/
plot.py
1179 lines (967 loc) · 38.5 KB
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plot.py
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##############################################################################
#
# plot.py: general wrappers for matplotlib plotting
#
# 'public' methods:
# end_print
# dens2d
# hist
# plot
# start_print
# scatterplot (like hogg_scatterplot)
# text
#
# this module also defines a custom matplotlib
# projection in which the polar azimuth increases
# clockwise (as in, the Galaxy viewed from the NGP)
#
#############################################################################
#############################################################################
#Copyright (c) 2010 - 2020, Jo Bovy
#All rights reserved.
#
#Redistribution and use in source and binary forms, with or without
#modification, are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission.
#
#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
#"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
#LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
#A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
#HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
#INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
#BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
#OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
#AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
#LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
#WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
#POSSIBILITY OF SUCH DAMAGE.
#############################################################################
import re
import matplotlib
import matplotlib.cm as cm
import matplotlib.pyplot as pyplot
import matplotlib.ticker as ticker
import numpy
from matplotlib import rc
from matplotlib.projections import PolarAxes, register_projection
from matplotlib.ticker import NullFormatter
from matplotlib.transforms import Affine2D, Bbox, IdentityTransform
from mpl_toolkits.mplot3d import \
Axes3D # Necessary for 3D plotting (projection = '3d')
from packaging.version import parse as parse_version
from scipy import interpolate, ndimage, special
_MPL_VERSION= parse_version(matplotlib.__version__)
from ..util.config import __config__
if __config__.getboolean('plot','seaborn-bovy-defaults'):
try:
import seaborn as sns
except: pass
else:
sns.set_style('ticks',
{'xtick.direction': 'in',
'ytick.direction': 'in',
'axes.labelsize': 18.0,
'axes.titlesize': 18.0,
'figure.figsize': numpy.array([ 6.64, 4. ]),
'grid.linewidth': 2.0,
'legend.fontsize': 18.0,
'lines.linewidth': 2.0,
'lines.markeredgewidth': 0.0,
'lines.markersize': 14.0,
'patch.linewidth': 0.6,
'xtick.labelsize': 16.0,
'xtick.major.pad': 14.0,
'xtick.major.width': 2.0,
'xtick.minor.width': 1.0,
'ytick.labelsize': 16.0,
'ytick.major.pad': 14.0,
'ytick.major.width': 2.0,})
_DEFAULTNCNTR= 10
def end_print(filename,**kwargs):
"""
NAME:
end_print
PURPOSE:
saves the current figure(s) to filename
INPUT:
filename - filename for plot (with extension)
OPTIONAL INPUTS:
format - file-format
OUTPUT:
(none)
HISTORY:
2009-12-23 - Written - Bovy (NYU)
"""
if 'format' in kwargs:
pyplot.savefig(filename,**kwargs)
else:
pyplot.savefig(filename,format=re.split(r'\.',filename)[-1],**kwargs)
pyplot.close()
def hist(x,xlabel=None,ylabel=None,overplot=False,**kwargs):
"""
NAME:
hist
PURPOSE:
wrapper around matplotlib's hist function
INPUT:
x - array to histogram
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
yrange - set the y-axis range
+all pyplot.hist keywords
OUTPUT:
(from the matplotlib docs:
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.hist)
The return value is a tuple (n, bins, patches)
or ([n0, n1, ...], bins, [patches0, patches1,...])
if the input contains multiple data
HISTORY:
2009-12-23 - Written - Bovy (NYU)
"""
if not overplot:
pyplot.figure()
if 'xrange' in kwargs:
xlimits= kwargs.pop('xrange')
if not 'range' in kwargs:
kwargs['range']= xlimits
xrangeSet= True
else: xrangeSet= False
if 'yrange' in kwargs:
ylimits= kwargs.pop('yrange')
yrangeSet= True
else: yrangeSet= False
out= pyplot.hist(x,**kwargs)
if overplot: return out
_add_axislabels(xlabel,ylabel)
if not 'range' in kwargs and not xrangeSet:
if isinstance(x,list):
xlimits=(numpy.array(x).min(),numpy.array(x).max())
else:
pyplot.xlim(x.min(),x.max())
elif xrangeSet:
pyplot.xlim(xlimits)
else:
pyplot.xlim(kwargs['range'])
if yrangeSet:
pyplot.ylim(ylimits)
_add_ticks()
return out
def plot(*args,**kwargs):
"""
NAME:
plot
PURPOSE:
wrapper around matplotlib's plot function
INPUT:
see http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
scatter= if True, use pyplot.scatter and its options etc.
