def plot_ratio(df,
bins=[np.linspace(0, .25, 100),
np.linspace(0, 1, 100)],
alpha=1,
cmap=None,
mc_field='mc',
stdc_field='stdc',
response_field='response'):
C, M = plt.meshgrid(*bins)
resp1 = plt.histogram2d(
df[df.loc[:, response_field] == 1].loc[:, stdc_field].values,
df[df.loc[:, response_field] == 1].loc[:, mc_field].values,
bins=bins)[0] + 1
#resp1[resp1==1] = np.nan
resp2 = plt.histogram2d(
df[df.loc[:, response_field] == -1].loc[:, stdc_field].values,
df[df.loc[:, response_field] == -1].loc[:, mc_field].values,
bins=bins)[0] + 1
#resp2[resp2==1] = np.nan
resp1 = resp1.astype(float) / np.nansum(resp1)
resp2 = resp2.astype(float) / np.nansum(resp2)
plane = np.log(resp1 / resp2)
#plane[plane==1] = np.nan
plt.pcolormesh(bins[1],
bins[0],
np.ma.masked_invalid(plane),
cmap=cmap,
vmin=-2.4,
vmax=2.4)
def hist2d(MC, varname1, varname2, varslice=None,
percentiles=[0.0027,0.0455,0.3173,0.5,0.75],
colors=[(0.4,0.4,1,0.2),(1,0.4,1,0.5),(1,0.2,0.2,0.5),(0.7,0.1,0.1,1),(0.5,0.05,0.05,1),(0.4,0.05,0.05,0.5)],
ticklabels=['3$\\sigma$','2$\\sigma$','1$\\sigma$','50%','25%'],
axis=None,
fignum=1,
contourcmd=pylab.contourf,
clear=False,
colorbar=True,
doerrellipse=False,
chain=None,
**kwargs):
"""
Create a 2D histogram of the MCMC data over some Trace range
"""
try: # if input is just a dict of arrays
if varslice is None:
histvals,xvals,yvals = pylab.histogram2d(MC[varname1].squeeze(),MC[varname2].squeeze(),**kwargs)
else:
histvals,xvals,yvals = pylab.histogram2d(MC[varname1][slice(*varslice)].squeeze(),MC[varname2][slice(*varslice)].squeeze(),**kwargs)
except TypeError:
if varslice is None:
histvals,xvals,yvals = pylab.histogram2d(
MC.trace(varname1,chain=chain)[:].squeeze(),
MC.trace(varname2,chain=chain)[:].squeeze(),
**kwargs)
else:
histvals,xvals,yvals = pylab.histogram2d(
MC.trace(varname1,chain=chain)[slice(*varslice)].squeeze(),
MC.trace(varname2,chain=chain)[slice(*varslice)].squeeze(),
**kwargs)
levels = [find_percentile(histvals, p*100) for p in percentiles]
if axis is None:
pylab.figure(fignum)
if clear:
pylab.clf()
axis = pylab.gca()
xax = np.linspace(xvals.min(),xvals.max(),histvals.shape[1])
yax = np.linspace(yvals.min(),yvals.max(),histvals.shape[0])
if axis is not None:
contourcmd = eval('axis.'+contourcmd.__name__)
cntr = contourcmd(xax, yax, histvals.swapaxes(0,1), np.unique(levels+[histvals.max()]), colors=colors)
# hack to fix opacity
axis.set_xlabel(varname1)
axis.set_ylabel(varname2)
if colorbar:
try:
cb = pylab.colorbar(cntr, ax=axis)
cb.ax.set_yticks(levels)
cb.ax.set_yticklabels(ticklabels)
except Exception as e:
print("Colorbar failed with exception {0}".format(e))
if doerrellipse:
errellipse(MC,varname1,varname2)
return axis
def plot_model(
df,
model,
bins=[np.linspace(0, 0.25, 100), np.linspace(0, 1, 100)],
hyperplane_only=False,
alpha=1,
cmap=None,
):
C, M = np.meshgrid(*bins)
resp1 = (
plt.histogram2d(
df[df.response == 1].stdc.values, df[df.response == 1].mc.values, bins=bins
)[0]
+ 1
)
resp1[resp1 == 1] = np.nan
resp2 = (
plt.histogram2d(
df[df.response == -1].stdc.values,
df[df.response == -1].mc.values,
bins=bins,
)[0]
+ 1
)
resp2[resp2 == 1] = np.nan
resp1 = resp1.astype(float) / np.nansum(resp1)
resp2 = resp2.astype(float) / np.nansum(resp2)
p = model.predict(np.vstack([M.ravel(), C.ravel(), 0 * np.ones(M.shape).ravel()]).T)
p = p.reshape(M.shape)
decision = (
lambda x: -(model.params.mc * x + model.params.Intercept) / model.params.stdc
)
if not hyperplane_only:
plane = np.log(resp1 / resp2)
plane[plane == 1] = np.nan
pcol = plt.pcolormesh(
bins[1],
bins[0],
np.ma.masked_invalid(plane),
cmap=cmap,
vmin=-2.4,
vmax=2.4,
rasterized=True,
linewidth=0,
)
pcol.set_edgecolor("face")
mind, maxd = plt.xlim()
plt.ylim(bins[0][0], bins[0][-1])
plt.xlim(bins[1][0], bins[1][-1])
plt.plot([mind, maxd], [decision(mind), decision(maxd)], "k", lw=2, alpha=alpha)
plt.plot([0, 0], [bins[0][0], bins[0][-1]], "k--", lw=2)
# ylim([0, 0.25])
# xlim([0, 1])
# contour(bins[1], bins[0], p.T, [0.5])
return bins, p
def plot_model(df,
model,
bins=[np.linspace(0, .25, 100),
np.linspace(0, 1, 100)],
hyperplane_only=False,
alpha=1,
cmap=None,
mc_field='mc',
stdc_field='stdc',
response_field='response',
plot_hyperplane=True):
C, M = plt.meshgrid(*bins)
resp1 = plt.histogram2d(
df[df.loc[:, response_field] == 1].loc[:, stdc_field].values,
df[df.loc[:, response_field] == 1].loc[:, mc_field].values,
bins=bins)[0] + 1
#resp1[resp1==1] = np.nan
resp2 = plt.histogram2d(
df[df.loc[:, response_field] == -1].loc[:, stdc_field].values,
df[df.loc[:, response_field] == -1].loc[:, mc_field].values,
bins=bins)[0] + 1
#resp2[resp2==1] = np.nan
resp1 = resp1.astype(float) / np.nansum(resp1)
resp2 = resp2.astype(float) / np.nansum(resp2)
p = model.predict(
np.vstack([M.ravel(),
C.ravel(), 0 * np.ones(M.shape).ravel()]).T)
p = p.reshape(M.shape)
decision = lambda x: - \
(model.params[mc_field] * x + model.params.Intercept) / \
model.params[stdc_field]
if not hyperplane_only:
plane = np.log(resp1 / resp2)
#plane[plane==1] = np.nan
plt.pcolormesh(bins[1],
bins[0],
np.ma.masked_invalid(plane),
cmap=cmap,
vmin=-2.4,
vmax=2.4)
mind, maxd = plt.xlim()
plt.ylim(bins[0][0], bins[0][-1])
plt.xlim(bins[1][0], bins[1][-1])
if plot_hyperplane:
plt.plot([mind, maxd], [decision(mind), decision(maxd)],
'k',
lw=2,
alpha=alpha)
plt.plot([0, 0], [bins[0][0], bins[0][-1]], 'k--', lw=2)
return bins
def plot_phases(in_file, plot_type, plot_log):
flags = ['histogram','phases']
plot_flag = 0
log_flag = 0
def no_log(x):
return x
fig = pylab.figure(1)
ax = fig.add_subplot(111)
try:
img = spimage.sp_image_read(in_file,0)
except:
raise IOError("Can't read %s." % in_file)
values = img.image.reshape(pylab.size(img.image))
if plot_log:
log_function = pylab.log
else:
log_function = no_log
if plot_type == PHASES:
hist = pylab.histogram(pylab.angle(values),bins=500)
ax.plot((hist[1][:-1]+hist[1][1:])/2.0,log_function(hist[0]))
elif plot_flag == HISTOGRAM:
hist = pylab.histogram2d(pylab.real(values),pylab.imag(values),bins=500)
ax.imshow(log_function(hist[0]),extent=(hist[2][0],hist[2][-1],-hist[1][-1],-hist[1][0]),interpolation='nearest')
else:
ax.plot(pylab.real(values),pylab.imag(values),'.')
