def plot(self, func, interp=True, plotter='imshow'):
import matplotlib as mpl
from matplotlib import pylab as pl
if interp:
lpi = self.interpolator(func)
z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
else:
y, x = np.mgrid[
self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
z = func(x, y)
z = np.where(np.isinf(z), 0.0, z)
extent = (self.xrange[0], self.xrange[1], self.yrange[0],
self.yrange[1])
pl.ioff()
pl.clf()
pl.hot() # Some like it hot
if plotter == 'imshow':
pl.imshow(np.nan_to_num(z),
interpolation='nearest',
extent=extent,
origin='lower')
elif plotter == 'contour':
Y, X = np.ogrid[
self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
pl.contour(np.ravel(X), np.ravel(Y), z, 20)
x = self.x
y = self.y
lc = mpl.collections.LineCollection(np.array([
((x[i], y[i]), (x[j], y[j])) for i, j in self.tri.edge_db
]),
colors=[(0, 0, 0, 0.2)])
ax = pl.gca()
ax.add_collection(lc)
if interp:
title = '%s Interpolant' % self.name
else:
title = 'Reference'
if hasattr(func, 'title'):
pl.title('%s: %s' % (func.title, title))
else:
pl.title(title)
pl.show()
pl.ion()
def plot(self, func, interp=True, plotter='imshow'):
import matplotlib as mpl
from matplotlib import pylab as pl
if interp:
lpi = self.interpolator(func)
z = lpi[self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
else:
y, x = np.mgrid[
self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
z = func(x, y)
z = np.where(np.isinf(z), 0.0, z)
extent = (self.xrange[0], self.xrange[1],
self.yrange[0], self.yrange[1])
pl.ioff()
pl.clf()
pl.hot() # Some like it hot
if plotter == 'imshow':
pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent,
origin='lower')
elif plotter == 'contour':
Y, X = np.ogrid[
self.yrange[0]:self.yrange[1]:complex(0, self.nrange),
self.xrange[0]:self.xrange[1]:complex(0, self.nrange)]
pl.contour(np.ravel(X), np.ravel(Y), z, 20)
x = self.x
y = self.y
lc = mpl.collections.LineCollection(
np.array([((x[i], y[i]), (x[j], y[j]))
for i, j in self.tri.edge_db]),
colors=[(0, 0, 0, 0.2)])
ax = pl.gca()
ax.add_collection(lc)
if interp:
title = '%s Interpolant' % self.name
else:
title = 'Reference'
if hasattr(func, 'title'):
pl.title('%s: %s' % (func.title, title))
else:
pl.title(title)
pl.show()
pl.ion()
def decision_boundary_plot(svm, features, vectors, labels, kernel, fileName = None, **args) :
title = None
if 'title' in args :
title = args['title']
xlabel = None
if 'xlabel' in args :
xlabel = args['xlabel']
ylabel = None
if 'ylabel' in args :
ylabel = args['ylabel']
fontsize = 'medium'
if 'fontsize' in args :
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args :
contourFontsize = args['contourFontsize']
showColorbar = True
if 'showColorbar' in args :
showColorbar = args['showColorbar']
show = True
if fileName is not None :
show = False
if 'show' in args :
show = args['show']
# setting up the grid
delta = 0.005
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float_)
gridX = numpy.zeros((len(x) *len(y), 2), numpy.float_)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if kernel.get_name() == 'Linear' and 'customwandb' in args:
kernel.init_optimization_svm(svm)
b=svm.get_bias()
w=kernel.get_w()
kernel.set_w(args['customwandb'][0])
svm.set_bias(args['customwandb'][1])
if kernel.get_name() == 'Linear' and 'drawarrow' in args:
kernel.init_optimization_svm(svm)
b=svm.get_bias()
w=kernel.get_w()
s=1.0/numpy.dot(w,w)/1.17
pylab.arrow(0,-b/w[1], w[0]*s,s*w[1], width=0.01, fc='#dddddd', ec='k')
grid_features = RealFeatures(numpy.transpose(gridX))
results = svm_test(svm, kernel, features, grid_features)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
Z[i][j] = results[n]
n += 1
cdict = {'red' :((0.0, 0.6, 0.6),(0.5, 0.8, 0.8),(1.0, 1.0, 1.0)),
