def plot_colormeshmatrix_reduced(
X, Y, ymin = None, ymax = None,
title = "plot_colormeshmatrix_reduced"):
print "plot_colormeshmatrix_reduced X.shape", X.shape, "Y.shape", Y.shape
# input_cols = [i for i in df.columns if i.startswith("X")]
# output_cols = [i for i in df.columns if i.startswith("Y")]
# Xs = df[input_cols]
# Ys = df[output_cols]
# numsamples = df.shape[0]
# print "plot_scattermatrix_reduced: numsamples = %d" % numsamples
# # numplots = Xs.shape[1] * Ys.shape[1]
# # print "numplots = %d" % numplots
cbar_orientation = "vertical" # "horizontal"
gs = gridspec.GridSpec(Y.shape[2], X.shape[2]/2)
pl.ioff()
fig = pl.figure()
fig.suptitle(title)
# # alpha = 1.0 / np.power(numsamples, 1.0/(Xs.shape[1] - 0))
# alpha = 0.2
# print "alpha", alpha
# cols = ["k", "b", "r", "g", "c", "m", "y"]
for i in range(X.shape[2]/2):
for j in range(Y.shape[2]):
# print "i, j", i, j, Xs, Ys
ax = fig.add_subplot(gs[j, i])
pcm = ax.pcolormesh(X[:,:,i], X[:,:,X.shape[2]/2+i], Y[:,:,j], vmin = ymin, vmax = ymax)
# ax.plot(Xs.as_matrix()[:,i], Ys.as_matrix()[:,j], "ko", alpha = alpha)
ax.set_xlabel("goal")
ax.set_ylabel("error")
cbar = fig.colorbar(mappable = pcm, ax=ax, orientation=cbar_orientation)
ax.set_aspect(1)
if SAVEPLOTS:
fig.savefig("fig_%03d_colormeshmatrix_reduced.pdf" % (fig.number), dpi=300)
fig.show()
python类pcolormesh()的实例源码
def _plot_features(out_dir, signal, sampling_rate, logmel, delta, delta_delta, specgram, filename):
try:
os.makedirs(out_dir)
except:
pass
sampling_interval = 1.0 / sampling_rate
times = np.arange(len(signal)) * sampling_interval
pylab.clf()
plt.rcParams['font.size'] = 18
pylab.figure(figsize=(len(signal) / 2000, 16))
ax1 = pylab.subplot(511)
pylab.plot(times, signal)
pylab.title("Waveform")
pylab.xlabel("Time [sec]")
pylab.ylabel("Amplitude")
pylab.xlim([0, len(signal) * sampling_interval])
ax2 = pylab.subplot(512)
specgram = np.log(specgram)
pylab.pcolormesh(np.arange(0, specgram.shape[0]), np.arange(0, specgram.shape[1]) * 8000 / specgram.shape[1], specgram.T, cmap=pylab.get_cmap("jet"))
pylab.title("Spectrogram")
pylab.xlabel("Time [sec]")
pylab.ylabel("Frequency [Hz]")
pylab.colorbar()
ax3 = pylab.subplot(513)
pylab.pcolormesh(np.arange(0, logmel.shape[0]), np.arange(1, 41), logmel.T, cmap=pylab.get_cmap("jet"))
pylab.title("Log mel filter bank features")
pylab.xlabel("Frame")
pylab.ylabel("Filter number")
pylab.colorbar()
ax4 = pylab.subplot(514)
pylab.pcolormesh(np.arange(0, delta.shape[0]), np.arange(1, 41), delta.T, cmap=pylab.get_cmap("jet"))
pylab.title("Deltas")
pylab.xlabel("Frame")
pylab.ylabel("Filter number")
pylab.colorbar()
ax5 = pylab.subplot(515)
pylab.pcolormesh(np.arange(0, delta_delta.shape[0]), np.arange(1, 41), delta_delta.T, cmap=pylab.get_cmap("jet"))
pylab.title("Delta-deltas")
pylab.xlabel("Frame")
pylab.ylabel("Filter number")
pylab.colorbar()
pylab.tight_layout()
pylab.savefig(os.path.join(out_dir, filename), bbox_inches="tight")
def densityPlot(targ_ra, targ_dec, data, iso, g_radius, nbhd, type):
"""Stellar density plot"""
mag_g = data[mag_g_dred_flag]
mag_r = data[mag_r_dred_flag]
if type == 'stars':
filter = star_filter(data)
plt.title('Stellar Density')
elif type == 'galaxies':
filter = galaxy_filter(data)
plt.title('Galactic Density')
elif type == 'blue_stars':
filter = blue_star_filter(data)
plt.title('Blue Stellar Density')
iso_filter = (iso.separation(mag_g, mag_r) < 0.1)
# projection of image
proj = ugali.utils.projector.Projector(targ_ra, targ_dec)
x, y = proj.sphereToImage(data[filter & iso_filter]['RA'], data[filter & iso_filter]['DEC']) # filter & iso_filter
bound = 0.5 #1.
