def box_plot_pandas(bin_vec, real_vec, ax=None):
"""
Wrapper around matplotlib's boxplot function.
Inputs
bin_vec: Series of labels
real_vec: Series of measurements to be grouped according to bin_vec
"""
_, ax = init_ax(ax)
bin_vec, real_vec = match_series(bin_vec, real_vec)
categories = bin_vec.value_counts().index
data = [real_vec[bin_vec == num] for num in categories]
bp = ax.boxplot(data,
positions=range(len(categories)),
widths=.3,
patch_artist=True)
if real_vec.name:
ax.set_ylabel(real_vec.name)
if bin_vec.name:
ax.set_xlabel(bin_vec.name)
[p.set_visible(False) for p in bp['fliers']]
[p.set_visible(False) for p in bp['caps']]
[p.set_visible(False) for p in bp['whiskers']]
for p in bp['medians']:
p.set_color(colors[0])
p.set_lw(3)
p.set_alpha(.8)
for i, p in enumerate(bp['boxes']):
p.set_color('grey')
p.set_lw(3)
p.set_alpha(.7)
if len(data[i]) < 3:
p.set_alpha(0)
def fancy_raster(df, cluster=False, cmap=plt.cm.get_cmap('Spectral'),
norm=None, ax=None):
if cluster:
d = sp.spatial.distance.pdist(df)
D = sp.spatial.distance.squareform(d)
Y = sp.cluster.hierarchy.linkage(D)
Z = sp.cluster.hierarchy.dendrogram(Y, no_plot=True)
order = Z['leaves']
df = df.ix[order, order]
_, ax = init_ax(ax, figsize=(12,8))
img = ax.imshow(df, interpolation='Nearest', cmap=cmap, norm=norm)
ax.set_yticks(range(len(df.index)))
ax.set_yticklabels(df.index)
ax.set_xticks(np.arange(len(df.columns)))
ax.set_xticklabels(df.columns, rotation=360-90, ha='center');
ax.hlines(np.arange(len(df.index)-1)+.5, -.5, len(df.columns)-.5,
color='white', lw=6)
ax.vlines(np.arange(len(df.columns)-1)+.5, -.5, len(df.index)-.5,
color='white', lw=6)
if cluster:
icoord = np.array(Z['icoord']) - np.array(Z['icoord']).min()
icoord = icoord * ((len(Z['leaves']) - 1) / icoord.max())
dcoord = -1*np.array(Z['dcoord']) - .7
for i,z,c in zip(icoord, dcoord, Z['color_list']):
ax.plot(i,z,color=c, lw=2, alpha=.8)
ax.tick_params(axis='x', top='off')
ax.set_frame_on(False)
return img
def fischer_bar_chart(bin_vec, response_vec, ax=None, filename=None):
fig, ax = init_ax(ax)
t = pd.crosstab(bin_vec, response_vec)
t.plot(kind='bar', ax=ax)
if filename is not None:
fig.savefig(filename)
return fig
def box_plot_pandas(bin_vec, real_vec, ax=None):
"""
Wrapper around matplotlib's boxplot function.
