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pImagePlots.py
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pImagePlots.py
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import numpy
import pylab
from scipy import fftpack
# For plotting an image in log scale
from matplotlib.colors import LogNorm
from pImage import PImage
class PImagePlots(PImage):
"""Inherits from PImage, which does all the 'math' work, but adds plotting capabilities.
These are separate to make it easier to use PImage from a non-interactive terminal. """
def showXSlice(self, y=None, source='image'):
"""Plot a 1d slice through the image (or fft or psd), at y (if None, defaults to center)."""
if y == None:
y = round(self.ny / 2.0)
if source == 'image':
x = numpy.arange(0, self.nx)
pylab.plot(x, self.image[y][:])
elif source == 'fft':
# have to adjust y for the fact that fimage has 'shifted' axis versus fimage2
# (but xfreq goes with fimage)
y = self.ny/2.0 - y
pylab.plot(self.xfreq, fftpack.ifftshift(self.fimage)[y][:].real, 'k.')
elif source == 'psd':
y = self.ny/2.0 - y
pylab.plot(self.xfreq, fftpack.ifftshift(self.psd2d)[y][:], 'k.')
elif source == 'acovf':
x = numpy.arange(0, self.nx)
pylab.plot(x, self.acovf[y][:])
else:
raise Exception('Source must be one of image/fft/psd/acovf')
return
def showYSlice(self, x=None):
"""Plot a 1d slice through the image, at x (if None, defaults to center)."""
if x == None:
x = round(self.nx/2.0)
if source == 'image':
y = numpy.arange(0, self.ny)
pylab.plot(y, self.image[:][x])
elif source == 'fft':
pylab.plot(self.yfreq, fftpack.ifftshift(self.fimage)[:][x].real, 'k.')
elif source == 'psd':
pylab.plot(self.yfreq, fftpack.ifftshift(self.psd2d)[:][x], 'k.')
elif source == 'acovf':
y = numpy.arange(0, self.ny)
pylab.plot(y, self.acovf[:][x])
else:
raise Exception('Source must be one of image/fft/psd/acovf')
return
def showImage(self, xlim=None, ylim=None, clims=None, cmap=None, copy=False, stats=True):
if copy:
# useful if you're going to apply a hanning filter or something later, but
# want to keep an imge of the original
image = numpy.copy(self.image)
else:
image = self.image
pylab.figure()
pylab.title('Image')
if xlim == None:
x0 = 0
x1 = self.nx
else:
x0 = xlim[0]
x1 = xlim[1]
if ylim == None:
y0 = 0
y1 = self.ny
else:
y0 = ylim[0]
y1 = ylim[1]
if clims == None:
pylab.imshow(image, origin='lower', cmap=cmap)
else:
pylab.imshow(image, origin='lower', vmin=clims[0], vmax=clims[1], cmap=cmap)
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar()
clims = cb.get_clim()
pylab.xlim(x0, x1)
pylab.ylim(y0, y1)
if stats:
statstxt = 'Mean/Stdev/Min/Max:\n %.2f/%.2f/%.2f/%.2f' %(numpy.mean(image), numpy.std(image), image.min(), image.max())
pylab.figtext(0.75, 0.03, statstxt)
return clims
def showImageI(self, xlim=None, ylim=None, clims=None, cmap=None):
pylab.figure()
pylab.title('Reconstructed Image')
if xlim == None:
x0 = 0
x1 = self.nx
else:
x0 = xlim[0]
x1 = xlim[1]
if ylim == None:
y0 = 0
y1 = self.ny
else:
y0 = ylim[0]
y1 = ylim[1]
if clims == None:
pylab.imshow(self.imageI.real, origin='lower', cmap=cmap)
else:
pylab.imshow(self.imageI.real, origin='lower', vmin=clims[0], vmax=clims[1], cmap=cmap)
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar()
pylab.xlim(x0, x1)
pylab.ylim(y0, y1)
return
