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_, _, ext = split_filename(fname)
if ext == '.tsv':
return pd.read_table(fname, index_col=0)
elif ext in ('.nii', '.nii.gz', '.gii'):
return nb.load(fname)
raise ValueError("Unknown file type!")
class DesignPlot(Visualization):
def _visualize(self, data, out_name):
from matplotlib import pyplot as plt
plt.set_cmap('viridis')
plot_and_save(out_name, nis.reporting.plot_design_matrix, data)
class DesignCorrelationPlotInputSpec(VisualizationInputSpec):
contrast_info = traits.List(traits.Dict)
class DesignCorrelationPlot(Visualization):
input_spec = DesignCorrelationPlotInputSpec
def _visualize(self, data, out_name):
contrast_matrix = pd.DataFrame({c['name']: c['weights'][0]
for c in self.inputs.contrast_info})
all_cols = list(data.columns)
evs = set(contrast_matrix.index)
if set(contrast_matrix.index) != all_cols[:len(evs)]:
ev_cols = [col for col in all_cols if col in evs]
confound_cols = [col for col in all_cols if col not in evs]
data = data[ev_cols + confound_cols]
plot_and_save(out_name, plot_corr_matrix,
class ContrastMatrixPlot(Visualization):
input_spec = ContrastMatrixPlotInputSpec
def _visualize(self, data, out_name):
contrast_matrix = pd.DataFrame({c['name']: c['weights'][0]
for c in self.inputs.contrast_info},
index=data.columns)
contrast_matrix.fillna(value=0, inplace=True)
if 'constant' in contrast_matrix.index:
contrast_matrix = contrast_matrix.drop(index='constant')
plot_and_save(out_name, plot_contrast_matrix, contrast_matrix,
ornt=self.inputs.orientation)
class GlassBrainPlotInputSpec(VisualizationInputSpec):
threshold = traits.Enum('auto', None, traits.Float(), usedefault=True)
vmax = traits.Float()
colormap = traits.Str('bwr', usedefault=True)
class GlassBrainPlot(Visualization):
input_spec = GlassBrainPlotInputSpec
def _visualize(self, data, out_name):
import numpy as np
vmax = self.inputs.vmax
if not isdefined(vmax):
vmax = None
abs_data = np.abs(data.get_fdata(dtype=np.float32))
pctile99 = np.percentile(abs_data, 99.99)
if abs_data.max() - pctile99 > 10:
vmax = pctile99
def _visualize(self, data, out_name):
contrast_matrix = pd.DataFrame({c['name']: c['weights'][0]
for c in self.inputs.contrast_info})
all_cols = list(data.columns)
evs = set(contrast_matrix.index)
if set(contrast_matrix.index) != all_cols[:len(evs)]:
ev_cols = [col for col in all_cols if col in evs]
confound_cols = [col for col in all_cols if col not in evs]
data = data[ev_cols + confound_cols]
plot_and_save(out_name, plot_corr_matrix,
data.drop(columns='constant').corr(),
len(evs))
class ContrastMatrixPlotInputSpec(VisualizationInputSpec):
contrast_info = traits.List(traits.Dict)
orientation = traits.Enum('horizontal', 'vertical', usedefault=True,
desc='Display orientation of contrast matrix')
class ContrastMatrixPlot(Visualization):
input_spec = ContrastMatrixPlotInputSpec
def _visualize(self, data, out_name):
contrast_matrix = pd.DataFrame({c['name']: c['weights'][0]
for c in self.inputs.contrast_info},
index=data.columns)
contrast_matrix.fillna(value=0, inplace=True)
if 'constant' in contrast_matrix.index:
contrast_matrix = contrast_matrix.drop(index='constant')
plot_and_save(out_name, plot_contrast_matrix, contrast_matrix,