本文整理汇总了Python中pylab.close方法的典型用法代码示例。如果您正苦于以下问题:Python pylab.close方法的具体用法?Python pylab.close怎么用?Python pylab.close使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pylab
的用法示例。
在下文中一共展示了pylab.close方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: output_groups
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def output_groups(tcs, alpha, mis, column_label, direction, thresh=0, prefix=''):
f = safe_open(prefix + '/groups.txt', 'w+')
h = safe_open(prefix + '/topics.txt', 'w+')
m, nv = mis.shape
annotate = lambda q, s: q if s >= 0 else '~' + q
for j in range(m):
f.write('Group num: %d, TC(X;Y_j): %0.3f\n' % (j, tcs[j]))
# inds = np.where(alpha[j] * mis[j] > thresh)[0]
inds = np.where(alpha[j] >= 1.)[0]
inds = inds[np.argsort(-alpha[j, inds] * mis[j, inds])]
for ind in inds:
f.write(column_label[ind] + u', %0.3f, %0.3f, %0.3f\n' % (
mis[j, ind], alpha[j, ind], mis[j, ind] * alpha[j, ind]))
#h.write(unicode(j) + u':' + u','.join([annotate(column_label[ind], direction[j,ind]) for ind in inds[:10]]) + u'\n')
h.write(str(j) + u':' + u','.join(
[annotate(column_label[ind], direction[j, ind]) for ind in inds[:10]]) + u'\n')
f.close()
h.close()
示例2: save_PTZ_metric2disk
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def save_PTZ_metric2disk(metrics,save_path):
import json
#metric_dict= {}
recall_ = list(metrics['metric']['recall'])
precision_ = list(metrics['metric']['precision'])
f_score = metrics['metric']['MaxF']
try:
iu = metrics['metric']['iu']
except KeyError:
iu = 0.0
cont_embedding = metrics['contrast_embedding']
metric_ = {'recall':recall_,'precision':precision_,'f-score':f_score,'iu':iu,
'contrast_embedding':cont_embedding}
file_ = open(save_path + '/metric.json', 'w')
file_.write(json.dumps(metric_, ensure_ascii=False, indent=2))
file_.close()
示例3: save_metric2disk
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def save_metric2disk(metrics,save_path):
import json
length = len(metrics)
metric_dict= {}
for i in range(length):
recall_ = list(metrics[i]['metric']['recall'])
name = metrics[i]['name']
precision_ = list(metrics[i]['metric']['precision'])
f_score = metrics[i]['metric']['MaxF']
try:
iu = metrics[i]['metric']['iu']
except KeyError:
iu = 0.0
metric_ = {'name':name,'recall':recall_,'precision':precision_,'f-score':f_score,'iu':iu}
metric_dict.setdefault(i,metric_)
file_ = open(save_path + '/metric.json', 'w')
file_.write(json.dumps(metric_dict, ensure_ascii=False, indent=2))
file_.close()
示例4: on_epoch_end
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
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_data_format() == 'channels_first':
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()
示例5: save
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def save(GUI):
global txtResultPath
if GUI:
import pylab as pl
import nest.raster_plot
import nest.voltage_trace
logger.debug("Saving IMAGES into {0}".format(SAVE_PATH))
for key in spikedetectors:
try:
nest.raster_plot.from_device(spikedetectors[key], hist=True)
pl.savefig(f_name_gen(SAVE_PATH, "spikes_" + key.lower()), dpi=dpi_n, format='png')
pl.close()
except Exception:
print(" * * * from {0} is NOTHING".format(key))
txtResultPath = SAVE_PATH + '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)
示例6: save_voltage
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def save_voltage(multimeters):
import h5py
print "Write to HDF5 file"
filename = "voltage.hdf5"
timestamp = datetime.datetime.now()
with h5py.File(filename, "w") as f:
f.attrs['default'] = 'entry'
f.attrs['file_name'] = filename
f.attrs['file_time'] = str(timestamp)
f.create_dataset(key, data=nest.GetStatus(multimeters[key], "events")[0]["V_m"])
f.close()
print "wrote file:", filename
# title = "Membrane potential"
# ev = nest.GetStatus(detec, "events")[0]
# with open("{0}@voltage_{1}.txt".format(txt_result_path, name), 'w') as f:
# f.write("Name: {0}, Title: {1}\n".format(name, title))
# print int(T / multimeter_param['interval'])
# for line in range(0, int(T / multimeter_param['interval'])):
# for index in range(0, N_volt):
# print "{0} {1} ".format(ev["times"][line], ev["V_m"][line])
# #f.write("\n")
# print "\n"
示例7: save_voltage
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def save_voltage(multimeters):