def showImageAndImageI(self, clims=None, climsI=None, title=None):
shrinkratio=0.6
pylab.figure()
if title!=None:
pylab.suptitle(title)
pylab.subplot(121)
pylab.title('Image')
if clims == None:
pylab.imshow(self.image, origin='lower')
else:
pylab.imshow(self.image, origin='lower', vmin=clims[0], vmax=clims[1])
pylab.colorbar(shrink=shrinkratio)
pylab.xticks(rotation=45)
pylab.subplot(122)
pylab.title('Reconstructed image')
if climsI == None:
pylab.imshow(self.imageI.real, origin='lower')
else:
pylab.imshow(self.imageI.real, origin='lower', vmin=climsI[0], vmax=climsI[1])
pylab.colorbar(shrink=shrinkratio)
pylab.xticks(rotation=45)
return
def showFft(self, real=True, imag=True, clims=None, log=False):
if ((real == True) & (imag==True)):
p = 2
shrinkratio=0.7
else:
p = 1
shrinkratio=0.9
pylab.figure()
if log:
from matplotlib.colors import LogNorm
norml = LogNorm()
if clims!=None:
if (clims[0] <0) | (clims[1]<0):
print 'Using a log scale and clims < 0 does not work ...'
norml = LogNorm(vmin=clims[0], vmax=clims[1])
else:
from matplotlib.colors import Normalize
norml = Normalize()
if clims!=None:
norml = Normalize(vmin=clims[0], vmax=clims[1])
if real:
pylab.subplot(1,p,1)
pylab.title('Real FFT')
pylab.imshow(self.fimage.real, origin='lower', norm=norml,
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xlabel('u')
pylab.ylabel('v')
cb = pylab.colorbar(shrink=shrinkratio)
if imag:
pylab.subplot(1,p,p)
pylab.title('Imaginary FFT')
pylab.imshow(self.fimage.imag, origin='lower', norm=norml,
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xlabel('u')
pylab.ylabel('v')
cb = pylab.colorbar(shrink=shrinkratio)
return
def showFftI(self, real=True, imag=True, clims=None, log=False):
if ((real == True) & (imag==True)):
p = 2
shrinkratio=0.7
else:
p = 1
shrinkratio=0.9
pylab.figure()
if log:
from matplotlib.colors import LogNorm
norml = LogNorm()
if clims!=None:
if (clims[0] <0) | (clims[1]<0):
print 'Using a log scale and clims < 0 does not work ...'
norml = LogNorm(vmin=clims[0], vmax=clims[1])
else:
from matplotlib.colors import Normalize
norml = Normalize()
if clims!=None:
norml = Normalize(vmin=clims[0], vmax=clims[1])
if real:
pylab.subplot(1,p,1)
pylab.title('Reconstructed Real FFT')
pylab.imshow(self.fimageI.real, origin='lower',
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xlabel('u')
pylab.ylabel('v')
cb = pylab.colorbar(shrink=shrinkratio)
if imag:
pylab.subplot(1,p,p)
pylab.title('Reconstructed Imaginary FFT')
if clims == None:
pylab.imshow(self.fimageI.imag, origin='lower',
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
else:
pylab.imshow(self.fimageI.imag, origin='lower', vmin=clims[0], vmax=clims[1],
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xlabel('u')
pylab.ylabel('v')
cb = pylab.colorbar(shrink=shrinkratio)
return
def showPsd2d(self, log=True):
pylab.figure()
pylab.title('2D Power Spectrum')
vmax = self.psd2d.max()
vmin = max(vmax-10e20, self.psd2d.min())
vmin = max(vmin, 0.0001)
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.psd2d, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(), \
self.yfreq.min(), self.yfreq.max()], \
norm=norml)
else:
pylab.imshow(self.psd2d, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(),
self.yfreq.min(), self.yfreq.max()])
cb = pylab.colorbar()
pylab.xlabel('u')
