def contour_hess(ax, c, m, xlim, ylim,
threshold=20, levels=10, bins=100, log_counts=True,
plot_args=None, contour_args=None):
"""Plot a CMD as a contour Hess diagram in high-density regions, and
as a scatter plot in low density regions.
Parameters
----------
ax :
The matplotlib Axes instance.
c : ndarray
The colour (x) coordinates of stars
m : ndarray
The magnitude (y) coordinates of stars
"""
default_plot_args = {'ms': 2.0, 'mfc': 'k', 'mec': 'None',
'rasterized': True, 'alpha': 0.3}
if plot_args is not None:
default_plot_args.update(plot_args)
default_contour_args = {'cmap': mpl.cm.gray_r,
'linestyles': 'None',
'linewidths': 0.,
'alpha': 1.}
if contour_args is not None:
default_contour_args.append(contour_args)
scatter_contour(c, m, levels=levels, threshold=threshold,
log_counts=log_counts,
histogram2d_args={'bins': bins,
'range': [[min(xlim), max(xlim)],
[min(ylim), max(ylim)]]},
plot_args=default_plot_args,
contour_args=default_contour_args,
ax=ax)
def contour_hess(ax,
c,
m,
xlim,
ylim,
threshold=20,
levels=10,
bins=100,
log_counts=True,
plot_args=None,
contour_args=None):
"""Plot a CMD as a contour Hess diagram in high-density regions, and
as a scatter plot in low density regions.
Parameters
----------
ax :
The matplotlib Axes instance.
c : ndarray
The colour (x) coordinates of stars
m : ndarray
The magnitude (y) coordinates of stars
"""
default_plot_args = {
'ms': 2.0,
'mfc': 'k',
'mec': 'None',
'rasterized': True,
'alpha': 0.3
}
if plot_args is not None:
default_plot_args.update(plot_args)
default_contour_args = {
'cmap': mpl.cm.gray_r,
'linestyles': 'None',
'linewidths': 0.,
'alpha': 1.
}
if contour_args is not None:
default_contour_args.append(contour_args)
scatter_contour(c,
m,
levels=levels,
threshold=threshold,
log_counts=log_counts,
histogram2d_args={
'bins': bins,
'range': [[min(xlim), max(xlim)],
[min(ylim), max(ylim)]]
},
plot_args=default_plot_args,
contour_args=default_contour_args,
ax=ax)
def hist2dscatter(x, y, nbins, threshold_value, axis=None, ms=0.5):
if axis == None:
axis = plt.gca()
scatter_contour(x,
y,
threshold=threshold_value,
log_counts=True,
ax=axis,
histogram2d_args=dict(bins=nbins),
plot_args=dict(marker='.',
markersize=ms,
linestyle='none',
color=plt.cm.plasma.colors[0]),
contour_args=dict(cmap=plt.cm.plasma))
def plotcmd(cpath, txt, blue='f475w', red='f814w', y='f814w'):
aestheticize()
df = pd.read_hdf('hlsp/final_cut.hdf5', key='data')
f1 = blue.lower() + '_vega'
f2 = red.lower() + '_vega'
f3 = y.lower() + '_vega'
inpath = cpath.contains_points(
np.array([df['ra'].values, df['dec'].values]).astype(float).T)
nstars = inpath.sum()
if nstars == 0:
raise Exception('Nothing found in image region.')
print('Number of stars found in region: {}'.format(nstars))
df_cut = df[inpath]
if df_cut.shape[0] == 0:
raise Exception('Empty dataframe.')
fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))
ax.plot(df_cut[f1] - df_cut[f2], df_cut[f3], 'k.', ms=4, alpha=0.)
xl = ax.get_xlim()
yl = ax.get_ylim()
sc = scatter_contour(df_cut[f1] - df_cut[f2],
df_cut[f3],
ax=ax,
threshold=100,
log_counts=False,
plot_args={
'color': 'k',
'alpha': 0.2
},
histogram2d_args={'bins': 40},
contour_args={
'cmap': 'viridis',
'zorder': 100
})
levels = sc[-1].levels
labels = ['Fewer\nstars', 'More\nstars']
cb = fig.colorbar(sc[-1])
cb.set_ticks([levels[0], levels[-1]])
cb.set_ticklabels(labels)
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.invert_yaxis()
ax.set_xlabel('{} - {}'.format(blue.upper(), red.upper()))
ax.set_ylabel('{}'.format(y.upper()))
title_str = txt.split(': ')[1].split('. ')[0]
coo = SkyCoord(*cpath.vertices[0], unit='deg')
coo_str = coo.to_string('hmsdms')
ax.set_title('{} ({})'.format(title_str, coo_str))
fig.tight_layout()
fig.set_dpi(300)
fig.savefig('cmd.png', dpi=300)
def plot_avmod_vs_av(avmod_images, av_datas, av_error_datas=None, limits=None,
fit=True, savedir='./', filename=None, show=True,
scale=('linear','linear'), title = '', gridsize=(100,100), std=None):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from matplotlib import cm
from astroML.plotting import scatter_contour
n = int(np.ceil(len(av_datas)**0.5))
if n**2 - n > len(av_datas):
nrows = n - 1
ncols = n
y_scaling = 1.0 - 1.0 / n
else:
nrows, ncols = n, n
y_scaling = 1.0
# set up plot aesthetics
# ----------------------
plt.close;plt.clf()
#plt.rcdefaults()
# color map
cmap = plt.cm.gnuplot
# color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
params = {'axes.color_cycle': color_cycle, # colors of different plots
}
#plt.rcparams.update(params)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(nrows, ncols),
ngrids=len(av_datas),
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Cycle through lists
for i in xrange(len(av_datas)):
av = av_datas[i]
avmod = avmod_images[i]
av_error_data = av_error_datas[i]
# Drop the NaNs from the images
if type(av_error_data) is float:
indices = np.where((av == av) &\
(avmod == avmod)
)
if type(av_error_data) is np.ndarray or \
type(av_error_data) is np.ma.core.MaskedArray or \
type(avmod_error) is np.ndarray or \
type(avmod_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) &\
(avmod == avmod) &\
(av_error_data == av_error_data)
)
av_nonans = av[indices]
avmod_nonans = avmod[indices]
# Fix error data types
if type(av_error_data) is np.ndarray:
av_error_data_nonans = av_error_data[indices]
else:
av_error_data_nonans = np.array(av_error_data[indices])
# Create plot
ax = imagegrid[i]
if 0:
image = ax.errorbar(av_nonans.ravel(),
avmod_nonans.ravel(),
xerr=(av_error_data_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
if 0:
image = ax.hexbin(av_nonans.ravel(),
avmod_nonans.ravel(),
#norm=matplotlib.colors.LogNorm(),
mincnt=1,
yscale=scale[1],
xscale=scale[0],
gridsize=gridsize,
cmap=cm.Greys,
#cmap=cm.gist_stern,
)
cb = ax.cax.colorbar(image,)
# Write label to colorbar
cb.set_label_text('Bin Counts',)
