python paperclip_Python pyplot.sca方法代码示例
本文整理匯總了Python中matplotlib.pyplot.sca方法的典型用法代碼示例。如果您正苦于以下問題：Python pyplot.sca方法的具體用法？Python pyplot.sca怎么用？Python pyplot.sca使用的例子？那么恭喜您, 這里精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊matplotlib.pyplot的用法示例。
在下文中一共展示了pyplot.sca方法的27個代碼示例，這些例子默認根據(jù)受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點贊，您的評價將有助于我們的系統(tǒng)推薦出更棒的Python代碼示例。
示例1: dplot_1ch
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def dplot_1ch(d, func, pgrid=True, ax=None,
figsize=(9, 4.5), fignum=None, nosuptitle=False, **kwargs):
"""Plot wrapper for single-spot measurements. Use `dplot` instead."""
global gui_status
if ax is None:
fig = plt.figure(num=fignum, figsize=figsize)
ax = fig.add_subplot(111)
else:
fig = ax.figure
s = d.name
if 'bg_mean' in d:
s += (' BG=%.1fk' % (d.bg_mean[Ph_sel('all')][0] * 1e-3))
if 'T' in d:
s += (u', T=%dμs' % (d.T[0] * 1e6))
if 'mburst' in d:
s += (', #bu=%d' % d.num_bursts[0])
if not nosuptitle:
ax.set_title(s, fontsize=12)
ax.grid(pgrid)
plt.sca(ax)
gui_status['first_plot_in_figure'] = True
func(d, **kwargs)
return ax
開發(fā)者ID:tritemio，項目名稱:FRETBursts，代碼行數(shù):25，
示例2: test_given_colors_levels_and_extends
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def test_given_colors_levels_and_extends():
_, axes = plt.subplots(2, 4)
data = np.arange(12).reshape(3, 4)
colors = ['red', 'yellow', 'pink', 'blue', 'black']
levels = [2, 4, 8, 10]
for i, ax in enumerate(axes.flatten()):
plt.sca(ax)
filled = i % 2 == 0.
extend = ['neither', 'min', 'max', 'both'][i // 2]
if filled:
last_color = -1 if extend in ['min', 'max'] else None
plt.contourf(data, colors=colors[:last_color], levels=levels,
extend=extend)
else:
last_level = -1 if extend == 'both' else None
plt.contour(data, colors=colors, levels=levels[:last_level],
extend=extend)
plt.colorbar()
開發(fā)者ID:miloharper，項目名稱:neural-network-animation，代碼行數(shù):26，
示例3: plot_sub_joint
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_sub_joint(self, func, subsample, **kwargs):
"""Draw a bivariate plot of `x` and `y`.
Parameters
----------
func : plotting callable
This must take two 1d arrays of data as the first two
positional arguments, and it must plot on the "current" axes.
kwargs : key, value mappings
Keyword argument are passed to the plotting function.
Returns
-------
self : JointGrid instance
Returns `self`.