colorbar= if True, and scatter==True, add colorbar
crange - range for colorbar of scatter==True
clabel= label for colorbar
overplot=True does not start a new figure and does not change the ranges and labels
gcf=True does not start a new figure (does change the ranges and labels)
onedhists - if True, make one-d histograms on the sides
onedhistcolor, onedhistfc, onedhistec
onedhistxnormed, onedhistynormed - normed keyword for one-d histograms
onedhistxweights, onedhistyweights - weights keyword for one-d histograms
bins= number of bins for onedhists
semilogx=, semilogy=, loglog= if True, plot logs
OUTPUT:
plot to output device, returns what pyplot.plot returns, or 3 Axes instances if onedhists=True
HISTORY:
2009-12-28 - Written - Bovy (NYU)
"""
overplot= kwargs.pop('overplot',False)
gcf= kwargs.pop('gcf',False)
onedhists= kwargs.pop('onedhists',False)
scatter= kwargs.pop('scatter',False)
loglog= kwargs.pop('loglog',False)
semilogx= kwargs.pop('semilogx',False)
semilogy= kwargs.pop('semilogy',False)
colorbar= kwargs.pop('colorbar',False)
onedhisttype= kwargs.pop('onedhisttype','step')
onedhistcolor= kwargs.pop('onedhistcolor','k')
onedhistfc= kwargs.pop('onedhistfc','w')
onedhistec= kwargs.pop('onedhistec','k')
onedhistxnormed= kwargs.pop('onedhistxnormed',True)
onedhistynormed= kwargs.pop('onedhistynormed',True)
onedhistxweights= kwargs.pop('onedhistxweights',None)
onedhistyweights= kwargs.pop('onedhistyweights',None)
if 'bins' in kwargs:
bins= kwargs['bins']
kwargs.pop('bins')
elif onedhists:
if isinstance(args[0],numpy.ndarray):
bins= round(0.3*numpy.sqrt(args[0].shape[0]))
elif isinstance(args[0],list):
bins= round(0.3*numpy.sqrt(len(args[0])))
else:
bins= 30
if onedhists:
if overplot or gcf: fig= pyplot.gcf()
else: fig= pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
elif not overplot and not gcf: pyplot.figure()
ax=pyplot.gca()
ax.set_autoscale_on(False)
xlabel= kwargs.pop('xlabel',None)
ylabel= kwargs.pop('ylabel',None)
clabel= kwargs.pop('clabel',None)
xlimits= kwargs.pop('xrange',None)
if xlimits is None:
if isinstance(args[0],list):
xlimits=(numpy.array(args[0]).min(),numpy.array(args[0]).max())
else:
xlimits=(args[0].min(),args[0].max())
ylimits= kwargs.pop('yrange',None)
if ylimits is None:
if isinstance(args[1],list):
ylimits=(numpy.array(args[1]).min(),numpy.array(args[1]).max())
else:
ylimits=(args[1].min(),args[1].max())
climits= kwargs.pop('crange',None)
if climits is None and scatter:
if 'c' in kwargs and isinstance(kwargs['c'],list):
climits=(numpy.array(kwargs['c']).min(),numpy.array(kwargs['c']).max())
elif 'c' in kwargs:
climits=(kwargs['c'].min(),kwargs['c'].max())
else:
climits= None
if scatter:
out= pyplot.scatter(*args,**kwargs)
elif loglog:
out= pyplot.loglog(*args,**kwargs)
elif semilogx:
out= pyplot.semilogx(*args,**kwargs)
elif semilogy:
out= pyplot.semilogy(*args,**kwargs)
else:
out= pyplot.plot(*args,**kwargs)
if overplot:
pass
else:
if semilogy:
ax= pyplot.gca()
ax.set_yscale('log')
elif semilogx:
ax= pyplot.gca()
ax.set_xscale('log')
elif loglog:
ax= pyplot.gca()
ax.set_xscale('log')
ax.set_yscale('log')
pyplot.xlim(*xlimits)
pyplot.ylim(*ylimits)
_add_axislabels(xlabel,ylabel)
if not semilogy and not semilogx and not loglog:
_add_ticks()
elif semilogy:
_add_ticks(xticks=True,yticks=False)
elif semilogx:
_add_ticks(yticks=True,xticks=False)
#Add colorbar
if colorbar:
cbar= pyplot.colorbar(out,fraction=0.15)
if _MPL_VERSION < parse_version('3.1'): # pragma: no cover
# https://matplotlib.org/3.1.0/api/api_changes.html#colorbarbase-inheritance
cbar.set_clim(*climits)
else:
cbar.mappable.set_clim(*climits)
if not clabel is None:
cbar.set_label(clabel)
#Add onedhists
if not onedhists:
return out
histx, edges, patches= axHistx.hist(args[0], bins=bins,
normed=onedhistxnormed,
weights=onedhistxweights,
histtype=onedhisttype,
range=sorted(xlimits),
color=onedhistcolor,fc=onedhistfc,
ec=onedhistec)
histy, edges, patches= axHisty.hist(args[1], bins=bins,
orientation='horizontal',
weights=onedhistyweights,
normed=onedhistynormed,
histtype=onedhisttype,
range=sorted(ylimits),
color=onedhistcolor,fc=onedhistfc,
ec=onedhistec)
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
axHistx.set_ylim( 0, 1.2*numpy.amax(histx))
axHisty.set_xlim( 0, 1.2*numpy.amax(histy))
return (axScatter,axHistx,axHisty)
def plot3d(*args,**kwargs):
"""
NAME:
plot3d
PURPOSE:
plot in 3d much as in 2d
INPUT:
see http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
overplot=True does not start a new figure
OUTPUT:
HISTORY:
2011-01-08 - Written - Bovy (NYU)
"""
overplot= kwargs.pop('overplot',False)
if not overplot: pyplot.figure()
ax= pyplot.gcf().add_subplot(projection='3d')
ax.set_autoscale_on(False)
xlabel= kwargs.pop('xlabel',None)
ylabel= kwargs.pop('ylabel',None)
zlabel= kwargs.pop('zlabel',None)
if 'xrange' in kwargs:
xlimits= kwargs.pop('xrange')
else:
if isinstance(args[0],list):
xlimits=(numpy.array(args[0]).min(),numpy.array(args[0]).max())
else:
xlimits=(args[0].min(),args[0].max())
if 'yrange' in kwargs:
ylimits= kwargs.pop('yrange')
else:
if isinstance(args[1],list):
ylimits=(numpy.array(args[1]).min(),numpy.array(args[1]).max())
else:
ylimits=(args[1].min(),args[1].max())
if 'zrange' in kwargs:
zlimits= kwargs.pop('zrange')
else:
if isinstance(args[2],list):
zlimits=(numpy.array(args[2]).min(),numpy.array(args[2]).max())
else:
zlimits=(args[1].min(),args[2].max())
out= pyplot.plot(*args,**kwargs)
if overplot:
pass
else:
if xlabel != None:
if xlabel[0] != '$':
thisxlabel=r'$'+xlabel+'$'
else:
thisxlabel=xlabel
ax.set_xlabel(thisxlabel)
if ylabel != None:
if ylabel[0] != '$':
thisylabel=r'$'+ylabel+'$'
else:
thisylabel=ylabel
ax.set_ylabel(thisylabel)
if zlabel != None:
if zlabel[0] != '$':
thiszlabel=r'$'+zlabel+'$'
else:
thiszlabel=zlabel
ax.set_zlabel(thiszlabel)
ax.set_xlim3d(*xlimits)
ax.set_ylim3d(*ylimits)
ax.set_zlim3d(*zlimits)
return out
def dens2d(X,**kwargs):
"""
NAME:
dens2d
PURPOSE:
plot a 2d density with optional contours
INPUT:
first argument is the density
matplotlib.pyplot.imshow keywords (see http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.imshow)
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
noaxes - don't plot any axes
overplot - if True, overplot
colorbar - if True, add colorbar
shrink= colorbar argument: shrink the colorbar by the factor (optional)
conditional - normalize each column separately (for probability densities, i.e., cntrmass=True)
gcf=True does not start a new figure (does change the ranges and labels)
Contours:
justcontours - if True, only draw contours
contours - if True, draw contours (10 by default)
levels - contour-levels
cntrmass - if True, the density is a probability and the levels are probability masses contained within the contour
cntrcolors - colors for contours (single color or array)
cntrlabel - label the contours
cntrlw, cntrls - linewidths and linestyles for contour
cntrlabelsize, cntrlabelcolors,cntrinline - contour arguments
cntrSmooth - use ndimage.gaussian_filter to smooth before contouring
onedhists - if True, make one-d histograms on the sides
onedhistcolor - histogram color
retAxes= return all Axes instances
retCont= return the contour instance
OUTPUT:
plot to output device, Axes instances depending on input
HISTORY:
2010-03-09 - Written - Bovy (NYU)
"""
overplot= kwargs.pop('overplot',False)
gcf= kwargs.pop('gcf',False)
if not overplot and not gcf:
pyplot.figure()
xlabel= kwargs.pop('xlabel',None)
ylabel= kwargs.pop('ylabel',None)
zlabel= kwargs.pop('zlabel',None)
if 'extent' in kwargs:
extent= kwargs.pop('extent')
else:
xlimits= kwargs.pop('xrange',[0,X.shape[1]])
ylimits= kwargs.pop('yrange',[0,X.shape[0]])
extent= xlimits+ylimits
if not 'aspect' in kwargs:
kwargs['aspect']= (xlimits[1]-xlimits[0])/float(ylimits[1]-ylimits[0])
noaxes= kwargs.pop('noaxes',False)
justcontours= kwargs.pop('justcontours',False)
if ('contours' in kwargs and kwargs['contours']) or \
'levels' in kwargs or justcontours or \
('cntrmass' in kwargs and kwargs['cntrmass']):
contours= True
else:
contours= False
kwargs.pop('contours',None)
if 'levels' in kwargs:
levels= kwargs['levels']
kwargs.pop('levels')
elif contours:
if 'cntrmass' in kwargs and kwargs['cntrmass']:
levels= numpy.linspace(0.,1.,_DEFAULTNCNTR)
elif True in numpy.isnan(numpy.array(X)):
levels= numpy.linspace(numpy.nanmin(X),numpy.nanmax(X),_DEFAULTNCNTR)
else:
levels= numpy.linspace(numpy.amin(X),numpy.amax(X),_DEFAULTNCNTR)
cntrmass= kwargs.pop('cntrmass',False)
conditional= kwargs.pop('conditional',False)
cntrcolors= kwargs.pop('cntrcolors','k')
cntrlabel= kwargs.pop('cntrlabel',False)
cntrlw= kwargs.pop('cntrlw',None)
cntrls= kwargs.pop('cntrls',None)
cntrSmooth= kwargs.pop('cntrSmooth',None)
cntrlabelsize= kwargs.pop('cntrlabelsize',None)
cntrlabelcolors= kwargs.pop('cntrlabelcolors',None)
cntrinline= kwargs.pop('cntrinline',None)
retCumImage= kwargs.pop('retCumImage',False)
cb= kwargs.pop('colorbar',False)
shrink= kwargs.pop('shrink',None)
onedhists= kwargs.pop('onedhists',False)
onedhistcolor= kwargs.pop('onedhistcolor','k')
retAxes= kwargs.pop('retAxes',False)
retCont= kwargs.pop('retCont',False)
if onedhists:
if overplot or gcf: fig= pyplot.gcf()
else: fig= pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
axHistx = pyplot.axes(rect_histx)
axHisty = pyplot.axes(rect_histy)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
ax=pyplot.gca()
ax.set_autoscale_on(False)
if conditional:
plotthis= X/numpy.tile(numpy.sum(X,axis=0),(X.shape[1],1))
else:
plotthis= X
if not justcontours:
out= pyplot.imshow(plotthis,extent=extent,**kwargs)
if not overplot:
pyplot.axis(extent)
_add_axislabels(xlabel,ylabel)
_add_ticks()
#Add colorbar
if cb and not justcontours:
if shrink is None:
shrink= numpy.amin([float(kwargs.pop('aspect',1.))*0.87,1.])