if 1:
l1 = scatter_contour(av_nonans.ravel(),
avmod_nonans.ravel(),
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=30,
range=((limits[0], limits[1]),
(limits[2], limits[3]))),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.7,
markersize=2),
contour_args=dict(
cmap=plt.cm.gray_r,
#cmap=cmap,
),
)
# Plot sensitivies
av_limit = np.median(av_error_datas[0])
ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
ax.plot((0, 10),
(0, 10),
color='0.5',
linewidth=3,
alpha=0.5)
if std is not None:
ax.fill_between((-std, 10),
(0, 10 + std),
(-2*std, 10 - std),
color='0.2',
alpha=0.2,
edgecolor='none'
)
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
if limits is not None:
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_xlabel(r'$A_V$ Data [mag]')
ax.set_ylabel(r'$A_V$ Model [mag]')
#ax.set_title(core_names[i])
if filename is not None:
plt.savefig(savedir + filename)
if show:
fig.show()
# Fetch the Stripe 82 standard star catalog
data = fetch_sdss_S82standards()
g = data['mmu_g']
r = data['mmu_r']
i = data['mmu_i']
#------------------------------------------------------------
# plot the results
ax = plt.axes()
scatter_contour(g - r,
r - i,
threshold=200,
log_counts=True,
ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker='.',
linestyle='none',
markersize=1,
color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.set_xlabel('g - r')
ax.set_ylabel('r - i')
ax.set_xlim(-0.5, 2.5)
ax.set_ylim(-0.5, 2.5)
plt.show()
def plot_avmod_vs_av(avmod_images,
av_datas,
av_error_datas=None,
limits=None,
fit=True,
savedir='./',
filename=None,
show=True,
scale=('linear', 'linear'),
title='',
gridsize=(100, 100),
std=None):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from matplotlib import cm
from astroML.plotting import scatter_contour
n = int(np.ceil(len(av_datas)**0.5))
if n**2 - n > len(av_datas):
nrows = n - 1
ncols = n
y_scaling = 1.0 - 1.0 / n
else:
nrows, ncols = n, n
y_scaling = 1.0
# set up plot aesthetics
# ----------------------
plt.close
plt.clf()
#plt.rcdefaults()
# color map
cmap = plt.cm.gnuplot
# color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
params = {
'axes.color_cycle': color_cycle, # colors of different plots
}
#plt.rcparams.update(params)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
imagegrid = ImageGrid(
fig,
(1, 1, 1),
nrows_ncols=(nrows, ncols),
ngrids=len(av_datas),
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Cycle through lists
for i in xrange(len(av_datas)):
av = av_datas[i]
avmod = avmod_images[i]
av_error_data = av_error_datas[i]
# Drop the NaNs from the images
if type(av_error_data) is float:
indices = np.where((av == av) &\
(avmod == avmod)
)
if type(av_error_data) is np.ndarray or \
type(av_error_data) is np.ma.core.MaskedArray or \
type(avmod_error) is np.ndarray or \
type(avmod_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) &\
(avmod == avmod) &\
(av_error_data == av_error_data)
)
av_nonans = av[indices]
avmod_nonans = avmod[indices]
# Fix error data types
if type(av_error_data) is np.ndarray:
av_error_data_nonans = av_error_data[indices]
else:
av_error_data_nonans = np.array(av_error_data[indices])
# Create plot
ax = imagegrid[i]
if 0:
image = ax.errorbar(av_nonans.ravel(),
avmod_nonans.ravel(),
xerr=(av_error_data_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3)
if 0:
image = ax.hexbin(
av_nonans.ravel(),
avmod_nonans.ravel(),
#norm=matplotlib.colors.LogNorm(),
mincnt=1,
yscale=scale[1],
xscale=scale[0],
gridsize=gridsize,
cmap=cm.Greys,
#cmap=cm.gist_stern,
)
cb = ax.cax.colorbar(image, )
# Write label to colorbar
cb.set_label_text('Bin Counts', )
if 1:
l1 = scatter_contour(
av_nonans.ravel(),
avmod_nonans.ravel(),
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=30,
range=((limits[0], limits[1]),
(limits[2], limits[3]))),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.7,
markersize=2),
contour_args=dict(cmap=plt.cm.gray_r,
#cmap=cmap,
),
)
# Plot sensitivies
av_limit = np.median(av_error_datas[0])
ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
ax.plot((0, 10), (0, 10), color='0.5', linewidth=3, alpha=0.5)
if std is not None:
ax.fill_between((-std, 10), (0, 10 + std), (-2 * std, 10 - std),
color='0.2',
alpha=0.2,
edgecolor='none')
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
ax.set_xlabel(r'$A_V$ Data [mag]')
ax.set_ylabel(r'$A_V$ Model [mag]')
#ax.set_title(core_names[i])
if filename is not None:
plt.savefig(savedir + filename)
if show:
fig.show()
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)
#------------------------------------------------------------
# Fetch the Stripe 82 standard star catalog
data = fetch_sdss_S82standards()
g = data['mmu_g']
r = data['mmu_r']
i = data['mmu_i']
#------------------------------------------------------------
# plot the results
fig, ax = plt.subplots(figsize=(5, 3.75))
scatter_contour(g - r, r - i, threshold=200, log_counts=True, ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.set_xlabel(r'${\rm g - r}$')
ax.set_ylabel(r'${\rm r - i}$')
ax.set_xlim(-0.6, 2.5)
ax.set_ylim(-0.6, 2.5)
plt.show()
def plot_av_vs_nhi(
nhi,
av,
av_error=None,
limits=None,
fit=True,
savedir="",
filename=None,
show=True,
fit_params=None,
contour_plot=True,
scale=("linear", "linear"),
title="",
gridsize=(100, 100),
std=None,
):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from matplotlib import cm
from astroML.plotting import scatter_contour
# set up plot aesthetics
# ----------------------
plt.close
plt.clf()
# plt.rcdefaults()
# color map
cmap = plt.cm.gnuplot
# color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
params = {"axes.color_cycle": color_cycle} # colors of different plots
# plt.rcparams.update(params)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
imagegrid = ImageGrid(
fig,
(1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0.25,
aspect=False,
label_mode="L",