return fig
def plot_phases(in_file, plot_type, plot_log):
plot_flag = 0
def no_log(x):
return x
fig = pylab.figure(1)
ax = fig.add_subplot(111)
try:
img = spimage.sp_image_read(in_file, 0)
except IOError:
raise IOError("Can't read %s." % in_file)
values = img.image.reshape(pylab.size(img.image))
if plot_log:
log_function = pylab.log
else:
log_function = no_log
if plot_type == PHASES:
hist = pylab.histogram(pylab.angle(values), bins=500)
ax.plot((hist[1][:-1] + hist[1][1:]) / 2, log_function(hist[0]))
elif plot_flag == HISTOGRAM:
hist = pylab.histogram2d(pylab.real(values),
pylab.imag(values),
bins=500)
ax.imshow(log_function(hist[0]),
extent=(hist[2][0], hist[2][-1], -hist[1][-1], -hist[1][0]),
interpolation='nearest')
else:
ax.plot(pylab.real(values), pylab.imag(values), '.')
return fig
def plot_joint_mass_age_pdf(cldata):
""" plot p(logmass, logage | cluster) """
grid = cldata['match']['grid']
pr = np.exp(-0.5 * grid['fit'])
y = np.unique(grid['age'])
dlogm = 0.1
bins = (np.arange(dlogm, 6, dlogm), get_bins_from_center(y))
n, bx, by = plt.histogram2d(grid['mass'],
grid['age'],
weights=pr,
bins=bins)
floor = n[n > 0].min()
plt.imshow(np.log10(n + 1e-3 * floor).T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.magma,
aspect='auto',
interpolation='nearest')
plt.contour(n.T,
figrc.nice_sigma_levels(n, [1, 2, 3]),
extent=(bx[0], bx[-1], by[0], by[-1]),
colors='k')
plt.xlabel('Log mass')
plt.ylabel('Log age')
def probcontour(xarr,yarr,style='black',smooth=2,bins=100,weights=None,label=None):
from scipy import ndimage
import numpy
H,xbins,ybins = pylab.histogram2d(xarr,yarr,bins=bins,weights=weights)
H = ndimage.gaussian_filter(H,smooth)
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl68 = sortH[cumH>cumH.max()*0.32].min()
lvl95 = sortH[cumH>cumH.max()*0.05].min()
lvl99 = sortH[cumH>cumH.max()*0.003].min()
if style == 'black':
pylab.contour(H.T,[lvl68,lvl95,lvl99],colors=style,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
else:
# Shaded areas:
if type(style) == str:
pylab.contourf(H.T,[lvl99,lvl95],colors=style,alpha=0.2,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=style,alpha=0.5,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,1e8],colors=style,alpha=0.9,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]),label=label)
else:
pylab.contourf(H.T,[lvl99,lvl95],colors=rgb_to_hex(rgb_alpha(style,0.2)),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=rgb_to_hex(rgb_alpha(style,0.5)),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,1e8],colors=rgb_to_hex(rgb_alpha(style,0.9)),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]),label=label)
def get_surfaces(x, y, filter, bins):
'''
Compute how often x and y occured before a specific
choice.
Parameters
----------
x, y: array containing mean contrast and std respectively
filter: array
True for choice 1, false for choice 2
bins: 2-list of arrays
Specifies edges for the 2D histogram.
'''
idx = filter > 0
resp1 = plt.histogram2d(x[idx], y[idx], bins=bins)[0]
resp2 = plt.histogram2d(x[~idx], y[~idx], bins=bins)[0]
return resp1, resp2
def expected_mean(data, bins, levels=[0.5]):
resp1 = plt.histogram2d(data.trans_stdc.values,
data.trans_mc.values,
weights=data.mc,
bins=bins)[0]
resp_count = plt.histogram2d(data.trans_stdc.values,
data.trans_mc.values,
bins=bins)[0]
expected = resp1 / resp_count.astype(float)
CS = plt.contour(center(bins[1]),
center(bins[0]),
np.ma.masked_invalid(expected),
levels,
colors='m')
zc = CS.collections[0]
plt.setp(zc, linewidth=2.5)
return expected
def draw_otf_spectrum(cube, figure=None, subplot=111, title='', grid=True,
font_family=None, tick_labels_size=9, xlim=None, ylim=None,
show=True, *args, **kwargs):
import time
import numpy
import analyse
import analyse.plotter
import matplotlib.figure
import matplotlib.colors
import pylab
t0 = time.time()
print('[%f] -- %f'%(time.time(), time.time()-t0))
if not isinstance(figure, matplotlib.figure.Figure):
if figure is None:
figure = pylab.figure()
else:
figure = pylab.figure(figsize=figure)
else:
show = False
if type(subplot) is int:
subplot = analyse.plotter.get_subplot(subplot)
elif type(subplot) in [list, tuple]:
if len(subplot) == 3:
subplot = analyse.plotter.get_subplot(subplot)
nz, ny, nx = cube.data.shape
spectra = cube.data.T.reshape(nx*ny, nz)
v = analyse.generate_axis(cube, axis=3) / 1000.
ax = figure.add_axes(subplot)
#[ax.plot(v, s, '.b', ms=1, mew=0, alpha=0.1) for s in spectra]
xd = numpy.array(list(v) * len(spectra))
yd = spectra.ravel()
yd[numpy.isnan(yd)] = 0
histmap, histx, histy = pylab.histogram2d(xd, yd, bins=501)
histmap = histmap.T
histmap[numpy.where(histmap==0)] = 0.3
histmap = numpy.log10(histmap)
histX, histY = numpy.meshgrid(histx, histy)
ax.pcolormesh(histX, histY, histmap, vmax=3.3)
ax.grid(grid)
ax.set_title(title, size=tick_labels_size+1)
if xlim is not None: ax.set_xlim(*xlim)
else: ax.set_xlim(xd.min(), xd.max())
if ylim is not None: ax.set_ylim(*ylim)
else: ax.set_ylim(yd.min(), yd.max())
tx = ax.get_xticks()
ty = ax.get_yticks()
ax.set_xticklabels(tx, size=tick_labels_size)
ax.set_yticklabels(ty, size=tick_labels_size)
if show:
pylab.show()
print('[%f] -- %f'%(time.time(), time.time()-t0))
return figure
def mi(x,y, bins=11):
"""Given two arrays x and y of equal length, return their mutual information in bits
"""
Hxy, xe, ye = pylab.histogram2d(x,y,bins=bins)
Hx = Hxy.sum(axis=1)
Hy = Hxy.sum(axis=0)
Pxy = Hxy/float(x.size)
Px = Hx/float(x.size)
Py = Hy/float(x.size)
pxy = Pxy.ravel()
px = Px.repeat(Py.size)
py = pylab.tile(Py, Px.size)
idx = pylab.find((pxy > 0) & (px > 0) & (py > 0))
return (pxy[idx]*pylab.log2(pxy[idx]/(px[idx]*py[idx]))).sum()
def plot_pm_radial():
"""
Plot a historgram of stars moving in a radial direction (in the
cluster outskirts and compare to the average histogram in all other
directions.
