From 1c7c7f0215ec38722cc7014fa96c114d4aac02c4 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Fri, 9 Feb 2024 12:53:07 +0100
Subject: [PATCH] use nanpercentile with corrected data and replace deprecated
np.int with int
---
notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index 3fa1efccd..0da892794 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -1148,7 +1148,7 @@
"fig = plt.figure(figsize=(10, 10))\n",
"corrected_ave = np.nansum(corrected, axis=(2, 3))\n",
"plt.scatter(corrected_ave.flatten()/10**6, blshift.flatten(), s=0.9)\n",
- "plt.xlim(np.percentile(corrected_ave/10**6, [2, 98]))\n",
+ "plt.xlim(np.nanpercentile(corrected_ave/10**6, [2, 98]))\n",
"plt.grid()\n",
"plt.xlabel('Illuminated corrected [MADU] ')\n",
"_ = plt.ylabel('Estimated baseline shift [ADU]')"
@@ -1216,7 +1216,7 @@
" display(Markdown(f'A mean across train: {tid}\\n'))\n",
" fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
- " data = np.mean(corrected, axis=0)\n",
+ " data = np.nanmean(corrected, axis=0)\n",
" vmin, vmax = np.nanpercentile(data, [5, 99.9])\n",
" ax = geom.plot_data_fast(data, ax=ax, vmin=vmin, vmax=vmax, cmap=cmap)\n",
" pass\n",
@@ -1249,7 +1249,7 @@
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
"vmin, vmax = get_range(corrected[cell_idx_preview], 5, -50)\n",
- "nbins = np.int((vmax + 50) / 2)\n",
+ "nbins = int((vmax + 50) / 2)\n",
"h = ax.hist(corrected[cell_idx_preview].flatten(),\n",
" bins=nbins, range=(-50, vmax),\n",
" histtype='stepfilled', log=True)\n",
@@ -1272,7 +1272,7 @@
"vmax = np.nanmax(corrected)\n",
"if vmax > 50000:\n",
" vmax = 50000\n",
- "nbins = np.int((vmax + 100) / 5)\n",
+ "nbins = int((vmax + 100) / 5)\n",
"h = ax.hist(corrected.flatten(), bins=nbins,\n",
" range=(-100, vmax), histtype='step', log=True, label = 'All')\n",
"ax.hist(corrected[gains == 0].flatten(), bins=nbins, range=(-100, vmax),\n",
--
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