPK4ZfD59n3OIdZOQ5qBXkxSdR7ahT1UfvWEIIUS5IISXKp4S9sGQgOHKhQS+49z1ZP68YFm05ycur9+N0qbSvHciHQ9tQxVuW0BFCiMKSQkqUPyknYFF/yEuDGh3hgflgNOudqlxxulQmf3eQeb+dAKDfTdV4s19zrCYZmSeEEEUhhZQoXzISYWEfyEyE0GYwaCmYPfVOVa5k5Tn4z9Jd/HgwEYD/u6MBY2+thyKXRYUQosikkBLlR06q1hJ1MQ6q1IKhy8EzQN9M5UxCWi4jF0Sz/2w6FpOBdx5oSe+WsnyOEEIUlxRSonyw58DngyBxL3iHaOvn+Ybpnapc2Refxr8XbCchPZcgbwtzH2pLm5pV9I4lhBDlmhRSwv05HfDVCDj1B1j9tZaowDp6pypX1h9I5InPd5Jjd1I/xIdPotoRGeildywhhCj3pJAS7k1V4ZsnIPY7MHnA4KUQ3kLvVOWGqqrM++0Eb3x3EFWFrvWDmTXkJvw8pHO+EEKUBCmkhHtb/yLsWgyKURudV7OT3onKDYfTxcur97N46ykABneowSv3NsVslGkihBCipEghJdzXb9Phj/e07fveh4Z36hqnPEnPtTN28Q42HUlGUeD5uxozskttGZknhBAlTAop4Z52LIQfX9a273gdWg3WN085cjolm5ELojmcmImn2ciMga24o6l0zBdCiNIghZRwPwfXaP2iADqPh06P6xqnPNlx6iKjPttOcqaNUD8r84a3o1k1f71jCSFEhSWFlHAvJzZpI/RUF7QeBj3+q3eicuOb3Wd5atlubA4XTSP8mDe8HWH+HnrHEkKICk0KKeE+zu3W5opy5kGje+Ce6bIIcSGoqsqsn4/y9g+HAejROIQZA1vjbZUfbyGEKG3ym1a4hwvHtFnLbRlQqyv0nwdG+e95PXkOJ8+t2MfyHWcAGNmlNs/d1RijQQpQIYQoC/JJJfSXfk5bPy/rPIS1gIFLwCyXpK7nYpaNRxfFsO1ECkaDwn/vbcqwf9XUO5YQQlQqUkgJfeVchEX9IPWUNlv50OXg4ad3Krd3IjmLEfOjOZGchY/VxKwhN3FLg6p6xxJCiEpHCimhH1s2LHkQkg6ATxgMWwU+IXqncntbjl9g9KIYUrPtVAvw5JOodjQM89U7lhBCVEpSSAl9OO2wbDic3goe/toixFXkstT1LI85wzMr9mB3qrSKDOCjh9pS1deqdywhhKi0pJASZc/lglVj4MgPYPKEwcsgtIneqdyay6Xy7vrDvP/zUQDubh7OOwNa4mE26pxMCCEqNymkROlxuSD1JJyPheTDYPaCqo3g4GrY+yUYTDDgM6jRQe+kbsNmc/LDoQTOpeZiMSncFBmIp9XI9PVHWLP3HABjutXl/+5oiEFG5gkhhO6kkBKl43wsbP8EDn0PmQngdGhzQhkM4LRpx/SZDQ3u0DenG1m4OY6PN50gMT0Hu1MFwGxUMBgMZNucmAwKb/ZrzgNtI3VOKoQQ4hIppETJOx8LP0+BuE2Qm6rdZzSDy/5XEeUZCFYZnXfJws1xTFsXS47dgaIYMBm1y3m5DhVwYlCgW8OqtK4RoHdUIYQQf2PQO4CoYFwuOLAaEveBPUu7z2jVWqNUp3ZbMWqdzbd+qLVUVXI2m5OPN50g1+7AajJgUEAB7K6/jvE0GzAqCj/sT8TlUnXLKoQQoiAppETJSjsN53aBPVsrkoxmwAWOHG2/waR9ASTHwplteiV1Gz8cSuB8Ri4eZhNO199bosCogNWokOdwkedwcjQpk/jUHJ0TCyGEuEQKKVGybJlgywKXE1DBpWpFFWgFlNHjz/XzFHDYICtZz7RuISEtD6eqYjKo2BwubH/2jzIZFDzNBkxGBVUFu1Mlz+EkyyateEII4S6kkBIly+IDFm8wGEEFnH+2nihGbaqD/IFmKpgs4B2sU1D3EeZvxQCk57r4s4bCYlTwMCkoioLTpdWeZqOC1WTE2yJdG4UQwl1IISVKln8khLcCgxnUP1tOFIM29QEKuBxagQUQ3BCqt9cpqPtoERGAUwWH66+RehajVkSpqord6cLLYsRqMlIvxIdqAZ46JxZCCHGJFFKiZBkMUOdmyE7mr4rJoHUud+SC888O597B0OFRMFbu1pV98WkMmLsFm1NFAYwGUBQFu0u7zJdtc2FUoHqgF8G+Vu5oGirzRwkhhBup3J9iouTlZcLaZ8GWoV3iswZoRZXLDihg9oSQJtD1SWjYS++0uvrxQCJPLN1Jts1JvRAf7m0RzvId8QXmkfIwG6gf6kvPpmHc0TSUeiGypp4QQrgTKaREyXHY4MthEB8DnlUg6nswexSc2TykMUR2qNQtUaqq8snvcbz+7QFUFbrWD+b9wTfh72lm9M11L5vZPMDbQrUAT2mJEkIIN1R5P81EyXI5YeWjcOwnMHvDkK8gtLG2L7B2pW99usThdPHfb/azaMspAAZ3qMEr9zbFbNSuslssRu5pUU3PiEIIIYpACilx41QVvn8a9q/QOpk/uBCqt9U7ldtJz7UzbslONh4+j6LA83c1ZmSX2iiKtDQJIUR5JYWUuHG/TIHojwEF+n0I9brrncjtnLmYzYj50RxOzMTTbGTGwFbc0TRM71hCCCFukBRS4sZsnQu/TtG275oGzfrrm8cN7Tx1kUc+iyE5M49QPyvzhrejWTV/vWMJIYQoAVJIieLb+5V2SQ+g27PQ/hF987ih7/aeY8IXu8hzuGgc7scnUW0J95d5oIQQoqKQQkoUz9Eftc7lqNB+FNwySe9EbkVVVT745RjT1sUC0L1RCDMHtcbbKj9yQghRkchvdVF0p6Phi2HaLOXN+kOvt/5cP08A2Bwunlu5l69izgAwonNtnr+7MUaZvkAIISocKaRE0SQdgiUPaAsR1+0OfeZos5kLAFKzbYxeFMOW4ykYFHjl3qYM61hL71hCCCFKiRRSovBST8HCvpBzEaq11aY5MFn0TuU24pKzGDE/muPJWfhYTbw/uDXdGoboHUsIIUQpkkJKFE5WslZEZZyFqo1gyDJtCRgBwLYTKYxauJ3UbDvVAjyZF9WWRmF+escSQghRyqSQEteXlwGL74cLR8E/EoauAK9AvVO5jRU7zjBp+R7sTpWW1f35aHhbQnw99I4lhBCiDEghJa7NkQdLB8PZneAVBMNWgb8sYQLayLz/rT/MzJ+OAnBX8zDeeaAVnhajzsmEEEKUFSmkxNW5nLB8JJzYCBYfGLocguvpncot5NqdTPxqD9/sPgvAY93qMvGOhrKwsBBCVDJSSIkrU1VYMwEOfgNGCwxcAhGt9U7lFpIz8xj12XZ2nErFZFCY3K85A9pG6h1LCCGEDqSQElf202uwYwEoBuj/MdS5Re9EbuFIYgYPz4/mzMUc/DxMzBnWhk51g/WOJYQQQidSSInLbf4ANr2jbd/zP2hyn7553MRvR5J5bHEMGbkOagZ58UlUO+pW9dE7lhBCCB25/UyK8fHxDB06lKCgIDw9PWnevDnbt2/P36+qKi+99BLh4eF4enrSo0cPjhw5UuA5UlJSGDJkCH5+fgQEBDBy5EgyMzPL+q2UD7uXwrpnte3bXoQ2UbrGcRdLtp5i+KfbyMh10K5WFVaO6SxFlBBCCPcupC5evEjnzp0xm818//33HDhwgHfeeYcqVarkHzN16lRmzpzJnDlz2Lp1K97e3vTs2ZPc3Nz8Y4YMGcL+/ftZv349a9asYePGjYwaNUqPt+TeDq+DVWO07X+Nga5P6ZvHDbhcKpO/O8hzK/fidKn0bV2NRf/uQKC3TEQqhBACFFVVVb1DXM0zzzzD77//zqZNm664X1VVIiIieOqpp/i///s/ANLS0ggNDWX+/PkMHDiQgwcP0qRJE6Kjo2nbti0Aa9eu5a677uLMmTNERERc9rx5eXnk5eXl305PTycyMpK0tDT8/CroJIuntsBnfcCRAy0elKVfgGybg/FLd/HDgUQAJvRowBPd66HIuoJCCFEupKen4+/vX6qf3279Sbl69Wratm3LAw88QEhICK1bt+ajjz7K33/ixAkSEhLo0aNH/n3+/v506NCBzZs3A7B582YCAgLyiyiAHj16YDAY2Lp16xVf980338Tf3z//KzKygo/IStwPSwZoRVT9nnDfrEpfRCWm5zLgw838cCARi9HAjIGt+E+P+lJECSGEKMCtPy2PHz/O7NmzqV+/PuvWreOxxx7jiSeeYMGCBQAkJCQAEBoaWuBxoaGh+fsSEhIICSm43pnJZCIwMDD/mH969tlnSUtLy/86ffp0Sb8193ExDhb2g9w0iPwXPDAfjGa9U+lq/9k07nv/d/bFpxPobWHJIx24r5VMQiqEEOJybj1qz+Vy0bZtWyZPngxA69at2bdvH3PmzGH48OGl9rpWqxWr1Vpqz+82MpO09fMyEyCkKQxeChYvvVPpasPBRB7/fCfZNid1q3rzaVR7agRV7nMihBDi6ty6RSo8PJwmTZoUuK9x48acOnUKgLCwMAASExMLHJOYmJi/LywsjKSkpAL7HQ4HKSkp+cdUSrlpsKg/pByHgJrarOWeVa7/uApKVVU++e0Ej3y2nWybk871glgxprMUUUIIIa7JrQupzp07ExsbW+C+w4cPU7NmTQBq165NWFgYGzZsyN+fnp7O1q1b6dixIwAdO3YkNTWVmJiY/GN++uknXC4XHTp0KIN34YbsufD5YEjYA95VYdhK8AvXO5VuHE4XL329n1fXHMClwqD2kcx/uD3+npX7EqcQQojrc+tLexMmTKBTp05MnjyZAQMGsG3bNubOncvcuXMBUBSF8ePH8/rrr1O/fn1q167Niy++SEREBH369AG0FqxevXrxyCOPMGfOHOx2O+PGjWPgwIFXHLFX4Tkd2vp5J38Dq5/WEhVUV+9UusnItTNuyU5+PXweRYFn72zEI13rSKdyIYQQheLWhVS7du1YuXIlzz77LK+++iq1a9dm+vTpDBkyJP+Yp59+mqysLEaNGkVqaipdunRh7dq1eHh45B+zePFixo0bR/fu3TEYDPTv35+ZM2fq8Zb0paqw5j9waA0YrTDocwhvqXcq3Zy5mM3I+duJTczAw2xg+oOt6dWsEl/uFUIIUWRuPY+UuyiLeSjKxPqX4PcZ2vp5AxZC43v0TqSbnacu8shnMSRn5hHia2Xe8HY0r+6vdywhhBAlqCw+v926RUqUoN9nakUUQO+ZlbqI+m7vOSZ8sYs8h4vG4X7MG96WiABPvWMJIYQoh6SQqgx2Lob1L2rbPV6Bm4bpm0cnqqrywS/HmLZOG8BwW6MQZg5qjY9VfgyEEEIUj3yCVHSHvoPVj2vbnR6HLuN1jaMXm8PF8yv3sizmDAAPd67FC3c3wWiQTuVCCCGKTwqpiizud1gWBaoTWg2B21/TO5EuUrNtjF4Uw5bjKRgU+O+9TXmoYy29YwkhhKgApJCqqM7tgc8HgjMPGt6l9YuqhEP645KzGDE/muPJWfhYTbw3uDW3Ngy5/gOFEEKIQpBCqiK6cEybtTwvHWp2hvs/AWPl+1ZvO5HCqIXbSc22Uy3Ak3lRbWkUVo5HXQohhHA7le/TtaLLSNDWz8tKgtDm2lxR5so3Im3lzjNM+movNqeLltX9+Wh4W0J8Pa7/QCGEEKIIpJCqSHIuwsJ+kHoSqtTWZi33qFxzI6mqyv/WH2bmT0cBuLNZGO8OaIWnxahzMiGEEBWRFFIVhS0blgyEpP3gE6qtn+cbqneqMpVrd/L0V3tYvfssAI91q8vEOxpikJF5QgghSokUUhWB066Nzju9Baz+MHQFBNbWO1WZupCZxyOfbWfHqVRMBoXJfZszoF2k3rGEEEJUcFJIlXcuF3w9Do6sA5MHDP4CwprpnapMHUnMYMSCaE6n5ODnYWLO0DZ0qhesdywhhBCVgBRS5Zmqwg8vwJ6loBhhwGdQs6PeqcrUb0eSeWxxDBm5DmoGeTFveDvqhfjoHUsIIUQlIYVUefbbu7Bllrbd5wNo0FPfPGXs822neGHVPpwulXa1qvDhsLYEelv0jiWEEKISkUKqvIqZDxte1bZ7ToaWA3WNU5acLpW31h5i7sbjAPRtXY0p/ZtjNcnIPCGEEGVLCqny6MBqWDNB2+7yJHQcq2+eMpRtc/CfpbtYfyARgAk9GvBE93oolXDWdiGEEPqTQqq8Of4rLB8JqgtuGg7dX9I7UZlJTM9l5IJo9sWnYzEamPZAC+5rVU3vWEIIISoxKaTKk7M7YelgcNqgcW+453+VZv28/WfTGDl/OwnpuQR6W5g7rA1tawXqHUsIIUQlJ4VUeZF8FBbdD7ZMqNUV+n0MhsrRJ2jDwUQe/3wn2TYndat682lUe2oEeekdSwghhJBCqlxIi4eFfSA7GcJbwsAlYK7468apqsqnv8fx+rcHcKnQuV4QHwxpg7+nWe9oQgghBCCFlPvLToFF/SDtNATVgyHLwcNP71SlzuF08co3B1i45SQAA9tF8lqfZpiNBp2TCSGEEH+RQsqd2bJgyQA4fwh8w7X183yq6p2q1GXk2hm3ZCe/Hj6PosCzdzbika51ZGSeEEIItyOFlLty2OCLYXAmGjwCtCIqoIbeqUrdmYvZjJy/ndjEDDzMBqY/2JpezcL0jiWEEEJckRRS7sjlglWPwbENYPaCIcsgpLHeqUrdrtOp/HvBdpIz8wjxtfLx8La0qB6gdywhhBDiqqSQ0pPTAae3wPlYbcHhGh0hoCasewb2fQUGEwxYCJHt9U5aIlwulZMpWWyPu0iu3UmDUB/a1AjEZDLw3d5zTPhiF3kOF43CfPkkqh0RAZ56RxZCCCGuSQopvcSuhU3vaEWU06bd5+EH3lUhcZ92u88cqN9Dv4wl6GhSBh/8dJRNRy+QmWdHVcFiMlCvqje1q/qwfEc8ALc1CmHmoNb4WOW/phBCCPcnn1Z6iF0L30+CrCSt1cnDH1xObYReprb0CV2fghYP6JuzhBxNyuC1NQfYHncRlwreFhOKopJjc7L7TBo7TqcBENWpFi/e0wSjQTqVCyGEKB+kkCprTgdsmQ3ZF7TLeWYvbXZypx1cdu0YsxeYPLW+UobyPdzf5VL5fs859sWnAxDgacJgUHC5VGxOJ05VO65usDcv3NVYiighhBDlSvn+lC6PzmyD5CParOQmq1ZE2XMh54K23+QJGODUH9rcUeVcfGoO0XEXyXM48bQYMRgUHE4X5zNt2JwuFMDLbCDb7mDH6Yt6xxVCCCGKRAqpspaVrPWJUgygGMGRp81YDmD2BM8qgAp5mdpyMOVcls1Bhk3rE2U2KOQ5XCRl2nC4VIwKBPtYsJgU7A6VC1k2veMKIYQQRSKX9sqadzAYLWDPAkcu5KQAqtY65Rn0V5Fl9QGLj95pb5i3xYSvxYyiQI7dSWqOAwCzUSHI24IKqA4Fs0m7LYQQQpQn0iJV1qq3h+D6Wudyp10rmowW8ArW9tuytHX0anQC/0h9s5aAagGetKtVBavJiM3hwmoy4GE2EOxjwaAo5NkcOFWVelV9uSmyit5xhRBCiCKRQqqsGU3wr8fAK0jrXG7y0EbtOWxa65SiaAsTN7m33Hc0BzAYFO5sEU6zan4oivJnnygjDqeLjFw7eU6VIG8rUZ1rYTKV//crhBCicpFPLj007AV3vgWhzbR+Upf6Q1l9odE90HMyVG2od8oSUy/ElxfvaULPJqH4epjJsTvJyHViUBSaRPjxUu8mdG8cqndMIYQQosgUVVVVvUO4u/T0dPz9/UlLS8PPz6/knvhKM5tXqVUhWqKu5FozmwshhBAlrdQ+v/9GOpvryWiCWl20r0rAYFCoHexD7eDy34leCCGEALm0J4QQQghRbFJICSGEEEIUkxRSQgghhBDFJIWUEEIIIUQxSSElhBBCCFFMUkgJIYQQQhSTFFJCCCGEEMUkhZQQQgghRDFJISWEEEIIUUxSSAkhhBBCFJMUUkIIIYQQxSRr7RXCpXWd09PTdU4ihBBCiMK69Ll96XO8NEghVQgZGRkAREZG6pxECCGEEEWVkZGBv79/qTy3opZmmVZBuFwuzp49i6+vL4qi6B2nSNLT04mMjOT06dP4+fnpHcetybkqHDlPhSPnqXDkPBWOnKfC+ed5UlWVjIwMIiIiMBhKpzeTtEgVgsFgoHr16nrHuCF+fn7yw1dIcq4KR85T4ch5Khw5T4Uj56lw/n6eSqsl6hLpbC6EEEIIUUxSSAkhhBBCFJMUUhWc1Wrl5Zdfxmq16h3F7cm5Khw5T4Uj56lw5DwVjpynwtHjPElncyGEEEKIYpIWKSGEEEKIYpJCSgghhBCimKSQEkIIIYQoJimkhBBCCCGKSQqpcio+Pp6hQ4cSFBSEp6cnzZs3Z/v27fn7VVXlpZdeIjw8HE9PT3r06MGRI0cKPEdKSgpDhgzBz8+PgIAARo4cSWZmZlm/lVLjdDp58cUXqV27Np6entStW5fXXnutwJpLlfU8bdy4kd69exMREYGiKKxatarA/pI6L3v27KFr1654eHgQGRnJ1KlTS/utlahrnSe73c6kSZNo3rw53t7eRERE8NBDD3H27NkCz1HZz9M/jR49GkVRmD59eoH75TxpDh48yL333ou/vz/e3t60a9eOU6dO5e/Pzc1l7NixBAUF4ePjQ//+/UlMTCzwHKdOneLuu+/Gy8uLkJAQJk6ciMPhKO23V2Kud54yMzMZN24c1atXx9PTkyZNmjBnzpwCx5TpeVJFuZOSkqLWrFlTjYqKUrdu3aoeP35