"""
if subsample > 0 and subsample < len(self.x):
indexes = np.random.choice(range(len(self.x)), subsample,
replace=False)
plot_x = np.array([self.x[i] for i in indexes])
plot_y = np.array([self.y[i] for i in indexes])
plt.sca(self.ax_joint)
func(plot_x, plot_y, **kwargs)
else:
plt.sca(self.ax_joint)
func(self.x, self.y, **kwargs)
return self
開發(fā)者ID:Noahs-ARK，項目名稱:idea_relations，代碼行數(shù):27，
示例4: plot_state
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_state(self, ax, x=None, color="b", normalize=True):
""" Plot the current state or a given state vector
Parameters:
-----------
ax: Axes Object
The axes to plot the state on
x: 2x0 array_like[float], optional
A state vector of the dynamics
Returns
-------
ax: Axes Object
The axes with the state plotted
"""
if x is None:
x = self.current_state
if normalize:
x, _ = self.normalize(x)
assert len(
x) == self.n_s, "x needs to have the same number of states as the dynamics"
plt.sca(ax)
ax.plot(x[0], x[1], color=color, marker="o", mew=1.2)
return ax
開發(fā)者ID:befelix，項目名稱:safe-exploration，代碼行數(shù):25，
示例5: plot_figures
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_figures(figures, nrows=1, ncols=1, width_ratios=None):
fig, axeslist = plt.subplots(ncols=ncols, nrows=nrows, gridspec_kw={'width_ratios': width_ratios})
for ind, (title, fig) in enumerate(figures):
axeslist.ravel()[ind].imshow(fig, cmap='gray', interpolation='nearest')
axeslist.ravel()[ind].set_title(title)
if TASK != 'Associative Recall' or ind == 0:
axeslist.ravel()[ind].set_xlabel('Time ------->')
if TASK == 'Associative Recall':
plt.sca(axeslist[1])
plt.xticks([0, 1, 2])
plt.sca(axeslist[2])
plt.xticks([0, 1, 2])
if TASK == 'Copy':
plt.sca(axeslist[1])
plt.yticks([])
plt.tight_layout()
開發(fā)者ID:MarkPKCollier，項目名稱:NeuralTuringMachine，代碼行數(shù):22，
示例6: plot_contours_in_slice
?點贊 6
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_contours_in_slice(self, slice_seg, target_axis):
"""Plots contour around the data in slice (after binarization)"""
plt.sca(target_axis)
contour_handles = list()
for index, label in enumerate(self.unique_labels_display):
binary_slice_seg = slice_seg == index
if not binary_slice_seg.any():
continue
ctr_h = plt.contour(binary_slice_seg,
levels=[cfg.contour_level, ],
colors=(self.color_for_label[index],),
linewidths=cfg.contour_line_width,
alpha=self.alpha_seg,
zorder=cfg.seg_zorder_freesurfer)
contour_handles.append(ctr_h)
return contour_handles
開發(fā)者ID:raamana，項目名稱:visualqc，代碼行數(shù):20，
示例7: matplotformat
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def matplotformat(self, ax, plot_y, plot_name, x_max):
plt.sca(ax)
plot_x = [i * 5 for i in range(len(plot_y))]
plt.xticks(np.linspace(0, x_max, (x_max // 50) + 1, dtype=np.int32))
plt.xlabel('Epochs', fontsize=16)
plt.ylabel('NLL by oracle', fontsize=16)
plt.title(plot_name)
plt.plot(plot_x, plot_y)
開發(fā)者ID:EternalFeather，項目名稱:Generative-adversarial-Nets-in-NLP，代碼行數(shù):10，
示例8: matplotformat
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def matplotformat(self, ax, plot_y, plot_name, x_max):
plt.sca(ax)
plot_x = [i * 5 for i in range(len(plot_y))]
plt.xticks(np.linspace(0, x_max, (x_max // 100) + 1, dtype=np.int32))
plt.xlabel('Epochs', fontsize=16)
plt.ylabel('NLL by oracle', fontsize=16)
plt.title(plot_name)
plt.plot(plot_x, plot_y)
開發(fā)者ID:EternalFeather，項目名稱:Generative-adversarial-Nets-in-NLP，代碼行數(shù):10，
示例9: visualize_predictions
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def visualize_predictions(prediction_seqs, label_seqs, num_classes,
fig_width=6.5, fig_height_per_seq=0.5):
""" Visualize predictions vs. ground truth.
Args:
prediction_seqs: A list of int NumPy arrays, each with shape
`[duration, 1]`.
label_seqs: A list of int NumPy arrays, each with shape `[duration, 1]`.
num_classes: An integer.
fig_width: A float. Figure width (inches).
fig_height_per_seq: A float. Figure height per sequence (inches).
Returns:
A tuple of the created figure, axes.
