def plot_rgb(image, name, label=None, label_color='w', label_size='large'):
"""
This will plot the r,g,b channels of an *image* of shape (N,M,3) or
(N,M,4). *name* is the prefix of the file name, which will be supplemented
with "_rgb.png." *label*, *label_color* and *label_size* may also be
specified.
"""
import pylab
Nvec = image.shape[0]
image[np.isnan(image)] = 0.0
if image.shape[2] >= 4:
image = image[:,:,:3]
pylab.clf()
pylab.gcf().set_dpi(100)
pylab.gcf().set_size_inches((Nvec/100.0, Nvec/100.0))
pylab.gcf().subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0, wspace=0.0, hspace=0.0)
pylab.imshow(image, interpolation='nearest')
if label is not None:
pylab.text(20, 20, label, color = label_color, size=label_size)
pylab.savefig("%s_rgb.png" % name)
pylab.clf()
python类savefig()的实例源码
def view_trigger_snippets_bis(trigger_snippets, elec_index, save=None):
fig = pylab.figure()
ax = fig.add_subplot(1, 1, 1)
for n in xrange(0, trigger_snippets.shape[2]):
y = trigger_snippets[:, elec_index, n]
x = numpy.arange(- (y.size - 1) / 2, (y.size - 1) / 2 + 1)
b = 0.5 + 0.5 * numpy.random.rand()
ax.plot(x, y, color=(0.0, 0.0, b), linestyle='solid')
ax.grid(True)
ax.set_xlim([numpy.amin(x), numpy.amax(x)])
ax.set_xlabel("time")
ax.set_ylabel("amplitude")
if save is None:
pylab.show()
else:
pylab.savefig(save)
pylab.close(fig)
return
def view_loss_curve(losss, title=None, save=None):
'''Plot loss curve'''
x_min = 1
x_max = len(losss) - 1
fig = pylab.figure()
ax = fig.gca()
ax.semilogy(range(x_min, x_max + 1), losss[1:], color='blue', linestyle='solid')
ax.grid(True, which='both')
if title is None:
ax.set_title("Loss curve")
else:
ax.set_title(title)
ax.set_xlabel("iteration")
ax.set_ylabel("loss")
ax.set_xlim([x_min - 1, x_max + 1])
if save is None:
pylab.show()
else:
pylab.savefig(save)
pylab.close(fig)
return
def plot_labeled_images_random(image_list, label_list, categories, n, title_str, ypixels, xpixels, seed, filename):
random.seed(seed)
index_sample = random.sample(range(len(image_list)), n)
plt.figure(figsize=(2*n, 2))
#plt.suptitle(title_str)
for i, ind in enumerate(index_sample):
ax = plt.subplot(1, n, i + 1)
plt.imshow(image_list[ind].reshape(ypixels, xpixels))
plt.gray()
ax.set_title(categories[label_list[ind]], fontsize=20)
ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False)
if 1:
pylab.savefig(filename, bbox_inches='tight')
else:
plt.show()
# plot_unlabeled_images_random: plots unlabeled images at random
def plot_unlabeled_images_random(image_list, n, title_str, ypixels, xpixels, seed, filename):
random.seed(seed)
index_sample = random.sample(range(len(image_list)), n)
plt.figure(figsize=(2*n, 2))
plt.suptitle(title_str)
for i, ind in enumerate(index_sample):
ax = plt.subplot(1, n, i + 1)
plt.imshow(image_list[ind].reshape(ypixels, xpixels))
plt.gray()
ax.get_xaxis().set_visible(False); ax.get_yaxis().set_visible(False)
if 1:
pylab.savefig(filename, bbox_inches='tight')
else:
plt.show()
# plot_compare: given test images and their reconstruction, we plot them for visual comparison
def plot_compare(x_test, decoded_imgs, filename):
n = 10
plt.figure(figsize=(2*n, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if 1:
pylab.savefig(filename, bbox_inches='tight')
else:
plt.show()
# plot_img: plots greyscale image
def display_results_figure(results, METRIC):
import pylab as pb
color = iter(pb.cm.rainbow(np.linspace(0, 1, len(results))))
plots = []
for method in results.keys():
x = []
y = []
for train_perc in sorted(results[method].keys()):
x.append(train_perc)
y.append(results[method][train_perc][0])
c = next(color)
(pi, ) = pb.plot(x, y, color=c)
plots.append(pi)
from matplotlib.font_manager import FontProperties
fontP = FontProperties()
fontP.set_size('small')
pb.legend(plots, map(method_name_mapper, results.keys()),
prop=fontP, bbox_to_anchor=(0.6, .65))
pb.xlabel('#Tweets from target rumour for training')
pb.ylabel('Accuracy')
pb.title(METRIC.__name__)
pb.savefig('incrementing_training_size.png')
def display_data(word_vectors, words, target_words=None):
target_matrix = word_vectors.copy()
if target_words:
target_words = [line.strip().lower() for line in open(target_words)][:2000]