cXbdunXr06NH8Y6ZMmaL6+/urq1atUnfv3q3ee++9au3atdWcnJz8Y3r16qW2bNlS3bJli7pp0ya1Xr166qBBg/R4S6XijTfeUIOCgtQ1a9aoJ06cUJctW6b6+PioM2bMyD+msp6n7777Tn3++efVFStWqIC6cuXKAvtL4rykpaWpoaGh6pAhQ9R9+/apn3/+uerp6al++OGHZfU2b9i1zlNqaqrao0cP9YsvvlAPHTqkbt68WW3fvr3apk2bAs9R2c/T361YsUJt2bKlGhERof7vf/8rsE/Ok6oePXpUDQwMVCdOnKju2LFDPXr0qPr111+riYmJ+ceMHj1ajYyMVDds2KBu375d/de//qV26tQpf7/D4VCbNWum9ujRQ925c6f63XffqcHBweqzzz5bVm/zhl3vPD3yyCNq3bp11Z9//lk9ceKE+uGHH6pGo1H9+uuv848py/MkhVQ5NGnSJLVLly5X3e9yudSwsDB12rRp+felpqaqVqtV/fzzz1VVVdUDBw6ogBodHZ1/zPfff68qiqLGx8eXXvgydPfdd6sjRowocF+/fv3UIUOGqKoq5+mSf/6iKqnz8sEHH6hVqlRR8/Ly8o+ZNGmS2rBhw1J+R6XjWgXCJdu2bVMB9eTJk6qqynn6uzNnzqjVqlVT9+3bp9asWbNAISXnSfPggw+qQ4cOvepjUlNTVbPZrC5btiz/voMHD6qAunnzZlVVtSLEYDCoCQkJ+cfMnj1b9fPzK3DuyosrnaemTZuqr776aoH7brrpJvX5559XVbXsz5Nc2iuHVq9eTdu2bXnggQcICQmhdevWfPTRR/n7T5w4QUJCAj169Mi/z9/fnw4dOrB582YANm/eTEBAAG3bts0/pkePHhgMBrZu3Vp2b6YUderUiQ0bNnD48GEAdu/ezW+//cadd94JyHm6mpI6L5s3b+bmm2/GYrHkH9OzZ09iY2O5ePFiGb2bspWWloaiKAQEBAByni5xuVwMGzaMiRMn0rRp08v2y3nSztG3335LgwYN6NmzJyEhIXTo0KHAZa2YmBjsdnuBn81GjRpRo0aNAj+bzZs3JzQ0NP+Ynj17kp6ezv79+8vs/ZSmTp06sXr1auLj41FVlZ9//pnDhw9zxx13AGV/nqSQKoeOHz/O7NmzqV+/PuvWreOxxx7jiSeeYMGCBQAkJCQAFPgPcun2pX0JCQmEhIQU2G8ymQgMDMw/prx75plnGDhwII0aNcJsNtO6dWvGjx/PkCFDADlPV1NS5yUhIeGKz/H316hIcnNzmTRpEoMGDcpfLFXOk+att97CZDLxxBNPXHG/nCdISkoiMzOTKVOm0KtXL3744Qf69u1Lv379+PXXXwHtfVoslvxC/ZJ//mxW5PME8N5779GkSROqV6+OxWKhV69ezJo1i5tvvhko+/NkKub7EDpyuVy0bduWyZMnA9C6dWv27dvHnDlzGD58uM7p3MeXX37J4sWLWbJkCU2bNmXXrl2MHz+eiIgIOU+iRNntdgYMGICqqsyePVvvOG4lJiaGGTNmsGPHDhRF0TuO23K5XADcd999TJgwAYBWrVrxxx9/MGfOHG655RY947mV9957jy1btrB69Wpq1qzJxo0bGTt2LBEREQVaocqKtEiVQ+Hh4TRp0qTAfY0bN84f2REWFgZw2QiFxMTE/H1hYWEkJSUV2O9wOEhJSck/prybOHFifqtU8+bNGTZsGBMmTODNN98E5DxdTUmdl7CwsCs+x99foyK4VESdPHmS9evX57dGgZwngE2bNpGUlESNGjUwmUyYTCZOnjzJU089Ra1atQA5TwDBwcGYTKbr/m632WykpqYWOOafP5sV+Tzl5OTw3HPP8e6779K7d29atGjBuHHjePDBB3n77beBsj9PUkiVQ507dyY2NrbAfYcPH6ZmzZoA1K5dm7CwMDZs2JC/Pz09na1bt9KxY0cAOnbsSGpqKjExMfnH/PTTT7hcLjp06FAG76L0ZWdnYzAU/C9uNBrz//KT83RlJXVeOnbsyMaNG7Hb7fnHrF+/noYNG1KlSpUyejel61IRdeTIEX788UeCgoIK7JfzBMOGDWPPnj3s2rUr/ysiIoKJEyeybt06QM4TgMVioV27dtf83d6mTRvMZnOBn83Y2FhOnTpV4Gdz7969BQrTSwX+P4u08shut2O326/5u73Mz1ORuqYLt7Bt2zbVZDKpb7zxhnrkyBF18eLFqpeXl7po0aL8Y6ZMmaIGBASoX3/9tbpnzx71vvvuu+Lw9datW6tbt25Vf/vtN7V+/frlflj/3w0fPlytVq1a/vQHK1asUIODg9Wnn346/5jKep4yMjLUnTt3qjt37lQB9d1331V37tyZP9qsJM5LamqqGhoaqg4bNkzdt2+funTpUtXLy6tcDVe/1nmy2Wzqvffeq1avXl3dtWuXeu7cufyvv4/6qezn6Ur+OWpPVeU8qao2PYTZbFbnzp2rHjlyRH3vvfdUo9Gobtq0Kf85Ro8erdaoUUP96aef1O3bt6sdO3ZUO3bsmL//0rD+O+64Q921a5e6du1atWrVquVq+oPrnadbbrlFbdq0qfrzzz+rx48fVz/99FPVw8ND/eCDD/KfoyzPkxRS5dQ333yjNmvWTLVarWqjRo3UuXPnFtjvcrnUF198UQ0NDVWtVqvavXt3NTY2tsAxFy5cUAcNGqT6+Piofn5+6sMPP6xmZGSU5dsoVenp6ep//vMftUaNGqqHh4dap04d9fnnny/wIVdZz9PPP/+sApd9DR8+XFXVkjsvu3fvVrt06aJarf/f3p2FRNn9cQD/TguN44KZa4u+uWRiWpia5oVZhC3UuJBG2ih6EY64kEIQKgkpgYoUiBRUpgRaFCZJGYShSFqmmOVSiqLBhJV4IWouc96L6MHJbZqG/PN/v58rn+ec55zfeW78cuYws0Fs2bJFXLly5W8t0SiWe08DAwOLtgEQ9fX10hj/9fe0mMWCFN/TDzdv3hSurq5CLpeL3bt3i+rqap0xJicnhVqtFhs3bhQKhUKEh4cLjUaj02dwcFAcPXpUmJiYCGtra5GRkSFmZmb+xhKNYqX3pNFoRHx8vNi8ebOQy+XC3d1dFBUVCa1WK43xN9+TTIh5X/NMRERERHrjGSkiIiIiAzFIERERERmIQYqIiIjIQAxSRERERAZikCIiIiIyEIMUERERkYEYpIiIiIgMxCBFREREZCAGKSIyGplMhurq6tUuQy/x8fEICwtb7TIWVVZWBktLy9Uug4j0wCBFRHr5/PkzUlJS4OzsjA0bNmDbtm04ceKEzg+DEhH916xb7QKI6H/f4OAggoKCYGlpiYKCAnh5eWFmZgZ1dXVITk5GT0/PapdIepiZmcH69etXuwyi/yvckSKiFanVashkMrx69QqRkZHYsWMHPD09cf78eTQ3N+v0/fr1K8LDw6FQKODm5oaamhqpbW5uDomJidi+fTtMTEzg7u6Oq1ev6jz/8yO3wsJCODg4YNOmTUhOTsbMzIzU559//kF+fj4SEhJgbm4OR0dH3LhxQ2ec4eFhREVFwdLSElZWVlAqlRgcHNR7zT8/Xqurq4OHhwfMzMxw5MgRaDQaqc+BAweQnp6u81xYWBji4+N1ar18+TJUKhXMzMzg5OSEmpoafPnyBUqlEmZmZvD29kZra+uCGqqrq+Hm5ga5XI7Q0FAMDw/rtD969Ag+Pj6Qy+VwdnZGbm4uZmdnpXaZTIbS0lKcPHkSpqamyMvL03v9RKQfBikiWtbo6CiePn2K5ORkmJqaLmj/9SxPbm4uoqKi8PbtWxw7dgwxMTEYHR0FAGi1WmzduhX3799HV1cXcnJycPHiRdy7d09njPr6evT396O+vh537txBWVkZysrKdPoUFRXB19cX7e3tUKvVSEpKQm9vL4AfOy+hoaEwNzdHY2MjmpqapCA0PT2t99onJiZQWFiIiooKNDQ0YGhoCJmZmXo//1NxcTGCgoLQ3t6O48eP4+zZs1CpVIiNjUVbWxtcXFygUqkw/zfkJyYmkJeXh/LycjQ1NWFsbAynT5+W2hsbG6FSqZCWloauri5cv34dZWVlC8LSpUuXEB4ejs7OTiQkJPx27US0AkFEtIyWlhYBQDx8+HDFvgBEVlaWdD0+Pi4AiCdPniz5THJysoiMjJSu4+LihJOTk5idnZXunTp1SkRHR0vXTk5OIjY2VrrWarXC1tZWlJaWCiGEqKioEO7u7kKr1Up9vn//LkxMTERdXZ00j1KpXLKu27dvCwCir69PuldSUiLs7Oyk6+DgYJGWlqbznFKpFHFxcUvWqtFoBACRnZ0t3Xv58qUAIDQajc7czc3NUp/u7m4BQLS0tAghhDh06JDIz8/XmbuiokI4ODhI1wBEenr6kmskoj/HM1JEtCwxb5dEH97e3tLfpqamsLCwwMjIiHSvpKQEt27dwtDQECYnJzE9PY09e/bojOHp6Ym1a9dK1w4ODujs7FxyHplMBnt7e2mejo4O9PX1wdzcXOeZqakp9Pf3670WhUIBFxcXnTrmr0Vf82u1s7MDAHh5eS24NzIyAnt7ewDAunXr4OfnJ/XZuXMnLC0t0d3dDX9/f3R0dKCpqUlnB2pubg5TU1OYmJiAQqEAAPj6+v52vUSkPwYpIlqWm5sbZDKZ3gfKfz3MLJPJoNVqAQCVlZXIzMxEUVERAgMDYW5ujoKCArS0tOg9hj59xsfHsXfvXty9e3dBfTY2NnqtY6k55gfLNWvWLAia889yLTaOTCZb8t6va1zO+Pg4cnNzERERsaBNLpdLfy/2cSwRGQ+DFBEty8rKCqGhoSgpKUFqauqCf8xjY2N6f+dRU1MT9u/fD7VaLd37nR0iffn4+KCqqgq2trawsLAw+vg/2djY6Bw+n5ubw7t37xASEvLHY8/OzqK1tRX+/v4AgN7eXoyNjcHDwwPAjzX29vbC1dX1j+ciIsPxsDkRraikpARzc3Pw9/fHgwcP8PHjR3R3d+PatWsIDAzUexw3Nze0trairq4OHz58QHZ2Nl6/fm30emNiYmBtbQ2lUonGxkYMDAzgxYsXSE1NxadPn4w2z8GDB1FbW4va2lr09PQgKSkJY2NjRhl7/fr1SElJQUtLC968eYP4+HgEBARIwSonJwfl5eXIzc3F+/fv0d3djcrKSmRlZRllfiLSD4MUEa3I2dkZbW1tCAkJQUZGBnbt2oXDhw/j+fPnKC0t1Xucc+fOISIiAtHR0di3bx++ffumsztlLAqFAg0NDXB0dERERAQ8PDyQmJiIqakpo+5QJSQkIC4uDiqVCsHBwXB2djbKbhTwYw0XLlzAmTNnEBQUBDMzM1RVVUntoaGhePz4MZ49ewY/Pz8EBASguLgYTk5ORpmfiPQjE797kpSIiIiIAHBHioiIiMhgDFJEREREBmKQIiIiIjIQgxQRERGRgRikiIiIiAzEIEVERERkIAYpIiIiIgMxSBEREREZiEGKiIiIyEAMUkREREQGYpAiIiIiMtC/13PQvs86gosAAAAASUVORK5CYII=", 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", 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" ] @@ -615,12 +641,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -683,14 +709,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Detection efficiency: [0.02615627 0.0133356 ]\n" + "Detection efficiency: [0.02613734 0.01332036]\n" ] } ], @@ -707,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -715,50 +741,50 @@ "output_type": "stream", "text": [ "Processing Co60_1...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4064 4065 4066]\n", - "Counts measured: [np.float64(47036.03830551509), np.float64(44205.39387658654)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(46957.38643825811), np.float64(44045.459628740195)]\n", "Processing Co60_2...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4026 4027 4028]\n", - "Counts measured: [np.float64(22368.399686140438), np.float64(22576.072847491916)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(22416.58388011696), np.float64(22529.586339447393)]\n", "Processing Co60_3...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", - "Counts measured: [np.float64(16154.528046739626), np.float64(13558.504178212668)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(16054.125444967927), np.float64(13553.682613733048)]\n", "Processing Co60_4...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4090 4091 4092]\n", - "Counts measured: [np.float64(29827.171495947478), np.float64(30058.52544959307)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(29843.318378842872), np.float64(29774.61769554247)]\n", "Processing Co60_5...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4038 4039 4040]\n", - "Counts measured: [np.float64(10901.980284982346), np.float64(9338.42098327135)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(10864.152988263319), np.float64(9305.379734783886)]\n", "Processing Cs137_1...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4084 4085 4086]\n", - "Counts measured: [np.float64(663460.7071836735)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(663133.8158949262)]\n", "Processing Cs137_2...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", - "Counts measured: [np.float64(1082378.6051444719)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(1081873.0672619161)]\n", "Processing Cs137_3...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4087 4088 4089]\n", - "Counts measured: [np.float64(1133356.7423398674)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(1132822.892638708)]\n", "Processing Cs137_4...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4057 4058 4059]\n", - "Counts measured: [np.float64(782959.8477999661)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(782585.5604400036)]\n", "Processing Mn54_1...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", - "Counts measured: [np.float64(8319.977883210617)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(8283.256725880987)]\n", "Processing Mn54_2...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", - "Counts measured: [np.float64(8913.022518906384)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(8872.47112105926)]\n", "Processing Mn54_3...