Python NiftiWrapper.from_filename方法代码示例

# 需要导入模块: from dcmstack.dcmmeta import NiftiWrapper [as 别名]
# 或者: from dcmstack.dcmmeta.NiftiWrapper import from_filename [as 别名]
 def _run_interface(self, runtime):
     src = NiftiWrapper.from_filename(self.inputs.src_file)
     dest_nii = nb.load(self.inputs.dest_file)
     dest = NiftiWrapper(dest_nii, make_empty=True)
     classes = src.meta_ext.get_valid_classes()
     if self.inputs.include_classes:
         classes = [cls 
                    for cls in classes 
                    if cls in self.inputs.include_classes
                   ]
     if self.inputs.exclude_classes:
         classes = [cls 
                    for cls in classes 
                    if not cls in self.inputs.exclude_classes
                   ]
     
     for cls in classes:
         src_dict = src.meta_ext.get_class_dict(cls)
         dest_dict = dest.meta_ext.get_class_dict(cls)
         dest_dict.update(src_dict)
         
     self.out_path = path.join(os.getcwd(), 
                               path.basename(self.inputs.dest_file))
     dest.to_filename(self.out_path)
     
     return runtime

# 需要导入模块: from dcmstack.dcmmeta import NiftiWrapper [as 别名]
# 或者: from dcmstack.dcmmeta.NiftiWrapper import from_filename [as 别名]
    def _run_interface(self, runtime):
        #If the 'meta_keys' input is a list, covert it to a dict
        self._make_name_map()
        nw = NiftiWrapper.from_filename(self.inputs.in_file)
        self.result = {}
        for meta_key, out_name in self._meta_keys.iteritems():
            self.result[out_name] = nw.meta_ext.get_values(meta_key)

        return runtime

# 需要导入模块: from dcmstack.dcmmeta import NiftiWrapper [as 别名]
# 或者: from dcmstack.dcmmeta.NiftiWrapper import from_filename [as 别名]
    def get_tr_and_sliceorder(dicom_files):
        import numpy as np
        import dcmstack, dicom
        from dcmstack.dcmmeta import NiftiWrapper

        nii_wrp = NiftiWrapper.from_filename(dicom_files)
        sliceorder = np.argsort(nii_wrp.meta_ext.get_values("CsaImage.MosaicRefAcqTimes")[0]).tolist()
        tr = nii_wrp.meta_ext.get_values("RepetitionTime")
        return tr / 1000.0, sliceorder

# 需要导入模块: from dcmstack.dcmmeta import NiftiWrapper [as 别名]
# 或者: from dcmstack.dcmmeta.NiftiWrapper import from_filename [as 别名]
 def get_tr_and_sliceorder(dicom_files, convention="french"):
     import numpy as np
     import dcmstack, dicom
     from dcmstack.dcmmeta import NiftiWrapper
     nii_wrp = NiftiWrapper.from_filename(dicom_files)
     if convention == "french":
         sliceorder = np.argsort(np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0])).tolist()
     elif convention == "SPM":
         sliceorder = np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0]).tolist()
     tr = nii_wrp.meta_ext.get_values('RepetitionTime')
     return tr/1000.,sliceorder

# 需要导入模块: from dcmstack.dcmmeta import NiftiWrapper [as 别名]
# 或者: from dcmstack.dcmmeta.NiftiWrapper import from_filename [as 别名]
def get_tr_and_sliceorder(dicom_files, convention="french"):
    import numpy as np
    import dcmstack, dicom
    from dcmstack.dcmmeta import NiftiWrapper
    nii_wrp = NiftiWrapper.from_filename(dicom_files)
    if convention == "french":
        sliceorder = np.argsort(np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0])).tolist()
    elif convention == "SPM":
        sliceorder = np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0]).tolist()
    tr = nii_wrp.meta_ext.get_values('RepetitionTime')
    return tr/1000.,sliceorder

    get_meta = pe.Node(util.Function(input_names=['dicom_files', 'convention'], output_names=['tr', 'sliceorder'], function=get_tr_and_sliceorder), name="get_meta")
    get_meta.inputs.convention = "french"

    wf.connect(datagrabber, "resting_nifti", get_meta, "dicom_files")

    preproc = create_rest_prep(name="bips_resting_preproc", fieldmap=False)
    zscore = preproc.get_node('z_score')
    preproc.remove_nodes([zscore])

    mod_realign = preproc.get_node("mod_realign")
    mod_realign.plugin_args = {"submit_specs":"request_memory=4000\n"}