"""
num_seqs = len(label_seqs)
max_seq_length = max([seq.shape[0] for seq in label_seqs])
figsize = (fig_width, num_seqs*fig_height_per_seq)
fig, axes = plt.subplots(nrows=num_seqs, ncols=1,
sharex=True, figsize=figsize)
for pred_seq, label_seq, ax in zip(prediction_seqs, label_seqs, axes):
plt.sca(ax)
plot_label_seq(label_seq, num_classes, 1)
plot_label_seq(pred_seq, num_classes, -1)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.xlim(0, max_seq_length)
plt.ylim(-2.75, 2.75)
plt.tight_layout()
return fig, axes
開發(fā)者ID:rdipietro，項目名稱:miccai-2016-surgical-activity-rec，代碼行數(shù):35，
示例10: implot
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def implot(im1, im2, im3, im4, im5, im6, im7, im8):
m = 4
n = 2
ims = [im1, im2, im3, im4, im5, im6, im7, im8]
for i in range(m*n):
ax = plt.subplot(m, n, i+1)
plt.sca(ax)
plt.imshow(ims[i])
開發(fā)者ID:wyf2017，項目名稱:DSMnet，代碼行數(shù):10，
示例11: plotChart
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plotChart(self, costList, misRateList, saveFigPath):
'''
繪制錯分率和損失函數(shù)值隨 epoch 變化的曲線。
:param costList: 訓(xùn)練過程中每個epoch的損失函數(shù)列表
:param misRateList: 訓(xùn)練過程中每個epoch的錯分率列表
:return:
'''
# 導(dǎo)入繪圖庫
import matplotlib.pyplot as plt
# 新建畫布
plt.figure('Perceptron Cost and Mis-classification Rate',figsize=(8, 9))
# 設(shè)定兩個子圖和位置關(guān)系
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
# 選擇子圖1并繪制損失函數(shù)值折線圖及相關(guān)坐標(biāo)軸
plt.sca(ax1)
plt.plot(xrange(1, len(costList)+1), costList, '--b*')
plt.xlabel('Epoch No.')
plt.ylabel('Cost')
plt.title('Plot of Cost Function')
plt.grid()
ax1.legend(u"Cost", loc='best')
# 選擇子圖2并繪制錯分率折線圖及相關(guān)坐標(biāo)軸
plt.sca(ax2)
plt.plot(xrange(1, len(misRateList)+1), misRateList, '-r*')
plt.xlabel('Epoch No.')
plt.ylabel('Mis-classification Rate')
plt.title('Plot of Mis-classification Rate')
plt.grid()
ax2.legend(u'Mis-classification Rate', loc='best')
# 顯示圖像并打印和保存
# 需要先保存再繪圖否則相當(dāng)于新建了一張新空白圖像然后保存
plt.savefig(saveFigPath)
plt.show()
################################### PART3 TEST ########################################
# 例子
開發(fā)者ID:ysh329，項目名稱:statistical-learning-methods-note，代碼行數(shù):42，
示例12: plotChart
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plotChart(self, costList, misRateList, saveFigPath):
'''
繪制錯分率和損失函數(shù)值隨 epoch 變化的曲線。
:param costList: 訓(xùn)練過程中每個epoch的損失函數(shù)列表
:param misRateList: 訓(xùn)練過程中每個epoch的錯分率列表
:return:
'''
# 導(dǎo)入繪圖庫
import matplotlib.pyplot as plt
# 新建畫布
plt.figure('Perceptron Cost and Mis-classification Rate', figsize=(8, 9))
# 設(shè)定兩個子圖和位置關(guān)系
ax1 = plt.subplot(211)
ax2 = plt.subplot(212)
# 選擇子圖1并繪制損失函數(shù)值折線圖及相關(guān)坐標(biāo)軸
plt.sca(ax1)
plt.plot(xrange(1, len(costList) + 1), costList, '--b*')
plt.xlabel('Epoch No.')
