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from builtins import zip 

import numbers 

import numpy as np 

import warnings 

import healpy as hp 

from matplotlib import colors 

from matplotlib import ticker 

import matplotlib.pyplot as plt 

import matplotlib.cm as cm 

from matplotlib.ticker import FuncFormatter 

import matplotlib as mpl 

from matplotlib.patches import Ellipse 

from matplotlib.collections import PatchCollection 

 

from lsst.sims.maf.utils import optimalBins, percentileClipping 

from .plotHandler import BasePlotter, applyZPNorm 

 

from lsst.sims.utils import _equatorialFromGalactic 

from .perceptual_rainbow import makePRCmap 

perceptual_rainbow = makePRCmap() 

 

__all__ = ['setColorLims', 'setColorMap', 'HealpixSkyMap', 'HealpixPowerSpectrum', 

'HealpixHistogram', 'OpsimHistogram', 'BaseHistogram', 

'BaseSkyMap', 'HealpixSDSSSkyMap', 'LambertSkyMap'] 

 

baseDefaultPlotDict = {'title': None, 'xlabel': None, 'label': None, 

'logScale': False, 'percentileClip': None, 'normVal': None, 'zp': None, 

'cbarFormat': None, 'cmap': perceptual_rainbow, 'cbar_edge': True, 'nTicks': 10, 

'colorMin': None, 'colorMax': None, 

'xMin': None, 'xMax': None, 'yMin': None, 'yMax': None, 

'labelsize': None, 'fontsize': None, 'figsize': None, 'subplot': 111} 

 

 

def setColorLims(metricValue, plotDict): 

"""Set up color bar limits.""" 

# Use plot dict if these values are set. 

colorMin = plotDict['colorMin'] 

colorMax = plotDict['colorMax'] 

# If not, try to use percentile clipping. 

if (plotDict['percentileClip'] is not None) & (np.size(metricValue.compressed()) > 0): 

pcMin, pcMax = percentileClipping(metricValue.compressed(), percentile=plotDict['percentileClip']) 

if colorMin is None: 

colorMin = pcMin 

if colorMax is None: 

colorMax = pcMax 

# If not, just use the data limits. 

if colorMin is None: 

colorMin = metricValue.compressed().min() 

if colorMax is None: 

colorMax = metricValue.compressed().max() 

# But make sure there is some range on the colorbar 

if colorMin == colorMax: 

colorMin = colorMin - 0.5 

colorMax = colorMax + 0.5 

return np.sort([colorMin, colorMax]) 

 

 

def setColorMap(plotDict): 

cmap = plotDict['cmap'] 

if cmap is None: 

cmap = 'perceptual_rainbow' 

if type(cmap) == str: 

cmap = getattr(cm, cmap) 

# Set background and masked pixel colors default healpy white and gray. 

cmap.set_over(cmap(1.0)) 

cmap.set_under('w') 

cmap.set_bad('gray') 

return cmap 

 

 

class HealpixSkyMap(BasePlotter): 

""" 

Generate a sky map of healpix metric values using healpy's mollweide view. 

""" 

def __init__(self): 

super(HealpixSkyMap, self).__init__() 

# Set the plotType 

self.plotType = 'SkyMap' 

self.objectPlotter = False 

# Set up the default plotting parameters. 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'rot': (0, 0, 0), 'flip': 'astro', 'coord': 'C'}) 

# Note: for alt/az sky maps using the healpix plotter, you can use 

# {'rot': (90, 90, 90), 'flip': 'geo'} 

 

def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None): 

""" 

Parameters 

---------- 

metricValue : numpy.ma.MaskedArray 

slicer : lsst.sims.maf.slicers.HealpixSlicer 

userPlotDict: dict 

Dictionary of plot parameters set by user (overrides default values). 

fignum : int 

Matplotlib figure number to use (default = None, starts new figure). 

 

Returns 

------- 

int 

Matplotlib figure number used to create the plot. 

