How to use get_node method in avocado

Best Python code snippet using avocado_python

preprocess.py

Source:preprocess.py

...29        30        self.preprocessing(preprocess, func_dic)31        self.get_warps(preprocess)32        33        preprocess.connect([(inputnode, preprocess.get_node('Fregistration'), [('T1w', 'T1w'),34                                                                              ('mask', 'mask')]),35                            #(inputnode, preprocess.get_node('bet'), [('T1w', 'inputnode.in_files')]),36                            (inputnode, preprocess.get_node('bet'), [('T1w', 'T1w')]),37                            (inputnode, preprocess.get_node('slicetimer'), [('TR', 'time_repetition')]),38                            (inputnode, preprocess.get_node('extract'), [('bold', 'in_file')]),39                            (inputnode, preprocess.get_node('warp'), [('mask', 'ref_file')]),40                            (inputnode, preprocess.get_node('prelim'), [('mask', 'reference')]),41                            (inputnode, preprocess.get_node('dilateref'), [('mask', 'in_file')]),42                            (inputnode, preprocess.get_node('Fmni'), [('mask', 'mniMask')]),43                            (preprocess.get_node('bet_strip'), preprocess.get_node('warp'), [('out_file', 'in_file')]),44                            (preprocess.get_node('bet_strip'), preprocess.get_node('invwarp'), [('out_file', 'brain')]),45                            (preprocess.get_node('Fregistration'), preprocess.get_node('invwarp'), [('out_mat', 'coregmat')]),46                            (preprocess.get_node('Fmni'), preprocess.get_node('invwarp'), [('brainmask', 'brainmask')]),47                            (preprocess.get_node('bet_strip'), preprocess.get_node('prelim'), [('out_file', 'in_file')]),48                            (preprocess.get_node('bet_strip'), preprocess.get_node('decision'), [('out_file', 'mask')]),49                            (preprocess.get_node('bet_strip'), preprocess.get_node('dilatebrain'), [('out_file', 'in_file')]),50                            (preprocess.get_node('warp'), preprocess.get_node('Fmni'), [('field_file', 'warp')]),51                            ])52        53        outnode = Node(IdentityInterface(fields=['smoothed', 'segmentations', 'warp_file', 'brain', 'brainmask', 'outliers', 'unsmoothed', 'invwarp', 'keepreg', 'keepsmooth']), name='outnode')54        55        preprocess.connect([(preprocess.get_node('bet_strip'), outnode, [('out_file', 'brain')]),56                            (preprocess.get_node('Fmni'), outnode, [('segmentations', 'segmentations')]),57                            (preprocess.get_node('Fsmooth'), outnode, [('smooth', 'smoothed')]),58                            (preprocess.get_node('Fsmooth'), outnode, [('files', 'keepsmooth')]),59                            #(preprocess.get_node('warp'), outnode, [('field_file', 'warp_file')]),60                            (preprocess.get_node('Fmni'), outnode, [('warp', 'warp_file')]),61                            (preprocess.get_node('invwarp'), outnode, [('invwarp', 'invwarp')]),62                            #(preprocess.get_node('mcflirt'), outnode, [('par_file', 'mc_par')]),63                            (preprocess.get_node('art'), outnode, [('outlier_files', 'outliers')]),64                            #(preprocess.get_node('Fregistration'), outnode, [('out_mat', 'coregmat')]),65                            (preprocess.get_node('Fregistration'), outnode, [('files', 'keepreg')]),66                            (preprocess.get_node('fillmask'), outnode, [('out_file', 'brainmask')]),67                            ])68        69        if 'rest' in self.task:70            preprocess.connect([(preprocess.get_node('Fregress'), outnode, [('forreho', 'unsmoothed')]),71                                ])72        else:73            preprocess.connect([(preprocess.get_node('Fmni'), outnode, [('warped', 'unsmoothed')]),74                                ])75        76        77        write = Node(Function(input_names=['base_dir', 'pipeline_st', 'task']+intermediates), name='write')78        write.inputs.function_str = get_sink(intermediates)79        write.inputs.base_dir = self.base_dir80        write.inputs.pipeline_st = self.pipeline81        write.inputs.task = self.task82        83        preprocess.connect([(preprocess.get_node('bet_strip'), write, [('out_file', 'brain')]),84                            (preprocess.get_node('Fsmooth'), write, [('smooth', 'smoothed')]),85                            (preprocess.get_node('Fregistration'), write, [('out_mat', 'coregmat')]),86                            (preprocess.get_node('fast'), write, [('tissue_class_files', 'segmentations')]),87                            (preprocess.get_node('warp'), write, [('field_file', 'warp_field'),88                                                                  ('warped_file', 'warp')]),89                            (preprocess.get_node('invwarp'), write, [('invwarp', 'invwarp')]),90                            (preprocess.get_node('art'), write, [('outlier_files', 'outliers'),91                                                                 ('plot_files', 'plots')]),92                            ])93        94        return preprocess95    96    def preprocessing(self, flow, func_dic):97        from preprocessing.functions import function_str, decision98        self.coregistration(flow, func_dic)99        100        extract = Node(ExtractROI(t_size=-1, output_type='NIFTI_GZ'), name='extract')101        mcflirt = Node(MCFLIRT(save_plots=True, output_type='NIFTI_GZ'), name='mcflirt')102        103        slicetimer = Node(SliceTimer(index_dir=False, interleaved=True, output_type='NIFTI_GZ'), name='slicetimer')104            105        func_str, input_names = function_str('smooth', func_dic)106        Fsmooth = Node(Function(input_names=input_names,107                                output_names=['smooth', 'files']), name='Fsmooth')108        Fsmooth.inputs.function_str = func_str109        110        decision = Node(Function(input_names=['mask', 'mc_mean', 'mc', 'st', 'slice_correct', 'mean_vol'],111                                 output_names=['start_img', 'corrected_img', 'mask'], function=decision), name='decision')112        decision.inputs.mean_vol = ''113        decision.inputs.st = ''114        115        art = Node(ArtifactDetect(norm_threshold=2,116                                  zintensity_threshold=3,117                                  mask_type='spm_global',118                                  parameter_source='FSL',119                                  use_differences=[True, False],120                                  plot_type='svg'),121                   name="art")122        123        fillmask = Node(UnaryMaths(operation='fillh'), name='fillmask')124        125        func_str, input_names = function_str('regress', func_dic)126        Fregress = Node(Function(input_names=input_names,127                                 output_names=['warped', 'forreho']), name='Fregress', mem_gb=3)128        Fregress.inputs.function_str = func_str129        130        if 'rest' in self.task:131            Fregress.inputs.rest = True132        else:133            Fregress.inputs.rest = False134            135        flow.connect([(extract, mcflirt, [('roi_file', 'in_file')]),136                      (mcflirt, slicetimer, [('out_file', 'in_file')]),137                      (mcflirt, decision, [('mean_img', 'mean_vol'),138                                           ('out_file', 'mc')]),139                      (mcflirt, art, [('par_file', 'realignment_parameters')]),140                      (slicetimer, decision, [('slice_time_corrected_file', 'st')]),141                      (decision, flow.get_node('Fregistration'), [('start_img', 'start_img'),142                                                                  ('corrected_img', 'corrected_img'),143                                                                  ('mask', 'brainmask')]),144                      (decision, flow.get_node('Fmni'), [('mask', 'brainmask')]),145                      (decision, flow.get_node('Fmni'), [('start_img', 'start_img')]),146                      (flow.get_node('Fmni'), flow.get_node('boldmask'), [('start_img', 'inputnode.in_file')]),147                      #(decision, flow.get_node('boldmask'), [('start_img', 'inputnode.in_file')]),148                      (flow.get_node('boldmask'), fillmask, [('outputnode.skull_stripped_file', 'in_file')]),149                      (flow.get_node('Fregistration'), art, [('warped', 'realigned_files')]),150                      (flow.get_node('Fmni'), Fregress, [('warped', 'unsmoothed')]),151                      (flow.get_node('Fmni'), Fregress, [('brainmask', 'mask')]),152                      (flow.get_node('Fmni'), Fregress, [('segmentations', 'segmentations')]),153                      #(art, Fregress, [('outlier_files', 'outliers')]),154                      (mcflirt, Fregress, [('par_file', 'mc_par')]),155                      (Fregress, Fsmooth, [('warped', 'warped')]),156                      (fillmask, Fsmooth, [('out_file', 'mask')]),157                      ])158        159    def coregistration(self, flow, func_dic):#SEARCH TO SEE IF BET ALREADY RUN AND OUTPUT SAVED160        from preprocessing.functions import function_str, strip_container161        from preprocessing.workflows import check4brains162        163        bet = Node(Function(input_names=['data_dir', 'T1w'], output_names='out_file', function=check4brains), name='bet') #init_brain_extraction_wf(name='bet')164        bet.inputs.data_dir = self.data_dir165        166        bet_strip = Node(Function(input_names='in_file', output_names='out_file', function=strip_container), name='bet_strip')...

