本文整理汇总了Python中matplotlib.image.NonUniformImage类的典型用法代码示例。如果您正苦于以下问题：Python NonUniformImage类的具体用法？Python NonUniformImage怎么用？Python NonUniformImage使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了NonUniformImage类的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: plot_spectrogram
def plot_spectrogram(spec, Xd=(0,1), Yd=(0,1), norm=colo.LogNorm(vmin=0.000001), figname=None):
#
x_min, x_max = Xd
y_min, y_max = Yd
#
fig = plt.figure(num=figname)
nf = len(spec)
for ch, data in enumerate(spec):
#print ch, data.shape
x = np.linspace(x_min, x_max, data.shape[0])
y = np.linspace(y_min, y_max, data.shape[1])
#print x[0],x[-1],y[0],y[-1]
ax = fig.add_subplot(nf*100+11+ch)
im = NonUniformImage(ax, interpolation='bilinear', cmap=cm.gray_r,
norm=norm)
im.set_data(x, y, data.T)
ax.images.append(im)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_title('Channel %d' % ch)
#ax.set_xlabel('timeline')
ax.set_ylabel('frequency')
print 'Statistics: max<%.3f> min<%.3f> mean<%.3f> median<%.3f>' % (data.max(), data.min(), data.mean(), np.median(data))
#
plt.show()示例2: plot_spectrogram
def plot_spectrogram(spec, Xd=(0,1), Yd=(0,1)):
import matplotlib
#matplotlib.use('GTKAgg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.image import NonUniformImage
import matplotlib.colors as colo
#
x_min, x_max = Xd
y_min, y_max = Yd
#
fig = plt.figure()
nf = len(spec)
for ch, data in enumerate(spec):
#print ch, data.shape
x = numpy.linspace(x_min, x_max, data.shape[0])
y = numpy.linspace(y_min, y_max, data.shape[1])
#print x[0],x[-1],y[0],y[-1]
ax = fig.add_subplot(nf*100+11+ch)
im = NonUniformImage(ax, interpolation='bilinear', cmap=cm.gray_r,
norm=colo.LogNorm(vmin=.00001))
im.set_data(x, y, data.T)
ax.images.append(im)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_title('Channel %d' % ch)
#ax.set_xlabel('timeline')
ax.set_ylabel('frequency')
print 'Statistics: max<%.3f> min<%.3f> mean<%.3f> median<%.3f>' % (data.max(), data.min(), data.mean(), numpy.median(data))
#
plt.show()示例3: _update3
def _update3(itr,D,x,soln,t,ax,time):
N = 100
#ax.clear()
buff = 200.0
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
ax.set_xlim((minx-buff/2,maxx+buff/2))
ax.set_ylim((miny-buff/2,maxy+buff/2))
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
ax.images = []
im =NonUniformImage(ax,interpolation='bilinear',
cmap='cubehelix',
extent=(minx,maxx,miny,maxy))
val = soln[itr](zip(xflat,yflat))
val = np.sqrt(np.sum(val**2,1))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
t.set_text('t: %s s' % time[itr])
return ax,t示例4: plotDensity
def plotDensity(ax, x, result):
N = len(result["moments"][0])
y = np.linspace(0, 1, len(result["density"][0]))
z = np.log(1 + np.array(zip(*result["density"])))
im = NonUniformImage(ax, norm=Normalize(0, 5, clip=True), interpolation="nearest", cmap=cm.Greys)
im.set_data(x, y, z)
ax.images.append(im)示例5: _do_plot2
def _do_plot2(x, y, z, fname, min_pitch):
fig = figure(figsize=(15,7.5+7.5/2))
#fig.suptitle('Narmour')
ax = fig.add_subplot(111)
im = NonUniformImage(ax, interpolation=None, extent=(min(x)-1, max(x)+1, min(y)-1, max(y)+1))
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(min(x)-1, max(x)+1)
ax.set_ylim(min(y)-1, max(y)+1)
def format_pitch(i, pos=None):
if int(i) != i: import ipdb;ipdb.set_trace()
return Note(int(i + min_pitch)%12).get_pitch_name()[:-1]