""" 

# Override the default plotting parameters with user specified values. 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

# Update the metric data with zeropoint or normalization. 

metricValue = applyZPNorm(metricValueIn, plotDict) 

# Generate a Mollweide full-sky plot. 

fig = plt.figure(fignum, figsize=plotDict['figsize']) 

# Set up color bar limits. 

clims = setColorLims(metricValue, plotDict) 

cmap = setColorMap(plotDict) 

# Set log scale? 

norm = None 

if plotDict['logScale']: 

norm = 'log' 

# Avoid trying to log scale when zero is in the range. 

if (norm == 'log') & ((clims[0] <= 0 <= clims[1]) or (clims[0] >= 0 >= clims[1])): 

# Try something simple 

above = metricValue[np.where(metricValue > 0)] 

if len(above) > 0: 

clims[0] = above.max() 

# If still bad, give up and turn off norm 

if ((clims[0] <= 0 <= clims[1]) or (clims[0] >= 0 >= clims[1])): 

norm = None 

warnings.warn("Using norm was set to log, but color limits pass through 0. " 

"Adjusting so plotting doesn't fail") 

if plotDict['coord'] == 'C': 

notext = True 

else: 

notext = False 

hp.mollview(metricValue.filled(slicer.badval), title=plotDict['title'], cbar=False, 

min=clims[0], max=clims[1], rot=plotDict['rot'], flip=plotDict['flip'], 

coord=plotDict['coord'], cmap=cmap, norm=norm, 

sub=plotDict['subplot'], fig=fig.number, notext=notext) 

# Add a graticule (grid) over the globe. 

hp.graticule(dpar=30, dmer=30, verbose=False) 

# Add colorbar (not using healpy default colorbar because we want more tickmarks). 

ax = plt.gca() 

im = ax.get_images()[0] 

# Add label. 

if plotDict['label'] is not None: 

plt.figtext(0.8, 0.8, '%s' % (plotDict['label'])) 

# Make a color bar. Supress silly colorbar warnings. 

with warnings.catch_warnings(): 

warnings.simplefilter("ignore") 

cb = plt.colorbar(im, shrink=0.75, aspect=25, pad=0.1, orientation='horizontal', 

format=plotDict['cbarFormat'], extendrect=True) 

cb.set_label(plotDict['xlabel'], fontsize=plotDict['fontsize']) 

if plotDict['labelsize'] is not None: 

cb.ax.tick_params(labelsize=plotDict['labelsize']) 

if norm == 'log': 

tick_locator = ticker.LogLocator(numticks=plotDict['nTicks']) 

cb.locator = tick_locator 

cb.update_ticks() 

if (plotDict['nTicks'] is not None) & (norm != 'log'): 

tick_locator = ticker.MaxNLocator(nbins=plotDict['nTicks']) 

cb.locator = tick_locator 

cb.update_ticks() 

# If outputing to PDF, this fixes the colorbar white stripes 

if plotDict['cbar_edge']: 

cb.solids.set_edgecolor("face") 

return fig.number 

 

 

class HealpixPowerSpectrum(BasePlotter): 

def __init__(self): 

self.plotType = 'PowerSpectrum' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'maxl': None, 'removeDipole': True, 'linestyle': '-'}) 

 

def __call__(self, metricValue, slicer, userPlotDict, fignum=None): 

""" 

Generate and plot the power spectrum of metricValue (calculated on a healpix grid). 

""" 

if 'Healpix' not in slicer.slicerName: 

raise ValueError('HealpixPowerSpectrum for use with healpix metricBundles.') 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

 

fig = plt.figure(fignum, figsize=plotDict['figsize']) 

ax = fig.add_subplot(plotDict['subplot']) 

# If the mask is True everywhere (no data), just plot zeros 

if False not in metricValue.mask: 

return None 

if plotDict['removeDipole']: 

cl = hp.anafast(hp.remove_dipole(metricValue.filled(slicer.badval)), lmax=plotDict['maxl']) 

else: 

cl = hp.anafast(metricValue.filled(slicer.badval), lmax=plotDict['maxl']) 

ell = np.arange(np.size(cl)) 

if plotDict['removeDipole']: 

condition = (ell > 1) 

else: 

condition = (ell > 0) 

ell = ell[condition] 

cl = cl[condition] 

