def fancy_plot(self):
"""Make a fancier looking sky map of the footprint.
"""
if not (fancyplot):
print(
'Cannot make this fancy plot; MAF plotting utilities unavailable.'
)
return None
# fieldRA / fieldDec are dictionaries - key=prop
slicer = slicers.OpsimFieldSlicer()
fignum = None
colorlist = [[1, 1, 0], [.5, 0, .5], [0, .25, .5], [0, 1, 0],
[0, 0, 0], [1, 0, 0], [.5, .5, 1]]
ci = 0
colors = {}
add_planes = True
for name in self.regions:
print(name)
# Modify slicer so we can use it for plotting.
slicer.slicePoints['ra'] = np.radians(self.regions[name]['ra'])
slicer.slicePoints['dec'] = np.radians(self.regions[name]['dec'])
fieldLocs = ma.MaskedArray(data=np.empty(len(self.regions[name]),
object),
mask=np.zeros(len(self.regions[name]),
bool),
fill_value=-99)
colors[name] = [
colorlist[ci][0], colorlist[ci][1], colorlist[ci][2], 0.4
]
ci += 1
if ci == len(colorlist):
ci = 0
for i in range(len(self.regions[name])):
fieldLocs.data[i] = colors[name]
skymap = plots.BaseSkyMap()
fignum = skymap(fieldLocs,
slicer, {
'metricIsColor': True,
'bgcolor': 'lightgray',
'raCen': 0,
'figsize': (10, 8),
'ecPlane': add_planes,
'mwZone': add_planes
},
fignum=fignum)
add_planes = False
fig = plt.figure(fignum)
labelcolors = []
labeltext = []
for name in self.regions:
el = Ellipse(
(0, 0),
0.03,
0.03,
fc=(colors[name][0], colors[name][1], colors[name][2]),
alpha=colors[name][3])
labelcolors.append(el)
labeltext.append(name)
plt.legend(labelcolors, labeltext, loc=(0.85, 0.9), fontsize='smaller')
return fig
def fancy_plot_Nvisits(self, cmap='viridis'):
"""Make a fancier looking sky map of the footprint.
"""
if not (fancyplot):
print(
'Cannot make this fancy plot; MAF plotting utilities unavailable.'
)
return None
# fieldRA / fieldDec are dictionaries - key=prop
slicer = slicers.OpsimFieldSlicer()
fignum = None
# Add a 'survey' that covers the whole sky, and then we put nvisits per survey into it.
regionlist = list(self.regions.keys())
self.define_survey_region('_all_nvisits', limits={'dec': [-90, 90]})
self.regions['_all_nvisits'] = self.regions['_all_nvisits'].assign(
Nvis=0)
for name in regionlist:
print(name)
tmp = self.regions['_all_nvisits']['Nvis'] + self.regions[name][
'Nvis']
tmp = np.where(np.isnan(tmp), 0, tmp)
self.regions['_all_nvisits']['Nvis'] += tmp
nvisits = ma.MaskedArray(data=self.regions['_all_nvisits']['Nvis'],
mask=np.zeros(
len(self.regions['_all_nvisits']), bool),
fill_value=-99)
nvisits.mask = np.where(nvisits == 0, True, False)
# Modify slicer so we can use it for plotting.
slicer.slicePoints['ra'] = np.radians(
self.regions['_all_nvisits']['ra'])
slicer.slicePoints['dec'] = np.radians(
self.regions['_all_nvisits']['dec'])
skymap = plots.BaseSkyMap()
fignum = skymap(nvisits,
slicer, {
'xlabel': 'Nvisits',
'cmap': cmap,
'raCen': 0,
'figsize': (10, 8),
'colorMin': 0,
'colorMax': 1000,
'ecPlane': True,
'mwZone': True
},
fignum=fignum)
del self.regions['_all_nvisits']
fig = plt.figure(fignum)
return fig
def plotFields(fields, match):
slicer = slicers.OpsimFieldSlicer()
