From 955da35a95b9e627d08ceae4d2f7b53e979429d8 Mon Sep 17 00:00:00 2001
From: Philipp Schmidt <philipp.schmidt@xfel.eu>
Date: Thu, 22 Feb 2024 10:39:11 +0100
Subject: [PATCH] Remove inner parallelization in Timepix centroiding
---
notebooks/Timepix/Compute_Timepix_Event_Centroids.ipynb | 7 ++-----
1 file changed, 2 insertions(+), 5 deletions(-)
diff --git a/notebooks/Timepix/Compute_Timepix_Event_Centroids.ipynb b/notebooks/Timepix/Compute_Timepix_Event_Centroids.ipynb
index 50a985dfd..036bb35f7 100755
--- a/notebooks/Timepix/Compute_Timepix_Event_Centroids.ipynb
+++ b/notebooks/Timepix/Compute_Timepix_Event_Centroids.ipynb
@@ -45,7 +45,6 @@
"clustering_epsilon = 2.0 # centroiding: The maximum distance between two samples for one to be considered as in the neighborhood of the other\n",
"clustering_tof_scale = 1e7 # centroiding: Scaling factor for the ToA axis so that the epsilon parameter in DB scan works in all 3 dimensions\n",
"clustering_min_samples = 2 # centroiding: minimum number of samples necessary for a cluster\n",
- "clustering_n_jobs = 5 # centroiding: (DBSCAN) The number of parallel jobs to run.\n",
"threshold_tot = 0 # raw data: minimum ToT necessary for a pixel to contain valid data\n",
"\n",
"raw_timewalk_lut_filepath = '' # fpath to look up table for timewalk correction relative to proposal path or empty string,\n",
@@ -249,7 +248,6 @@
" clustering_epsilon=2,\n",
" clustering_tof_scale=1e7,\n",
" clustering_min_samples=3,\n",
- " clustering_n_jobs=1,\n",
" centroiding_timewalk_lut=None):\n",
" # format input data\n",
" _tpx_data = {\n",
@@ -271,7 +269,7 @@
" # clustering (identify clusters in 2d data (x,y,tof) that belong to a single hit,\n",
" # each sample belonging to a cluster is labeled with an integer cluster id no)\n",
" _tpx_data = pre_clustering_filter(_tpx_data, tot_threshold=threshold_tot)\n",
- " _tpx_data[\"labels\"] = clustering(_tpx_data, epsilon=clustering_epsilon, tof_scale=clustering_tof_scale, min_samples=clustering_min_samples, n_jobs=clustering_n_jobs)\n",
+ " _tpx_data[\"labels\"] = clustering(_tpx_data, epsilon=clustering_epsilon, tof_scale=clustering_tof_scale, min_samples=clustering_min_samples)\n",
" _tpx_data = post_clustering_filter(_tpx_data)\n",
" # compute centroid data (reduce cluster of samples to a single point with properties)\n",
" if _tpx_data[\"labels\"] is None or _tpx_data[\"labels\"].size == 0:\n",
@@ -301,7 +299,7 @@
" missing_centroids = num_centroids > max_num_centroids\n",
"\n",
" if num_centroids > max_num_centroids:\n",
- " warn('number of centroids larger than definde maximum, some data cannot be written to disk')\n",
+ " warn('Number of centroids is larger than the defined maximum, some data cannot be written to disk')\n",
"\n",
" for key in centroid_dt.names:\n",
" out_data[index, :num_centroids][key] = centroids[key]\n",
@@ -385,7 +383,6 @@
" clustering_epsilon=clustering_epsilon,\n",
" clustering_tof_scale=clustering_tof_scale,\n",
" clustering_min_samples=clustering_min_samples,\n",
- " clustering_n_jobs=clustering_n_jobs,\n",
" centroiding_timewalk_lut=centroiding_timewalk_lut)\n",
"\n",
"\n",
--
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