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4086 4087 4088]\n", - "Counts measured: [np.float64(3577.3883280784066)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(3578.3870157813676)]\n", "Processing Na22_2...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4093 4094 4095]\n", - "Counts measured: [np.float64(2218849.9724823623), np.float64(502280.9213963469)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(2218104.329563081), np.float64(502104.2113759391)]\n", "Processing Na22_3...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4067 4068 4069]\n", - "Counts measured: [np.float64(1351072.877852661), np.float64(371085.7465765631)]\n", + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(1350638.6115192047), np.float64(370990.75368719886)]\n", "Processing Na22_4...\n", - "[ 0.78384389 -38.37449615] [ 0 1 2 ... 4091 4092 4093]\n", - "Counts measured: [np.float64(2131183.0539309), np.float64(484562.51706595556)]\n" + "[ 0.78350295 -37.75182068] [ 0 1 2 ... 4094 4095 4096]\n", + "Counts measured: [np.float64(2130508.5872191284), np.float64(484392.7750372546)]\n" ] } ], @@ -816,12 +842,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -853,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -883,12 +909,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -908,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -916,17 +942,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n", + "\n", "Processing zirconium_1...\n", - "\n", + "\n", "Processing zirconium_2...\n", - "\n", + "\n", "Processing zirconium_3...\n", - "\n" + "\n" ] } ], @@ -979,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -989,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -997,17 +1023,17 @@ "output_type": "stream", "text": [ "Processing niobium_1...\n", - "\n", + "\n", "Processing niobium_2...\n", - "\n", + "\n", "Processing niobium_3...\n", - "\n", + "\n", "Processing zirconium_1...\n", - "\n", + "\n", "Processing zirconium_2...\n", - "\n", + "\n", "Processing zirconium_3...\n", - "\n" + "\n" ] } ], @@ -1025,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1033,30 +1059,30 @@ "output_type": "stream", "text": [ "Processing channel 4...\n", - "niobium_1: [421177.56667845] ± [5568.95764857] emmited gamma rays\n", - "niobium_1: 9.75e+07 neutrons/s\n", - "niobium_2: [1095916.4761057] ± [8983.17846382] emmited gamma rays\n", - "niobium_2: 1.07e+08 neutrons/s\n", - "niobium_3: [899693.81776096] ± [8139.33020337] emmited gamma rays\n", - "niobium_3: 8.99e+07 neutrons/s\n", - "zirconium_1: [1678703.37830234] ± [10951.01206566] emmited gamma rays\n", - "zirconium_1: 8.79e+07 neutrons/s\n", - "zirconium_2: [1366007.96450431] ± [9878.565664] emmited gamma rays\n", - "zirconium_2: 8.48e+07 neutrons/s\n", - "zirconium_3: [1968107.97886165] ± [11857.461856] emmited gamma rays\n", - "zirconium_3: 8.40e+07 neutrons/s\n", + "niobium_1: [425116.86783661] ± [5600.39547916] emmited gamma rays\n", + "niobium_1: 9.84e+07 neutrons/s\n", + "niobium_2: [1114068.28261905] ± [9066.09849059] emmited gamma rays\n", + "niobium_2: 1.08e+08 neutrons/s\n", + "niobium_3: [904166.22420225] ± [8167.49105503] emmited gamma rays\n", + "niobium_3: 9.03e+07 neutrons/s\n", + "zirconium_1: [1656561.40179429] ± [10888.6344659] emmited gamma rays\n", + "zirconium_1: 8.67e+07 neutrons/s\n", + "zirconium_2: [1370576.32770845] ± [9904.24244678] emmited gamma rays\n", + "zirconium_2: 8.51e+07 neutrons/s\n", + "zirconium_3: [1965135.61962815] ± [11859.48727681] emmited gamma rays\n", + "zirconium_3: 8.39e+07 neutrons/s\n", "Processing channel 5...\n", - "niobium_1: [605163.6341688] ± [6675.40639871] emmited gamma rays\n", - "niobium_1: 1.22e+08 neutrons/s\n", - "niobium_2: [1407211.31183843] ± [10179.37135757] emmited gamma rays\n", + "niobium_1: [601205.7136999] ± [6660.02834282] emmited gamma rays\n", + "niobium_1: 1.21e+08 neutrons/s\n", + "niobium_2: [1411473.67191165] ± [10204.71590298] emmited gamma rays\n", "niobium_2: 1.24e+08 neutrons/s\n", - "niobium_3: [1403706.02542878] ± [10166.68532395] emmited gamma rays\n", - "niobium_3: 1.30e+08 neutrons/s\n", - "zirconium_1: [2290779.86918969] ± [12792.60393814] emmited gamma rays\n", - "zirconium_1: 1.14e+08 neutrons/s\n", - "zirconium_2: [2200967.12008692] ± [12539.32193514] emmited gamma rays\n", + "niobium_3: [1405390.0211149] ± [10182.70027162] emmited gamma rays\n", + "niobium_3: 1.31e+08 neutrons/s\n", + "zirconium_1: [2297027.91382572] ± [12821.91182926] emmited gamma rays\n", + "zirconium_1: 1.15e+08 neutrons/s\n", + "zirconium_2: [2209564.50370307] ± [12575.43427949] emmited gamma rays\n", "zirconium_2: 1.23e+08 neutrons/s\n", - "zirconium_3: [3136271.66467644] ± [14968.35630193] emmited gamma rays\n", + "zirconium_3: [3141098.68436108] ± [14993.75603572] emmited gamma rays\n", "zirconium_3: 1.25e+08 neutrons/s\n" ] } @@ -1094,25 +1120,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Processing channel 4...\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n" + "Processing channel 4...\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -1124,18 +1144,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Processing channel 5...\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n", - "[ 0 1 2 ... 4093 4094 4095]\n" + "Processing channel 5...\n" ] }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 9dfa7f2..22e4e26 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -47,13 +47,15 @@ class Detector: _spectrum: Union[NDArray[np.float64], None] = None _bin_edges: Union[NDArray[np.float64], None] = None - def __init__(self, channel_nb) -> None: + def __init__(self, channel_nb, nb_digitizer_bins=4096) -> None: """ Initialize a Detector object. Args: channel_nb: channel number of the detector + nb_digitizer_bins: number of digitizer bins for the detector. """ self.channel_nb = channel_nb + self.nb_digitizer_bins = nb_digitizer_bins self.events = np.empty((0, 2)) # Initialize as empty 2D array with 2 columns self.live_count_time = None self.real_count_time = None @@ -96,9 +98,12 @@ def get_energy_hist( energy_values = np.nan_to_num(energy_values, nan=0) if bins is None: - bins = np.arange( - int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + 1 - ) + if self.nb_digitizer_bins == None: + bins = np.arange( + int(np.nanmin(energy_values)), int(np.nanmax(energy_values)) + 1 + ) + else: + bins = np.arange(self.nb_digitizer_bins + 1) return np.histogram(energy_values, bins=bins) @@ -106,42 +111,38 @@ def get_energy_hist_background_substract( self, background_detector: "Detector", bins: Union[NDArray[np.float64], None] = None, + live_or_real: str = "live", ) -> Tuple[np.ndarray, np.ndarray]: + """Get the energy histogram of the detector events with background subtraction. + + Args: + background_detector: _description_ + bins: _description_. Defaults to None. + live_or_real: When doing the background sub decide whether the background + histogram is scaled by live or real count time. + """ assert ( self.channel_nb == background_detector.channel_nb ), f"Channel number mismatch: {self.channel_nb} != {background_detector.channel_nb}" - ps_to_seconds = 1e-12 raw_hist, raw_bin_edges = self.get_energy_hist(bins=bins) + b_hist, _ = background_detector.get_energy_hist(bins=raw_bin_edges) - # If background spectrum and bin edges are already calculated, return them - if ( - background_detector._spectrum is not None - and background_detector._bin_edges is not None - ): - raise ValueError("Background spectrum and bin edges must be calculated.") - - background_times = background_detector.events[:, 0].copy() - background_energies = background_detector.events[:, 1].copy() - - if self.real_count_time < background_detector.real_count_time: - # get background counts for the duration of the sample count - end_ind = np.nanargmin( - np.abs( - self.real_count_time / ps_to_seconds - - (background_times - background_times[0]) - ) - ) - b_hist, _ = np.histogram( - background_energies[: end_ind + 1], - bins=raw_bin_edges, + if live_or_real == "live": + # Scale background histogram by live count time + b_hist = b_hist * ( + self.live_count_time / background_detector.live_count_time ) - else: - b_hist, _ = np.histogram(background_energies, bins=raw_bin_edges) + elif live_or_real == "real": + # Scale background histogram by real count time b_hist = b_hist * ( self.real_count_time / background_detector.real_count_time ) + else: + raise ValueError( + f"Invalid live_or_real value: {live_or_real}. Use 'live' or 'real'." + ) hist_background_substracted = raw_hist - b_hist diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 505c042..a8a8bc8 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -431,7 +431,8 @@ def measured_spectrum(energies): # RUN computed_hist, _ = detector_meas.get_energy_hist_background_substract( - background_detector=background_detector + background_detector=background_detector, + live_or_real="real", ) # TEST @@ -558,6 +559,7 @@ def test_check_source_detection_efficiency(expected_efficiency): background_measurement = compass.Measurement("background") bg_detector = compass.Detector(channel_nb=1) bg_detector.real_count_time = 0.5 + bg_detector.live_count_time = bg_detector.real_count_time background_measurement.detectors = [bg_detector] # RUN @@ -614,7 +616,7 @@ def test_get_calibration_data(a, b): measurement = compass.CheckSourceMeasurement(name="test measurement") measurement.check_source = check_source measurement.start_time = datetime.datetime(2024, 11, 7) - detector = compass.Detector(channel_nb=channel_nb) + detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None) energy_events = np.random.normal( loc=energy_channel, scale=30, size=int(nb_events_measured) ) @@ -626,14 +628,16 @@ def test_get_calibration_data(a, b): time_events = np.random.uniform(0, 100, size=int(nb_events_measured)) detector.events = np.column_stack((time_events, energy_events)) detector.real_count_time = 100 + detector.live_count_time = detector.real_count_time measurement.detectors = [detector] measurements.append(measurement) # create background measurement background_measurement = compass.Measurement("background") - bg_detector = compass.Detector(channel_nb=channel_nb) - bg_detector.real_count_time = 100 + bg_detector = compass.Detector(channel_nb=channel_nb, nb_digitizer_bins=None) + bg_detector.live_count_time = 100 + bg_detector.real_count_time = bg_detector.live_count_time background_measurement.detectors = [bg_detector] # RUN @@ -963,38 +967,46 @@ def test_activationfoil_density_thickness_validation(): cross_section=20.0, ) - with pytest.raises(ValueError, match="Thickness and density must either both be floats or both be None."): + with pytest.raises( + ValueError, + match="Thickness and density must either both be floats or both be None.", + ): ActivationFoil(reaction=reaction, mass=1.0, name="foil", density=1.0) - with pytest.raises(ValueError, match="Thickness and density must either both be floats or both be None."): + with pytest.raises( + ValueError, + match="Thickness and density must either both be floats or both be None.", + ): ActivationFoil(reaction=reaction, mass=1.0, name="foil", thickness=0.1) -def create_test_measurement(name: str, num_detectors: int = 2, num_events: int = 100) -> compass.Measurement: +def create_test_measurement( + name: str, num_detectors: int = 2, num_events: int = 100 +) -> compass.Measurement: """ Helper function to create a test measurement with synthetic data. """ measurement = compass.Measurement(name) - + # Set start and stop times measurement.start_time = datetime.datetime(2025, 1, 1, 10, 0, 0) measurement.stop_time = datetime.datetime(2025, 1, 1, 10, 15, 0) - + # Create detectors with synthetic events for channel_nb in range(num_detectors): detector = compass.Detector(channel_nb) - + # Generate synthetic events (time in ps, energy) times = np.random.uniform(0, 1e12, num_events) # Random times in ps energies = np.random.uniform(100, 1000, num_events) # Random energies detector.events = np.column_stack((times, energies)) - + # Set timing information detector.live_count_time = 900.0 # 15 minutes detector.real_count_time = 900.0 - + measurement.detectors.append(detector) - + return measurement @@ -1003,30 +1015,32 @@ def test_measurement_to_h5_single(tmpdir): Test the Measurement.to_h5 method for a single measurement. """ # Create test measurement - measurement = create_test_measurement("test_measurement", num_detectors=2, num_events=50) - + measurement = create_test_measurement( + "test_measurement", num_detectors=2, num_events=50 + ) + # Save to HDF5 h5_file = os.path.join(tmpdir, "test_single.h5") measurement.to_h5(h5_file, mode="w") - + # Verify file exists and has correct structure assert os.path.exists(h5_file) - + with h5py.File(h5_file, "r") as f: # Check measurement group exists assert "test_measurement" in f measurement_group = f["test_measurement"] - + # Check attributes assert "start_time" in measurement_group.attrs assert "stop_time" in measurement_group.attrs assert measurement_group.attrs["start_time"] == "2025-01-01T10:00:00" assert measurement_group.attrs["stop_time"] == "2025-01-01T10:15:00" - + # Check detectors assert "detector_0" in measurement_group assert "detector_1" in measurement_group - + # Check detector data detector_group = measurement_group["detector_0"] assert "events" in detector_group @@ -1041,26 +1055,30 @@ def test_measurement_to_h5_append_mode(tmpdir): Test the Measurement.to_h5 method with append mode for multiple measurements. """ # Create test measurements - measurement1 = create_test_measurement("measurement_1", num_detectors=1, num_events=30) - measurement2 = create_test_measurement("measurement_2", num_detectors=2, num_events=40) - + measurement1 = create_test_measurement( + "measurement_1", num_detectors=1, num_events=30 + ) + measurement2 = create_test_measurement( + "measurement_2", num_detectors=2, num_events=40 + ) + h5_file = os.path.join(tmpdir, "test_append.h5") - + # Save first measurement measurement1.to_h5(h5_file, mode="w") - + # Append second measurement measurement2.to_h5(h5_file, mode="a") - + # Verify both measurements are in the file with h5py.File(h5_file, "r") as f: assert "measurement_1" in f assert "measurement_2" in f - + # Check first measurement assert "detector_0" in f["measurement_1"] assert f["measurement_1"]["detector_0"]["events"].shape[0] == 30 - + # Check second measurement assert "detector_0" in f["measurement_2"] assert "detector_1" in f["measurement_2"] @@ -1074,19 +1092,19 @@ def test_measurement_to_h5_overwrite_existing(tmpdir): # Create initial measurement measurement1 = create_test_measurement("same_name", num_detectors=1, num_events=30) measurement1.detectors[0].live_count_time = 100.0 - + # Create updated measurement with same name measurement2 = create_test_measurement("same_name", num_detectors=1, num_events=50) measurement2.detectors[0].live_count_time = 200.0 - + h5_file = os.path.join(tmpdir, "test_overwrite.h5") - + # Save first measurement measurement1.to_h5(h5_file, mode="w") - + # Overwrite with second measurement measurement2.to_h5(h5_file, mode="a") - + # Verify only the second measurement data remains with h5py.File(h5_file, "r") as f: assert "same_name" in f @@ -1105,23 +1123,23 @@ def test_measurement_write_multiple_to_h5(tmpdir): create_test_measurement("exp_2", num_detectors=2, num_events=30), create_test_measurement("exp_3", num_detectors=3, num_events=40), ] - + h5_file = os.path.join(tmpdir, "test_multiple.h5") - + # Write all measurements to file compass.Measurement.write_multiple_to_h5(measurements, h5_file) - + # Verify all measurements are in the file with h5py.File(h5_file, "r") as f: assert "exp_1" in f assert "exp_2" in f assert "exp_3" in f - + # Check each measurement has correct number of detectors assert len([k for k in f["exp_1"].keys() if k.startswith("detector_")]) == 1 assert len([k for k in f["exp_2"].keys() if k.startswith("detector_")]) == 2 assert len([k for k in f["exp_3"].keys() if k.startswith("detector_")]) == 3 - + # Check event counts assert f["exp_1"]["detector_0"]["events"].shape[0] == 20 assert f["exp_2"]["detector_0"]["events"].shape[0] == 30 @@ -1133,19 +1151,23 @@ def test_measurement_from_h5_single(tmpdir): Test the Measurement.from_h5 method for loading a single measurement. """ # Create and save a test measurement - original_measurement = create_test_measurement("test_load", num_detectors=2, num_events=35) + original_measurement = create_test_measurement( + "test_load", num_detectors=2, num_events=35 + ) h5_file = os.path.join(tmpdir, "test_load_single.h5") original_measurement.to_h5(h5_file) - + # Load the measurement back - loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="test_load") - + loaded_measurement = compass.Measurement.from_h5( + h5_file, measurement_name="test_load" + ) + # Verify loaded measurement matches original assert loaded_measurement.name == "test_load" assert loaded_measurement.start_time == original_measurement.start_time assert loaded_measurement.stop_time == original_measurement.stop_time assert len(loaded_measurement.detectors) == 2 - + # Check detector data for i, detector in enumerate(loaded_measurement.detectors): original_detector = original_measurement.detectors[i] @@ -1164,23 +1186,23 @@ def test_measurement_from_h5_all_measurements(tmpdir): create_test_measurement("load_1", num_detectors=1, num_events=25), create_test_measurement("load_2", num_detectors=2, num_events=35), ] - + h5_file = os.path.join(tmpdir, "test_load_all.h5") compass.Measurement.write_multiple_to_h5(measurements, h5_file) - + # Load