rows = [words.index(word) for word in target_words if word in words]
target_matrix = target_matrix[rows,:]
else:
rows = np.random.choice(len(word_vectors), size=1000, replace=False)
target_matrix = target_matrix[rows,:]
reduced_matrix = tsne(target_matrix, 2);
Plot.figure(figsize=(200, 200), dpi=100)
max_x = np.amax(reduced_matrix, axis=0)[0]
max_y = np.amax(reduced_matrix, axis=0)[1]
Plot.xlim((-max_x,max_x))
Plot.ylim((-max_y,max_y))
Plot.scatter(reduced_matrix[:, 0], reduced_matrix[:, 1], 20);
for row_id in range(0, len(rows)):
target_word = words[rows[row_id]]
x = reduced_matrix[row_id, 0]
y = reduced_matrix[row_id, 1]
Plot.annotate(target_word, (x,y))
Plot.savefig("word_vectors.png");
def on_epoch_end(self, epoch, logs={}):
self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % epoch))
self.show_edit_distance(256)
word_batch = next(self.text_img_gen)[0]
res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
for i in range(self.num_display_words):
pylab.subplot(self.num_display_words, 1, i + 1)
if K.image_dim_ordering() == 'th':
the_input = word_batch['the_input'][i, 0, :, :]
else:
the_input = word_batch['the_input'][i, :, :, 0]
pylab.imshow(the_input, cmap='Greys_r')
pylab.xlabel('Truth = \'%s\' Decoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
fig = pylab.gcf()
fig.set_size_inches(10, 12)
pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % epoch))
pylab.close()
# Input Parameters
def plot_evaluation_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
average_scores = [0]
median_scores = [0]
for n in xrange(len(csv_evaluation)):
params = csv_evaluation[n]
episodes.append(params[0])
average_scores.append(params[1])
median_scores.append(params[2])
pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("average score")
pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir)
pylab.clf()
pylab.plot(0, 0)
pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("median score")
pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir)
def plot(self, filename):
r"""Save an image file of the transfer function.
This function loads up matplotlib, plots the transfer function and saves.
Parameters
----------
filename : string
The file to save out the plot as.
Examples
--------
>>> tf = TransferFunction( (-10.0, -5.0) )
>>> tf.add_gaussian(-9.0, 0.01, 1.0)
>>> tf.plot("sample.png")
"""
import matplotlib
matplotlib.use("Agg")
import pylab
pylab.clf()
pylab.plot(self.x, self.y, 'xk-')
pylab.xlim(*self.x_bounds)
pylab.ylim(0.0, 1.0)
pylab.savefig(filename)
def save(GUI):
global txtResultPath
if GUI:
import pylab as pl
import nest.raster_plot
import nest.voltage_trace
for key in spikedetectors:
try:
nest.raster_plot.from_device(spikedetectors[key], hist=True)
pl.savefig(f_name_gen("", "spikes_" + key.lower()), dpi=dpi_n, format='png')
pl.close()
except Exception:
print(" * * * from {0} is NOTHING".format(key))
txtResultPath = 'txt/'
logger.debug("Saving TEXT into {0}".format(txtResultPath))
if not os.path.exists(txtResultPath):
os.mkdir(txtResultPath)
for key in spikedetectors:
save_spikes(spikedetectors[key], name=key)
with open(txtResultPath + 'timeSimulation.txt', 'w') as f:
for item in times:
f.write(item)
def on_epoch_end(self, epoch, logs={}):
self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
self.show_edit_distance(256)
word_batch = next(self.text_img_gen)[0]
res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
if word_batch['the_input'][0].shape[0] < 256:
cols = 2
else:
cols = 1
for i in range(self.num_display_words):
pylab.subplot(self.num_display_words // cols, cols, i + 1)
if K.image_dim_ordering() == 'th':
the_input = word_batch['the_input'][i, 0, :, :]
else:
the_input = word_batch['the_input'][i, :, :, 0]
pylab.imshow(the_input.T, cmap='Greys_r')
pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
fig = pylab.gcf()
fig.set_size_inches(10, 13)
pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
pylab.close()
image_ocr_gpu.py 文件源码
项目：keras-mxnet-benchmarks
作者: sandeep-krishnamurthy
项目源码
文件源码
阅读 32
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def on_epoch_end(self, epoch, logs={}):
self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
