/usr/lib/python2.7/dist-packages/healpy/projaxes.py is in python-healpy 1.10.3-2.
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# This file is part of Healpy.
#
# Healpy is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Healpy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Healpy; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
#
# For more information about Healpy, see http://code.google.com/p/healpy
#
from . import projector as P
from . import rotator as R
from . import pixelfunc
import matplotlib
import matplotlib.axes
import numpy as np
import six
from ._healpy_pixel_lib import UNSEEN
pi = np.pi
dtor = pi/180.
class SphericalProjAxes(matplotlib.axes.Axes):
"""Define a special Axes to take care of spherical projection.
Parameters
----------
projection : a SphericalProj class or a class derived from it.
type of projection
rot : list or string
define rotation. See rotator.
coord : list or string
define coordinate system. See rotator.
coordprec : number of digit after floating point for coordinates display.
format : format string for value display.
Notes
-----
Other keywords from Axes (see Axes).
"""
def __init__(self, ProjClass, *args, **kwds):
if not issubclass(ProjClass, P.SphericalProj):
raise TypeError("First argument must be a SphericalProj class "
"(or derived from)")
self.proj = ProjClass(rot = kwds.pop('rot',None),
coord = kwds.pop('coord',None),
flipconv = kwds.pop('flipconv',None),
**kwds.pop('arrayinfo', {}))
kwds.setdefault('format','%g')
kwds.setdefault('coordprec',2)
kwds['aspect'] = 'equal'
super(SphericalProjAxes,self).__init__(*args, **kwds)
self.axis('off')
self.set_autoscale_on(False)
xmin,xmax,ymin,ymax = self.proj.get_extent()
self.set_xlim(xmin,xmax)
self.set_ylim(ymin,ymax)
dx,dy = self.proj.ang2xy(pi/2.,1.*dtor,direct=True)
self._segment_threshold = 16.*np.sqrt(dx**2+dy**2)
self._segment_step_rad = 0.1*pi/180
self._do_border = True
self._gratdef = {}
self._gratdef['local'] = False
self._gratdef['dpar'] = 30.
def set_format(self,f):
"""Set the format string for value display
"""
self._format=f
return f
def set_coordprec(self,n):
"""Set the number of digits after floating point for coord display.
"""
self._coordprec = n
def format_coord(self,x,y):
"""Format the coordinate for display in status bar. Take projection
into account.
"""
format=self._format+' at '
pos=self.get_lonlat(x,y)
if pos is None or np.isnan(pos).any(): return ''
lon,lat = np.around(pos,decimals=self._coordprec)
val = self.get_value(x,y)
if val is None:
format = '%s'
val = ''
elif type(val) is str: format='%s @ '
coordsys = self.proj.coordsysstr
if coordsys != '':
res=(format+'(%g, %g) in %s')%(val,lon,lat,
coordsys[0:3])
else:
res=(format+'lon=%g, lat=%g')%(val,lon,lat)
return res
def get_lonlat(self,x,y):
"""Get the coordinate in the coord system of the image, in lon/lat in deg.
"""
lon,lat = self.proj.xy2ang(x,y,lonlat=True)
return lon,lat
def get_value(self,x,y):
"""Get the value of the map at position x,y
"""
if len(self.get_images()) < 1:
return None
im = self.get_images()[-1]
arr=im.get_array()
i,j = self.proj.xy2ij(x,y)
if i is None or j is None:
return None
elif arr.mask is not np.ma.nomask and arr.mask[i,j]:
return 'UNSEEN'
else:
return arr[i,j]
def projmap(self,map,vec2pix_func,vmin=None,vmax=None,badval=UNSEEN,
cmap=None,norm=None,rot=None,coord=None,**kwds):
"""Project a map on the SphericalProjAxes.
Parameters
----------
map : array-like
The map to project.
vec2pix_func : function
The function describing the pixelisation.
vmin, vmax : float, scalars
min and max value to use instead of min max of the map
badval : float
The value of the bad pixels
cmap : a color map
The colormap to use (see matplotlib.cm)
rot : sequence
In the form (lon, lat, psi) (unit: degree):the center of the map is
at (lon, lat) and rotated by angle psi around that direction.
coord : {'G', 'E', 'C', None}
The coordinate system of the map ('G','E' or 'C'), rotate
the map if different from the axes coord syst.
