Merge branch 'master' into OSSPS_gamma

.bandit

@@ -0,0 +1,2 @@
+skips:
+  - "B101" # warning about assert

.codacy.yml

@@ -1,2 +1,4 @@
 exclude_paths:
-  - '**.md'
+  - '**.md'
+assert_used:
+  - skips: ['*test*.py', 'test*.py']

.mailmap

@@ -23,5 +23,5 @@ Gemma Fardell <[email protected]> <[email protected]>
 Sam D. Porter <[email protected]>
 Matthew Strugari <[email protected]>
 Matthew Strugari <[email protected]> <[email protected]>
-
+Toni Reeves <[email protected]>
 

CHANGES.md

@@ -22,9 +22,6 @@
   - Several allocate methods in STIR.py, Gadgetron.py and Reg.py are replaced with just one allocate in DataContainer class that does not copy data between Python and C++.
   - `return None` in the method `Datacontainer.shape()` replaced with more Pythonesque `return (0,)`.
 
-* MR
-  - Handling of "irregular" ISMRMRD acquisitions hit what appears to be a bug in recent Gadgetron (HEAD detached at 0670db84). A quick fix applied, Gadgetron issue to be raised.
-
 ## v3.5.0
 
 * GitHub Action: remove temporarily the Ubuntu 20.04 build, #1178

CMakeLists.txt

@@ -177,7 +177,7 @@ else(DISABLE_PYTHON)
 endif(DISABLE_PYTHON)
 
 
-option(DISABLE_Matlab "Disable building SIRF matlab support" OFF)
+option(DISABLE_Matlab "Disable building SIRF matlab support" ON)
 if (DISABLE_Matlab)
   message(STATUS "Matlab support disabled")
 else(DISABLE_Matlab)

README.md

@@ -10,11 +10,12 @@ This software is the main output of [SyneRBI](https://www.ccpsynerbi.ac.uk), the
 Platform for Synergistic Reconstruction for Biomedical Imaging (formerly CCP PETMR).
 
 Please start with our latest [User's Guide](doc/UserGuide.md).
-See [our Wiki page for installation instructions](https://github.com/SyneRBI/SIRF/wiki/Installation-instructions).
 
-You can also [download a VM](https://doi.org/10.5281/zenodo.2707911) with SIRF and related software preinstalled.
+## How to obtain SIRF
+There are multiple ways to obtain a binary version of SIRF, please check them out in our documentation page [how to obtain SIRF](https://github.com/SyneRBI/SIRF/wiki/How-to-obtain-SIRF)
+or [our Wiki page for installation instructions](https://github.com/SyneRBI/SIRF/wiki/Installation-instructions).
 
-# Where is everything installed?
+## Where is everything installed?
 
 SIRF largely follows the usual directory structure with some minor tweaks. When using
 the default options when building SIRF, you will get (most of the) following, depending
@@ -40,7 +41,7 @@ what was found/built:
 [gh-action-link]: https://github.com/SyneRBI/SIRF/actions/workflows/build-test.yml
 [travis-badge]: https://travis-ci.org/SyneRBI/SIRF.svg?branch=master
 [travis]: https://travis-ci.org/SyneRBI/SIRF
-[style-badge]: https://api.codacy.com/project/badge/Grade/392861b4085f4f438d12c41029f86b47
-[style-link]: https://www.codacy.com/gh/SyneRBI/SIRF?utm_source=github.com&utm_medium=referral&utm_content=SyneRBI/SIRF&utm_campaign=Badge_Grade
+[style-badge]: https://app.codacy.com/project/badge/Grade/392861b4085f4f438d12c41029f86b47
+[style-link]: https://app.codacy.com/gh/SyneRBI/SIRF/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade
 [zenodo-badge]: https://zenodo.org/badge/DOI/10.5281/zenodo.2707911.svg
 [zenodo-link]: https://doi.org/10.5281/zenodo.2707911

examples/Python/MR/acquisition_data.py

@@ -38,10 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
-from ast import literal_eval
+from sirf.Utilities import error, examples_data_path, existing_filepath
 
-# import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+# import MR engine module
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -57,7 +58,7 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_file
-    acq_data = AcquisitionData(input_file, True)
+    acq_data = mr.AcquisitionData(input_file, True)
 
     # the raw k-space data is a list of different readouts
     # of different data type (e.g. noise correlation data, navigator data,
@@ -85,7 +86,7 @@ def main():
 