# Modify slicer so we can use it for plotting.
slicer.slicePoints['ra'] = fields['ra']
slicer.slicePoints['dec'] = fields['dec']
fieldLocs = ma.MaskedArray(data=np.zeros(len(fields['ra']), float),
mask=np.ones(len(fields['ra']), int),
fill_value=-99)
fieldLocs.data[match] = 1.0
fieldLocs.mask[match] = 0
skymap = plots.BaseSkyMap()
skymap(fieldLocs, slicer, {
'colorMin': 0,
'colorMax': 1,
'xlabel': 'Field Locations'
})
plt.show()
def plotFields(fieldRA, fieldDec):
slicer = slicers.OpsimFieldSlicer()
fignum = None
colorlist = [[1, 0, 0], [1, 1, 0], [0, 1, 0], [0, .25, .5], [.5, 0, .5],
[0, 0, 0], [.5, .5, 1]]
ci = 0
colors = {}
for prop in fieldRA:
# Modify slicer so we can use it for plotting.
slicer.slicePoints['ra'] = np.radians(fieldRA[prop])
slicer.slicePoints['dec'] = np.radians(fieldDec[prop])
fieldLocs = ma.MaskedArray(data=np.empty(len(fieldRA[prop]), object),
mask=np.zeros(len(fieldRA[prop]), bool),
fill_value=-99)
colors[prop] = [
colorlist[ci][0], colorlist[ci][1], colorlist[ci][2], 0.4
]
ci += 1
if ci == len(colorlist):
ci = 0
for i in xrange(len(fieldRA[prop])):
fieldLocs.data[i] = colors[prop]
skymap = plots.BaseSkyMap()
fignum = skymap(fieldLocs,
slicer, {
'metricIsColor': True,
'bgcolor': 'lightgray',
'raCen': 0
},
fignum=fignum)
plt.figure(fignum)
labelcolors = []
labeltext = []
for prop in fieldRA:
el = Ellipse((0, 0),
0.03,
0.03,
fc=(colors[prop][0], colors[prop][1], colors[prop][2]),
alpha=colors[prop][3])
labelcolors.append(el)
labeltext.append(prop.rstrip('.conf'))
plt.legend(labelcolors, labeltext, loc=(0.85, 0.9), fontsize='smaller')
plt.show()
def runSlices(opsimName,
metadata,
simdata,
fields,
bins,
args,
opsDb,
verbose=False):
# Set up the movie slicer.
movieslicer = setupMovieSlicer(simdata, bins)
# Set up formatting for output suffix.
sliceformat = '%s0%dd' % ('%', int(np.log10(len(movieslicer))) + 1)
# Get the telescope latitude info.
lat_tele = Site(name='LSST').latitude_rad
# Run through the movie slicer slicePoints and generate plots at each point.
for i, ms in enumerate(movieslicer):
t = time.time()
slicenumber = sliceformat % (i)
if verbose:
print(slicenumber)
# Set up metrics.
if args.movieStepsize != 0:
tstep = args.movieStepsize
else:
tstep = ms['slicePoint']['binRight'] - bins[i]
if tstep > 1:
tstep = 40. / 24. / 60. / 60.
# Add simple view of time to plot label.
times_from_start = ms['slicePoint']['binRight'] - (int(bins[0]) +
0.16 - 0.5)
# Opsim years are 365 days (not 365.25)
years = int(times_from_start / 365)
days = times_from_start - years * 365
plotlabel = 'Year %d Day %.4f' % (years, days)
# Set up metrics.
metricList, plotDictList = setupMetrics(
opsimName,
metadata,
plotlabel=plotlabel,
t0=ms['slicePoint']['binRight'],
tStep=tstep,
years=years,
verbose=verbose)
# Identify the subset of simdata in the movieslicer 'data slice'
simdatasubset = simdata[ms['idxs']]
# Set up opsim slicer on subset of simdata provided by movieslicer
opslicer = slicers.OpsimFieldSlicer(
simDataFieldIdColName='opsimFieldId',
fieldIdColName='opsimFieldId')
# Set up metricBundles to combine metrics, plotdicts and slicer.
bundles = []
sqlconstraint = ''
for metric, plotDict in zip(metricList, plotDictList):
bundles.append(
metricBundles.MetricBundle(metric,
opslicer,
constraint=sqlconstraint,
metadata=metadata,
runName=opsimName,
plotDict=plotDict))
