def drawSmoothCatalog(self, catalog, label=None, **kwargs):
ax = plt.gca()
ra,dec = catalog.ra_dec
x, y = sphere2image(self.ra,self.dec,ra,dec)
delta_x = self.radius/100.
smoothing = 2*delta_x
bins = numpy.arange(-self.radius, self.radius + 1.e-10, delta_x)
h, xbins, ybins = numpy.histogram2d(x, y, bins=[bins, bins])
blur = nd.filters.gaussian_filter(h.T, smoothing / delta_x)
defaults = dict(cmap='gray_r',rasterized=True)
kwargs = dict(defaults.items()+kwargs.items())
xx,yy = np.meshgrid(xbins,ybins)
im = drawProjImage(xx,yy,blur,coord='C',**kwargs)
if label:
plt.text(0.05, 0.95, label, fontsize=10, ha='left', va='top',
color='k', transform=pylab.gca().transAxes,
bbox=dict(facecolor='white', alpha=1., edgecolor='none'))
python类arange()的实例源码
def plot_bar_chart(page, datasets, dataset_labels, dataset_colors, x_group_labels, err=0, title=None, xlabel='Bins', ylabel='Counts'):
assert len(datasets) == len(dataset_colors) == len(dataset_labels)
for dataset in datasets:
assert len(dataset) == len(datasets[0])
assert len(dataset) == len(x_group_labels)
num_x_groups = len(datasets[0])
x_group_locations = pylab.arange(num_x_groups)
width = 1.0 / float(len(datasets)+1)
figure = pylab.figure()
axis = figure.add_subplot(111)
bars = []
for i in xrange(len(datasets)):
bar = axis.bar(x_group_locations + (width*i), datasets[i], width, yerr=err, color=dataset_colors[i], error_kw=dict(ecolor='pink', lw=3, capsize=6, capthick=3))
bars.append(bar)
if title is not None:
axis.set_title(title)
if ylabel is not None:
axis.set_ylabel(ylabel)
if xlabel is not None:
axis.set_xlabel(xlabel)
axis.set_xticks(x_group_locations + width*len(datasets)/2)
x_tick_names = axis.set_xticklabels(x_group_labels)
rot = 0 if num_x_groups == 1 else 15
pylab.setp(x_tick_names, rotation=rot, fontsize=10)
axis.set_xlim(-width, num_x_groups)
y_tick_names = axis.get_yticklabels()
pylab.setp(y_tick_names, rotation=0, fontsize=10)
axis.legend([bar[0] for bar in bars], dataset_labels)
page.savefig()
pylab.close()
def test_dT_max_impact(nmpc, sim, start=0.1, end=0.8, step=0.02, ymax=200,
sample_size=200):
xvals = pylab.arange(start, end + step, step)
f = nmpc.update_dT_max
label = '$\\Delta t_\\mathsf{max}$ (s)'
return test_dT_impact(
xvals, f, nmpc, sim, start, end, step, ymax, sample_size, label)
def test_dT_min_impact(nmpc, sim, start=0.002, end=0.2, step=0.002, ymin=200,
sample_size=20):
xvals = pylab.arange(start, end + step, step)
f = nmpc.update_dT_min
label = '$\\Delta t_\\mathsf{min}$ (s)'
return test_dT_impact(
xvals, f, nmpc, sim, start, end, step, ymin, sample_size, label)
def drawHessDiagram(self,catalog=None):
ax = plt.gca()
if not catalog: catalog = self.get_stars()
r_peak = self.kernel.extension
angsep = ugali.utils.projector.angsep(self.ra, self.dec, catalog.ra, catalog.dec)
cut_inner = (angsep < r_peak)
cut_annulus = (angsep > 0.5) & (angsep < 1.) # deg
mmin, mmax = 16., 24.
