From 065185c36e05d56695cca5bf70a567a3f03df9d5 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Fri, 9 Feb 2024 09:30:51 +0100
Subject: [PATCH] Last changes after more testing - replace np.percentile(,
[0,100]) with min(), max() - put back histogram plots with get_range for no
need of change. - improve gain plot and add clamp function
---
.../AGIPD/AGIPD_Correct_and_Verify.ipynb | 38 +++++++++++--------
1 file changed, 22 insertions(+), 16 deletions(-)
diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index b9048f088..3fa1efccd 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -167,6 +167,7 @@
" CellRange,\n",
" LitFrameSelection,\n",
")\n",
+ "from cal_tools.ana_tools import get_range\n",
"from cal_tools.calcat_interface import (\n",
" AGIPD_CalibrationData,\n",
" CalCatError,\n",
@@ -1045,7 +1046,7 @@
"metadata": {},
"outputs": [],
"source": [
- "vmin, vmax = np.percentile(corrected, [0, 100])\n",
+ "vmin, vmax = np.nanmin(corrected), np.nanmax(corrected)\n",
"hist, bins_x, bins_y = calgs.histogram2d(\n",
" corrected.flatten().astype(np.float32),\n",
" gains.flatten().astype(np.float32), bins=(100, 3),\n",
@@ -1087,17 +1088,22 @@
"source": [
"pulse_range = [np.min(pulseId[pulseId>=0]), np.max(pulseId[pulseId>=0])]\n",
"\n",
+ "\n",
+ "def clamp(value, min_value, max_value):\n",
+ " return max(min_value, min(value, max_value))\n",
+ "\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",
"\n",
"mean_data = np.nanmean(corrected, axis=(2, 3))\n",
- "vmin, vmax = np.percentile(mean_data, [0, 100])\n",
+ "vmin, vmax = mean_data.min(), mean_data.max()\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=[[max(vmin, -50), min(vmax, 1000)], pulse_range],\n",
+ " range=[[clamp(vmin, -50, -0.2), min(vmax, 1000)], pulse_range],\n",
")\n",
"do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Pulse id\")\n",
"if vmax > 1000: # a zoom out plot.\n",
@@ -1105,7 +1111,7 @@
" mean_data.flatten().astype(np.float32),\n",
" pulseId.flatten().astype(np.float32),\n",
" bins=(100, int(pulse_range[1])),\n",
- " range=[[max(vmin, -50), min(vmax, 20000)], pulse_range]\n",
+ " range=[[clamp(vmin, -50, -0.2), min(vmax, 20000)], pulse_range]\n",
" )\n",
" do_2d_plot(hist, (bins_x, bins_y), \"Signal (ADU)\", \"Pulse id\")"
]
@@ -1242,10 +1248,10 @@
"source": [
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
- "vmin, vmax = np.nanpercentile(corrected[cell_idx_preview], [5, 100])\n",
- "nbins = int((vmax + 50) / 2)\n",
+ "vmin, vmax = get_range(corrected[cell_idx_preview], 5, -50)\n",
+ "nbins = np.int((vmax + 50) / 2)\n",
"h = ax.hist(corrected[cell_idx_preview].flatten(),\n",
- " bins=nbins, range=(vmin, vmax),\n",
+ " bins=nbins, range=(-50, vmax),\n",
" histtype='stepfilled', log=True)\n",
"plt.xlabel('[ADU]')\n",
"plt.ylabel('Counts')\n",
@@ -1262,18 +1268,18 @@
"source": [
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
- "vmin, vmax = np.nanpercentile(corrected, [5, 100])\n",
+ "vmin, vmax = get_range(corrected, 10, -100)\n",
+ "vmax = np.nanmax(corrected)\n",
"if vmax > 50000:\n",
" vmax = 50000\n",
- "nbins = int((vmax + 100) / 5)\n",
- "hist_range=(vmin, vmax)\n",
+ "nbins = np.int((vmax + 100) / 5)\n",
"h = ax.hist(corrected.flatten(), bins=nbins,\n",
- " range=hist_range, histtype='step', log=True, label = 'All')\n",
- "ax.hist(corrected[gains == 0].flatten(), bins=nbins, range=hist_range,\n",
+ " range=(-100, vmax), histtype='step', log=True, label = 'All')\n",
+ "ax.hist(corrected[gains == 0].flatten(), bins=nbins, range=(-100, vmax),\n",
" alpha=0.5, log=True, label='High gain', color='green')\n",
- "ax.hist(corrected[gains == 1].flatten(), bins=nbins, range=hist_range,\n",
+ "ax.hist(corrected[gains == 1].flatten(), bins=nbins, range=(-100, vmax),\n",
" alpha=0.5, log=True, label='Medium gain', color='red')\n",
- "ax.hist(corrected[gains == 2].flatten(), bins=nbins, range=hist_range,\n",
+ "ax.hist(corrected[gains == 2].flatten(), bins=nbins, range=(-100, vmax),\n",
" alpha=0.5, log=True, label='Low gain', color='yellow')\n",
"ax.legend()\n",
"ax.grid()\n",
@@ -1301,9 +1307,9 @@
"source": [
"fig = plt.figure(figsize=(20, 10))\n",
"ax = fig.add_subplot(111)\n",
- "vmin, vmax = np.percentile(np.mean(gains, axis=0), [0, 100])\n",
"ax = geom.plot_data_fast(\n",
- " np.mean(gains, axis=0), ax=ax, vmin=vmin, vmax=vmax, cmap=cmap)\n",
+ " np.max(gains, axis=0), ax=ax,\n",
+ " cmap=cmap, vmin=-0.3, vmax=2.3) # Extend cmap for wrong gain values.\n",
"pass"
]
},
--
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