directory tidy

.github/workflows/run.yml

@@ -3,7 +3,7 @@ on:
   push:
     branches: ['**']
     tags: ['**']
-    paths-ignore: ['LICENSE*', 'LICENCE*', 'README.md']
+    paths-ignore: ['LICENSE*', 'LICENCE*', 'README.md', 'SIRF_data_preparation/*']
 jobs:
   check:
     runs-on: ubuntu-latest

.gitignore

@@ -3,7 +3,6 @@ tmp*/
 errr.txt
 info.txt
 warn.txt
-__pycache__
+__pycache__/
 *.s
 *.hs
-

src/SIRF_data_preparation/PET_plot_functions.py → SIRF_data_preparation/PET_plot_functions.py

@@ -1,24 +1,6 @@
-'''
-Some functions for plotting used in the examples
-'''
-
-## CCP SyneRBI Synergistic Image Reconstruction Framework (SIRF)
-## Copyright 2020-2021 University College London
-##
-## This is software developed for the Collaborative Computational
-## Project in Synergistic Reconstruction for Biomedical Imaging (formerly CCP PETMR)
-## (http://www.ccpsynerbi.ac.uk/).
-##
-## Licensed under the Apache License, Version 2.0 (the "License");
-##   you may not use this file except in compliance with the License.
-##   You may obtain a copy of the License at
-##       http://www.apache.org/licenses/LICENSE-2.0
-##   Unless required by applicable law or agreed to in writing, software
-##   distributed under the License is distributed on an "AS IS" BASIS,
-##   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-##   See the License for the specific language governing permissions and
-##   limitations under the License.
-
+'''Some functions for plotting used in the examples'''
+# Licence: Apache-2.0
+# Copyright 2020-2021 University College London
 import sirf.STIR as PET
 import numpy as np
 import matplotlib.pyplot as plt
@@ -50,4 +32,3 @@ def plot_sinogram_profile(prompts, randoms=None, scatter=None, sumaxis=(0,1), se
             plt.plot(np.sum(scatter.as_array() + randoms_as_array, axis=sumaxis)[select,:], label='randoms+scatter')
     ax.legend()
     plt.show()
-

lib/data_utilities/__init__.py → SIRF_data_preparation/data_utilities.py

@@ -1,26 +1,16 @@
 '''Library of Siemens data preparation utilities.'''
-
-# Author: Ashley Gillman, Kris Thielemans, Evgueni Ovtchinnikov
+# Authors: Ashley Gillman, Kris Thielemans, Evgueni Ovtchinnikov
+# Licence: Apache-2.0
 # Copyright (C) 2021 Commonwealth Scientific and Industrial Research Organisation
 # Copyright (C) 2024 University College London
 # Copyright (C) 2024 STFC, UK Research and Innovation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-#       http://www.apache.org/licenses/LICENSE-2.0
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
 import os
 import importlib
 pet = importlib.import_module('sirf.STIR')
 import logging
 
 logger = logging.getLogger("PETRIC")
+DATA_PATH = '/home/sirfuser/devel/SyneRBI-Challenge/data'
 
 
 def the_data_path(*data_type):
@@ -30,13 +20,7 @@ def the_data_path(*data_type):
     data_type: either 'PET', 'MR' or 'Synergistic', or use multiple arguments for
     subdirectories like the_data_path('PET', 'mMR', 'NEMA_IQ').
     '''
-    try:
-        from .data_path import data_path
-    except ImportError:
-        raise RuntimeError(
-            "Path to data not found.")
-
-    return os.path.join(data_path, *data_type)
+    return os.path.join(DATA_PATH, *data_type)
 
 
 def fix_siemens_norm_EOL(in_filename, out_filename):
@@ -132,7 +116,7 @@ def prepare_challenge_Siemens_data(data_path, challenge_data_path, intermediate_
     logger.info('data shape: %s' % repr(prompts.shape))
     logger.info('prompts norm: %f' % prompts.norm())
     logger.info('randoms norm: %f' % randoms.norm())
-    
+
     logger.info(f'writing prompts to {f_prompts} and randoms to {f_randoms}')
     prompts.write(f_prompts)
     randoms.write(f_randoms)
@@ -176,8 +160,7 @@ def prepare_challenge_Siemens_data(data_path, challenge_data_path, intermediate_
     asm_mf.normalise(background)
     logger.info('norm of the additive term: %f' % background.norm())
     logger.info(f'writing additive term to {f_additive}')
-    
+
     background.write(f_additive)
 
     return
-