cmin, cmax = -0.5, 1.0
mbins = np.linspace(mmin, mmax, 150)
cbins = np.linspace(cmin, cmax, 150)
color = catalog.color[cut_annulus]
mag = catalog.mag[cut_annulus]
h, xbins, ybins = numpy.histogram2d(color, mag, bins=[cbins,mbins])
blur = nd.filters.gaussian_filter(h.T, 2)
kwargs = dict(extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()],
cmap='gray_r', aspect='auto', origin='lower',
rasterized=True, interpolation='none')
ax.imshow(blur, **kwargs)
pylab.scatter(catalog.color[cut_inner], catalog.mag[cut_inner],
c='red', s=7, edgecolor='none')# label=r'$r < %.2f$ deg'%(r_peak))
ugali.utils.plotting.drawIsochrone(self.isochrone, c='b', zorder=10)
ax.set_xlim(-0.5, 1.)
ax.set_ylim(24., 16.)
plt.xlabel(r'$g - r$')
plt.ylabel(r'$g$')
plt.xticks([-0.5, 0., 0.5, 1.])
plt.yticks(numpy.arange(mmax - 1., mmin - 1., -1.))
radius_string = (r'${\rm r}<%.1f$ arcmin'%( 60 * r_peak))
pylab.text(0.05, 0.95, radius_string,
fontsize=10, ha='left', va='top', color='red',
transform=pylab.gca().transAxes,
bbox=dict(facecolor='white', alpha=1., edgecolor='none'))
def drawMembersCMD(self,data):
ax = plt.gca()
if isinstance(data,basestring):
filename = data
data = pyfits.open(filename)[1].data
xmin, xmax = -0.25,0.25
ymin, ymax = -0.25,0.25
mmin, mmax = 16., 24.
cmin, cmax = -0.5, 1.0
mbins = np.linspace(mmin, mmax, 150)
cbins = np.linspace(cmin, cmax, 150)
mag_1 = data[self.config['catalog']['mag_1_field']]
mag_2 = data[self.config['catalog']['mag_2_field']]
x_prob, y_prob = sphere2image(self.ra, self.dec, data['RA'], data['DEC'])
sel = (x_prob > xmin)&(x_prob < xmax) & (y_prob > ymin)&(y_prob < ymax)
sel_prob = data['PROB'][sel] > 5.e-2
index_sort = numpy.argsort(data['PROB'][sel][sel_prob])
plt.scatter(data['COLOR'][sel][~sel_prob], mag_1[sel][~sel_prob],
marker='o',s=2,c='0.75',edgecolor='none')
sc = pylab.scatter(data['COLOR'][sel][sel_prob][index_sort], mag_1[sel][sel_prob][index_sort],
c=data['PROB'][sel][sel_prob][index_sort],
marker='o', s=10, edgecolor='none', cmap='jet', vmin=0., vmax=1)
pylab.xlim(cmin, cmax)
pylab.ylim(mmax, mmin)
pylab.xlabel(r'$g - r$')
pylab.ylabel(r'$g$')
#axes[1].yaxis.set_major_locator(MaxNLocator(prune='lower'))
pylab.xticks([-0.5, 0., 0.5, 1.])
pylab.yticks(numpy.arange(mmax - 1., mmin - 1., -1.))
ugali.utils.plotting.drawIsochrone(self.isochrone, c='k', zorder=10)
pylab.text(0.05, 0.95, r'$\Sigma p_{i} = %i$'%(data['PROB'].sum()),
fontsize=10, horizontalalignment='left', verticalalignment='top', color='k', transform=pylab.gca().transAxes,
bbox=dict(facecolor='white', alpha=1., edgecolor='none'))
divider = make_axes_locatable(pylab.gca())
ax_cb = divider.new_horizontal(size="7%", pad=0.1)
plt.gcf().add_axes(ax_cb)
pylab.colorbar(sc, cax=ax_cb, orientation='vertical', ticks=[0, 0.2, 0.4, 0.6, 0.8, 1.0], label='Membership Probability')
ax_cb.yaxis.tick_right()
def drawDensityProfile(self, catalog=None):
rmax = 24. # arcmin
bins = numpy.arange(0, rmax + 1.e-10, 2.)
