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Merge pull request #21 from spm/enh-type-system
[Enhancement] Matlab-like type system
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‎.mpython/templates/class_header.py‎

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from <pkgname>.__wrapper__ import Runtime, MatlabClass
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class <classname>(MatlabClass):
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def __init__(self, *args, **kwargs):
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<docstring>
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super().__init__()

‎.mpython/templates/docstring.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/<rfilepath> )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""

‎examples/DCM_ERP.py‎

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"""
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analyse some ERP data (mismatch negativity ERP SPM file from SPM-webpages)
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This is an example batch script to analyse two evoked responses with an
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assumed 5 sources.
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To try this out on your data (the date of this example don't exist in your SPM8 distribution),
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you have to change 'Pbase' to your own analysis-directory, and choose a name ('DCM.xY.Dfile')
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of an existing SPM for M/EEG-file with at least two evoked responses.
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"""
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import os
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import spm
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num = spm.Array.from_any
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# Please replace filenames etc. by your own.
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# ----------------------------------------------------------------------
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spm.spm('defaults', 'EEG')
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# Data and analysis directories
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# ----------------------------------------------------------------------
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Pbase = os.path.dirname(__file__) # directory with your data,
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Pdata = os.path.join(Pbase, '.') # data directory in Pbase
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Panalysis = os.path.join(Pbase, '.') # analysis directory in Pbase
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os.chdir(Pbase)
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# Data filename
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# ----------------------------------------------------------------------
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DCM = spm.Struct()
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DCM.xY.Dfile = 'maeMdfspm8_subject1'
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# Parameters and options used for setting up model
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# ----------------------------------------------------------------------
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DCM.options.analysis = 'ERP' # analyze evoked responses
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DCM.options.model = 'ERP' # ERP model
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DCM.options.spatial = 'IMG' # spatial model
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DCM.options.trials = num([1, 2]) # index of ERPs within ERP/ERF file
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DCM.options.Tdcm[0] = 0 # start of peri-stimulus time to be modelled
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DCM.options.Tdcm[1] = 200 # end of peri-stimulus time to be modelled
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DCM.options.Nmodes = 8 # nr of modes for data selection
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DCM.options.h = 1 # nr of DCT components
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DCM.options.onset = 60 # selection of onset (prior mean)
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DCM.options.D = 1 # downsampling
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# ----------------------------------------------------------------------
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# Data and spatial model
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# ----------------------------------------------------------------------
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DCM = spm.spm_dcm_erp_data(DCM)
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# ----------------------------------------------------------------------
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# Location priors for dipoles
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# ----------------------------------------------------------------------
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DCM.Lpos = num([
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[-42, -22, 7],
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[46, -14, 8],
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[-61, -32, 8],
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[59, -25, 8],
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[46, 20, 8]
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]).T
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DCM.Sname = ['left AI', 'right A1', 'left STG', 'right STG', 'right IFG']
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Nareas = DCM.Lpos.shape[1]
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# ----------------------------------------------------------------------
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# Spatial model
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# ----------------------------------------------------------------------
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DCM = spm.spm_dcm_erp_dipfit(DCM)
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# ----------------------------------------------------------------------
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# Specify connectivity model
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# ----------------------------------------------------------------------
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os.chdir(Panalysis)
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DCM.A = spm.Cell()
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DCM.B = spm.Cell()
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DCM.A[0] = spm.Array(Nareas, Nareas)
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DCM.A[0][2, 0] = 1
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DCM.A[0][3, 1] = 1
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DCM.A[0][4, 3] = 1
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DCM.A[1] = spm.Array(Nareas, Nareas)
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DCM.A[1][0, 2] = 1
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DCM.A[1][1, 3] = 1
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DCM.A[1][3, 4] = 1
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DCM.A[2] = spm.Array(Nareas, Nareas)
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DCM.A[2][3, 2] = 1
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DCM.A[2][2, 3] = 1
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DCM.B[0] = DCM.A[0] + DCM.A[1]
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DCM.B[0][0, 0] = 1
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DCM.B[0][1, 1] = 1
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DCM.C = num([[1, 1, 0, 0, 0]]).T
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# ----------------------------------------------------------------------
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# Between trial effects
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# ----------------------------------------------------------------------
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DCM.xU.X = num([[0, 1]]).T
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DCM.xU.name = ['rare']
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# ----------------------------------------------------------------------
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# Invert
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# ----------------------------------------------------------------------
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DCM.name = 'DCMexample'
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DCM = spm.spm_dcm_erp(DCM)