'green':((0.0, 0.6, 0.6),(0.5, 0.8, 0.8),(1.0, 1.0, 1.0)),
'blue' :((0.0, 0.6, 0.6),(0.5, 0.8, 0.8),(1.0, 1.0, 1.0)),
}
my_cmap = matplotlib.colors.LinearSegmentedColormap('lightgray',cdict,256)
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear', origin='lower',
cmap=my_cmap, extent=(xmin,xmax,ymin,ymax) )
if 'decisionboundaryonly' in args:
C1 = pylab.contour(numpy.transpose(Z),
[0],
origin='lower',
linewidths=(3),
colors = ['k'],
extent=(xmin,xmax,ymin,ymax))
else:
C1 = pylab.contour(numpy.transpose(Z),
[-1,0,1],
origin='lower',
linewidths=(1,3,1),
colors = ['k','k'],
extent=(xmin,xmax,ymin,ymax))
pylab.clabel(C1,
inline=1,
fmt='%1.1f',
fontsize=contourFontsize)
# plot the data
lab=labels.get_labels()
vec=numpy.array(vectors)
idx=numpy.where(lab==-1)[0]
pylab.scatter(vec[idx,0], vec[idx,1], s=300, c='#4444ff', marker='o', alpha=0.8, zorder=100)
idx=numpy.where(lab==+1)[0]
pylab.scatter(vec[idx,0], vec[idx,1], s=500, c='#ff4444', marker='s', alpha=0.8, zorder=100)
# plot SVs
if not 'decisionboundaryonly' in args:
training_outputs = svm_test(svm, kernel, features, features)
sv_idx=numpy.where(abs(training_outputs)<=1.01)[0]
pylab.scatter(vec[sv_idx,0], vec[sv_idx,1], s=100, c='k', marker='o', alpha=0.8, zorder=100)
if 'showmovedpoint' in args:
x=-0.779838709677
y=-0.1375
pylab.scatter([x], [y], s=300, c='#4e4e61', marker='o', alpha=1, zorder=100, edgecolor='#454548')
pylab.arrow(x,y-0.1, 0, -0.8/1.5, width=0.01, fc='#dddddd', ec='k')
#pylab.show()
if title is not None :
pylab.title(title, fontsize=fontsize)
if ylabel:
pylab.ylabel(ylabel,fontsize=fontsize)
if xlabel:
pylab.xlabel(xlabel,fontsize=fontsize)
if showColorbar :
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None :
pylab.savefig(fileName)
if show :
pylab.show()
def decision_boundary_plot(svm,
features,
vectors,
labels,
kernel,
fileName=None,
**args):
title = None
if 'title' in args:
title = args['title']
xlabel = None
if 'xlabel' in args:
xlabel = args['xlabel']
ylabel = None
if 'ylabel' in args:
ylabel = args['ylabel']
fontsize = 'medium'
if 'fontsize' in args:
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args:
contourFontsize = args['contourFontsize']
showColorbar = True
if 'showColorbar' in args:
showColorbar = args['showColorbar']
show = True
if fileName is not None:
show = False
if 'show' in args:
show = args['show']
# setting up the grid
delta = 0.005
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float_)
gridX = numpy.zeros((len(x) * len(y), 2), numpy.float_)
n = 0
for i in range(len(x)):
for j in range(len(y)):
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if kernel.get_name() == 'Linear' and 'customwandb' in args:
kernel.init_optimization_svm(svm)
b = svm.get_bias()
w = kernel.get_w()
kernel.set_w(args['customwandb'][0])
svm.set_bias(args['customwandb'][1])
if kernel.get_name() == 'Linear' and 'drawarrow' in args:
kernel.init_optimization_svm(svm)
b = svm.get_bias()
w = kernel.get_w()
s = 1.0 / numpy.dot(w, w) / 1.17
pylab.arrow(0,
-b / w[1],
w[0] * s,
s * w[1],
width=0.01,
fc='#dddddd',
ec='k')
grid_features = RealFeatures(numpy.transpose(gridX))
results = svm_test(svm, kernel, features, grid_features)
n = 0
for i in range(len(x)):
for j in range(len(y)):
Z[i][j] = results[n]
n += 1
cdict = {
'red': ((0.0, 0.6, 0.6), (0.5, 0.8, 0.8), (1.0, 1.0, 1.0)),
'green': ((0.0, 0.6, 0.6), (0.5, 0.8, 0.8), (1.0, 1.0, 1.0)),
'blue': ((0.0, 0.6, 0.6), (0.5, 0.8, 0.8), (1.0, 1.0, 1.0)),
}
my_cmap = matplotlib.colors.LinearSegmentedColormap(
'lightgray', cdict, 256)
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear',
origin='lower',
cmap=my_cmap,
extent=(xmin, xmax, ymin, ymax))
if 'decisionboundaryonly' in args:
C1 = pylab.contour(numpy.transpose(Z), [0],
origin='lower',
linewidths=(3),
colors=['k'],
extent=(xmin, xmax, ymin, ymax))
else:
C1 = pylab.contour(numpy.transpose(Z), [-1, 0, 1],
origin='lower',
linewidths=(1, 3, 1),
colors=['k', 'k'],
extent=(xmin, xmax, ymin, ymax))
pylab.clabel(C1, inline=1, fmt='%1.1f', fontsize=contourFontsize)
# plot the data
lab = labels.get_labels()
vec = numpy.array(vectors)
idx = numpy.where(lab == -1)[0]
pylab.scatter(vec[idx, 0],
vec[idx, 1],
s=300,
c='#4444ff',
marker='o',
alpha=0.8,
zorder=100)
idx = numpy.where(lab == +1)[0]
pylab.scatter(vec[idx, 0],
vec[idx, 1],
s=500,
c='#ff4444',
marker='s',
alpha=0.8,
zorder=100)
# plot SVs
if not 'decisionboundaryonly' in args:
training_outputs = svm_test(svm, kernel, features, features)
sv_idx = numpy.where(abs(training_outputs) <= 1.01)[0]
pylab.scatter(vec[sv_idx, 0],
vec[sv_idx, 1],
s=100,
c='k',
marker='o',
alpha=0.8,
zorder=100)
if 'showmovedpoint' in args:
x = -0.779838709677
y = -0.1375
pylab.scatter([x], [y],
s=300,
c='#4e4e61',
marker='o',
alpha=1,
zorder=100,
edgecolor='#454548')
pylab.arrow(x,
y - 0.1,
0,
-0.8 / 1.5,
width=0.01,
fc='#dddddd',
ec='k')
#pylab.show()
if title is not None:
pylab.title(title, fontsize=fontsize)
if ylabel:
pylab.ylabel(ylabel, fontsize=fontsize)
if xlabel:
pylab.xlabel(xlabel, fontsize=fontsize)
if showColorbar:
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None:
pylab.savefig(fileName)
if show:
pylab.show()
#% For any number of bars beyond 4, just make up random colors.
if numberOfBars > 4.:
barColorMap[4:numberOfBars,0:3.] = np.random.rand((numberOfBars-4.), 3.)
elif button == 2.:
#% Example of using colormap with random colors
barColorMap = np.random.rand(numberOfBars, 3.)
elif button == 3.:
#% Example of using pre-defined jet colormap
barColorMap = plt.jet(numberOfBars)
elif button == 4.:
#% Example of using pre-defined Hot colormap
barColorMap = plt.hot(numberOfBars)
else:
#% Example of using pre-defined lines colormap
barColorMap = lines(numberOfBars)
#% Plot each number one at a time, calling bar() for each y value.
for b in np.arange(1., (numberOfBars)+1):
#% Plot one single bar as a separate bar series.
#% Fancy up the graph.
plt.grid(on)
caption = sprintf('Data plotted in %d barseries, each with a different color', length(y))
plt.title(caption, 'FontSize', fontSize)
plt.xlabel('x', 'FontSize', fontSize)
def decisionSurface(classifier, fileName=None, **args):
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args:
numContours = args['numContours']
title = None
if 'title' in args:
title = args['title']
markersize = 5
fontsize = 'medium'
if 'markersize' in args:
markersize = args['markersize']
if 'fontsize' in args:
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args:
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args:
showColorbar = args['showColorbar']
show = True
if fileName is not None:
show = False
if 'show' in args:
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args:
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) * len(y), 2), numpy.float)
n = 0
for i in range(len(x)):
for j in range(len(y)):
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container:
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else:
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)):
for j in range(len(y)):
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear',
origin='lower',
cmap=pylab.cm.gray,
extent=(xmin, xmax, ymin, ymax))
if numContours == 1:
C = pylab.contour(numpy.transpose(Z), [0],
origin='lower',
linewidths=(3),
colors='black',
extent=(xmin, xmax, ymin, ymax))
elif numContours == 3:
C = pylab.contour(numpy.transpose(Z), [-1, 0, 1],
origin='lower',
linewidths=(1, 3, 1),
colors='black',