ax.set_xlabel('Segunda nota')
ax.axes.xaxis.set_major_formatter(ticker.FuncFormatter(format_pitch))
ax.axes.xaxis.set_major_locator(ticker.MultipleLocator(base=1.0))
ax.set_ylabel('Primer nota')
ax.axes.yaxis.set_major_formatter(ticker.FuncFormatter(format_pitch))
ax.axes.yaxis.set_major_locator(ticker.MultipleLocator())
cb = plt.colorbar(im)
pylab.grid(True)
pylab.savefig(fname)
pylab.close()示例6: test_nonuniformimage_setdata
def test_nonuniformimage_setdata():
ax = plt.gca()
im = NonUniformImage(ax)
x = np.arange(3, dtype=np.float64)
y = np.arange(4, dtype=np.float64)
z = np.arange(12, dtype=np.float64).reshape((4, 3))
im.set_data(x, y, z)
x[0] = y[0] = z[0, 0] = 9.9
assert im._A[0, 0] == im._Ax[0] == im._Ay[0] == 0, 'value changed'示例7: plot_interpolant
def plot_interpolant(D,interp,x,title='',dim=1,ax=None,scatter=False):
if ax is None:
fig,ax = plt.subplots()
plt.gca().set_aspect('equal', adjustable='box')
ax.set_title(title)
buff = 400.0
N = 150
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
ax.set_xlim((minx-buff,maxx+buff))
ax.set_ylim((miny-buff,maxy+buff))
if dim == 1:
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
points = np.zeros((len(xflat),2))
points[:,0] = xflat
points[:,1] = yflat
val = interp(points)
#val[(np.sqrt(xflat**2+yflat**2) > 6371),:] = 0.0
im =NonUniformImage(ax,interpolation='bilinear',cmap='cubehelix_r',extent=(minx,maxx,miny,maxy))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
if scatter == True:
p = ax.scatter(x[:,0],
x[:,1],
c='gray',edgecolor='none',zorder=2,s=10)
cbar = plt.colorbar(im)
if dim == 2:
ax.quiver(x[::3,0],x[::3,1],interp(x)[::3,0],interp(x)[::3,1],color='gray',scale=4000.0,zorder=20)
return ax示例8: plot_interpolant
def plot_interpolant(D,interp,x,title='figure'):
buff = 100.0
fig,ax = plt.subplots()
plt.gca().set_aspect('equal', adjustable='box')
plt.title(title,fontsize=16)
N = 200
minx = np.min(x[:,0])
maxx = np.max(x[:,0])
miny = np.min(x[:,1])
maxy = np.max(x[:,1])
xitp = np.linspace(minx,maxx,N)
yitp = np.linspace(miny,maxy,N)
xgrid,ygrid = np.meshgrid(xitp,yitp)
xflat = xgrid.flatten()
yflat = ygrid.flatten()
points = np.zeros((len(xflat),2))
points[:,0] = xflat
points[:,1] = yflat
val = interp(points)
val[(np.sqrt(xflat**2+yflat**2) > 6371),:] = 0.0
square = Polygon([(minx-buff,miny-buff),
(minx-buff,maxy+buff),
(maxx+buff,maxy+buff),
(maxx+buff,miny-buff),
(minx-buff,miny-buff)])
#help(D)
im =NonUniformImage(ax,interpolation='bilinear',cmap='cubehelix',extent=(minx,maxx,miny,maxy))
im.set_data(xitp,yitp,np.reshape(val,(N,N)))
ax.images.append(im)
ax.add_artist(PolygonPatch(square.difference(D),alpha=1.0,color='k',zorder=1))
p = ax.scatter(x[:,0],
x[:,1],
c='gray',edgecolor='none',zorder=2,s=10)
cbar = plt.colorbar(im)
cbar.ax.set_ylabel(title)
ax.set_xlim((minx-buff,maxx+buff))
ax.set_ylim((miny-buff,maxy+buff))
#fig.colorbar(p)
return fig示例9: _do_plot
def _do_plot(x, y, z, fname, max_interval, reference_note=None):
fig = figure(figsize=(15,7.5+7.5/2))
#fig.suptitle('Narmour')
ax = fig.add_subplot(111)
im = NonUniformImage(ax, interpolation=None, extent=(min(x), max(x), min(y), max(y)))
im.set_data(x, y, z)
ax.images.append(im)
ax.set_xlim(min(x), max(x))
ax.set_ylim(min(y), max(y))
def format_interval_w_ref_note(reference_note):
def format_interval(i, pos=None):
if int(i) != i: import ipdb;ipdb.set_trace()
