<filename>nipy/algorithms/diagnostics/tsdiffplot.py
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
''' plot tsdiffana parameters '''
from __future__ import absolute_import

import numpy as np

import nipy
from .timediff import time_slice_diffs

from nipy.externals.six import string_types

def plot_tsdiffs(results, axes=None):
    ''' Plotting routine for time series difference metrics

    Requires matplotlib

    Parameters
    ----------
    results : dict
       Results of format returned from
       :func:`nipy.algorithms.diagnostics.time_slice_diff`
    '''
    import matplotlib.pyplot as plt
    T = len(results['volume_means'])
    S = results['slice_mean_diff2'].shape[1]
    mean_means = np.mean(results['volume_means'])
    scaled_slice_diff = results['slice_mean_diff2'] / mean_means

    if axes is None:
        n_plots = 4
        fig = plt.figure()
        fig.set_size_inches([10,10])
        axes = [plt.subplot(n_plots, 1, i+1) for i in range(n_plots)]

    def xmax_labels(ax, val, xlabel, ylabel):
        xlims = ax.axis()
        ax.axis((0, val) + xlims[2:])
        ax.set_xlabel(xlabel)
        ax.set_ylabel(ylabel)

    # plot of mean volume variance
    ax = axes[0]
    ax.plot(results['volume_mean_diff2'] / mean_means)
    xmax_labels(ax, T-1, 'Difference image number', 'Scaled variance')

    # plot of diff by slice
    ax = axes[1]
    #Set up the color map for the different slices:
    X, Y = np.meshgrid(np.arange(scaled_slice_diff.shape[0]),
                       np.arange(scaled_slice_diff.shape[1]))

    # Use HSV in order to code the slices from bottom to top:
    ax.scatter(X.T.ravel(),scaled_slice_diff.ravel(),
               c=Y.T.ravel(),cmap=plt.cm.hsv,
               alpha=0.2)

    xmax_labels(ax, T-1,
                'Difference image number',
                'Slice by slice variance')

    # mean intensity
    ax = axes[2]
    ax.plot(results['volume_means'] / mean_means)
    xmax_labels(ax, T,
                'Image number',
                'Scaled mean \n voxel intensity')

    # slice plots min max mean
    ax = axes[3]
    ax.hold(True)
    ax.plot(np.mean(scaled_slice_diff, 0), 'k')
    ax.plot(np.min(scaled_slice_diff, 0), 'b')
    ax.plot(np.max(scaled_slice_diff, 0), 'r')
    ax.hold(False)
    xmax_labels(ax, S+1,
                'Slice number',
                'Max/mean/min \n slice variation')
    return axes


@np.deprecate_with_doc('Please see docstring for alternative code')
def plot_tsdiffs_image(img, axes=None, show=True):
    ''' Plot time series diagnostics for image

    This function is deprecated; please use something like::

        results = time_slice_diff_image(img, slice_axis=2)
        plot_tsdiffs(results)

    instead.

    Parameters
    ----------
    img : image-like or filename str
       image on which to do diagnostics
    axes : None or sequence, optional
       Axes on which to plot the diagnostics.  If None, then we create a figure
       and subplots for the plots.  Sequence should have length
       >=4.
    show : {True, False}, optional
       If True, show the figure after plotting it

    Returns
    -------
    axes : Matplotlib axes
       Axes on which we have done the plots.   Will be same as `axes` input if
       `axes` input was not None
    '''
    if isinstance(img, string_types):
        title = img
    else:
        title = 'Difference plots'
    img = nipy.as_image(img)
    res = time_slice_diffs(img)
    axes = plot_tsdiffs(res, axes)
    axes[0].set_title(title)
    if show:
        # show the plot
        import matplotlib.pyplot as plt
        plt.show()
    return axes



<filename>nipy/algorithms/diagnostics/tsdiffplot.py
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
''' plot tsdiffana parameters '''
from __future__ import absolute_import

import numpy as np

import nipy
from .timediff import time_slice_diffs

from nipy.externals.six import string_types

def plot_tsdiffs(results, axes=None):
    ''' Plotting routine for time series difference metrics