share_all=True,
# cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Drop the NaNs from the images
if type(av_error) is float:
indices = np.where((av == av) & (nhi == nhi))
if type(av_error) is np.ndarray or type(av_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) & (nhi == nhi) & (av_error == av_error))
av_nonans = av[indices]
nhi_nonans = nhi[indices]
# Fix error data types
if type(av_error) is np.ndarray:
av_error_nonans = av_error[indices]
else:
av_error_nonans = np.array(av_error[indices])
# Create plot
ax = imagegrid[0]
if limits is not None:
contour_range = ((limits[0], limits[1]), (limits[2], limits[3]))
else:
contour_range = limits
print contour_plot
if contour_plot:
l1 = scatter_contour(
nhi_nonans.ravel(),
av_nonans.ravel(),
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=30, range=contour_range),
plot_args=dict(marker="o", linestyle="none", color="black", alpha=0.7, markersize=2),
contour_args=dict(
cmap=plt.cm.gray_r,
# cmap=cmap,
),
)
else:
image = ax.errorbar(
nhi_nonans.ravel(),
av_nonans.ravel(),
xerr=(av_error_nonans.ravel()),
alpha=0.2,
color="k",
marker="^",
ecolor="k",
linestyle="None",
markersize=3,
)
# Plot sensitivies
# av_limit = np.median(av_errors[0])
# ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
if 1:
p = np.polyfit(nhi_nonans.ravel(), av_nonans.ravel(), deg=1)
x_fit = np.linspace(-10, 100, 100)
y_fit = fit_params["dgr"] * x_fit + fit_params["intercept"]
y_poly_fit = p[0] * x_fit + p[1]
ax.plot(
x_fit,
y_fit,
# color='0.5',
linestyle="--",
linewidth=2,
alpha=1,
)
ax.plot(x_fit, y_poly_fit, color="r", linestyle="--", linewidth=2, alpha=1)
print ("fitted intercept = {0:.1f} mag".format(p[1]))
std = None
if std is not None:
ax.fill_between((-std, 10), (0, 10 + std), (-2 * std, 10 - std), color="0.2", alpha=0.2, edgecolor="none")
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx="clip")
ax.set_yscale(scale[1], nonposy="clip")
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
ax.set_xlabel(r"$N($H$\textsc{i}) \times\,10^{20}$ cm$^{-3}$")
ax.set_ylabel(r"$A_V$ [mag]")
# ax.set_title(core_names[i])
if filename is not None:
plt.savefig(savedir + filename)
if show:
fig.show()
def plot_av_vs_nhi(nhi, av, av_error=None, limits=None,
fit=True, savedir='', filename=None, show=True, fit_params=None,
contour_plot=True,
scale=('linear','linear'), title = '', gridsize=(100,100), std=None):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from matplotlib import cm
from astroML.plotting import scatter_contour
# set up plot aesthetics
# ----------------------
plt.close;plt.clf()
#plt.rcdefaults()
# color map
cmap = plt.cm.gnuplot
# color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
params = {'axes.color_cycle': color_cycle, # colors of different plots
}
#plt.rcparams.update(params)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
imagegrid = ImageGrid(fig, (1,1,1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Drop the NaNs from the images
if type(av_error) is float:
indices = np.where((av == av) &\
(nhi == nhi)
)
if type(av_error) is np.ndarray or \
type(av_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) &\
(nhi == nhi) &\
(av_error == av_error)
)
av_nonans = av[indices]
nhi_nonans = nhi[indices]
# Fix error data types
if type(av_error) is np.ndarray:
av_error_nonans = av_error[indices]
else:
av_error_nonans = np.array(av_error[indices])
# Create plot
ax = imagegrid[0]
if limits is not None:
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
else:
contour_range = limits
print contour_plot
if contour_plot:
l1 = scatter_contour(nhi_nonans.ravel(),
av_nonans.ravel(),
threshold=2,
log_counts=0,
levels=5,
ax=ax,
histogram2d_args=dict(bins=30,
range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.7,
markersize=2),
contour_args=dict(
cmap=plt.cm.gray_r,
#cmap=cmap,
),
)
else:
image = ax.errorbar(nhi_nonans.ravel(),
av_nonans.ravel(),
xerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
# Plot sensitivies
#av_limit = np.median(av_errors[0])
#ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
if 1:
p = np.polyfit(nhi_nonans.ravel(), av_nonans.ravel(), deg=1)
x_fit = np.linspace(-10, 100, 100)
y_fit = fit_params['dgr'] * x_fit + fit_params['intercept']
y_poly_fit = p[0] * x_fit + p[1]
ax.plot(x_fit,
y_fit,
#color='0.5',
linestyle='--',
linewidth=2,
alpha=1)
ax.plot(x_fit,
y_poly_fit,
color='r',
linestyle='--',
linewidth=2,
alpha=1)
print('fitted intercept = {0:.1f} mag'.format(p[1]))
std = None
if std is not None:
ax.fill_between((-std, 10),
(0, 10 + std),
(-2*std, 10 - std),
color='0.2',
alpha=0.2,
edgecolor='none'
)
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
if limits is not None:
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_xlabel(r'$N($H$\textsc{i}) \times\,10^{20}$ cm$^{-3}$')
ax.set_ylabel(r'$A_V$ [mag]')
#ax.set_title(core_names[i])
if filename is not None:
plt.savefig(savedir + filename)
if show:
fig.show()
def doAstr511HW2(truth, obs, diskLF, haloLF):
"""Make a six-panel plot illustrating Astr511 homework #2"""
# a) Define a ``gold parallax sample" by piObs/piErr > 10 and compare distance
# modulus from the parallax measurement (D/kpc=1 milliarcsec/piObs)
# to the distance modulus determined from r and Mr listed in the ``truth" file
piObs = obs[14]
piErr = obs[15]
piSNR = piObs / piErr
D = np.where(piObs>0, 1/piObs, 1.0e6)
mask = (piSNR>10)
Dok = D[mask]
DM = 5*np.log10(Dok/0.01)
DMtrue = truth[4] - truth[8]
diffDM = DM - DMtrue[mask]
if (0):
print ' mean =', np.mean(diffDM)
print ' rms =', np.std(diffDM)
med, sigG = median_sigmaG(diffDM)
print 'median =', med
print 'sigmaG =', sigG
### make vectors of x=distance modulus and y=absolute magnitude to run Cminus
# sample faint limit
rFaintLimit = 24.5
# use either true distances, or estimated from parallax
if (0):
# using "truth" values of distance modulus, DM, to compute LF
Mr = truth[8]
rTrue = truth[4]