return Note(int(reference_note.pitch + i - max_interval-1)%12).get_pitch_name()[:-1]
return format_interval
def format_interval_wo_ref_note(x, pos=None):
if int(x) != x: import ipdb;ipdb.set_trace()
return int(x-max_interval-1)
if reference_note is not None:
format_interval= format_interval_w_ref_note(reference_note)
else:
format_interval= format_interval_wo_ref_note
ax.set_xlabel('Intervalo realizado')
ax.axes.xaxis.set_major_formatter(ticker.FuncFormatter(format_interval_wo_ref_note))
ax.axes.xaxis.set_major_locator(ticker.MultipleLocator(base=1.0))
if reference_note is not None:
ax.set_ylabel('Segunda nota')
else:
ax.set_ylabel('Intervalo implicativo')
ax.axes.yaxis.set_major_formatter(ticker.FuncFormatter(format_interval))
ax.axes.yaxis.set_major_locator(ticker.MultipleLocator())
cb = plt.colorbar(im)
pylab.grid(True)
pylab.savefig(fname)
pylab.close()示例10: execute
def execute(self):
pylab.ioff()
self.figure = pylab.figure()
self.figure.canvas.mpl_connect('motion_notify_event', self.dataPrinter)
x = self.fieldContainer.dimensions[-1].data
y = self.fieldContainer.dimensions[-2].data
xmin=scipy.amin(x)
xmax=scipy.amax(x)
ymin=scipy.amin(y)
ymax=scipy.amax(y)
#Support for images with non uniform axes adapted
#from python-matplotlib-doc/examples/pcolor_nonuniform.py
ax = self.figure.add_subplot(111)
vmin = self.fieldContainer.attributes.get('vmin', None)
vmax = self.fieldContainer.attributes.get('vmax', None)
if vmin is not None:
vmin /= self.fieldContainer.unit
if vmax is not None:
vmax /= self.fieldContainer.unit
if MPL_LT_0_98_1 or self.fieldContainer.isLinearlyDiscretised():
pylab.imshow(self.fieldContainer.maskedData,
aspect='auto',
interpolation='nearest',
vmin=vmin,
vmax=vmax,
origin='lower',
extent=(xmin, xmax, ymin, ymax))
pylab.colorbar(format=F(self.fieldContainer), ax=ax)
else:
im = NonUniformImage(ax, extent=(xmin,xmax,ymin,ymax))
if vmin is not None or vmax is not None:
im.set_clim(vmin, vmax)
im.set_data(x, y, self.fieldContainer.maskedData)
else:
im.set_data(x, y, self.fieldContainer.maskedData)
im.autoscale_None()
ax.images.append(im)
ax.set_xlim(xmin,xmax)
ax.set_ylim(ymin,ymax)
pylab.colorbar(im,format=F(self.fieldContainer), ax=ax)
pylab.xlabel(self.fieldContainer.dimensions[-1].shortlabel)
pylab.ylabel(self.fieldContainer.dimensions[-2].shortlabel)
pylab.title(self.fieldContainer.label)
#ax=pylab.gca()
if self.show:
pylab.ion()
pylab.show()示例11: plot_img
def plot_img(img, filename='image.png', xlim=None, ylim=None, title="", xlabel="", ylabel=""):
#
if not xlim: xlim = (0, img.shape[1] - 1)
if not ylim: ylim = (0, img.shape[0] - 1)
x = numpy.linspace(xlim[0], xlim[1], img.shape[1])
y = numpy.linspace(ylim[0], ylim[1], img.shape[0])
#
fig = plt.figure()
ax = fig.add_subplot(111)
im = NonUniformImage(ax, cmap=cm.Greys)#, norm=colo.LogNorm(vmin=.00001))
im.set_data(x, y, img)
ax.images.append(im)
#
ax.set_xlim(*xlim)
ax.set_ylim(*ylim)
if title: ax.set_title(title)
if xlabel: ax.set_xlabel(xlabel)
if ylabel: ax.set_ylabel(ylabel)
#
plt.show()
plt.savefig(filename)示例12: plot_stacked_time_series_image
def plot_stacked_time_series_image(self, fig, ax, x, y, z, title='', ylabel='',
cbar_title='', title_font={}, axis_font={}, tick_font = {},
**kwargs):
'''
This plot is a stacked time series that uses NonUniformImage with regualrly spaced ydata from
a linear interpolation. Designed to support FRF ADCP data.
