Commit statistics as examples

examples/analysis.py

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+def define_limits(phs_list):
+    '''Returns the x-y coordinates limits (min, max)
+    for a list of persistence diagrams
+    '''
+    ph = view.plot.collapse(phs_list)
+    xlims = [min(np.transpose(ph)[0]), max(np.transpose(ph)[0])]
+    ylims = [min(np.transpose(ph)[1]), max(np.transpose(ph)[1])]
+
+    return xlims, ylims
+
+
+def persistence_image(ph, norm_factor=None, xlims=None, ylims=None):
+    '''Create the data for the generation of the persistence image.
+    If norm_factor is provided the data will be normalized based on this,
+    otherwise they will be normalized to 1.
+    If xlims, ylims are provided the data will be scaled accordingly.   
+    '''
+    from scipy import stats
+    import numpy as np
+
+    if xlims is None:
+        xlims = [min(np.transpose(ph)[0]), max(np.transpose(ph)[0])]
+    if ylims is None:
+        ylims = [min(np.transpose(ph)[1]), max(np.transpose(ph)[1])]
+
+    X, Y = np.mgrid[xlims[0]:xlims[1]:100j, ylims[0]:ylims[1]:100j]
+
+    values = np.transpose(ph)
+    kernel = stats.gaussian_kde(values)
+    positions = np.vstack([X.ravel(), Y.ravel()])
+    Z = np.reshape(kernel(positions).T, X.shape)
+
+    if norm_factor is None:
+        norm_factor = np.max(Z)
+
+    Zn = Z / norm_factor
+
+    return Zn
+
+
+def average_ph_image(images_list):
+    '''Generates a normalized average image
+    from a list of images. Careful: images should be
+    at the same scale (x-y) for appropriate comparison.
+    '''
+    average_imgs = images_list[0]
+
+    for im in images_list[1:]:
+        average_imgs = np.add(average_imgs, im)
+
+    average_imgs = average_imgs / len(images_list)
+
+    return average_imgs
+
+
+def img_diff(Z1, Z2, norm=True):
+    """Takes as input two images
+       as exported from the gaussian kernel
+       plotting function, and returns
+       their absolute difference:
+       diff(abs(Z1 - Z2))
+    """
+    if norm:
+        Z1 = Z1 / Z1.max()
+        Z2 = Z2 / Z2.max()
+
+    return Z1 - Z2
+
+
+def plot_imgs(img, new_fig=True, subplot=111, title='', xlims=None, ylims=None, cmap=cm.jet, vmin=0, vmax=1., masked=False, threshold=0.01):
+    '''Plots the gaussian kernel of the input image.
+    '''
+    from scipy import stats
+    from view import common
+    import numpy as np
+
+    if xlims is None:
+        xlims = (0,100)
+    if ylims is None:
+        ylims = (0,100)
+
+    fig, ax = common.get_figure(new_fig=new_fig, subplot=subplot)
+
+    if masked:
+        img = np.ma.masked_where((threshold > np.abs(img)), img)
+
+    cax= ax.imshow(np.rot90(img), vmin=vmin, vmax=vmax, cmap=cmap, interpolation='bilinear', extent=xlims+ylims)
+
+    kwargs = {}
+
+    kwargs['xlim'] = xlims
+    kwargs['ylim'] = ylims
+    kwargs['title'] = title
+
+    plt.colorbar(cax)
+
+    ax.set_aspect('equal')
+
+    return common.plot_style(fig=fig, ax=ax, aspect='equal')

examples/distances_example.py

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+import tmd
+import view
+
+pop1 = tmd.io.load_population(directory1)
+pop2 = tmd.io.load_population(directory2)
+phs1 = [tmd.methods.get_ph_neuron(n, neurite_type='axon') for n in pop1.neurons]
+phs2 = [tmd.methods.get_ph_neuron(n, neurite_type='axon') for n in pop2.neurons]
+
+# Normalize the limits
+xlims, ylims = define_limits(phs1 + phs2)
+# Create average images for populations
+imgs1 = [persistence_image(p, xlims=xlims, ylims=ylims) for p in phs1]
+IMG1 = average_ph_image(imgs1)
+imgs2 = [persistence_image(p, xlims=xlims, ylims=ylims) for p in phs2]
+IMG2 = average_ph_image(imgs2)
+
+# You can plot the images if you want to create pretty figures
+average_figure1 = plot_imgs(IMG1, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
+average_figure2 = plot_imgs(IMG2, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
+
+# Create the diffence between the two images
+DIMG = img_diff(IMG1, IMG2) # subtracts IMG2 from IMG1 so anything red IMG1 has more of it and anything blue IMG2 has more of it - or that's how it is supposed to be :)
+# Plot the difference between them
+diff_image = plot_imgs(DIMG, vmin=-1.0, vmax=1.0) # vmin, vmax important to see changes
+# Quantify the absolute distance between the two averages
+dist = np.sum(np.abs(DIMG))