# Plot the results. 

plt.plot(ell, (cl * ell * (ell + 1)) / 2.0 / np.pi, 

color=plotDict['color'], linestyle=plotDict['linestyle'], label=plotDict['label']) 

if cl.max() > 0 and plotDict['logScale']: 

plt.yscale('log') 

plt.xlabel(r'$l$', fontsize=plotDict['fontsize']) 

plt.ylabel(r'$l(l+1)C_l/(2\pi)$', fontsize=plotDict['fontsize']) 

if plotDict['labelsize'] is not None: 

plt.tick_params(axis='x', labelsize=plotDict['labelsize']) 

plt.tick_params(axis='y', labelsize=plotDict['labelsize']) 

if plotDict['title'] is not None: 

plt.title(plotDict['title']) 

# Return figure number (so we can reuse/add onto/save this figure if desired). 

return fig.number 

 

 

class HealpixHistogram(BasePlotter): 

def __init__(self): 

self.plotType = 'Histogram' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'ylabel': 'Area (1000s of square degrees)', 

'bins': None, 'binsize': None, 'cumulative': False, 

'scale': None, 'linestyle': '-'}) 

self.baseHist = BaseHistogram() 

 

def __call__(self, metricValue, slicer, userPlotDict, fignum=None): 

""" 

Histogram metricValue for all healpix points. 

""" 

if 'Healpix' not in slicer.slicerName: 

raise ValueError('HealpixHistogram is for use with healpix slicer.') 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

if plotDict['scale'] is None: 

plotDict['scale'] = (hp.nside2pixarea(slicer.nside, degrees=True) / 1000.0) 

fignum = self.baseHist(metricValue, slicer, plotDict, fignum=fignum) 

return fignum 

 

 

class OpsimHistogram(BasePlotter): 

def __init__(self): 

self.plotType = 'Histogram' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'ylabel': 'Number of Fields', 'yaxisformat': '%d', 

'bins': None, 'binsize': None, 'cumulative': False, 

'scale': 1.0, 'linestyle': '-'}) 

self.baseHist = BaseHistogram() 

 

def __call__(self, metricValue, slicer, userPlotDict, fignum=None): 

""" 

Histogram metricValue for all healpix points. 

""" 

if slicer.slicerName != 'OpsimFieldSlicer': 

raise ValueError('OpsimHistogram is for use with OpsimFieldSlicer.') 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

fignum = self.baseHist(metricValue, slicer, plotDict, fignum=fignum) 

return fignum 

 

 

class BaseHistogram(BasePlotter): 

def __init__(self): 

self.plotType = 'Histogram' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'ylabel': 'Count', 'bins': None, 'binsize': None, 'cumulative': False, 

'scale': 1.0, 'yaxisformat': '%.3f', 'linestyle': '-'}) 

 

def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None): 

""" 

Plot a histogram of metricValues (such as would come from a spatial slicer). 

""" 

# Adjust metric values by zeropoint or normVal, and use 'compressed' version of masked array. 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

metricValue = applyZPNorm(metricValueIn, plotDict) 

metricValue = metricValue.compressed() 

# Toss any NaNs or infs 

metricValue = metricValue[np.isfinite(metricValue)] 

# Determine percentile clipped X range, if set. (and xmin/max not set). 

if plotDict['xMin'] is None and plotDict['xMax'] is None: 

if plotDict['percentileClip']: 

plotDict['xMin'], plotDict['xMax'] = percentileClipping(metricValue, 

percentile=plotDict['percentileClip']) 

# Set the histogram range values, to avoid cases of trying to histogram single-valued data. 

# First we try to use the range specified by a user, if there is one. Then use the data if not. 

# all of this only works if plotDict is not cumulative. 

histRange = [plotDict['xMin'], plotDict['xMax']] 

if histRange[0] is None: 

histRange[0] = metricValue.min() 

if histRange[1] is None: 

histRange[1] = metricValue.max() 