plt.ylabel('Cost')
plt.title('Plot of Cost Function')
plt.grid()
ax1.legend(u"Cost", loc='best')
# 選擇子圖2并繪制錯分率折線圖及相關(guān)坐標(biāo)軸
plt.sca(ax2)
plt.plot(xrange(1, len(misRateList) + 1), misRateList, '-r*')
plt.xlabel('Epoch No.')
plt.ylabel('Mis-classification Rate')
plt.title('Plot of Mis-classification Rate')
plt.grid()
ax2.legend(u'Mis-classification Rate', loc='best')
# 顯示圖像并打印和保存
# 需要先保存再繪圖否則相當(dāng)于新建了一張新空白圖像然后保存
plt.savefig(saveFigPath)
plt.show()
################################### PART3 TEST ########################################
# 例子
開發(fā)者ID:ysh329，項目名稱:statistical-learning-methods-note，代碼行數(shù):42，
示例13: plot_ellipsoid_2D
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_ellipsoid_2D(p, q, ax, n_points=100, color="r"):
""" Plot an ellipsoid in 2D
TODO: Untested!
Parameters
----------
p: 3x1 array[float]
Center of the ellipsoid
q: 3x3 array[float]
Shape matrix of the ellipsoid
ax: matplotlib.Axes object
Ax on which to plot the ellipsoid
Returns
-------
ax: matplotlib.Axes object
The Ax containing the ellipsoid
"""
plt.sca(ax)
r = nLa.cholesky(q).T; # checks spd inside the function
t = np.linspace(0, 2 * np.pi, n_points);
z = [np.cos(t), np.sin(t)];
ellipse = np.dot(r, z) + p;
handle, = ax.plot(ellipse[0, :], ellipse[1, :], color)
return ax, handle
開發(fā)者ID:befelix，項目名稱:safe-exploration，代碼行數(shù):29，
示例14: plot_ellipsoid_trajectory
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_ellipsoid_trajectory(self, p, q, vis_safety_bounds=True, ax=None,
color="r"):
""" Plot the reachability ellipsoids given in observation space
TODO: Need more principled way to transform ellipsoid to internal states
Parameters
----------
p: n x n_s array[float]
The ellipsoid centers of the trajectory
q: n x n_s x n_s ndarray[float]
The shape matrices of the trajectory
vis_safety_bounds: bool, optional
Visualize the safety bounds of the system
"""
new_ax = False
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
new_ax = True
plt.sca(ax)
n, n_s = np.shape(p)
handles = [None] * n
for i in range(n):
p_i = cas_reshape(p[i, :], (n_s, 1)) + self.p_origin.reshape((n_s, 1))
q_i = cas_reshape(q[i, :], (self.n_s, self.n_s))
ax, handles[i] = plot_ellipsoid_2D(p_i, q_i, ax, color=color)
# ax = plot_ellipsoid_2D(p_i,q_i,ax,color = color)
if vis_safety_bounds:
ax = self.plot_safety_bounds(ax)
if new_ax:
plt.show()
return ax, handles
開發(fā)者ID:befelix，項目名稱:safe-exploration，代碼行數(shù):41，
示例15: plot_ellipsoid_2D
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_ellipsoid_2D(p, q, ax, n_points = 100, color = "r"):
""" Plot an ellipsoid in 2D
TODO: Untested!
