In [1]:
# Basic notebook imports
%matplotlib inline
import matplotlib
import pylab as plt
import numpy as np
import healpy as hp
We start by importing several classes from the skymap module and setting a few constants that we will use in this example.
In [2]:
# Import skymap and some of it's basic map classes
import skymap
from skymap import Skymap,McBrydeSkymap,OrthoSkymap
from skymap import SurveySkymap,SurveyMcBryde,SurveyOrtho
from skymap import DESSkymap,BlissSkymap
SKYMAPS = [Skymap,McBrydeSkymap,OrthoSkymap]
SURVEYS = [SurveySkymap,SurveyMcBryde,SurveyOrtho]
ZOOMS = [DESSkymap,BlissSkymap]
In the last chapter we saw how we could create skymaps with different projections using the projection keywork argument to the Skymap initializer. However, for ease of use we have also defined a set of subclasses for commonly used projections.
In [3]:
fig,axes = plt.subplots(1,3,figsize=(20,4))
for i,cls in enumerate(SKYMAPS):
plt.sca(axes[i])
smap = cls()
plt.title(cls.__name__)
Notice that the OrthoSkymap now has it's meridians labeled (something that basemap would complain about).
In addition to facilitating the generation of specific projections, Skymap also has a number of astronomically oriented methods. For example, you can draw an outline of the Milky Way
In [4]:
fig,axes = plt.subplots(1,3,figsize=(20,4))
for i,cls in enumerate(SKYMAPS):
plt.sca(axes[i])
smap = cls()
smap.draw_milky_way()
smap.draw_lmc()
smap.draw_smc()
plt.title(cls.__name__)
In addition to methods to plot astronomical objects, Skymap has a number of methods to plot useful quantities when observing. (Careful, the 'cyl' projection can be a bit weird when the tissot used to plot the airmass crosses lon=0.)
In [5]:
fig,axes = plt.subplots(1,3,figsize=(20,4))
for i,cls in enumerate(SKYMAPS):
plt.sca(axes[i])
smap = cls(date='2018/11/02 01:00:00')
smap.draw_airmass(airmass=1.4,color='b')
smap.draw_zenith(alpha=1.0,radius=2.0,color='g',)
smap.draw_lmc()
smap.draw_smc()
In addition to the projection subclasses of Skymap, there are also survey-specific subclasses. The SurveySkymap object has a number of methods for plotting individual surveys.
In [6]:
fig,axes = plt.subplots(1,3,figsize=(20,4))
for i,cls in enumerate(SURVEYS):
plt.sca(axes[i])
smap = cls()
smap.draw_des(label='DES')
smap.draw_maglites(label='MagLiteS')
smap.draw_bliss(label='BLISS')
plt.title('Footprints (%s)'%cls.__name__)
plt.legend(loc='upper right')
Many surveys don't span the entire sky and it is useful to zoom in on the region specifically covered. To do this, we use the SurveyZoom subclasses.
In [7]:
plt.figure()
smap = DESSkymap()
smap.draw_des(label='DES')
smap.draw_maglites(label='MagLiteS')
smap.draw_bliss(label='BLISS')
plt.suptitle('DES Zoom Footprint')
plt.legend(loc='upper left',fontsize=10)
Out[7]:
Unfortunately, the ZoomSkymap is rather fragile because it relies on an AxisArtist to plot the curvilinear coordinate grid. Hopefully we can make it more robust in the future.