# Need to have some range of values on the histogram, or it will fail. 

if histRange[0] == histRange[1]: 

warnings.warn('Histogram range was single-valued; expanding default range.') 

histRange[1] = histRange[0] + 1.0 

# Set up the bins for the histogram. User specified 'bins' overrides 'binsize'. 

# Note that 'bins' could be a single number or an array, simply passed to plt.histogram. 

if plotDict['bins'] is not None: 

bins = plotDict['bins'] 

elif plotDict['binsize'] is not None: 

# If generating a cumulative histogram, want to use full range of data (but with given binsize). 

# .. but if user set histRange to be wider than full range of data, then 

# extend bins to cover this range, so we can make prettier plots. 

if plotDict['cumulative']: 

if plotDict['xMin'] is not None: 

# Potentially, expand the range for the cumulative histogram. 

bmin = np.min([metricValue.min(), plotDict['xMin']]) 

else: 

bmin = metricValue.min() 

if plotDict['xMax'] is not None: 

bmax = np.max([metricValue.max(), plotDict['xMax']]) 

else: 

bmax = metricValue.max() 

bins = np.arange(bmin, bmax + plotDict['binsize'] / 2.0, plotDict['binsize']) 

# Otherwise, not cumulative so just use metric values, without potential expansion. 

else: 

bins = np.arange(histRange[0], histRange[1] + plotDict['binsize'] / 2.0, plotDict['binsize']) 

# Catch edge-case where there is only 1 bin value 

if bins.size < 2: 

bins = np.arange(bins.min() - plotDict['binsize'] * 2.0, 

bins.max() + plotDict['binsize'] * 2.0, plotDict['binsize']) 

else: 

# If user did not specify bins or binsize, then we try to figure out a good number of bins. 

bins = optimalBins(metricValue) 

# Generate plots. 

fig = plt.figure(fignum, figsize=plotDict['figsize']) 

ax = fig.add_subplot(plotDict['subplot']) 

# Check if any data falls within histRange, because otherwise histogram generation will fail. 

if isinstance(bins, np.ndarray): 

condition = ((metricValue >= bins.min()) & (metricValue <= bins.max())) 

else: 

condition = ((metricValue >= histRange[0]) & (metricValue <= histRange[1])) 

plotValue = metricValue[condition] 

if len(plotValue) == 0: 

# No data is within histRange/bins. So let's just make a simple histogram anyway. 

n, b, p = plt.hist(metricValue, bins=50, histtype='step', cumulative=plotDict['cumulative'], 

log=plotDict['logScale'], label=plotDict['label'], 

color=plotDict['color']) 

else: 

# There is data to plot, and we've already ensured histRange/bins are more than single value. 

n, b, p = plt.hist(metricValue, bins=bins, range=histRange, 

histtype='step', log=plotDict['logScale'], 

cumulative=plotDict['cumulative'], 

label=plotDict['label'], color=plotDict['color']) 

hist_ylims = plt.ylim() 

if n.max() > hist_ylims[1]: 

plt.ylim(ymax = n.max()) 

if n.min() < hist_ylims[0] and not plotDict['logScale']: 

plt.ylim(ymin = n.min()) 

# Fill in axes labels and limits. 

# Option to use 'scale' to turn y axis into area or other value. 

 

def mjrFormatter(y, pos): 

if not isinstance(plotDict['scale'], numbers.Number): 

raise ValueError('plotDict["scale"] must be a number to scale the y axis.') 

return plotDict['yaxisformat'] % (y * plotDict['scale']) 

 

ax.yaxis.set_major_formatter(FuncFormatter(mjrFormatter)) 

# Set optional x, y limits. 

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']) 

# Set/Add various labels. 

plt.xlabel(plotDict['xlabel'], fontsize=plotDict['fontsize']) 

plt.ylabel(plotDict['ylabel'], fontsize=plotDict['fontsize']) 

plt.title(plotDict['title']) 

if plotDict['labelsize'] is not None: 

plt.tick_params(axis='x', labelsize=plotDict['labelsize']) 

plt.tick_params(axis='y', labelsize=plotDict['labelsize']) 