Notes
-----
Other keywords are transmitted to :func:`matplotlib.Axes.imshow`
"""
img = self.proj.projmap(map,vec2pix_func,rot=rot,coord=coord)
w = ~( np.isnan(img) |
np.isinf(img) |
pixelfunc.mask_bad(img, badval = badval) )
try:
if vmin is None: vmin = img[w].min()
except ValueError:
vmin = 0.
try:
if vmax is None: vmax = img[w].max()
except ValueError:
vmax = 0.
if vmin > vmax:
vmin = vmax
if vmin == vmax:
vmin -= 1.
vmax += 1.
cm,nn = get_color_table(vmin,vmax,img[w],cmap=cmap,norm=norm)
ext = self.proj.get_extent()
img = np.ma.masked_values(img, badval)
aximg = self.imshow(img,extent = ext,cmap=cm,norm=nn,
interpolation='nearest',origin='lower',
vmin=vmin,vmax=vmax,**kwds)
xmin,xmax,ymin,ymax = self.proj.get_extent()
self.set_xlim(xmin,xmax)
self.set_ylim(ymin,ymax)
return img
def projplot(self,*args,**kwds):
"""projplot is a wrapper around :func:`matplotlib.Axes.plot` to take into account the
spherical projection.
You can call this function as::
projplot(theta, phi) # plot a line going through points at coord (theta, phi)
projplot(theta, phi, 'bo') # plot 'o' in blue at coord (theta, phi)
projplot(thetaphi) # plot a line going through points at coord (thetaphi[0], thetaphi[1])
projplot(thetaphi, 'bx') # idem but with blue 'x'
Parameters
----------
theta, phi : float, array-like
Coordinates of point to plot. Can be put into one 2-d array, first line is
then *theta* and second line is *phi*. See *lonlat* parameter for unit.
fmt : str
A format string (see :func:`matplotlib.Axes.plot` for details)
lonlat : bool, optional
If True, theta and phi are interpreted as longitude and latitude
in degree, otherwise, as colatitude and longitude in radian
coord : {'E', 'G', 'C', None}
The coordinate system of the points, only used if the coordinate
coordinate system of the Axes has been defined and in this
case, a rotation is performed
rot : None or sequence
rotation to be applied =(lon, lat, psi) : lon, lat will be position of the
new Z axis, and psi is rotation around this axis, all in degree.
if None, no rotation is performed
direct : bool
if True, the rotation to center the projection is not
taken into account
Notes
-----
Other keywords are passed to :func:`matplotlib.Axes.plot`.
See Also
--------
projscatter, projtext
"""
fmt = None
if len(args) < 1:
raise ValueError("No argument given")
if len(args) == 1:
theta,phi = np.asarray(args[0])
elif len(args) == 2:
if type(args[1]) is str:
fmt=args[1]
theta,phi = np.asarray(args[0])
else:
theta,phi = np.asarray(args[0]),np.asarray(args[1])
elif len(args) == 3:
if type(args[2]) is not str:
raise TypeError("Third argument must be a string")
else:
theta,phi = np.asarray(args[0]),np.asarray(args[1])
fmt = args[2]
else:
raise TypeError("Three args maximum")
rot=kwds.pop('rot',None)
if rot is not None:
rot = np.array(np.atleast_1d(rot),copy=1)
rot.resize(3)
rot[1] = rot[1]-90.
coord=self.proj.mkcoord(kwds.pop('coord',None))[::-1]
lonlat=kwds.pop('lonlat',False)
vec = R.dir2vec(theta,phi,lonlat=lonlat)
vec = (R.Rotator(rot=rot,coord=coord,eulertype='Y')).I(vec)
x,y = self.proj.vec2xy(vec,direct=kwds.pop('direct',False))
x,y = self._make_segment(x,y,threshold=kwds.pop('threshold',
self._segment_threshold))
thelines = []
for xx,yy in zip(x,y):
if fmt is not None:
try: # works in matplotlib 1.3 and earlier
linestyle, marker, color = matplotlib.axes._process_plot_format(fmt)
except: # matplotlib 1.4 and later
linestyle, marker, color = matplotlib.axes._axes._process_plot_format(fmt)
kwds.setdefault('linestyle',linestyle)
kwds.setdefault('marker',marker)
if color is not None: kwds.setdefault('color',color)
l = matplotlib.lines.Line2D(xx,yy,**kwds)
self.add_line(l)
thelines.append(l)
return thelines
def projscatter(self,theta, phi=None,*args,**kwds):
"""Projscatter is a wrapper around :func:`matplotlib.Axes.scatter` to take into account the
spherical projection.
