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moPlots.py
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moPlots.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.patches import Ellipse
from matplotlib.collections import PatchCollection
from lsst.sims.maf.plots import BasePlotter
mag_sun = -27.1 # apparent r band magnitude of the sun. this sets the band for the magnitude limit.
# see http://www.ucolick.org/~cnaw/sun.html for apparent magnitudes in other bands.
mag_sun = -26.74 # apparent V band magnitude of the Sun (our H mags translate to V band)
km_per_au = 1.496e8
m_per_km = 1000
class MetricVsH(BasePlotter):
"""
Plot metric values versus H.
Marginalize over metric values in each H bin using 'npReduce'.
"""
def __init__(self):
self.plotType = 'MetricVsH'
self.objectPlotter = False
self.defaultPlotDict = {'title':None, 'xlabel':'H (mag)', 'ylabel':None, 'label':None,
'npReduce':None, 'nbins':None, 'albedo':None, 'Hmark':None}
self.minHrange=1.0
def __call__(self, metricValue, slicer, userPlotDict, fignum=None):
if 'linestyle' not in userPlotDict:
userPlotDict['linestyle'] = '-'
fig = plt.figure(fignum)
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
Hvals = slicer.slicePoints['H']
reduceFunc = plotDict['npReduce']
if reduceFunc is None:
reduceFunc = np.mean
if Hvals.shape == metricValue.shape:
# We have a simple set of values to plot against H.
# This may be due to running a secondary metric, such as completeness.
mVals = metricValue.filled()
elif len(Hvals) == slicer.slicerShape[1]:
# Using cloned H distribution.
# Apply 'npReduce' method directly to metric values, and plot at matching H values.
mVals = reduceFunc(metricValue, axis=0)
else:
# Probably each object has its own H value.
hrange = Hvals.max() - Hvals.min()
minH = Hvals.min()
if hrange < self.minHrange:
hrange = self.minHrange
minH = Hvals.min() - hrange/2.0
nbins = plotDict['nbins']
if nbins is None:
nbins = 30
stepsize = hrange / float(nbins)
bins = np.arange(minH, minH + hrange + stepsize/2.0, stepsize)
# In each bin of H, calculate the 'npReduce' value of the corresponding metricValues.
inds = np.digitize(Hvals, bins)
inds = inds-1
mVals = np.zeros(len(bins), float)
for i in range(len(bins)):
match = metricValue[inds == i]
if len(match) == 0:
mVals[i] = slicer.badval
else:
mVals[i] = reduceFunc(match.filled())
Hvals = bins
plt.plot(Hvals, mVals, color=plotDict['color'], linestyle=plotDict['linestyle'],
label=plotDict['label'])
if 'xMin' in plotDict:
plt.xlim(xmin = plotDict['xMin'])
if 'xMax' in plotDict:
plt.xlim(xmax = plotDict['xMax'])
if 'yMin' in plotDict:
plt.ylim(ymin = plotDict['yMin'])
if 'yMax' in plotDict:
plt.ylim(ymax = plotDict['yMax'])
# Convert Hvals to diameter, using 'albedo'
albedo = plotDict['albedo']
y = 1.0
if albedo is not None:
ax = plt.axes()
ax2 = ax.twiny()
Hmin, Hmax = ax.get_xlim()
dmax = 2.0 * np.sqrt(10**((mag_sun - Hmin - 2.5*np.log10(albedo))/2.5))
dmin = 2.0 * np.sqrt(10**((mag_sun - Hmax - 2.5*np.log10(albedo))/2.5))
dmax = dmax * km_per_au * m_per_km
dmin = dmin * km_per_au * m_per_km
ax2.set_xlim(dmax, dmin)
ax2.set_xscale('log')
ax2.set_xlabel('D (m)', labelpad=-10, horizontalalignment='right')
ax2.grid(False)
plt.sca(ax)
y = 1.1
plt.grid(True)
if plotDict['Hmark'] is not None:
plt.axvline(x=plotDict['Hmark'], color='r', linestyle=':', alpha=0.3)
plt.title(plotDict['title'], y=y)
plt.xlabel(plotDict['xlabel'])
plt.ylabel(plotDict['ylabel'])
plt.tight_layout()
return fig.number
class MetricVsOrbit(BasePlotter):
"""
Plot metric values (at a particular H value) vs. orbital parameters.
Marginalize over metric values in each orbital bin using 'npReduce'.
"""
def __init__(self, xaxis='q', yaxis='e'):
self.plotType = 'MetricVsOrbit_%s%s' %(xaxis, yaxis)
self.objectPlotter = False
self.defaultPlotDict = {'title':None, 'xlabel':xaxis, 'ylabel':yaxis,
'xaxis':xaxis, 'yaxis':yaxis,
'label':None, 'cmap':cm.cubehelix,
'npReduce':None,
'nxbins':None, 'nybins':None, 'levels':None,
'Hval':None, 'Hwidth':None}
def __call__(self, metricValue, slicer, userPlotDict, fignum=None):
fig = plt.figure(fignum)
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
xvals = slicer.slicePoints['orbits'][plotDict['xaxis']]
yvals = slicer.slicePoints['orbits'][plotDict['yaxis']]
# Set x/y bins.
nxbins = plotDict['nxbins']
nybins = plotDict['nybins']
if nxbins is None:
nxbins = 100
if nybins is None:
nybins = 100
if 'xbins' in plotDict:
xbins = plotDict['xbins']
else:
xbinsize = (xvals.max() - xvals.min())/float(nxbins)
xbins = np.arange(xvals.min(), xvals.max() + xbinsize/2.0, xbinsize)
if 'ybins' in plotDict:
ybins = plotDict['ybins']
else:
ybinsize = (yvals.max() - yvals.min())/float(nybins)
ybins = np.arange(yvals.min(), yvals.max() + ybinsize/2.0, ybinsize)
nxbins = len(xbins)
nybins = len(ybins)
