def plot_sdss_filters():
Xref = fetch_vega_spectrum()
Xref[:, 1] /= 2.1 * Xref[:, 1].max()
#----------------------------------------------------------------------
# Plot filters in color with a single spectrum
fig, ax = plt.subplots()
ax.plot(Xref[:, 0], Xref[:, 1], '-k', lw=2)
for f, c in zip('ugriz', 'bgrmk'):
X = fetch_filter(f)
ax.fill(X[:, 0], X[:, 1], ec=c, fc=c, alpha=0.4)
kwargs = dict(fontsize=20, ha='center', va='center', alpha=0.5)
ax.text(3500, 0.02, 'u', color='b', **kwargs)
ax.text(4600, 0.02, 'g', color='g', **kwargs)
ax.text(6100, 0.02, 'r', color='r', **kwargs)
ax.text(7500, 0.02, 'i', color='m', **kwargs)
ax.text(8800, 0.02, 'z', color='k', **kwargs)
ax.set_xlim(3000, 11000)
ax.set_title('SDSS Filters and Reference Spectrum')
ax.set_xlabel('Wavelength (Angstroms)')
ax.set_ylabel('normalized flux / filter transmission')
def plot_sdss_filters():
Xref = fetch_vega_spectrum()
Xref[:, 1] /= 2.1 * Xref[:, 1].max()
#----------------------------------------------------------------------
# Plot filters in color with a single spectrum
fig, ax = plt.subplots()
ax.plot(Xref[:, 0], Xref[:, 1], '-k', lw=2)
for f,c in zip('ugriz', 'bgrmk'):
X = fetch_filter(f)
ax.fill(X[:, 0], X[:, 1], ec=c, fc=c, alpha=0.4)
kwargs = dict(fontsize=20, ha='center', va='center', alpha=0.5)
ax.text(3500, 0.02, 'u', color='b', **kwargs)
ax.text(4600, 0.02, 'g', color='g', **kwargs)
ax.text(6100, 0.02, 'r', color='r', **kwargs)
ax.text(7500, 0.02, 'i', color='m', **kwargs)
ax.text(8800, 0.02, 'z', color='k', **kwargs)
ax.set_xlim(3000, 11000)
ax.set_title('SDSS Filters and Reference Spectrum')
ax.set_xlabel('Wavelength (Angstroms)')
ax.set_ylabel('normalized flux / filter transmission')
def plot_redshifts():
Xref = fetch_vega_spectrum()
Xref[:, 1] /= 2.1 * Xref[:, 1].max()
#----------------------------------------------------------------------
# Plot filters in gray with several redshifted spectra
fig, ax = plt.subplots()
redshifts = [0.0, 0.4, 0.8]
colors = 'bgr'
for z, c in zip(redshifts, colors):
plt.plot((1. + z) * Xref[:, 0], Xref[:, 1], color=c)
ax.add_patch(Arrow(4200, 0.47, 1300, 0, lw=0, width=0.05, color='r'))
ax.add_patch(Arrow(5800, 0.47, 1250, 0, lw=0, width=0.05, color='r'))
ax.text(3800, 0.49, 'z = 0.0', fontsize=14, color=colors[0])
ax.text(5500, 0.49, 'z = 0.4', fontsize=14, color=colors[1])
ax.text(7300, 0.49, 'z = 0.8', fontsize=14, color=colors[2])
for f in 'ugriz':
X = fetch_filter(f)
ax.fill(X[:, 0], X[:, 1], ec='k', fc='k', alpha=0.2)
kwargs = dict(fontsize=20, color='gray', ha='center', va='center')
ax.text(3500, 0.02, 'u', **kwargs)
ax.text(4600, 0.02, 'g', **kwargs)
ax.text(6100, 0.02, 'r', **kwargs)
ax.text(7500, 0.02, 'i', **kwargs)
ax.text(8800, 0.02, 'z', **kwargs)
ax.set_xlim(3000, 11000)
ax.set_ylim(0, 0.55)
ax.set_title('Redshifting of a Spectrum')
ax.set_xlabel('Observed Wavelength (Angstroms)')
ax.set_ylabel('normalized flux / filter transmission')
def plot_redshifts():
Xref = fetch_vega_spectrum()
Xref[:, 1] /= 2.1 * Xref[:, 1].max()
#----------------------------------------------------------------------
# Plot filters in gray with several redshifted spectra
fig, ax = plt.subplots()
redshifts = [0.0, 0.4, 0.8]
