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visualize.py
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visualize.py
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import logging
from spn import MARG_IND
import numpy
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as pyplot
import matplotlib.gridspec as gridspec
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import ticker
from matplotlib.colors import LogNorm
# from spn.utils import get_best_value_from_frame
# import seaborn
from collections import defaultdict
from collections import Counter
color_bi_list = [(1., 1., 1.), (0., 0., 0.)]
binary_cmap = matplotlib.colors.ListedColormap(color_bi_list)
inv_binary_cmap = matplotlib.colors.ListedColormap(list(reversed(color_bi_list)))
color_tri_list = [(1., 1., 1.), (0., 0., 0.), (1., 0., 0.), ]
ternary_cmap = matplotlib.colors.ListedColormap([(1., 0., 0.), (1., 1., 1.), (0., 0., 0.)])
inv_ternary_cmap = matplotlib.colors.ListedColormap([(1., 0., 0.), (0., 0., 0.), (1., 1., 1.)])
checkboard_bg_gray = (0.59, 0.597, 0.59)
checkboard_fg_gray = (0.394, 0.398, 0.39)
quaternary_cmap = matplotlib.colors.ListedColormap([checkboard_bg_gray,
checkboard_fg_gray,
(1., 1., 1.),
(0., 0., 0.)])
inv_quaternary_cmap = matplotlib.colors.ListedColormap([checkboard_bg_gray,
checkboard_fg_gray,
(0., 0., 0.),
(1., 1., 1.)])
quaternary_white_cmap = matplotlib.colors.ListedColormap([checkboard_bg_gray,
checkboard_fg_gray,
(1., 1., 1.)])
inv_quaternary_white_cmap = matplotlib.colors.ListedColormap([checkboard_bg_gray,
checkboard_fg_gray,
(0., 0., 0.)])
#
# changing font size
# seaborn.set_context("poster", font_scale=1.7, rc={'font.size': 32,
# # 'axes.labelsize': fontSize,
# # 'xtick.labelsize': fontSize,
# # 'ytick.labelsize': fontSize,
# # 'legend.fontsize': fontSize,
# 'text.usetex': True
# })
# matplotlib.rcParams.update({'font.size': 22})
def beautify_with_seaborn():
#
import seaborn
seaborn.set_style('white')
seaborn.despine(trim=True)
seaborn.set_context('poster')
def make_checkboard_bg(img,
bg_value=-1,
checkboard_pattern=[-1, -2]):
#
# create checkboard image
wr = 0 if img.shape[1] % 2 == 0 else 1
hr = 0 if img.shape[0] % 2 == 0 else 1
w = img.shape[1] // 2 + wr
h = img.shape[0] // 2 + hr
re = numpy.r_[w * checkboard_pattern]
ro = numpy.r_[w * list(reversed(checkboard_pattern))]
checkboard_img = numpy.row_stack(h * (re, ro))
fg_indexes = img != bg_value
checkboard_img[fg_indexes] = img[fg_indexes]
return checkboard_img[:img.shape[0], :img.shape[1]]
def make_double_checkboard_bg(img,
bg_value=-1,
checkboard_pattern=[-1, -1, -2, -2]):
#
# create checkboard image
wr = 0 if img.shape[1] % 2 == 0 else 1
hr = 0 if img.shape[0] % 2 == 0 else 1
w = img.shape[1] // 2 + wr
h = img.shape[0] // 2 + hr
re1 = numpy.r_[w * checkboard_pattern]
re2 = numpy.r_[w * checkboard_pattern]
ro1 = numpy.r_[w * list(reversed(checkboard_pattern))]
ro2 = numpy.r_[w * list(reversed(checkboard_pattern))]
checkboard_img = numpy.row_stack(h * (re1, re2, ro1, ro2))
fg_indexes = img != bg_value
checkboard_img[fg_indexes] = img[fg_indexes]
return checkboard_img[:img.shape[0], :img.shape[1]]
def visualize_curves(curves,
output=None,
labels=None,
lines=None,
linestyles=None,
linewidths=None,
palette='husl',
markers=None,
loc=None,
colors=None,
fig_size=(10, 8)):
"""
WRITEME
"""
import seaborn
seaborn.set_style('white')
seaborn.set_context(rc={'lines.markeredgewidth': 0.1})
seaborn.set_context('poster')
n_curves = len(curves)
n_lines = len(lines)
#
# default legend location, upper right
if loc is None:
loc = 3
#
# setting the palette
# seaborn.set_palette(palette, n_colors=(n_curves + n_lines))
if colors is None:
colors = [seaborn.color_palette("husl", 12)[1],
seaborn.color_palette("husl", 12)[4],
seaborn.color_palette("husl")[0],
seaborn.color_palette("husl", 12)[8],
seaborn.color_palette("husl", 12)[9]]
print(colors)
#
# default linestyle
default_linestyle = '-'
if linestyles is None:
linestyles = [default_linestyle for i in range(n_curves)]
default_width = 6
if linewidths is None:
linewidths = [default_width for i in range(n_curves)]
if markers is None:
# markers = ['s', 'D', '2', '3', '1']
markers = ['3', '1', '2', '3', 's']
fig, ax = pyplot.subplots(figsize=fig_size)
for i, curve in enumerate(curves):
curve_x, curve_y = curve
if labels is not None:
label = labels[i]
line = ax.plot(curve_x, curve_y,
label=label,
linestyle=linestyles[i],
linewidth=linewidths[i],
marker=markers[i],
mew=0.1,
color=colors[i],
markeredgecolor='none'
)
else:
line = ax.plot(curve_x, curve_y,
linestyle=linestyles[i],
linewidth=linewidths[i],
marker=markers[i],
mew=0.1,
color=colors[i],
markeredgecolor='none'
)
#
# lastly plotting straight lines, if present
if lines is not None:
default_linestyles = ['--', '-.', ':']
for i, line_y in enumerate(lines):
#
# this feels a little bit hackish, assuming all share the same axis
prototypical_x_axis = curves[0][0]
start_x = prototypical_x_axis[0]
end_x = prototypical_x_axis[-1]
ax.plot([start_x, end_x],
[line_y, line_y],
linestyle=default_linestyles[i],
color=colors[i + len(curves)],
linewidth=default_width) # linestyles[i + n_curves])
#
# setting up the legend
if labels is not None:
legend = ax.legend(labels, loc=loc)
seaborn.despine()
pyplot.xlabel('# components')
pyplot.ylabel('test ll')
if output is not None:
# fig = pyplot.gcf()
# fig_width = 18.5
# fig_height = 10.5
# dpi = 150
# fig.set_size_inches(fig_width, fig_height)
# fig.savefig(output,
# # additional_artists=[legend],
# dpi=dpi,
# bbox_inches='tight')
# pyplot.close(fig)
pp = PdfPages(output + '.pdf')
pp.savefig(fig)
pp.close()
else:
#
# shall this be mutually exclusive with file saving?
pyplot.show()
DATASET_LIST = ['nltcs', 'msnbc', 'kdd',
'plants', 'baudio', 'jester', 'bnetflix',
'accidents', 'tretail', 'pumsb_star',
'dna', 'kosarek', 'msweb',
'book', 'tmovie', 'cwebkb',
'cr52', 'c20ng', 'bbc', 'ad']
def visualize_histograms(histograms,
output=None,
labels=DATASET_LIST,
linestyles=None,
rotation=90,
legend=None,
y_log=False,
colors=['seagreen', 'orange', 'cornflowerblue']):
"""
Plotting histograms one near the other
"""
import seaborn
n_histograms = len(histograms)
#
# assuming homogeneous data leengths
# TODO: better error checking
n_ticks = len(histograms[0])
bin_width = 1 / (n_histograms + 1)
bins = [[i + j * bin_width for i in range(n_ticks)]
for j in range(1, n_histograms + 1)]
#
# setting up seaborn
seaborn.set_style("white")
seaborn.set_context("poster")
# seaborn.set_palette(palette, n_colors=n_histograms)
fig, ax = pyplot.subplots()
if legend is not None:
_legend = pyplot.legend(legend)
#
# setting labels
middle_histogram = n_histograms // 2 + 1 # if n_histograms > 1 else 0
pyplot.xticks(bins[middle_histogram], DATASET_LIST)
if rotation is not None:
locs, labels = pyplot.xticks()
pyplot.setp(labels, rotation=90)
#
# actual plotting
print(histograms)
for i, histogram in enumerate(histograms):
ax.bar(bins[i], histogram, width=bin_width,
facecolor=colors[i], edgecolor="none",
log=y_log)
seaborn.despine()
if output is not None:
pp = PdfPages(output)
pp.savefig(fig)