"""
d = atpy.Table(wd1_data)
idx = np.where(d.P > 0.9)
# Identify the clusters central position
# h, xedges, yedges = py.histogram2d(d.x_F814W, d.y_F814W, weights=d.P)
h, xedges, yedges = py.histogram2d(d.x_F814W[idx], d.y_F814W[idx])
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
py.imshow(h, extent=extent, interpolation=None)
py.colorbar()
def plot_pm_radial():
"""
Plot a historgram of stars moving in a radial direction (in the
cluster outskirts and compare to the average histogram in all other
directions.
"""
d = atpy.Table(wd1_data)
idx = np.where(d.P > 0.9)
# Identify the clusters central position
# h, xedges, yedges = py.histogram2d(d.x_F814W, d.y_F814W, weights=d.P)
h, xedges, yedges = py.histogram2d(d.x_F814W[idx], d.y_F814W[idx])
extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]]
py.imshow(h, extent=extent, interpolation=None)
py.colorbar()
def update_histogram_plt_data(self, histogram_save_data):
self.hist_data = histogram_save_data
bins = (self.Gbins, self.Dbins)
G_breaking = histogram_save_data.get_G_breaking()
D_breaking = histogram_save_data.get_D_breaking()
G_making = histogram_save_data.get_G_making()
break_histogram = pl.histogram(G_breaking, self.Gbins)
make_histogram = pl.histogram(G_making, self.Gbins)
self.histo1D_breaking_sum = self.histo1D_breaking_sum + break_histogram[
0]
self.histo1D_making_sum = self.histo1D_making_sum + make_histogram[0]
break_histogram2D = pl.histogram2d(G_breaking, D_breaking, bins)
self.histo2D_breaking_sum = self.histo2D_breaking_sum + break_histogram2D[
0]
def pdf2d(ax,ay,imp,xbins,ybins,smooth,color,style):
from scipy import ndimage
pylab.xlim([xbins[0],xbins[-1]])
pylab.ylim([ybins[0],ybins[-1]])
# npts = int((ax.size/4)**0.5)
H,x,y = pylab.histogram2d(ax,ay,weights=imp,bins=[xbins,ybins])
# Smooth the PDF:
H = ndimage.gaussian_filter(H,smooth)
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl00 = 2*sortH.max()
lvl68 = sortH[cumH>cumH.max()*0.32].min()
lvl95 = sortH[cumH>cumH.max()*0.05].min()
lvl99 = sortH[cumH>cumH.max()*0.003].min()
# print "2D histogram: min,max = ",H.min(),H.max()
# print "Contour levels: ",[lvl00,lvl68,lvl95,lvl99]
if style == 'shaded':
# Plot shaded areas first:
pylab.contourf(H.T,[lvl99,lvl95],colors=color,alpha=0.1,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=color,alpha=0.4,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,lvl00],colors=color,alpha=0.7,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# endif
# Always plot outlines:
pylab.contour(H.T,[lvl68,lvl95,lvl99],colors=color,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
return
def pdf2d(ax,ay,imp,xbins,ybins,smooth,color="grey",style='shaded'):
from scipy import ndimage
pylab.xlim([xbins[0],xbins[-1]])
pylab.ylim([ybins[0],ybins[-1]])
# npts = int((ax.size/4)**0.5)
H,x,y = pylab.histogram2d(ax,ay,weights=imp,bins=[xbins,ybins])
H = ndimage.gaussian_filter(H,smooth)
norm = np.sum(H.flatten())
H = H * (norm > 0.0) / (norm + (norm == 0.0))
sortH = np.sort(H.flatten())
cumH = sortH.cumsum()
# Set contours to show number of objects *outside* the contours
fracs=[0.3, 0.1, 0.03, 0.01, 0.003, 0.001]
labels=[]
lvls=[]
for f in fracs:
lvls.append( sortH[cumH>cumH.max()*f].min() )
labels.append(str(1-f))
print "2D histogram: min,max = ",H.min(),H.max()
print "Contour levels: ",lvls
if style == 'shaded':
# Plot shaded areas first:
for num in range(len(lvls)-1):
pylab.contourf(H.T,[lvls[num],lvls[num+1]],colors=color,alpha=np.sqrt(fracs[num]),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# endif
# Always plot outlines:
CS=pylab.contour(H.T,lvls,colors='black',\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
plt.clabel(CS,fontsize=10,text=labels)
return
def mutual_information(x,y, bins=11): """Given two arrays x and y of equal length, return their mutual information in bits >>> N = 10000 >>> xi = pylab.randn(N) >>> xi[pylab.find(xi>0)] = 1 >>> xi[pylab.find(xi<=0)] = 0 >>> yi = xi >>> print round(mutual_information(xi, yi, 1000),2) #One bit of info 1.0 >>> N = 10000 >>> xi = pylab.uniform(size=N) >>> yi = pylab.floor(xi*8) >>> print round(mutual_information(xi, yi, 1000),2) #Three bits of info 3.0 >>> N = 100000 >>> xi = pylab.randn(N) >>> yi = pylab.randn(N) >>> print round(mutual_information(xi, yi),2) #Should be zero given enough data and not too sparse binning 0.0 """ Hxy, xe, ye = pylab.histogram2d(x,y,bins=bins) Hx = Hxy.sum(axis=1) Hy = Hxy.sum(axis=0) Pxy = Hxy/float(x.size) Px = Hx/float(x.size) Py = Hy/float(x.size) pxy = Pxy.ravel() px = Px.repeat(Py.size) py = pylab.tile(Py, Px.size) idx = pylab.find((pxy > 0) & (px > 0) & (py > 0)) mi = (pxy[idx]*pylab.log2(pxy[idx]/(px[idx]*py[idx]))).sum() return mi
def mutual_information(x, y, bins=11):
"""Given two arrays x and y of equal length, return their mutual information in bits
>>> N = 10000
>>> xi = pylab.randn(N)
>>> xi[pylab.find(xi>0)] = 1
>>> xi[pylab.find(xi<=0)] = 0
>>> yi = xi
>>> print round(mutual_information(xi, yi, 1000),2) #One bit of info
1.0
>>> N = 10000
>>> xi = pylab.uniform(size=N)
>>> yi = pylab.floor(xi*8)
>>> print round(mutual_information(xi, yi, 1000),2) #Three bits of info
3.0
>>> N = 100000
>>> xi = pylab.randn(N)
>>> yi = pylab.randn(N)
>>> print round(mutual_information(xi, yi),2) #Should be zero given enough data and not too sparse binning
0.0
"""
Hxy, xe, ye = pylab.histogram2d(x, y, bins=bins)
Hx = Hxy.sum(axis=1)
Hy = Hxy.sum(axis=0)
Pxy = Hxy / float(x.size)
Px = Hx / float(x.size)
Py = Hy / float(x.size)
pxy = Pxy.ravel()
px = Px.repeat(Py.size)
py = pylab.tile(Py, Px.size)
idx = pylab.find((pxy > 0) & (px > 0) & (py > 0))
mi = (pxy[idx] * pylab.log2(pxy[idx] / (px[idx] * py[idx]))).sum()
return mi
def probcontour(xarr,
yarr,
style='lines',
color='k',
smooth=2,
bins=100,
weights=None,
linewidths=1,
linestyles='solid',
nlevels=3):
from scipy import ndimage
import numpy
H, xbins, ybins = pylab.histogram2d(xarr, yarr, bins=bins, weights=weights)
H = ndimage.gaussian_filter(H, smooth)
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl68 = sortH[cumH > cumH.max() * 0.32].min()
lvl95 = sortH[cumH > cumH.max() * 0.05].min()
lvl99 = sortH[cumH > cumH.max() * 0.003].min()
