From 4c7161422c931bfc6cfdf0697049c5a944f94c4b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20Le=20Guyader?= <loic.le.guyader@xfel.eu>
Date: Wed, 28 Jul 2021 13:48:16 +0200
Subject: [PATCH] Inspect nl correction with unscaled data
---
src/toolbox_scs/routines/boz.py | 24 +++++++++++-------------
1 file changed, 11 insertions(+), 13 deletions(-)
diff --git a/src/toolbox_scs/routines/boz.py b/src/toolbox_scs/routines/boz.py
index f3ebbe2..916d29a 100644
--- a/src/toolbox_scs/routines/boz.py
+++ b/src/toolbox_scs/routines/boz.py
@@ -1254,7 +1254,7 @@ def inspect_correction(params, gain=None):
scale = 1e-6
- f, axs = plt.subplots(3, 3, figsize=(6, 6), sharex=True, sharey=True)
+ f, axs = plt.subplots(3, 3, figsize=(6, 6), sharex=True)
# nbins = np.linspace(0.01, 1.0, 100)
@@ -1276,16 +1276,11 @@ def inspect_correction(params, gain=None):
snr_v = snr(good_d[n].values.flatten(),
good_d[r].values.flatten(), verbose=True)
- if k == 0:
- m = np.nanmean(good_d[n].values.flatten()
- /good_d[r].values.flatten())
- else:
- m = 1
-
+ m = snr_v['direct']['mu']
h, xedges, yedges, img = axs[l, k].hist2d(
g*scale*good_d[r].values.flatten(),
- good_d[n].values.flatten()/good_d[r].values.flatten()/m,
- [photon_scale, np.linspace(0.95, 1.05, 150)],
+ good_d[n].values.flatten()/good_d[r].values.flatten(),
+ [photon_scale, np.linspace(0.95, 1.05, 150)*m],
cmap='Blues',
vmax=200,
norm=LogNorm(),
@@ -1293,13 +1288,14 @@ def inspect_correction(params, gain=None):
)
h, xedges, yedges, img2 = axs[l, k].hist2d(
g*scale*sat_d[r].values.flatten(),
- sat_d[n].values.flatten()/sat_d[r].values.flatten()/m,
- [photon_scale, np.linspace(0.95, 1.05, 150)],
+ sat_d[n].values.flatten()/sat_d[r].values.flatten(),
+ [photon_scale, np.linspace(0.95, 1.05, 150)*m],
cmap='Reds',
vmax=200,
norm=LogNorm(),
# alpha=0.5 # make the plot looks ugly with lots of white lines
)
+
v = snr_v['direct']['mu']/snr_v['direct']['s']
axs[l, k].text(0.4, 0.15, f'SNR: {v:.0f}',
transform = axs[l, k].transAxes)
@@ -1310,9 +1306,11 @@ def inspect_correction(params, gain=None):
# axs[l, k].plot(3*nbins, 1+np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
# axs[l, k].plot(3*nbins, 1-np.sqrt(2/(1e6*nbins)), c='C1', ls='--')
+ axs[l, k].set_ylim([0.95*m, 1.05*m])
+
for k in range(3):
- for l in range(3):
- axs[l, k].set_ylim([0.95, 1.05])
+ #for l in range(3):
+ # axs[l, k].set_ylim([0.95, 1.05])
if gain:
axs[2, k].set_xlabel('#ph (10$^6$)')
else:
--
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