pp.close()
def jitter(arr, std=.02):
stdev = std * (max(arr) - min(arr))
return arr + numpy.random.randn(len(arr)) * stdev
def plot_depth_vs_size(frame_list,
labels,
depth_col_label='n_levels:',
size_col_label='n_edges:',
fig_size=(9, 8),
save_path=None,
pdf=False,
colors=None,
markers=None,
jitter_points=(.04, .04),
marker_size=80):
import seaborn
if not colors:
colors = seaborn.color_palette("husl")
if not markers:
markers = ['o' for _frame in frame_list]
fig = pyplot.figure(figsize=fig_size)
ax1 = fig.add_subplot(111)
# fig.suptitle('nltcs')
pyplot.xlabel('depth')
pyplot.ylabel('# edges')
for i, frame in enumerate(frame_list):
ax1.scatter(x=jitter(frame[depth_col_label].values, jitter_points[0]),
y=jitter(frame[size_col_label].values, jitter_points[1]),
c=colors[i],
edgecolor='none',
marker=markers[i],
label=labels[i],
s=marker_size)
seaborn.despine()
pyplot.legend(loc='upper right')
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
return ax1
def get_best_value_from_frame(frame,
best_col,
attribute=None):
if not attribute:
attribute = best_col
best_values = frame[frame[best_col] == frame[best_col].max()][attribute].values
assert len(best_values) == 1
return best_values[0]
def plot_comparative_histograms(frame_lists,
col_name,
best_col_name,
x_labels,
y_label,
save_path=None,
linestyles=None,
rotation=90,
legend=None,
fig_size=(10, 8),
y_log=False,
colors=None,
pdf=False):
"""
Plotting bars one near the other
"""
import seaborn
if not colors:
colors = seaborn.color_palette("husl")
#
# extracting histograms from frames
histograms = [[] for _list in frame_lists]
for i, f_list in enumerate(frame_lists):
for frame in f_list:
param_value = get_best_value_from_frame(frame, best_col_name, col_name)
histograms[i].append(param_value)
n_histograms = len(histograms)
#
# assuming homogeneous data lengths
# TODO: better error checking
n_ticks = len(histograms[0])
bin_width = 1 / (n_histograms + 1)
bins = [[i + j * bin_width for i in range(n_ticks)]
for j in range(1, n_histograms + 1)]
fig = pyplot.figure(figsize=fig_size)
ax = fig.add_subplot(111)
# fig.suptitle('nltcs')
pyplot.xlabel('datasets')
pyplot.ylabel(y_label)
if legend is not None:
_legend = pyplot.legend(legend)
#
# setting labels
middle_histogram = n_histograms // 2 + 1 # if n_histograms > 1 else 0
pyplot.xticks(bins[middle_histogram], x_labels)
if rotation is not None:
locs, labels = pyplot.xticks()
pyplot.setp(labels, rotation=90)
#
# actual plotting
print(histograms)
for i, histogram in enumerate(histograms):
ax.bar(bins[i], histogram, width=bin_width,
facecolor=colors[i], edgecolor="none",
log=y_log)
seaborn.despine()
pyplot.legend(loc='upper right')
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
return ax
def plot_m_by_n_images(images,
m, n,
fig_size=(12, 12),
cmap=matplotlib.cm.binary,
w_space=0.1,
h_space=0.1,
dpi=900,
save_path=None,
pdf=False):
"""
Plot images in a mxn tiling
"""
print(w_space, h_space)
gs1 = gridspec.GridSpec(m, n)
gs1.update(wspace=w_space, hspace=h_space)
print(len(images))
fig = pyplot.figure(figsize=fig_size, dpi=dpi)
for x in range(m):
for y in range(n):
id = n * x + y
if id < len(images):
ax = fig.add_subplot(gs1[id])
ax.matshow(images[id], cmap=cmap)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
# pyplot.tight_layout()
pyplot.subplots_adjust(left=None, right=None, wspace=w_space, hspace=h_space)
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
def plot_m_by_n_images_list(images,
m, n,
fig_size=(12, 12),
cmap=[matplotlib.cm.binary],
w_space=0.1,
h_space=0.1,
dpi=900,
