diff --git a/pes_to_spec/test/prepare_plots.py b/pes_to_spec/test/prepare_plots.py
index 12ca07cd4d98a448665ee6a1d876d0adc2d294ed..75ba648acf981744ba1d4e7033d07e6cde185cb5 100755
--- a/pes_to_spec/test/prepare_plots.py
+++ b/pes_to_spec/test/prepare_plots.py
@@ -3,6 +3,8 @@
import os
import re
+from typing import Optional, Tuple, Dict
+
import matplotlib
matplotlib.use('Agg')
import pandas as pd
@@ -238,7 +240,7 @@ def plot_wiener(df: pd.DataFrame, filename: str):
fig.savefig(filename)
plt.close(fig)
-def plot_pes(df: pd.DataFrame, channel:str, filename: str):
+def plot_pes(df: pd.DataFrame, channel: Dict[str, int], filename: str, fast_range: Optional[Tuple[int, int]]=None, Ne1s: Optional[Tuple[int, int]]=None, label: Optional[Dict[str, str]]=None, refs: Optional[Dict[str, Dict[int, float]]]=None, counts_to_mv: Optional[float]=None):
"""
Plot low-resolution spectrum.
@@ -255,31 +257,92 @@ def plot_pes(df: pd.DataFrame, channel:str, filename: str):
last = last-270
print("Range:", first, last)
sel = (df.bin >= first) & (df.bin < last)
- x = df.loc[sel, "bin"]
- if channel == "sum":
- y = df.loc[sel, [k for k in df.columns if "channel_" in k]].sum(axis=1)
- ax.plot(x, y, c='b', lw=5)
- elif isinstance(channel, list):
- for ch in channel:
- sch = ch.replace('_', ' ')
- y = df.loc[sel, ch]
- ax.plot(x, y, lw=5, label=sch)
- else:
- y = df.loc[sel, channel]
- ax.plot(x, y, c='b', lw=5)
- ax.legend(frameon=False)
+ x = df.loc[sel, "bin"].to_numpy()
+ col = dict()
+ colors = ["tab:red", "tab:blue"]
+ p = list()
+ # plot each channel
+ for ich, ch in enumerate(channel.keys()):
+ if label is None:
+ sch = ch.replace('_', '')[-2:]
+ else:
+ sch = label[ch]
+ y = df.loc[sel, ch].to_numpy().astype(np.float32)
+ if counts_to_mv is not None:
+ y *= counts_to_mv
+ c = colors[ich]
+ col[ch] = c
+ p += [ax.plot(x, y, lw=2, c=c, label=sch)]
ax.set(title=f"",
- xlabel="Time-of-flight index",
- ylabel="Counts [a.u.]")
+ ylim=(0, None),
+ #xlabel="Time-of-flight index",
+ xlabel="Samples",
+ ylabel="Counts [a.u.]" if counts_to_mv is None else "Digitizer reading [mV]")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
+ minY, maxY = ax.get_ylim()
+ # show reference energy lines
+ if refs is not None:
+ for ich, ch in enumerate(channel.keys()):
+ for tof, energy in refs[ch].items():
+ ax.axvline(tof, 0, 0.5 + ich*0.17, ls='-.', lw=1, c=col[ch])
+ ax.text(tof-1, (0.51 + ich*0.18)*maxY, f"{energy} eV", fontsize=14, rotation="vertical", color=col[ch])
+ # show prompt line
+ for ch, prompt in channel.items():
+ ax.axvline(x=prompt, ls='--', lw=1, c=col[ch])
+ ax.text(prompt-3, 0.5*maxY, "Prompt", fontsize=16, rotation="vertical", color=col[ch])
+ # show the fast electrons range
+ if fast_range is not None:
+ x1, x2 = fast_range
+ xtext = int(x1 + (x2 - x1)*0.3)
+ ytext = 0.9*maxY
+ ax.fill_between([x1, x2], minY, maxY, alpha=0.2, facecolor="tab:olive")
+ ax.text(xtext, ytext, "Valence", fontsize=18, fontweight='bold')
+ ax.text(xtext, ytext-0.05*maxY, "Auger", fontsize=18, fontweight='bold')
+ # show the Ne 1s range
+ if Ne1s is not None:
+ x1, x2 = Ne1s
+ xtext = int(x1 + (x2 - x1)*0.3)
+ ytext = 0.9*maxY
+ ax.fill_between([x1, x2], minY, maxY, alpha=0.2, facecolor="tab:cyan")
+ ax.text(xtext, ytext, "Ne 1s", fontsize=22, fontweight='bold')
+ ns_per_sample = 0.5
+ cax = dict()
+ def f_(ch):
+ return (lambda kk: (np.array(kk) - int(channel[ch]))*ns_per_sample)
+ def i_(ch):
+ return (lambda kk: np.array(kk)/ns_per_sample + int(channel[ch]))
+ forward_ = {ch: f_(ch) for ch in channel}
+ inverse_ = {ch: i_(ch) for ch in channel}
+ for ich, (ch, prompt) in enumerate(channel.items()):
+ cax[ch] = ax.secondary_xaxis(1.0+0.07*ich, functions=(forward_[ch], inverse_[ch]))
+ #cax[ch].spines['left'].set_visible(False)
+ cax[ch].spines['top'].set_position(('outward', 10))
+ cax[ch].spines['top'].set_color(col[ch])
+ cax[ch].tick_params(axis='x', colors=col[ch], labelsize=16)
+ if ich == len(channel)-1:
+ cax[ch].set_xlabel('Time-of-flight [ns]', fontsize=16)
+ #cax[ch].xaxis.label.set_color(col[ch])
+ #cax[ch].title.set_color(col[ch])
+ ax.legend(frameon=False, loc='center')
plt.tight_layout()
fig.savefig(filename)
plt.close(fig)
if __name__ == '__main__':
indir = 'p900331r69t70'
- channel = ['channel_1_A', 'channel_4_A', 'channel_3_B']
+ channel = {'channel_4_A': 2639,
+ 'channel_3_B': 2646,
+ }
+ label = {'channel_4_A': r'22.5$^\circ$',
+ 'channel_3_B': r'225$^\circ$',
+ }
+ Ne1s = (2710, 2742)
+ fast_range = (2650, 2670)
+ refs={'channel_4_A': {2716:1002.5, 2722:997.5},
+ 'channel_3_B': {2723:1002.5, 2729:997.5}
+ }
+ counts_to_mv = 40.0/100.0
#channel = 'sum'
#for fname in os.listdir(indir):
# if re.match(r'test_q100_[0-9]*\.csv', fname):
@@ -290,7 +353,9 @@ if __name__ == '__main__':
for fname in ('test_q100_1724098413', 'test_q100_1724098596', 'test_q50_1724099445'):
plot_final(pd.read_csv(f'{indir}/{fname}.csv'), f'{fname}.pdf')
- plot_pes(pd.read_csv(f'{indir}/{fname}_pes.csv'), channel, f'{fname}_pes.pdf')
+ plot_pes(pd.read_csv(f'{indir}/{fname}_pes.csv'), channel, f'{fname}_pes.pdf',
+ fast_range=fast_range, Ne1s=Ne1s, label=label, refs=refs,
+ counts_to_mv=counts_to_mv)
plot_chi2(pd.read_csv(f'{indir}/quality.csv'), f'chi2_prepca.pdf')
plot_chi2_intensity(pd.read_csv(f'{indir}/quality.csv'), f'intensity_vs_chi2_prepca.pdf')