From a88cf030ddeeba2b5de6129b39a594953b2e0a3e Mon Sep 17 00:00:00 2001
From: Philipp Schmidt <philipp.schmidt@xfel.eu>
Date: Fri, 8 Oct 2021 13:50:57 +0200
Subject: [PATCH] Narrow down sequences when using PPU filtering in AGIPD
correct
---
.../AGIPD/AGIPD_Correct_and_Verify.ipynb | 68 +++++++++++--------
1 file changed, 40 insertions(+), 28 deletions(-)
diff --git a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
index 34038d771..c0eb25b22 100644
--- a/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
+++ b/notebooks/AGIPD/AGIPD_Correct_and_Verify.ipynb
@@ -124,7 +124,7 @@
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import yaml\n",
- "from extra_data import RunDirectory, stack_detector_data\n",
+ "from extra_data import RunDirectory, stack_detector_data, by_id\n",
"from extra_geom import AGIPD_1MGeometry, AGIPD_500K2GGeometry\n",
"from matplotlib import cm as colormap\n",
"from matplotlib.colors import LogNorm\n",
@@ -279,6 +279,45 @@
"execution_count": null,
"metadata": {},
"outputs": [],
+ "source": [
+ "if use_ppu_device:\n",
+ " # Obtain trains to process if using a pulse picker device.\n",
+ " dc = RunDirectory(in_folder / f'r{run:04d}')\n",
+ "\n",
+ " # Will throw an uncaught exception if the device is wrong.\n",
+ " seq_start = run[use_ppu_device, 'trainTrigger.sequenceStart.value'].ndarray()\n",
+ "\n",
+ " # The trains picked are the unique values of trainTrigger.sequenceStart\n",
+ " # minus the first (previous trigger before this run).\n",
+ " train_ids = np.unique(seq_start)[1:] + ppu_train_offset\n",
+ "\n",
+ " print(f'PPU device {use_ppu_device} triggered for {len(train_ids)} trains')\n",
+ " \n",
+ " # Since we got the DataCollection already, narrow down the files we open.\n",
+ " # This hardcodes the receiver_id and path_template parameters currently, but this\n",
+ " # will disappear with moving the entire notebook to EXtra-data.\n",
+ " subdc = dc.select_trains(by_id[train_ids]).select(f'{karabo_id}/DET/*CH0:xtdf')\n",
+ " sequences = sorted({int(f.filename[-8:-3]) for f in subdc.files})\n",
+ "\n",
+ "elif train_ids[0] != [-1]:\n",
+ " # Specific trains passed by parameter, convert to ndarray.\n",
+ " train_ids = np.array(train_ids)\n",
+ " \n",
+ " print(f'Processing up to {len(train_ids)} manually selected trains')\n",
+ "else:\n",
+ " # Process all trains.\n",
+ " train_ids = None\n",
+ " \n",
+ " print(f'Processing all valid trains')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
"source": [
"# set everything up filewise\n",
"mapped_files, _, total_sequences, _, _ = cal_tools.tools.map_modules_from_folder(\n",
@@ -385,33 +424,6 @@
"print(f\"• Photon Energy: {photon_energy}\")"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "if use_ppu_device:\n",
- " # Obtain trains to process if using a pulse picker device.\n",
- " run = RunDirectory(in_folder / f'r{run:04d}')\n",
- "\n",
- " # Will throw an uncaught exception if the device is wrong.\n",
- " seq_start = run[use_ppu_device, 'trainTrigger.sequenceStart.value'].ndarray()\n",
- "\n",
- " # The trains picked are the unique values of trainTrigger.sequenceStart\n",
- " # minus the first (previous trigger before this run).\n",
- " train_ids = np.unique(seq_start)[1:] + ppu_train_offset\n",
- "\n",
- " print(f'PPU device {use_ppu_device} triggered for {len(train_ids)} trains')\n",
- "\n",
- "elif train_ids[0] != [-1]:\n",
- " # Specific trains passed by parameter, convert to ndarray.\n",
- " train_ids = np.array(train_ids)\n",
- "else:\n",
- " # Process all trains.\n",
- " train_ids = None"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
--
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