from builtins import zip
import numpy as np
import matplotlib.pyplot as plt
from lsst.sims.maf.utils import percentileClipping
from .plotHandler import BasePlotter
__all__ = ['OneDBinnedData']
[docs]class OneDBinnedData(BasePlotter):
def __init__(self):
self.plotType = 'BinnedData'
self.objectPlotter = False
self.defaultPlotDict = {'title': None, 'label': None, 'xlabel': None, 'ylabel': None,
'filled': False, 'alpha': 0.5, 'linestyle': '-', 'linewidth': 1,
'logScale': False, 'percentileClip': None,
'xMin': None, 'xMax': None, 'yMin': None, 'yMax': None,
'fontsize': None, 'figsize': None, 'grid': False}
[docs] def __call__(self, metricValues, slicer, userPlotDict, fignum=None):
"""
Plot a set of oneD binned metric data.
"""
if slicer.slicerName != 'OneDSlicer':
raise ValueError('OneDBinnedData plotter is for use with OneDSlicer')
if 'bins' not in slicer.slicePoints:
errMessage = 'OneDSlicer must contain "bins" in slicePoints metadata.'
errMessage += ' SlicePoints only contains keys %s.' % (slicer.slicePoints.keys())
raise ValueError(errMessage)
plotDict = {}
plotDict.update(self.defaultPlotDict)
plotDict.update(userPlotDict)
fig = plt.figure(fignum, figsize=plotDict['figsize'])
# Plot the histogrammed data.
leftedge = slicer.slicePoints['bins'][:-1]
width = np.diff(slicer.slicePoints['bins'])
if plotDict['filled']:
plt.bar(leftedge, metricValues.filled(), width, label=plotDict['label'],
linewidth=0, alpha=plotDict['alpha'], log=plotDict['logScale'],
color=plotDict['color'])
else:
good = np.where(metricValues.mask == False)
x = np.ravel(list(zip(leftedge[good], leftedge[good] + width[good])))
y = np.ravel(list(zip(metricValues[good], metricValues[good])))
if plotDict['logScale']:
plt.semilogy(x, y, label=plotDict['label'], color=plotDict['color'],
linestyle=plotDict['linestyle'], linewidth=plotDict['linewidth'],
alpha=plotDict['alpha'])
else:
plt.plot(x, y, label=plotDict['label'], color=plotDict['color'],
linestyle=plotDict['linestyle'], linewidth=plotDict['linewidth'],
alpha=plotDict['alpha'])
if 'ylabel' in plotDict:
plt.ylabel(plotDict['ylabel'], fontsize=plotDict['fontsize'])
if 'xlabel' in plotDict:
plt.xlabel(plotDict['xlabel'], fontsize=plotDict['fontsize'])
# Set y limits (either from values in args, percentileClipping or compressed data values).
if plotDict['percentileClip'] is not None:
yMin, yMax = percentileClipping(metricValues.compressed(),
percentile=plotDict['percentileClip'])
if plotDict['yMin'] is None:
plotDict['yMin'] = yMin
if plotDict['yMax'] is None:
plotDict['yMax'] = yMax
plt.grid(plotDict['grid'], alpha=0.3)
if plotDict['yMin'] is None and metricValues.filled().min() == 0:
plotDict['yMin'] = 0
# Set y and x limits, if provided.
if plotDict['yMin'] is not None:
plt.ylim(ymin=plotDict['yMin'])
if plotDict['yMax'] is not None:
plt.ylim(ymax=plotDict['yMax'])
if plotDict['xMin'] is not None:
plt.xlim(xmin=plotDict['xMin'])
if plotDict['xMax'] is not None:
plt.xlim(xmax=plotDict['xMax'])
plt.title(plotDict['title'])
return fig.number