    # inputs
    preproc.inputs.inputspec.motion_correct_node = 'nipy'
    ad = preproc.get_node('artifactdetect')
    preproc.disconnect(mod_realign,'parameter_source',
        ad,'parameter_source')
    ad.inputs.parameter_source = "NiPy"

    preproc.inputs.inputspec.realign_parameters = {"loops":[5],
                                                   "speedup":[5]}
    preproc.inputs.inputspec.do_whitening = False
    preproc.inputs.inputspec.timepoints_to_remove = 4
    preproc.inputs.inputspec.smooth_type = 'susan'
    preproc.inputs.inputspec.do_despike = False
    preproc.inputs.inputspec.surface_fwhm = 0.0
    preproc.inputs.inputspec.num_noise_components = 6
    preproc.inputs.inputspec.regress_before_PCA = False
    preproc.get_node('fwhm_input').iterables = ('fwhm', [5])
    preproc.get_node('take_mean_art').get_node('strict_artifact_detect').inputs.save_plot = True
#preproc.get_node('take_mean_art').get_node('strict_artifact_detect').overwrite=True
    preproc.inputs.inputspec.ad_normthresh = 1
    preproc.inputs.inputspec.ad_zthresh = 3
    preproc.inputs.inputspec.do_slicetime = True
    preproc.inputs.inputspec.compcor_select = [True, True]
    preproc.inputs.inputspec.filter_type = 'fsl'
    preproc.get_node('bandpass_filter').iterables = [('highpass_freq', [0.01]), ('lowpass_freq', [0.1])]
#     preproc.inputs.inputspec.highpass_freq = 0.01
#     preproc.inputs.inputspec.lowpass_freq = 0.1
    #[motion_params, composite_norm, compcorr_components, global_signal, art_outliers, motion derivatives]
    preproc.inputs.inputspec.reg_params = [True, True, True, False, True, False]
    preproc.inputs.inputspec.fssubject_dir = freesurferdir
    wf.connect(get_meta, "tr", preproc, "inputspec.tr")
    wf.connect(get_meta, "sliceorder", preproc, "inputspec.sliceorder")
    wf.connect(subject_id_infosource, "subject_id", preproc, 'inputspec.fssubject_id')
    wf.connect(datagrabber, "resting_nifti", preproc, "inputspec.func")

 #   report_wf = create_preproc_report_wf(resultsdir + "/reports")
 #   report_wf.inputs.inputspec.fssubjects_dir = preproc.inputs.inputspec.fssubject_dir

    def pick_full_brain_ribbon(l):
        import os
        for path in l:
            if os.path.split(path)[1] == "ribbon.mgz":
                return path

 #   wf.connect(preproc,"artifactdetect.plot_files", report_wf, "inputspec.art_detect_plot")
 #   wf.connect(preproc,"take_mean_art.weighted_mean.mean_image", report_wf, "inputspec.mean_epi")
 #   wf.connect(preproc,("getmask.register.out_reg_file", list_to_filename), report_wf, "inputspec.reg_file")
 #   wf.connect(preproc,("getmask.fssource.ribbon",pick_full_brain_ribbon), report_wf, "inputspec.ribbon")
 #   wf.connect(preproc,("CompCor.tsnr.tsnr_file", list_to_filename), report_wf, "inputspec.tsnr_file")
 #   wf.connect(subject_id_infosource, "subject_id", report_wf, "inputspec.subject_id")

    ds = pe.Node(nio.DataSink(), name="datasink", overwrite=True)
    ds.inputs.base_directory = os.path.join(resultsdir, "volumes")
    wf.connect(preproc, 'bandpass_filter.out_file', ds, "preprocessed_resting")
    wf.connect(preproc, 'getmask.register.out_fsl_file', ds, "func2anat_transform")
    wf.connect(preproc, 'outputspec.mask', ds, "epi_mask")
    wf.write_graph()

    wf.run(plugin="MultiProc")