save_path=None,
pdf=False,
show=False):
"""
Plot images in a mxn tiling
"""
print(w_space, h_space)
gs1 = gridspec.GridSpec(m, n)
gs1.update(wspace=w_space, hspace=h_space)
assert len(images) == len(cmap)
print(len(images))
fig = pyplot.figure(figsize=fig_size, dpi=dpi)
for x in range(m):
for y in range(n):
id = n * x + y
if id < len(images):
ax = fig.add_subplot(gs1[id])
ax.matshow(images[id], cmap=cmap[id], vmin=-0.00001)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
# pyplot.tight_layout()
pyplot.subplots_adjust(left=None, right=None, wspace=w_space, hspace=h_space)
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
if show:
pyplot.show()
def plot_m_by_n_by_p_by_q_images(images_lists,
m, n, p, q,
fig_size=(16, 16),
cmap=matplotlib.cm.binary,
save_path=None,
pdf=False):
"""
Plot images in a (mxp + p - 1) x (nxq + q - 1) tiling
"""
fig = pyplot.figure(figsize=fig_size)
tot_rows = m * p + p - 1
tot_cols = n * q + q - 1
for i, images in enumerate(images_lists):
i_row = i // q
i_col = i - q * i_row
# print('i', i, i_row, i_col)
for j, img in enumerate(images):
j_row = j // n
j_col = j - n * j_row
if j < m * n:
# print('j', j, j_row, j_col)
t_row = i_row * m + i_row + j_row
t_col = i_col * n + i_col + j_col
id = tot_cols * t_row + t_col
# print('id', id)
ax = fig.add_subplot(tot_rows,
tot_cols, id + 1)
ax.matshow(img, cmap=cmap)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
pyplot.tight_layout()
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
#
# axes utils
from mpl_toolkits.axes_grid1 import make_axes_locatable
def plot_m_by_n_heatmaps(images,
min_max_list,
m, n,
cmaps,
fig_size=(12, 12),
w_space=0.1,
h_space=0.1,
colorbars=False,
dpi=900,
save_path=None,
pdf=False):
"""
Plot images in a mxn tiling
"""
import seaborn
seaborn.set_style('white')
seaborn.set_context('poster')
assert len(min_max_list) == len(images)
print(len(images))
gs1 = gridspec.GridSpec(m, n)
gs1.update(wspace=w_space, hspace=h_space)
fig = pyplot.figure(figsize=fig_size, dpi=dpi)
for x in range(m):
for y in range(n):
id = n * x + y
if id < len(images):
# ax = fig.add_subplot(m, n, id + 1)
ax = fig.add_subplot(gs1[id])
if id > 0:
norm = None # LogNorm(vmin=min_act, vmax=max_act)
print('min max', id, min_max_list[id])
img = ax.matshow(images[id],
cmap=cmaps[id],
vmin=min_max_list[id][0],
vmax=min_max_list[id][1],
norm=norm)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
pyplot.axis('off')
if colorbars:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
pyplot.colorbar(img, cax=cax)
else:
img = ax.matshow(images[id], cmap=cmaps[id])
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
pyplot.axis('off')
# pyplot.tight_layout()
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
def array_2_mat(array, n_rows, n_cols):
array = numpy.array(array, copy=True)
return array.reshape(n_rows, n_cols)
def tiling_sizes(n_images, n_cols=None):
n_rows = None
if n_cols is None:
n_rows = int(numpy.sqrt(n_images))
n_cols = n_rows
else:
n_rows = max(n_images // n_cols, 1)
rem_tiles = n_images - n_rows * n_cols
if rem_tiles > 0:
n_rem_rows, n_rem_cols = tiling_sizes(rem_tiles, n_cols)
return n_rows + n_rem_rows, n_cols
return n_rows, n_cols
def scope_histogram(spn,
fig_size=(12, 5),
dpi=900,
ylim=None,
xlim=None,
save_path=None,
pdf=False,
show=True):
import seaborn
seaborn.set_style('white')
# seaborn.despine(trim=True)
seaborn.set_context('poster', font_scale=1.8)
scope_dict = defaultdict(list)
for node in spn.top_down_nodes():
scope = None
if hasattr(node, 'var_scope'):
scope = node.var_scope