if style == 'lines':
#pylab.contour(H.T,[lvl99,lvl95,lvl68],colors=color,\
# extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]), linewidths=linewidths, linestyles=linestyles)
pylab.contour(H.T,[lvl95,lvl68],colors=color,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]), linewidths=linewidths, linestyles=linestyles)
else:
# Shaded areas:
if type(color) == str:
pylab.contourf(H.T,[lvl95,lvl68],colors=color,alpha=0.5,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,1e8],colors=color,alpha=0.9,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
else:
pylab.contourf(H.T,[lvl95,lvl68],colors=rgb_to_hex(rgb_alpha(color,0.5)),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,1e8],colors=rgb_to_hex(rgb_alpha(color,0.9)),\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
def pdf2d(ax, ay, imp, xbins, ybins, smooth, color, style):
from scipy import ndimage
pylab.xlim([xbins[0], xbins[-1]])
pylab.ylim([ybins[0], ybins[-1]])
# npts = int((ax.size/4)**0.5)
H, x, y = pylab.histogram2d(ax, ay, weights=imp, bins=[xbins, ybins])
# Smooth the PDF:
H = ndimage.gaussian_filter(H, smooth)
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl00 = 2 * sortH.max()
lvl68 = sortH[cumH > cumH.max() * 0.32].min()
lvl95 = sortH[cumH > cumH.max() * 0.05].min()
lvl99 = sortH[cumH > cumH.max() * 0.003].min()
# print "2D histogram: min,max = ",H.min(),H.max()
# print "Contour levels: ",[lvl00,lvl68,lvl95,lvl99]
if style == 'shaded':
# Plot shaded areas first:
pylab.contourf(H.T,[lvl99,lvl95],colors=color,alpha=0.1,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=color,alpha=0.4,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,lvl00],colors=color,alpha=0.7,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# endif
# Always plot outlines:
pylab.contour(H.T,[lvl68,lvl95,lvl99],colors=color,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
def hist2d_with_outliers(x, y, xbins, ybins, nout): ''' Creates a 2D histogram from the given data, and returns a list of the indices in the data of points that lie in low-occupancy cells (where the histogram counts is < "nout"). The "xbins" and "ybins" arguments are passed to numpy.histogram2d. You probably want to show the histogram with: (H, outliers, xe, ye) = hist2d_with_outliers(x, y, 10, 10, 10) imshow(H, extent=(min(xe), max(xe), min(ye), max(ye)), aspect='auto') plot(x[outliers], y[outliers], 'r.') Returns: (H, outliers, xe, ye) H: 2D histogram image outliers: array of integer indices of the outliers xe: x edges chosen by histgram2d ye: y edges chosen by histgram2d ''' # returns (density image, indices of outliers) (H,xe,ye) = histogram2d(x, y, (xbins,ybins)) Out = array([]).astype(int) for i in range(len(xe)-1): for j in range(len(ye)-1): if H[i,j] > nout: continue if H[i,j] == 0: continue H[i,j] = 0 Out = append(Out, flatnonzero((x >= xe[i]) * (x < xe[i+1]) * (y >= ye[j]) * (y < ye[j+1]))) return (H.T, Out, xe, ye)
def plot_cmd_with_model(cldata):
cmd = cldata['match']['cmd']
x = cmd['color']
y = cmd['mag']
dx = get_bins_from_center(np.unique(x))
dy = get_bins_from_center(np.unique(y))
plt.figure()
plot_cmd(cldata, c='w', edgecolor='k', alpha=1.)
n, bx, by = plt.histogram2d(cmd['color'],
cmd['mag'],
bins=(dx, dy),
weights=cmd['n_mod'])
plt.imshow(n.T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.Blues,
aspect='auto',
interpolation='nearest')
# plt.ylim(plt.ylim()[1], 20)
plt.xlim(bx[0], bx[-1])
plt.ylim(by[-1], 20)
figrc.hide_axis('top right'.split())
plt.colorbar()
plt.tight_layout()
def plot_model(df,
model=None,
bins=[np.linspace(0, .3, 26),
np.linspace(0, 1, 26)],
hyperplane_only=False,
alpha=1,
mincnt=10,
cmap='RdBu_r',
fieldy='stdc',
fieldx='mc',
vmin=-2.4,
vmax=2.4,
predicted_surface=False,
contrast=None):
C, M = np.meshgrid(*bins)
if model is not None:
if not predicted_surface:
N = 50
resp1 = np.ones((len(bins[0]) - 1, len(bins[1]) - 1))
resp2 = np.ones((len(bins[0]) - 1, len(bins[1]) - 1))
if contrast is None:
contrast = get_contrast(df)
ps = expit(model(contrast))
for i in range(N):
idx = bernoulli.rvs(ps).astype(bool)
resp1 += plt.histogram2d(df[fieldy].values[idx],
df[fieldx].values[idx],
bins=bins)[0]
resp2 += plt.histogram2d(df[fieldy].values[~idx],
df[fieldx].values[~idx],
bins=bins)[0]
resp2[(resp2) <= (mincnt * N)] = np.nan
resp1[(resp1) <= (mincnt * N)] = np.nan
resp1, resp2 = resp1 / N, resp2 / N
resp1 = resp1.astype(float) / np.nansum(resp1)
resp2 = resp2.astype(float) / np.nansum(resp2)
plane = np.log(resp1 / resp2)
plane[plane == 1] = np.nan
else:
if contrast is None:
contrast = get_contrast(df)
ps = expit(model(contrast))
plane = get_predicted_surface(contrast.std(1), contrast.mean(1),
ps, bins, mincnt)
else:
idx = df.response > 0
resp1 = plt.histogram2d(
df[fieldy].values[idx], df[fieldx].values[idx], bins=bins)[0] + 1
resp1[resp1 == 1] = np.nan
resp2 = plt.histogram2d(
df[fieldy].values[~idx], df[fieldx].values[~idx], bins=bins)[0] + 1
resp2[resp2 == 1] = np.nan
resp1[resp1 < mincnt] = np.nan
resp2[resp2 < mincnt] = np.nan
resp1 = resp1.astype(float) / np.nansum(resp1)
resp2 = resp2.astype(float) / np.nansum(resp2)
plane = np.log(resp1 / resp2)
plane[plane == 1] = np.nan
pcol = plt.pcolormesh(bins[1],
bins[0],
np.ma.masked_invalid(plane),
cmap=cmap,
vmin=vmin,
vmax=vmax,
rasterized=True,
linewidth=0)
pcol.set_edgecolor('face')
if predicted_surface:
pcol = plt.contour(bins[1][1:], bins[0][1:],
np.ma.masked_invalid(plane), [0.35, 0.5, 0.65])
mind, maxd = plt.xlim()
plt.ylim(bins[0][0], bins[0][-1])
plt.xlim(bins[1][0], bins[1][-1])
plt.plot([0, 0], [bins[0][0], bins[0][-1]], 'k--', lw=2)
plot_flag = flag
elif flag in log_flags:
log_flag = flag
else:
print "unknown flag %s" % flag
if log_flag == 'log':
log_function = pylab.log
else:
log_function = no_log
if plot_flag == 'phases':
hist = pylab.histogram(pylab.angle(values),bins=50)
ax.plot((hist[1][:-1]+hist[1][1:])/2.0,log_function(hist[0]))
elif plot_flag == 'histogram':
hist = pylab.histogram2d(pylab.real(values),pylab.imag(values),bins=500)
ax.imshow(log_function(hist[0]),extent=(hist[2][0],hist[2][-1],-hist[1][-1],-hist[1][0]),interpolation='nearest')
else:
ax.plot(pylab.real(values),pylab.imag(values),'.')