You can call this function as::
projscatter(theta, phi) # plot points at coord (theta, phi)
projplot(thetaphi) # plot points at coord (thetaphi[0], thetaphi[1])
Parameters
----------
theta, phi : float, array-like
Coordinates of point to plot. Can be put into one 2-d array, first line is
then *theta* and second line is *phi*. See *lonlat* parameter for unit.
lonlat : bool, optional
If True, theta and phi are interpreted as longitude and latitude
in degree, otherwise, as colatitude and longitude in radian
coord : {'E', 'G', 'C', None}, optional
The coordinate system of the points, only used if the coordinate
coordinate system of the axes has been defined and in this
case, a rotation is performed
rot : None or sequence, optional
rotation to be applied =(lon, lat, psi) : lon, lat will be position of the
new Z axis, and psi is rotation around this axis, all in degree.
if None, no rotation is performed
direct : bool, optional
if True, the rotation to center the projection is not
taken into account
Notes
-----
Other keywords are passed to :func:`matplotlib.Axes.plot`.
See Also
--------
projplot, projtext
"""
save_input_data = hasattr(self.figure, 'zoomtool')
if save_input_data:
input_data = (theta, phi, args, kwds.copy())
if phi is None:
theta,phi = np.asarray(theta)
else:
theta, phi = np.asarray(theta), np.asarray(phi)
rot=kwds.pop('rot',None)
if rot is not None:
rot = np.array(np.atleast_1d(rot),copy=1)
rot.resize(3)
rot[1] = rot[1]-90.
coord=self.proj.mkcoord(kwds.pop('coord',None))[::-1]
lonlat=kwds.pop('lonlat',False)
vec = R.dir2vec(theta,phi,lonlat=lonlat)
vec = (R.Rotator(rot=rot,coord=coord,eulertype='Y')).I(vec)
x,y = self.proj.vec2xy(vec,direct=kwds.pop('direct',False))
s = self.scatter(x, y, *args, **kwds)
if save_input_data:
if not hasattr(self, '_scatter_data'):
self._scatter_data = []
self._scatter_data.append((s, input_data))
return s
def projtext(self, theta, phi, s, **kwds):
"""Projtext is a wrapper around :func:`matplotlib.Axes.text` to take into account the
spherical projection.
Parameters
----------
theta, phi : float, array-like
Coordinates of point to plot. Can be put into one 2-d array, first line is
then *theta* and second line is *phi*. See *lonlat* parameter for unit.
text : str
The text to be displayed.
lonlat : bool, optional
If True, theta and phi are interpreted as longitude and latitude
in degree, otherwise, as colatitude and longitude in radian
coord : {'E', 'G', 'C', None}, optional
The coordinate system of the points, only used if the coordinate
coordinate system of the axes has been defined and in this
case, a rotation is performed
rot : None or sequence, optional
rotation to be applied =(lon, lat, psi) : lon, lat will be position of the
new Z axis, and psi is rotation around this axis, all in degree.
if None, no rotation is performed
direct : bool, optional
if True, the rotation to center the projection is not
taken into account
Notes
-----
Other keywords are passed to :func:`matplotlib.Axes.text`.
See Also
--------
projplot, projscatter
"""
if phi is None:
theta,phi = np.asarray(theta)
else:
theta, phi = np.asarray(theta), np.asarray(phi)
rot=kwds.pop('rot',None)
if rot is not None:
rot = np.array(np.atleast_1d(rot),copy=1)
rot.resize(3)
rot[1] = rot[1]-90.
coord=self.proj.mkcoord(kwds.pop('coord',None))[::-1]
lonlat=kwds.pop('lonlat',False)
vec = R.dir2vec(theta,phi,lonlat=lonlat)
vec = (R.Rotator(rot=rot,coord=coord,eulertype='Y')).I(vec)
x,y = self.proj.vec2xy(vec,direct=kwds.pop('direct',False))
return self.text(x,y,s,**kwds)
def _make_segment(self,x,y,threshold=None):
if threshold is None:
threshold = self._segment_threshold
x,y=np.atleast_1d(x),np.atleast_1d(y)
d2 = np.sqrt((np.roll(x,1)-x)**2+(np.roll(y,1)-y)**2)
w=np.where(d2 > threshold)[0]
#w=w[w!=0]
xx=[]
yy=[]
if len(w) == 1:
x=np.roll(x,-w[0])
y=np.roll(y,-w[0])
xx.append(x)
yy.append(y)
elif len(w) >= 2:
xx.append(x[0:w[0]])
yy.append(y[0:w[0]])
for i in six.moves.xrange(len(w)-1):
xx.append(x[w[i]:w[i+1]])
yy.append(y[w[i]:w[i+1]])
xx.append(x[w[-1]:])
yy.append(y[w[-1]:])
else:
xx.append(x)
yy.append(y)
return xx,yy
def get_parallel_interval(self,vx,vy=None,vz=None):
"""Get the min and max value of theta of the parallel to cover the
field of view.