‎examples/auditory_batch.py‎

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"""
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This batch script analyses the Auditory fMRI dataset available from the
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SPM website:
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http://www.fil.ion.ucl.ac.uk/spm/data/auditory/
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as described in the SPM manual:
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http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#Chap:data:auditory
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________________________________________________________________________
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Copyright (C) 2014 Wellcome Trust Centre for Neuroimaging
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Matlab: Guillaume Flandin
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Python: Yael Balbastre
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"""
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import zipfile
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import os.path as op
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import spm
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num = spm.Array.from_any
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# Directory containing the Auditory data
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# ----------------------------------------------------------------------
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url = 'https://www.fil.ion.ucl.ac.uk/spm/download/data/MoAEpilot/MoAEpilot.zip'
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data_path = op.dirname(__file__)
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zip_path = op.join(data_path, 'MoAEpilot.zip')
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print(f'{"Downloading Auditory dataset...":<40s}')
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spm.Runtime.call("urlwrite", url, zip_path)
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with zipfile.ZipFile(zip_path, 'r') as f:
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f.extractall(data_path)
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print(f" {'...done':30s}\n")
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# Initialise SPM
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# ----------------------------------------------------------------------
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spm.spm('Defaults', 'fMRI')
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spm.spm_jobman('initcfg', nargout=0)
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# spm.spm_get_defaults('cmdline', true)
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# ======================================================================
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# PREAMBLE: DUMMY SCANS
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# ======================================================================
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f = spm.spm_select('FPList', op.join(data_path, 'fM00223'), r'^f.*\.img$')
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matlabbatch = spm.Cell()
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matlabbatch(0).cfg_basicio.file_dir.dir_ops.cfg_mkdir.parent = [data_path]
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matlabbatch(0).cfg_basicio.file_dir.dir_ops.cfg_mkdir.name = 'dummy'
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matlabbatch(0).cfg_basicio.file_dir.file_ops.file_move.files = f[:12]
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matlabbatch(0).cfg_basicio.file_dir.file_ops.file_move.action.moveto = [op.join(data_path, 'dummy')]
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spm.spm_jobman('run', matlabbatch, nargout=0)
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# ======================================================================
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# SPATIAL PREPROCESSING
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# ======================================================================
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f = spm.spm_select('FPList', op.join(data_path,'fM00223'), r'^f.*\.img$')
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a = spm.spm_select('FPList', op.join(data_path,'sM00223'), r'^s.*\.img$')
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matlabbatch = spm.Cell()
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# Realign
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# ----------------------------------------------------------------------
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matlabbatch(0).spm.spatial.realign.estwrite.data = [f]
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matlabbatch(0).spm.spatial.realign.estwrite.roptions.which = num([0, 1])
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# Coregister
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# ----------------------------------------------------------------------
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matlabbatch(1).spm.spatial.coreg.estimate.ref = [spm.spm_file(f[0, 0], 'prefix', 'mean')]
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matlabbatch(1).spm.spatial.coreg.estimate.source = [a]
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# Segment
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# ----------------------------------------------------------------------
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matlabbatch(2).spm.spatial.preproc.channel.vols = [a]
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matlabbatch(2).spm.spatial.preproc.channel.write = num([0, 1])
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matlabbatch(2).spm.spatial.preproc.warp.write = num([0, 1])
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# Normalise: Write
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# ----------------------------------------------------------------------
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matlabbatch(3).spm.spatial.normalise.write.subj["def"] = [spm.spm_file(a, 'prefix', 'y_', 'ext', 'nii')]
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matlabbatch(3).spm.spatial.normalise.write.subj.resample = f
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matlabbatch(3).spm.spatial.normalise.write.woptions.vox = num([3, 3, 3])
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matlabbatch(4).spm.spatial.normalise.write.subj["def"] = [spm.spm_file(a, 'prefix', 'y_', 'ext', 'nii')]
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matlabbatch(4).spm.spatial.normalise.write.subj.resample = [spm.spm_file(a, 'prefix', 'm', 'ext', 'nii')]
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matlabbatch(4).spm.spatial.normalise.write.woptions.vox = num([1, 1, 3])
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# Smooth