return fig
if __name__ == "__main__":
try:
plot_phases(sys.argv[1],*sys.argv[2:])
pylab.show()
except:
print """
Usage: plot_phases datafile [flags]
zspec_master = pylab.array(zspec_master)
zphot_master = pylab.array(zphot_master)
dz1pluszspec_master = (zphot_master - zspec_master) / (1 + zspec_master)
nmad = mypy.nmad(dz1pluszspec_master)
foutlier = len(pylab.find(
abs(dz1pluszspec_master) >= 0.15)) * 1. / len(dz1pluszspec_master)
### major panel: all fields
nbins = 120
clo, chi = 0, 1.05 ### colorbar limits
cmap = pylab.cm.spectral
hist2d, xedges, yedges = pylab.histogram2d(zspec_master,
zphot_master,
bins=(nbins, nbins),
range=([axis1[0], axis1[1]],
[axis1[2], axis1[3]]))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
nlo, nhi = hist2d.min(), hist2d.max()
hist2d[pylab.where(hist2d == 0)] = pylab.nan
hist2d[pylab.where(hist2d > 0)] = pylab.log10(hist2d[pylab.where(hist2d > 0)])
zphotzspec_map = sp_master.imshow(hist2d.T[::-1],
extent=extent,
interpolation='nearest',
cmap=cmap)
zphotzspec_map.set_clim(0, chi * zphotzspec_map.get_clim()[1])
sp_master.set_aspect('auto')
def plot_match_cmd_results(cldata):
cmd = cldata['match']['cmd']
x = cmd['color']
y = cmd['mag']
dx = get_bins_from_center(np.unique(x))
dy = get_bins_from_center(np.unique(y))
plt.figure(figsize=(10, 8))
ax = plt.subplot(221)
n, bx, by = plt.histogram2d(cmd['color'],
cmd['mag'],
bins=(dx, dy),
weights=cmd['n_obs'])
plt.imshow(n.T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.gray_r,
aspect='auto',
interpolation='nearest')
figrc.hide_axis('top right'.split())
plt.colorbar()
plt.title('observed')
plt.subplot(222, sharex=ax, sharey=ax)
n, bx, by = plt.histogram2d(cmd['color'],
cmd['mag'],
bins=(dx, dy),
weights=cmd['n_mod'])
plt.imshow(n.T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.gray_r,
aspect='auto',
interpolation='nearest')
figrc.hide_axis('top right'.split())
plt.colorbar()
plt.title('modeled')
plt.subplot(223, sharex=ax, sharey=ax)
n, bx, by = plt.histogram2d(cmd['color'],
cmd['mag'],
bins=(dx, dy),
weights=cmd['n_diff'])
plt.imshow(n.T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.gray_r,
aspect='auto',
interpolation='nearest')
figrc.hide_axis('top right'.split())
plt.colorbar()
plt.title('residuals')
plt.subplot(224, sharex=ax, sharey=ax)
n, bx, by = plt.histogram2d(cmd['color'],
cmd['mag'],
bins=(dx, dy),
weights=cmd['sig'])
plt.imshow(n.T,
origin='lower',
extent=(bx[0], bx[-1], by[0], by[-1]),
cmap=plt.cm.gray_r,
aspect='auto',
interpolation='nearest')
plt.ylim(plt.ylim()[1], 20)
figrc.hide_axis('top right'.split())
plt.colorbar()
plt.title('significance')
plt.tight_layout()
def pdf2d(ax, ay, imp, xbins, ybins, smooth, color, style, conditional=False):
from scipy import ndimage
pylab.xlim([xbins[0], xbins[-1]])
pylab.ylim([ybins[0], ybins[-1]])
# npts = int((ax.size/4)**0.5)
H, x, y = pylab.histogram2d(ax, ay, weights=imp, bins=[xbins, ybins])
totalmass = sum(H.flatten())
# Smooth the histogram into a PDF:
if conditional:
ncolumns = len(H[0, :])
totalstdev = numpy.sqrt(numpy.var(ay))
for i in range(ncolumns):
p = H[i, :]
# Need to choose smoothing scale carefully here! Columns with fewer points need
# bigger smoothing scales:
norm = numpy.sum(p)
if (norm > 0.0):
yy = ybins[1:]
mean, stdev, Neff, N95 = pappy.meansd(yy, wht=p)
blur = (smooth + smooth * (stdev / totalstdev)**2)
# print "i,norm,totalmass, smooth,blur = ",i,norm,totalmass,smooth,blur
H[i, :] = ndimage.gaussian_filter1d(p.flatten(), blur)
else:
H = ndimage.gaussian_filter(H, smooth)
# For a conditional PDF Pr(y|x), normalise PDF in columns (constant x):
if conditional:
for i in range(len(H[0, :])):
p = H[i, :]
norm = numpy.sum(p.flatten())
# Can only estimate conditional where there are enough points! Rough
# estimate - focus on 99.9% of the mass:
norm = norm * (norm > 0.001 * totalmass)
H[i, :] = p * (norm > 0.0) / (norm + (norm == 0.0))
else:
norm = numpy.sum(H.flatten())
H = H * (norm > 0.0) / (norm + (norm == 0.0))
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl00 = 2 * sortH.max()
lvl68 = sortH[cumH > cumH.max() * 0.32].min()
lvl95 = sortH[cumH > cumH.max() * 0.05].min()
lvl997 = sortH[cumH > cumH.max() * 0.003].min()
# print "2D histogram: min,max = ",H.min(),H.max()
# print "Contour levels: ",[lvl00,lvl68,lvl95,lvl997]
if style == 'shaded':
# Plot shaded areas first:
pylab.contourf(H.T,[lvl997,lvl95],colors=color,alpha=0.1,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=color,alpha=0.4,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,lvl00],colors=color,alpha=0.7,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# endif
# Always plot outlines:
pylab.contour(H.T,[lvl68,lvl95,lvl997],colors=color,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# pylab.contour(H.T,[lvl68,lvl95],colors=color,\
# extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
return
pylab.plot(range(-nburn, nplot), mu1[:nburn+nplot], label='stage 1')
pylab.plot(range(-nburn, nplot), mu2[:nburn+nplot], label='stage 2')
pylab.ylabel('mean')
pylab.xlim(-nburn, nplot)
pylab.axvspan(-nburn, 0, facecolor='r', alpha=0.2)
pylab.legend()
pylab.subplot(2, 1, 2)
pylab.plot(range(-nburn, nplot), M[:nburn+nplot])
pylab.ylabel('transition point')
pylab.xlim(-nburn, nplot)
pylab.axvspan(-nburn, 0, facecolor='r', alpha=0.2)
pylab.xlabel('step')
# plot probability distribution in mu1-mu2 plane
fig = pylab.figure()
(Z, xedges, yedges) = pylab.histogram2d(mu1[nburn:], mu2[nburn:], bins=20,
normed=True)
X = 0.5*(xedges[:-1]+xedges[1:]) # centers of histogram bins
Y = 0.5*(yedges[:-1]+yedges[1:]) # centers of histogram bins
(Y, X) = pylab.meshgrid(Y, X)
axis = fig.gca(projection='3d', azim=-50, elev=20)
