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clf()的实例源码
def plot(self,**kwargs):
"""plot landscape (kwargs are passed on to imshow()
Use interpolation='bilinear' or 'bicubic' for a smooth
surface. Default is 'nearest', which shows exact bin
boundaries.
"""
import pylab
kwargs.setdefault('interpolation','nearest')
pylab.clf()
pylab.xlabel('x')
pylab.ylabel('y')
pylab.imshow(self.pmf_masked.T,**kwargs)
pylab.colorbar()
pylab.show()
def display_pr_curve(precision, recall):
# following examples from sklearn
# TODO: f1 operating point
import pylab as plt
# Plot Precision-Recall curve
plt.clf()
plt.plot(recall, precision, label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall example: Max f1={0:0.2f}'.format(max_f1))
plt.legend(loc="lower left")
plt.show()
def plot(self, fontsize=16):
"""Create the barplot from the stats file"""
from sequana.lazy import pylab
from sequana.lazy import pandas as pd
pylab.clf()
df = pd.DataFrame(self._parse_data()['rules'])
ts = df.ix['mean-runtime']
total_time = df.ix['mean-runtime'].sum()
#ts['total'] = self._parse_data()['total_runtime'] / float(self.N)
ts['total'] = total_time
ts.sort_values(inplace=True)
ts.plot.barh(fontsize=fontsize)
pylab.grid(True)
pylab.xlabel("Seconds (s)", fontsize=fontsize)
try:
pylab.tight_layout()
except:
pass
def edgescatter(self, ps):
for ei,X in enumerate(self.edges):
i,j = X[:2]
matchdRA, matchdDec = X[10:12]
mu = X[9]
A = self.alignments[ei]
plt.clf()
if len(matchdRA) > 1000:
plothist(matchdRA, matchdDec, 101)
else:
plt.plot(matchdRA, matchdDec, 'k.', alpha=0.5)
plt.axvline(0, color='0.5')
plt.axhline(0, color='0.5')
plt.axvline(mu[0], color='b')
plt.axhline(mu[1], color='b')
for nsig in [1,2]:
X,Y = A.getContours(nsigma=nsig)
plt.plot(X, Y, 'b-')
plt.xlabel('delta-RA (arcsec)')
plt.ylabel('delta-Dec (arcsec)')
plt.axis('scaled')
ps.savefig()
def plotaffine(aff, RR, DD, exag=1000, affineOnly=False, doclf=True, **kwargs):
import pylab as plt
if doclf:
plt.clf()
if affineOnly:
dr,dd = aff.getAffineOffset(RR, DD)
else:
rr,dd = aff.apply(RR, DD)
dr = rr - RR
dd = dd - DD
#plt.plot(RR, DD, 'r.')
#plt.plot(RR + dr*exag, DD + dd*exag, 'bx')
plt.quiver(RR, DD, exag*dr, exag*dd,
angles='xy', scale_units='xy', scale=1,
pivot='middle', color='b', **kwargs)
#pivot='tail'
ax = plt.axis()
plt.plot([aff.getReferenceRa()], [aff.getReferenceDec()], 'r+', mew=2, ms=5)
plt.axis(ax)
esuf = ''
if exag != 1.:
esuf = ' (x %g)' % exag
plt.title('Affine transformation found' + esuf)
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 show(self):
r"""Display an image of the transfer function
This function loads up matplotlib and displays the current transfer function.
Parameters
----------
Examples
--------
>>> tf = TransferFunction( (-10.0, -5.0) )
>>> tf.add_gaussian(-9.0, 0.01, 1.0)
>>> tf.show()
"""
import pylab
pylab.clf()
pylab.plot(self.x, self.y, 'xk-')
pylab.xlim(*self.x_bounds)
pylab.ylim(0.0, 1.0)
pylab.draw()
def show_mpl(self, im, enhance=True, clear_fig=True):
if self._pylab is None:
import pylab
self._pylab = pylab
if self._render_figure is None:
self._render_figure = self._pylab.figure(1)
if clear_fig: self._render_figure.clf()
if enhance:
nz = im[im > 0.0]
nim = im / (nz.mean() + 6.0 * np.std(nz))
nim[nim > 1.0] = 1.0
nim[nim < 0.0] = 0.0
del nz
else:
nim = im
ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper')
return ax
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 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 plot_2D_heat_map(states,p,labels, inter=False):
import pylab as pl
X = np.unique(states[0,:])
Y = np.unique(states[1,:])
X_len = len(X)
Y_len = len(Y)
Z = np.zeros((X.max()+1,Y.max()+1))
for i in range(len(p)):
Z[states[0,i],states[1,i]] = p[i]
pl.clf()
pl.imshow(Z.T, origin='lower')
pl.xlabel(labels[0])
pl.ylabel(labels[1])
if inter== True:
pl.draw()
else:
pl.show()
def plot_2D_contour(states,p,labels,inter=False):
import pylab as pl
from pyme.statistics import expectation as EXP
exp = EXP((states,p))
X = np.unique(states[0,:])
Y = np.unique(states[1,:])
X_len = len(X)
Y_len = len(Y)
Z = np.zeros((X.max()+1,Y.max()+1))
for i in range(len(p)):