     # inspect the first readout flag
     flags0 = acq_data.get_ISMRMRD_info('flags', range(1))
-    if flags0 & IMAGE_DATA_MASK:
+    if flags0 & mr.IMAGE_DATA_MASK:
         print('first readout is image data')
     else:
         # should see this if input data file is test_2D_2x.h5
@@ -143,7 +144,7 @@ def main():
     # direction. So far only the removal of readout oversampling and noise and
     # asymmetric echo adjusting is implemented
     print('pre-processing acquisition data...')
-    processed_acq_data = preprocess_acquisition_data(acq_data).abs()
+    processed_acq_data = mr.preprocess_acquisition_data(acq_data).abs()
     acq_max = processed_acq_data.maximum(processed_acq_data*10)
     acq_min = processed_acq_data.minimum(processed_acq_data*10)
     print('norm of acq_data: %f' % processed_acq_data.norm())

examples/Python/MR/acquisition_model.py

@@ -38,8 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -55,23 +58,21 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_file
-    AcquisitionData.set_storage_scheme('memory')
-    acq_data = AcquisitionData(input_file)
+    mr.AcquisitionData.set_storage_scheme('memory')
+    acq_data = mr.AcquisitionData(input_file)
     print('---\n acquisition data norm: %e' % acq_data.norm())
 
     # pre-process acquisition data
     print('---\n pre-processing acquisition data...')
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
     print('---\n processed acquisition data norm: %e' % processed_data.norm())
 
     # perform reconstruction to obtain a meaningful ImageData object
-    # (cannot be obtained in any other way at present)
-#    if processed_data.is_undersampled():
     if acq_data.is_undersampled():
-        recon = CartesianGRAPPAReconstructor()
+        recon = mr.CartesianGRAPPAReconstructor()
         recon.compute_gfactors(False)
     else:
-        recon = FullySampledReconstructor()
+        recon = mr.FullySampledReconstructor()
     recon.set_input(processed_data)
     print('---\n reconstructing...')
     recon.process()
@@ -104,24 +105,20 @@ def main():
     print('patient table positions:')
     print(ptp)
 
-    # sort processed acquisition data;
-    # sorting currently performed with respect to (in this order):
-    #    - repetition
-    #    - slice
-    #    - kspace encode step 1
+    # sort processed acquisition data
     print('---\n sorting acquisition data...')
     processed_data.sort()
 
     # compute coil sensitivity maps
     print('---\n computing coil sensitivity maps...')
-    csms = CoilSensitivityData()
+    csms = mr.CoilSensitivityData()
     csms.calculate(processed_data)
 
     # create acquisition model based on the acquisition parameters
     # stored in processed_data and image parameters stored in reconstructed_images
-##    acq_model = AcquisitionModel(processed_data, reconstructed_images)
-    acq_model = AcquisitionModel()
-    acq_model.set_up(processed_data, reconstructed_images)
+    acq_model = mr.AcquisitionModel(processed_data, reconstructed_images)
+##    acq_model = mr.AcquisitionModel()
+##    acq_model.set_up(processed_data, reconstructed_images)
     acq_model.set_coil_sensitivity_maps(csms)
 
     # use the acquisition model (forward projection) to produce simulated
@@ -134,7 +131,8 @@ def main():
         simulated_acq_data.write(output_file)
 
     # display simulated acquisition data
-    #simulated_acq_data.show(title = 'Simulated acquisition data (magnitude)')
+    if show_plot:
+        simulated_acq_data.show(title = 'Simulated acquisition data (magnitude)')
 
     print('\n--- Computing the norm of the acquisition model operator...')
     acqm_norm = acq_model.norm()

examples/Python/MR/coil_sensitivity_maps.py

@@ -38,10 +38,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
-from pUtilities import *
+from sirf.Utilities import error, examples_data_path, existing_filepath, show_3D_array
 
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -59,20 +60,20 @@ def main():
     input_file = existing_filepath(data_path, data_file)
     #
     # acquisition data will be read from an HDF file input_file
-    AcquisitionData.set_storage_scheme('memory')
+    mr.AcquisitionData.set_storage_scheme('memory')
 
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
     #
     # pre-process acquisition data
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
     #
     # sort k-space data into a 2D Cartesian matrix for each coil
     processed_data.sort()
 
     # 2. Calculate coil sensitivity maps directly from the raw k-space data:
 
     # create coil sensitivity object
-    CSMs = CoilSensitivityData()
+    CSMs = mr.CoilSensitivityData()
     #
     # set number of smoothing iterations to suppress noise
     CSMs.smoothness = nit
@@ -98,13 +99,13 @@ def main():
     # SSRS and Inati methods:
 
     # create coil images object
-    CIs = CoilImagesData()
+    CIs = mr.CoilImagesData()
     # calculate coil sensitivity maps by dividing each coil image data by the
     # Square-Root-of-the-Sum-of-Squares over all coils (SRSS);
     # (niter = nit) sets the number of smoothing iterations applied
     # to the image data prior to the calculation of the coil sensitivity maps
     CIs.calculate(processed_data)
-    CSs = CoilSensitivityData()
+    CSs = mr.CoilSensitivityData()
     print('B) calculating from coil images...')
     CSs.calculate(CIs, method='SRSS(niter=%d)' % nit)
     diff = CSs - CSMs

examples/Python/MR/fully_sampled_recon.py

@@ -38,8 +38,13 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+import numpy
+
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -58,7 +63,7 @@ def main():
     # bruker_to_ismrmrd on https://github.com/ismrmrd/.
     # Acquisition data will be read from an HDF file input_file
     print('---\n reading in file %s...' % input_file)
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
 