# Return figure number 

return fig.number 

 

 

class BaseSkyMap(BasePlotter): 

def __init__(self): 

self.plotType = 'SkyMap' 

self.objectPlotter = False # unless 'metricIsColor' is true.. 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'projection': 'aitoff', 'radius': np.radians(1.75), 'alpha': 1.0, 

'plotMask': False, 'metricIsColor': False, 'cbar': True, 

'raCen': 0.0, 'mwZone': True, 'bgcolor': 'gray'}) 

 

def _plot_tissot_ellipse(self, lon, lat, radius, ax=None, **kwargs): 

"""Plot Tissot Ellipse/Tissot Indicatrix 

 

Parameters 

---------- 

lon : float or array_like 

longitude-like of ellipse centers (radians) 

lat : float or array_like 

latitude-like of ellipse centers (radians) 

radius : float or array_like 

radius of ellipses (radians) 

ax : Axes object (optional) 

matplotlib axes instance on which to draw ellipses. 

 

Other Parameters 

---------------- 

other keyword arguments will be passed to matplotlib.patches.Ellipse. 

 

# The code in this method adapted from astroML, which is BSD-licensed. 

# See http: //github.com/astroML/astroML for details. 

""" 

# Code adapted from astroML, which is BSD-licensed. 

# See http: //github.com/astroML/astroML for details. 

ellipses = [] 

if ax is None: 

ax = plt.gca() 

for l, b, diam in np.broadcast(lon, lat, radius * 2.0): 

el = Ellipse((l, b), diam / np.cos(b), diam, **kwargs) 

ellipses.append(el) 

return ellipses 

 

def _plot_ecliptic(self, raCen=0, ax=None): 

""" 

Plot a red line at location of ecliptic. 

""" 

if ax is None: 

ax = plt.gca() 

ecinc = 23.439291 * (np.pi / 180.0) 

ra_ec = np.arange(0, np.pi * 2., (np.pi * 2. / 360.)) 

dec_ec = np.sin(ra_ec) * ecinc 

lon = -(ra_ec - raCen - np.pi) % (np.pi * 2) - np.pi 

ax.plot(lon, dec_ec, 'r.', markersize=1.8, alpha=0.4) 

 

def _plot_mwZone(self, raCen=0, peakWidth=np.radians(10.), taperLength=np.radians(80.), ax=None): 

""" 

Plot blue lines to mark the milky way galactic exclusion zone. 

""" 

if ax is None: 

ax = plt.gca() 

# Calculate galactic coordinates for mw location. 

step = 0.02 

galL = np.arange(-np.pi, np.pi + step / 2., step) 

val = peakWidth * np.cos(galL / taperLength * np.pi / 2.) 

galB1 = np.where(np.abs(galL) <= taperLength, val, 0) 

galB2 = np.where(np.abs(galL) <= taperLength, -val, 0) 

# Convert to ra/dec. 

# Convert to lon/lat and plot. 

ra, dec = _equatorialFromGalactic(galL, galB1) 

lon = -(ra - raCen - np.pi) % (np.pi * 2) - np.pi 

ax.plot(lon, dec, 'b.', markersize=1.8, alpha=0.4) 

ra, dec = _equatorialFromGalactic(galL, galB2) 

lon = -(ra - raCen - np.pi) % (np.pi * 2) - np.pi 

ax.plot(lon, dec, 'b.', markersize=1.8, alpha=0.4) 

 

def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None): 

""" 

Plot the sky map of metricValue for a generic spatial slicer. 

