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378 | def samples_nd(samples, points=[], **kwargs):
opts = {
# what to plot on triagonal and diagonal subplots
'upper': 'hist', # hist/scatter/None
'diag': 'hist', # hist/None
#'lower': None, # hist/scatter/None # TODO: implement
# title and legend
'title': None,
'legend': False,
# labels
'labels': [], # for dimensions
'labels_points': [], # for points
'labels_samples': [], # for samples
# colors
'samples_colors': plt.rcParams['axes.prop_cycle'].by_key()['color'],
'points_colors': plt.rcParams['axes.prop_cycle'].by_key()['color'],
# subset
'subset': None,
# axes limits
'limits': [],
# ticks
'ticks': [],
'tickformatter': mpl.ticker.FormatStrFormatter('%g'),
'tick_labels': None,
# options for hist
'hist_diag': {
'alpha': 1.,
'bins': 25,
'density': False,
'histtype': 'step'
},
'hist_offdiag': {
#'edgecolor': 'none',
#'linewidth': 0.0,
'bins': 25,
},
# options for kde
'kde_diag': {
'bw_method': 'scott',
'bins': 100,
'color': 'black'
},
'kde_offdiag': {
'bw_method': 'scott',
'bins': 25
},
# options for contour
'contour_offdiag': {
'levels': [0.68]
},
# options for scatter
'scatter_offdiag': {
'alpha': 0.5,
'edgecolor': 'none',
'rasterized': False,
},
# options for plot
'plot_offdiag': {},
# formatting points (scale, markers)
'points_diag': {
},
'points_offdiag': {
'marker': '.',
'markersize': 20,
},
# matplotlib style
'style': os.path.join(os.path.dirname(__file__), 'matplotlibrc'),
# other options
'fig_size': (10, 10),
'fig_bg_colors':
{'upper': None,
'diag': None,
'lower': None},
'fig_subplots_adjust': {
'top': 0.9,
},
'subplots': {
},
'despine': {
'offset': 5,
},
'title_format': {
'fontsize': 16
},
}
# TODO: add color map support
# TODO: automatically determine good bin sizes for histograms
# TODO: get rid of seaborn dependency for despine
# TODO: add legend (if legend is True)
samples_nd.defaults = opts.copy()
opts = _update(opts, kwargs)
# Prepare samples
if type(samples) != list:
samples = [samples]
# Prepare points
if type(points) != list:
points = [points]
points = [np.atleast_2d(p) for p in points]
# Dimensions
dim = samples[0].shape[1]
num_samples = samples[0].shape[0]
# TODO: add asserts checking compatiblity of dimensions
# Prepare labels
if opts['labels'] == [] or opts['labels'] is None:
labels_dim = ['dim {}'.format(i+1) for i in range(dim)]
else:
labels_dim = opts['labels']
# Prepare limits
if opts['limits'] == [] or opts['limits'] is None:
limits = []
for d in range(dim):
min = +np.inf
max = -np.inf
for sample in samples:
min_ = sample[:, d].min()
min = min_ if min_ < min else min
max_ = sample[:, d].max()
max = max_ if max_ > max else max
limits.append([min, max])
else:
if len(opts['limits']) == 1:
limits = [opts['limits'][0] for _ in range(dim)]
else:
limits = opts['limits']
# Prepare ticks
if opts['ticks'] == [] or opts['ticks'] is None:
ticks = None
else:
if len(opts['ticks']) == 1:
ticks = [opts['ticks'][0] for _ in range(dim)]
else:
ticks = opts['ticks']
# Prepare diag/upper/lower
if type(opts['diag']) is not list:
opts['diag'] = [opts['diag'] for _ in range(len(samples))]
if type(opts['upper']) is not list:
opts['upper'] = [opts['upper'] for _ in range(len(samples))]
#if type(opts['lower']) is not list:
# opts['lower'] = [opts['lower'] for _ in range(len(samples))]
opts['lower'] = None
# Style
if opts['style'] in ['dark', 'light']:
style = os.path.join(
os.path.dirname(__file__),
'matplotlib_{}.style'.format(opts['style']))
else:
style = opts['style'];
# Apply custom style as context
with mpl.rc_context(fname=style):
# Figure out if we subset the plot
subset = opts['subset']
if subset is None:
rows = cols = dim
subset = [i for i in range(dim)]
else:
if type(subset) == int:
subset = [subset]
elif type(subset) == list:
pass
else:
raise NotImplementedError
rows = cols = len(subset)
fig, axes = plt.subplots(rows, cols, figsize=opts['fig_size'], **opts['subplots'])
axes = axes.reshape(rows, cols)
# Style figure
fig.subplots_adjust(**opts['fig_subplots_adjust'])
fig.suptitle(opts['title'], **opts['title_format'])
# Style axes
row_idx = -1
for row in range(dim):
if row not in subset:
continue
else:
row_idx += 1
col_idx = -1
for col in range(dim):
if col not in subset:
continue
else:
col_idx += 1
if row == col:
current = 'diag'
elif row < col:
current = 'upper'
else:
current = 'lower'
ax = axes[row_idx, col_idx]
plt.sca(ax)
# Background color
if current in opts['fig_bg_colors'] and \
opts['fig_bg_colors'][current] is not None:
ax.set_facecolor(
opts['fig_bg_colors'][current])
# Axes
if opts[current] is None:
ax.axis('off')
continue
# Limits
if limits is not None:
ax.set_xlim(
(limits[col][0], limits[col][1]))
if current is not 'diag':
ax.set_ylim(
(limits[row][0], limits[row][1]))
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
# Ticks
if ticks is not None:
ax.set_xticks(
(ticks[col][0], ticks[col][1]))
if current is not 'diag':
ax.set_yticks(
(ticks[row][0], ticks[row][1]))
# Despine
despine(ax=ax, **opts['despine'])
# Formatting axes
if current == 'diag': # off-diagnoals
if opts['lower'] is None or col == dim-1:
_format_axis(ax, xhide=False, xlabel=labels_dim[col],
yhide=True, tickformatter=opts['tickformatter'])
else:
_format_axis(ax, xhide=True, yhide=True)
else: # off-diagnoals
if row == dim-1:
_format_axis(ax, xhide=False, xlabel=labels_dim[col],
yhide=True, tickformatter=opts['tickformatter'])
else:
_format_axis(ax, xhide=True, yhide=True)
if opts['tick_labels'] is not None:
ax.set_xticklabels(
(str(opts['tick_labels'][col][0]), str(opts['tick_labels'][col][1])))
# Diagonals
if current == 'diag':
if len(samples) > 0:
for n, v in enumerate(samples):
if opts['diag'][n] == 'hist':
h = plt.hist(
v[:, row],
color=opts['samples_colors'][n],
**opts['hist_diag']
)
elif opts['diag'][n] == 'kde':
density = gaussian_kde(
v[:, row],
bw_method=opts['kde_diag']['bw_method'])
xs = np.linspace(xmin, xmax, opts['kde_diag']['bins'])
ys = density(xs)
h = plt.plot(xs, ys,
color=opts['samples_colors'][n],
)
else:
pass
if len(points) > 0:
extent = ax.get_ylim()
for n, v in enumerate(points):
h = plt.plot(
[v[:, row], v[:, row]],
extent,
color=opts['points_colors'][n],
**opts['points_diag']
)
# Off-diagonals
else:
if len(samples) > 0:
for n, v in enumerate(samples):
if opts['upper'][n] == 'hist' or opts['upper'][n] == 'hist2d':
hist, xedges, yedges = np.histogram2d(
v[:, col], v[:, row], range=[
[limits[col][0], limits[col][1]],
[limits[row][0], limits[row][1]]],
**opts['hist_offdiag'])
h = plt.imshow(hist.T,
origin='lower',
extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],
aspect='auto'
)
elif opts['upper'][n] in ['kde', 'kde2d', 'contour', 'contourf']:
density = gaussian_kde(v[:, [col, row]].T, bw_method=opts['kde_offdiag']['bw_method'])
X, Y = np.meshgrid(np.linspace(limits[col][0], limits[col][1], opts['kde_offdiag']['bins']),
np.linspace(limits[row][0], limits[row][1], opts['kde_offdiag']['bins']))
positions = np.vstack([X.ravel(), Y.ravel()])
Z = np.reshape(density(positions).T, X.shape)
if opts['upper'][n] == 'kde' or opts['upper'][n] == 'kde2d':
h = plt.imshow(Z,
extent=[limits[col][0], limits[col][1], limits[row][0], limits[row][1]],
origin='lower',
aspect='auto',
)
elif opts['upper'][n] == 'contour':
Z = (Z - Z.min())/(Z.max() - Z.min())
h = plt.contour(X, Y, Z,
origin='lower',
extent=[limits[col][0], limits[col][1], limits[row][0], limits[row][1]],
colors=opts['samples_colors'][n],
**opts['contour_offdiag']
)
else:
pass
elif opts['upper'][n] == 'scatter':
h = plt.scatter(
v[:, col], v[:, row],
color=opts['samples_colors'][n],
**opts['scatter_offdiag']
)
elif opts['upper'][n] == 'plot':
h = plt.plot(
v[:, col], v[:, row],
color=opts['samples_colors'][n],
**opts['plot_offdiag']
)
else:
pass
if len(points) > 0:
for n, v in enumerate(points):
h = plt.plot(
v[:, col], v[:, row],
color=opts['points_colors'][n],
**opts['points_offdiag']
)
if len(subset) < dim:
for row in range(len(subset)):
ax = axes[row, len(subset)-1]
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
text_kwargs = {'fontsize': plt.rcParams['font.size']*2.}
ax.text(x1 + (x1 - x0) / 8., (y0 + y1) / 2., '...', **text_kwargs)
if row == len(subset)-1:
ax.text(x1 + (x1 - x0) / 12., y0 - (y1 - y0) / 1.5, '...', rotation=-45, **text_kwargs)
return fig, axes
|