all measurements loaded_measurements = compass.Measurement.from_h5(h5_file) - + # Verify we got all measurements assert len(loaded_measurements) == 2 loaded_names = [m.name for m in loaded_measurements] assert "load_1" in loaded_names assert "load_2" in loaded_names - + # Find corresponding measurements load_1 = next(m for m in loaded_measurements if m.name == "load_1") load_2 = next(m for m in loaded_measurements if m.name == "load_2") - + assert len(load_1.detectors) == 1 assert len(load_2.detectors) == 2 assert load_1.detectors[0].events.shape[0] == 25 @@ -1195,7 +1217,7 @@ def test_measurement_from_h5_nonexistent_measurement(tmpdir): measurement = create_test_measurement("existing", num_detectors=1, num_events=10) h5_file = os.path.join(tmpdir, "test_nonexistent.h5") measurement.to_h5(h5_file) - + # Try to load a non-existent measurement with pytest.raises(ValueError, match="Measurement 'nonexistent' not found in file"): compass.Measurement.from_h5(h5_file, measurement_name="nonexistent") @@ -1209,29 +1231,33 @@ def test_measurement_h5_roundtrip(tmpdir): measurement = compass.Measurement("roundtrip_test") measurement.start_time = datetime.datetime(2025, 7, 2, 14, 30, 0) measurement.stop_time = datetime.datetime(2025, 7, 2, 15, 0, 0) - + # Create detector with specific events detector = compass.Detector(channel_nb=5) - detector.events = np.array([ - [1000000000, 150.5], # time in ps, energy - [2000000000, 250.7], - [3000000000, 350.9], - ]) + detector.events = np.array( + [ + [1000000000, 150.5], # time in ps, energy + [2000000000, 250.7], + [3000000000, 350.9], + ] + ) detector.live_count_time = 1800.0 detector.real_count_time = 1800.0 measurement.detectors = [detector] - + # Save and load h5_file = os.path.join(tmpdir, "roundtrip.h5") measurement.to_h5(h5_file) - loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="roundtrip_test") - + loaded_measurement = compass.Measurement.from_h5( + h5_file, measurement_name="roundtrip_test" + ) + # Verify exact data integrity assert loaded_measurement.name == "roundtrip_test" assert loaded_measurement.start_time == measurement.start_time assert loaded_measurement.stop_time == measurement.stop_time assert len(loaded_measurement.detectors) == 1 - + loaded_detector = loaded_measurement.detectors[0] assert loaded_detector.channel_nb == 5 assert loaded_detector.live_count_time == 1800.0 @@ -1248,12 +1274,14 @@ def test_measurement_h5_empty_measurement(tmpdir): measurement.start_time = datetime.datetime(2025, 1, 1, 12, 0, 0) measurement.stop_time = datetime.datetime(2025, 1, 1, 12, 30, 0) measurement.detectors = [] # No detectors - + # Save and load h5_file = os.path.join(tmpdir, "empty.h5") measurement.to_h5(h5_file) - loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="empty_test") - + loaded_measurement = compass.Measurement.from_h5( + h5_file, measurement_name="empty_test" + ) + # Verify empty measurement assert loaded_measurement.name == "empty_test" assert loaded_measurement.start_time == measurement.start_time @@ -1269,51 +1297,55 @@ def test_measurement_h5_roundtrip_spectrum_only(tmpdir): measurement = compass.Measurement("roundtrip_spectrum_test") measurement.start_time = datetime.datetime(2025, 7, 2, 14, 30, 0) measurement.stop_time = datetime.datetime(2025, 7, 2, 15, 0, 0) - + # Create detector with specific events that will create a predictable spectrum detector = compass.Detector(channel_nb=5) # Create events with integer energies for predictable histogram - detector.events = np.array([ - [1000000000, 100.0], # time in ps, energy - [2000000000, 100.0], # Same energy -> 2 counts in bin 100 - [3000000000, 200.0], # Different energy -> 1 count in bin 200 - [4000000000, 200.0], # Same energy -> 2 counts in bin 200 - [5000000000, 300.0], # Different energy -> 1 count in bin 300 - [5000000000, 300.0], # Same energy -> 2 counts in bin 300 - [5000000000, 400.0], # Different energy -> 1 count in bin 400 - ]) + detector.events = np.array( + [ + [1000000000, 100.0], # time in ps, energy + [2000000000, 100.0], # Same energy -> 2 counts in bin 100 + [3000000000, 200.0], # Different energy -> 1 count in bin 200 + [4000000000, 200.0], # Same energy -> 2 counts in bin 200 + [5000000000, 300.0], # Different energy -> 1 count in bin 300 + [5000000000, 300.0], # Same energy -> 2 counts in bin 300 + [5000000000, 400.0], # Different energy -> 1 count in bin 400 + ] + ) detector.live_count_time = 1800.0 detector.real_count_time = 1800.0 measurement.detectors = [detector] - + # Get the expected spectrum before saving expected_hist, expected_bin_edges = detector.get_energy_hist(bins=None) - + # Save with spectrum_only=True and load h5_file = os.path.join(tmpdir, "roundtrip_spectrum.h5") measurement.to_h5(h5_file, spectrum_only=True) - loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="roundtrip_spectrum_test") - + loaded_measurement = compass.Measurement.from_h5( + h5_file, measurement_name="roundtrip_spectrum_test" + ) + # Verify basic measurement data integrity assert loaded_measurement.name == "roundtrip_spectrum_test" assert loaded_measurement.start_time == measurement.start_time assert loaded_measurement.stop_time == measurement.stop_time assert len(loaded_measurement.detectors) == 1 - + loaded_detector = loaded_measurement.detectors[0] assert loaded_detector.channel_nb == 5 assert loaded_detector.live_count_time == 1800.0 assert loaded_detector.real_count_time == 1800.0 - + # Verify events array is empty (spectrum_only mode) assert loaded_detector.events.shape[0] == 0 - + # Verify spectrum data is present and correct - assert hasattr(loaded_detector, 'spectrum') - assert hasattr(loaded_detector, 'bin_edges') + assert hasattr(loaded_detector, "spectrum") + assert hasattr(loaded_detector, "bin_edges") np.testing.assert_array_equal(loaded_detector.spectrum, expected_hist) np.testing.assert_array_equal(loaded_detector.bin_edges, expected_bin_edges) - + # Verify the spectrum contains expected counts # The exact bin positions depend on the histogram implementation print(f"Spectrum: {loaded_detector.spectrum}") @@ -1326,37 +1358,39 @@ def test_measurement_h5_spectrum_only_file_structure(tmpdir): Test that spectrum_only mode creates the correct HDF5 file structure. """ # Create measurement with events - measurement = create_test_measurement("spectrum_structure_test", num_detectors=1, num_events=100) - + measurement = create_test_measurement( + "spectrum_structure_test", num_detectors=1, num_events=100 + ) + # Save with spectrum_only=True h5_file = os.path.join(tmpdir, "spectrum_structure.h5") measurement.to_h5(h5_file, spectrum_only=True) - + # Verify file structure with h5py.File(h5_file, "r") as f: assert "spectrum_structure_test" in f measurement_group = f["spectrum_structure_test"] - + # Check measurement attributes assert "start_time" in measurement_group.attrs assert "stop_time" in measurement_group.attrs - + # Check detector group assert "detector_0" in measurement_group detector_group = measurement_group["detector_0"] - + # In spectrum_only mode, should have spectrum and bin_edges, but empty events assert "spectrum" in detector_group assert "bin_edges" in detector_group assert "events" in detector_group - + # Events should be empty array assert detector_group["events"].shape[0] == 0 - + # Spectrum should have data assert detector_group["spectrum"].shape[0] > 0 assert detector_group["bin_edges"].shape[0] > 0 - + # Timing attributes should still be present assert "live_count_time" in detector_group.attrs assert "real_count_time" in detector_group.attrs @@ -1368,23 +1402,23 @@ def test_measurement_h5_spectrum_only_vs_full_size_comparison(tmpdir): """ # Create measurement with many events to see file size difference measurement = create_test_measurement("size_test", num_detectors=1, num_events=1000) - + # Save in both modes h5_file_full = os.path.join(tmpdir, "full_events.h5") h5_file_spectrum = os.path.join(tmpdir, "spectrum_only.h5") - + measurement.to_h5(h5_file_full, spectrum_only=False) measurement.to_h5(h5_file_spectrum, spectrum_only=True) - + # Compare file sizes full_size = os.path.getsize(h5_file_full) spectrum_size = os.path.getsize(h5_file_spectrum) - + # Spectrum-only file should be smaller (unless histogram has more bins than events) # At minimum, both files should exist and have reasonable sizes assert full_size > 0 assert spectrum_size > 0 - + # For 1000 events, the full file should typically be larger # (though this could depend on the specific data and compression) print(f"Full events file size: {full_size} bytes") @@ -1399,49 +1433,54 @@ def test_measurement_h5_spectrum_only_analysis_capability(tmpdir): measurement = compass.Measurement("analysis_test") measurement.start_time = datetime.datetime(2025, 7, 2, 10, 0, 