""" 

if 'ra' not in slicer.slicePoints or 'dec' not in slicer.slicePoints: 

errMessage = 'SpatialSlicer must contain "ra" and "dec" in slicePoints metadata.' 

errMessage += ' SlicePoints only contains keys %s.' % (slicer.slicePoints.keys()) 

raise ValueError(errMessage) 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

metricValue = applyZPNorm(metricValueIn, plotDict) 

 

fig = plt.figure(fignum, figsize=plotDict['figsize']) 

# other projections available include 

# ['aitoff', 'hammer', 'lambert', 'mollweide', 'polar', 'rectilinear'] 

ax = fig.add_subplot(plotDict['subplot'], projection=plotDict['projection']) 

# Set up valid datapoints and colormin/max values. 

if plotDict['plotMask']: 

# Plot all data points. 

mask = np.ones(len(metricValue), dtype='bool') 

else: 

# Only plot points which are not masked. Flip numpy ma mask where 'False' == 'good'. 

good = ~metricValue.mask 

 

# Add ellipses at RA/Dec locations - but don't add colors yet. 

lon = -(slicer.slicePoints['ra'][good] - plotDict['raCen'] - np.pi) % (np.pi * 2) - np.pi 

ellipses = self._plot_tissot_ellipse(lon, slicer.slicePoints['dec'][good], 

plotDict['radius'], rasterized=True, ax=ax) 

if plotDict['metricIsColor']: 

current = None 

for ellipse, mVal in zip(ellipses, metricValue.data[good]): 

if mVal[3] > 1: 

ellipse.set_alpha(1.0) 

ellipse.set_facecolor((mVal[0], mVal[1], mVal[2])) 

ellipse.set_edgecolor('k') 

current = ellipse 

else: 

ellipse.set_alpha(mVal[3]) 

ellipse.set_color((mVal[0], mVal[1], mVal[2])) 

ax.add_patch(ellipse) 

if current: 

ax.add_patch(current) 

else: 

# Determine color min/max values. metricValue.compressed = non-masked points. 

clims = setColorLims(metricValue, plotDict) 

# Determine whether or not to use auto-log scale. 

if plotDict['logScale'] == 'auto': 

if clims[0] > 0: 

if np.log10(clims[1]) - np.log10(clims[0]) > 3: 

plotDict['logScale'] = True 

else: 

plotDict['logScale'] = False 

else: 

plotDict['logScale'] = False 

if plotDict['logScale']: 

# Move min/max values to things that can be marked on the colorbar. 

#clims[0] = 10 ** (int(np.log10(clims[0]))) 

#clims[1] = 10 ** (int(np.log10(clims[1]))) 

norml = colors.LogNorm() 

p = PatchCollection(ellipses, cmap=plotDict['cmap'], alpha=plotDict['alpha'], 

linewidth=0, edgecolor=None, norm=norml, rasterized=True) 

else: 

p = PatchCollection(ellipses, cmap=plotDict['cmap'], alpha=plotDict['alpha'], 

linewidth=0, edgecolor=None, rasterized=True) 

p.set_array(metricValue.data[good]) 

p.set_clim(clims) 

ax.add_collection(p) 

# Add color bar (with optional setting of limits) 

if plotDict['cbar']: 

cb = plt.colorbar(p, aspect=25, extendrect=True, orientation='horizontal', 

format=plotDict['cbarFormat']) 

# If outputing to PDF, this fixes the colorbar white stripes 

if plotDict['cbar_edge']: 

cb.solids.set_edgecolor("face") 

cb.set_label(plotDict['xlabel'], fontsize=plotDict['fontsize']) 

cb.ax.tick_params(labelsize=plotDict['labelsize']) 

tick_locator = ticker.MaxNLocator(nbins=plotDict['nTicks']) 

cb.locator = tick_locator 

cb.update_ticks() 

# Add ecliptic 

self._plot_ecliptic(plotDict['raCen'], ax=ax) 

if plotDict['mwZone']: 

self._plot_mwZone(plotDict['raCen'], ax=ax) 

ax.grid(True, zorder=1) 

ax.xaxis.set_ticklabels([]) 

if plotDict['bgcolor'] is not None: 

ax.set_facecolor(plotDict['bgcolor']) 