extent=(xmin, xmax, ymin, ymax))
else:
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin, xmax, ymin, ymax))
pylab.clabel(C, inline=1, fmt='%1.1f', fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None:
pylab.title(title, fontsize=fontsize)
if showColorbar:
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None:
pylab.savefig(fileName)
if show:
pylab.show()
def decisionSurface(classifier, fileName = None, **args) :
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args :
numContours = args['numContours']
title = None
if 'title' in args :
title = args['title']
markersize=5
fontsize = 'medium'
if 'markersize' in args :
markersize = args['markersize']
if 'fontsize' in args :
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args :
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args :
showColorbar = args['showColorbar']
show = True
if fileName is not None :
show = False
if 'show' in args :
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args :
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) *len(y), 2), numpy.float)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container :
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else :
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear', origin='lower',
cmap=pylab.cm.gray, extent=(xmin,xmax,ymin,ymax) )
if numContours == 1 :
C = pylab.contour(numpy.transpose(Z),
[0],
origin='lower',
linewidths=(3),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
elif numContours == 3 :
C = pylab.contour(numpy.transpose(Z),
[-1,0,1],
origin='lower',
linewidths=(1,3,1),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
else :
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin,xmax,ymin,ymax))
pylab.clabel(C,
inline=1,
fmt='%1.1f',
fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None :
pylab.title(title, fontsize=fontsize)
if showColorbar :
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None :
pylab.savefig(fileName)
if show :
pylab.show()
x = np.linspace(0, 20)
plt.plot(x, J(0, x), label='J0')
plt.plot(x, J(1, x), label='J1')
plt.plot(x, J(2, x), label='J2')
plt.legend(("$J_0$", "$J_1$", "$J_2$"))
plt.show()
# part(b)
lamda = 0.5
k = 2 * np.pi / lamda
r = np.linspace(0, 1e-6)
x, y = np.mgrid[-1:1:100j, -1:1:100j]
# print(x, y)
r = np.sqrt(x**2 + y**2)
I = (J(1, r * k) / k / r)**2
plt.imshow(I, vmax=0.01)
plt.hot()
plt.title(
" Density plot of the intensity of the circular diffraction pattern of a point light source"
)
plt.show()
plt.show()
def main(self, fname=None, show=False,
figurename=None, save=False, along_orbit=False, set_cmap=True):
# if len(a.digitization_list) < 100:
# return
if set_cmap:
plt.hot()
fig = plt.figure(figsize=(8.27, 11.69), dpi=70)
n = 8 + 4 + 1 + 1 + 1
hr = np.ones(n)
hr[-4:] = 0.5
g = mpl.gridspec.GridSpec(n,1, hspace=0.1, height_ratios=hr,
bottom=0.06, right=0.89)
axes = []
prev = None
for i in range(n):
axes.append(plt.subplot(g[i], sharex=prev))
axes[i].set_xlim(self.extent[0], self.extent[1])
axes[i].yaxis.set_major_locator(
mpl.ticker.MaxNLocator(prune='upper', nbins=5,
steps=[1,2,5,10]))
l = celsius.SpiceetLocator()
axes[i].xaxis.set_major_locator(l)
axes[i].xaxis.set_major_formatter(
celsius.SpiceetFormatter(locator=l))
prev = axes[-1]
axit = iter(axes)
self.plot_aspera_ima(ax=next(axit), inverted=False)
self.plot_aspera_els(ax=next(axit))
self.plot_mod_b(ax=next(axit))
self.plot_ne(ax=next(axit))
self.plot_timeseries(ax=next(axit))
self.plot_frequency_range(ax=next(axit), f_min=0.0, f_max=0.2,
colorbar=True)
# self.plot_frequency_range(ax=axit.next(), f_min=0.2, f_max=0.5)
self.plot_frequency(ax=next(axit), f=0.5)
# self.plot_frequency(ax=axit.next(), f=0.75)
self.plot_frequency(ax=next(axit), f=1.)