0) measurement.stop_time = datetime.datetime(2025, 7, 2, 10, 30, 0) - + detector = compass.Detector(channel_nb=1) # Create events with known energy distribution - energies = np.concatenate([ - np.full(50, 500.0), # 50 events at 500 keV - np.full(30, 600.0), # 30 events at 600 keV - np.full(20, 700.0), # 20 events at 700 keV - ]) + energies = np.concatenate( + [ + np.full(50, 500.0), # 50 events at 500 keV + np.full(30, 600.0), # 30 events at 600 keV + np.full(20, 700.0), # 20 events at 700 keV + ] + ) times = np.random.uniform(0, 1e12, len(energies)) detector.events = np.column_stack((times, energies)) detector.live_count_time = 1800.0 detector.real_count_time = 1800.0 measurement.detectors = [detector] - + # Save with spectrum_only=True h5_file = os.path.join(tmpdir, "analysis_spectrum.h5") measurement.to_h5(h5_file, spectrum_only=True) - + # Load and analyze spectrum - loaded_measurement = compass.Measurement.from_h5(h5_file, measurement_name="analysis_test") + loaded_measurement = compass.Measurement.from_h5( + h5_file, measurement_name="analysis_test" + ) loaded_detector = loaded_measurement.detectors[0] - + # Verify we can analyze the spectrum - assert hasattr(loaded_detector, 'spectrum') - assert hasattr(loaded_detector, 'bin_edges') - + assert hasattr(loaded_detector, "spectrum") + assert hasattr(loaded_detector, "bin_edges") + # Check total counts total_counts = np.sum(loaded_detector.spectrum) assert total_counts == 100 # 50 + 30 + 20 - + # Check that peak energies are preserved in the spectrum # Find bin centers bin_centers = (loaded_detector.bin_edges[:-1] + loaded_detector.bin_edges[1:]) / 2 - + # Find peaks in the spectrum (simple approach) - peak_indices = np.where(loaded_detector.spectrum > 15)[0] # Bins with significant counts + peak_indices = np.where(loaded_detector.spectrum > 15)[ + 0 + ] # Bins with significant counts peak_energies = bin_centers[peak_indices] - + # Should have peaks near our input energies (500, 600, 700) assert len(peak_energies) >= 3, "Should find at least 3 energy peaks" - + # Verify the spectrum structure makes sense assert loaded_detector.spectrum.dtype in [np.int32, np.int64, np.uint32, np.uint64] assert loaded_detector.bin_edges.dtype in [np.int32, np.int64, np.uint32, np.uint64] assert len(loaded_detector.bin_edges) == len(loaded_detector.spectrum) + 1 - From 44b628d19f010a30e098ac8cd8c074eea470ea4b Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Jul 2025 19:43:11 -0400 Subject: [PATCH 129/137] fixed get_events() for waveform csv --- .../activation_foils/compass.py | 47 ++++++++++++++----- 1 file changed, 35 insertions(+), 12 deletions(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 22e4e26..619172a 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -938,26 +938,49 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar time_values[ch] = np.empty(0) energy_values[ch] = np.empty(0) for i, filename in enumerate(data_filenames[ch]): + print(f'Processing File {i}') # only the first file has a header if i == 0: - header = 0 + # determine the column names + # + # Typically, setting the header argument to 1 + # would normally work, but on some CoMPASS csv + # files, specifically those with waveform data, + # the column header has far fewer entries + # than the number of columns in the csv file. + # This is due to the "SAMPLES" column, which + # contains the waveform data actually being made + # up of the 7th-nth column of an n column csv file. + # + # So to mitigate this, we will read in the header + # manually and determine which column of + # the dataset to read in. + first_row_df = pd.read_csv(csv_file_path, + delimiter=";", + header=None, + nrows=1) + column_names = first_row_df.to_numpy()[0] + # Determine which column applies to time and energy + time_col = np.where(column_names=="TIMETAG")[0][0] + energy_col = np.where(column_names=="ENERGY")[0][0] + # First csv file has header, so skip it + # because we already read it in + skiprows=1 else: - header = None + # For subsequent csv files, don't skip any rows + # as there won't be any header + skiprows=0 csv_file_path = os.path.join(directory, filename) - df = pd.read_csv(csv_file_path, delimiter=";", header=header) + df = pd.read_csv(csv_file_path, + delimiter=";", + header=None, + skiprows=skiprows) - # read the header and store in names - if i == 0: - names = df.columns.values - else: - # apply the column names if not the first file - df.columns = names - - time_data = df["TIMETAG"].to_numpy() - energy_data = df["ENERGY"].to_numpy() + time_data = df[time_col].to_numpy() + energy_data = df[energy_col].to_numpy() # Extract and append the energy data to the list time_values[ch] = np.concatenate([time_values[ch], time_data]) From 47471aefcf1c5a13140fd5970638d890852eb57f Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Jul 2025 20:11:13 -0400 Subject: [PATCH 130/137] Fixed csv_file_path bug --- libra_toolbox/neutron_detection/activation_foils/compass.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/libra_toolbox/neutron_detection/activation_foils/compass.py b/libra_toolbox/neutron_detection/activation_foils/compass.py index 619172a..a57e9fe 100644 --- a/libra_toolbox/neutron_detection/activation_foils/compass.py +++ b/libra_toolbox/neutron_detection/activation_foils/compass.py @@ -940,6 +940,8 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar for i, filename in enumerate(data_filenames[ch]): print(f'Processing File {i}') + csv_file_path = os.path.join(directory, filename) + # only the first file has a header if i == 0: # determine the column names @@ -972,7 +974,6 @@ def get_events(directory: str) -> Tuple[Dict[int, np.ndarray], Dict[int, np.ndar # as there won't be any header skiprows=0 - csv_file_path = os.path.join(directory, filename) df = pd.read_csv(csv_file_path, delimiter=";", From 55720c05e116243ffbaa7234860884e48026ebf2 Mon Sep 17 00:00:00 2001 From: cdunn314 Date: Fri, 25 Jul 2025 20:27:20 -0400 Subject: [PATCH 131/137] test for reading in csvs with waveform data --- ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 0 ...92_Co60_0_872uCi_19Mar2014_250318_run2.csv | 0 ..._Co60_0_872uCi_19Mar2014_250318_run2_1.csv | 0 ...0_Co60_0_872uCi_19Mar2014_20241213_4in.CSV | 7 ++++++ ...Co60_0_872uCi_19Mar2014_20241213_4in_1.CSV | 2 ++ test/neutron_detection/test_compass.py | 25 +++++++++++-------- 6 files changed, 23 insertions(+), 11 deletions(-) rename test/neutron_detection/compass_test_data/events/{ => no_waveforms}/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv (100%) rename test/neutron_detection/compass_test_data/events/{ => no_waveforms}/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv (100%) rename test/neutron_detection/compass_test_data/events/{ => no_waveforms}/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv (100%) create mode 100644 test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in.CSV create mode 100644 test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in_1.CSV diff --git a/test/neutron_detection/compass_test_data/events/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv similarity index 100% rename from test/neutron_detection/compass_test_data/events/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv rename to test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH15@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv diff --git a/test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv b/test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv similarity index 100% rename from test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv rename to test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2.csv diff --git a/test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv b/test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv similarity index 100% rename from test/neutron_detection/compass_test_data/events/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv rename to test/neutron_detection/compass_test_data/events/no_waveforms/Data_CH5@V1725_292_Co60_0_872uCi_19Mar2014_250318_run2_1.csv diff --git a/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in.CSV b/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in.CSV new file mode 100644 index 0000000..a4f58d2 --- /dev/null +++ b/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in.CSV @@ -0,0 +1,7 @@ +BOARD;CHANNEL;TIMETAG;ENERGY;ENERGYSHORT;FLAGS;PROBE_CODE;SAMPLES 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\ No newline at end of file diff --git a/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in_1.CSV b/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in_1.CSV new file mode 100644 index 0000000..9810254 --- /dev/null +++ b/test/neutron_detection/compass_test_data/events/waveforms/Data_CH4@DT5725_1360_Co60_0_872uCi_19Mar2014_20241213_4in_1.CSV @@ -0,0 +1,2 @@ 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\ No newline at end of file diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index a8a8bc8..7e1b50a 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -97,32 +97,35 @@ def test_sort_compass_files(tmpdir, base_name: str, expected_filenames: dict): @pytest.mark.parametrize( - "expected_time, expected_energy, expected_idx", + "waveform_directory, expected_time, expected_energy, expected_idx, expected_keys, test_ch", [ - (6685836624, 515, 5), - (11116032249, 568, 6), - (1623550122, 589, -1), - (535148093, 1237, -2), + ("no_waveforms", 6685836624, 515, 5, [5, 15], 5), + ("no_waveforms", 11116032249, 568, 6, [5, 15], 5), + ("no_waveforms", 1623550122, 589, -1, [5, 15], 5), + ("no_waveforms", 535148093, 1237, -2, [5, 15], 5), + ("waveforms", 80413091, 1727, 0, [4], 4), + ("waveforms", 2850906749, 1539, 2, [4], 4), + ("waveforms", 14300873206559, 1700, 6, [4], 4) ], ) -def test_get_events(expected_time, expected_energy, expected_idx): +def test_get_events_no_waveforms(waveform_directory, expected_time, + expected_energy, expected_idx, + expected_keys, test_ch): """ Test the get_events function from the compass module. Checks that specific time and energy values are returned for a given channel """ - test_directory = Path(__file__).parent / "compass_test_data/events" + test_directory = Path(__file__).parent / "compass_test_data/events" / waveform_directory times, energies = compass.get_events(test_directory) assert isinstance(times, dict) assert isinstance(energies, dict) - expected_keys = [5, 15] for key in expected_keys: assert key in times assert key in energies - ch = 5 - assert times[ch][expected_idx] == expected_time - assert energies[ch][expected_idx] == expected_energy + assert times[test_ch][expected_idx] == expected_time + assert energies[test_ch][expected_idx] == expected_energy utc_minus5 = datetime.timezone(datetime.timedelta(hours=-5)) From 981ecc5b843042ee78b4526c6509e84d79f53ccb Mon Sep 17 00:00:00 2001 From: Collin Dunn Date: Wed, 30 Jul 2025 12:33:36 -0400 Subject: [PATCH 132/137] changed test function name --- test/neutron_detection/test_compass.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/neutron_detection/test_compass.py b/test/neutron_detection/test_compass.py index 7e1b50a..d01f2e2 100644 --- a/test/neutron_detection/test_compass.py +++ b/test/neutron_detection/test_compass.py @@ -108,9 +108,9 @@ def test_sort_compass_files(tmpdir, base_name: str, expected_filenames: dict): ("waveforms", 14300873206559, 1700, 6, [4], 4) ], ) -def test_get_events_no_waveforms(waveform_directory, expected_time, - expected_energy, expected_idx, - expected_keys, test_ch): +def test_get_events(waveform_directory, expected_time, + expected_energy, expected_idx, + expected_keys, test_ch): """ Test the get_events function from the compass module. Checks that specific time and energy values are returned for a given channel From e798efed99d444d764c63b6b854179f22ce853f6 Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 31 Jul 2025 15:48:15 -0400 Subject: [PATCH 133/137] some comments --- libra_toolbox/neutronics/__init__.py | 3 ++- libra_toolbox/neutronics/materials.py | 29 ++++++++++++++------------- 2 files changed, 17 insertions(+), 15 deletions(-) diff --git a/libra_toolbox/neutronics/__init__.py b/libra_toolbox/neutronics/__init__.py index 3bbb7ad..3fd285d 100644 --- a/libra_toolbox/neutronics/__init__.py +++ b/libra_toolbox/neutronics/__init__.py @@ -1 +1,2 @@ -from .neutron_source import * \ No newline at end of file +from .neutron_source import * +import materials diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py index 27a63b6..5d3cdea 100644 --- a/libra_toolbox/neutronics/materials.py +++ b/libra_toolbox/neutronics/materials.py @@ -11,7 +11,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302. https://doi.org/10.1063/1.555590 """ - temp = temp + 273.15 # Convert temperature from Celsius to Kelvin + temp = temp + 273.15 # Convert temperature from Celsius to Kelvin C = LiCl_frac * 100 # Convert molar concentration to molar percent a = 2.25621 @@ -25,7 +25,6 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): rho = a + b * C + c * temp + d * C**2 \ + e * C**3 + f * temp * C**2 + g * C * temp**2 - return rho @@ -53,7 +52,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): # SS304.temperature = 700 + 273 SS304.add_element('C', 0.000800, "wo") SS304.add_element('Mn', 0.020000, "wo") -SS304.add_element('P', 0.000450 , "wo") +SS304.add_element('P', 0.000450, "wo") SS304.add_element('S', 0.000300, "wo") SS304.add_element('Si', 0.010000, "wo") SS304.add_element('Cr', 0.190000, "wo") @@ -81,7 +80,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): # air air = openmc.Material(name="Air") -air.add_element("C", 0.00012399 , 'wo') +air.add_element("C", 0.00012399, 'wo') air.add_element('N', 0.75527, 'wo') air.add_element('O', 0.23178, 'wo') air.add_element('Ar', 0.012827, 'wo') @@ -92,48 +91,50 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): epoxy.add_element('C', 0.70, 'wo') epoxy.add_element('H', 0.08, 'wo') epoxy.add_element('O', 0.15, 'wo') -epoxy.add_element('N', 0.07, 'wo') -epoxy.set_density('g/cm3', 1.2) +epoxy.add_element('N', 0.07, 'wo') +epoxy.set_density('g/cm3', 1.2) # helium @5psig -pressure = 34473.8 # Pa ~ 5 psig +pressure = 34473.8 # Pa ~ 5 psig temperature = 300 # K R_he = 2077 # J/(kg*K) -density = pressure / (R_he * temperature) # in kg/cm^3 -density *= 1 / 1000 # in g/cm^3 +density = pressure / (R_he * temperature) # in kg/cm^3 +density *= 1 / 1000 # in g/cm^3 he = openmc.Material(name="Helium") he.add_element('He', 1.0, 'ao') he.set_density('g/cm3', density) -# PbLi - natural - pure +# PbLi - eutectic - natural - pure pbli = openmc.Material(name="pbli") pbli.add_element("Pb", 84.2, "ao") pbli.add_element("Li", 15.2, "ao") pbli.set_density("g/cm3", 11) +# lif-bef - eutectic - natural - pure flibe = openmc.Material(name="flibe") flibe.add_element("Li", 2.0, "ao") flibe.add_element("Be", 1.0, "ao") flibe.add_element("F", 4.0, "ao") flibe.set_density("g/cm3", 1.94) -# lif-licl - natural - pure +# lif-licl - eutectic - natural - pure cllif_nat = openmc.Material(name='ClLiF natural') LiCl_frac = 0.695 # at.fr. cllif_nat.add_element('F', .5*(1 - LiCl_frac), 'ao') cllif_nat.add_element('Li', 1.0, 'ao') cllif_nat.add_element('Cl', .5*LiCl_frac, 'ao') -cllif_nat.set_density('g/cm3', get_exp_cllif_density(650)) # 69.5 at. % LiCL at 650 C +cllif_nat.set_density('g/cm3', get_exp_cllif_density(650) + ) # 69.5 at. % LiCL at 650 C -# lif-licl - natural - EuF3 spiced +# lif-licl - eutectic - natural - EuF3 spiced spicyclif = openmc.Material(name="spicyclif") spicyclif.add_element("F", .15935, "wo") spicyclif.add_element("Li", .17857, "wo") spicyclif.add_element("Cl", .6340, "wo") spicyclif.add_element("Eu", .0279, "wo") -# FLiNaK - natural - pure +# FLiNaK - eutectic - natural - pure flinak = openmc.Material(name="flinak") flinak.add_element("F", 50, "ao") flinak.add_element("Li", 23.25, "ao") From 7f19fc38f187f30ccd470fa551033d7e2d011ea1 Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Fri, 13 Dec 2024 10:42:47 -0500 Subject: [PATCH 134/137] + neutronics materials file --- libra_toolbox/neutronics/materials.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py new file mode 100644 index 0000000..aa4b4b0 --- /dev/null +++ b/libra_toolbox/neutronics/materials.py @@ -0,0 +1 @@ +import openmc \ No newline at end of file From c46c7218f3dddb140e33e9b02929f8d37668d760 Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 19 Dec 2024 13:51:02 -0500 Subject: [PATCH 135/137] - neutronics materials file --- libra_toolbox/neutronics/materials.py | 1 - 1 file changed, 1 deletion(-) delete mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py deleted file mode 100644 index aa4b4b0..0000000 --- a/libra_toolbox/neutronics/materials.py +++ /dev/null @@ -1 +0,0 @@ -import openmc \ No newline at end of file From cbb5900fdf97d6e76836be78b01a7b3be05055e3 Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 19 Dec 2024 16:46:00 -0500 Subject: [PATCH 136/137] + neutronics mat file & examples --- libra_toolbox/neutronics/materials.py | 142 ++++++++++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 libra_toolbox/neutronics/materials.py diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py new file mode 100644 index 0000000..27a63b6 --- /dev/null +++ b/libra_toolbox/neutronics/materials.py @@ -0,0 +1,142 @@ +import openmc + + +def get_exp_cllif_density(temp, LiCl_frac=0.695): + """ Calculates density of ClLiF [g/cc] from temperature in Celsius + and molar concentration of LiCl. Valid for 660 C - 1000 C. + Source: + G. J. Janz, R. P. T. Tomkins, C. B. Allen; + Molten