# Add label. 

if plotDict['label'] is not None: 

plt.figtext(0.75, 0.9, '%s' % plotDict['label'], fontsize=plotDict['fontsize']) 

if plotDict['title'] is not None: 

plt.text(0.5, 1.09, plotDict['title'], horizontalalignment='center', 

transform=ax.transAxes, fontsize=plotDict['fontsize']) 

return fig.number 

 

 

class HealpixSDSSSkyMap(BasePlotter): 

def __init__(self): 

self.plotType = 'SkyMap' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'cbarFormat': '%.2f', 

'raMin': -90, 'raMax': 90, 'raLen': 45, 

'decMin': -2., 'decMax': 2.}) 

 

def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None): 

 

""" 

Plot the sky map of metricValue using healpy cartview plots in thin strips. 

raMin: Minimum RA to plot (deg) 

raMax: Max RA to plot (deg). Note raMin/raMax define the centers that will be plotted. 

raLen: Length of the plotted strips in degrees 

decMin: minimum dec value to plot 

decMax: max dec value to plot 

metricValueIn: metric values 

""" 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

metricValue = applyZPNorm(metricValueIn, plotDict) 

norm = None 

if plotDict['logScale']: 

norm = 'log' 

clims = setColorLims(metricValue, plotDict) 

cmap = setColorMap(plotDict) 

racenters = np.arange(plotDict['raMin'], plotDict['raMax'], plotDict['raLen']) 

nframes = racenters.size 

fig = plt.figure(fignum) 

# Do not specify or use plotDict['subplot'] because this is done in each call to hp.cartview. 

for i, racenter in enumerate(racenters): 

if i == 0: 

useTitle = plotDict['title'] + ' /n' + '%i < RA < %i' % (racenter - plotDict['raLen'], 

racenter + plotDict['raLen']) 

else: 

useTitle = '%i < RA < %i' % (racenter - plotDict['raLen'], racenter + plotDict['raLen']) 

hp.cartview(metricValue.filled(slicer.badval), title=useTitle, cbar=False, 

min=clims[0], max=clims[1], flip='astro', rot=(racenter, 0, 0), 

cmap=cmap, norm=norm, lonra=[-plotDict['raLen'], plotDict['raLen']], 

latra=[plotDict['decMin'], plotDict['decMax']], sub=(nframes + 1, 1, i + 1), fig=fig) 

hp.graticule(dpar=20, dmer=20, verbose=False) 

# Add colorbar (not using healpy default colorbar because want more tickmarks). 

ax = fig.add_axes([0.1, .15, .8, .075]) # left, bottom, width, height 

# Add label. 

if plotDict['label'] is not None: 

plt.figtext(0.8, 0.9, '%s' % plotDict['label']) 

# Make the colorbar as a seperate figure, 

# from http: //matplotlib.org/examples/api/colorbar_only.html 

cnorm = colors.Normalize(vmin=clims[0], vmax=clims[1]) 

# supress silly colorbar warnings 

with warnings.catch_warnings(): 

warnings.simplefilter("ignore") 

cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap, norm=cnorm, 

orientation='horizontal', format=plotDict['cbarFormat']) 

cb.set_label(plotDict['xlabel']) 

cb.ax.tick_params(labelsize=plotDict['labelsize']) 

if norm == 'log': 

tick_locator = ticker.LogLocator(numticks=plotDict['nTicks']) 

cb.locator = tick_locator 

cb.update_ticks() 

if (plotDict['nTicks'] is not None) & (norm != 'log'): 

tick_locator = ticker.MaxNLocator(nbins=plotDict['nTicks']) 

cb.locator = tick_locator 

cb.update_ticks() 

# If outputing to PDF, this fixes the colorbar white stripes 

if plotDict['cbar_edge']: 

cb.solids.set_edgecolor("face") 

fig = plt.gcf() 

return fig.number 

 

 

class LambertSkyMap(BasePlotter): 

""" 

Use basemap and contour to make a Lambertian projection. 

Note that the plotDict can include a 'basemap' key with a dictionary of 

arbitrary kwargs to use with the call to Basemap. 