centers = 0.5 * (bins[1:] + bins[0:-1])
area = numpy.pi * (bins[1:]**2 - bins[0:-1]**2)
r_peak = self.kernel.extension
stars = self.get_stars()
angsep = ugali.utils.projector.angsep(self.ra, self.dec,
stars.ra, stars.dec)
angsep_arcmin = angsep * 60 # arcmin
cut_iso = self.isochrone_selection(stars)
h = numpy.histogram(angsep_arcmin[(angsep_arcmin < rmax) & cut_iso], bins=bins)[0]
h_out = numpy.histogram(angsep_arcmin[(angsep_arcmin < rmax) & (~cut_iso)], bins=bins)[0]
gals = self.get_galaxies()
if len(gals):
angsep_gal = ugali.utils.projector.angsep(self.ra, self.dec,
gals.ra, gals.dec)
angsep_gal_arcmin = angsep_gal * 60 # arcmin
cut_iso_gal = self.isochrone_selection(gals)
h_gal = np.histogram(angsep_gal_arcmin[(angsep_gal_arcmin < rmax) & cut_iso_gal], bins=bins)[0]
h_gal_out = np.histogram(angsep_gal_arcmin[(angsep_gal_arcmin < rmax) & (~cut_iso_gal)], bins=bins)[0]
plt.plot(centers, h/area, c='red', label='Filtered Stars')
plt.errorbar(centers, h/area, yerr=(numpy.sqrt(h) / area), ecolor='red', c='red')
plt.scatter(centers, h/area, edgecolor='none', c='red', zorder=22)
plt.plot(centers, h_out/area, c='gray', label='Unfiltered Stars')
plt.errorbar(centers, h_out/area, yerr=(numpy.sqrt(h_out) / area), ecolor='gray', c='gray')
plt.scatter(centers, h_out/area, edgecolor='none', c='gray', zorder=21)
if len(gals):
plt.plot(centers, h_gal/area, c='black', label='Filtered Galaxies')
plt.errorbar(centers, h_gal/area, yerr=(numpy.sqrt(h_gal) / area), ecolor='black', c='black')
plt.scatter(centers, h_gal/area, edgecolor='none', c='black', zorder=20)
plt.xlabel('Angular Separation (arcmin)')
plt.ylabel(r'Density (arcmin$^{-2}$)')
plt.xlim(0., rmax)
ymax = pylab.ylim()[1]
#pylab.ylim(0, ymax)
pylab.ylim(0, 12)
pylab.legend(loc='upper right', frameon=False, fontsize=10)
def draw_slices(hist, func=np.sum, **kwargs):
""" Draw horizontal and vertical slices through histogram """
from mpl_toolkits.axes_grid1 import make_axes_locatable
kwargs.setdefault('ls','-')
ax = plt.gca()
data = hist
# Slices
vslice = func(data,axis=0)
hslice = func(data,axis=1)
npix = np.array(data.shape)
#xlim,ylim = plt.array(zip([0,0],npix-1))
xlim = ax.get_xlim()
ylim = ax.get_ylim()
#extent = ax.get_extent()
#xlim =extent[:2]
#ylim = extent[2:]
# Bin centers
xbin = np.linspace(xlim[0],xlim[1],len(vslice))#+0.5
ybin = np.linspace(ylim[0],ylim[1],len(hslice))#+0.5
divider = make_axes_locatable(ax)
#gh2 = pywcsgrid2.GridHelperSimple(wcs=self.header, axis_nums=[2, 1])
hax = divider.append_axes("right", size=1.2, pad=0.05,sharey=ax,
axes_class=axes_divider.LocatableAxes)
hax.axis["left"].toggle(label=False, ticklabels=False)
#hax.plot(hslice, plt.arange(*ylim)+0.5,'-') # Bin center
hax.plot(hslice, ybin, **kwargs) # Bin center
hax.xaxis.set_major_locator(MaxNLocator(4,prune='both'))
hax.set_ylim(*ylim)
#gh1 = pywcsgrid2.GridHelperSimple(wcs=self.header, axis_nums=[0, 2])
vax = divider.append_axes("top", size=1.2, pad=0.05, sharex=ax,
axes_class=axes_divider.LocatableAxes)
vax.axis["bottom"].toggle(label=False, ticklabels=False)
vax.plot(xbin, vslice, **kwargs)
vax.yaxis.set_major_locator(MaxNLocator(4,prune='lower'))
vax.set_xlim(*xlim)
return vax,hax