import h5py
print "Write to HDF5 file"
filename = "voltage.hdf5"
timestamp = datetime.datetime.now()
with h5py.File(filename, "w") as f:
f.attrs['default'] = 'entry'
f.attrs['file_name'] = filename
f.attrs['file_time'] = str(timestamp)
f.create_dataset(key, data=nest.GetStatus(multimeters[key], "events")[0]["V_m"])
f.close()
print "wrote file:", filename
#title = "Membrane potential"
#ev = nest.GetStatus(detec, "events")[0]
#with open("{0}@voltage_{1}.txt".format(txt_result_path, name), 'w') as f:
# f.write("Name: {0}, Title: {1}\n".format(name, title))
# print int(T / multimeter_param['interval'])
# for line in range(0, int(T / multimeter_param['interval'])):
# for index in range(0, N_volt):
# print "{0} {1} ".format(ev["times"][line], ev["V_m"][line])
# #f.write("\n")
# print "\n"
示例8: output_groups
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def output_groups(tcs, alpha, mis, column_label, thresh=0, prefix=''):
f = safe_open(prefix + '/text_files/groups.txt', 'w+')
g = safe_open(prefix + '/text_files/groups_no_overlaps.txt', 'w+')
m, nv = mis.shape
for j in range(m):
f.write('Group num: %d, TC(X;Y_j): %0.3f\n' % (j, tcs[j]))
g.write('Group num: %d, TC(X;Y_j): %0.3f\n' % (j, tcs[j]))
inds = np.where(alpha[j] * mis[j] > thresh)[0]
inds = inds[np.argsort(-alpha[j, inds] * mis[j, inds])]
for ind in inds:
f.write(column_label[ind] + ', %0.3f, %0.3f, %0.3f\n' % (
mis[j, ind], alpha[j, ind], mis[j, ind] * alpha[j, ind]))
inds = np.where(alpha[j] == 1)[0]
inds = inds[np.argsort(- mis[j, inds])]
for ind in inds:
g.write(column_label[ind] + ', %0.3f\n' % mis[j, ind])
f.close()
g.close()
示例9: plot_convergence
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def plot_convergence(history, prefix='', prefix2=''):
plt.figure(figsize=(8, 5))
ax = plt.subplot(111)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.plot(history["TC"], '-', lw=2.5, color=tableau20[0])
x = len(history["TC"])
y = np.max(history["TC"])
plt.text(0.5 * x, 0.8 * y, "TC", fontsize=18, fontweight='bold', color=tableau20[0])
if "additivity" in history:
plt.plot(history["additivity"], '-', lw=2.5, color=tableau20[1])
plt.text(0.5 * x, 0.3 * y, "additivity", fontsize=18, fontweight='bold', color=tableau20[1])
plt.ylabel('TC', fontsize=12, fontweight='bold')
plt.xlabel('# Iterations', fontsize=12, fontweight='bold')
plt.suptitle('Convergence', fontsize=12)
filename = '{}/summary/convergence{}.pdf'.format(prefix, prefix2)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
plt.savefig(filename, bbox_inches="tight")
plt.close('all')
return True
示例10: plot_heatmaps
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def plot_heatmaps(data, mis, column_label, cont, topk=30, prefix=''):
cmap = sns.cubehelix_palette(as_cmap=True, light=.9)
m, nv = mis.shape
for j in range(m):
inds = np.argsort(- mis[j, :])[:topk]
if len(inds) >= 2:
plt.clf()
order = np.argsort(cont[:,j])
subdata = data[:, inds][order].T
subdata -= np.nanmean(subdata, axis=1, keepdims=True)
subdata /= np.nanstd(subdata, axis=1, keepdims=True)
columns = [column_label[i] for i in inds]
sns.heatmap(subdata, vmin=-3, vmax=3, cmap=cmap, yticklabels=columns, xticklabels=False, mask=np.isnan(subdata))
filename = '{}/heatmaps/group_num={}.png'.format(prefix, j)
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
plt.title("Latent factor {}".format(j))
plt.yticks(rotation=0)
plt.savefig(filename, bbox_inches='tight')
plt.close('all')
#plot_rels(data[:, inds], map(lambda q: column_label[q], inds), colors=cont[:, j],
# outfile=prefix + '/relationships/group_num=' + str(j), latent=labels[:, j], alpha=0.1)
示例11: plot
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def plot(t, plots, shot_ind):
n = len(plots)
for i in range(0,n):
label, data = plots[i]
plt = py.subplot(n, 1, i+1)
plt.tick_params(labelsize=8)
py.grid()
py.xlim([t[0], t[-1]])
py.ylabel(label)
py.plot(t, data, 'k-')
py.scatter(t[shot_ind], data[shot_ind], marker='*', c='g')
py.xlabel("Time")
py.show()
py.close()
示例12: draw_graph_row
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def draw_graph_row(graphs,
index=0,
contract=True,
n_graphs_per_line=5,
size=4,
xlim=None,
ylim=None,
**args):
"""draw_graph_row."""
