From 0936bd51f4628849e0a3659df3a7e37c022ee4c9 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Tue, 25 Apr 2023 09:28:47 +0200
Subject: [PATCH] revert back uneeded plotting changes
---
...Jungfrau_Gain_Correct_and_Verify_NBC.ipynb | 42 +++++++++----------
1 file changed, 20 insertions(+), 22 deletions(-)
diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index 7c73286e3..c2b26937b 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -707,7 +707,7 @@
" # Reading CORR data for plotting.\n",
" jf_corr = components.JUNGFRAU(\n",
" corr_dc,\n",
- " detector_name=karabo_id, n_modules=nmods, st_modno=st_modno,\n",
+ " detector_name=karabo_id,\n",
" ).select_trains(np.s_[:plot_trains])\n",
" tid, jf_corr_data = next(iter(jf_corr.trains(require_all=True)))\n",
"\n",
@@ -715,31 +715,32 @@
"# TODO: Fix the case if not all modules were requested to be corrected.\n",
"# For example if only one modules was corrected. An assertion error is expected\n",
"# at `geom.plot_data_fast`, while plotting corrected images.\n",
- "corrected = jf_corr.get_array(\"data.adc\")[:, :, cell_idx_preview, ...]\n",
+ "corrected = jf_corr.get_array(\"data.adc\")[:, :, cell_idx_preview, ...].values\n",
"corrected_train = jf_corr_data[\"data.adc\"][\n",
" :, cell_idx_preview, ...\n",
- "] # loose the train axis.\n",
+ "].values # loose the train axis.\n",
"\n",
- "mask = jf_corr.get_array(\"data.mask\")[:, :, cell_idx_preview, ...]\n",
- "mask_train = jf_corr_data[\"data.mask\"][:, cell_idx_preview, ...]\n",
+ "mask = jf_corr.get_array(\"data.mask\")[:, :, cell_idx_preview, ...].values\n",
+ "mask_train = jf_corr_data[\"data.mask\"][:, cell_idx_preview, ...].values\n",
"\n",
"with RunDirectory(f\"{in_folder}/r{run:04d}/\", f\"*S{first_seq:05d}*\", _use_voview=False) as raw_dc:\n",
"\n",
" # Reading RAW data for plotting.\n",
" jf_raw = components.JUNGFRAU(\n",
- " raw_dc, detector_name=karabo_id, n_modules=nmods, st_modno=st_modno).select_trains(\n",
+ " raw_dc, detector_name=karabo_id).select_trains(\n",
" np.s_[:plot_trains]\n",
" )\n",
"\n",
- "raw = jf_raw.get_array(\"data.adc\")[:, :, cell_idx_preview, ...]\n",
+ "raw = jf_raw.get_array(\"data.adc\")[:, :, cell_idx_preview, ...].values\n",
"raw_train = (\n",
" jf_raw.select_trains(by_id[[tid]])\n",
" .get_array(\"data.adc\")[:, 0, cell_idx_preview, ...]\n",
+ " .values\n",
")\n",
"\n",
- "gain = jf_raw.get_array(\"data.gain\")[:, :, cell_idx_preview, ...]\n",
+ "gain = jf_raw.get_array(\"data.gain\")[:, :, cell_idx_preview, ...].values\n",
"gain_train_cells = (\n",
- " jf_raw.select_trains(by_id[[tid]]).get_array(\"data.gain\")[:, :, :, ...]\n",
+ " jf_raw.select_trains(by_id[[tid]]).get_array(\"data.gain\")[:, :, :, ...].values\n",
")"
]
},
@@ -760,13 +761,12 @@
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"raw_mean = np.mean(raw, axis=1)\n",
- "raw_mean_arr = raw_mean.values\n",
"\n",
"geom.plot_data_fast(\n",
" raw_mean,\n",
" ax=ax,\n",
- " vmin=min(0.75*np.median(raw_mean_arr[raw_mean_arr > 0]), 2000),\n",
- " vmax=max(1.5*np.median(raw_mean_arr[raw_mean_arr > 0]), 16000),\n",
+ " vmin=min(0.75*np.median(raw_mean[raw_mean > 0]), 2000),\n",
+ " vmax=max(1.5*np.median(raw_mean[raw_mean > 0]), 16000),\n",
" cmap=\"jet\",\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
@@ -791,9 +791,8 @@
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"corrected_mean = np.mean(corrected, axis=1)\n",
- "corrected_mean_arr = corrected_mean.values\n",
- "_corrected_vmin = min(0.75*np.median(corrected_mean_arr[corrected_mean_arr > 0]), -0.5)\n",
- "_corrected_vmax = max(2.*np.median(corrected_mean_arr[corrected_mean_arr > 0]), 100)\n",
+ "_corrected_vmin = min(0.75*np.median(corrected_mean[corrected_mean > 0]), -0.5)\n",
+ "_corrected_vmax = max(2.*np.median(corrected_mean[corrected_mean > 0]), 100)\n",
"\n",
"mean_plot_kwargs = dict(\n",
" vmin=_corrected_vmin, vmax=_corrected_vmax, cmap=\"jet\"\n",
@@ -822,7 +821,7 @@
"source": [
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"corrected_masked = corrected.copy()\n",
- "corrected_masked_mean = corrected_masked.where(mask == 0).mean(dim=\"train\", skipna=False)\n",
+ "corrected_masked_mean = np.nanmean(corrected_masked, axis=1)\n",
"del corrected_masked\n",
"\n",
"if not strixel_sensor:\n",
@@ -849,10 +848,9 @@
"display(Markdown((f\"#### A single image from train {tid}\")))\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
- "median_corr_train = np.median(corrected_train.values[corrected_train.values > 0])\n",
"single_plot_kwargs = dict(\n",
- " vmin=min(0.75 * median_corr_train, -0.5),\n",
- " vmax=max(2.0 * median_corr_train, 100),\n",
+ " vmin=min(0.75 * np.median(corrected_train[corrected_train > 0]), -0.5),\n",
+ " vmax=max(2.0 * np.median(corrected_train[corrected_train > 0]), 100),\n",
" cmap=\"jet\"\n",
")\n",
"\n",
@@ -916,8 +914,8 @@
"for i, mod in enumerate(karabo_da):\n",
" pdu = da_to_pdu[mod]\n",
" h, ex, ey = np.histogram2d(\n",
- " raw[i].values.flatten(),\n",
- " gain[i].values.flatten(),\n",
+ " raw[i].flatten(),\n",
+ " gain[i].flatten(),\n",
" bins=[100, 4],\n",
" range=[[0, 10000], [0, 4]],\n",
" )\n",
@@ -946,7 +944,7 @@
"for i, mod in enumerate(karabo_da):\n",
" pdu = da_to_pdu[mod]\n",
" fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(18, 10))\n",
- " corrected_flatten = corrected[i].values.flatten()\n",
+ " corrected_flatten = corrected[i].flatten()\n",
" for ax, hist_range in zip(axs, [(-100, 1000), (-1000, 10000)]):\n",
" h = ax.hist(\n",
" corrected_flatten,\n",
--
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