Inputs
bin_vec: Series of labels
real_vec: Series of measurements to be grouped according to bin_vec
"""
_, ax = init_ax(ax)
bin_vec, real_vec = match_series(bin_vec, real_vec)
categories = bin_vec.value_counts().index
data = [real_vec[bin_vec == num] for num in categories]
bp = ax.boxplot(data, positions=range(len(categories)), widths=.3,
patch_artist=True)
if real_vec.name:
ax.set_ylabel(real_vec.name)
if bin_vec.name:
ax.set_xlabel(bin_vec.name)
[p.set_visible(False) for p in bp['fliers']]
[p.set_visible(False) for p in bp['caps']]
[p.set_visible(False) for p in bp['whiskers']]
for p in bp['medians']:
p.set_color(colors[0])
p.set_lw(3)
p.set_alpha(.8)
for i, p in enumerate(bp['boxes']):
p.set_color('grey')
p.set_lw(3)
p.set_alpha(.7)
if len(data[i]) < 3:
p.set_alpha(0)
def fancy_raster(df, cluster=False, cmap=plt.cm.get_cmap('Spectral'),
norm=None, ax=None):
if cluster:
d = sp.spatial.distance.pdist(df)
D = sp.spatial.distance.squareform(d)
Y = sp.cluster.hierarchy.linkage(D)
Z = sp.cluster.hierarchy.dendrogram(Y, no_plot=True)
order = Z['leaves']
df = df.ix[order, order]
_, ax = init_ax(ax, figsize=(12, 8))
img = ax.imshow(df, interpolation='Nearest', cmap=cmap, norm=norm)
ax.set_yticks(range(len(df.index)))
ax.set_yticklabels(df.index)
ax.set_xticks(np.arange(len(df.columns)))
ax.set_xticklabels(df.columns, rotation=360 - 90, ha='center');
ax.hlines(np.arange(len(df.index) - 1) + .5, -.5, len(df.columns) - .5,
color='white', lw=6)
ax.vlines(np.arange(len(df.columns) - 1) + .5, -.5, len(df.index) - .5,
color='white', lw=6)
if cluster:
icoord = np.array(Z['icoord']) - np.array(Z['icoord']).min()
icoord = icoord * ((len(Z['leaves']) - 1) / icoord.max())
dcoord = -1 * np.array(Z['dcoord']) - .7
for i, z, c in zip(icoord, dcoord, Z['color_list']):
ax.plot(i, z, color=c, lw=2, alpha=.8)
ax.tick_params(axis='x', top='off')
ax.set_frame_on(False)
return img
def fischer_bar_chart(bin_vec, response_vec, ax=None, filename=None):
fig, ax = init_ax(ax)
t = pd.crosstab(bin_vec, response_vec)
t.plot(kind='bar', ax=ax)
if filename is not None:
fig.savefig(filename)
return fig
def violin_plot_pandas(bin_vec,
real_vec,
ann='p',
order=None,
ax=None,
filename=None):
"""
http://pyinsci.blogspot.com/2009/09/violin-plot-with-matplotlib.html
Wrapper around matplotlib's boxplot function to add violin profile.
Inputs
bin_vec: Series of labels
real_vec: Series of measurements to be grouped according to bin_vec
"""
fig, ax = init_ax(ax)
ax.set_ylabel(real_vec.name)
ax.set_xlabel(bin_vec.name)
bin_vec, real_vec = match_series(bin_vec, real_vec)
try:
if order is None:
categories = bin_vec.value_counts().index
else:
categories = order
_violin_plot(ax, [real_vec[bin_vec == num] for num in categories],
pos=categories,
bp=True)
ax.set_xticklabels(
[str(c) + '\n(n=%i)' % sum(bin_vec == c) for c in categories])
except:
box_plot_pandas(bin_vec, real_vec, ax=ax)
#if type(bin_vec.name) == str:
# ax.set_title(str(bin_vec.name) + ' x ' + str(real_vec.name))
p_value = Stats.kruskal_pandas(bin_vec, real_vec)['p']
if ann == 'p_fancy':
ax.annotate('$p = {}$'.format(latex_float(p_value)), (.95, -.02),
xycoords='axes fraction',
ha='right',
va='bottom',
size=14)
if ann == 'p':
ax.annotate('p = {0:.1e}'.format(p_value), (.95, .02),
xycoords='axes fraction',
ha='right',
va='bottom',
size=12)
elif ann is not None:
ax.annotate(ann, (.95, .02),
xycoords='axes fraction',
ha='right',
va='bottom',
size=12)
if filename is not None:
fig.savefig(filename)
return
def histo_compare(hit_vec, response_vec, ax=None):
'''
Split response_vec by hit_vec and compared histograms.
Also plots the kde of the whole response_vec.
'''
fig, ax = init_ax(ax)
kde1 = sp.stats.gaussian_kde(response_vec)
x_eval = np.linspace(min(response_vec), max(response_vec), num=200)
ax.plot(x_eval, kde1(x_eval), 'k-')
miss, hit = split_a_by_b(response_vec, hit_vec)
ax.hist(miss, bins=20, normed=True, alpha=.2, label='WT');
ax.hist(hit, bins=10, normed=True, alpha=.5, label='Mut');
ax.legend()
return fig
def histo_compare(hit_vec, response_vec, ax=None):
'''
Split response_vec by hit_vec and compared histograms.
Also plots the kde of the whole response_vec.