CB1= pyplot.colorbar(out,shrink=shrink)
if not zlabel is None:
if zlabel[0] != '$':
thiszlabel=r'$'+zlabel+'$'
else:
thiszlabel=zlabel
CB1.set_label(thiszlabel)
if contours or retCumImage:
aspect= kwargs.get('aspect',None)
origin= kwargs.get('origin',None)
if cntrmass:
#Sum from the top down!
plotthis[numpy.isnan(plotthis)]= 0.
sortindx= numpy.argsort(plotthis.flatten())[::-1]
cumul= numpy.cumsum(numpy.sort(plotthis.flatten())[::-1])/numpy.sum(plotthis.flatten())
cntrThis= numpy.zeros(numpy.prod(plotthis.shape))
cntrThis[sortindx]= cumul
cntrThis= numpy.reshape(cntrThis,plotthis.shape)
else:
cntrThis= plotthis
if contours:
if not cntrSmooth is None:
cntrThis= ndimage.gaussian_filter(cntrThis,cntrSmooth,
mode='nearest')
cont= pyplot.contour(cntrThis,levels,colors=cntrcolors,
linewidths=cntrlw,extent=extent,aspect=aspect,
linestyles=cntrls,origin=origin)
if cntrlabel:
pyplot.clabel(cont,fontsize=cntrlabelsize,
colors=cntrlabelcolors,
inline=cntrinline)
if noaxes:
ax.set_axis_off()
#Add onedhists
if not onedhists:
if retCumImage:
return cntrThis
elif retAxes:
return pyplot.gca()
elif retCont:
return cont
elif justcontours:
return cntrThis
else:
return out
histx= numpy.nansum(X.T,axis=1)*numpy.fabs(ylimits[1]-ylimits[0])/X.shape[1] #nansum bc nan is *no dens value*
histy= numpy.nansum(X.T,axis=0)*numpy.fabs(xlimits[1]-xlimits[0])/X.shape[0]
histx[numpy.isnan(histx)]= 0.
histy[numpy.isnan(histy)]= 0.
dx= (extent[1]-extent[0])/float(len(histx))
axHistx.plot(numpy.linspace(extent[0]+dx,extent[1]-dx,len(histx)),histx,
drawstyle='steps-mid',color=onedhistcolor)
dy= (extent[3]-extent[2])/float(len(histy))
axHisty.plot(histy,numpy.linspace(extent[2]+dy,extent[3]-dy,len(histy)),
drawstyle='steps-mid',color=onedhistcolor)
axHistx.set_xlim( axScatter.get_xlim() )
axHisty.set_ylim( axScatter.get_ylim() )
axHistx.set_ylim( 0, 1.2*numpy.amax(histx))
axHisty.set_xlim( 0, 1.2*numpy.amax(histy))
if retCumImage:
return cntrThis
elif retAxes:
return (axScatter,axHistx,axHisty)
elif justcontours:
return cntrThis
else:
return out
def start_print(fig_width=5,fig_height=5,axes_labelsize=16,
text_fontsize=11,legend_fontsize=12,
xtick_labelsize=10,ytick_labelsize=10,
xtick_minor_size=2,ytick_minor_size=2,
xtick_major_size=4,ytick_major_size=4):
"""
NAME:
start_print
PURPOSE:
setup a figure for plotting
INPUT:
fig_width - width in inches
fig_height - height in inches
axes_labelsize - size of the axis-labels
text_fontsize - font-size of the text (if any)
legend_fontsize - font-size of the legend (if any)
xtick_labelsize - size of the x-axis labels
ytick_labelsize - size of the y-axis labels
xtick_minor_size - size of the minor x-ticks
ytick_minor_size - size of the minor y-ticks
OUTPUT:
(none)
HISTORY:
2009-12-23 - Written - Bovy (NYU)
"""
fig_size = [fig_width,fig_height]
params = {'axes.labelsize': axes_labelsize,
'font.size': text_fontsize,
'legend.fontsize': legend_fontsize,
'xtick.labelsize':xtick_labelsize,
'ytick.labelsize':ytick_labelsize,
'text.usetex': True,
'figure.figsize': fig_size,
'xtick.major.size' : xtick_major_size,
'ytick.major.size' : ytick_major_size,
'xtick.minor.size' : xtick_minor_size,
'ytick.minor.size' : ytick_minor_size,
'legend.numpoints':1,
'xtick.top': True,
'xtick.direction': 'in',
'ytick.right': True,
'ytick.direction': 'in'}
pyplot.rcParams.update(params)
rc('text.latex', preamble=r'\usepackage{amsmath}'+'\n'
+r'\usepackage{amssymb}')
def text(*args,**kwargs):
"""
NAME:
text
PURPOSE:
thin wrapper around matplotlib's text and annotate