pylab.ylabel('v')
return
def showPhasesI(self):
pylab.figure()
pylab.title('PSD Phases')
pylab.imshow(self.phasespecI, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(),
self.yfreq.min(), self.yfreq.max()])
cb = pylab.colorbar()
pylab.xlabel('u')
pylab.ylabel('v')
return
def showPhases(self):
pylab.figure()
pylab.title('PSD Phases')
pylab.imshow(self.phasespec, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(),
self.yfreq.min(), self.yfreq.max()])
cb = pylab.colorbar()
pylab.xlabel('u')
pylab.ylabel('v')
return
def showPsd2dI(self, log=True):
pylab.figure()
pylab.title('Reconstructed 2D Power Spectrum')
vmax = self.psd2dI.max()
vmin = max(vmax-10e20, self.psd2dI.min())
vmin = max(vmin, 0.0001)
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.psd2dI, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(), \
self.yfreq.min(), self.yfreq.max()], norm=norml)
else:
pylab.imshow(self.psd2dI, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(),
self.yfreq.min(), self.yfreq.max()])
cb = pylab.colorbar()
pylab.xlabel('u')
pylab.ylabel('v')
return
def showPsd1d(self, comparison=None, linear=False, legendlabels=['Image', 'Comparison']):
pylab.figure()
# limit y scale
ymax = self.psd1d.max()
if comparison != None:
ymax = max(ymax, comparison.psd1d.max())
ymin = max(ymax - 10e15, self.psd1d.min())
if comparison != None:
ymin = min(self.psd1d.min(), comparison.psd1d.min())
ymin = max(ymax - 10e15, ymin)
ymin = max(ymin, 1e-12)
xmax = max(self.xcen, self.ycen) - min(self.padx, self.pady)
xmin = 0.5
pylab.subplots_adjust(left=0.15, right=0.9, wspace=0.35, hspace=0.2)
pylab.subplot(121)
pylab.plot(self.rfreq, self.psd1d, 'b-')
if comparison != None:
pylab.plot(comparison.rfreq, comparison.psd1d, 'r-')
pylab.ylim(ymin, ymax)
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
if linear:
pylab.yscale('linear')
pylab.yticks(fontsize='smaller')
pylab.xlabel('Frequency')
pylab.ylabel('1-D Power Spectrum')
pylab.subplot(122)
pylab.plot(self.psdx, self.psd1d, 'b-', label=legendlabels[0])
if comparison != None:
pylab.plot(comparison.psdx, comparison.psd1d, 'r-', label=legendlabels[1])
pylab.xlim(xmin, xmax)
pylab.ylim(ymin, ymax)
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
if linear:
pylab.xscale('linear')
pylab.yscale('linear')
pylab.yticks(fontsize='smaller')
pylab.xlabel('Spatial scale (pix)')
pylab.grid()
if comparison!=None:
pylab.legend(numpoints=1, fancybox=True, loc='lower right', fontsize='smaller')
pylab.suptitle('1D Power Spectrum: min_npix %.0f, min_dr %.2f' %(self.min_npix, self.min_dr))
return
def showAcovf2d(self, real=True, imag=False, clims=None, log=False):
if ((real == True) & (imag==True)):
p = 2
shrinkratio=0.7
else:
p = 1
shrinkratio=0.9
pylab.figure()
if clims != None:
vmin = clims[0]
vmax = clims[1]
if real:
if clims == None:
vmax = self.acovf.max().real
vmin = max(vmax-10e20, self.acovf.min().real)
if log:
vmax = max(vmax, 0.00001)
vmin = max(vmin, 0.000001)
pylab.subplot(1,p,1)
pylab.title('Real ACovF')
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.acovf.real, origin='lower', norm=norml, extent=[0-self.xcen, self.nx-self.xcen,
0-self.ycen, self.ny-self.ycen])
else:
pylab.imshow(self.acovf.real, origin='lower', vmin=vmin, vmax=vmax,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
if imag:
if clims == None:
vmax = self.acovf.max().imag
vmin = max(vmax-10e20, self.acovf.min().imag)
if log:
vmax = max(vmax, 0.00001)
vmin = max(vmin, 0.000001)
pylab.subplot(1,p,p)
pylab.title('Imaginary ACovF')
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.acovf.imag, origin='lower', norm=norml, extent=[-self.xcen, self.nx-self.xcen,
-self.ycen, self.ny-self.ycen])
else:
pylab.imshow(self.acovf.imag, origin='lower', vmin=vmin, vmax=vmax,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
return
def showAcovf2dI(self, real=True, imag=True, clims=None, log=False):
if ((real == True) & (imag==True)):
p = 2
shrinkratio=0.7
else:
p = 1
shrinkratio=0.9
pylab.figure()
if clims != None:
vmin = clims[0]
vmax = clims[1]
if real:
if clims == None:
vmax = self.acovfI.max().real
vmin = max(vmax-10e20, self.acovfI.min().real)
if log:
vmax = max(vmax, 0.00001)
vmin = max(vmin, 0.000001)
pylab.subplot(1,p,1)
pylab.title('Reconstructed Real ACovF')
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.acovfI.real, origin='lower', norm=norml,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
else:
pylab.imshow(self.acovfI.real, origin='lower', vmin=vmin, vmax=vmax,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
if imag:
if clims == None:
vmax = self.acovfI.max().imag
vmin = max(vmax-10e20, self.acovfI.min().imag)
if log:
vmax = max(vmax, 0.00001)
vmin = max(vmin, 0.000001)
pylab.subplot(1,p,p)
pylab.title('Reconstructed Imag. ACovF')
if log==True:
from matplotlib.colors import LogNorm
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.acovfI.imag, origin='lower', norm=norml,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
else:
pylab.imshow(self.acovfI.imag, origin='lower', vmin=vmin, vmax=vmax,
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
return
def showAcovf1d(self, linear=False, comparison=None, legendlabels=['Image', 'Comparison']):
pylab.figure()
try:
pylab.title('1D ACovF: min_npix %.0f, min_dr %.2f' %(self.min_npix, self.min_dr))
except:
pylab.title('1D ACovF')
maxscale_image = (numpy.sqrt((self.nx/2.0 - self.padx)**2 + (self.ny/2.0 - self.pady)**2))
condition = (self.acovfx <= maxscale_image)
pylab.plot(self.acovfx[condition], self.acovf1d[condition], 'b-', label=legendlabels[0])
if comparison != None:
pylab.plot(comparison.acovfx[condition], comparison.acovf1d[condition], 'r-', label=legendlabels[1])
if linear:
pylab.yscale('linear')
else:
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
#pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
pylab.xscale('linear')
pylab.xlabel('Spatial Scale (pix)')
pylab.ylabel('1d ACovF')
pylab.grid()
if comparison!=None:
pylab.legend(numpoints=1, fancybox=True, loc='upper right', fontsize='smaller')
return
def showSf(self, linear=False, comparison=None, legendlabels=['Image', 'Comparison']):
pylab.figure()
pylab.title('1-d SF: min_npix %.0f, min_dr %.2f' %(self.min_npix, self.min_dr))
pylab.plot(self.sfx, self.sf, 'b-', label=legendlabels[0])
if comparison != None:
pylab.plot(comparison.sfx, comparison.sf, 'r-', label=legendlabels[1])
if linear:
pylab.yscale('linear')
else:
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
#pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
pylab.xscale('linear')
pylab.xlabel('Spatial Scale (pix)')
pylab.ylabel('1-d SF')
pylab.grid()
