本文整理汇总了Python中matplotlib.pylab.subplots方法的典型用法代码示例。如果您正苦于以下问题:Python pylab.subplots方法的具体用法?Python pylab.subplots怎么用?Python pylab.subplots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matplotlib.pylab
的用法示例。
在下文中一共展示了pylab.subplots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: test_plotting
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def test_plotting(self):
"""
This test is just to document current use in libraries in case of refactoring
"""
corrs = np.array([.6, .2, .1, .001])
errs = np.array([.1, .05, .04, .0005])
fig, ax1 = plt.subplots(1, 1, figsize=(5, 5))
cca.plot_correlations(corrs, errs, ax=ax1, color='blue')
cca.plot_correlations(corrs * .1, errs, ax=ax1, color='orange')
# Shuffle data
# ...
# fig, ax1 = plt.subplots(1,1,figsize(10,10))
# plot_correlations(corrs, ... , ax=ax1, color='blue')
# plot_correlations(shuffled_coors, ..., ax=ax1, color='red')
# plt.show()
示例2: plot_metrics
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_metrics(metric_list, save_path=None):
# runs through each test case and adds a set of bars to a plot. Saves
f, (ax1) = plt.subplots(1, 1)
plt.grid(True)
print_metrics(metric_list)
bar_metrics(metric_list[0], ax1, index=0)
bar_metrics(metric_list[1], ax1, index=1)
bar_metrics(metric_list[2], ax1, index=2)
if save_path is None:
save_path = "img/bar_" + key + ".png"
plt.savefig(save_path, dpi=400)
示例3: create_figure
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def create_figure(im_size, figsize_max=MAX_FIGURE_SIZE):
""" create an empty figure of image size maximise maximal size
:param tuple(int,int) im_size:
:param float figsize_max:
:return:
>>> fig, ax = create_figure((100, 150))
>>> isinstance(fig, plt.Figure)
True
"""
assert len(im_size) >= 2, 'not valid image size - %r' % im_size
size = np.array(im_size[:2])
fig_size = size[::-1] / float(size.max()) * figsize_max
fig, ax = plt.subplots(figsize=fig_size)
return fig, ax
示例4: plot
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot(self):
try:
import pandas as pd
import matplotlib.pylab as plt
df = pd.DataFrame(self.history).set_index(['id', 'generation']).fillna(0)
population_size = sum(df.iloc[0].values)
n_populations = df.reset_index()['id'].nunique()
fig, axes = plt.subplots(nrows=n_populations, figsize=(12, 2*n_populations),
sharex='all', sharey='all', squeeze=False)
for row, (_, pop) in zip(axes, df.groupby('id')):
ax = row[0]
pop.reset_index(level='id', drop=True).plot(ax=ax)
ax.set_ylim([0, population_size])
ax.set_xlabel('iteration')
ax.set_ylabel('# w/ preference')
if n_populations > 1:
for i in range(0, df.reset_index().generation.max(), 50):
ax.axvline(i)
plt.show()
except ImportError:
print("If you install matplotlib and pandas you will get a pretty plot.")
示例5: plot_trajectory
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_trajectory(name):
STEPS = 600
DELTA = 1 if name != 'linear' else 0.1
trajectory = create_trajectory(name, STEPS)
x = [trajectory.get_position_at(i * DELTA).x for i in range(STEPS)]
y = [trajectory.get_position_at(i * DELTA).y for i in range(STEPS)]
trajectory_fig, trajectory_plot = plt.subplots(1, 1)
trajectory_plot.plot(x, y, label='trajectory', lw=3)
trajectory_plot.set_title(name.title() + ' Trajectory', fontsize=20)
trajectory_plot.set_xlabel(r'$x{\rm[m]}$', fontsize=18)
trajectory_plot.set_ylabel(r'$y{\rm[m]}$', fontsize=18)
trajectory_plot.legend(loc=0)
trajectory_plot.grid()
plt.show()
示例6: plot_alignment_to_numpy
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_alignment_to_numpy(alignment, info=None):
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(alignment, aspect='auto', origin='lower',
interpolation='none')
fig.colorbar(im, ax=ax)
xlabel = 'Decoder timestep'
if info is not None:
xlabel += '\n\n' + info
plt.xlabel(xlabel)
plt.ylabel('Encoder timestep')
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
示例7: train
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def train(self):
"""
训练
"""
training_set,test_set, training_inputs, training_target, test_inputs, test_targets = self.getData()
eth_model = self.buildModel(training_inputs, 1, 20)
training_target = (training_set["eth_Close"][self.window_len:].values /
training_set['eth_Close'][:-self.window_len].values) - 1
eth_history = eth_model.fit(training_inputs, training_target,
epochs=self.epochs, batch_size=self.batch_size,
verbose=self.verbose, shuffle=True)
fig, ax1 = plt.subplots(1, 1)
ax1.plot(eth_history.epoch, eth_history.history['loss'])
ax1.set_title('Training Loss')
ax1.set_ylabel('MAE',fontsize=12)
ax1.set_xlabel('# Epochs',fontsize=12)
plt.show()
示例8: real
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def real(val, outline=None, ax=None, cbar=False, cmap='RdBu', outline_alpha=0.5):
"""Plots the real part of 'val', optionally overlaying an outline of 'outline'
"""
if ax is None:
fig, ax = plt.subplots(1, 1, constrained_layout=True)
vmax = np.abs(val).max()
h = ax.imshow(np.real(val.T), cmap=cmap, origin='lower left', vmin=-vmax, vmax=vmax)
if outline is not None:
ax.contour(outline.T, 0, colors='k', alpha=outline_alpha)
ax.set_ylabel('y')
ax.set_xlabel('x')
if cbar:
plt.colorbar(h, ax=ax)
return ax
示例9: abs
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def abs(val, outline=None, ax=None, cbar=False, cmap='magma', outline_alpha=0.5, outline_val=None):
"""Plots the absolute value of 'val', optionally overlaying an outline of 'outline'
"""
if ax is None:
fig, ax = plt.subplots(1, 1, constrained_layout=True)
vmax = np.abs(val).max()
h = ax.imshow(np.abs(val.T), cmap=cmap, origin='lower left', vmin=0, vmax=vmax)
if outline_val is None and outline is not None: outline_val = 0.5*(outline.min()+outline.max())
if outline is not None:
ax.contour(outline.T, [outline_val], colors='w', alpha=outline_alpha)
ax.set_ylabel('y')
ax.set_xlabel('x')
if cbar:
plt.colorbar(h, ax=ax)
return ax
示例10: test_fields_H
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def test_fields_H(self):
F = fdtd(self.eps_r, dL=self.dL, npml=self.pml)
fig, ax = plt.subplots(figsize=(10, 10))
im = ax.pcolormesh(np.zeros((self.Nx, self.Ny)), cmap='RdBu')
for t_index in range(self.steps):
fields = F.forward(Jx=self.gaussian(t_index))
if t_index % self.skip_rate == 0:
max_E = np.abs(fields['Hz']).max()
im.set_array(fields['Hz'][:, :, 0].ravel())
im.set_clim([-1, 1])
plt.pause(0.001)
ax.set_title('time = {}'.format(t_index))
示例11: plot_gate_outputs_to_numpy
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
fig, ax = plt.subplots(figsize=(12, 3))
ax.scatter(
range(len(gate_targets)), gate_targets, alpha=0.5, color='green', marker='+', s=1, label='target',
)
ax.scatter(
range(len(gate_outputs)), gate_outputs, alpha=0.5, color='red', marker='.', s=1, label='predicted',
)
plt.xlabel("Frames (Green target, Red predicted)")
plt.ylabel("Gate State")
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
示例12: plot_true_and_augmented_data
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_true_and_augmented_data(sample,noised_sample,label,n_examples):
output_dir = os.path.split(FLAGS.output)[0]
# Save augmented data
plt.clf()
fig, ax = plt.subplots(3,1)
for t in range(noised_sample.shape[1]):
ax[t].plot(noised_sample[:,t])
ax[t].set_xlabel('time (samples)')
ax[t].set_ylabel('amplitude')
ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label))
plt.savefig(os.path.join(output_dir, "augmented_data",
'augmented_{:03d}.pdf'.format(n_examples)))
plt.close()
# Save true data
plt.clf()
fig, ax = plt.subplots(3,1)
for t in range(sample.shape[1]):
ax[t].plot(sample[:,t])
ax[t].set_xlabel('time (samples)')
ax[t].set_ylabel('amplitude')
ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label))
plt.savefig(os.path.join(output_dir, "true_data",
'true__{:03d}.pdf'.format(n_examples)))
plt.close()
示例13: plot_losses
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def plot_losses(losses_d, losses_g, filename):
losses_d = np.array(losses_d)
fig, axes = plt.subplots(3, 2, figsize=(8, 8))
axes = axes.flatten()
axes[0].plot(losses_d[:, 0])
axes[1].plot(losses_d[:, 1])
axes[2].plot(losses_d[:, 2])
axes[3].plot(losses_d[:, 3])
axes[4].plot(losses_g)
axes[0].set_title("losses_d")
axes[1].set_title("losses_d_real")
axes[2].set_title("losses_d_fake")
axes[3].set_title("losses_d_gp")
axes[4].set_title("losses_g")
plt.tight_layout()
plt.savefig(filename)
plt.close()
开发者ID:PacktPublishing,项目名称:Hands-On-Generative-Adversarial-Networks-with-Keras,代码行数:19,代码来源:resnet_wgan_gp_cifar10_train.py
示例14: showVector
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def showVector(df,columnName):
# print(df.columns)
#可以显示vecter(polygon,point)数据。show vector
multi=2
fig, ax = plt.subplots(figsize=(14*multi, 8*multi))
df.plot(column=columnName,
categorical=True,
legend=True,
scheme='QUANTILES',
cmap='RdBu', #'OrRd'
ax=ax)
# df.plot()
# adjust legend location
leg = ax.get_legend()
# leg.set_bbox_to_anchor((1.15,0.5))
ax.set_axis_off()
plt.show()
# As provided in the answer by Divakar https://stackoverflow.com/questions/41190852/most-efficient-way-to-forward-fill-nan-values-in-numpy-array
示例15: showVector
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import subplots [as 别名]
def showVector(df,columnName):
# print(df.columns)
#可以显示vecter(polygon,point)数据。show vector
multi=2
fig, ax = plt.subplots(figsize=(14*multi, 8*multi))
df.plot(column=columnName,
categorical=True,
legend=True,
scheme='QUANTILES',
cmap='RdBu', #'OrRd'
ax=ax)
# df.plot()
# adjust legend location
leg = ax.get_legend()
# leg.set_bbox_to_anchor((1.15,0.5))
ax.set_axis_off()
plt.show()
#Spatial_Autocorrelation_for_Areal_Unit_Data
开发者ID:richieBao,项目名称:python-urbanPlanning,代码行数:23,代码来源:Exploratory Spatial Data Analysis in PySAL.py