use keywords:
'bottom_left=True'
'bottom_right=True'
'top_left=True'
'top_right=True'
'title=True'
to place the text in one of the corners or use it as the title
INPUT:
see matplotlib's text
(http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.text)
OUTPUT:
prints text on the current figure
HISTORY:
2010-01-26 - Written - Bovy (NYU)
"""
if kwargs.pop('title',False):
pyplot.annotate(args[0],(0.5,1.05),xycoords='axes fraction',
horizontalalignment='center',
verticalalignment='top',**kwargs)
elif kwargs.pop('bottom_left',False):
pyplot.annotate(args[0],(0.05,0.05),xycoords='axes fraction',**kwargs)
elif kwargs.pop('bottom_right',False):
pyplot.annotate(args[0],(0.95,0.05),xycoords='axes fraction',
horizontalalignment='right',**kwargs)
elif kwargs.pop('top_right',False):
pyplot.annotate(args[0],(0.95,0.95),xycoords='axes fraction',
horizontalalignment='right',
verticalalignment='top',**kwargs)
elif kwargs.pop('top_left',False):
pyplot.annotate(args[0],(0.05,0.95),xycoords='axes fraction',
verticalalignment='top',**kwargs)
else:
pyplot.text(*args,**kwargs)
def scatterplot(x,y,*args,**kwargs):
"""
NAME:
scatterplot
PURPOSE:
make a 'smart' scatterplot that is a density plot in high-density
regions and a regular scatterplot for outliers
INPUT:
x, y
xlabel - (raw string!) x-axis label, LaTeX math mode, no $s needed
ylabel - (raw string!) y-axis label, LaTeX math mode, no $s needed
xrange
yrange
bins - number of bins to use in each dimension
weights - data-weights
aspect - aspect ratio
conditional - normalize each column separately (for probability densities, i.e., cntrmass=True)
gcf=True does not start a new figure (does change the ranges and labels)
contours - if False, don't plot contours
justcontours - if True, only draw contours, no density
cntrcolors - color of contours (can be array as for dens2d)
cntrlw, cntrls - linewidths and linestyles for contour
cntrSmooth - use ndimage.gaussian_filter to smooth before contouring
levels - contour-levels; data points outside of the last level will be individually shown (so, e.g., if this list is descending, contours and data points will be overplotted)
onedhists - if True, make one-d histograms on the sides
onedhistx - if True, make one-d histograms on the side of the x distribution
onedhisty - if True, make one-d histograms on the side of the y distribution
onedhistcolor, onedhistfc, onedhistec
onedhistxnormed, onedhistynormed - normed keyword for one-d histograms
onedhistxweights, onedhistyweights - weights keyword for one-d histograms
cmap= cmap for density plot
hist= and edges= - you can supply the histogram of the data yourself, this can be useful if you want to censor the data, both need to be set and calculated using scipy.histogramdd with the given range
retAxes= return all Axes instances
OUTPUT:
plot to output device, Axes instance(s) or not, depending on input
HISTORY:
2010-04-15 - Written - Bovy (NYU)
"""
xlabel= kwargs.pop('xlabel',None)
ylabel= kwargs.pop('ylabel',None)
if 'xrange' in kwargs:
xrange= kwargs.pop('xrange')
else:
if isinstance(x,list): xrange=[numpy.amin(x),numpy.amax(x)]
else: xrange=[x.min(),x.max()]
if 'yrange' in kwargs:
yrange= kwargs.pop('yrange')
else:
if isinstance(y,list): yrange=[numpy.amin(y),numpy.amax(y)]
else: yrange=[y.min(),y.max()]
ndata= len(x)
bins= kwargs.pop('bins',round(0.3*numpy.sqrt(ndata)))
weights= kwargs.pop('weights',None)
levels= kwargs.pop('levels',special.erf(numpy.arange(1,4)/numpy.sqrt(2.)))