# Remove (default) stackers from bundles, because we've already run them above on the original data.
for mb in bundles:
mb.stackerList = []
bundledict = metricBundles.makeBundlesDictFromList(bundles)
# Set up metricBundleGroup to handle metrics calculation + plotting
bg = metricBundles.MetricBundleGroup(bundledict,
opsDb,
outDir=args.outDir,
resultsDb=None,
saveEarly=False)
# 'Hack' bundleGroup to just go ahead and run the metrics, without querying the database.
simData = simdatasubset
bg.fieldData = fields
bg.setCurrent(sqlconstraint)
bg.runCurrent(constraint=sqlconstraint, simData=simData)
# Plot data each metric, for this slice of the movie, adding slicenumber as a suffix for output plots.
# Plotting here, rather than automatically via sliceMetric method because we're going to rotate the sky,
# and add extra legend info and figure text (for FilterColors metric).
ph = plots.PlotHandler(outDir=args.outDir,
figformat='png',
dpi=72,
thumbnail=False,
savefig=False)
obsnow = np.where(simdatasubset['observationStartMJD'] ==
simdatasubset['observationStartMJD'].max())[0]
raCen = np.radians(
np.mean(simdatasubset[obsnow]['observationStartLST']))
# Calculate horizon location.
horizonlon, horizonlat = addHorizon(lat_telescope=lat_tele)
# Create the plot for each metric and save it (after some additional manipulation).
for mb in bundles:
ph.setMetricBundles([mb])
fignum = ph.plot(plotFunc=plots.BaseSkyMap(),
plotDicts={'raCen': raCen})
fig = plt.figure(fignum)
ax = plt.gca()
# Add horizon and zenith.
plt.plot(horizonlon, horizonlat, 'k.', alpha=0.3, markersize=1.8)
plt.plot(0, lat_tele, 'k+')
# For the FilterColors metric, add some extra items.
if mb.metric.name == 'FilterColors':
# Add the time stamp info (plotlabel) with a fancybox.
plt.figtext(0.75,
0.9,
'%s' % (plotlabel),
bbox=dict(boxstyle='Round, pad=0.7',
fc='w',
ec='k',
alpha=0.5))
# Add a legend for the filters.
filterstacker = stackers.FilterColorStacker()
for i, f in enumerate(['u', 'g', 'r', 'i', 'z', 'y']):
plt.figtext(0.92,
0.55 - i * 0.035,
f,
color=filterstacker.filter_rgb_map[f])
# Add a moon.
moonRA = np.radians(np.mean(simdatasubset[obsnow]['moonRA']))
lon = -(moonRA - raCen - np.pi) % (np.pi * 2) - np.pi
moonDec = np.radians(np.mean(simdatasubset[obsnow]['moonDec']))
# Note that moonphase is 0-100 (translate to 0-1). 0=new.
moonPhase = np.mean(simdatasubset[obsnow]['moonPhase']) / 100.
alpha = np.max([moonPhase, 0.15])
circle = Circle((lon, moonDec),
radius=0.05,
color='k',
alpha=alpha)
ax.add_patch(circle)
# Add some explanatory text.
ecliptic = Line2D([], [], color='r', label="Ecliptic plane")
galaxy = Line2D([], [], color='b', label="Galactic plane")
horizon = Line2D([], [],
color='k',
alpha=0.3,
label="20 deg elevation limit")
moon = Line2D([], [],
color='k',
linestyle='',
marker='o',
markersize=8,
alpha=alpha,
label="\nMoon (Dark=Full)\n (Light=New)")
zenith = Line2D([], [],
color='k',
linestyle='',
marker='+',
markersize=5,
label="Zenith")
plt.legend(
handles=[horizon, zenith, galaxy, ecliptic, moon],
loc=[0.1, -0.35],
ncol=3,
frameon=False,
title=
'Aitoff plot showing HA/Dec of simulated survey pointings',
numpoints=1,
fontsize='small')
# Save figure.
plt.savefig(os.path.join(
args.outDir,
mb.metric.name + '_' + slicenumber + '_SkyMap.png'),
format='png',
dpi=72)
plt.close('all')
dt, t = dtime(t)
if verbose:
print('Ran and plotted slice %s of movieslicer in %f s' %
(slicenumber, dt))