From ec56e1813312fc4fee2162241d5d6828ebd2d1f6 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Thu, 26 Jan 2023 18:46:20 +0100
Subject: [PATCH] Nicer plots
---
pes_to_spec/test/offline_analysis.py | 46 +++++++++++++++++++++++-----
1 file changed, 39 insertions(+), 7 deletions(-)
diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py
index 7224c07..782aab6 100755
--- a/pes_to_spec/test/offline_analysis.py
+++ b/pes_to_spec/test/offline_analysis.py
@@ -15,6 +15,8 @@ matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
+from mpl_toolkits.axes_grid.inset_locator import (inset_axes, InsetPosition,
+ mark_inset)
from typing import Dict, Optional
@@ -23,7 +25,7 @@ import pandas as pd
SMALL_SIZE = 12
MEDIUM_SIZE = 18
-BIGGER_SIZE = 24
+BIGGER_SIZE = 22
plt.rc('font', size=BIGGER_SIZE) # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title
@@ -53,7 +55,7 @@ def plot_pes(filename: str, pes_raw_int: np.ndarray, first: int, last: int):
fig.savefig(filename)
plt.close(fig)
-def plot_result(filename: str, spec_pred: Dict[str, np.ndarray], spec_smooth: np.ndarray, spec_raw_pe: np.ndarray, spec_raw_int: Optional[np.ndarray]=None):
+def plot_result(filename: str, spec_pred: Dict[str, np.ndarray], spec_smooth: np.ndarray, spec_raw_pe: np.ndarray, spec_raw_int: Optional[np.ndarray]=None, pes: Optional[np.ndarray]=None, pes_to_show: Optional[str]="", pes_bin: Optional[np.ndarray]=None):
"""
Plot result with uncertainty band.
@@ -63,6 +65,9 @@ def plot_result(filename: str, spec_pred: Dict[str, np.ndarray], spec_smooth: np
spec_smooth: Smoothened expected result with shape (features,).
spec_raw_pe: x axis with the photon energy in eV.
spec_raw_int: Original true expected result with shape (features,).
+ pes: PES spectrum for the inset.
+ pes_to_show: Name of the channel shown.
+ pes_bin: PES bins.
"""
fig = plt.figure(figsize=(12, 8))
@@ -71,19 +76,39 @@ def plot_result(filename: str, spec_pred: Dict[str, np.ndarray], spec_smooth: np
unc_stat = np.mean(spec_pred["unc"])
unc_pca = np.mean(spec_pred["pca"])
unc = np.sqrt(unc_stat**2 + unc_pca**2)
- ax.plot(spec_raw_pe, spec_smooth, c='b', lw=3, label="High-resolution measurement (smoothened)")
- ax.plot(spec_raw_pe, spec_pred["expected"], c='r', lw=3, label="High-resolution prediction")
- ax.fill_between(spec_raw_pe, spec_pred["expected"] - unc, spec_pred["expected"] + unc, facecolor='red', alpha=0.6, label="68% unc.")
+ ax.plot(spec_raw_pe, spec_smooth, c='b', lw=3, label="High-res. measurement (smoothened)")
+ ax.plot(spec_raw_pe, spec_pred["expected"], c='r', ls='--', lw=3, label="High-res. prediction")
+ #ax.fill_between(spec_raw_pe, spec_pred["expected"] - unc, spec_pred["expected"] + unc, facecolor='green', alpha=0.6, label="68% unc.")
+ ax.fill_between(spec_raw_pe, spec_pred["expected"] - unc, spec_pred["expected"] + unc, facecolor='gold', alpha=0.5, label="68% unc.")
#ax.fill_between(spec_raw_pe, spec_pred["expected"] - unc_stat, spec_pred["expected"] + unc_stat, facecolor='red', alpha=0.6, label="68% unc. (stat.)")
#ax.fill_between(spec_raw_pe, spec_pred["expected"] - unc_pca, spec_pred["expected"] + unc_pca, facecolor='magenta', alpha=0.6, label="68% unc. (syst., PCA)")
#if spec_raw_int is not None:
# ax.plot(spec_raw_pe, spec_raw_int, c='b', lw=1, ls='--', label="High-resolution measurement")
Y = np.amax(spec_smooth)
- ax.legend(frameon=False, borderaxespad=0)
+ ax.legend(frameon=False, borderaxespad=0, loc='upper left')
ax.set(title=f"", #avg(stat unc) = {unc_stat}, avg(pca unc) = {unc_pca}",
xlabel="Photon energy [eV]",
ylabel="Intensity",
ylim=(0, 1.2*Y))
+ if pes is not None:
+ ax2 = plt.axes([0,0,1,1])
+ # Manually set the position and relative size of the inset axes within ax1
+ ip = InsetPosition(ax, [0.65,0.6,0.35,0.4])
+ ax2.set_axes_locator(ip)
+ Ypes = np.amax(pes)
+ ax2.plot(pes_bin, pes, c='black', lw=3)
+ ax2.set(title=f"Low-resolution example data",
+ xlabel="Bin",
+ ylabel=f"{pes_to_show}",
+ ylim=(0, 1.2*Ypes),
+ #labelsize=SMALL_SIZE,
+ #xticklabels=dict(fontdict=dict(fontsize=SMALL_SIZE)),
+ #yticklabels=dict(fontdict=dict(fontsize=SMALL_SIZE)),
+ )
+ ax2.title.set_size(SMALL_SIZE)
+ ax2.xaxis.label.set_size(SMALL_SIZE)
+ ax2.yaxis.label.set_size(SMALL_SIZE)
+ ax2.tick_params(axis='both', which='major', labelsize=SMALL_SIZE)
fig.savefig(filename)
plt.close(fig)
@@ -191,6 +216,9 @@ def main():
print("Plotting")
spec_smooth = model.preprocess_high_res(spec_raw_int)
first, last = model.get_low_resolution_range()
+ first += 10
+ last -= 100
+ pes_to_show = 'channel_1_D'
# plot
for tid in test_tids:
idx = np.where(tid==tids)[0][0]
@@ -200,7 +228,11 @@ def main():
for k, item in spec_pred.items()},
spec_smooth[idx, :],
spec_raw_pe[idx, :],
- spec_raw_int[idx, :])
+ spec_raw_int[idx, :],
+ pes=-pes_raw[pes_to_show][idx, first:last],
+ pes_to_show=pes_to_show.replace('_', ' '),
+ pes_bin=np.arange(first, last)
+ )
for ch in channels:
plot_pes(f"test_pes_{tid}_{ch}.png", pes_raw[ch][idx, first:last], first, last)
--
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