# Identify the relevant metricValues for the Hvalue we want to plot.
Hvals = slicer.slicePoints['H']
Hwidth = plotDict['Hwidth']
if Hwidth is None:
Hwidth = 1.0
if len(Hvals) == slicer.slicerShape[1]:
if plotDict['Hval'] is None:
Hidx = len(Hvals) / 2
Hval = Hvals[Hidx]
else:
Hval = plotDict['Hval']
Hidx = np.where(np.abs(Hvals - Hval) == np.abs(Hvals - Hval).min())[0]
Hidx = Hidx[0]
else:
if plotDict['Hval'] is None:
Hval = np.median(Hvals)
Hidx = np.where(np.abs(Hvals - Hval) <= Hwidth/2.0)[0]
else:
Hval = plotDict['Hvals']
Hidx = np.where(np.abs(Hvals - Hval) <= Hwidth/2.0)[0]
if len(Hvals) == slicer.slicerShape[1]:
mVals = np.swapaxes(metricValue, 1, 0)[Hidx].filled()
else:
mVals = metricValue[Hidx].filled()
# Calculate the npReduce'd metric values at each x/y bin.
if 'colorMin' in plotDict:
badval = plotDict['colorMin'] - 1
else:
badval = slicer.badval
binvals = np.zeros((nybins, nxbins), dtype='float') + badval
xidxs = np.digitize(xvals, xbins) - 1
yidxs = np.digitize(yvals, ybins) - 1
reduceFunc = plotDict['npReduce']
if reduceFunc is None:
reduceFunc = np.mean
for iy in range(nybins):
ymatch = np.where(yidxs == iy)[0]
for ix in range(nxbins):
xmatch = np.where(xidxs[ymatch] == ix)[0]
matchVals = mVals[ymatch][xmatch]
if len(matchVals) > 0:
binvals[iy][ix] = reduceFunc(matchVals)
xi, yi = np.meshgrid(xbins, ybins)
if 'colorMin' in plotDict:
vMin = plotDict['colorMin']
else:
vMin = binvals.min()
if 'colorMax' in plotDict:
vMax = plotDict['colorMax']
else:
vMax = binvals.max()
nlevels = plotDict['levels']
if nlevels is None:
nlevels = 200
levels = np.arange(vMin, vMax, (vMax-vMin)/float(nlevels))
plt.contourf(xi, yi, binvals, levels, extend='max',
zorder=0, cmap=plotDict['cmap'])
cbar = plt.colorbar()
label = plotDict['label']
if label is None:
label = ''
cbar.set_label(label + ' @ H=%.1f' %(Hval))
plt.title(plotDict['title'])
plt.xlabel(plotDict['xlabel'])
plt.ylabel(plotDict['ylabel'])
return fig.number
class MetricVsOrbitPoints(BasePlotter):
"""
Plot metric values (at a particular H value) as function of orbital parameters,
using points for each metric value.
"""
def __init__(self, xaxis='q', yaxis='e'):
self.plotType = 'MetricVsOrbit'
self.objectPlotter = False
self.defaultPlotDict = {'title':None, 'xlabel':xaxis, 'ylabel':yaxis,
'label':None, 'cmap':cm.cubehelix,
'xaxis':xaxis, 'yaxis':yaxis,
'Hval':None, 'Hwidth':None,
'foregroundPoints':True, 'backgroundPoints':False}
def __call__(self, metricValue, slicer, userPlotDict, fignum=None):
fig = plt.figure(fignum)
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
xvals = slicer.slicePoints['orbits'][plotDict['xaxis']]
yvals = slicer.slicePoints['orbits'][plotDict['yaxis']]
# Identify the relevant metricValues for the Hvalue we want to plot.
Hvals = slicer.slicePoints['H']
Hwidth = plotDict['Hwidth']
if Hwidth is None:
Hwidth = 1.0
if plotDict['Hval'] is None:
if len(Hvals) == slicer.slicerShape[1]:
Hidx = len(Hvals) / 2
Hval = Hvals[Hidx]
else:
Hval = np.median(Hvals)
Hidx = np.where(np.abs(Hvals - Hval) <= Hwidth/2.0)[0]
if len(Hvals) == slicer.slicerShape[1]:
mVals = np.swapaxes(metricValue, 1, 0)[Hidx]
else:
mVals = metricValue[Hidx]
if 'colorMin' in plotDict:
vMin = plotDict['colorMin']
else:
vMin = mVals.min()
if 'colorMax' in plotDict:
vMax = plotDict['colorMax']
else:
vMax = mVals.max()
if plotDict['backgroundPoints']:
# This isn't quite right for the condition .. but will do for now.
condition = np.where(mVals == 0)
plt.plot(xvals[condition], yvals[condition], 'r.', markersize=4, alpha=0.5, zorder=3)
if plotDict['foregroundPoints']:
plt.scatter(xvals, yvals, c=mVals, vmin=vMin, vmax=vMax,
cmap=plotDict['cmap'], s=15, alpha=0.8, zorder=0)
cb = plt.colorbar()
plt.title(plotDict['title'])
plt.xlabel(plotDict['xlabel'])
plt.ylabel(plotDict['ylabel'])
return fig.number