surf = axis.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=pylab.cm.jet)
axis.contour(X, Y, Z, zdir='z', offset=-pylab.amax(Z))
axis.contourf(X, Y, Z, 50, zdir='x', offset=min(mu1[nburn:]), colors='c')
axis.contourf(X, Y, Z, 50, zdir='y', offset=max(mu2[nburn:]), colors='c')
axis.set_xlabel('mu1')
axis.set_ylabel('mu2')
axis.set_zlabel('probability')
axis.set_zlim(-pylab.amax(Z), pylab.amax(Z))
axis.plot([mu1true], [mu2true], [-pylab.amax(Z)], 'r*')
fig.colorbar(surf)
pylab.show()
sp1.axis(axlims)
sp1.grid()
sp1.set_xlabel('%s$_{AUTO}$' % filternames[fiducial_filter])
sp1.set_ylabel('( %s - %s )$_{APER}$' % (filternames[fiducial_filter], filtername))
inds = pylab.find((cat.use == 1) &
(fluxes[i][0] > 0) &
(fluxes[i][1] > 0) &
(fluxes[fiducial_filter][0] > 0) &
(fluxes[fiducial_filter][1] > 0))
color = -2.5 * pylab.log10(fluxes[fiducial_filter][0][inds] / fluxes[i][0][inds])
magnitude = 25 - 2.5 * pylab.log10(fluxes[fiducial_filter][1][inds])
hist2d, xedges, yedges = pylab.histogram2d(magnitude, color, bins=(500, 500))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
hist2d[pylab.where(hist2d > 0)] = pylab.log10(hist2d[pylab.where(hist2d > 0)]) + 0.2
asdf = sp1.imshow(hist2d.T, extent=extent, interpolation='nearest', cmap=pylab.cm.gray_r)
pylab.savefig('colorDist%02i_%s-%s_%s.png' % (i+1, filternames[fiducial_filter], filtername, version))
sp1.clear()
pylab.close()
def pdf2d(ax,ay,imp,xbins,ybins,smooth,color,style,conditional=False):
from scipy import ndimage
pylab.xlim([xbins[0],xbins[-1]])
pylab.ylim([ybins[0],ybins[-1]])
# npts = int((ax.size/4)**0.5)
H,x,y = pylab.histogram2d(ax,ay,weights=imp,bins=[xbins,ybins])
totalmass = sum(H.flatten())
# Smooth the histogram into a PDF:
if conditional:
ncolumns = len(H[0,:])
totalstdev = numpy.sqrt(numpy.var(ay))
for i in range(ncolumns):
p = H[i,:]
# Need to choose smoothing scale carefully here! Columns with fewer points need
# bigger smoothing scales:
norm = numpy.sum(p)
if (norm > 0.0):
yy = ybins[1:]
mean,stdev,Neff,N95 = pappy.meansd(yy,wht=p)
blur = (smooth + smooth*(stdev/totalstdev)**2)
# print "i,norm,totalmass, smooth,blur = ",i,norm,totalmass,smooth,blur
H[i,:] = ndimage.gaussian_filter1d(p.flatten(),blur)
else:
H = ndimage.gaussian_filter(H,smooth)
# For a conditional PDF Pr(y|x), normalise PDF in columns (constant x):
if conditional:
for i in range(len(H[0,:])):
p = H[i,:]
norm = numpy.sum(p.flatten())
# Can only estimate conditional where there are enough points! Rough
# estimate - focus on 99.9% of the mass:
norm = norm * (norm > 0.001*totalmass)
H[i,:] = p * (norm > 0.0) / (norm + (norm == 0.0))
else:
norm = numpy.sum(H.flatten())
H = H * (norm > 0.0) / (norm + (norm == 0.0))
sortH = numpy.sort(H.flatten())
cumH = sortH.cumsum()
# 1, 2, 3-sigma, for the old school:
lvl00 = 2*sortH.max()
lvl68 = sortH[cumH>cumH.max()*0.32].min()
lvl95 = sortH[cumH>cumH.max()*0.05].min()
lvl997 = sortH[cumH>cumH.max()*0.003].min()
# print "2D histogram: min,max = ",H.min(),H.max()
# print "Contour levels: ",[lvl00,lvl68,lvl95,lvl997]
if style == 'shaded':
# Plot shaded areas first:
pylab.contourf(H.T,[lvl997,lvl95],colors=color,alpha=0.1,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl95,lvl68],colors=color,alpha=0.4,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
pylab.contourf(H.T,[lvl68,lvl00],colors=color,alpha=0.7,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# endif
# Always plot outlines:
pylab.contour(H.T,[lvl68,lvl95,lvl997],colors=color,\
extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
# pylab.contour(H.T,[lvl68,lvl95],colors=color,\
# extent=(xbins[0],xbins[-1],ybins[0],ybins[-1]))
return
#sp.plot(f.fout.lmass[f.inds_spatial][inds], f.overdensities[f.inds_spatial][inds],
# ls='', marker='o', ms=1, zorder=1)
zspec_master = pylab.array(zspec_master)
z_master = pylab.array(z_master)
v_master = pylab.array(v_master)
m_master = pylab.array(m_master)
cmap = pylab.cm.gray_r
nbins = 5
hist2d, xedges, yedges = pylab.histogram2d(m_master, v_master,
bins=(len(mlims)*nbins+2*nbins, len(vlims)*nbins+2*nbins),
range=([min(mlims)-dm, max(mlims)+dm], [min(vlims)-dv, max(vlims)+dv]))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
nlo, nhi = hist2d.min(), hist2d.max()
hist2d[pylab.where(hist2d == 0)] = pylab.nan
hist2d[pylab.where(hist2d > 0)] = pylab.log10(hist2d[pylab.where(hist2d > 0)])
nhi = hist2d[pylab.where(-pylab.isnan(hist2d))].max()
#colormap = sp.imshow(hist2d.T[::-1,:], extent=extent, vmin=-0.3, vmax=nhi+0.3, interpolation='nearest', cmap=cmap)
colormap = sp.imshow(hist2d.T, extent=extent, vmin=-0.3, vmax=nhi+0.3, interpolation='nearest', cmap=cmap)
"""
font = FontProperties()
font.set_family('sans-serif')
sp1.axhline(0.25 / 2, color='k', lw=2, ls='--', zorder=2)
sp1.axhline(-0.25 / 2, color='yellow', lw=4, ls='-', zorder=2)
sp1.axhline(-0.25 / 2, color='k', lw=2, ls='--', zorder=2)
sp2.axhline(0.25 / 2, color='yellow', lw=4, ls='-', zorder=2)
sp2.axhline(0.25 / 2, color='k', lw=2, ls='--', zorder=2)
sp2.axhline(-0.25 / 2, color='yellow', lw=4, ls='-', zorder=2)
sp2.axhline(-0.25 / 2, color='k', lw=2, ls='--', zorder=2)
#sp1.plot(lmass_afta_1d, lmass_b4_1d - lmass_afta_1d, ls='', marker='o', mec='gray', ms=1, zorder=1)
#sp2.plot(z_1d, lmass_b4_1d - lmass_afta_1d, ls='', marker='o', mec='gray', ms=1, zorder=1)
nbins1, nbins2 = 70, 70
hist2d, xedges, yedges = pylab.histogram2d(lmass_afta_1d,
lmass_b4_1d - lmass_afta_1d,
bins=(nbins1, nbins2),
range=([ax1[0],
ax1[1]], [ax1[2], ax1[3]]))
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
asdf = sp1.imshow(pylab.log10(hist2d.T + 1),
extent=extent,
interpolation='nearest',
cmap=pylab.cm.Greens)
asdf.set_clim(0, pylab.log10(hist2d.max()) * 1.)