    Requires matplotlib

    Parameters
    ----------
    results : dict
       Results of format returned from
       :func:`nipy.algorithms.diagnostics.time_slice_diff`
    '''
    import matplotlib.pyplot as plt
    T = len(results['volume_means'])
    S = results['slice_mean_diff2'].shape[1]
    mean_means = np.mean(results['volume_means'])
    scaled_slice_diff = results['slice_mean_diff2'] / mean_means

    if axes is None:
        n_plots = 4
        fig = plt.figure()
        fig.set_size_inches([10,10])
        axes = [plt.subplot(n_plots, 1, i+1) for i in range(n_plots)]

    def xmax_labels(ax, val, xlabel, ylabel):
        xlims = ax.axis()
        ax.axis((0, val) + xlims[2:])
        ax.set_xlabel(xlabel)
        ax.set_ylabel(ylabel)

    # plot of mean volume variance
    ax = axes[0]
    ax.plot(results['volume_mean_diff2'] / mean_means)
    xmax_labels(ax, T-1, 'Difference image number', 'Scaled variance')

    # plot of diff by slice
    ax = axes[1]
    #Set up the color map for the different slices:
    X, Y = np.meshgrid(np.arange(scaled_slice_diff.shape[0]),
                       np.arange(scaled_slice_diff.shape[1]))

    # Use HSV in order to code the slices from bottom to top:
    ax.scatter(X.T.ravel(),scaled_slice_diff.ravel(),
               c=Y.T.ravel(),cmap=plt.cm.hsv,
               alpha=0.2)

    xmax_labels(ax, T-1,
                'Difference image number',
                'Slice by slice variance')

    # mean intensity
    ax = axes[2]
    ax.plot(results['volume_means'] / mean_means)
    xmax_labels(ax, T,
                'Image number',
                'Scaled mean \n voxel intensity')

    # slice plots min max mean
    ax = axes[3]
    ax.hold(True)
    ax.plot(np.mean(scaled_slice_diff, 0), 'k')
    ax.plot(np.min(scaled_slice_diff, 0), 'b')
    ax.plot(np.max(scaled_slice_diff, 0), 'r')
    ax.hold(False)
    xmax_labels(ax, S+1,
                'Slice number',
                'Max/mean/min \n slice variation')
    return axes


@np.deprecate_with_doc('Please see docstring for alternative code')
def plot_tsdiffs_image(img, axes=None, show=True):
    ''' Plot time series diagnostics for image

    This function is deprecated; please use something like::

        results = time_slice_diff_image(img, slice_axis=2)
        plot_tsdiffs(results)

    instead.

    Parameters
    ----------
    img : image-like or filename str
       image on which to do diagnostics
    axes : None or sequence, optional
       Axes on which to plot the diagnostics.  If None, then we create a figure
       and subplots for the plots.  Sequence should have length
       >=4.
    show : {True, False}, optional
       If True, show the figure after plotting it

    Returns
    -------
    axes : Matplotlib axes
       Axes on which we have done the plots.   Will be same as `axes` input if
       `axes` input was not None
    '''
    if isinstance(img, string_types):
        title = img
    else:
        title = 'Difference plots'
    img = nipy.as_image(img)
    res = time_slice_diffs(img)
    axes = plot_tsdiffs(res, axes)
    axes[0].set_title(title)
    if show:
        # show the plot
        import matplotlib.pyplot as plt
        plt.show()
    return axes



# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: plot tsdiffana parameters Plotting routine for time series difference metrics      Requires matplotlib      Parameters     ----------     results : dict        Results of format returned from        :func:`nipy.algorithms.diagnostics.time_slice_diff` # plot of mean volume variance # plot of diff by slice #Set up the color map for the different slices: # Use HSV in order to code the slices from bottom to top: # mean intensity # slice plots min max mean Plot time series diagnostics for image      This function is deprecated; please use something like::          results = time_slice_diff_image(img, slice_axis=2)         plot_tsdiffs(results)      instead.      Parameters     ----------     img : image-like or filename str        image on which to do diagnostics     axes : None or sequence, optional        Axes on which to plot the diagnostics.  If None, then we create a figure        and subplots for the plots.  Sequence should have length        >=4.     show : {True, False}, optional        If True, show the figure after plotting it      Returns     -------     axes : Matplotlib axes        Axes on which we have done the plots.   Will be same as `axes` input if        `axes` input was not None # show the plot