steps = 100.
bins = np.linspace(-bound, bound, steps)
signal = np.histogram2d(x, y, bins=[bins, bins])[0]
sigma = 0.01 * (0.25 * np.arctan(0.25*g_radius*60. - 1.5) + 1.3) # full range, arctan
convolution = scipy.ndimage.filters.gaussian_filter(signal, sigma/(bound/steps))
plt.pcolormesh(bins, bins, convolution.T, cmap='Greys')
plt.xlim(bound, -bound)
plt.ylim(-bound, bound)
plt.gca().set_aspect('equal')
plt.xlabel(r'$\Delta \alpha$ (deg)')
plt.ylabel(r'$\Delta \delta$ (deg)')
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size = '5%', pad=0)
plt.colorbar(cax=cax)
def hessPlot(targ_ra, targ_dec, data, iso, g_radius, nbhd):
"""Hess plot"""
mag_g = data[mag_g_dred_flag]
mag_r = data[mag_r_dred_flag]
filter_s = star_filter(data)
plt.title('Hess')
c1 = SkyCoord(targ_ra, targ_dec, unit='deg')
r_near = 2.*g_radius # annulus begins at 3*g_radius away from centroid
r_far = np.sqrt(5.)*g_radius # annulus has same area as inner area
inner = (c1.separation(SkyCoord(data['RA'], data['DEC'], unit='deg')).deg < g_radius)
outer = (c1.separation(SkyCoord(data['RA'], data['DEC'], unit='deg')).deg > r_near) & (c1.separation(SkyCoord(data['RA'], data['DEC'], unit='deg')).deg < r_far)
xbins = np.arange(-0.5, 1.1, 0.1)
ybins = np.arange(16., 24.5, 0.5)
foreground = np.histogram2d(mag_g[inner & filter_s] - mag_r[inner & filter_s], mag_g[inner & filter_s], bins=[xbins, ybins])
background = np.histogram2d(mag_g[outer & filter_s] - mag_r[outer & filter_s], mag_g[outer & filter_s], bins=[xbins, ybins])
fg = foreground[0].T
bg = background[0].T
fg_abs = np.absolute(fg)
bg_abs = np.absolute(bg)
mask_abs = fg_abs + bg_abs
mask_abs[mask_abs == 0.] = np.nan # mask signficiant zeroes
signal = fg - bg
signal = np.ma.array(signal, mask=np.isnan(mask_abs)) # mask nan
cmap = matplotlib.cm.viridis
cmap.set_bad('w', 1.)
plt.pcolormesh(xbins, ybins, signal, cmap=cmap)
plt.colorbar()
ugali.utils.plotting.drawIsochrone(iso, lw=2, c='k', zorder=10, label='Isocrhone')
plt.axis([-0.5, 1.0, 16, 24])
plt.gca().invert_yaxis()
plt.gca().set_aspect(1./4.)
plt.xlabel('g-r (mag)')
plt.ylabel('g (mag)')
#ax = plt.gca()
#divider = make_axes_locatable(ax)
#cax = divider.append_axes('right', size = '5%', pad=0)
#plt.colorbar(cax=cax)