sp1.set_aspect('auto')
nbins1, nbins2 = 70, 70
hist2d, xedges, yedges = pylab.histogram2d(z_1d,
lmass_b4_1d - lmass_afta_1d,
bins=(nbins1, nbins2),
range=([ax2[0],
vj_zspec, uv_zspec = pylab.array(vj_zspec), pylab.array(uv_zspec) vj_zphot_err, uv_zphot_err = pylab.array(vj_zphot_err), pylab.array(uv_zphot_err) vj_zspec_err, uv_zspec_err = pylab.array(vj_zspec_err), pylab.array(uv_zspec_err) ################################# ### plotting photometric sample ################################# nbins_zphot = 75 xlims, ylims = [-0.1, 2.1], [0.3, 2.5] hist2d_zphot, xedges_zphot, yedges_zphot = pylab.histogram2d(vj_zphot, uv_zphot, bins=(nbins_zphot, nbins_zphot), range=(xlims, ylims)) extent_zphot = [xedges_zphot[0], xedges_zphot[-1], yedges_zphot[0], yedges_zphot[-1]] nlo, nhi = hist2d_zphot.min(), hist2d_zphot.max() dn = abs(nlo - nhi) cmap_colors = [pylab.cm.gray_r(i) for i in pylab.linspace(0, 1, 15)] cmap = pylab.cm.gray_r cmap = matplotlib.colors.ListedColormap(cmap_colors, name='custom grays', N=None) colormap = sp1.imshow(hist2d_zphot.T, extent=extent_zphot, interpolation='nearest', cmap=cmap) colormap.set_clim(nlo+dn*0.03, nhi-dn*0.03) colormap.set_clim(nlo+dn*0.02, nhi-dn*0.03)
def plot_pairwise_envelope(imageRoot, comboDir, magCut=100, radCut=10000,
drBinSize=5, dmBinSize=0.5, calcArea=False):
"""
For all pairs of stars in a starlist, plot up the separation and
delta-magnitude. Overplot the Starfinder PSF on the plots.
imageRoot = e.g. mag06maylgs2_arch_f1_kp
comboDir = e.g. /u/ghezgroup/data/gc/06maylgs2/combo/
magCut = Only plot pairs where the primary (brighter star) is brighter
than a certain magnitude (default=100).
radCut = Only plot pairs where the radius of the primary (brighter star)
is less than a certain radius in pixels (default=10,000).
"""
image_file = comboDir + imageRoot + '.fits'
psf_file = comboDir + imageRoot + '_psf.fits'
#psf_file = imageRoot + '_psf2d_2asec.fits'
lis_file = comboDir + 'starfinder/' + imageRoot + '_rms.lis'
outSuffix = '%s_m%g_r%d' % (imageRoot.replace('mag', ''), magCut, radCut)
lis = starTables.StarfinderList(lis_file, hasErrors=True)
img = pyfits.getdata(image_file)
psf2D = pyfits.getdata(psf_file)
# Sort by brightness
sidx = lis.mag.argsort()
mag = lis.mag[sidx]
x = lis.x[sidx]
y = lis.y[sidx]
xe = lis.xerr[sidx]
ye = lis.yerr[sidx]
asterr = (xe + ye) / 2.0
# # Make a cut based on the astrometric at a given magnitude.
# starsPerBin = 30
# numBins = int( math.ceil(len(x) / starsPerBin) )
# keep = np.array([], dtype=int)
# for ii in range(numBins):
# lo = ii * starsPerBin
# hi = (ii+1) * starsPerBin
# asterr_median = np.median( asterr[lo:hi] )
# asterr_stddev = asterr[lo:hi].std()
# # Keep only those below median + 3*stddev
# cutoff = asterr_median + (2.0 * asterr_stddev)
# tmp = np.where(asterr[lo:hi] < cutoff)[0]
# tmp += lo
# keep = np.append(keep, tmp)
# print 'Stars from %.2f - %.2f have median(asterr) = %.4f std(asterr) = %.4f' % (mag[lo:hi].min(), mag[lo:hi].max(), asterr_median, asterr_stddev)
# print 'Throwing out %d of %d stars with outlying astrometric errors' % \
# (len(x) - len(keep), len(x))
# mag = mag[keep]
# x = x[keep]
# y = y[keep]
starCount = len(mag)
magMatrix = np.tile(mag, (starCount, 1))
xMatrix = np.tile(x, (starCount, 1))
yMatrix = np.tile(y, (starCount, 1))
# Do the matrix calculation, then take the upper triangule
dm = util.triu2flat(magMatrix.transpose() - magMatrix)
dx = util.triu2flat(xMatrix.transpose() - xMatrix)
dy = util.triu2flat(yMatrix.transpose() - yMatrix)
dr = np.hypot(dx, dy)
# Also pull out the x and y positions (and magnitudes) of the
# primary star in each pair.
x = util.triu2flat(xMatrix.transpose())
y = util.triu2flat(yMatrix.transpose())
m = util.triu2flat(magMatrix.transpose())
xmid = (x.max() - x.min()) / 2.0
ymid = (y.max() - y.min()) / 2.0
r = np.hypot(x - xmid, y - ymid)
# Calculate the edges of the detectors
edge_xlo = x.min()
edge_xhi = x.max()
edge_ylo = y.min()
edge_yhi = y.max()
# Azimuthally average the PSF
psf_r, psf_f, psf_std, psf_n = radialProfile.azimuthalAverage(psf2D)
psf = -2.5 * np.log10(psf_f[0] / psf_f) # convert to magnitudes
# Trim down to a manageable size
idx = np.where((dr > 0) & (dr < 500) & (m < magCut) & (r < radCut))[0]
dm = dm[idx]
dx = dx[idx]
dy = dy[idx]
dr = dr[idx]
x = x[idx]
y = y[idx]
r = r[idx]
m = m[idx]
py.close(2)
py.figure(2, figsize=(8, 6))
# ##########
# Plot 2D scatter of positions
# ##########
py.clf()
py.scatter(dx, dy, s=2, c=dm, marker='o', edgecolor='none')
py.xlabel('X (pixels)')
py.ylabel('Y (pixels)')
py.title('MagCut < %g, RadCut < %d' % (magCut, radCut))
cbar = py.colorbar()
cbar.set_label('Delta Magnitude')
print 'Saving plots/pairs_xy_%s.png' % outSuffix
py.savefig('plots/pairs_xy_%s.png' % outSuffix)
# ##########
# Plot 2D histogram of positions
# ##########
(xy, x_edges, y_edges) = py.histogram2d(dx, dy, bins=[25, 25])
dx_bin_size = (x_edges[1] - x_edges[0]) * 0.00995
dy_bin_size = (y_edges[1] - y_edges[0]) * 0.00995
xy /= dx_bin_size * dy_bin_size
py.clf()
py.imshow(xy.transpose(),
extent=[x_edges[0], x_edges[-1], y_edges[0], y_edges[-1]],
cmap=py.cm.gray_r)
py.axis('equal')
py.xlabel('X (pixel)')
py.ylabel('Y (pixel)')
py.title('MagCut < %g, RadCut < %d' % (magCut, radCut))
cbar = py.colorbar()
cbar.set_label('stars / arcsec^2')
print 'Saving plots/pairs_xy_hist_%s.png' % outSuffix
py.savefig('plots/pairs_xy_hist_%s.png' % outSuffix)
# ##########
# Grid up into magnitude and distance 2D bins
# ##########
dr_bins = np.arange(0, 501, drBinSize)
dm_bins = np.arange(-11, 0.1, dmBinSize)
(h, r_edges, m_edges) = py.histogram2d(dr, dm, bins=[dr_bins,dm_bins])
