![]() ![]() ![]() suptitle ( 'SDSS+2MASS magnitudes' ) plt. figure ( figsize = ( 10, 10 )) ax = MultiAxes ( 7, hspace = 0.05, wspace = 0.05, fig = fig ) ax. This algorithm determines which fields are used as the 'primary' observation of any given region of sky and which catalog entries are the 'primary' detections of the given source. Scans are obtained along stripes spaced 2.5° in survey latitude. To flag possible problems in the image processing or photometric or astrometric calibration To give quantitative measures of the accuracy of the SDSS. zeros (( len ( data ), 7 )) colors = data - data colors = data - data colors = data - data colors = data - data colors = data - data colors = data - data colors = data - data labels = bins = fig = plt. suptitle ( 'SDSS magnitudes' ) #- # Plot datacross-matched with 2MASS data = fetch_sdss_S82standards ( crossmatch_2mass = True ) colors = np. figure ( figsize = ( 10, 10 )) ax = MultiAxes ( 4, hspace = 0.05, wspace = 0.05, fig = fig ) ax. We discuss several results made possible by accurate SDSS astrometric measurements in a large sky area, with emphasis on asteroids and stellar proper. zeros (( len ( data ), 4 )) colors = data - data colors = data - data colors = data - data colors = data - data labels = bins = fig = plt. ![]() # Author: Jake VanderPlas # License: BSD # The figure is an example from astroML: see import numpy as np from matplotlib import pyplot as plt from astroML.datasets import fetch_sdss_S82standards from otting import MultiAxes #- # Plot SDSS data alone data = fetch_sdss_S82standards () colors = np. ![]()
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