'''
if not title_font:
title_font = title_font_default
if not axis_font:
axis_font = axis_font_default
# z = np.ma.array(z, mask=np.isnan(z))
h = NonUniformImage(ax, interpolation='bilinear', extent=(min(x), max(x), min(y), max(y)),
cmap=plt.cm.jet)
h.set_data(x, y, z)
ax.images.append(h)
ax.set_xlim(min(x), max(x))
ax.set_ylim(min(y), max(y))
# h = plt.pcolormesh(x, y, z, shading='gouraud', **kwargs)
# h = plt.pcolormesh(x, y, z, **kwargs)
if ylabel:
ax.set_ylabel(ylabel, **axis_font)
if title:
ax.set_title(title, **title_font)
# plt.axis('tight')
ax.xaxis_date()
date_list = mdates.num2date(x)
self.get_time_label(ax, date_list)
fig.autofmt_xdate()
# if invert:
ax.invert_yaxis()
cbar = plt.colorbar(h)
if cbar_title:
cbar.ax.set_ylabel(cbar_title, **axis_font)
ax.grid(True)
if tick_font:
ax.tick_params(**tick_font)示例13: plot_time_frequency
def plot_time_frequency(spectrum, interpolation='bilinear',
background_color=None, clim=None, dbscale=True, **kwargs):
"""
Time-frequency plot. Modeled after image_nonuniform.py example
spectrum is a dataframe with frequencies in columns and time in rows
"""
if spectrum is None:
return None
times = spectrum.index
freqs = spectrum.columns
if dbscale:
z = 10 * np.log10(spectrum.T)
else:
z = spectrum.T
ax = plt.figure().add_subplot(111)
extent = (times[0], times[-1], freqs[0], freqs[-1])
im = NonUniformImage(ax, interpolation=interpolation, extent=extent)
if background_color:
im.get_cmap().set_bad(kwargs['background_color'])
else:
z[np.isnan(z)] = 0.0 # replace missing values with 0 color
if clim:
im.set_clim(clim)
if 'cmap' in kwargs:
im.set_cmap(kwargs['cmap'])
im.set_data(times, freqs, z)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.images.append(im)
if 'colorbar_label' in kwargs:
plt.colorbar(im, label=kwargs['colorbar_label'])
else:
plt.colorbar(im, label='Power (dB/Hz)')
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
return plt.gcf() 示例14: plot2d
def plot2d(x, y, z, ax=None, cmap='RdGy', norm=None, **kw):
""" Plot dataset using NonUniformImage class
Parameters
----------
x : (nx,)
y : (ny,)
z : (nx,nz)
"""
from matplotlib.image import NonUniformImage
if ax is None:
fig = plt.gcf()
ax = fig.add_subplot(111)
xlim = (x.min(), x.max())
ylim = (y.min(), y.max())
im = NonUniformImage(ax,
interpolation='bilinear',
extent=xlim + ylim,
cmap=cmap)
if norm is not None:
im.set_norm(norm)
im.set_data(x, y, z, **kw)
ax.images.append(im)
#plt.colorbar(im)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
def update(z):
return im.set_data(x, y, z, **kw)
return im, update示例15: abs
scatf1 = abs(fudge*2.0*velocity.value/1.064) # single bounce scatter frequency Virgo Scatter eqn 3
scatf2 = 2.0*scatf1
scatf3 = 3.0*scatf1
scatf4 = 4.0*scatf1
scatf5 = 5.0*scatf1
# print max scatter values and their times
print 'max scatter f1 = ' + str(max(scatf1)) + ' Hz'
tofmax = times[argmax(scatf2)]
tofmaxgps = tofmax + start_time
print 'time of max f2 = ' + str(tofmax) + ' s, GPS=' + str(tofmaxgps)
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(211)
# Plot Spectrogram
if plotspec==1:
im1 = NonUniformImage(ax1, interpolation='bilinear',extent=(min(t),max(t),10,55),cmap='jet')
im1.set_data(t,freq,20.0*log10(Pxx))
if witness_base=="GDS-CALIB_STRAIN":
print "setting color limits for STRAIN"
im1.set_clim(-1000,-800)
elif witness_base=="ASC-AS_B_RF45_Q_YAW_OUT_DQ" or witness_base=="ASC-AS_B_RF36_Q_PIT_OUT_DQ" or witness_base=="ASC-AS_A_RF45_Q_PIT_OUT_DQ" or witness_base=="LSC-MICH_IN1_DQ":
im1.set_clim(-200,20)
elif witness_base == "OMC-LSC_SERVO_OUT_DQ":
im1.set_clim(-240,-85)
ax1.images.append(im1)
#cbar1 = fig.colorbar(im1)
#cbar1.set_clim(-120,-40)
# plot fringe prediction timeseries
#ax1.plot(times,scatf5, c='blue', linewidth='0.2', label='f5')
ax1.plot(times,scatf4, c='purple', linewidth='0.4', label='f4')