self.show_edit_distance(256)
word_batch = next(self.text_img_gen)[0]
res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
if word_batch['the_input'][0].shape[0] < 256:
cols = 2
else:
cols = 1
for i in range(self.num_display_words):
pylab.subplot(self.num_display_words // cols, cols, i + 1)
if K.image_dim_ordering() == 'th':
the_input = word_batch['the_input'][i, 0, :, :]
else:
the_input = word_batch['the_input'][i, :, :, 0]
pylab.imshow(the_input.T, cmap='Greys_r')
pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
fig = pylab.gcf()
fig.set_size_inches(10, 13)
pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
pylab.close()
def tile_images(image_batch, image_width=28, image_height=28, image_channel=1, dir=None, filename="images"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
pylab.gray()
for m in range(100):
pylab.subplot(10, 10, m + 1)
pylab.imshow(image_batch[m].reshape((image_width, image_height)), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def scatter_labeled_z(z_batch, label_batch, filename="labeled_z"):
fig = pylab.gcf()
fig.set_size_inches(20.0, 16.0)
pylab.clf()
colors = ["#2103c8", "#0e960e", "#e40402","#05aaa8","#ac02ab","#aba808","#151515","#94a169", "#bec9cd", "#6a6551"]
for n in range(z_batch.shape[0]):
result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[label_batch[n]], s=40, marker="o", edgecolors='none')
classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
recs = []
for i in range(0, len(colors)):
recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i]))
ax = pylab.subplot(111)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5))
pylab.xticks(pylab.arange(-4, 5))
pylab.yticks(pylab.arange(-4, 5))
pylab.xlabel("z1")
pylab.ylabel("z2")
pylab.savefig(filename)
def visualize_bin_list(self, bin_list, path):
"""
Will create a histogram of all bin_list entries and save it to the specified path
"""
# TODO use savelogic here
for jj, bin_entry in enumerate(bin_list):
hist_x, hist_y = self._traceanalysis_logic.calculate_histogram(bin_entry, num_bins=50)
pb.plot(hist_x[0:len(hist_y)], hist_y)
fname = 'bin_' + str(jj) + '.png'
savepath = os.path.join(path, fname)
pb.savefig(savepath)
pb.close()
# =========================================================================
# Connecting to GUI
# =========================================================================
# absolutely not working at the moment.
def stat_personal(self):
if not os.path.exists(self.file_path + self.ip.ip):
os.mkdir(self.file_path + self.ip.ip)
print('make dir %s' % self.ip.ip)
try:
items = self.ip.info_set.count()
except:
return 0
my_info = Info.objects.filter(ip = self.ip).order_by('date')
dates = list(range(len(my_info)))
bmis = [info.get_bmi() for info in my_info]
pl.figure('my', figsize = (5.2, 2.8), dpi = 100)
pl.plot(dates, bmis, '*-', color = '#20b2aa', linewidth = 1.5)
pl.ylabel(u'BMI?', fontproperties = zhfont)
pl.ylim(0.0, 50.0)
pl.savefig(self.file_path + self.ip.ip + '/my.jpg')
pl.cla()
return items
4(improved-7).py 文件源码
项目：computational_physics_N2014301020117
作者: yukangnineteen
项目源码
文件源码
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def show_results(self):
pl.plot(self.t1, self.n_A1, 'b--', label='A1: Time Step = 0.05')
pl.plot(self.t1, self.n_B1, 'b', label='B1: Time Step = 0.05')
pl.plot(self.t2, self.n_A2, 'g--', label='A2: Time Step = 0.1')
pl.plot(self.t2, self.n_B2, 'g', label='B2: Time Step = 0.1')
pl.plot(self.t1, self.n_A1_true, 'r--', label='True A1: Time Step = 0.05')
pl.plot(self.t1, self.n_B1_true, 'r', label='True B1: Time Step = 0.05')
pl.plot(self.t2, self.n_A2_true, 'c--', label='True A2: Time Step = 0.1')
pl.plot(self.t2, self.n_B2_true, 'c', label='True B2: Time Step = 0.1')
pl.title('Double Decay Probelm-Approximation Compared with True in Defferent Time Steps')
pl.xlim(0.0, 0.1)
pl.ylim(0.0, 100.0)
pl.xlabel('time ($s$)')
pl.ylabel('Number of Nuclei')
pl.legend(loc='best', shadow=True, fontsize='small')
pl.grid(True)
pl.savefig("computational_physics homework 4(improved-7).png")
def plotdata(obsmode,spectrum,val,odict,sdict,
instr,fieldname,outdir,outname):
isetting=P.isinteractive()
P.ioff()
P.clf()
P.plot(obsmode,val,'.')
