def get_stars_and_galaxies(Nstars=5000, Ngals=5000):
"""Get the subset of star/galaxy data to plot"""
data = fetch_imaging_sample()
objtype = data['type']
stars = data[objtype == 6][:Nstars]
galaxies = data[objtype == 3][:Ngals]
return stars, galaxies
def get_stars_and_galaxies(Nstars=5000, Ngals=5000):
"""Get the subset of star/galaxy data to plot"""
data = fetch_imaging_sample()
objtype = data['type']
stars = data[objtype == 6][:Nstars]
galaxies = data[objtype == 3][:Ngals]
return stars, galaxies
# Author: Jake VanderPlas <*****@*****.**>
# License: BSD
# The figure is an example from astroML: see http://astroML.github.com
import numpy as np
from matplotlib import pyplot as plt
from astroML.datasets import fetch_imaging_sample
#------------------------------------------------------------
# Get the star/galaxy data
data = fetch_imaging_sample()
objtype = data['type']
stars = data[objtype == 6][:5000]
galaxies = data[objtype == 3][:5000]
#------------------------------------------------------------
# Plot the stars and galaxies
plot_kwargs = dict(color='k', linestyle='none', marker='.', markersize=1)
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.plot(galaxies['gRaw'] - galaxies['rRaw'],
galaxies['rRaw'],
**plot_kwargs)
ax2 = fig.add_subplot(223, sharex=ax1)
ax2.plot(galaxies['gRaw'] - galaxies['rRaw'],
galaxies['rRaw'] - galaxies['iRaw'],
**plot_kwargs)
"""
SDSS Imaging
============
This example shows how to load the magnitude data from the SDSS imaging
catalog, and plot colors and magnitudes of the stars and galaxies.
"""
# Author: Jake VanderPlas <*****@*****.**>
# License: BSD
# The figure is an example from astroML: see http://astroML.github.com
import numpy as np
from matplotlib import pyplot as plt
from astroML.datasets import fetch_imaging_sample
#------------------------------------------------------------
# Get the star/galaxy data
data = fetch_imaging_sample()
objtype = data['type']
stars = data[objtype == 6][:5000]
galaxies = data[objtype == 3][:5000]
#------------------------------------------------------------
# Plot the stars and galaxies
plot_kwargs = dict(color='k', linestyle='none', marker='.', markersize=1)
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax1.plot(galaxies['gRaw'] - galaxies['rRaw'],
galaxies['rRaw'],
from astroML.density_estimation import XDGMM from astroML.crossmatch import crossmatch from astroML.datasets import fetch_sdss_S82standards, fetch_imaging_sample from astroML.plotting.tools import draw_ellipse from astroML.decorators import pickle_results from astroML.stats import sigmaG #------------------------------------------------------------ # define u-g-r-i-z extinction from Berry et al, arXiv 1111.4985 # multiply extinction by A_r extinction_vector = np.array([1.810, 1.400, 1.0, 0.759, 0.561]) #---------------------------------------------------------------------- # Fetch and process the noisy imaging data data_noisy = fetch_imaging_sample() # select only stars data_noisy = data_noisy[data_noisy['type'] == 6] # Get the extinction-corrected magnitudes for each band X = np.vstack([data_noisy[f + 'RawPSF'] for f in 'ugriz']).T Xerr = np.vstack([data_noisy[f + 'psfErr'] for f in 'ugriz']).T # extinction terms from Berry et al, arXiv 1111.4985 X -= (extinction_vector * data_noisy['rExtSFD'][:, None]) #---------------------------------------------------------------------- # Fetch and process the stacked imaging data data_stacked = fetch_sdss_S82standards()
#---------------------------------------------------------------------- # This function adjusts matplotlib settings for a uniform feel in the textbook. # 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) #------------------------------------------------------------ # define u-g-r-i-z extinction from Berry et al, arXiv 1111.4985 # multiply extinction by A_r extinction_vector = np.array([1.810, 1.400, 1.0, 0.759, 0.561]) #---------------------------------------------------------------------- # Fetch and process the noisy imaging data data_noisy = fetch_imaging_sample() # select only stars data_noisy = data_noisy[data_noisy['type'] == 6] # Get the extinction-corrected magnitudes for each band X = np.vstack([data_noisy[f + 'RawPSF'] for f in 'ugriz']).T Xerr = np.vstack([data_noisy[f + 'psfErr'] for f in 'ugriz']).T # extinction terms from Berry et al, arXiv 1111.4985 X -= (extinction_vector * data_noisy['rExtSFD'][:, None]) #---------------------------------------------------------------------- # Fetch and process the stacked imaging data data_stacked = fetch_sdss_S82standards()