DMtrue = rTrue - Mr
# type could be estimated from colors...
typeWD = truth[13]
# here the sample is defined using true apparent magnitude
maskC = (rTrue < rFaintLimit) & (typeWD == 1)
xC = DMtrue[maskC]
yC = Mr[maskC]
else:
# observed apparent magnitude
rObs = obs[6]
if (1):
# fit a 4-th order polynomial to Mr(gr) relation (high-SNR sample)
A, B, C, D, E = fitPhotomParallax(truth, obs)
# and now apply
gr = obs[4]-obs[6]
MrPP = WDppFit(gr, A, B, C, D, E)
else:
# testing here: take true Mr...
MrPP = truth[8]
# here the sample is defined using observed apparent magnitude
DMobs = rObs - MrPP
# type could be estimated from colors...
typeWD = truth[13]
maskC = (rObs < rFaintLimit) & (typeWD == 1)
xC = DMobs[maskC]
yC = MrPP[maskC]
## the sample boundary is a simple linear function because using DM
xCmax = rFaintLimit - yC
yCmax = rFaintLimit - xC
## split disk and halo, assume populations are known
pop = truth[14]
popC = pop[maskC]
maskD = (popC==1)
xCD = xC[maskD]
yCD = yC[maskD]
xCmaxD = xCmax[maskD]
yCmaxD = yCmax[maskD]
maskH = (popC==2)
xCH = xC[maskH]
yCH = yC[maskH]
xCmaxH = xCmax[maskH]
yCmaxH = yCmax[maskH]
print 'observed sample size of xCD (disk)', np.size(xCD)
print 'observed sample size of xCH (halo)', np.size(xCH)
### and now corrected Mr distributions: application of the Cminus method
## note that Cminus returns **cumulative** distribution functions
## however, bootstrap_Cminus (which calls Cminus) then does conversion
## and returns **differential** distribution functions
#### this is where we run Cminus method (or use stored results) ####
archive_file = 'CminusZI.npz'
if not os.path.exists(archive_file):
print ("- computing bootstrapped differential luminosity function ")
#------------------------------------------------------------
# aux arrays
x_fit = np.linspace(0, 15, 42)
y_fit = np.linspace(10, 17, 42)
#------------------------------------------------------------
## compute the Cminus distributions (with bootstrap)
# disk subsample
print ("Disk sample with %i points" % len(xCD))
xD_dist, dxD_dist, yD_dist, dyD_dist = bootstrap_Cminus(xCD, yCD, xCmaxD, yCmaxD,
x_fit, y_fit, Nbootstraps=20, normalize=True)
# halo subsample
print ("Halo sample with %i points" % len(xCH))
xH_dist, dxH_dist, yH_dist, dyH_dist = bootstrap_Cminus(xCH, yCH, xCmaxH, yCmaxH,
x_fit, y_fit, Nbootstraps=20, normalize=True)
np.savez(archive_file,
x_fit=x_fit, xD_dist=xD_dist, dxD_dist=dxD_dist,
y_fit=y_fit, yD_dist=yD_dist, dyD_dist=dyD_dist)
archive_file = 'CminusZIhalo.npz'
np.savez(archive_file,
xH_dist=xH_dist, dxH_dist=dxH_dist,
yH_dist=yH_dist, dyH_dist=dyH_dist)
#------------------------------------------------------------
else:
print "- using ZI's precomputed bootstrapped luminosity function results"
# disk
archive_file = 'CminusZI.npz'
archive = np.load(archive_file)
x_fit = archive['x_fit']
xD_dist = archive['xD_dist']
dxD_dist = archive['dxD_dist']
y_fit = archive['y_fit']
yD_dist = archive['yD_dist']
dyD_dist = archive['dyD_dist']
# halo
archive_file = 'CminusZIhalo.npz'
archive = np.load(archive_file)
xH_dist = archive['xH_dist']
dxH_dist = archive['dxH_dist']
yH_dist = archive['yH_dist']
dyH_dist = archive['dyH_dist']
# bin mid positions for plotting the results:
x_mid = 0.5 * (x_fit[1:] + x_fit[:-1])
y_mid = 0.5 * (y_fit[1:] + y_fit[:-1])
### determine normalization of luminosity functions and number density distributions
## we need to renormalize LFs: what we have are differential distributions
## normalized so that their cumulative LF is 1 at the last point;
## we need to account for the fractional sky coverage and for flux selection effect
## and do a bit of extrapolation to our position (DM=0) at the end
# 1) the fraction of covered sky: we know that the sample is defined by bGal > bMin
bMin = 60.0 # in degrees
# in steradians, given b > 60deg, dOmega = 2*pi*[1-cos(90-60)] = 0.8418 sr
dOmega = 2*math.radians(180)*(1-math.cos(math.radians(90-bMin)))
# 2) renormalization constants due to selection effects
# these correspond to the left-hand side of eq. 12 from lecture 4 (page 26)
DMmax0 = 11.0
MrMax0 = rFaintLimit - DMmax0
NmaskD = ((xCD < DMmax0) & (yCD < MrMax0))
Ndisk = np.sum(NmaskD)
NmaskH = ((xCH < DMmax0) & (yCH < MrMax0))
Nhalo = np.sum(NmaskH)
print 'Size of volume-limited samples D and H:', Ndisk, ' and ', Nhalo, '(DM<', DMmax0, ', Mr<', MrMax0, ')'
