Merge pull request #4 from ybuch/3-improve-documentation-and-batch-an…

…alysis-script

3 improve documentation and batch analysis script

README.md

@@ -7,11 +7,16 @@ Alignment: run `full_align.sh` for each geo_id (telescope and DUT alignment) and
 Masking: run `mask_generator.sh` which generates a mask file from a default mask (masking not used frontends) and a yaml file (list of disabeled pixels in the data taking). Please nothe that the default masks do not work for all runs since the frontends were also changed within geo_ids! These masks were adapted by hand and a set of working masks is stored under initial_masks.
 
 Analysis: is done using jobsub which is installed under your corry installation (see corry manual and below). A csv file has to be created with `data_searcher.sh` which contains information of geo and data for each run before starting the analysis.
+## Software
+
+Root: 6.24/06, ROOT 6.26/10 seem to work both.
+Eudaq: https://github.com/bpilsl/eudaq/tree/monopix2
+Corryvreckan: Master branch cf10f1ad0a4ff3e21eb8bdc7235664727f3393f4
 
 
 ## conf
 
-This folder contains all config files for corry which are used for the alignment as well as scripts. The analysis is done via jobsub (see below)
+This folder contains all config files for corry which are used for the alignment as well as scripts.
 
 ### full_align.sh
 
@@ -34,14 +39,6 @@ Folder containing used mask files (applied_masks) and scripts for generating mas
 
 This script copies a default mask from the default folder (containing information about the masking of the not activated frontends) and appends the information of the yaml files depending on the runnumber (by using extract_masked_pixels.py). The output is stored in the folder applied_masks. Please note that the default masks are not propper for all runs since changes in the masking werde done throughout equal geo_ids. Already finished masks can be found in the folder initial_masks.
 
-## jobsub
-
-This folder contains necessary files for jobsub which has to be started from your local corry installation (`<path_to_corry>/jobsub python3 jobsub.py -c <path_to_repo>/jobsub/analysis_jobsub.cfg> -csv <path_to_repo>/jobsub/data_p.csv -s -j 5 <runnumbers>`). For further information about jobsub please have a look at the corry manual. As example csv file, data_examle.csv is given.
-
-### analysis_jobsub.cfg
-
-config file used by jobsub
-
-### data_searcher.sh
+### analysis
 
-This script takes `W02R05_standard.csv`, `W05R15_TID.csv` or `W08R06_p.csv` as input and generates the needed csv file for jobsub. 
+Use conf/analyze.py. This convenience script does a run specific DUT alignment and analyses the run using the analysis.conf. 

conf/analyze.py

@@ -1,31 +1,51 @@
+'''
+Usage: python3 analyze.py --start 1400 --stop 1568
+'''
+
 import argparse
 import glob
 import re
 import os
 import pandas as pd
 
+
+# These paths might need to be changed
 data_folder = '/home/bgnet2/s3_cloud/beam_data/desy'
 corry_bin = '~/corryvreckan/bin/corry'
+
+# These paths do not need to be changed.
 geo_path_tel = '../geo/full_aligned'
-geo_path_run = '../geo/temp'
 dut_align_conf = '../conf/align_dut_mpx2.conf'
 analysis_conf = '../conf/analysis.conf'
 run_align_folder = '../geo/run_align'
 analysis_folder = '../analysis'
-run_start = 1400
-run_stop = 1401
-do_masking_per_run = True
+
+do_align_per_run = True
 do_analysis = True
 number_of_events_align = 100000
-number_of_events_analyze = 100000000
 
+parser = argparse.ArgumentParser(description='corry analysis wrapper')
+parser.add_argument('-r', help='run number', default = 0)
+parser.add_argument('--start', help='run number start', default = 0)
+parser.add_argument('--stop', help='run number stop', default = 0)
+parser.add_argument('-n', help='number of events for analysis', default = 100000000)
 
-df = pd.read_csv("../run_properties.csv", sep=",")
-print(df)
-data_in_files = glob.glob(data_folder + '/*.raw')
-# print('available raws: ', data_in_files)
+args = parser.parse_args()
 
+number_of_events_analyze = args.n
 
+if args.r == 0 and (args.start == 0 or args.stop == 0):
+    print("Either use run number or start/stop to give a range of runs to be analyzed.")
+
+if args.r != 0:
+    run_start = args.r
+    run_stop = args.r
+else:
+    run_start = args.start
+    run_stop = args.stop
+
+df = pd.read_csv("../run_properties.csv", sep=",")
+data_in_files = glob.glob(data_folder + '/*.raw')
 
 for current_run in range(run_start,run_stop+1):
     if not current_run in df['run_number'].unique():
@@ -40,16 +60,17 @@
         m = re.search(f'telescope.+run(0*{current_run})', f)
         if m:
             current_tel_file = f
-
+    if current_dut_file == '' or current_tel_file == '':
+        print(f'raw_files empty for run {current_run}')
+        continue
     print(f'processing tel: {current_tel_file}, dut: {current_dut_file}')
     geo_id = df.loc[df['run_number'] == current_run]['geoid'].values[0]
-    print(type(geo_id))
     tel_full_aligned = geo_path_tel+'/'+f'geo_id{geo_id}_full_aligned.geo'
     run_aligned = run_align_folder+'/'+f'alignment_run_{current_run}.geo'
     run_aligned_histo = '/'+f'alignment_run_{current_run}.root'
     analysis_histo = analysis_folder+'/'+f'analysis_run_{current_run}.root'
 
-    if do_masking_per_run:
+    if do_align_per_run:
         print(f'Running alignment for run {current_run}')
         corry_cmd = f'{corry_bin} -c {dut_align_conf} -o number_of_events={number_of_events_align} -o output_directory={run_align_folder} -o detectors_file={tel_full_aligned} -o detectors_file_updated={run_aligned} -o histogram_file={run_aligned_histo} -o EventLoaderEUDAQ2.file_name={current_tel_file} -o EventLoaderEUDAQ2:Monopix2_0.file_name={current_dut_file}'
         print(corry_cmd)
@@ -69,4 +90,5 @@
     if do_analysis:
         print(f'Running anaylsis for run {current_run}')
         corry_cmd = f'{corry_bin} -c {analysis_conf} -o number_of_events={number_of_events_analyze} -o output_directory={analysis_folder} -o detectors_file={run_aligned} -o histogram_file={analysis_histo} -o EventLoaderEUDAQ2.file_name={current_tel_file} -o EventLoaderEUDAQ2:Monopix2_0.file_name={current_dut_file}'
+        print(corry_cmd)
         os.system(corry_cmd)

geo/full_aligned/geo_id10_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 0deg,0deg,89.7485deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id3_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 180deg,180deg,0.116024deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id5_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 0deg,0deg,89.8731deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id6_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 2.24422deg,-1.25592deg,89.8deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id7_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 0deg,0deg,89.6663deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id8_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 0deg,0deg,89.72deg
 orientation_mode = "xyz"

geo/full_aligned/geo_id9_full_aligned.geo

@@ -40,7 +40,7 @@ type = "mimosa26"
 
 [Monopix2_0]
 coordinates = "cartesian"
-material_budget = 0.00075
+material_budget = 0.0032
 number_of_pixels = 512, 512
 orientation = 0deg,0deg,89.7388deg
 orientation_mode = "xyz"