def load_rrlyrae_data():
"""Load the RR Lyrae data.
This will be used in several examples.
"""
# ----------------------------------------------------------------------
# Load data
rrlyrae = fetch_rrlyrae_mags()
standards = fetch_sdss_S82standards()
# perform color cuts on standard stars
# these come from eqns 1-4 of Sesar et al 2010, ApJ 708:717
u_g = standards["mmu_u"] - standards["mmu_g"]
g_r = standards["mmu_g"] - standards["mmu_r"]
r_i = standards["mmu_r"] - standards["mmu_i"]
i_z = standards["mmu_i"] - standards["mmu_z"]
standards = standards[
(u_g > 0.7)
& (u_g < 1.35)
& (g_r > -0.15)
& (g_r < 0.4)
& (r_i > -0.15)
& (r_i < 0.22)
& (i_z > -0.21)
& (i_z < 0.25)
]
# ----------------------------------------------------------------------
# get magnitudes and colors; split into train and test sets
mags_rr = np.vstack([rrlyrae[f + "mag"] for f in "ugriz"])
colors_rr = mags_rr[:-1] - mags_rr[1:]
mags_st = np.vstack([standards["mmu_" + f] for f in "ugriz"])
colors_st = mags_st[:-1] - mags_st[1:]
# stack the two sets of colors together
X = np.vstack((colors_st.T, colors_rr.T))
y = np.zeros(X.shape[0])
y[-colors_rr.shape[1] :] = 1
return X, y
def load_rrlyrae_data():
"""Load the RR Lyrae data.
This will be used in several examples.
"""
#----------------------------------------------------------------------
# Load data
rrlyrae = fetch_rrlyrae_mags()
standards = fetch_sdss_S82standards()
# perform color cuts on standard stars
# these come from eqns 1-4 of Sesar et al 2010, ApJ 708:717
u_g = standards['mmu_u'] - standards['mmu_g']
g_r = standards['mmu_g'] - standards['mmu_r']
r_i = standards['mmu_r'] - standards['mmu_i']
i_z = standards['mmu_i'] - standards['mmu_z']
standards = standards[(u_g > 0.7) & (u_g < 1.35) &
(g_r > -0.15) & (g_r < 0.4) &
(r_i > -0.15) & (r_i < 0.22) &
(i_z > -0.21) & (i_z < 0.25)]
#----------------------------------------------------------------------
# get magnitudes and colors; split into train and test sets
mags_rr = np.vstack([rrlyrae[f + 'mag'] for f in 'ugriz'])
colors_rr = mags_rr[:-1] - mags_rr[1:]
mags_st = np.vstack([standards['mmu_' + f] for f in 'ugriz'])
colors_st = mags_st[:-1] - mags_st[1:]
# stack the two sets of colors together
X = np.vstack((colors_st.T, colors_rr.T))
y = np.zeros(X.shape[0])
y[-colors_rr.shape[1]:] = 1
return X, y
more effectively show very dense scatter plots.
"""
# Author: Jake VanderPlas <*****@*****.**>
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
from matplotlib import pyplot as plt
from astroML.plotting import scatter_contour
from astroML.datasets import fetch_sdss_S82standards
#------------------------------------------------------------
# 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='.',
from astroML.plotting import scatter_contour
from astroML.datasets import 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)
#------------------------------------------------------------
# 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'${\rm r - i}$')
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()
# cut to RA, DEC range of imaging sample
RA = data_stacked['RA']
DEC = data_stacked['DEC']
data_stacked = data_stacked[(RA > 0) & (RA < 10) &
(DEC > -1) & (DEC < 1)]
# get stacked magnitudes for each band
Y = np.vstack([data_stacked['mmu_' + f] for f in 'ugriz']).T
Yerr = np.vstack([data_stacked['msig_' + f] for f in 'ugriz']).T
# extinction terms from Berry et al, arXiv 1111.4985
Y -= (extinction_vector * data_stacked['A_r'][:, None])
# quality cuts
This demonstrates how to fetch and plot the colors of the SDSS Stripe 82
standard stars, both alone and with the cross-matched 2MASS colors.
"""
# 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_sdss_S82standards
from astroML.plotting import MultiAxes
#------------------------------------------------------------
# Plot SDSS data alone
data = fetch_sdss_S82standards()
colors = np.zeros((len(data), 4))
colors[:, 0] = data['mmu_u'] - data['mmu_g']
colors[:, 1] = data['mmu_g'] - data['mmu_r']
colors[:, 2] = data['mmu_r'] - data['mmu_i']
colors[:, 3] = data['mmu_i'] - data['mmu_z']
labels = ['u-g', 'g-r', 'r-i', 'i-z']
bins = [
np.linspace(0.0, 3.5, 100),
np.linspace(0, 2, 100),
np.linspace(-0.2, 1.8, 100),
np.linspace(-0.2, 1.0, 100)
standard stars, both alone and with the cross-matched 2MASS colors.
"""
# 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_sdss_S82standards
from astroML.plotting import MultiAxes
#------------------------------------------------------------
# Plot SDSS data alone
data = fetch_sdss_S82standards()
colors = np.zeros((len(data), 4))
colors[:, 0] = data['mmu_u'] - data['mmu_g']
colors[:, 1] = data['mmu_g'] - data['mmu_r']
colors[:, 2] = data['mmu_r'] - data['mmu_i']
colors[:, 3] = data['mmu_i'] - data['mmu_z']
labels = ['u-g', 'g-r', 'r-i', 'i-z']
bins = [np.linspace(0.0, 3.5, 100),
np.linspace(0, 2, 100),
np.linspace(-0.2, 1.8, 100),
np.linspace(-0.2, 1.0, 100)]
# 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() # cut to RA, DEC range of imaging sample RA = data_stacked['RA'] DEC = data_stacked['DEC'] data_stacked = data_stacked[(RA > 0) & (RA < 10) & (DEC > -1) & (DEC < 1)] # get stacked magnitudes for each band Y = np.vstack([data_stacked['mmu_' + f] for f in 'ugriz']).T Yerr = np.vstack([data_stacked['msig_' + f] for f in 'ugriz']).T # extinction terms from Berry et al, arXiv 1111.4985 Y -= (extinction_vector * data_stacked['A_r'][:, None]) # quality cuts g = Y[:, 1]
from astroML.datasets import fetch_sdss_S82standards
import matplotlib.pyplot as plt
data_unmatched = fetch_sdss_S82standards()
g_u = data_unmatched['mmu_g']
r_u = data_unmatched['mmu_r']
i_u = data_unmatched['mmu_i']
data_matched = fetch_sdss_S82standards(crossmatch_2mass=True)
g_m = data_matched['mmu_g']
r_m = data_matched['mmu_r']
i_m = data_matched['mmu_i']
# Plot of the unmatched (all) stars
plt.figure(figsize=[16, 9])
plt.scatter(g_u - r_u, r_u - i_u, s=4, c='black')
plt.xlabel('g-r color')
plt.ylabel('r-i color')
plt.xlim(-2, 4)
plt.ylim(-2, 4)
plt.title('Non-crossmatched SDSS Data')
plt.savefig('unmatched.png')
plt.close()
# Plot of the matched stars
fig = plt.figure(figsize=[16, 9])
plt.scatter(g_m - r_m, r_m - i_m, s=4, c='black')
plt.xlabel('g-r color')
plt.ylabel('r-i color')
plt.xlim(-2, 4)
plt.ylim(-2, 4)