aspect= kwargs.pop('aspect',(xrange[1]-xrange[0])/(yrange[1]-yrange[0]))
conditional= kwargs.pop('conditional',False)
contours= kwargs.pop('contours',True)
justcontours= kwargs.pop('justcontours',False)
cntrcolors= kwargs.pop('cntrcolors','k')
cntrlw= kwargs.pop('cntrlw',None)
cntrls= kwargs.pop('cntrls',None)
cntrSmooth= kwargs.pop('cntrSmooth',None)
onedhists= kwargs.pop('onedhists',False)
onedhistx= kwargs.pop('onedhistx',onedhists)
onedhisty= kwargs.pop('onedhisty',onedhists)
onedhisttype= kwargs.pop('onedhisttype','step')
onedhistcolor= kwargs.pop('onedhistcolor','k')
onedhistfc= kwargs.pop('onedhistfc','w')
onedhistec= kwargs.pop('onedhistec','k')
onedhistls= kwargs.pop('onedhistls','solid')
onedhistlw= kwargs.pop('onedhistlw',None)
onedhistsbins= kwargs.pop('onedhistsbins',round(0.3*numpy.sqrt(ndata)))
overplot= kwargs.pop('overplot',False)
gcf= kwargs.pop('gcf',False)
cmap= kwargs.pop('cmap',cm.gist_yarg)
onedhistxnormed= kwargs.pop('onedhistxnormed',True)
onedhistynormed= kwargs.pop('onedhistynormed',True)
onedhistxweights= kwargs.pop('onedhistxweights',weights)
onedhistyweights= kwargs.pop('onedhistyweights',weights)
retAxes= kwargs.pop('retAxes',False)
if onedhists or onedhistx or onedhisty:
if overplot or gcf: fig= pyplot.gcf()
else: fig= pyplot.figure()
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
axScatter = pyplot.axes(rect_scatter)
if onedhistx:
axHistx = pyplot.axes(rect_histx)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_formatter(nullfmt)
if onedhisty:
axHisty = pyplot.axes(rect_histy)
# no labels
axHisty.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
fig.sca(axScatter)
data= numpy.array([x,y]).T
if 'hist' in kwargs and 'edges' in kwargs:
hist=kwargs['hist']
kwargs.pop('hist')
edges=kwargs['edges']
kwargs.pop('edges')
else:
hist, edges= numpy.histogramdd(data,bins=bins,range=[xrange,yrange],
weights=weights)
if contours:
cumimage= dens2d(hist.T,contours=contours,levels=levels,
cntrmass=contours,cntrSmooth=cntrSmooth,
cntrcolors=cntrcolors,cmap=cmap,origin='lower',
xrange=xrange,yrange=yrange,xlabel=xlabel,
ylabel=ylabel,interpolation='nearest',
retCumImage=True,aspect=aspect,
conditional=conditional,
cntrlw=cntrlw,cntrls=cntrls,
justcontours=justcontours,zorder=5*justcontours,
overplot=(gcf or onedhists or overplot or onedhistx or onedhisty))
else:
cumimage= dens2d(hist.T,contours=contours,
cntrcolors=cntrcolors,
cmap=cmap,origin='lower',
xrange=xrange,yrange=yrange,xlabel=xlabel,
ylabel=ylabel,interpolation='nearest',
conditional=conditional,
retCumImage=True,aspect=aspect,
cntrlw=cntrlw,cntrls=cntrls,
overplot=(gcf or onedhists or overplot or onedhistx or onedhisty))
#Set axes and labels
pyplot.axis(list(xrange)+list(yrange))
if not overplot:
_add_axislabels(xlabel,ylabel)
_add_ticks()
binxs= []
xedge= edges[0]
for ii in range(len(xedge)-1):
binxs.append((xedge[ii]+xedge[ii+1])/2.)
binxs= numpy.array(binxs)
binys= []
yedge= edges[1]
for ii in range(len(yedge)-1):
binys.append((yedge[ii]+yedge[ii+1])/2.)
binys= numpy.array(binys)
cumInterp= interpolate.RectBivariateSpline(binxs,binys,cumimage.T,
kx=1,ky=1)
cums= []