## Size of volume-limited samples D and H: 49791 and 2065 (DM< 11.0 , Mr< 13.5 )
# now we need to find what fraction of the total sample (without flux selection effects)
# is found for x < DMmax0 and y < MrMax0. To do so, we need to integrate differential
# distributions returned by bootstrap_Cminus up to x=DMmax0 and y=MrMax0
# (NB: we could have EASILY read off these from the original cumulative distributions returned
# from Cminus, but unfortunately bootstrap_Cminus returns only differential distributions;
# a design bug??)
# n.b. verified that all four distributions integrate to 1
XfracD = IntDiffDistr(x_fit, xD_dist, DMmax0)
YfracD = IntDiffDistr(y_fit, yD_dist, MrMax0)
XfracH = IntDiffDistr(x_fit, xH_dist, DMmax0)
YfracH = IntDiffDistr(y_fit, yH_dist, MrMax0)
# and use eq. 12 to obtain normalization constant C for both subsamples
Cdisk = Ndisk / (XfracD * YfracD)
Chalo = Nhalo / (XfracH * YfracH)
print 'Cdisk = ', Cdisk, 'Chalo = ', Chalo
## Cdisk = 608,082 Chalo = 565,598
## number density normalization
# need to go from per DM to per pc: dDM/dV = 5 / [ln(10) * dOmega * D^3]
Dpc = 10 * 10**(0.2*x_mid)
# the units are "number of stars per pc^3" (of all absolute magnitudes Mr)
# note that here we assume isotropic sky distribution (division by dOmega!)
rhoD = Cdisk * 5/np.log(10)/dOmega / Dpc**3 * xD_dist
rhoH = Chalo * 5/np.log(10)/dOmega / Dpc**3 * xH_dist
# same fractional errors remain after renormalization
dxD_distN = rhoD * dxD_dist / xD_dist
dxH_distN = rhoH * dxH_dist / xH_dist
# differential luminosity function: per pc3 and mag, ** at the solar position **
# to get proper normalization, we simply multiply differential distributions (whose
# integrals are already normalized to 1) by the local number density distributions)
# Therefore, we need to extrapolate rhoD and rhoH to D=0
# and then use these values as normalization factors
# The choice of extrapolation function is tricky (much more so than in case of
# interpolation): based on our "prior knowledge", we choose
# for disk: fit exponential disk, ln(rho) = ln(rho0) - H*D for 100 < D/pc < 500
xFitExpDisk = Dpc[(Dpc > 100) & (Dpc < 500)]
yFitExpDisk = np.log(rhoD[(Dpc > 100) & (Dpc < 500)])
A, H = curve_fit(ExpDiskDensity, xFitExpDisk, yFitExpDisk)[0]
rhoD0 = np.exp(A)
print 'rhoD0', rhoD0, ' scale height H (pc) =', 1/H
# and for halo: simply take the median value for 500 < D/pc < 2000
rhoH0 = np.median(rhoH[(Dpc > 500) & (Dpc < 2000)])
print 'rhoH0', rhoH0
# and now renormalize (rhoD0, rhoH0 = 0.0047 and 0.0000217)
# the units are "number of stars per pc^3 AND per mag" (at D=0)
yD_distN = yD_dist * rhoD0
yH_distN = yH_dist * rhoH0
# same fractional errors remain after renormalization
dyD_distN = yD_distN * dyD_dist / yD_dist
dyH_distN = yH_distN * dyH_dist / yH_dist
### PLOTTING ###
plot_kwargs = dict(color='k', linestyle='none', marker='.', markersize=1)
plt.subplots_adjust(bottom=0.09, top=0.96, left=0.09, right=0.96, wspace=0.31, hspace=0.32)
# plot the distribution of the distance modulus difference and compute its median
# and root-mean-square scatter (hint: beware of outliers and clip at 3sigma!)
hist, bins = np.histogram(diffDM, bins=50) ## N.B. diffDM defined at the top
center = (bins[:-1]+bins[1:])/2
ax1 = plt.subplot(3,2,1)
ax1.plot(center, hist, drawstyle='steps')
ax1.set_xlim(-1, 1)
ax1.set_xlabel(r'$\mathrm{\Delta DM (mag)}$')
ax1.set_ylabel(r'$\mathrm{dN/d(\Delta DM)}$')
ax1.set_title('parallax SNR>10')
ax1.plot(label=r'mpj legend')
plt.text(0.25, 0.9,
r'$\bar{x}=-0.023$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.9),
ha='center', va='center', transform=plt.gca().transAxes)
plt.text(0.75, 0.9,
r'$med.=-0.035$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.9),
ha='center', va='center', transform=plt.gca().transAxes)
plt.text(0.2, 0.7,
r'$\sigma=0.158$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.9),
ha='center', va='center', transform=plt.gca().transAxes)
plt.text(0.75, 0.7,
r'$\sigma_G=0.138$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.9),
ha='center', va='center', transform=plt.gca().transAxes)
# plot uncorrected Mr vs. DM for disk
ax2 = plt.subplot(3,2,2)
ax2.set_xlim(4, 15)
ax2.set_ylim(17, 10)
ax2.set_xlabel(r'$\mathrm{DM}$')
ax2.set_ylabel(r'$\mathrm{M_{r}}$')
scatter_contour(xCD, yCD, threshold=20, log_counts=True, ax=ax2,
histogram2d_args=dict(bins=40),
plot_args=dict(marker='.', linestyle='none',
markersize=1, color='black'),
contour_args=dict(cmap=plt.cm.bone))
# ax2.plot(xCmaxD, yCD, color='blue')
plt.text(0.75, 0.20,
r'$disk$',
fontsize=22, bbox=dict(ec='k', fc='w', alpha=0.3),
ha='center', va='center', transform=plt.gca().transAxes)
# plot uncorrected Mr vs. DM for halo
ax3 = plt.subplot(3,2,3)
ax3.set_xlim(4, 15)
ax3.set_ylim(17, 10)
ax3.set_xlabel(r'$\mathrm{DM}$')
ax3.set_ylabel(r'$\mathrm{M_{r}}$')
scatter_contour(xCH, yCH, threshold=20, log_counts=True, ax=ax3,
histogram2d_args=dict(bins=40),
plot_args=dict(marker='.', linestyle='none',
markersize=1, color='black'),
contour_args=dict(cmap=plt.cm.bone))