elif hasattr(node, 'var'):
scope = frozenset(node.var)
scope_dict[len(scope)].append(node)
max_scope_len = max(scope_dict.keys())
scope_list = [0] * max_scope_len
for scope_len, nodes in scope_dict.items():
scope_list[scope_len - 1] = len(nodes)
print(scope_list)
fig, ax = pyplot.subplots(figsize=fig_size)
# ax.bar(numpy.arange(max_scope_len),
# scope_list,
# log=True)
# width = 3e-3
width = 0.1
for i in range(0, len(scope_list)):
# x_pos = [10 ** (numpy.log10(i) - width),
# 10 ** (numpy.log10(i) - width),
# 10 ** (numpy.log10(i) + width),
# 10 ** (numpy.log10(i) + width)]
x_pos = [i - width + 1, i - width + 1, i + width + 1, i + width + 1]
y_pos = [0,
scope_list[i],
scope_list[i],
0]
ax.fill(x_pos,
y_pos, 'black')
ax.set_yscale('log')
# ax.set_xscale('log')
if xlim:
ax.set_xlim([xlim[0], xlim[1]])
else:
ax.set_xlim([-1, len(scope_list) + 1])
if ylim:
ax.set_ylim([0.1, ylim])
pyplot.xlabel('scope length')
pyplot.ylabel('# nodes')
pyplot.tight_layout()
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
if show:
pyplot.show()
# rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
return scope_list
def scope_maps(spns,
height=20,
fig_size=(12, 4),
dpi=900,
# cmap=matplotlib.cm.jet,
cmap=matplotlib.cm.binary,
min_val=None,
max_val=None,
xlim=None,
w_space=0.0,
h_space=1.1,
save_path=None,
pdf=False):
import seaborn
seaborn.set_style('white')
seaborn.despine(trim=True)
seaborn.set_context('poster', font_scale=1.8)
scope_lists = []
for spn in spns:
scope_dict = defaultdict(list)
for node in spn.top_down_nodes():
scope = None
if hasattr(node, 'var_scope'):
scope = node.var_scope
elif hasattr(node, 'var'):
scope = frozenset(node.var)
scope_dict[len(scope)].append(node)
max_scope_len = max(scope_dict.keys())
scope_list = [0] * max_scope_len
for scope_len, nodes in scope_dict.items():
scope_list[scope_len - 1] = len(nodes)
print(scope_list)
scope_lists.append(scope_list)
m = len(spns)
n = 1
gs1 = gridspec.GridSpec(m, n)
gs1.update(wspace=w_space, hspace=h_space)
if xlim is None:
xlim = len(scope_lists[0])
height_val = xlim * (fig_size[1] / len(spns)) / fig_size[0]
step = 99 if xlim // 10 > 10 else 29
fig = pyplot.figure(figsize=fig_size, dpi=dpi)
for i in range(m):
norm = None
ax = fig.add_subplot(gs1[i])
matrix_map = numpy.log10(numpy.array(scope_lists[i][:xlim]) + 1).reshape(1, xlim)
matrix_map = numpy.lib.pad(matrix_map, ((0, 0), (5, 5)), 'constant')
matrix_map = numpy.repeat(matrix_map, height_val, axis=0)
print(matrix_map)
img = ax.matshow(matrix_map,
cmap=cmap,
vmin=min_val,
vmax=max_val,
norm=norm)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]))
# pyplot.axis('off')
ax.set_xticks(numpy.arange(1, xlim, step))
ax.set_xlabel('scope length')
# pyplot.ylabel('# nodes')
# pyplot.tight_layout()
if save_path:
fig.savefig(save_path + '.svg')
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig)
pp.close()
pyplot.show()
# rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
return scope_list
def scope_map_layerwise(spn,
fig_size=(20, 1),
dpi=900,
# cmap=matplotlib.cm.jet,
cmap=matplotlib.cm.binary,
xlim=None,
w_space=-100.0,
h_space=-100.0,
save_path=None,
pdf=False,
show=True):
import seaborn
cmap.set_under('white')
seaborn.set_style('white')
# seaborn.despine(trim=True)
seaborn.set_context('poster', font_scale=1.8)
max_scope_len = max([len(node.var_scope) for node in spn.top_down_nodes()
if hasattr(node, 'var_scope')])
scope_lists = []
for layer in spn.bottom_up_layers():
scope_dict = defaultdict(list)
for node in layer.nodes():
scope = None