P.ylabel('(pysyn-syn)/syn')
P.xlabel('obsmode')
P.title("%s: %s"%(instr,fieldname))
P.savefig(os.path.join(outdir,outname+'_obsmode.ps'))
P.clf()
P.plot(spectrum,val,'.')
P.ylabel('(pysyn-syn)/syn')
P.xlabel('spectrum')
P.title("%s: %s"%(instr,fieldname))
P.savefig(os.path.join(outdir,outname+'_spectrum.ps'))
matplotlib.interactive(isetting)
def visualize_reconstruction(xp, model, x, visualization_dir, epoch, gpu=False):
x_variable = chainer.Variable(xp.asarray(x))
_x = model.decode(model.encode(x_variable), test=True)
_x.to_cpu()
_x = _x.data
fig = pylab.gcf()
fig.set_size_inches(8.0, 8.0)
pylab.clf()
pylab.gray()
for m in range(50):
i = m / 10
j = m % 10
pylab.subplot(10, 10, 20 * i + j + 1, xticks=[], yticks=[])
pylab.imshow(x[m].reshape((28, 28)), interpolation="none")
pylab.subplot(10, 10, 20 * i + j + 10 + 1, xticks=[], yticks=[])
pylab.imshow(_x[m].reshape((28, 28)), interpolation="none")
# pylab.imshow(np.clip((_x_batch.data[m] + 1.0) / 2.0, 0.0, 1.0).reshape(
# (config.img_channel, config.img_width, config.img_width)), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/reconstruction_{}.png".format(visualization_dir, epoch))
# pylab.show()
def plot_evaluation_episode_reward():
pylab.clf()
sns.set_context("poster")
pylab.plot(0, 0)
episodes = [0]
average_scores = [0]
median_scores = [0]
for n in xrange(len(csv_evaluation)):
params = csv_evaluation[n]
episodes.append(params[0])
average_scores.append(params[1])
median_scores.append(params[2])
pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("average score")
pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir)
pylab.clf()
pylab.plot(0, 0)
pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2)
pylab.xlabel("episodes")
pylab.ylabel("median score")
pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir)
def on_epoch_end(self, epoch, logs={}):
self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
self.show_edit_distance(256)
word_batch = next(self.text_img_gen)[0]
res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
if word_batch['the_input'][0].shape[0] < 256:
cols = 2
else:
cols = 1
for i in range(self.num_display_words):
pylab.subplot(self.num_display_words // cols, cols, i + 1)
if K.image_dim_ordering() == 'th':
the_input = word_batch['the_input'][i, 0, :, :]
else:
the_input = word_batch['the_input'][i, :, :, 0]
pylab.imshow(the_input.T, cmap='Greys_r')
pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
fig = pylab.gcf()
fig.set_size_inches(10, 13)
pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
pylab.close()
def plot_kde(data, dir=None, filename="kde", color="Greens"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
bg_color = sns.color_palette(color, n_colors=256)[0]
ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2)
ax.set_axis_bgcolor(bg_color)
kde = ax.get_figure()
pylab.xlim(-4, 4)
pylab.ylim(-4, 4)
kde.savefig("{}/{}.png".format(dir, filename))
def plot_kde(data, dir=None, filename="kde", color="Greens"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
bg_color = sns.color_palette(color, n_colors=256)[0]
ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2)
ax.set_axis_bgcolor(bg_color)
kde = ax.get_figure()
pylab.xlim(-4, 4)
pylab.ylim(-4, 4)
kde.savefig("{}/{}".format(dir, filename))
def tile_binary_images(x, dir=None, filename="x", row=10, col=10):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(col * 2, row * 2)
pylab.clf()
pylab.gray()
