How to use the fitlins.interfaces.visualizations.Visualization function in fitlins

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
        if isinstance(data, nb.Cifti2Image):
            plot_dscalar(data, vmax=vmax, threshold=self.inputs.threshold,
                         cmap=self.inputs.colormap, output_file=out_name)
        else:
            nlp.plot_glass_brain(data, colorbar=True, plot_abs=False,

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,
                      data.drop(columns='constant').corr(),
                      len(evs))

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,
                      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)

newpath=runtime.cwd,
                                   use_ext=False)
        self._visualize(data, out_name)
        self._results['figure'] = out_name
        return runtime

    def _load_data(self, fname):
        _, _, 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})