""" 

 

def __init__(self): 

self.plotType = 'SkyMap' 

self.objectPlotter = False 

self.defaultPlotDict = {} 

self.defaultPlotDict.update(baseDefaultPlotDict) 

self.defaultPlotDict.update({'basemap': {'projection': 'nplaea', 'boundinglat': 1, 'lon_0': 180, 

'resolution': None, 'celestial': False, 'round': False}, 

'levels': 200, 'cbarFormat': '%i', 'norm': None}) 

 

def __call__(self, metricValueIn, slicer, userPlotDict, fignum=None): 

 

if 'ra' not in slicer.slicePoints or 'dec' not in slicer.slicePoints: 

errMessage = 'SpatialSlicer must contain "ra" and "dec" in slicePoints metadata.' 

errMessage += ' SlicePoints only contains keys %s.' % (slicer.slicePoints.keys()) 

raise ValueError(errMessage) 

 

plotDict = {} 

plotDict.update(self.defaultPlotDict) 

plotDict.update(userPlotDict) 

 

metricValue = applyZPNorm(metricValueIn, plotDict) 

clims = setColorLims(metricValue, plotDict) 

# Calculate the levels to use for the contour 

if np.size(plotDict['levels']) > 1: 

levels = plotDict['levels'] 

else: 

step = (clims[1] - clims[0]) / plotDict['levels'] 

levels = np.arange(clims[0], clims[1] + step, step) 

 

fig = plt.figure(fignum, figsize=plotDict['figsize']) 

ax = fig.add_subplot(plotDict['subplot']) 

 

# if using anaconda, to get basemap: 

# conda install basemap 

# Note, this should be possible without basemap, but there are 

# matplotlib bugs: 

# http: //stackoverflow.com/questions/31975303/matplotlib-tricontourf-with-an-axis-projection 

try: 

from mpl_toolkits.basemap import Basemap 

except ModuleNotFoundError: 

raise('To use this plotting function, please install Basemap into your python distribution') 

 

m = Basemap(**plotDict['basemap']) 

# Contour the plot first to remove any anti-aliasing artifacts. Doesn't seem to work though. See: 

# http: //stackoverflow.com/questions/15822159/aliasing-when-saving-matplotlib\ 

# -filled-contour-plot-to-pdf-or-eps 

# tmpContour = m.contour(np.degrees(slicer.slicePoints['ra']), 

# np.degrees(slicer.slicePoints['dec']), 

# metricValue.filled(np.min(clims)-1), levels, tri=True, 

# cmap=plotDict['cmap'], ax=ax, latlon=True, 

# lw=1) 

 

# Set masked values to be below the lowest contour level. 

if plotDict['norm'] == 'log': 

z_val = metricValue.filled(np.min(clims)-0.9) 

norm = colors.LogNorm(vmin=z_val.min(), vmax=z_val.max()) 

else: 

norm = plotDict['norm'] 

CS = m.contourf(np.degrees(slicer.slicePoints['ra']), 

np.degrees(slicer.slicePoints['dec']), 

metricValue.filled(np.min(clims)-0.9), levels, tri=True, 

cmap=plotDict['cmap'], ax=ax, latlon=True, norm=norm) 

 

# Try to fix the ugly pdf contour problem 

for c in CS.collections: 

c.set_edgecolor("face") 

 

para = np.arange(0, 89, 20) 

m.drawparallels(para, labels=[False, True, True, False]) 

m.drawmeridians(np.arange(-180, 181, 60), labels=[True, False, False, False]) 

cb = plt.colorbar(CS, format=plotDict['cbarFormat']) 

cb.set_label(plotDict['xlabel']) 

if plotDict['labelsize'] is not None: 

cb.ax.tick_params(labelsize=plotDict['labelsize']) 

# Pop in an extra line to raise the title a bit 

ax.set_title(plotDict['title']+'\n ') 

# If outputing to PDF, this fixes the colorbar white stripes 

if plotDict['cbar_edge']: 

cb.solids.set_edgecolor("face") 

return fig.number