for m in range(row * col):
pylab.subplot(row, col, m + 1)
pylab.imshow(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def main():
data = pd.read_table('../Real_Values.txt').get_values()
x = [float(d) for d in data]
test = np.array([669, 592, 664, 1005, 699, 401, 646, 472, 598, 681, 1126, 1260, 562, 491, 714, 530, 521, 687, 776, 802, 499, 536, 871, 801, 965, 768, 381, 497, 458, 699, 549, 427, 358, 219, 635, 756, 775, 969, 598, 630, 649, 722, 835, 812, 724, 966, 778, 584, 697, 737, 777, 1059, 1218, 848, 713, 884, 879, 1056, 1273, 1848, 780, 1206, 1404, 1444, 1412, 1493, 1576, 1178, 836, 1087, 1101, 1082, 775, 698, 620, 651, 731, 906, 958, 1039, 1105, 620, 576, 707, 888, 1052, 1072, 1357, 768, 986, 816, 889, 973, 983, 1351, 1266, 1053, 1879, 2085, 2419, 1880, 2045, 2212, 1491, 1378, 1524, 1231, 1577, 2459, 1848, 1506, 1589, 1386, 1111, 1180, 1075, 1595, 1309, 2092, 1846, 2321, 2036, 3587, 1637, 1416, 1432, 1110, 1135, 1233, 1439, 894, 628, 967, 1176, 1069, 1193, 1771, 1199, 888, 1155, 1254, 1403, 1502, 1692, 1187, 1110, 1382, 1808, 2039, 1810, 1819, 1408, 803, 1568, 1227, 1270, 1268, 1535, 873, 1006, 1328, 1733, 1352, 1906, 2029, 1734, 1314, 1810, 1540, 1958, 1420, 1530, 1126, 721, 771, 874, 997, 1186, 1415, 973, 1146, 1147, 1079, 3854, 3407, 2257, 1200, 734, 1051, 1030, 1370, 2422, 1531, 1062, 530, 1030, 1061, 1249, 2080, 2251, 1190, 756, 1161, 1053, 1063, 932, 1604, 1130, 744, 930, 948, 1107, 1161, 1194, 1366, 1155, 785, 602, 903, 1142, 1410, 1256, 742, 985, 1037, 1067, 1196, 1412, 1127, 779, 911, 989, 946, 888, 1349, 1124, 761, 994, 1068, 971, 1157, 1558, 1223, 782, 2790, 1835, 1444, 1098, 1399, 1255, 950, 1110, 1345, 1224, 1092, 1446, 1210, 1122, 1259, 1181, 1035, 1325, 1481, 1278, 769, 911, 876, 877, 950, 1383, 980, 705, 888, 877, 638, 1065, 1142, 1090, 1316, 1270, 1048, 1256, 1009, 1175, 1176, 870, 856, 860])
n_predict = 100
extrapolation = fourierExtrapolation(x, n_predict)
pl.figure()
pl.plot(np.arange(len(x), len(extrapolation) + len(x)), extrapolation, 'r', label = 'extrapolation')
pl.plot(x, 'b', label = 'Given Data', linewidth = 3)
pl.legend()
pl.ylabel('BPM')
pl.xlabel('Sample')
pl.title('Fourier Extrapolation')
pl.savefig('FourierExtrapolation.png')
#pl.show()
with open('Fourier_PredValues.txt', 'w') as out:
out.write(str([e for e in extrapolation]).strip('[]'))
def plot_kde(data, dir=None, filename="kde", color="Greens"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
bg_color = sns.color_palette(color, n_colors=256)[0]
ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2)
ax.set_axis_bgcolor(bg_color)
kde = ax.get_figure()
pylab.xlim(-4, 4)
pylab.ylim(-4, 4)
kde.savefig("{}/{}.png".format(dir, filename))
def plot_kde(data, dir=None, filename="kde", color="Greens"):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(16.0, 16.0)
pylab.clf()
bg_color = sns.color_palette(color, n_colors=256)[0]
ax = sns.kdeplot(data[:, 0], data[:,1], shade=True, cmap=color, n_levels=30, clip=[[-4, 4]]*2)
ax.set_axis_bgcolor(bg_color)
kde = ax.get_figure()
pylab.xlim(-4, 4)
pylab.ylim(-4, 4)
kde.savefig("{}/{}".format(dir, filename))
def tile_binary_images(x, dir=None, filename="x", row=10, col=10):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
fig = pylab.gcf()
fig.set_size_inches(col * 2, row * 2)
pylab.clf()
pylab.gray()
for m in range(row * col):
pylab.subplot(row, col, m + 1)
pylab.imshow(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