if hasattr(node, 'var_scope'):
scope = node.var_scope
elif hasattr(node, 'var'):
scope = frozenset(node.var)
scope_dict[len(scope)].append(node)
# max_scope_len = max(scope_dict.keys())
scope_list = [0] * max_scope_len
for scope_len, nodes in scope_dict.items():
scope_list[scope_len - 1] = len(nodes)
print(scope_list)
scope_lists.append(scope_list)
m = len(scope_lists)
n = 1
gs1 = gridspec.GridSpec(m, n)
gs1.update(wspace=w_space, hspace=h_space)
fig = pyplot.figure(figsize=fig_size, dpi=dpi)
maps = []
min_val = numpy.Inf
max_val = -numpy.inf
eps = numpy.spacing(0.0)
norm = None
if xlim is None:
xlim = len(scope_lists[0])
height_val = max(1, xlim * (fig_size[1] / len(scope_lists)) / fig_size[0])
step = 100 if xlim // 10 > 50 else 30
print('height:', height_val, xlim, step)
for i in range(m):
# print(scope_lists[i])
# matrix_map = numpy.log10(numpy.array(scope_lists[i][:xlim])).reshape(1, xlim)
matrix_map = numpy.zeros(xlim)
matrix_map[numpy.array(scope_lists[i][:xlim]) > 0] = 1
matrix_map = matrix_map.reshape(1, xlim)
matrix_map = numpy.lib.pad(matrix_map, ((0, 0), (5, 5)), 'constant')
matrix_map = numpy.repeat(matrix_map, height_val, axis=0)
# print(matrix_map)
maps.append(matrix_map)
min_val = min([matrix_map.min(), min_val])
max_val = max([matrix_map.max(), max_val])
print('minmax', min_val, max_val)
show_all_y_labels = True if len(maps) < 10 else False
n_layers = len(maps)
for i in range(n_layers):
ax = fig.add_subplot(gs1[i], frameon=False)
# if i > 0:
# step = 100 if xlim // 10 > 10 else 30
# matrix_map = numpy.repeat(maps[i], height_val, axis=0)
img = ax.matshow(maps[i],
cmap=cmap,
vmin=min_val + eps,
vmax=max_val,
norm=norm)
pyplot.xticks(numpy.array([]))
pyplot.yticks(numpy.array([]), rotation=90)
# ax.yaxis.set_rotate_label(False)
if show_all_y_labels or i % 2 == 1:
ax.set_ylabel(str(n_layers - i - 1), rotation=0, ha='right', va='center')
# pyplot.axis('off')
if i == 0:
ticks = [1] + [i for i in range(step, xlim, step)] # + [xlim]
ax.set_xticks(ticks)
fig.text(0.03, 0.5, 'depth', rotation=90, ha='center', va='center', fontsize=32)
# ax.set_xticks(numpy.arange(1, xlim, step))
ax.set_xlabel('scope length')
# pyplot.ylabel('# nodes')
# pyplot.tight_layout()
# fig.subplots_adjust(wspace=0, hspace=0)
if save_path:
fig.savefig(save_path + '.svg', bbox_inches='tight', pad_inches=0)
if pdf:
pp = PdfPages(save_path + '.pdf')
pp.savefig(fig, bbox_inches='tight', pad_inches=0)
pp.close()
if show:
pyplot.show()
# rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)
return scope_list
def multiple_scope_histogram(spns,
fig_size=(14, 5),
dpi=900,
save_path=None,
y_log=True,
colors=None,
pdf=False):
import seaborn
seaborn.set_style('white')
# seaborn.despine(trim=True)
seaborn.set_context('poster')
if not colors:
# colors = seaborn.color_palette("husl")
colors = ['red', 'green', 'black']
n_ticks = 0
spn_scope_lists = []
for spn in spns:
scope_dict = defaultdict(list)
for node in spn.top_down_nodes():
scope = None
if hasattr(node, 'var_scope'):
scope = node.var_scope
elif hasattr(node, 'var'):
scope = frozenset(node.var)
scope_dict[len(scope)].append(node)
#
# assuming all spns to have the same scope
max_scope_len = max(scope_dict.keys())
n_ticks = max(max_scope_len, 0)
scope_list = [0] * max_scope_len
for scope_len, nodes in scope_dict.items():
scope_list[scope_len - 1] = len(nodes)
print(scope_list)
spn_scope_lists.append(scope_list)
n_histograms = len(spns)
bin_width = 1 / (n_histograms + 1)
bins = [[i + j * bin_width for i in range(n_ticks)]
for j in range(1, n_histograms + 1)]