def PlotLikelihoods(measure='DM',
model=model,
typ='near',
nside=nside,
plot_every=1,
absolute=False):
## plots evolution of likelihood function with z
## determine redshift to be plotted
if typ == 'near':
redshifts = redshift_skymaps_near[::plot_every]
elif typ == 'far':
redshifts = redshift_skymaps[::plot_every]
## plot likelihood function for each redshift
color = cm.rainbow(np.linspace(0, 1, len(redshifts[1:])))
for z, c in zip(redshifts[1:], color):
PlotLikelihood(z,
measure=measure,
typ=typ,
model=model,
nside=nside,
absolute=absolute,
color=c)
## care for labels
plt.ylabel('P(%s|%s)' % (measure, 'd' if typ == 'near' else 'z'))
plt.xlabel(r"%s (%s)" % (measure, units[measure]))
if measure in ['DM', 'SM'] or absolute:
plt.xscale('log')
plt.yscale('log')
plt.legend()
## save to file
plt.savefig(root_likelihoods + '%s_%s_%s_likelihood.png' %
(model, typ, '|%s|' % measure if absolute else measure))
plt.close()
def plot_spikes(df, sample_type, ax=None, mi=None, mx=None):
if ax is None:
fig, ax = plt.subplots()
else:
fig = None
x = df[df['Sample Type'] == sample_type]['x']
y = df[df['Sample Type'] == sample_type]['z']
s = df[df['Sample Type'] == sample_type]['volume']
if mi is None:
mi = min(s)
mx = max(s)
norm = np.array([((i - mi) / (mx - mi)) * 100 for i in s])
colors_to_use = cm.rainbow(norm / max(norm))
colmap = cm.ScalarMappable(cmap=cm.rainbow)
colmap.set_array(colors_to_use)
t = ax.scatter(x, y, c=colors_to_use, s=norm, marker='o')
ax.set_xlim(150, 400)
if fig is not None:
fig.colorbar(colmap)
return (fig, ax)
return colmap
def plot_spike(df, spikename, ax=None, mi=None, mx=None):
if ax is None:
fig, ax = plt.subplots()
x = df[df['Sample name'] == spikename]['x']
y = df[df['Sample name'] == spikename]['z']
s = df[df['Sample name'] == spikename]['volume']
if mi is None:
mi = min(s)
mx = max(s)
norm = np.array([((i - mi) / (mx - mi)) * 75 for i in s])
colors_to_use = cm.rainbow(norm / max(norm))
colmap = cm.ScalarMappable(cmap=cm.rainbow)
colmap.set_array(colors_to_use)
ax.scatter(x, y, c=colors_to_use, s=norm, marker='o')
ax.set_xlim(0, 512)
fid = list(df[df['Sample name'] == spikename]['folderid'])[0]
ax.set_title('{0}\n{1}'.format(spikename, fid))
# if fig:
# fig.colorbar(colmap)
# return (fig, ax)
return colmap
def plot_av_image(av_image=None, header=None, title=None, limits=None,
savedir='./', filename=None, show=True, av_mask=None, co_mask=None,
av_threshold=None, av_thresholds=None):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import ImageGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
import matplotlib.cm as cm
import matplotlib.lines as mlines
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
fontScale = 15
params = {#'backend': .pdf',
'axes.labelsize': fontScale,
'axes.titlesize': fontScale,
'text.fontsize': fontScale,
'legend.fontsize': fontScale*3/4,
'xtick.labelsize': fontScale,
'ytick.labelsize': fontScale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': (8, 7),
'figure.titlesize': fontScale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
nrows_ncols=(1,1)
ngrids=1
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=1,
axes_class=(wcs.Axes,
dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
# create axes
ax = imagegrid[0]
cmap = cm.pink # colormap
cmap = cm.gray # colormap
# show the image
im = ax.imshow(av_image,
interpolation='nearest',origin='lower',
cmap=cmap,
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk4")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension (J2000)',)
ax.set_ylabel('Declination (J2000)',)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# plot limits
if limits is not None:
ax.set_xlim(limits[0],limits[2])
ax.set_ylim(limits[1],limits[3])
# Write label to colorbar
cb.set_label_text(r'A$_V$ (Mag)',)
# Show contour masks
cs_co = ax.contour(co_mask,
levels=(bad_pix,),
origin='lower',
colors='r',
linestyles='-')
if av_mask is not None:
cs_av = ax.contour(av_mask,
levels=(bad_pix,),
origin='lower',
colors='c',
linestyles='solid')
# Legend
co_line = mlines.Line2D([], [],
color='r',
linestyle='--',
label=r'CO threshold = 2$\times \sigma_{\rm CO}$')
if av_thresholds is not None:
colors = cm.rainbow(np.linspace(0, 1, len(av_thresholds)))
for i, av_threshold in enumerate(av_thresholds):
label = r'$A_V$ threshold = {0:1f} mag'.format(av_threshold)
av_line = mlines.Line2D([], [],
color=colors[i],
linestyle='solid',
label=label)
elif av_threshold is not None and av_mask is not None:
label = r'$A_V$ threshold = {0:1f} mag'.format(av_threshold)
av_line = mlines.Line2D([], [],
color='c',
linestyle='solid',
label=label)
#ax.clabel(cs_co, inline=1, fontsize=10)
#ax.clabel(cs_av, inline=1, fontsize=10)
#cs_co.collections.set_label(r'CO threshold = 2$\times \sigma_{\rm CO}$')
#cs_av.collections.set_label(r'$A_V$ threshold = ' + \
# '{0:1f} mag'.format(av_threshold))
lines = [cs_co.collections[0], cs_av.collections[0]]
labels = [r'CO threshold = 2$\times\ \sigma_{\rm CO}$',
r'$A_V$ threshold = {0:.1f} mag'.format(av_threshold)]
ax.legend(lines, labels,)
#bbox_to_anchor=(1,1),
#loc=3,
#ncol=2,
#mode="expand",
#borderaxespad=0.)
