From 12b11ae579142da99a8e341f8b3fe8b13fe3ab33 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 14 Aug 2023 17:33:26 +0200
Subject: [PATCH] Added script to only do plots.
---
pes_to_spec/test/prepare_plots.py | 142 ++++++++++++++++++++++++++++++
1 file changed, 142 insertions(+)
create mode 100755 pes_to_spec/test/prepare_plots.py
diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py
new file mode 100755
index 0000000..a348a88
--- /dev/null
+++ b/pes_to_spec/test/prepare_plots.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+import seaborn as sns
+
+def plot_final(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ ax.plot(df.energy, df.spec, c='b', lw=3, label="Grating spectrometer")
+ ax.plot(df.energy, df.prediction, c='r', ls='--', lw=3, label="Prediction")
+ ax.fill_between(df.energy, df.prediction - df.unc, df.prediction + df.unc, facecolor='gold', alpha=0.5, label="68% unc. (total)")
+ ax.fill_between(df.energy, df.prediction - df.unc_pca, df.prediction + df.unc_pca, facecolor='magenta', alpha=0.5, label="68% unc. (PCA only)")
+ Y = np.amax(df.spec)
+ ax.legend(frameon=False, borderaxespad=0, loc='upper left')
+ ax.set_title(f"Beam intensity: {df.beam_intensity.iloc[0]*1e-3:.1f} mJ", loc="left")
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ ax.set(
+ xlabel="Photon energy [eV]",
+ ylabel="Intensity [a.u.]",
+ ylim=(0, 1.3*Y))
+ fig.savefig(filename)
+ plt.close(fig)
+
+def plot_chi2(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ sns.histplot(x=df.chi2_prepca/df.ndof.iloc[0], kde=True, linewidth=3, ax=ax)
+ ax.set(title=f"",
+ xlabel=r"$\chi^2/$ndof",
+ ylabel="Counts [a.u.]",
+ xlim=(0, 5),
+ )
+ fig.savefig(filename)
+ plt.close(fig)
+
+def plot_rmse_intensity(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ ax.scatter(df.rmse, df.xgm_flux_t, c='r', s=30)
+ ax = plt.gca()
+ ax.set(title=f"",
+ xlabel=r"Root-mean-squared error",
+ ylabel="Beam intensity [uJ]",
+ )
+ fig.savefig(filename)
+ plt.close(fig)
+
+def plot_chi2_intensity(df: pd.DataFrame, filename: str):
+ fig = plt.figure(figsize=(12, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ ax.scatter(df.chi2_prepca/df.ndof.iloc[0], df.xgm_flux_t, c='r', s=30)
+ ax = plt.gca()
+ ax.set(title=f"",
+ xlabel=r"$\chi^2/$ndof",
+ ylabel="Beam intensity [uJ]",
+ xlim=(0, 5),
+ ylim=(0, df.xgm_flux_t.mean() + 3*df.xgm_flux_t.std())
+ )
+ fig.savefig(filename)
+ plt.close(fig)
+
+def pca_variance_plot(df: pd.DataFrame, filename: str):
+ """
+ Plot variance contribution.
+
+ Args:
+ filename: Output file name.
+ variance_ratio: Contribution of each component's variance.
+
+ """
+ fig = plt.figure(figsize=(8, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ c = np.cumsum(df.variance_ratio)
+ n_comp = df.n_comp.iloc[0]
+ ax.bar(1+np.arange(len(df.variance_ratio)), df.variance_ratio*100, color='tab:red', alpha=0.3, label="Per component")
+ ax.plot(1+np.arange(len(df.variance_ratio)), c*100, c='tab:blue', lw=5, label="Cumulative")
+ ax.plot([n_comp, n_comp], [0, c[n_comp]*100], lw=3, ls='--', c='m', label="Components kept")
+ ax.plot([0, n_comp], [c[n_comp]*100, c[n_comp]*100], lw=3, ls='--', c='m')
+ ax.legend(frameon=False)
+ print(f"PCA plot: total n. components: {len(df.variance_ratio)}")
+ x_max = np.where(c > 0.99)[0][0]
+ print(f"Fraction of variance: {c[n_comp]}")
+ ax.set_yscale('log')
+ ax.set(title=f"",
+ xlabel="Component",
+ ylabel="Variance [%]",
+ xlim=(1, x_max),
+ ylim=(0.1, 100))
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ plt.tight_layout()
+ fig.savefig(filename)
+ plt.close(fig)
+
+def plot_pes(df: pd.DataFrame, channel:str, filename: str):
+ """
+ Plot low-resolution spectrum.
+
+ Args:
+ filename: Output file name.
+ pes_raw_int: Low-resolution spectrum.
+
+ """
+ fig = plt.figure(figsize=(16, 8))
+ gs = GridSpec(1, 1)
+ ax = fig.add_subplot(gs[0, 0])
+ ax.plot(df.bin[df.range], df.loc[:, channel], c='b', lw=3)
+ #ax.legend()
+ ax.set(title=f"",
+ xlabel="Time-of-flight index",
+ ylabel="Counts [a.u.]")
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ fig.savefig(filename)
+ plt.close(fig)
+
+if __name__ == '__main__':
+ channel = 'channel_4_A'
+ fname = 'test_q100_1724098413'
+ plot_final(pd.read_csv(f'{fname}.csv'), f'{fname}.pdf')
+ plot_pes(pd.read_csv(f'{fname}_pes.csv'), channel, f'{fname}_{channel}.pdf')
+
+ fname = 'test_q100_1724098596'
+ plot_final(pd.read_csv(f'{fname}.csv'), f'{fname}.pdf')
+ plot_pes(pd.read_csv(f'{fname}_pes.csv'), channel, f'{fname}_{channel}.pdf')
+
+ plot_chi2(pd.read_csv(f'quality.csv'), f'chi2_prepca.pdf')
+ plot_chi2_intensity(pd.read_csv(f'quality.csv'), f'intensity_vs_chi2_prepca.pdf')
+ plot_rmse_intensity(pd.read_csv(f'quality.csv'), f'intensity_vs_rmse.pdf')
+
+ pca_variance_plot(pd.read_csv(f'pca_spec.csv'), f'pca_spec.pdf')
+ pca_variance_plot(pd.read_csv(f'pca_pes.csv'), f'pca_pes.pdf')
+
--
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