Parameters
----------
p: 3x1 array[float]
Center of the ellipsoid
q: 3x3 array[float]
Shape matrix of the ellipsoid
ax: matplotlib.Axes object
Ax on which to plot the ellipsoid
Returns
-------
ax: matplotlib.Axes object
The Ax containing the ellipsoid
"""
plt.sca(ax)
r = nLa.cholesky(q).T; #checks spd inside the function
t = np.linspace(0, 2*np.pi, n_points);
z = [np.cos(t), np.sin(t)];
ellipse = np.dot(r,z) + p;
handle, = ax.plot(ellipse[0,:], ellipse[1,:],color)
return ax, handle
開發(fā)者ID:befelix，項目名稱:safe-exploration，代碼行數(shù):29，
示例16: show_word_scores_heatmap
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def show_word_scores_heatmap(score_tensor_tup, x_ticks, y_ticks, nrows=1, ncols=1, titles=None, figsize=(8, 8), fontsize=14):
def colorbar(mappable):
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="1%", pad=0.1)
return fig.colorbar(mappable, cax=cax)
if not isinstance(score_tensor_tup, tuple):
score_tensor_tup = (score_tensor_tup, )
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)
for idx, ax in enumerate(axs):
score_tensor = score_tensor_tup[idx]
img = ax.matshow(score_tensor.numpy())
plt.sca(ax)
plt.xticks(range(score_tensor.size(1)), x_ticks, fontsize=fontsize)
plt.yticks(range(score_tensor.size(0)), y_ticks, fontsize=fontsize)
if titles is not None:
plt.title(titles[idx], fontsize=fontsize + 2)
colorbar(img)
for ax in axs:
ax.set_aspect('auto')
plt.tight_layout(h_pad=1)
plt.show()
開發(fā)者ID:thunlp，項目名稱:DIAG-NRE，代碼行數(shù):29，
示例17: add_to_plots
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def add_to_plots(plots, input):
FAIL_STEPS = MAX_NUMBER_OF_STEPS_NAVIGATION + 1
environment, mode, result = input
steps = []
success_rate = float(sum([value for value, _, _ in result])) / float(len(result))
print environment, mode, success_rate
for success, length, _ in result:
if success:
steps.append(length)
else:
steps.append(FAIL_STEPS)
steps.sort()
cumulative = {}
for index, step in enumerate(steps):
if step < FAIL_STEPS:
cumulative[step] = float(index + 1) / float(len(steps))
else:
cumulative[step] = success_rate
if environment in plots:
figure, axes = plots[environment]
plt.sca(axes)
else:
figure, axes = plt.subplots()
plots[environment] = figure, axes
sorted_cumulative = sorted(cumulative.items())
# print sorted_cumulative
x = [0] + [value for value, _ in sorted_cumulative] + [FAIL_STEPS]
y = [0] + [value for _, value in sorted_cumulative] + [success_rate]
y = [SUCCESS_SCALING * value for value in y]
plt.plot(x, y, METHOD_TO_COLOR[mode], linewidth=LINEWIDTH, label=METHOD_TO_LEGEND[mode])
plt.title(ENVIRONMENT_TO_PAPER_TITLE[environment], fontsize=TITLE_FONT)
plt.xlabel('Steps', fontsize=AXIS_LABEL_FONT)
if ENVIRONMENT_TO_PAPER_TITLE[environment] in ['Test-1', 'Test-5', 'Val-1']:
plt.ylabel('Success rate', fontsize=AXIS_LABEL_FONT)
plt.axis([0, FAIL_STEPS, 0, 1.0 * SUCCESS_SCALING])
plt.grid(linestyle='dotted')
print ENVIRONMENT_TO_PAPER_TITLE[environment]
if ENVIRONMENT_TO_PAPER_TITLE[environment] in ['Val-3']:
leg = plt.legend(shadow=True, fontsize=LEGEND_FONT, loc='upper left', fancybox=True, framealpha=1.0)
for legobj in leg.legendHandles:
legobj.set_linewidth(LEGEND_LINE_WIDTH)
開發(fā)者ID:nsavinov，項目名稱:SPTM，代碼行數(shù):43，
示例18: identify_images
?點贊 5
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# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def identify_images(zip_file):
"""Interactively identify images from a folder, writing the labels to an array for later training"""
with TemporaryZipDirectory(zip_file) as zfiles:
filepaths = get_files(zfiles, is_dicom)
labels = np.zeros(len(filepaths))
split_val = 25
length = len(filepaths)