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# ----------------------------------------------------------------------
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matlabbatch(5).spm.spatial.smooth.data = spm.spm_file(f, 'prefix', 'w')[:, None]
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matlabbatch(5).spm.spatial.smooth.fwhm = num([6, 6, 6])
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spm.spm_jobman('run', matlabbatch, nargout=0)
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# ======================================================================
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# GLM SPECIFICATION, ESTIMATION, INFERENCE, RESULTS
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# ======================================================================
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f = spm.spm_select('FPList', op.join(data_path,'fM00223'), r'^swf.*\.img$')
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matlabbatch = spm.Cell()
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# Output Directory
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# ----------------------------------------------------------------------
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matlabbatch(0).cfg_basicio.file_dir.dir_ops.cfg_mkdir.parent = [data_path]
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matlabbatch(0).cfg_basicio.file_dir.dir_ops.cfg_mkdir.name = 'GLM'
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# Model Specification
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# ----------------------------------------------------------------------
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matlabbatch(1).spm.stats.fmri_spec.dir = [op.join(data_path, 'GLM')]
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matlabbatch(1).spm.stats.fmri_spec.timing.units = 'scans'
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matlabbatch(1).spm.stats.fmri_spec.timing.RT = 7
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matlabbatch(1).spm.stats.fmri_spec.sess.scans = f
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matlabbatch(1).spm.stats.fmri_spec.sess.cond.name = 'active'
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matlabbatch(1).spm.stats.fmri_spec.sess.cond.onset = num(range(6, 86, 12))
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matlabbatch(1).spm.stats.fmri_spec.sess.cond.duration = 6
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# Model Estimation
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# ----------------------------------------------------------------------
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matlabbatch(2).spm.stats.fmri_est.spmmat = [op.join(data_path, 'GLM', 'SPM.mat')]
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# Contrasts
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# ----------------------------------------------------------------------
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matlabbatch(3).spm.stats.con.spmmat = [op.join(data_path, 'GLM', 'SPM.mat')]
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matlabbatch(3).spm.stats.con.consess(0).tcon.name = 'Listening > Rest'
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matlabbatch(3).spm.stats.con.consess(0).tcon.weights = num([1, 0])
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matlabbatch(3).spm.stats.con.consess(1).tcon.name = 'Rest > Listening'
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matlabbatch(3).spm.stats.con.consess(1).tcon.weights = num([-1, 0])
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# Inference Results
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# ----------------------------------------------------------------------
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matlabbatch(4).spm.stats.results.spmmat = [op.join(data_path, 'GLM', 'SPM.mat')]
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matlabbatch(4).spm.stats.results.conspec.contrasts = 1
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matlabbatch(4).spm.stats.results.conspec.threshdesc = 'FWE'
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matlabbatch(4).spm.stats.results.conspec.thresh = 0.05
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matlabbatch(4).spm.stats.results.conspec.extent = 0
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matlabbatch(4).spm.stats.results.print = False
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# Rendering
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# ----------------------------------------------------------------------
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matlabbatch(5).spm.util.render.display.rendfile = [op.join(spm.spm('Dir'), 'canonical', 'cortex_20484.surf.gii')]
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matlabbatch(5).spm.util.render.display.conspec.spmmat = [op.join(data_path, 'GLM', 'SPM.mat')]
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matlabbatch(5).spm.util.render.display.conspec.contrasts = 1
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matlabbatch(5).spm.util.render.display.conspec.threshdesc = 'FWE'
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matlabbatch(5).spm.util.render.display.conspec.thresh = 0.05
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matlabbatch(5).spm.util.render.display.conspec.extent = 0
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spm.spm_jobman('run', matlabbatch, nargout=0)

‎spm/__compat/loadxml.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/loadxml.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("loadxml", *args, **kwargs)

‎spm/__compat/savexml.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/savexml.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("savexml", *args, **kwargs, nargout=0)

‎spm/__compat/spm_add.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/spm_add.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("spm_add", *args, **kwargs)

‎spm/__compat/spm_adjmean_fmri_ui.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/spm_adjmean_fmri_ui.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("spm_adjmean_fmri_ui", *args, **kwargs, nargout=0)

‎spm/__compat/spm_adjmean_ui.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/spm_adjmean_ui.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("spm_adjmean_ui", *args, **kwargs, nargout=0)

‎spm/__compat/spm_atranspa.py‎

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[Matlab code]( https://github.com/spm/spm/blob/main/compat/spm_atranspa.m )
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Copyright (C) 2024-2024 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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Copyright (C) 1995-2025 Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL
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"""
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return Runtime.call("spm_atranspa", *args, **kwargs)

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