How to use get_node method in avocado

Best Python code snippet using avocado_python

preprocess.py

Source: preprocess.py Github

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...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')...

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graphs.py

Source: graphs.py Github

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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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