rounds = int(math.ceil(length / split_val))
for n in range(rounds):
fig, axes = plt.subplots(5, 5, figsize=(10, 10))
for axis, (idx, fp) in zip(axes.flatten(), enumerate(filepaths[split_val*n:split_val*(n+1)])):
img = load(fp)
plt.sca(axis)
plt.imshow(img, cmap=plt.cm.Greys)
plt.axis('off')
plt.title(idx+split_val*n)
plt.show()
not_done = True
while not_done:
label = input("Input the HU indices as a number or range. E.g. '66' or '25-47'. Type 'done' when finished:")
if label == 'done':
not_done = False
else:
items = label.split('-')
if len(items) > 1:
labels[int(items[0]):int(items[1])] = 1
else:
labels[int(items[0])] = 1
scaled_features = np.zeros((len(filepaths), 10000), dtype=np.float32)
for idx, fp in enumerate(filepaths):
scaled_features[idx, :] = process_image(fp)
dir2write = osp.dirname(zip_file)
np.save(osp.join(dir2write, 'images_' + osp.splitext(osp.basename(zip_file))[0]), scaled_features)
np.save(osp.join(dir2write, 'labels_' + osp.splitext(osp.basename(zip_file))[0]), labels)
os.remove(zip_file)
開發(fā)者ID:jrkerns，項目名稱:pylinac，代碼行數(shù):37，
示例19: _plot_analyzed_subimage
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def _plot_analyzed_subimage(self, subimage: str, show: bool=True, ax: plt.Axes=None):
"""Plot an individual piece of the VMAT analysis.
Parameters
----------
subimage : str
Specifies which image to plot.
show : bool
Whether to actually plot the image.
ax : matplotlib Axes, None
If None (default), creates a new figure to plot to, otherwise plots to the given axes.
"""
plt.ioff()
if ax is None:
fig, ax = plt.subplots()
# plot DMLC or OPEN image
if subimage in (DMLC, OPEN):
if subimage == DMLC:
img = self.dmlc_image
elif subimage == OPEN:
img = self.open_image
ax.imshow(img, cmap=get_dicom_cmap())
self._draw_segments(ax)
plt.sca(ax)
plt.axis('off')
plt.tight_layout()
# plot profile
elif subimage == PROFILE:
dmlc_prof, open_prof = self._median_profiles((self.dmlc_image, self.open_image))
ax.plot(dmlc_prof.values, label='DMLC')
ax.plot(open_prof.values, label='Open')
ax.autoscale(axis='x', tight=True)
ax.legend(loc=8, fontsize='large')
ax.grid()
if show:
plt.show()
開發(fā)者ID:jrkerns，項目名稱:pylinac，代碼行數(shù):41，
示例20: plot_lcurve
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def plot_lcurve(self, ax=None, guides=True):
"""
Make a plot of the data-misfit x regularization values.
The estimated corner value is shown as a blue triangle.
Parameters:
* ax : matplotlib Axes
If not ``None``, will plot the curve on this Axes instance.
* guides : True or False
Plot vertical and horizontal lines across the corner value.
"""
if ax is None:
ax = mpl.gca()
else:
mpl.sca(ax)
x, y = self.dnorm, self.mnorm
if self.loglog:
mpl.loglog(x, y, '.-k')
else:
mpl.plot(x, y, '.-k')
if guides:
vmin, vmax = ax.get_ybound()
mpl.vlines(x[self.corner_], vmin, vmax)
vmin, vmax = ax.get_xbound()
mpl.hlines(y[self.corner_], vmin, vmax)
mpl.plot(x[self.corner_], y[self.corner_], '^b', markersize=10)
mpl.xlabel('Data misfit(data norm)')
mpl.ylabel('Regularization(model norm)')
開發(fā)者ID:igp-gravity，項目名稱:geoist，代碼行數(shù):33，
示例21: test_simple_hinton
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def test_simple_hinton(self):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.sca(ax) # To test the case when ax=None
weight = np.random.randn(20, 20)
plots.hinton(weight, add_legend=True)
return fig
開發(fā)者ID:itdxer，項目名稱:neupy，代碼行數(shù):11，
示例22: add_colorbar
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def add_colorbar(im=None, axes=None, cs=None, label = None, aspect=30, location="right", pad_fraction=1, **kwargs):
"""
Add a colorbar to a plot (im).
