feat: pull CAD generated service meshes into simulation

compact/tracking/cable_services.xml

@@ -0,0 +1,32 @@
+<!-- SPDX-License-Identifier: LGPL-3.0-or-later -->
+<!-- Copyright (C) 2022 Wouter Deconinck, Whitney Armstrong -->
+
+<lccdd>
+  <materials>
+    <material name="0.000000_0.000000_1.000000_0.000000_0.000000">
+      <D value="7.85" unit="g/cm3"/>
+      <fraction n="1" ref="Fe"/>
+    </material>
+    <material name="1.000000_0.000000_0.000000_0.000000_0.000000">
+      <D value="7.85" unit="g/cm3"/>
+      <fraction n="1" ref="Fe"/>
+    </material>
+    <material name="1.000000_0.501961_0.000000_0.000000_0.000000">
+      <D value="7.85" unit="g/cm3"/>
+      <fraction n="1" ref="Fe"/>
+    </material>
+    <material name="1.000000_0.501961_0.250980_0.000000_0.000000">
+      <D value="7.85" unit="g/cm3"/>
+      <fraction n="1" ref="Fe"/>
+    </material>
+  </materials>
+
+  <detectors>
+    <detector name="Services" type="DD4hep_TestShape_Creator">
+      <check>
+	<shape type="CAD_MultiVolume" ref="meshes/FullCableSpaceEstimate.obj" unit="m">
+        </shape>
+      </check>
+    </detector>
+  </detectors>
+</lccdd>

compact/tracking/get_meshes.xml

@@ -0,0 +1,12 @@
+<!-- SPDX-License-Identifier: LGPL-3.0-or-later -->
+<!-- Copyright (C) 2022 Wouter Deconinck, Whitney Armstrong -->
+
+<lccdd>
+  <plugins>
+    <plugin name="epic_FileLoader">
+      <arg value="cache:$DETECTOR_PATH:/opt/detector"/>
+      <arg value="file:meshes/FullCableSpaceEstimate.obj"/>
+      <arg value="url:https://github.com/eic/epic-data/raw/meshes/meshes/tracking/FullCableSpaceEstimate.obj"/>
+    </plugin>
+  </plugins>
+</lccdd>

configurations/services_only.yml

@@ -0,0 +1,4 @@
+features:
+  tracking:
+    get_meshes:
+    cable_services:

scripts/subdetector_tests/g4_material_scan.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+# Copyright (C) 2023 Chao Peng
+'''
+    A script to use g4MaterialScan and collect the results
+'''
+
+import os
+import argparse
+import subprocess
+import pandas as pd
+import numpy as np
+from io import StringIO
+from collections import OrderedDict as odict
+
+pd.set_option('display.max_rows', 1000)
+PROGRESS_STEP = 10
+
+
+'''
+    A parser function to convert output from g4MaterialScan to a pandas dataframe
+    compact: is path to compact file
+    start_point: a list or tuple for 3D coordinates (e.g., 0,0,0)
+    direction: a lit or tuple for direction (e.g., 0.1,0.2,1.0)
+    return: a dataframe for materialScan results
+'''
+def g4_material_scan(compact, start_point, direction, timeout=200):
+    EXEC_NAME = 'g4MaterialScan'
+    cmd = '{} --compact={} --position=\"{},{},{}\" --direction=\"{},{},{}\"'\
+          .format(EXEC_NAME, compact, *np.hstack([start_point, direction]))
+    output = os.popen(cmd).read()
+    # output = subprocess.check_output(cmd.split(' '), timeout=timeout).decode('UTF-8')
+
+    # find material scan lines
+    lines = []
+    add_line = False
+
+    for l in output.split('\n'):
+        if add_line:
+            lines.append(l.strip())
+        if l.strip().startswith('MaterialScan'):
+            add_line = True
+
+
+    # NOTE: the code below is for materialScan output as of 03/05/2023
+    # it may need change if the output format is changed
+    scans = []
+    first_digit = False
+    for i, l in enumerate(lines):
+        line = l.strip('| ')
+        if not first_digit and not line[:1].isdigit():
+            continue
+        first_digit = True
+        if not line[:1].isdigit():
+            break
+        # break coordinates for endpoint, which has a format of (x, y, z)
+        scans.append(line.strip('| ').translate({ord(i): None for i in '()'}).replace(',', ' '))
+
+    cols = [
+        'material', 'Z', 'A', 'density',
+        'rad_length', 'int_length', 'thickness', 'path_length',
+        'int_X0', 'int_lamda', 'end_x', 'end_y', 'end_z'
+        ]
+
+    dft = pd.read_csv(StringIO('\n'.join(scans)), sep='\s+', header=None, index_col=0, names=cols)
+    return dft.astype({key: np.float64 for key in cols[1:]})
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+            dest='compact',
+            help='Top-level xml file of the detector description.'
+            )
+    parser.add_argument(
+            '-o', '--output', default='g4_materials.csv',
+            help='A csv file contains the scan info.'
+            )
+    parser.add_argument(
+            '--start-point', default='0,0,0',
+            help='Start point of the scan, use the format \"x,y,z\", unit is cm.'
+            )
+    parser.add_argument(
+            '--eta-min', type=float, default=-4.0,
+            help='Minimum eta for the scan.'
+            )
+    parser.add_argument(
+            '--eta-max', type=float, default=4.0,
+            help='Minimum eta for the scan.'
+            )
+    parser.add_argument(
+            '--eta-nbins', type=int, default=801,
+            help='Number of bins for the eta scan.'
+            )
+    parser.add_argument(
+            '--phi', type=float, default=20.,
+            help='Phi angle of the scan, unit is degree.'
+            )
+    args = parser.parse_args()
+
+    if not os.path.exists(args.compact):
+        print('Cannot find {}'.format(args.compact))
+        exit(-1)
+
+    start_point = np.array([float(v.strip()) for v in args.start_point.split(',')])
+    etas = np.linspace(args.eta_min, args.eta_max, args.eta_nbins)
+    mats_indices = odict()
+    # a data buffer for the X0 values of (eta, material), 50 should be large enough
+    data = np.zeros(shape=(len(etas), 50))
+    for i, eta in enumerate(etas):
+        if i % PROGRESS_STEP == 0:
+            print('Scanned {:d}/{:d} lines for {:.2f} < eta < {:.2f}'.format(i, len(etas), etas[0], etas[-1]))
+        direction = (np.cos(args.phi/180.*np.pi), np.sin(args.phi/180.*np.pi), np.sinh(eta))
+        dfa = g4_material_scan(args.compact, start_point, direction)
+        dfa.loc[:, 'X0'] = dfa['int_X0'].diff(1).fillna(dfa['int_X0'])
+        for mat, xval in dfa.groupby('material')['X0'].sum().items():
+            if mat not in mats_indices:
+                mats_indices[mat] = len(mats_indices)
+            j = mats_indices.get(mat)
+            data[i, j] = xval
+
+    result = pd.DataFrame(columns=mats_indices.keys(), index=etas, data=data[:, :len(mats_indices)])
+    result.to_csv(args.output)

src/FileLoader.cpp

@@ -36,7 +36,7 @@ long load_file(Detector& /* desc */, int argc, char** argv)
 {
   // argument parsing
   std::string cache, file, url;
-  std::string cmd("curl --retry 5 -f {0} -o {1}");
+  std::string cmd("curl --location --retry 5 -f {0} -o {1}");
   for (int i = 0; i < argc && argv[i]; ++i) {
     if (0 == std::strncmp("cache:", argv[i], 6))
       cache = (argv[i] + 6);