# ax3.plot(xCH, yCmaxH, '.k', markersize=0.1, color='blue')
plt.text(0.75, 0.20,
r'$halo$',
fontsize=22, bbox=dict(ec='k', fc='w', alpha=0.3),
ha='center', va='center', transform=plt.gca().transAxes)
# uncorrected Mr distributions
ax4 = plt.subplot(3,2,4)
ax4.hist(yCD, bins=30, log=True, histtype='step', color='red')
ax4.hist(yCH, bins=30, log=True, histtype='step', color='blue')
ax4.set_xlim(10, 17)
ax4.set_ylim(1, 50000)
ax4.set_xlabel(r'$\mathrm{M_r}$')
ax4.set_ylabel(r'$\mathrm{dN/dM_r}$')
plt.text(0.52, 0.20,
r'$uncorrected \, M_r \, distribution$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.3),
ha='center', va='center', transform=plt.gca().transAxes)
## now plot the two remaining panels: corrected rho and Mr distributions
# density distributions
ax5 = plt.subplot(325, yscale='log')
ax5.errorbar(Dpc, rhoD, dxD_distN, fmt='^k', ecolor='k', lw=1, color='red', label='disk')
ax5.errorbar(Dpc, rhoH, dxH_distN, fmt='^k', ecolor='k', lw=1, color='blue', label='halo')
ax5.set_xlim(0, 5000)
ax5.set_ylim(0.000001, 0.01)
ax5.set_xlabel(r'$\mathrm{D (pc)}$')
ax5.set_ylabel(r'$\mathrm{\rho(D)} (pc^{-3}) $')
ax5.legend(loc='upper right')
# luminosity functions
ax6 = plt.subplot(326, yscale='log')
ax6.errorbar(y_mid, yD_distN, dyD_distN, fmt='^k', ecolor='k', lw=1, color='red')
ax6.errorbar(y_mid, yH_distN, dyH_distN, fmt='^k', ecolor='k', lw=1, color='blue')
# true luminosity functions
MbinD = diskLF[0]
psiD = diskLF[1]
MbinH = haloLF[0]
psiH = haloLF[1] / 200
ax6.plot(MbinD, psiD, color='red')
ax6.plot(MbinH, psiH, color='blue')
plt.text(0.62, 0.15,
r'$\Psi \, in \, pc^{-3} mag^{-1}$',
fontsize=12, bbox=dict(ec='k', fc='w', alpha=0.3),
ha='center', va='center', transform=plt.gca().transAxes)
ax6.set_xlim(10, 17)
ax6.set_xlabel(r'$\mathrm{M_r}$')
ax6.set_ylabel(r'$\Psi(M_r)=\mathrm{dN^2/dV \,dM_r}$')
plt.savefig('Astr511HW2.png')
plt.show()
def print_chi(data_dict, axis):
""" Make the chi plot
Some more doc if need be ...
"""
#plt.clf()
#fig = plt.figure()
#axis = fig.add_subplot(111)
mags = data_dict['SDSS_g_mMed']
chi = data_dict['delflx'] / data_dict['delflxerr']
# remove silly values
m = ( mags > 17 ) * (mags< 20)
mags = mags[m]; chi = chi[m]
print('We throw away points with magnitude < 15, so that we are left with %d'%len(chi)+" points")
nlev = 15
scatter_contour(mags, chi, levels=nlev, threshold=50, log_counts=True, ax=axis,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=' ', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.jet))
step = (max(chi)-min(chi)) / nlev
print('Colourscale ranges from %f to %f, with 15 levels, i.e. steps every %f '%\
(min(chi), max(chi), step) )
rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))
rms_std = lambda x : np.std(x)
# The code below starts at 40 bins and calculates standard deviation sigma,
# and robust sigma G for each bin, and finds the smallest number of bins
# where there are no NaNs in any bin.
nbins1 = 40
diagnostic = 10
while diagnostic > 0 :
rms1_all = binned_statistic(mags, chi, statistic=rms_std, bins=nbins1)
nbins1 = nbins1 - 2
diagnostic = np.isnan(rms1_all[0]).sum()
print('For standard deviation we use %d bins' %nbins1)
nbins2 = 40
diagnostic = 10
while diagnostic > 0 :
rms2_all = binned_statistic(mags, chi, statistic=rms_robust, bins=nbins2)
nbins2 = nbins2 - 2
diagnostic = np.isnan(rms1_all[0]).sum()
print('For robust sigma G we use %d bins' %nbins2)
# Evaluated at the same bins as respective standard deviations...
bin_means1 = binned_statistic(mags, chi, statistic = 'mean', bins=nbins1+2)[0]
bin_means2 = binned_statistic(mags, chi, statistic = 'mean', bins=nbins2+2)[0]
chi_rms1 = rms1_all[0]
mags1 = rms1_all[1][:-1] # number of bins is one more than that of values
chi_rms2 = rms2_all[0]
mags2 = rms2_all[1][:-1]
#return mags1, mags2, bin_means1, bin_means2, chi_rms1, chi_rms2
sym = 56 # symbol size
axis.scatter(mags1, bin_means1 + 2*chi_rms1,s=sym, alpha=1.0, color='yellow', edgecolors='black')
axis.scatter(mags1, bin_means1 - 2*chi_rms1,s=sym, alpha=1.0, color='yellow', edgecolors='black')
axis.scatter(mags2, bin_means2 + 2*chi_rms2,s=sym, alpha=1.0, color='orange', edgecolors='black' )
axis.scatter(mags2, bin_means2 - 2*chi_rms2,s=sym, alpha=1.0, color='orange', edgecolors='black' )
axis.set_xlim(xmin=17)
axis.set_ylim(ymin=-6, ymax=6)
axis.xaxis.set_label_position('top')
axis.xaxis.tick_top()
axis.set_xlabel('SDSS g mag' )
axis.set_ylabel(r'$\chi $')
axis.set_xticks([17,18,19,20])
axis.set_xticklabels(['17','18','19','20'])
def plot_L_vs_T(T,
L,
masses=None,
filename=None,
show=False,
limits=None,
scale=['linear', 'linear']):
# Import external modules
import numpy as np
import math
import pyfits as pf
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import ImageGrid
from astroML.plotting import scatter_contour
# Set up plot aesthetics
# ----------------------
plt.close
plt.clf()
plt.rcdefaults()
# Color map
cmap = plt.cm.gnuplot
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 3)]
font_scale = 13
line_weight = 600
font_weight = 600
params = {
'axes.color_cycle': color_cycle, # colors of different plots
