From 700abf84c789ce2f7b60dac433be191d38027184 Mon Sep 17 00:00:00 2001
From: ahmedk <karim.ahmed@xfel.eu>
Date: Thu, 7 Jul 2022 11:13:06 +0200
Subject: [PATCH] rebase to master, fix conflicts, and rearrange plots
---
...Jungfrau_Gain_Correct_and_Verify_NBC.ipynb | 162 +++++++++++-------
1 file changed, 99 insertions(+), 63 deletions(-)
diff --git a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
index 492907b29..d7bf6d8b0 100644
--- a/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
+++ b/notebooks/Jungfrau/Jungfrau_Gain_Correct_and_Verify_NBC.ipynb
@@ -40,6 +40,7 @@
"overwrite = True # set to True if existing data should be overwritten\n",
"relative_gain = True # do relative gain correction\n",
"limit_images = 0 # ONLY FOR TESTING. process only first N images, Use 0 to process all.\n",
+ "cell_id_preview = 15 # cell Id used for preview in single-shot plots\n",
"\n",
"# Parameters for retrieving calibration constants\n",
"manual_slow_data = False # if true, use manually entered bias_voltage and integration_time values\n",
@@ -90,6 +91,7 @@
"from cal_tools.tools import (\n",
" get_constant_from_db_and_time,\n",
" get_dir_creation_date,\n",
+ " get_pdu_from_db,\n",
" map_seq_files,\n",
" write_compressed_frames,\n",
")\n",
@@ -505,6 +507,18 @@
" gain = seq_dc[instrument_src_kda, \"data.gain\"].ndarray()\n",
" memcells = seq_dc[instrument_src_kda, \"data.memoryCell\"].ndarray()\n",
"\n",
+ " if memory_cells > 1:\n",
+ " # For plotting, assuming that memory cells are sorted the same for all trains.\n",
+ " found_cells = memcells[0] == cell_id_preview\n",
+ " if any(found_cells):\n",
+ " cell_idx_preview = np.where(found_cells)[0][0]\n",
+ " else:\n",
+ " print(f\"WARNING: The selected cell_id_preview {cell_id_preview} is not available in burst mode.\"\n",
+ " f\"Previewing cell `{memcells[0]}`.\")\n",
+ " cell_idx_preview = 0\n",
+ " else:\n",
+ " cell_idx_preview = 0\n",
+ "\n",
" context.map(correct_train, data)\n",
" step_timer.done_step(f'Correction time.')\n",
"\n",
@@ -605,28 +619,28 @@
"source": [
"first_seq = 0 if sequences == [-1] else sequences[0]\n",
"\n",
- "# Reading CORR data for plotting.\n",
- "jf_corr = components.JUNGFRAU(\n",
- " RunDirectory(out_folder, f'*{run}*S{first_seq:05d}*'),\n",
- " detector_name=karabo_id,\n",
- ")\n",
- "tid, jf_corr_data = next(iter(jf_corr.trains(require_all=True)))\n",
+ "with RunDirectory(out_folder, f'*{run}*S{first_seq:05d}*') as corr_dc:\n",
+ " # Reading CORR data for plotting.\n",
+ " jf_corr = components.JUNGFRAU(\n",
+ " corr_dc,\n",
+ " detector_name=karabo_id,\n",
+ " )\n",
+ " tid, jf_corr_data = next(iter(jf_corr.trains(require_all=True)))\n",
"\n",
- "# Show cell_idx 0 TODO: replace with a choice of cell_idx_preview\n",
"# Shape = [modules, cells, trains, x, y]\n",
- "corrected = jf_corr.get_array(\"data.adc\")[:, :, 0, ...].values # pick cell_idx 0 for all trains.\n",
- "corrected_train = jf_corr_data[\"data.adc\"][:, 0, ...].values # loose the train axis.\n",
+ "corrected = jf_corr.get_array(\"data.adc\")[:, :, cell_idx_preview, ...].values\n",
+ "corrected_train = jf_corr_data[\"data.adc\"][:, cell_idx_preview, ...].values # loose the train axis.\n",
"\n",
- "mask = jf_corr.get_array(\"data.mask\")[:, :, 0, ...].values\n",
- "mask_train = jf_corr_data[\"data.mask\"][:, 0, ...].values\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",
- "# Reading RAW data for plotting.\n",
- "jf_raw = components.JUNGFRAU(\n",
- " RunDirectory(f'{in_folder}/r{run:04d}/', f'*S{first_seq:05d}*'),\n",
- " detector_name=karabo_id,\n",
- ")\n",
+ "with RunDirectory(f'{in_folder}/r{run:04d}/', f'*S{first_seq:05d}*') as raw_dc:\n",
+ "\n",
+ " # Reading RAW data for plotting.\n",
+ " jf_raw = components.JUNGFRAU(raw_dc, detector_name=karabo_id)\n",
+ "\n",
+ " raw = jf_raw.get_array(\"data.adc\")[:, :, cell_idx_preview, ...].values\n",
"\n",
- "raw = jf_raw.get_array(\"data.adc\")[:, :, 0, ...].values\n",
