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python paperclip_Python pyplot.sca方法代码示例

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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(jìn)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(gè)代碼示例，這些例子默認(rèn)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(diǎn)贊，您的評(píng)價(jià)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(diǎn)贊 6

# 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(xiàng)目名稱:FRETBursts，代碼行數(shù):25，

示例2: test_given_colors_levels_and_extends

?點(diǎn)贊 6

# 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(xiàng)目名稱:neural-network-animation，代碼行數(shù):26，

示例3: plot_sub_joint

?點(diǎn)贊 6

# 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(xiàng)目名稱:idea_relations，代碼行數(shù):27，

示例4: plot_state

?點(diǎn)贊 6

# 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(xiàng)目名稱:safe-exploration，代碼行數(shù):25，

示例5: plot_figures

?點(diǎn)贊 6

# 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(xiàng)目名稱:NeuralTuringMachine，代碼行數(shù):22，

示例6: plot_contours_in_slice

?點(diǎn)贊 6

# 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(xiàng)目名稱:visualqc，代碼行數(shù):20，

示例7: matplotformat

?點(diǎn)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 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(xiàng)目名稱:Generative-adversarial-Nets-in-NLP，代碼行數(shù):10，

示例8: matplotformat

?點(diǎn)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 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(xiàng)目名稱:Generative-adversarial-Nets-in-NLP，代碼行數(shù):10，

示例9: visualize_predictions

?點(diǎn)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 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(xiàng)目名稱:miccai-2016-surgical-activity-rec，代碼行數(shù):35，

示例10: implot

?點(diǎn)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 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(xiàng)目名稱:DSMnet，代碼行數(shù):10，

示例11: plotChart

?點(diǎn)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 plotChart(self, costList, misRateList, saveFigPath):

'''

繪制錯(cuò)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(gè)epoch的損失函數(shù)列表

:param misRateList: 訓(xùn)練過程中每個(gè)epoch的錯(cuò)分率列表

:return:

'''

# 導(dǎo)入繪圖庫(kù)

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(gè)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(cuò)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(xiàng)目名稱:statistical-learning-methods-note，代碼行數(shù):42，

示例12: plotChart

?點(diǎn)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 plotChart(self, costList, misRateList, saveFigPath):

'''

繪制錯(cuò)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(gè)epoch的損失函數(shù)列表

:param misRateList: 訓(xùn)練過程中每個(gè)epoch的錯(cuò)分率列表

:return:

'''

# 導(dǎo)入繪圖庫(kù)

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(gè)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(cuò)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(xiàng)目名稱:statistical-learning-methods-note，代碼行數(shù):42，

示例13: plot_ellipsoid_2D

?點(diǎn)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_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(xiàng)目名稱:safe-exploration，代碼行數(shù):29，

示例14: plot_ellipsoid_trajectory

?點(diǎn)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_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(xiàng)目名稱:safe-exploration，代碼行數(shù):41，

示例15: plot_ellipsoid_2D

?點(diǎn)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_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(xiàng)目名稱:safe-exploration，代碼行數(shù):29，

示例16: show_word_scores_heatmap

?點(diǎn)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(xiàng)目名稱:DIAG-NRE，代碼行數(shù):29，

示例17: add_to_plots

?點(diǎn)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(xiàng)目名稱:SPTM，代碼行數(shù):43，

示例18: identify_images

?點(diǎn)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 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(xiàng)目名稱:pylinac，代碼行數(shù):37，

示例19: _plot_analyzed_subimage

?點(diǎn)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(xiàng)目名稱:pylinac，代碼行數(shù):41，

示例20: plot_lcurve

?點(diǎn)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(xiàng)目名稱:geoist，代碼行數(shù):33，

示例21: test_simple_hinton

?點(diǎn)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(xiàng)目名稱:neupy，代碼行數(shù):11，

示例22: add_colorbar

?點(diǎn)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(xiàng)目名稱:gempy，代碼行數(shù):32，

示例23: add_colorbar

?點(diǎn)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(xiàng)目名稱:gempy，代碼行數(shù):11，

示例24: add_colorbar

?點(diǎn)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`.

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