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
#'axes.weight': line_weight,
'axes.linewidth': 1.2,
'axes.labelweight': font_weight,
'legend.fontsize': font_scale * 3 / 4,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': font_weight,
'font.serif': 'computer modern roman',
'text.fontsize': font_scale,
'text.usetex': True,
#'text.latex.preamble': r'\usepackage[T1]{fontenc}',
#'font.family': 'sans-serif',
'figure.figsize': (5, 5),
'figure.dpi': 600,
'backend': 'pdf',
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
ax = fig.add_subplot(111)
bins = (T[::-1], L)
bins = 15
L = np.log10(L)
scale = 'linear', 'linear'
limits = [40000, 500, -3, 6]
l1 = scatter_contour(
T,
L,
threshold=1,
log_counts=True,
levels=6,
ax=ax,
histogram2d_args=dict(
bins=bins,
range=((limits[1], limits[0]), (limits[2], limits[3])),
),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.7,
markersize=2),
contour_args=dict(cmap=plt.cm.gray_r,
#cmap=cmap,
),
)
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
ax.set_xscale(scale[0])
ax.set_yscale(scale[1])
# Adjust asthetics
ax.set_xlabel(r'$T_{\rm eff}$ [K]')
ax.set_ylabel(r'log$_{10}( L $ [L$_\odot$])')
ax.locator_params(nbins=5, axis='x')
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()
plt.xlim(-5.0, 5.0)
plt.ylim(-5.0, 5.0)
iplot=iplot+1
plt.subplot(2,2,iplot)
threshold=10
# note ax=plt within arguments
plt.title("scatter_contour: '.', markersize=2, color='red'")
label='data: ' + str(len(xdata))
scatter_contour(xdata, ydata, threshold=threshold, log_counts=True,
histogram2d_args=dict(bins=40),
ax=plt,
plot_args=dict(marker='.', linestyle='none',
markersize=2, color='red',
markeredgecolor='red',
label=label),
contour_args=dict(cmap=plt.cm.rainbow))
plt.xlim(-5.0, 5.0)
plt.ylim(-5.0, 5.0)
iplot=iplot+1
plt.subplot(2,2,iplot)
threshold=10
plt.title("scatter_contour: 'o', markersize=2, color='blue'")
scatter_contour(xdata, ydata, threshold=threshold, log_counts=True,
histogram2d_args=dict(bins=20),
ax=plt,
plot_args=dict(marker='o', linestyle='none',
def plot_colorcolor():
# ADD MW SATELLITES?
# READ SPECTRA
file = SAGA_DIR + '/data/saga_spectra_dirty.fits.gz'
allspec = Table.read(file)
m1 = allspec['ZQUALITY'] > 2
m2 = allspec['REMOVE'] == -1
allspec = allspec[m1 & m2]
# msat = allspec['SATS'] == 1
msat1 = allspec['SPEC_Z'] > 0.005
msat2 = allspec['SPEC_Z'] < 0.02
msat = msat1 & msat2
mname1 = allspec['SATS'] == 1
mname2 = allspec['HOST_SAGA_NAME'] != ''
mname = mname1 & mname2
gr = allspec['g'] - allspec['r']
ri = allspec['r'] - allspec['i']
fmag = allspec['r'] > 17.7
bmag = allspec['r'] <= 17.7
gal = allspec['PHOTPTYPE'] == 3
star = allspec['PHOTPTYPE'] == 6
xl = [-0.3, 2.0]
yl = [-0.8, 1.3]
xl1 = gr > xl[0]
xl2 = gr < xl[1]
yl1 = ri > yl[0]
yl2 = ri < yl[1]
lims = xl1 & xl2 & yl1 & yl2
fig = plt.figure(figsize=(7, 7))
fig.subplots_adjust(left=0.1,
right=0.95,
wspace=0.05,
bottom=0.1,
top=0.95,
hspace=0.05)
thr = 60
##############################
ax = fig.add_subplot(2, 2, 1)
scatter_contour(gr[gal & bmag & lims],
ri[gal & bmag & lims],
threshold=thr,
log_counts=True,
ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none',
color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[gal & bmag & msat], ri[gal & bmag & msat], 'b.', label='low-z')
ax.plot(gr[gal & bmag & msat & mname],
ri[gal & bmag & msat & mname],
'r.',
label='satellites')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0] + 0.02, yl[1] - 0.2, 'Bright Galaxies, r $<$ 17.7')
ax.get_xaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
plt.legend(loc=4, fontsize=9)
ax.set_ylabel('(r-i)')
##############################
ax = fig.add_subplot(2, 2, 2)
scatter_contour(gr[gal & fmag & lims],
ri[gal & fmag & lims],
threshold=thr,
log_counts=True,
ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none',
color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[gal & fmag & msat], ri[gal & fmag & msat], 'b.')
ax.plot(gr[gal & fmag & msat & mname], ri[gal & fmag & msat & mname], 'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0] + 0.02, yl[1] - 0.2, 'Faint Galaxies, r $>$ 17.7')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
##############################
ax = fig.add_subplot(2, 2, 3)
scatter_contour(gr[star & bmag & lims],
ri[star & bmag & lims],
threshold=thr,
log_counts=True,
ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none',
color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[star & bmag & msat], ri[star & bmag & msat], 'b.')
ax.plot(gr[star & bmag & msat & mname], ri[star & bmag & msat & mname],
'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0] + 0.03, yl[1] - 0.2, 'Bright Stars, r $<$ 17.7')
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
ax.set_xlabel('(g-r)')
ax.set_ylabel('(r-i)')
##############################
ax = fig.add_subplot(2, 2, 4)
scatter_contour(gr[star & fmag & lims],
ri[star & fmag & lims],
threshold=thr,
log_counts=True,
ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none',
color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[star & fmag & msat], ri[star & fmag & msat], 'b.')