colors = 'bgr'
for z, c in zip(redshifts, colors):
plt.plot((1. + z) * Xref[:, 0], Xref[:, 1], color=c)
ax.add_patch(Arrow(4200, 0.47, 1300, 0, lw=0, width=0.05, color='r'))
ax.add_patch(Arrow(5800, 0.47, 1250, 0, lw=0, width=0.05, color='r'))
ax.text(3800, 0.49, 'z = 0.0', fontsize=14, color=colors[0])
ax.text(5500, 0.49, 'z = 0.4', fontsize=14, color=colors[1])
ax.text(7300, 0.49, 'z = 0.8', fontsize=14, color=colors[2])
for f in 'ugriz':
X = fetch_filter(f)
ax.fill(X[:, 0], X[:, 1], ec='k', fc='k', alpha=0.2)
kwargs = dict(fontsize=20, color='gray', ha='center', va='center')
ax.text(3500, 0.02, 'u', **kwargs)
ax.text(4600, 0.02, 'g', **kwargs)
ax.text(6100, 0.02, 'r', **kwargs)
ax.text(7500, 0.02, 'i', **kwargs)
ax.text(8800, 0.02, 'z', **kwargs)
ax.set_xlim(3000, 11000)
ax.set_ylim(0, 0.55)
ax.set_title('Redshifting of a Spectrum')
ax.set_xlabel('Observed Wavelength (Angstroms)')
ax.set_ylabel('normalized flux / filter transmission')
# Faces data: this will be stored in the scikit_learn_data
# sub-directory of your home folder
faces = datasets.fetch_olivetti_faces()
print "Successfully fetched olivetti faces data"
#------------------------------------------------------------
# SDSS galaxy data: this will be stored in notebooks/datasets/data
sys.path.append(os.path.abspath('notebooks'))
from datasets import fetch_sdss_galaxy_mags
colors = fetch_sdss_galaxy_mags()
print "Successfully fetched SDSS galaxy data"
#------------------------------------------------------------
# SDSS filters & vega spectrum: stored in notebooks/figures/downloads
from figures.sdss_filters import fetch_filter, fetch_vega_spectrum
spectrum = fetch_vega_spectrum()
print "Successfully fetched vega spectrum"
filters = [fetch_filter(f) for f in 'ugriz']
print "Successfully fetched SDSS filters"
########NEW FILE########
__FILENAME__ = galaxy_mags
# This download script comes from astroML: http://astroml.github.com
import os
import urllib
import numpy as np
#----------------------------------------------------------------------
# Tools for querying the SDSS database using SQL
PUBLIC_URL = 'http://cas.sdss.org/public/en/tools/search/x_sql.asp'
The data are only a few megabytes, but conference wireless is
often not very reliable...
"""
import os
import sys
from sklearn import datasets
#------------------------------------------------------------
# Faces data: this will be stored in the scikit_learn_data
# sub-directory of your home folder
faces = datasets.fetch_olivetti_faces()
print "Successfully fetched olivetti faces data"
#------------------------------------------------------------
# SDSS galaxy data: this will be stored in notebooks/datasets/data
sys.path.append(os.path.abspath('notebooks'))
from datasets import fetch_sdss_galaxy_mags
colors = fetch_sdss_galaxy_mags()
print "Successfully fetched SDSS galaxy data"
#------------------------------------------------------------
# SDSS filters & vega spectrum: stored in notebooks/figures/downloads
from figures.sdss_filters import fetch_filter, fetch_vega_spectrum
spectrum = fetch_vega_spectrum()
print "Successfully fetched vega spectrum"
filters = [fetch_filter(f) for f in 'ugriz']
print "Successfully fetched SDSS filters"
