From d59052c40b8ff68cdce1a3ae9ea058a7099abdf1 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Thu, 23 May 2024 14:10:53 +0200
Subject: [PATCH] fix: no need to use keep_data_dims function and use available
functionalities in extra
---
...Jungfrau_Gain_Correct_and_Verify_NBC.ipynb | 83 +++++++------------
1 file changed, 28 insertions(+), 55 deletions(-)
diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index 668a7a165..059e49b4b 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -100,7 +100,6 @@
"from cal_tools.step_timing import StepTimer\n",
"from cal_tools.tools import (\n",
" calcat_creation_time,\n",
- " keep_data_dims,\n",
" map_seq_files,\n",
" write_constants_fragment,\n",
")\n",
@@ -780,51 +779,45 @@
"step_timer.start()\n",
"first_seq = 0 if sequences == [-1] else sequences[0]\n",
"\n",
- "corrected_files = [\n",
+ "seq_corrected_files = [\n",
" out_folder / f for f in fnmatch.filter(corrected_files, f\"*{run}*S{first_seq:05d}*\")\n",
"]\n",
- "with DataCollection.from_paths(corrected_files) as corr_dc:\n",
+ "\n",
+ "# TODO: replace with CALCAT value.\n",
+ "if \"1M\" in karabo_id:\n",
+ " nmods = 2\n",
+ "elif \"4M\" in karabo_id:\n",
+ " nmods = 8\n",
+ "else: # 500K\n",
+ " nmods = 1\n",
+ "\n",
+ "with DataCollection.from_paths(seq_corrected_files) as corr_dc:\n",
" # Reading CORR data for plotting.\n",
" jf_corr = components.JUNGFRAU(\n",
" corr_dc,\n",
" detector_name=karabo_id,\n",
+ " n_modules=nmods,\n",
" ).select_trains(np.s_[:plot_trains])\n",
" tid, jf_corr_data = next(iter(jf_corr.trains(require_all=True)))\n",
- "det_mod_start = jf_corr._modnos_start_at # TODO: replace with CALCAT value.\n",
- "available_modules_indices = list(jf_corr.modno_to_source.keys())\n",
+ "\n",
"# Shape = [modules, trains, cells, x, y]\n",
- "corrected = jf_corr.get_array(\"data.adc\")[:, :, cell_idx_preview, ...].values\n",
- "corrected_train = keep_data_dims( # loose the train axis.\n",
- " jf_corr_data[\"data.adc\"][:, cell_idx_preview, ...].values,\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
+ "corrected = jf_corr[\"data.adc\"].ndarray(module_gaps=True)[:, :, cell_idx_preview, ...]\n",
+ "corrected_train = jf_corr_data[\"data.adc\"][:, cell_idx_preview, ...] # loose the train axis.\n",
"\n",
- "mask = jf_corr.get_array(\"data.mask\")[:, :, cell_idx_preview, ...].values\n",
- "mask_train = keep_data_dims(\n",
- " jf_corr_data[\"data.mask\"][:, cell_idx_preview, ...].values,\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
+ "mask = jf_corr[\"data.mask\"].ndarray(module_gaps=True)[:, :, cell_idx_preview, ...]\n",
+ "mask_train = jf_corr_data[\"data.mask\"][:, cell_idx_preview, ...]\n",
"\n",
"with RunDirectory(f\"{in_folder}/r{run:04d}/\", f\"*S{first_seq:05d}*\", _use_voview=False) as raw_dc:\n",
" # Reading RAW data for plotting.\n",
- " jf_raw = components.JUNGFRAU(raw_dc, detector_name=karabo_id).select_trains(\n",
- " np.s_[:plot_trains]\n",
- " )\n",
+ " jf_raw = components.JUNGFRAU(\n",
+ " raw_dc, detector_name=karabo_id, n_modules=nmods\n",
+ " ).select_trains(np.s_[:plot_trains])\n",
"\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",
+ "raw = jf_raw[\"data.adc\"].ndarray(module_gaps=True)[:, :, cell_idx_preview, ...]\n",
"\n",
- "gain = jf_raw.get_array(\"data.gain\")[:, :, cell_idx_preview, ...].values\n",
+ "gain = jf_raw[\"data.gain\"].ndarray(module_gaps=True)[:, :, cell_idx_preview, ...]\n",
"gain_train_cells = (\n",
- " jf_raw.select_trains(by_id[[tid]]).get_array(\"data.gain\")[:, :, :, ...].values\n",
+ " jf_raw.select_trains(by_id[[tid]], )[\"data.gain\"].ndarray(module_gaps=True)[:, :, :, ...]\n",
")\n",
"step_timer.done_step(\"Prepared data for plotting\")"
]
@@ -845,13 +838,8 @@
"print(f\"The per pixel mean of the first {raw.shape[1]} trains of the first sequence file\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
- "raw_mean = keep_data_dims(\n",
- " np.mean(raw, axis=1),\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
- "vmin, vmax = np.percentile(raw_mean, [5, 95])\n",
+ "raw_mean = np.nanmean(raw, axis=1)\n",
+ "vmin, vmax = np.nanpercentile(raw_mean, [5, 95])\n",
"geom.plot_data_fast(\n",
" raw_mean,\n",
" ax=ax,\n",
@@ -879,12 +867,7 @@
"print(f\"The per pixel mean of the first {corrected.shape[1]} trains of the first sequence file\")\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
- "corrected_mean = keep_data_dims(\n",
- " np.nanmean(corrected, axis=1),\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
+ "corrected_mean = np.nanmean(corrected, axis=1)\n",
"vmin, vmax = np.nanpercentile(corrected_mean, [5, 95])\n",
"\n",
"mean_plot_kwargs = dict(vmin=vmin, vmax=vmax)\n",
@@ -920,12 +903,7 @@
"fig, ax = plt.subplots(figsize=(18, 10))\n",
"corrected_masked = corrected.copy()\n",
"corrected_masked[mask != 0] = np.nan\n",
- "corrected_masked_mean = keep_data_dims(\n",
- " np.nanmean(corrected_masked, axis=1),\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
+ "corrected_masked_mean = np.nanmean(corrected_masked, axis=1)\n",
"del corrected_masked\n",
"\n",
"if not strixel_sensor:\n",
@@ -1080,12 +1058,7 @@
"display(Markdown((f\"#### The per pixel maximum of train {tid} of the GAIN data\")))\n",
"\n",
"fig, ax = plt.subplots(figsize=(18, 10))\n",
- "gain_max = keep_data_dims(\n",
- " np.max(gain_train_cells, axis=(1, 2)),\n",
- " geom.expected_data_shape,\n",
- " available_modules_indices,\n",
- " det_mod_start\n",
- ")\n",
+ "gain_max = np.max(gain_train_cells, axis=(1, 2))\n",
"\n",
"geom.plot_data_fast(\n",
" gain_max,\n",
--
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