Args:
im: plt imshow
label: label of the colorbar
axes:
cs: Contourset
aspect: the higher, the smaller the colorbar is
pad_fraction:
**kwargs:
Returns: A perfect colorbar, no matter the plot.
"""
if axes is None:
axes = im.axes
divider = axes_grid1.make_axes_locatable(axes)
width = axes_grid1.axes_size.AxesY(axes, aspect=2. / aspect)
pad = axes_grid1.axes_size.Fraction(pad_fraction, width)
current_ax = plt.gca()
cax = divider.append_axes(location, size=width, pad=pad)
plt.sca(current_ax)
if cs:
cbar = axes.figure.colorbar(cs, cax=cax, **kwargs)
else:
if im is not None:
cbar = axes.figure.colorbar(im, cax=cax, **kwargs)
cbar.set_label(label)
return cbar
開發(fā)者ID:cgre-aachen，項目名稱:gempy，代碼行數(shù):32，
示例23: add_colorbar
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def add_colorbar(self, im, aspect=20, pad_fraction=1, **kwargs):
"""Add a vertical color bar to an image plot. Source: stackoverflow"""
divider = axes_grid1.make_axes_locatable(im.axes)
width = axes_grid1.axes_size.AxesY(im.axes, aspect=2. / aspect)
pad = axes_grid1.axes_size.Fraction(pad_fraction, width)
current_ax = plt.gca()
cax = divider.append_axes("right", size=width, pad=pad)
plt.sca(current_ax)
return im.axes.figure.colorbar(im, cax=cax, **kwargs)
開發(fā)者ID:cgre-aachen，項目名稱:gempy，代碼行數(shù):11，
示例24: add_colorbar
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def add_colorbar(im, *, aspect=20, pad=0.5, **kwargs):
r"""Add a vertical color bar to a plot.
Parameters
----------
im : ScalarMappable
The output of `sfs.plot2d.amplitude()`, `sfs.plot2d.level()` or any
other `matplotlib.cm.ScalarMappable`.
aspect : float, optional
Aspect ratio of the colorbar. Strictly speaking, since the
colorbar is vertical, it's actually the inverse of the aspect
ratio.
pad : float, optional
Space between image plot and colorbar, as a fraction of the
width of the colorbar.
.. note:: The *pad* argument of
:meth:`matplotlib.figure.Figure.colorbar` has a
slightly different meaning ("fraction of original
axes")!
\**kwargs
All further arguments are forwarded to
:meth:`matplotlib.figure.Figure.colorbar`.
See Also
--------
matplotlib.pyplot.colorbar
"""
ax = im.axes
divider = _axes_grid1.make_axes_locatable(ax)
width = _axes_grid1.axes_size.AxesY(ax, aspect=1/aspect)
pad = _axes_grid1.axes_size.Fraction(pad, width)
current_ax = _plt.gca()
cax = divider.append_axes("right", size=width, pad=pad)
_plt.sca(current_ax)
return ax.figure.colorbar(im, cax=cax, orientation='vertical', **kwargs)
開發(fā)者ID:sfstoolbox，項目名稱:sfs-python，代碼行數(shù):39，
示例25: add_colorbar
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def add_colorbar(im, aspect=20, pad_fraction=0.5,):
"""Add a vertical color bar to an image plot."""