if comparison!=None:
pylab.legend(numpoints=1, fancybox=True, loc='lower right', fontsize='smaller')
return
def plotAll(self, title=None):
pylab.figure()
pylab.subplots_adjust(left=0.1, right=0.97, wspace=0.45, hspace=0.2)
shrinkratio = 0.7
#
ax1 = pylab.subplot2grid((2,3),(0,0))
pylab.imshow(self.image, origin='lower')
pylab.xticks(rotation=45)
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
clims = cb.get_clim()
pylab.title('Image', fontsize=12)
#
ax2 = pylab.subplot2grid((2,3), (1,0))
pylab.imshow(self.fimage.real, origin='lower', vmin=clims[0], vmax=clims[1],
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xticks(rotation=45)
pylab.xlabel('u')
pylab.ylabel('v')
cb = pylab.colorbar(shrink=shrinkratio)
pylab.title('Real FFT', fontsize=12)
#
ax3 = pylab.subplot2grid((2,3), (0, 1))
pylab.imshow(self.acovf.real, origin='lower',
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xticks(rotation=45)
pylab.xlabel('X')
pylab.ylabel('Y')
cb = pylab.colorbar(shrink=shrinkratio)
pylab.title('2D ACovF', fontsize=12)
#
ax4 = pylab.subplot2grid((2,3), (1,1))
vmax = self.psd2d.max()
vmin = max(vmax - 10.e20, self.psd2d.min())
vmin = max(vmin, 0.01)
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.psd2d, origin='lower', extent=[self.xfreq.min(), self.xfreq.max(), \
self.yfreq.min(), self.yfreq.max()], \
norm=norml)
cb = pylab.colorbar(shrink=shrinkratio)
pylab.xticks(rotation=45)
pylab.xlabel('u')
pylab.ylabel('v')
pylab.title('2D PSD', fontsize=12)
#
ax5 = pylab.subplot2grid((2,3),(0,2))
pylab.plot(self.acovfx, self.acovf1d, 'k-')
#pylab.xscale('linear')
pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
pylab.xticks(rotation=45)
pylab.title('1D ACovF', fontsize=12)
#
ax6 = pylab.subplot2grid((2,3), (1,2))
pylab.plot(self.psdx, self.psd1d, 'k-')
pylab.yscale('log', subsy=[2,3,4,5,6,7,8,9])
pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
#pylab.xticks(rotation=45)
#pylab.xlabel('Pix')
pylab.xlabel('1D PSD', fontsize=12)
if title!=None:
pylab.suptitle(title, fontsize=14)
return
def _colorbarTicks(self, vmin, vmax, steps=4., log=False):
if not(log):
stepsize = (vmax-vmin)/float(steps)
ticks = numpy.arange(vmin, vmax+stepsize, stepsize)
cbformat = '%.2f'
if (vmax-vmin) < 0.01:
cbformat = '%.1e'
if log:
vmintmp = numpy.log10(vmin)
vmaxtmp = numpy.log10(vmax)
stepsize = (vmaxtmp - vmintmp)/float(steps)
ticks = numpy.arange(vmintmp, vmaxtmp+stepsize/2.0, stepsize)
ticks = numpy.floor(ticks)
ticks = 10**ticks
cbformat='%.0e'
return ticks, cbformat
def plotMore(self, title=None, useClims=True):
pylab.figure()
pylab.subplots_adjust(left=0.1, right=0.95, bottom=0.1, wspace=0.45, hspace=0.45)
shrinkratio = 0.7
#
ax1 = pylab.subplot2grid((3,3),(0,0))
pylab.imshow(self.image, origin='lower')
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('X / Y', fontsize='smaller')
if useClims:
clims = [self.image.min(), self.image.max()]
ticks, cbformat = self._colorbarTicks(clims[0], clims[1])
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks, format=cbformat)
else:
pylab.colorbar(shrink=shrinkratio)
pylab.title('Image', fontsize=12)
#
ax2 = pylab.subplot2grid((3,3), (0,1))
if useClims:
pylab.imshow(self.fimage.real, origin='lower', vmin=clims[0], vmax=clims[1],
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
else:
pylab.imshow(self.fimage.real, origin='lower',
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('u / v', fontsize='smaller')
if useClims:
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks, format=cbformat)
else:
pylab.colorbar(shrink=shrinkratio)
pylab.title('Real FFT', fontsize=12)
#
ax3 = pylab.subplot2grid((3,3), (0,2))
if useClims:
pylab.imshow(self.fimage.imag, origin='lower', vmin=clims[0], vmax=clims[1],
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
else:
pylab.imshow(self.fimage.imag, origin='lower',
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('u / v', fontsize='smaller')
if useClims:
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks, format=cbformat)
else:
pylab.colorbar(shrink=shrinkratio)
pylab.title('Imag FFT', fontsize=12)
#
ax4 = pylab.subplot2grid((3,3), (1,0))
pylab.imshow(self.phasespec, origin='lower',
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('u / v', fontsize='smaller')
if useClims:
ticks, cbformat = self._colorbarTicks(-numpy.pi, numpy.pi)
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks, format=cbformat)
else:
pylab.colorbar(shrink=shrinkratio)
pylab.title('Phase Spec.', fontsize=12)
#
ax5 = pylab.subplot2grid((3,3), (1,1))
vmax = self.psd2d.max()
vmin = max(vmax - 10.e20, self.psd2d.min())
vmin = max(vmin, 0.0001)
norml = LogNorm(vmin=vmin, vmax=vmax)
pylab.imshow(self.psd2d, origin='lower', norm=norml,
extent=[self.xfreq.min(), self.xfreq.max(), self.yfreq.min(), self.yfreq.max()])
ticks, cbformat = self._colorbarTicks(vmin, vmax, log=True)
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks)
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('u / v', fontsize='smaller')
pylab.title('2D PSD', fontsize=12)
#
ax6 = pylab.subplot2grid((3,3), (1, 2))
pylab.imshow(self.acovf.real, origin='lower',
extent=[-self.xcen, self.nx-self.xcen, -self.ycen, self.ny-self.ycen])
pylab.xticks(rotation=45, fontsize='smaller')
pylab.yticks(fontsize='smaller')
pylab.ylabel('X / Y', fontsize='smaller')
if useClims:
ticks, cbformat = self._colorbarTicks(0, self.acovf.real.max())
cb = pylab.colorbar(shrink=shrinkratio, ticks=ticks, format=cbformat)
else:
pylab.colorbar(shrink=shrinkratio)
pylab.title('2D ACovF', fontsize=12)
#
ax7 = pylab.subplot2grid((3,2), (2,0))
pylab.plot(self.rfreq, self.psd1d, 'k-')
pylab.yscale('log')#, subsy=[2,3,4,5,6,7,8,9])
pylab.xscale('linear')
pylab.xticks(rotation=45)
ylim = pylab.ylim()
ticks, cbformat = self._colorbarTicks(ylim[0], ylim[1], log=True)
pylab.yticks(ticks)
pylab.grid()
pylab.xlabel('u')
pylab.ylabel('1D PSD', fontsize=12)
#
ax8 = pylab.subplot2grid((3,2),(2,1))
pylab.plot(self.acovfx, self.acovf1d, 'k-')
pylab.xscale('linear')
#pylab.xscale('log', subsx=[2,3,4,5,6,7,8,9])
pylab.yscale('log')#, subsy=[2,3,4,5,6,7,8,9])
#pylab.xticks(rotation=45)
ylim = pylab.ylim()
try:
self.hanningFilter = True
vmin = self.acovf1d[self.acovfx > (self.acovfx.max()*4./5.)].mean()
vmin = max(vmin, ylim[0])
except AttributeError:
vmin = ylim[0]
ticks, cbformat = self._colorbarTicks(vmin, ylim[1], log=True)
xmax = max(self.xcen, self.ycen) - min(self.padx, self.pady)
xmin = 0.5
pylab.xlim(xmin, xmax)
pylab.ylim(vmin, ylim[1])
pylab.yticks(ticks)
pylab.grid()
pylab.xlabel('X')
pylab.ylabel('1D ACovF', fontsize=12)
if title!=None:
pylab.suptitle(title, fontsize=14)
return