# self.plot_frequency(ax=axit.next(), f=1.52)
self.plot_frequency(ax=next(axit), f=2.)
self.plot_tec(ax=next(axit))
# twx = plt.twinx()
# t = mex.sub_surface.read_tec(self.start_time, self.finish_time)
# good = t['FLAG'] == 1
# plt.plot(t['EPHEMERIS_TIME'][good], t['TEC'][good], 'k.', mew=0.)
# plt.ylabel(r'$TEC / m^{-2}$')
# plt.yscale('log')
# plt.ylim(3E13, 2E16)
# self.plot_profiles(ax=axit.next())
# self.plot_profiles_delta(ax=axit.next())
# self.plot_peak_altitude(ax=axit.next())
# self.plot_peak_density(ax=axit.next())
self.plot_profiles(ax=next(axit))
# self.plot_tec(ax=axit.next())
self.plot_altitude(ax=next(axit))
self.plot_lat(ax=next(axit))
self.plot_lon(ax=next(axit))
self.plot_sza(ax=next(axit))
# axes[-1].xaxis.set_major_formatter(celsius.SpiceetFormatter())
for i in range(n-1):
plt.setp( axes[i].get_xticklabels(), visible=False )
axes[i].set_xlim(self.extent[0], self.extent[1])
l = celsius.SpiceetLocator()
axes[i].xaxis.set_major_locator(l)
axes[i].xaxis.set_major_formatter(
celsius.SpiceetFormatter(locator=l))
plt.annotate("Orbit %d, plot start: %s, newest digitization: %s" % (
self.orbit,
celsius.spiceet_to_utcstr(self.extent[0],fmt='C')[0:17], self._newest),
(0.5, 0.93), xycoords='figure fraction', ha='center')
if save:
if figurename is None:
fname = mex.locate_data_directory() + ('ais_plots/v0.9/%05d/%d.pdf' % ((self.orbit // 1000) * 1000, self.orbit))
else:
fname = figurename
print('Writing %s' % fname)
d = os.path.dirname(fname)
if not os.path.exists(d) and d:
os.makedirs(d)
plt.savefig(fname)
if show:
plt.show()
else:
plt.close(fig)
plt.close('all')
if along_orbit:
# fig = plt.figure()
fig, ax = plt.subplots(2, 1, squeeze=True, figsize=(4,4), dpi=70, num=plt.gcf().number + 1)
plt.subplots_adjust(hspace=0.3,wspace=0.0, right=0.85)
self.density_along_orbit(ax[0], vmax=4.)
self.modb_along_orbit(ax[1], vmax=100.)
if save:
fname = mex.locate_data_directory() + ('ais_plots/A0_v0.9/%05d/%d.pdf' % ((self.orbit // 1000) * 1000, self.orbit))
print('Writing %s' % fname)
d = os.path.dirname(fname)
if not os.path.exists(d):
os.makedirs(d)
plt.savefig(fname)
if show:
plt.show()
else:
plt.close(fig)
plt.close('all')
header['CRPIX2'] -= header['NAXIS2'] / 2 - n / 2 # correct for wrong centering #header['CRPIX1'] += 7 #header['CRPIX2'] += 7 # header['NAXIS1'] = n header['NAXIS2'] = n # reload data with new header tod, projection, header, obs = csh.load_data(filenames, header=header) # get weighted backprojection bpj = projection.transpose(tod) weights = projection.transpose(tod.ones(tod.shape)) pipe = bpj / weights # get the model dense matrix P = lo.aslinearoperator(projection.aslinearoperator()) C = csh.averaging(tod.shape, factor) M = C * P Md = M.todense() MtMd = (M.T * M).todense() Pd = P.todense() PtPd = (P.T * P).todense() plt.imshow(MtMd, interpolation="nearest") a = plt.gca() a.set_xticks(()) a.set_yticks(()) plt.show() plt.hot()
#do_regular_survey(copy.deepcopy(routes),Preal,P,M,S,X,D,opts)
# plot the results - regular survey
path_colours = ['b','g','b']
path_linestyles = ['-','--','-.']
pylab.figure()
pylab.matshow(Preal[:,:,1],fignum=0)
for g in range(opts['num_groups']):
pylab.plot(routes[g][:,1],routes[g][:,0],c=path_colours[g],ls=path_linestyles[g],linewidth=2)
for k in range(opts['stops_per_group']):
pylab.plot(survey_locations_by_group_reg[g][k][1],survey_locations_by_group_reg[g][k][0],
ls='None',marker='o',color=path_colours[g],markersize=10,markeredgewidth=2)
pylab.title(r'$w(I(x))=1$')
pylab.hot()
ax = pylab.gca()
ax.set_xlim(0,100)
ax.set_ylim(100,0)
ax.set_xticks([])
ax.set_yticks([])
if save_figures:
pylab.gca().set_xticks([])
pylab.gca().set_yticks([])
pylab.gcf().set_figwidth(3.5)
pylab.gcf().set_figheight(3.5)
pylab.savefig('locations-regular.pdf', bbox_inches='tight')
# plot the results - optimised survey
