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src/a/s/astroML-0.2/examples/datasets/plot_sdss_specgals.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.datasets import fetch_sdss_specgals
 
data = fetch_sdss_specgals()

src/a/s/astroML-HEAD/examples/datasets/plot_sdss_specgals.py   astroML(Download)
from matplotlib import pyplot as plt
 
from astroML.datasets import fetch_sdss_specgals
 
data = fetch_sdss_specgals()

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_tree.py   astroML(Download)
 
from sklearn.tree import DecisionTreeRegressor
from astroML.datasets import fetch_sdss_specgals
 
#----------------------------------------------------------------------
#------------------------------------------------------------
# Fetch data and prepare it for the computation
data = fetch_sdss_specgals()
 
# put magnitudes in a matrix

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_forest.py   astroML(Download)
 
from sklearn.ensemble import RandomForestRegressor
from astroML.datasets import fetch_sdss_specgals
from astroML.decorators import pickle_results
 
#------------------------------------------------------------
# Fetch and prepare the data
data = fetch_sdss_specgals()
 
# put magnitudes in a matrix

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_boosting.py   astroML(Download)
 
from sklearn.ensemble import GradientBoostingRegressor
from astroML.datasets import fetch_sdss_specgals
from astroML.decorators import pickle_results
 
#------------------------------------------------------------
# Fetch and prepare the data
data = fetch_sdss_specgals()
 
# put magnitudes in a matrix

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_basic.py   astroML(Download)
from sklearn.metrics.pairwise import euclidean_distances
 
from astroML.datasets import fetch_sdss_specgals
 
#----------------------------------------------------------------------
np.random.seed(0)
 
data = fetch_sdss_specgals()
 
# put magnitudes in a matrix

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_bagging.py   astroML(Download)
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
 
from astroML.datasets import fetch_sdss_specgals
 
#----------------------------------------------------------------------
setup_text_plots(fontsize=8, usetex=True)
 
data = fetch_sdss_specgals()
 
# put magnitudes in a matrix

src/a/s/astroML-0.2/book_figures/chapter6/fig_correlation_function.py   astroML(Download)
 
from astroML.decorators import pickle_results
from astroML.datasets import fetch_sdss_specgals
from astroML.correlation import bootstrap_two_point_angular
 
#------------------------------------------------------------
# Get data and do some quality cuts
data = fetch_sdss_specgals()
m_max = 17.7
 

src/a/s/astroML-0.2/book_figures/chapter4/fig_lyndenbell_gals.py   astroML(Download)
from astroML.lumfunc import binned_Cminus, bootstrap_Cminus
from astroML.cosmology import Cosmology
from astroML.datasets import fetch_sdss_specgals
 
#----------------------------------------------------------------------
#------------------------------------------------------------
# Get the data and perform redshift/magnitude cuts
data = fetch_sdss_specgals()
 
z_min = 0.08

src/a/s/astroML-0.2/book_figures/chapter1/fig_SDSS_specgals.py   astroML(Download)
import numpy as np
from matplotlib import pyplot as plt
from astroML.datasets import fetch_sdss_specgals
 
#----------------------------------------------------------------------
#------------------------------------------------------------
# Fetch spectroscopic galaxy data
data = fetch_sdss_specgals()
data = data[:10000]
 

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