ax.plot(gr[star & fmag & msat & mname], ri[star & fmag & msat & mname],
'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0] + 0.03, yl[1] - 0.2, 'Faint Stars, r $>$ 17.7')
ax.get_yaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
ax.set_xlabel('(g-r)')
plt.show()
plt.savefig('fig_colorcolor.pdf')
# DO OBJECTS OUTSIDE OF STRAIGHT COLOR REQUIRES,
# PASS CRITERIA INCLUDING ERRORS?
lowz = allspec[msat]
gr = lowz['g'] - lowz['r']
ri = lowz['r'] - lowz['i']
m1 = gr > 0.8
m2 = ri > 0.5
for obj in lowz[m1 | m2]:
gmr = obj['g'] - obj['r']
rmi = obj['r'] - obj['i']
gmre = obj['g'] - obj['r'] - 2 * obj['g_err'] - 2 * obj['r_err']
rmie = obj['r'] - obj['i'] - 2 * obj['r_err'] - 2 * obj['i_err']
# print gmr,gmre,obj['SATS'],obj['PHOTPTYPE'],obj['SPEC_Z'],obj['REMOVE']
# print rmi,rmie
if gmre > 0.8 and rmie > 0.5:
print obj['SPEC_Z'], obj['RA'], obj['DEC'], obj['r']
print 'STARS'
star = lowz['PHOTPTYPE'] == 6
for obj in lowz[star]:
print obj['SPEC_Z'], obj['RA'], obj['DEC'], obj['TELNAME'], obj[
'HOST_NSAID'], obj['SATS'], obj['REMOVE']
def plot_colorcolor():
# ADD MW SATELLITES?
# READ SPECTRA
file = SAGA_DIR +'/data/saga_spectra_dirty.fits.gz'
allspec = Table.read(file)
m1 = allspec['ZQUALITY'] > 2
m2 = allspec['REMOVE'] == -1
allspec = allspec[m1&m2]
# msat = allspec['SATS'] == 1
msat1 = allspec['SPEC_Z'] > 0.005
msat2 = allspec['SPEC_Z'] < 0.02
msat = msat1 & msat2
mname1 = allspec['SATS'] == 1
mname2 = allspec['HOST_SAGA_NAME'] != ''
mname = mname1&mname2
gr = allspec['g'] - allspec['r']
ri = allspec['r'] - allspec['i']
fmag = allspec['r'] > 17.7
bmag = allspec['r'] <= 17.7
gal = allspec['PHOTPTYPE'] == 3
star = allspec['PHOTPTYPE'] == 6
xl = [-0.3,2.0]
yl=[-0.8,1.3]
xl1 = gr > xl[0]
xl2 = gr < xl[1]
yl1 = ri > yl[0]
yl2 = ri < yl[1]
lims = xl1&xl2&yl1&yl2
fig = plt.figure(figsize=(7, 7))
fig.subplots_adjust(left=0.1, right=0.95, wspace=0.05,
bottom=0.1, top=0.95, hspace=0.05)
thr = 60
##############################
ax = fig.add_subplot(2, 2, 1)
scatter_contour(gr[gal&bmag&lims], ri[gal&bmag&lims], threshold=thr, log_counts=True, ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[gal&bmag&msat], ri[gal&bmag&msat],'b.',label = 'low-z')
ax.plot(gr[gal&bmag&msat&mname], ri[gal&bmag&msat&mname],'r.',label='satellites')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0]+0.02,yl[1]-0.2,'Bright Galaxies, r $<$ 17.7')
ax.get_xaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
plt.legend(loc=4,fontsize=9)
ax.set_ylabel('(r-i)')
##############################
ax = fig.add_subplot(2, 2, 2)
scatter_contour(gr[gal&fmag&lims], ri[gal&fmag&lims], threshold=thr, log_counts=True, ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[gal&fmag&msat], ri[gal&fmag&msat],'b.')
ax.plot(gr[gal&fmag&msat&mname], ri[gal&fmag&msat&mname],'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0]+0.02,yl[1]-0.2,'Faint Galaxies, r $>$ 17.7')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
##############################
ax = fig.add_subplot(2, 2, 3)
scatter_contour(gr[star&bmag&lims], ri[star&bmag&lims], threshold=thr, log_counts=True, ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[star&bmag&msat], ri[star&bmag&msat],'b.')
ax.plot(gr[star&bmag&msat&mname], ri[star&bmag&msat&mname],'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0]+0.03,yl[1]-0.2,'Bright Stars, r $<$ 17.7')
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
ax.set_xlabel('(g-r)')
ax.set_ylabel('(r-i)')
##############################
ax = fig.add_subplot(2, 2, 4)
scatter_contour(gr[star&fmag&lims], ri[star&fmag&lims], threshold=thr, log_counts=True, ax=ax,
histogram2d_args=dict(bins=40),
plot_args=dict(marker=',', linestyle='none', color='black'),
contour_args=dict(cmap=plt.cm.bone))
ax.plot(gr[star&fmag&msat], ri[star&fmag&msat],'b.')
ax.plot(gr[star&fmag&msat&mname], ri[star&fmag&msat&mname],'r.')
ax.set_xlim(xl)
ax.set_ylim(yl)
ax.text(xl[0]+0.03,yl[1]-0.2,'Faint Stars, r $>$ 17.7')
ax.get_yaxis().set_visible(False)
plt.axvline(0.8, c='k', ls=':')
plt.axhline(0.5, c='k', ls=':')
ax.set_xlabel('(g-r)')
plt.show()
plt.savefig('fig_colorcolor.pdf')
# DO OBJECTS OUTSIDE OF STRAIGHT COLOR REQUIRES,
# PASS CRITERIA INCLUDING ERRORS?
lowz = allspec[msat]
gr = lowz['g'] - lowz['r']
ri = lowz['r'] - lowz['i']
m1 = gr > 0.8
m2 = ri > 0.5
for obj in lowz[m1|m2]:
gmr = obj['g'] - obj['r']
rmi = obj['r'] - obj['i']
gmre = obj['g'] - obj['r'] - 2*obj['g_err'] - 2*obj['r_err']
rmie = obj['r'] - obj['i'] - 2*obj['r_err'] - 2*obj['i_err']
# print gmr,gmre,obj['SATS'],obj['PHOTPTYPE'],obj['SPEC_Z'],obj['REMOVE']
# print rmi,rmie
if gmre > 0.8 and rmie > 0.5:
print obj['SPEC_Z'],obj['RA'],obj['DEC'],obj['r']
print 'STARS'
star = lowz['PHOTPTYPE'] ==6
for obj in lowz[star]:
print obj['SPEC_Z'],obj['RA'],obj['DEC'],obj['TELNAME'],obj['HOST_NSAID'],obj['SATS'],obj['REMOVE']