if title is not None:
fig.suptitle(title, fontsize=fontScale)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()
def fig2_b(df_orig, ax=None, mi=None, mx=None, use_fig=False, fig=None):
df = df_orig.copy(deep=True)
mono_names = list(
filter(lambda n: True if 'wild' not in n else False,
einkorn['Sample name'].unique()))[:13]
mono_names.extend(
list(
filter(lambda n: True if 'wild' in n else False,
einkorn['Sample name'].unique())))
df = df[df['Sample name'].isin(mono_names)]
df.ix[df['Sample Type'] == 'T. monococcum',
'x'] = df[df['Sample Type'] == 'T. monococcum']['x'] + 512
if ax is None:
fig, ax = plt.subplots()
x = df['x']
y = df['z']
s = df['volume']
if mi is None:
mi = min(s)
mx = max(s)
norm = np.array([((i - mi) / (mx - mi)) * 100 for i in s])
colors_to_use = cm.rainbow(norm / max(norm))
colmap = cm.ScalarMappable(cmap=cm.rainbow)
colmap.set_array(colors_to_use)
t = ax.scatter(x, y, c=colors_to_use, s=norm, marker='o')
if not use_fig:
colbar = fig.colorbar(colmap, ticks=[0, 0.5, 1])
colbar.ax.set_yticklabels([
r'{0:3.2f}mm$^3$'.format(mi),
r'{0:3.2f}mm$^3$'.format(df['volume'].mean()),
r'{0:3.2f}mm$^3$'.format(mx)
])
x1 = [300, 800]
squad = ['T. beoticum', 'T. monococcum']
ax.set_xticks(x1)
ax.set_xticklabels(squad, minor=False)
return (fig, ax)
else:
colbar = fig.colorbar(colmap, ticks=[0, 0.5, 1], ax=ax)
colbar.ax.set_yticklabels([
r'{0:3.2f}mm$^3$'.format(mi),
r'{0:3.2f}mm$^3$'.format(df['volume'].mean()),
r'{0:3.2f}mm$^3$'.format(mx)
])
x1 = [300, 800]
squad = ['T. beoticum', 'T. monococcum']
ax.set_xticks(x1)
ax.set_xticklabels(squad, minor=False)
return colmap
def plot_av_image(av_image=None,
header=None,
title=None,
limits=None,
savedir='./',
filename=None,
show=True,
av_mask=None,
co_mask=None,
av_threshold=None,
av_thresholds=None):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import ImageGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
import matplotlib.cm as cm
import matplotlib.lines as mlines
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
fontScale = 15
params = { #'backend': .pdf',
'axes.labelsize': fontScale,
'axes.titlesize': fontScale,
'text.fontsize': fontScale,
'legend.fontsize': fontScale * 3 / 4,
'xtick.labelsize': fontScale,
'ytick.labelsize': fontScale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': (8, 7),
'figure.titlesize': fontScale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
nrows_ncols = (1, 1)
ngrids = 1
imagegrid = ImageGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=1,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
# create axes
ax = imagegrid[0]
cmap = cm.pink # colormap
cmap = cm.gray # colormap
# show the image
im = ax.imshow(
av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk4")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension (J2000)', )
ax.set_ylabel('Declination (J2000)', )
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# plot limits
if limits is not None:
ax.set_xlim(limits[0], limits[2])
ax.set_ylim(limits[1], limits[3])
# Write label to colorbar
cb.set_label_text(r'A$_V$ (Mag)', )
# Show contour masks
cs_co = ax.contour(co_mask,
levels=(bad_pix, ),
origin='lower',
colors='r',
linestyles='-')
if av_mask is not None:
cs_av = ax.contour(av_mask,
levels=(bad_pix, ),
origin='lower',
colors='c',
linestyles='solid')
# Legend
co_line = mlines.Line2D([], [],
color='r',
linestyle='--',
label=r'CO threshold = 2$\times \sigma_{\rm CO}$')
if av_thresholds is not None:
colors = cm.rainbow(np.linspace(0, 1, len(av_thresholds)))
for i, av_threshold in enumerate(av_thresholds):
label = r'$A_V$ threshold = {0:1f} mag'.format(av_threshold)
av_line = mlines.Line2D([], [],
color=colors[i],
linestyle='solid',
label=label)
elif av_threshold is not None and av_mask is not None:
label = r'$A_V$ threshold = {0:1f} mag'.format(av_threshold)
av_line = mlines.Line2D([], [],
color='c',
linestyle='solid',
label=label)
#ax.clabel(cs_co, inline=1, fontsize=10)
#ax.clabel(cs_av, inline=1, fontsize=10)
#cs_co.collections.set_label(r'CO threshold = 2$\times \sigma_{\rm CO}$')
#cs_av.collections.set_label(r'$A_V$ threshold = ' + \
# '{0:1f} mag'.format(av_threshold))
lines = [cs_co.collections[0], cs_av.collections[0]]
labels = [
r'CO threshold = 2$\times\ \sigma_{\rm CO}$',
r'$A_V$ threshold = {0:.1f} mag'.format(av_threshold)
]
ax.legend(
lines,
labels,
)
#bbox_to_anchor=(1,1),
#loc=3,
#ncol=2,
#mode="expand",
#borderaxespad=0.)
if title is not None:
fig.suptitle(title, fontsize=fontScale)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()