From 6c02cdd8abf51913f7c69e901ece32e684619b98 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Thu, 8 Feb 2024 14:49:08 +0100
Subject: [PATCH] remove unneeded flatten operations an data conversions
---
.../AGIPD/AGIPD_Correct_and_Verify.ipynb | 37 +++++++++++--------
1 file changed, 22 insertions(+), 15 deletions(-)
diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index 29f40ffe8..32f9b4aa7 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -1015,13 +1015,16 @@
"metadata": {},
"outputs": [],
"source": [
+ "raw_float = raw.astype(np.float32)\n",
+ "signal = raw[:, 0, ...]\n",
+ "gain = raw[:, 1, ...]\n",
"hist, bins_x, bins_y = calgs.histogram2d(\n",
- " raw[:,0,...].flatten().astype(np.float32),\n",
- " raw[:,1,...].flatten().astype(np.float32),\n",
+ " signal.flatten().astype(np.float32),\n",
+ " gain.flatten().astype(np.float32),\n",
" bins=(100, 100),\n",
" range=[\n",
- " np.percentile(raw[:, 0, ...].astype(np.float32), [0.02, 99.8]),\n",
- " np.percentile(raw[:, 1, ...].astype(np.float32), [0.02, 99.8]),\n",
+ " np.percentile(signal, [0.02, 99.8]),\n",
+ " np.percentile(gain, [0.02, 99.8]),\n",
" ],\n",
" )\n",
"do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Analogue gain (ADU)\")"
@@ -1046,7 +1049,7 @@
" corrected.flatten().astype(np.float32),\n",
" gains.flatten().astype(np.float32), bins=(100, 3),\n",
" range=[\n",
- " np.percentile(corrected.flatten().astype(np.float32), [0, 100]),\n",
+ " np.percentile(corrected, [0, 100]),\n",
" [0, 3]\n",
" ],\n",
" )\n",
@@ -1080,7 +1083,8 @@
"metadata": {},
"outputs": [],
"source": [
- "pulse_range = np.percentile(pulseId.astype(np.float32), [0, 100])\n",
+ "pulse_range = [np.min(pulseId[pulseId>=0]), np.max(pulseId[pulseId>=0])]\n",
+ "\n",
"# Modify pulse_range, if only one pulse is selected.\n",
"if pulse_range[0] == pulse_range[1]:\n",
" pulse_range = [0, pulse_range[1]+int(acq_rate)]\n",
@@ -1090,12 +1094,15 @@
" mean_data.flatten().astype(np.float32),\n",
" pulseId.flatten().astype(np.float32),\n",
" bins=(100, int(pulse_range[1])),\n",
- " range=[\n",
- " np.percentile(mean_data.astype(np.float32), [0, 100]),\n",
- " pulse_range\n",
- " ],\n",
+ " range=[[-50, 1000], pulse_range],\n",
+ ")\n",
+ "do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Pulse id\")\n",
+ "hist, bins_x, bins_y = calgs.histogram2d(\n",
+ " mean_data.flatten().astype(np.float32),\n",
+ " pulseId.flatten().astype(np.float32),\n",
+ " bins=(100, int(pulse_range[1])),\n",
+ " range=[[-50, 200000], pulse_range]\n",
")\n",
- "\n",
"do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Pulse id\")"
]
},
@@ -1131,7 +1138,7 @@
"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.flatten()/10**6, [2, 98]))\n",
+ "plt.xlim(np.percentile(corrected_ave/10**6, [2, 98]))\n",
"plt.grid()\n",
"plt.xlabel('Illuminated corrected [MADU] ')\n",
"_ = plt.ylabel('Estimated baseline shift [ADU]')"
@@ -1200,7 +1207,7 @@
" fig = plt.figure(figsize=(20, 10))\n",
" ax = fig.add_subplot(111)\n",
" data = np.mean(corrected, axis=0)\n",
- " vmin, vmax = np.percentile(data, [5, 99.9])\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",
"else:\n",
@@ -1217,7 +1224,7 @@
"display(Markdown(f'A single shot of the CORRECTED image from cell {cell_id_preview} \\n'))\n",
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
- "vmin, vmax = np.percentile(corrected[cell_idx_preview], [5, 99.9])\n",
+ "vmin, vmax = np.nanpercentile(corrected[cell_idx_preview], [5, 99.9])\n",
"ax = geom.plot_data_fast(\n",
" corrected[cell_idx_preview], ax=ax, vmin=vmin, vmax=vmax, cmap=cmap)\n",
"pass"
@@ -1231,7 +1238,7 @@
"source": [
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
- "vmin, vmax = np.percentile(corrected[cell_idx_preview], [5, 100])\n",
+ "vmin, vmax = np.nanpercentile(corrected[cell_idx_preview], [5, 100])\n",
"nbins = int((vmax + 50) / 2)\n",
"h = ax.hist(corrected[cell_idx_preview].flatten(),\n",
" bins=nbins, range=(vmin, vmax),\n",
--
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