dim = len(graphs)
size_y = size
size_x = size * n_graphs_per_line * args.get('size_x_to_y_ratio', 1)
plt.figure(figsize=(size_x, size_y))
if xlim is not None:
plt.xlim(xlim)
plt.ylim(ylim)
else:
plt.xlim(xmax=3)
for i in range(dim):
plt.subplot(1, n_graphs_per_line, i + 1)
graph = graphs[i]
draw_graph(graph,
size=None,
pos=graph.graph.get('pos_dict', None),
**args)
if args.get('file_name', None) is None:
plt.show()
else:
row_file_name = '%d_' % (index) + args['file_name']
plt.savefig(row_file_name,
bbox_inches='tight',
transparent=True,
pad_inches=0)
plt.close()
示例13: saveBEVImageWithAxes
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def saveBEVImageWithAxes(data, outputname, cmap = None, xlabel = 'x [m]', ylabel = 'z [m]', rangeX = [-10, 10], rangeXpx = None, numDeltaX = 5, rangeZ = [7, 62], rangeZpx = None, numDeltaZ = 5, fontSize = 16):
'''
:param data:
:param outputname:
:param cmap:
'''
aspect_ratio = float(data.shape[1])/data.shape[0]
fig = pylab.figure()
Scale = 8
# add +1 to get axis text
fig.set_size_inches(Scale*aspect_ratio+1,Scale*1)
ax = pylab.gca()
#ax.set_axis_off()
#fig.add_axes(ax)
if cmap != None:
pylab.set_cmap(cmap)
#ax.imshow(data, interpolation='nearest', aspect = 'normal')
ax.imshow(data, interpolation='nearest')
if rangeXpx == None:
rangeXpx = (0, data.shape[1])
if rangeZpx == None:
rangeZpx = (0, data.shape[0])
modBev_plot(ax, rangeX, rangeXpx, numDeltaX, rangeZ, rangeZpx, numDeltaZ, fontSize, xlabel = xlabel, ylabel = ylabel)
#plt.savefig(outputname, bbox_inches='tight', dpi = dpi)
pylab.savefig(outputname, dpi = data.shape[0]/Scale)
pylab.close()
fig.clear()
示例14: test_bayesian_mnist
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def test_bayesian_mnist(self):
import pylab as plt
# Create a astroNN neural network instance and set the basic parameter
net = MNIST_BCNN()
net.task = 'classification'
net.callbacks = ErrorOnNaN()
net.max_epochs = 1
# Train the neural network
net.train(x_train, y_train)
net.save('mnist_bcnn_test')
net.plot_dense_stats()
plt.close() # Travis-CI memory error??
net.evaluate(x_test, utils.to_categorical(y_test, 10))
pred, pred_err = net.test(x_test)
test_num = y_test.shape[0]
assert (np.sum(pred == y_test)) / test_num > 0.9 # assert accuracy
net_reloaded = load_folder("mnist_bcnn_test")
net_reloaded.mc_num = 3 # prevent memory issue on Tavis CI
prediction_loaded = net_reloaded.test(x_test[:200])
net_reloaded.folder_name = None # set to None so it can be saved
net_reloaded.save()
load_folder(net_reloaded.folder_name) # ignore pycharm warning, its not None
示例15: explorer
# 需要导入模块: import pylab [as 别名]
# 或者: from pylab import close [as 别名]
def explorer():
for c in range(0, len(captchas), 77):
e = del_line(captchas[c])
pl.figure(c)
for i, p in enumerate(split_pic(e)):
pl.subplot(221+i)
char = e[:, p[0]:p[1]]
y1, y2 = split_y(char)
pl.imshow(regularize(char[y1:y2, :]), cmap=pl.cm.Greys)
pl.show()
if raw_input() == 'q':
pl.close('all')
break