graphs.py

Source:graphs.py

1import networkx as nx2import matplotlib.pyplot as plt3G = nx.DiGraph()4n = 85def get_node(num_we, num_wf):6    return str(num_we) + 'WE_' + str(num_wf) + 'WF'7def get_brg_1():8    G.add_nodes_from([str(k) + 'WE_' + str(n - k) + 'WF' for k in range(0, n + 1)])9    G.add_edge(get_node(0, 8), get_node(1, 7))10    G.add_edge(get_node(1, 7), get_node(2, 6))11    G.add_edge(get_node(2, 6), get_node(3, 5))12    G.add_edge(get_node(3, 5), get_node(2, 6))13    G.add_edge(get_node(4, 4), get_node(3, 5))14    G.add_edge(get_node(4, 4), get_node(5, 3))15    G.add_edge(get_node(5, 3), get_node(6, 2))16    G.add_edge(get_node(7, 1), get_node(6, 2))17    G.add_edge(get_node(8, 0), get_node(7, 1))18    pos = nx.circular_layout(G)19    nx.draw(G, pos, node_size=3400, with_labels=True, font_color='w')20    nx.draw_networkx_nodes(G, pos, nodelist=[get_node(0, 8), get_node(1, 7), get_node(4, 4), get_node(5, 3), get_node(7, 1), get_node(8, 0)], node_color='b', node_size=3400)21    nx.draw_networkx_nodes(G, pos, nodelist=[get_node(2, 6), get_node(3, 5), get_node(6, 2)], node_color='b', node_size=3400, alpha=0.25)22    plt.show()23def get_brg_2():24    G.add_nodes_from([str(k) + 'WE_' + str(n - k) + 'WF' for k in range(0, n + 1)])25    G.add_edge(get_node(0, 8), get_node(1, 7))26    G.add_edge(get_node(1, 7), get_node(2, 6))27    G.add_edge(get_node(2, 6), get_node(3, 5))28    G.add_edge(get_node(3, 5), get_node(4, 4))29    G.add_edge(get_node(4, 4), get_node(5, 3))30    G.add_edge(get_node(5, 3), get_node(6, 2))31    G.add_edge(get_node(6, 2), get_node(7, 1))32    G.add_edge(get_node(7, 1), get_node(8, 0))33    pos = nx.circular_layout(G)34    nx.draw(G, pos, node_size=3400, with_labels=True, font_color='w')35    nx.draw_networkx_nodes(G, pos, nodelist=[get_node(0, 8),36                                             get_node(1, 7),37                                             get_node(2, 6),38                                             get_node(3, 5),39                                             get_node(4, 4),40                                             get_node(5, 3),41                                             get_node(6, 2),42                                             get_node(7, 1)], node_color='b', node_size=3400)43    nx.draw_networkx_nodes(G, pos, nodelist=[get_node(8, 0)], node_color='b', node_size=3400, alpha=0.25)44    plt.show()...

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