Z[states[0,i],states[1,i]] = p[i]
Z = np.where(Z < 1e-8,0.0,Z)
pl.clf()
XX, YY = np.meshgrid(X,Y)
pl.contour(range(X.max()+1),range(Y.max()+1),Z.T)
pl.axhline(y=exp[1])
pl.axvline(x=exp[0])
pl.xlabel(labels[0])
pl.ylabel(labels[1])
if inter == True:
pl.draw()
else:
pl.show()
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 visualiseNormObject(self):
shape = (2*self.extent, 2*self.extent)
pylab.ion()
pylab.clf()
#pylab.set_cmap("bone")
pylab.hot()
pylab.title("image: %s" % self.fitsFile)
pylab.imshow(np.reshape(self.signPreserveNorm(), shape, order="F"), interpolation="nearest")
pylab.plot(np.arange(0,2*self.extent), self.extent*np.ones((2*self.extent,)), "r--")
pylab.plot(self.extent*np.ones((2*self.extent,)), np.arange(0,2*self.extent), "r--")
pylab.colorbar()
pylab.ylim(-1, 2*self.extent)
pylab.xlim(-1, 2*self.extent)
pylab.xlabel("Pixels")
pylab.ylabel("Pixels")
pylab.show()
def visualiseNormObject(self):
shape = (2*self.extent, 2*self.extent)
pylab.ion()
pylab.clf()
#pylab.set_cmap("bone")
pylab.hot()
pylab.title("image: %s" % self.fitsFile)
pylab.imshow(np.reshape(self.signPreserveNorm(), shape, order="F"), interpolation="nearest")
pylab.plot(np.arange(0,2*self.extent), self.extent*np.ones((2*self.extent,)), "r--")
pylab.plot(self.extent*np.ones((2*self.extent,)), np.arange(0,2*self.extent), "r--")
pylab.colorbar()
pylab.ylim(-1, 2*self.extent)
pylab.xlim(-1, 2*self.extent)
pylab.xlabel("Pixels")
pylab.ylabel("Pixels")
pylab.show()
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 tile_binary_images(x, dir=None, filename="x"):
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(np.clip(x[m], 0, 1), interpolation="none")
pylab.axis("off")
pylab.savefig("{}/{}.png".format(dir, filename))
def plot_z(z, dir=None, filename="z", xticks_range=None, yticks_range=None):
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()
for n in xrange(z.shape[0]):
result = pylab.scatter(z[n, 0], z[n, 1], s=40, marker="o", edgecolors='none')
pylab.xlabel("z1")
pylab.ylabel("z2")
if xticks_range is not None:
pylab.xticks(pylab.arange(-xticks_range, xticks_range + 1))
if yticks_range is not None:
pylab.yticks(pylab.arange(-yticks_range, yticks_range + 1))
pylab.savefig("{}/{}.png".format(dir, filename))
def plot_multiple_rocs_separate(rocList,title='', labels = None, equal_aspect = True):
""" Plot multiples ROC curves as separate at the same painting area. """
pylab.clf()
pylab.title(title)
for ix, r in enumerate(rocList):
ax = pylab.subplot(4,4,ix+1)
pylab.ylim((0,1))
pylab.xlim((0,1))
ax.set_yticklabels([])
ax.set_xticklabels([])
if equal_aspect:
cax = pylab.gca()
cax.set_aspect('equal')
if not labels:
labels = ['' for x in rocList]
pylab.text(0.2,0.1,labels[ix],fontsize=8)
pylab.plot([x[0] for x in r.derived_points],[y[1] for y in r.derived_points], 'r-',linewidth=2)
pylab.show()
def plot(self,title='',include_baseline=False,equal_aspect=True):
""" Method that generates a plot of the ROC curve
Parameters:
title: Title of the chart
include_baseline: Add the baseline plot line if it's True
equal_aspect: Aspects to be equal for all plot
"""
pylab.clf()
pylab.plot([x[0] for x in self.derived_points], [y[1] for y in self.derived_points], self.linestyle)
if include_baseline:
pylab.plot([0.0,1.0], [0.0,1.0],'k-.')
pylab.ylim((0,1))
pylab.xlim((0,1))
pylab.xticks(pylab.arange(0,1.1,.1))
pylab.yticks(pylab.arange(0,1.1,.1))
pylab.grid(True)
if equal_aspect:
cax = pylab.gca()
cax.set_aspect('equal')
pylab.xlabel('1 - Specificity')
pylab.ylabel('Sensitivity')
pylab.title(title)
pylab.show()
def visualize_x(reconstructed_x_batch, image_width=28, image_height=28, image_channel=1, dir=None):
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()
if image_channel == 1:
pylab.gray()
for m in range(100):
pylab.subplot(10, 10, m + 1)
if image_channel == 1:
pylab.imshow(reconstructed_x_batch[m].reshape((image_width, image_height)), interpolation="none")
elif image_channel == 3:
pylab.imshow(reconstructed_x_batch[m].reshape((image_channel, image_width, image_height)), interpolation="none")
pylab.axis("off")
pylab.savefig("%s/reconstructed_x.png" % dir)