     # pre-process acquired k-space data:
     # prior to image reconstruction several pre-processing steps such as 
@@ -67,12 +72,12 @@ def main():
     # direction. So far only the removal of readout oversampling and noise and
     # asymmetric echo adjusting is implemented
     print('---\n pre-processing acquisition data...')
-    processed_data = preprocess_acquisition_data(acq_data)
+    processed_data = mr.preprocess_acquisition_data(acq_data)
 
     # set up reconstruction:
     # create a reconstruction object using 2D inverse Fourier transform and
     # provide pre-processed k-space data as input
-    recon = FullySampledReconstructor()
+    recon = mr.FullySampledReconstructor()
     recon.set_input(processed_data)
 
     # perform reconstruction

examples/Python/MR/grappa_and_steepest_descent.py

@@ -42,8 +42,13 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+import numpy
+
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -70,16 +75,16 @@ def main():
     input_file = existing_filepath(data_path, data_file)
 
     # acquisition data will be read from an HDF file input_data
-    AcquisitionData.set_storage_scheme('memory')
-    acq_data = AcquisitionData(input_file)
+    mr.AcquisitionData.set_storage_scheme('memory')
+    acq_data = mr.AcquisitionData(input_file)
 
     # pre-process acquisition data
     print('---\n pre-processing acquisition data...')
-    preprocessed_data = preprocess_acquisition_data(acq_data)
+    preprocessed_data = mr.preprocess_acquisition_data(acq_data)
     preprocessed_data_norm = preprocessed_data.norm()
 
     # perform reconstruction
-    recon = CartesianGRAPPAReconstructor()
+    recon = mr.CartesianGRAPPAReconstructor()
     recon.set_input(preprocessed_data)
     recon.compute_gfactors(False)
     print('---\n reconstructing...')
@@ -95,7 +100,7 @@ def main():
         return
 
     # compute coil sensitivity maps
-    csms = CoilSensitivityData()
+    csms = mr.CoilSensitivityData()
     print('---\n sorting acquisition data...')
     preprocessed_data.sort()
     print('---\n computing sensitivity maps...')
@@ -104,7 +109,7 @@ def main():
     # create acquisition model based on the acquisition parameters
     # stored in preprocessed_data and image parameters stored in
     # image_data
-    acq_model = AcquisitionModel(preprocessed_data, image_data)
+    acq_model = mr.AcquisitionModel(preprocessed_data, image_data)
     acq_model.set_coil_sensitivity_maps(csms)
 
     start_val = 0.001
@@ -131,7 +136,7 @@ def main():
         image_data = image_data - grad * tau
         if (i%10 == 0 or i == niter - 1) and show_plot:
             it = i + 1
-            title = 'Steepest-descent-refined image data, iteration %d' % it
+            title = 'Image data computed by steepest descent at iteration %d' % it
             image_data.show(zyx=zyx, slice=slc, title=title, cmap=None, \
                             postpone=(i < niter - 1))
 

examples/Python/MR/grappa_basic.py

@@ -57,8 +57,11 @@
 from docopt import docopt
 args = docopt(__doc__, version=__version__)
 
+from sirf.Utilities import error, examples_data_path, existing_filepath
+
 # import engine module
-exec('from sirf.' + args['--engine'] + ' import *')
+import importlib
+mr = importlib.import_module('sirf.' + args['--engine'])
 
 # process command-line options
 data_file = args['--file']
@@ -82,19 +85,19 @@ def main():
 
     # Create an acquisition container of type AcquisitionData
     print('---\n reading in file %s...' % input_file)
-    AcquisitionData.set_storage_scheme('memory')
+    mr.AcquisitionData.set_storage_scheme('memory')
 
-    acq_data = AcquisitionData(input_file)
+    acq_data = mr.AcquisitionData(input_file)
 
     # Pre-process this input data.
     # (Currently this is a Python script that just sets up a 3 chain gadget.
     # In the future it will be independent of the MR recon engine.)
     print('---\n pre-processing acquisition data...')
-    preprocessed_data = preprocess_acquisition_data(acq_data)
+    preprocessed_data = mr.preprocess_acquisition_data(acq_data)
 
     # Perform reconstruction of the preprocessed data.
     # 1. set the reconstruction to be for Cartesian GRAPPA data.
-    recon = CartesianGRAPPAReconstructor();
+    recon = mr.CartesianGRAPPAReconstructor();
 
     # 2. set the reconstruction input to be the data we just preprocessed.
     recon.set_input(preprocessed_data);