'''
fig, ax = init_ax(ax)
kde1 = sp.stats.gaussian_kde(response_vec)
x_eval = np.linspace(min(response_vec), max(response_vec), num=200)
ax.plot(x_eval, kde1(x_eval), 'k-')
miss, hit = split_a_by_b(response_vec, hit_vec)
ax.hist(miss, bins=20, normed=True, alpha=.2, label='WT');
ax.hist(hit, bins=10, normed=True, alpha=.5, label='Mut');
ax.legend()
return fig
def series_scatter(s1, s2, ax=None, ann='p', filename=None, **plot_args):
fig, ax = init_ax(ax, figsize=(6,4))
if 's' not in plot_args:
plot_args['s'] = 75
if 'alpha' not in plot_args:
plot_args['alpha'] = .5
ax.scatter(*match_series(s1, s2), **plot_args)
ax.set_xlabel(s1.name)
ax.set_ylabel(s2.name)
if ann == 'p':
ax.annotate('p = {0:.1e}'.format(Tests.spearman_pandas(s1, s2)['p']), (.95, -.02),
xycoords='axes fraction', ha='right',va='bottom', size=14)
if ann == 'fancy_p':
ax.annotate('$p = {}$'.format(latex_float(Tests.spearman_pandas(s1, s2)['p'])), (.95, -.02),
xycoords='axes fraction', ha='right',va='bottom', size=14)
if filename is not None:
fig.savefig(filename)
def series_scatter(s1, s2, ax=None, ann='p', filename=None, **plot_args):
fig, ax = init_ax(ax, figsize=(6, 4))
if 's' not in plot_args:
plot_args['s'] = 75
if 'alpha' not in plot_args:
plot_args['alpha'] = .5
ax.scatter(*match_series(s1, s2), **plot_args)
ax.set_xlabel(s1.name)
ax.set_ylabel(s2.name)
if ann == 'p':
ax.annotate('p = {0:.1e}'.format(Tests.spearman_pandas(s1, s2)['p']), (.95, -.02),
xycoords='axes fraction', ha='right', va='bottom', size=14)
if ann == 'fancy_p':
ax.annotate('$p = {}$'.format(latex_float(Tests.spearman_pandas(s1, s2)['p'])), (.95, -.02),
xycoords='axes fraction', ha='right', va='bottom', size=14)
if filename is not None:
fig.savefig(filename)
def violin_plot_pandas(bin_vec, real_vec, ann='p', order=None, ax=None,
filename=None):
"""
http://pyinsci.blogspot.com/2009/09/violin-plot-with-matplotlib.html
Wrapper around matplotlib's boxplot function to add violin profile.
Inputs
bin_vec: Series of labels
real_vec: Series of measurements to be grouped according to bin_vec
"""
fig, ax = init_ax(ax)
ax.set_ylabel(real_vec.name)
ax.set_xlabel(bin_vec.name)
bin_vec, real_vec = match_series(bin_vec, real_vec)
try:
if order is None:
categories = bin_vec.value_counts().index
else:
categories = order
_violin_plot(ax, [real_vec[bin_vec == num] for num in categories],
pos=categories, bp=True)
ax.set_xticklabels([str(c) + '\n(n=%i)' % sum(bin_vec == c)
for c in categories])
except:
box_plot_pandas(bin_vec, real_vec, ax=ax)
#if type(bin_vec.name) == str:
# ax.set_title(str(bin_vec.name) + ' x ' + str(real_vec.name))
p_value = Stats.kruskal_pandas(bin_vec, real_vec)['p']
if ann == 'p_fancy':
ax.annotate('$p = {}$'.format(latex_float(p_value)), (.95, -.02),
xycoords='axes fraction', ha='right', va='bottom', size=14)
if ann == 'p':
ax.annotate('p = {0:.1e}'.format(p_value), (.95, .02),
xycoords='axes fraction', ha='right', va='bottom', size=12)
elif ann is not None:
ax.annotate(ann, (.95, .02), xycoords='axes fraction', ha='right',
va='bottom', size=12)
if filename is not None:
fig.savefig(filename)
return
def count_plot(vec, name=None, ax=None):
_, ax = init_ax(ax)
vec.value_counts().sort_index().plot(kind='bar', ax=ax)
ax.set_ylabel('# of Patients')
ax.set_xlabel(name if name is not None else vec.name)
def count_plot(vec, name=None, ax=None):
_, ax = init_ax(ax)
vec.value_counts().sort_index().plot(kind='bar', ax=ax)
ax.set_ylabel('# of Patients')
ax.set_xlabel(name if name is not None else vec.name)