Salts: Volume 4, Part 4 + Mixed Halide Melts Electrical Conductance, Density, Viscosity, and Surface Tension Data. + J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302. + https://doi.org/10.1063/1.555590 + """ + temp = temp + 273.15 # Convert temperature from Celsius to Kelvin + C = LiCl_frac * 100 # Convert molar concentration to molar percent + + a = 2.25621 + b = -8.20475e-3 + c = -4.09235e-4 + d = 6.37250e-5 + e = -2.52846e-7 + f = 8.73570e-9 + g = -5.11184e-10 + + rho = a + b * C + c * temp + d * C**2 \ + + e * C**3 + f * temp * C**2 + g * C * temp**2 + + + return rho + + +# Define Materials +# Source: PNNL Materials Compendium April 2021 +# PNNL-15870, Rev. 2 +inconel625 = openmc.Material(name='Inconel 625') +inconel625.set_density('g/cm3', 8.44) +inconel625.add_element('C', 0.000990, 'wo') +inconel625.add_element('Al', 0.003960, 'wo') +inconel625.add_element('Si', 0.004950, 'wo') +inconel625.add_element('P', 0.000148, 'wo') +inconel625.add_element('S', 0.000148, 'wo') +inconel625.add_element('Ti', 0.003960, 'wo') +inconel625.add_element('Cr', 0.215000, 'wo') +inconel625.add_element('Mn', 0.004950, 'wo') +inconel625.add_element('Fe', 0.049495, 'wo') +inconel625.add_element('Co', 0.009899, 'wo') +inconel625.add_element('Ni', 0.580000, 'wo') +inconel625.add_element('Nb', 0.036500, 'wo') +inconel625.add_element('Mo', 0.090000, 'wo') + +# Stainless Steel 304 from PNNL Materials Compendium (PNNL-15870 Rev2) +SS304 = openmc.Material(name="Stainless Steel 304") +# SS304.temperature = 700 + 273 +SS304.add_element('C', 0.000800, "wo") +SS304.add_element('Mn', 0.020000, "wo") +SS304.add_element('P', 0.000450 , "wo") +SS304.add_element('S', 0.000300, "wo") +SS304.add_element('Si', 0.010000, "wo") +SS304.add_element('Cr', 0.190000, "wo") +SS304.add_element('Ni', 0.095000, "wo") +SS304.add_element('Fe', 0.683450, "wo") +SS304.set_density("g/cm3", 8.00) + +# Using Microtherm with 1 a% Al2O3, 27 a% ZrO2, and 72 a% SiO2 +# https://www.foundryservice.com/product/microporous-silica-insulating-boards-mintherm-microtherm-1925of-grades/ +firebrick = openmc.Material(name="Firebrick") +# Estimate average temperature of Firebrick to be around 300 C +# Firebrick.temperature = 273 + 300 +firebrick.add_element('Al', 0.004, 'ao') +firebrick.add_element('O', 0.666, 'ao') +firebrick.add_element('Si', 0.240, 'ao') +firebrick.add_element('Zr', 0.090, 'ao') +firebrick.set_density('g/cm3', 0.30) + +# alumina insulation +# data from https://precision-ceramics.com/materials/alumina/ +alumina = openmc.Material(name='Alumina insulation') +alumina.add_element('O', 0.6, 'ao') +alumina.add_element('Al', 0.4, 'ao') +alumina.set_density('g/cm3', 3.98) + +# air +air = openmc.Material(name="Air") +air.add_element("C", 0.00012399 , 'wo') +air.add_element('N', 0.75527, 'wo') +air.add_element('O', 0.23178, 'wo') +air.add_element('Ar', 0.012827, 'wo') +air.set_density('g/cm3', 0.0012) + +# epoxy +epoxy = openmc.Material(name='Epoxy') +epoxy.add_element('C', 0.70, 'wo') +epoxy.add_element('H', 0.08, 'wo') +epoxy.add_element('O', 0.15, 'wo') +epoxy.add_element('N', 0.07, 'wo') +epoxy.set_density('g/cm3', 1.2) + +# helium @5psig +pressure = 34473.8 # Pa ~ 5 psig +temperature = 300 # K +R_he = 2077 # J/(kg*K) +density = pressure / (R_he * temperature) # in kg/cm^3 +density *= 1 / 1000 # in g/cm^3 +he = openmc.Material(name="Helium") +he.add_element('He', 1.0, 'ao') +he.set_density('g/cm3', density) + +# PbLi - natural - pure +pbli = openmc.Material(name="pbli") +pbli.add_element("Pb", 84.2, "ao") +pbli.add_element("Li", 15.2, "ao") +pbli.set_density("g/cm3", 11) + +flibe = openmc.Material(name="flibe") +flibe.add_element("Li", 2.0, "ao") +flibe.add_element("Be", 1.0, "ao") +flibe.add_element("F", 4.0, "ao") +flibe.set_density("g/cm3", 1.94) + +# lif-licl - natural - pure +cllif_nat = openmc.Material(name='ClLiF natural') +LiCl_frac = 0.695 # at.fr. + +cllif_nat.add_element('F', .5*(1 - LiCl_frac), 'ao') +cllif_nat.add_element('Li', 1.0, 'ao') +cllif_nat.add_element('Cl', .5*LiCl_frac, 'ao') +cllif_nat.set_density('g/cm3', get_exp_cllif_density(650)) # 69.5 at. % LiCL at 650 C + +# lif-licl - natural - EuF3 spiced +spicyclif = openmc.Material(name="spicyclif") +spicyclif.add_element("F", .15935, "wo") +spicyclif.add_element("Li", .17857, "wo") +spicyclif.add_element("Cl", .6340, "wo") +spicyclif.add_element("Eu", .0279, "wo") + +# FLiNaK - natural - pure +flinak = openmc.Material(name="flinak") +flinak.add_element("F", 50, "ao") +flinak.add_element("Li", 23.25, "ao") +flinak.add_element("Na", 5.75, "ao") +flinak.add_element("K", 21, "ao") +flinak.set_density("g/cm3", 2.020) From 1bf7ebabd6e3e4b089b07e8d9c1b5434791d1a8f Mon Sep 17 00:00:00 2001 From: Stefano Segantin Date: Thu, 31 Jul 2025 15:48:15 -0400 Subject: [PATCH 137/137] some comments --- libra_toolbox/neutronics/__init__.py | 1 + libra_toolbox/neutronics/materials.py | 29 ++++++++++++++------------- 2 files changed, 16 insertions(+), 14 deletions(-) diff --git a/libra_toolbox/neutronics/__init__.py b/libra_toolbox/neutronics/__init__.py index c116466..a6cad33 100644 --- a/libra_toolbox/neutronics/__init__.py +++ b/libra_toolbox/neutronics/__init__.py @@ -1,2 +1,3 @@ from .neutron_source import * from .vault import * +import materials diff --git a/libra_toolbox/neutronics/materials.py b/libra_toolbox/neutronics/materials.py index 27a63b6..5d3cdea 100644 --- a/libra_toolbox/neutronics/materials.py +++ b/libra_toolbox/neutronics/materials.py @@ -11,7 +11,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): J. Phys. Chem. Ref. Data 1 January 1979; 8 (1): 125–302. https://doi.org/10.1063/1.555590 """ - temp = temp + 273.15 # Convert temperature from Celsius to Kelvin + temp = temp + 273.15 # Convert temperature from Celsius to Kelvin C = LiCl_frac * 100 # Convert molar concentration to molar percent a = 2.25621 @@ -25,7 +25,6 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): rho = a + b * C + c * temp + d * C**2 \ + e * C**3 + f * temp * C**2 + g * C * temp**2 - return rho @@ -53,7 +52,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): # SS304.temperature = 700 + 273 SS304.add_element('C', 0.000800, "wo") SS304.add_element('Mn', 0.020000, "wo") -SS304.add_element('P', 0.000450 , "wo") +SS304.add_element('P', 0.000450, "wo") SS304.add_element('S', 0.000300, "wo") SS304.add_element('Si', 0.010000, "wo") SS304.add_element('Cr', 0.190000, "wo") @@ -81,7 +80,7 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): # air air = openmc.Material(name="Air") -air.add_element("C", 0.00012399 , 'wo') +air.add_element("C", 0.00012399, 'wo') air.add_element('N', 0.75527, 'wo') air.add_element('O', 0.23178, 'wo') air.add_element('Ar', 0.012827, 'wo') @@ -92,48 +91,50 @@ def get_exp_cllif_density(temp, LiCl_frac=0.695): epoxy.add_element('C', 0.70, 'wo') epoxy.add_element('H', 0.08, 'wo') epoxy.add_element('O', 0.15, 'wo') -epoxy.add_element('N', 0.07, 'wo') -epoxy.set_density('g/cm3', 1.2) +epoxy.add_element('N', 0.07, 'wo') +epoxy.set_density('g/cm3', 1.2) # helium @5psig -pressure = 34473.8 # Pa ~ 5 psig +pressure = 34473.8 # Pa ~ 5 psig temperature = 300 # K R_he = 2077 # J/(kg*K) -density = pressure / (R_he * temperature) # in kg/cm^3 -density *= 1 / 1000 # in g/cm^3 +density = pressure / (R_he * temperature) # in kg/cm^3 +density *= 1 / 1000 # in g/cm^3 he = openmc.Material(name="Helium") he.add_element('He', 1.0, 'ao') he.set_density('g/cm3', density) -# PbLi - natural - pure +# PbLi - eutectic - natural - pure pbli = openmc.Material(name="pbli") pbli.add_element("Pb", 84.2, "ao") pbli.add_element("Li", 15.2, "ao") pbli.set_density("g/cm3", 11) +# lif-bef - eutectic - natural - pure flibe = openmc.Material(name="flibe") flibe.add_element("Li", 2.0, "ao") flibe.add_element("Be", 1.0, "ao") flibe.add_element("F", 4.0, "ao") flibe.set_density("g/cm3", 1.94) -# lif-licl - natural - pure +# lif-licl - eutectic - natural - pure cllif_nat = openmc.Material(name='ClLiF natural') LiCl_frac = 0.695 # at.fr. cllif_nat.add_element('F', .5*(1 - LiCl_frac), 'ao') cllif_nat.add_element('Li', 1.0, 'ao') cllif_nat.add_element('Cl', .5*LiCl_frac, 'ao') -cllif_nat.set_density('g/cm3', get_exp_cllif_density(650)) # 69.5 at. % LiCL at 650 C +cllif_nat.set_density('g/cm3', get_exp_cllif_density(650) + ) # 69.5 at. % LiCL at 650 C -# lif-licl - natural - EuF3 spiced +# lif-licl - eutectic - natural - EuF3 spiced spicyclif = openmc.Material(name="spicyclif") spicyclif.add_element("F", .15935, "wo") spicyclif.add_element("Li", .17857, "wo") spicyclif.add_element("Cl", .6340, "wo") spicyclif.add_element("Eu", .0279, "wo") -# FLiNaK - natural - pure +# FLiNaK - eutectic - natural - pure flinak = openmc.Material(name="flinak") flinak.add_element("F", 50, "ao") flinak.add_element("Li", 23.25, "ao")