# Calculate the area covered for each star, taking into account
# edge limits. Use a dummy grid to calculate this for each star.
grid_pix_size = 1.0
grid_side = np.arange(-500, 501, grid_pix_size)
grid_y, grid_x = np.meshgrid(grid_side, grid_side)
grid_r = np.hypot(grid_x, grid_y)
areaFile = 'area_%s_dr%d.dat' % (outSuffix, drBinSize)
if os.path.isfile(areaFile) and not calcArea:
print 'Loading area file: %s' % areaFile
_area = open(areaFile, 'r')
area_at_r = pickle.load(_area)
_area.close()
if len(area_at_r) != len(dr_bins)-1:
print 'Problem with area_at_r in %s' % areaFile
print ' len(area_at_r) = %d' % len(area_at_r)
print ' len(dr_bins)-1 = %d' % (len(dr_bins) - 1)
else:
print 'Creating area file: %s' % areaFile
area_at_r = np.zeros(len(dr_bins)-1, dtype=float)
for rr in range(len(dr_bins)-1):
idx = np.where((grid_r > r_edges[rr]) & (grid_r <= r_edges[rr+1]))
rgrid_x = grid_x[idx[0], idx[1]]
rgrid_y = grid_y[idx[0], idx[1]]
for ss in range(len(dr)):
sgrid_x = rgrid_x + x[ss]
sgrid_y = rgrid_y + y[ss]
sdx = np.where((sgrid_x > edge_xlo) & (sgrid_x < edge_xhi) &
(sgrid_y > edge_ylo) & (sgrid_y < edge_yhi))[0]
area_at_r[rr] += len(sdx)
area_at_r[rr] *= 0.00995**2 * grid_pix_size**2 / len(dr)
area_formula = (r_edges[rr+1]**2 - r_edges[rr]**2)
area_formula *= math.pi * 0.00995**2
print '%3d < r <= %3d %.5f vs. %.5f' % \
(r_edges[rr], r_edges[rr+1], area_at_r[rr], area_formula)
_area = open(areaFile, 'w')
pickle.dump(area_at_r, _area)
_area.close()
for ii in range(h.shape[0]):
h[ii] /= area_at_r[ii] * dmBinSize
# Calculate a 2nd histogram for a sliding window in magnitude space. This
# means we have to code our own 2D histogram.
dr_bins2 = dr_bins
dm_bins2 = np.arange(-11, 0.1, dmBinSize/5.0)
h2 = np.zeros((len(dr_bins2)-1, len(dm_bins2)), dtype=float)
for rr in range(len(dr_bins2)-1):
for mm in range(len(dm_bins2)):
dm_lo = dm_bins2[mm] - (dmBinSize / 2.0)
dm_hi = dm_bins2[mm] + (dmBinSize / 2.0)
idx = np.where((dr >= dr_bins2[rr]) & (dr < dr_bins2[rr+1]) &
(dm >= dm_lo) & (dm < dm_hi))[0]
h2[rr, mm] = len(idx) / (area_at_r[rr] * dmBinSize)
#m_edges = dm_bins2
#h = h2
#r_edges = dr_bins2
# Now calculate the envelope by taking the average from
# outside 2"
critDist = np.zeros(h.shape[1], dtype=float)
idx = np.where(r_edges[:-1] > 2.0/0.00995)[0]
h_avg_hi_r = np.median(h[idx[0]:, :], axis=0)
h_std_hi_r = h[idx[0]:, :].std(axis=0)
r_center = r_edges[0:-1] + (drBinSize / 2.0)
m_center = m_edges[0:-1] + (dmBinSize / 2.0)
#m_center = m_edges
for mm in range(len(m_edges)-1):
# Walk from the inside out until we get to a pixel
# that has a density of at least avgDensity / 3.0
idx = np.where(h[:, mm] > (h_avg_hi_r[mm] / 3.0))[0]
if len(idx) > 0:
critDist[mm] = r_edges[idx[0]]
print mm, m_edges[mm], critDist[mm], h_avg_hi_r[mm]
# ##########
# Make a 2D version of the envelope (smoothed)
# - from 0 > dm > -8, use the PSF
# - from dm < -8, use a smoothed version of the envelope.
# ##########
# Trim the envelope down to dm < -8
idx = np.where((m_center < -8) & (critDist > 0))[0]
int_dist = critDist[idx]
int_mag = m_center[idx]
foo = int_dist.argsort()
int_dist = int_dist[foo]
int_mag = int_mag[foo]
tck = interpolate.splrep(int_dist, int_mag, s=0, k=1)
env_r = np.arange(400)
env_m = interpolate.splev(env_r, tck)
# Graft just the tail onto the existing PSF. (past dm < -8)
npsf_side = np.arange(-200, 201, 1)
npsf_y, npsf_x = np.meshgrid(npsf_side, npsf_side)
npsf_r = np.hypot(npsf_x, npsf_y)
npsf2D = np.zeros(npsf_r.shape, dtype=float)
npsf1D = np.zeros(npsf_r.shape, dtype=float)
# Set to the 1D azimuthally averaged PSF.
tck_psf = interpolate.splrep(psf_r, psf_f)
idx = np.where(npsf_r < 100)
npsf1D[idx[0], idx[1]] = interpolate.splev(npsf_r[idx[0], idx[1]], tck_psf)
npsf2D[100:300, 100:300] = psf2D
# Add in the halo.
idx = np.where(env_m < -8.25)[0]
startingDistance = env_r[idx].min()
idx = np.where(npsf_r >= startingDistance)
halo = interpolate.splev(npsf_r[idx[0], idx[1]], tck)
halo = psf_f[0] / 10**(-0.4 * halo)
npsf1D[idx[0], idx[1]] = np.hypot(npsf1D[idx[0], idx[1]], halo)
npsf2D[idx[0], idx[1]] = np.hypot(npsf2D[idx[0], idx[1]], halo)
# Possible choices of the envelope include:
# azimuthally averaged version
env_file = imageRoot + '_env1D.fits'
if os.access(env_file, os.F_OK): os.remove(env_file)
pyfits.writeto(env_file, npsf1D)
# full 2D structure inside ~1"
env_file = imageRoot + '_env2D.fits'
if os.access(env_file, os.F_OK): os.remove(env_file)
pyfits.writeto(env_file, npsf2D)
# the original PSF
env_file = imageRoot + '_psf2D.fits'
if os.access(env_file, os.F_OK): os.remove(env_file)
pyfits.writeto(env_file, psf2D)
# For plotting purposes get the 1D evelope back out again.
env_r, env_f, env_std, env_n = radialProfile.azimuthalAverage(npsf2D)
env_m = -2.5 * np.log10(env_f[0] / env_f) # convert to magnitudes
# ##########
# Plot points of dr vs. dm (unbinned)
# ##########
py.clf()
py.plot(dr, dm, 'k.', ms=2)
py.plot(psf_r, psf, 'b-')
py.axis([0, 500, -11, 0])
py.xlabel('X (pixel)')
py.ylabel('Y (pixel)')
py.title('MagCut < %g, RadCut < %d' % (magCut, radCut))
print 'Saving plots/pairs_rm_%s.png' % outSuffix
py.savefig('plots/pairs_rm_%s.png' % outSuffix)
# ##########
# Plot dr vs. dm in a 2D histogram
# ##########
hmask = np.ma.masked_where(h.transpose() == 0, h.transpose())
r_edges = r_edges * 0.00995
py.clf()
py.imshow(hmask,
extent=[r_edges[0], r_edges[-1], m_edges[0], m_edges[-1]],
cmap=py.cm.spectral_r)
py.plot(psf_r * 0.00995, psf, 'b-')
py.plot(critDist * 0.00995, m_center, 'r.-')
py.plot(env_r * 0.00995, env_m, 'g.-')
py.axis('tight')
py.axis([0, 5.0, -11, 0])
py.xlabel('Radius (arcsec)')
py.ylabel('Delta Magnitude')
py.title('MagCut < %g, RadCut < %d' % (magCut, radCut))
cbar = py.colorbar()
cbar.set_label('stars / (mag * arcsec^2)')
print 'Saving plots/pairs_rm_hist_%s.png' % outSuffix
py.savefig('plots/pairs_rm_hist_%s.png' % outSuffix)
