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added mean and std to plot

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mazzol_a
2026-07-27 10:43:10 +02:00
parent 89ed7fdf14
commit 19c3f41bc3
1 changed files with 27 additions and 25 deletions
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jfcal/Plotter.py
+27 -25
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@@ -7,6 +7,8 @@ from jfcal.utils import create_histogram_from_data
import matplotlib.pyplot as plt
import numpy as np
from enum import Enum
class Parameters(Enum):
@@ -81,7 +83,7 @@ class Plotter:
The axis with the histogram and fitted function plot.
"""
function_parameters = self.calibration.fit_results["par"][pixel[0],pixel[1],:]
function_parameters = self.calibration.fit_result["par"][pixel[0],pixel[1],:]
variable_name_width = 8
variable_width = 8
@@ -125,21 +127,21 @@ class Plotter:
"""
match parameter_name:
case Parameters.ELASTIC_SCATTERING_INTERCEPT.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 0], parameter_name="Elastic scattering intercept", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 0], parameter_name="Elastic scattering intercept", suppress_outliers=suppress_outliers)
case Parameters.ELASTIC_SCATTERING_SLOPE.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 1], parameter_name="Elastic scattering slope", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 1], parameter_name="Elastic scattering slope", suppress_outliers=suppress_outliers)
case Parameters.K_ALPHA_MEAN.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 2], parameter_name="Cu K_alpha mean", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 2], parameter_name="Cu K_alpha mean", suppress_outliers=suppress_outliers)
case Parameters.SIGMA.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 3], parameter_name="Charge sharing sigma", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 3], parameter_name="Charge sharing sigma", suppress_outliers=suppress_outliers)
case Parameters.K_ALPHA_AMPLITUDE.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 4], parameter_name="Cu K_alpha amplitude", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 4], parameter_name="Cu K_alpha amplitude", suppress_outliers=suppress_outliers)
case Parameters.RATIO_AMPLITUDE_CHARGE_SHARING.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 5], parameter_name="Charge sharing amplitude ratio", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 5], parameter_name="Charge sharing amplitude ratio", suppress_outliers=suppress_outliers)
case Parameters.RATIO_MEAN_K_BETA.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 6], parameter_name="Cu K_beta mean ratio", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 6], parameter_name="Cu K_beta mean ratio", suppress_outliers=suppress_outliers)
case Parameters.RATIO_AMPLITUDE_K_BETA.value:
plot_parameter(self.calibration.fit_results["par"][:, :, 7], parameter_name="Cu K_beta amplitude ratio", suppress_outliers=suppress_outliers)
plot_parameter(self.calibration.fit_result["par"][:, :, 7], parameter_name="Cu K_beta amplitude ratio", suppress_outliers=suppress_outliers)
case _:
raise ValueError(f"Unknown parameter name: {parameter_name}. Valid options are: {[param.value for param in Parameters]}")
@@ -169,37 +171,37 @@ class Plotter:
match parameter_name:
case Parameters.ELASTIC_SCATTERING_INTERCEPT.value:
data = self.calibration.fit_results["par"][:, :, 0].flatten()
data = self.calibration.fit_result["par"][:, :, 0].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering intercept", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering intercept", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.ELASTIC_SCATTERING_SLOPE.value:
data = self.calibration.fit_results["par"][:, :, 1].flatten()
data = self.calibration.fit_result["par"][:, :, 1].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering slope", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering slope", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.K_ALPHA_MEAN.value:
data = self.calibration.fit_results["par"][:, :, 2].flatten()
data = self.calibration.fit_result["par"][:, :, 2].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ mean", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ mean", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.SIGMA.value:
data = self.calibration.fit_results["par"][:, :, 3].flatten()
data = self.calibration.fit_result["par"][:, :, 3].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$\sigma$", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$\sigma$", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.K_ALPHA_AMPLITUDE.value:
data = self.calibration.fit_results["par"][:, :, 4].flatten()
data = self.calibration.fit_result["par"][:, :, 4].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ amplitude", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ amplitude", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.RATIO_AMPLITUDE_CHARGE_SHARING.value:
data = self.calibration.fit_results["par"][:, :, 5].flatten()
data = self.calibration.fit_result["par"][:, :, 5].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio amplitude charge sharing", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio amplitude charge sharing", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.RATIO_MEAN_K_BETA.value:
data = self.calibration.fit_results["par"][:, :, 6].flatten()
data = self.calibration.fit_result["par"][:, :, 6].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ mean", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ mean", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case Parameters.RATIO_AMPLITUDE_K_BETA.value:
data = self.calibration.fit_results["par"][:, :, 7].flatten()
data = self.calibration.fit_result["par"][:, :, 7].flatten()
histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ amplitude", axis=axis)
ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ amplitude", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
case _:
raise ValueError(f"Unknown parameter name: {parameter_name}. Valid options are: {[param.value for param in Parameters]}")