from mpl_toolkits import axes_grid1
divider = axes_grid1.make_axes_locatable(im.axes)
width = axes_grid1.axes_size.AxesY(im.axes, aspect=1./aspect)
pad = axes_grid1.axes_size.Fraction(pad_fraction, width)
current_ax = plt.gca()
cax = divider.append_axes("right", size=width, pad=pad)
plt.sca(current_ax)
return im.axes.figure.colorbar(im, cax=cax)
開發(fā)者ID:bird-house，項目名稱:flyingpigeon，代碼行數(shù):14，
示例26: create_pseudo_random_code
?點贊 5
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def create_pseudo_random_code(clen=10000, rseed=0, verbose=False):
"""
Create waveform files for hfradar
Juha Vierinen
"""
Npt = 200 # number of points to plot, just for plotting, arbitrary
"""
seed is a way of reproducing the random code without having to store all actual codes.
the seed can then act as a sort of station_id.
"""
seed(rseed)
"""
generate a uniform random phase modulated (complex) signal 'sig".
It's single precision floating point for SDR, since DAC is typically <= 16 bits!
"""
sig = np.exp(1j * 2.0 * np.pi * random(clen)).astype("complex64")
if stuffr is not None:
stuffr.plot_cts(sig[:Npt])
if verbose and hist is not None:
fg, ax = subplots(3, 1)
sca(ax[0])
hist(sig.real) # ,50)
sca(ax[1])
hist(sig.imag)
# hist(random(clen))
return sig
開發(fā)者ID:scivision，項目名稱:piradar，代碼行數(shù):33，
示例27: cruise_plot
?點贊 4
?
# 需要導(dǎo)入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import sca [as 別名]
def cruise_plot(sys, t, y, t_hill=5, vref=20, antiwindup=False,
linetype='b-', subplots=[None, None]):
# Figure out the plot bounds and indices
v_min = vref-1.2; v_max = vref+0.5; v_ind = sys.find_output('v')
u_min = 0; u_max = 2 if antiwindup else 1; u_ind = sys.find_output('u')
# Make sure the upper and lower bounds on v are OK
while max(y[v_ind]) > v_max: v_max += 1
while min(y[v_ind]) < v_min: v_min -= 1
# Create arrays for return values
subplot_axes = list(subplots)
# Velocity profile
if subplot_axes[0] is None:
subplot_axes[0] = plt.subplot(2, 1, 1)
else:
plt.sca(subplots[0])
plt.plot(t, y[v_ind], linetype)
plt.plot(t, vref*np.ones(t.shape), 'k-')
plt.plot([t_hill, t_hill], [v_min, v_max], 'k--')
plt.axis([0, t[-1], v_min, v_max])
plt.xlabel('Time $t$ [s]')
plt.ylabel('Velocity $v$ [m/s]')
# Commanded input profile
if subplot_axes[1] is None:
subplot_axes[1] = plt.subplot(2, 1, 2)
else:
plt.sca(subplots[1])
plt.plot(t, y[u_ind], 'r--' if antiwindup else linetype)
plt.plot([t_hill, t_hill], [u_min, u_max], 'k--')
plt.axis([0, t[-1], u_min, u_max])
plt.xlabel('Time $t$ [s]')
plt.ylabel('Throttle $u$')
# Applied input profile
if antiwindup:
# TODO: plot the actual signal from the process?
plt.plot(t, np.clip(y[u_ind], 0, 1), linetype)
plt.legend(['Commanded', 'Applied'], frameon=False)
return subplot_axes
# Define the time and input vectors
開發(fā)者ID:python-control，項目名稱:python-control，代碼行數(shù):47，
注：本文中的matplotlib.pyplot.sca方法示例整理自Github/MSDocs等源碼及文檔管理平臺，相關(guān)代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權(quán)歸原作者所有，傳播和使用請參考對應(yīng)項目的License；未經(jīng)允許，請勿轉(zhuǎn)載。
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