"raw_train = jf_raw.select_trains(\n",
" by_id[[tid]]).get_array(\"data.adc\")[:, 0, 0, ...].values\n",
"\n",
@@ -650,47 +664,13 @@
"metadata": {},
"outputs": [],
"source": [
- "def do_2d_plot(data, edges, y_axis, x_axis, title):\n",
- " fig = plt.figure(figsize=(10, 10))\n",
- " ax = fig.add_subplot(111)\n",
- " extent = [\n",
- " np.min(edges[1]),\n",
- " np.max(edges[1]),\n",
- " np.min(edges[0]),\n",
- " np.max(edges[0]),\n",
- " ]\n",
- "\n",
- " im = ax.imshow(\n",
- " data[::-1, :],\n",
- " extent=extent,\n",
- " aspect=\"auto\",\n",
- " norm=LogNorm(vmin=1, vmax=np.max(data))\n",
- " )\n",
- " ax.set_xlabel(x_axis)\n",
- " ax.set_ylabel(y_axis)\n",
- " ax.set_title(title)\n",
- " cb = fig.colorbar(im)\n",
- " cb.set_label(\"Counts\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i, mod in enumerate(karabo_da): \n",
- " h, ex, ey = np.histogram2d(\n",
- " raw[i].flatten(),\n",
- " gain[i].flatten(),\n",
- " bins=[100, 4],\n",
- " range=[[0, 10000], [0, 4]],\n",
- " )\n",
- " do_2d_plot(\n",
- " h, (ex, ey),\n",
- " \"Signal (ADU)\",\n",
- " \"Gain Bit Value (high gain=0[00], medium gain=1[01], low gain=3[11])\",\n",
- " f\"Module {mod}\")"
+ "db_modules = get_pdu_from_db(\n",
+ " karabo_id=karabo_id,\n",
+ " karabo_da=karabo_da,\n",
+ " constant=Constants.jungfrau.Offset(),\n",
+ " condition=condition,\n",
+ " cal_db_interface=cal_db_interface,\n",
+ " snapshot_at=creation_time)"
]
},
{
@@ -718,7 +698,7 @@
" cmap=\"jet\",\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
- "ax.set_title(f'{karabo_id}', size=18)\n",
+ "ax.set_title(f'{karabo_id} - Mean RAW', size=18)\n",
"plt.show()"
]
},
@@ -747,7 +727,7 @@
" cmap=\"jet\",\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
- "ax.set_title(f'{karabo_id}', size=18)\n",
+ "ax.set_title(f'{karabo_id} - Mean CORRECTED', size=18)\n",
"\n",
"plt.show()"
]
@@ -769,11 +749,67 @@
" cmap=\"jet\",\n",
" colorbar={'shrink': 1, 'pad': 0.01},\n",
")\n",
- "ax.set_title(f'{karabo_id}', size=18)\n",
+ "ax.set_title(f'{karabo_id} - CORRECTED train: {tid}', size=18)\n",
"\n",
"plt.show()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def do_2d_plot(data, edges, y_axis, x_axis, title):\n",
+ " fig = plt.figure(figsize=(10, 10))\n",
+ " ax = fig.add_subplot(111)\n",
+ " extent = [\n",
+ " np.min(edges[1]),\n",
+ " np.max(edges[1]),\n",
+ " np.min(edges[0]),\n",
+ " np.max(edges[0]),\n",
+ " ]\n",
+ "\n",
+ " im = ax.imshow(\n",
+ " data[::-1, :],\n",
+ " extent=extent,\n",
+ " aspect=\"auto\",\n",
+ " norm=LogNorm(vmin=1, vmax=np.max(data))\n",
+ " )\n",
+ " ax.set_xlabel(x_axis)\n",
+ " ax.set_ylabel(y_axis)\n",
+ " ax.set_title(title)\n",
+ " cb = fig.colorbar(im)\n",
+ " cb.set_label(\"Counts\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Gain Bit Value"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i, (pdu, mod) in enumerate(zip(db_modules, karabo_da)): \n",
+ " h, ex, ey = np.histogram2d(\n",
+ " raw[i].flatten(),\n",
+ " gain[i].flatten(),\n",
+ " bins=[100, 4],\n",
+ " range=[[0, 10000], [0, 4]],\n",
+ " )\n",
+ " do_2d_plot(\n",
+ " h, (ex, ey),\n",
+ " \"Signal (ADU)\",\n",
+ " \"Gain Bit Value (high gain=0[00], medium gain=1[01], low gain=3[11])\",\n",
+ " f\"Module {mod} ({pdu})\")"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -787,7 +823,7 @@
"metadata": {},
"outputs": [],
"source": [
- "for i, mod in enumerate(karabo_da): \n",
+ "for i, (pdu, mod) in enumerate(zip(db_modules, karabo_da)): \n",
" fig, axs = plt.subplots(nrows=2, ncols=1, figsize=(18, 10))\n",
" corrected_flatten = corrected[i].flatten()\n",
" for ax, hist_range in zip(axs, [(-100, 1000), (-1000, 10000)]):\n",
@@ -799,7 +835,7 @@
" )\n",
" l = ax.set_xlabel(\"Signal (keV)\")\n",
" l = ax.set_ylabel(\"Counts\")\n",
- " _ = ax.set_title(f'Module {mod}')"
+ " _ = ax.set_title(f'Module {mod} ({pdu})')"
]
},
{
--
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