Input:
- the normalized vector of the direction of the center of the
projection, in the reference frame of the graticule.
Return:
- vmin,vmax : between 0 and pi, vmin<vmax, the interval of theta
for the parallels crossing the field of view
"""
if vy is None and vz is None:
vx,vy,vz = vx
elif vy is None or vz is None:
raise ValueError("Both vy and vz must be given or both not given")
a = np.arccos(vz)
fov = self.proj.get_fov()
vmin = max(0., a-fov/2.)
vmax = min(pi, a+fov/2.)
return vmin,vmax
def get_meridian_interval(self, vx, vy=None, vz=None):
"""Get the min and max value of phi of the meridians to cover the field
of view.
Input:
- the normalized vector of the direction of the center of the
projection, in the reference frame of the graticule.
Return:
- vmin,vmax : the interval of phi for the
meridians crossing the field of view.
"""
if vy is None and vz is None:
vx,vy,vz = vx
elif vy is None or vz is None:
raise ValueError("Both vy and vz must be given or both not given")
fov = self.proj.get_fov()
th = np.arccos(vz)
if th <= fov/2.: # test whether north pole is visible
return -np.pi,np.pi
if abs(th-pi) <= fov/2.: # test whether south pole is visible
return -np.pi,np.pi
sth = np.sin(th)
phi0 = np.arctan2(vy,vx)
return phi0 - fov/sth/2., phi0 + fov/sth/2.
def graticule(self,dpar=None,dmer=None,coord=None,local=None,verbose=True,**kwds):
"""Draw a graticule.
Input:
- dpar: angular separation between parallels in degree
- dmer: angular separation between meridians in degree
- coord: coordinate system of the graticule ('G', 'E' or 'C')
- local: if True, no rotation performed at all
"""
gratargs = (dpar,dmer,coord,local)
gratkwds = kwds
if dpar is None: dpar=self._gratdef['dpar']
if local is None: local=self._gratdef['local']
if dmer is None: dmer = dpar
dpar = abs(dpar)*dtor
dmer = abs(dmer)*dtor
if not local:
vec = R.dir2vec(self.proj.get_center())
vec0 = R.Rotator(coord=self.proj.mkcoord(coord=coord)).I(vec)
else:
vec = (1,0,0)
vec0 = (1,0,0)
u_pmin,u_pmax = kwds.pop('pmax',None),kwds.pop('pmin',None)
u_mmin,u_mmax = kwds.pop('mmin',None),kwds.pop('mmax',None)
if u_pmin: u_pmin = (pi/2.-u_pmin*dtor)%pi
if u_pmax: u_pmax = (pi/2.-u_pmax*dtor)%pi
if u_mmin: u_mmin = ( ((u_mmin+180.)%360)-180)*dtor
if u_mmax: u_mmax = ( ((u_mmax+180.)%360)-180)*dtor
pmin,pmax = self.get_parallel_interval(vec0)
mmin,mmax = self.get_meridian_interval(vec0)
if u_pmin: pmin = u_pmin
if u_pmax: pmax = u_pmax
if u_mmin: mmin = u_mmin
if u_mmax: mmax = u_pmax
if verbose:
print('{0} {1} {2} {3}'.format(
pmin/dtor, pmax/dtor, mmin/dtor, mmax/dtor))
if not kwds.pop('force',False):
dpar,dmer = self._get_interv_graticule(pmin,pmax,dpar,
mmin,mmax,dmer,
verbose=verbose)
theta_list = np.around(np.arange(pmin,pmax+0.5*dpar,dpar)/dpar)*dpar
phi_list = np.around(np.arange(mmin,mmax+0.5*dmer,dmer)/dmer)*dmer
theta = np.arange(pmin,pmax,min((pmax-pmin)/100.,
self._segment_step_rad))
phi = np.arange(mmin,mmax,min((mmax-mmin)/100.,
self._segment_step_rad))
equator = False
gratlines = []
kwds.setdefault('lw',1)
kwds.setdefault('color','k')
for t in theta_list:
if abs(t-pi/2.)<1.e-10:
fmt = '-'
equator=True
elif abs(t) < 1.e-10: # special case: north pole
t = 1.e-10
fmt = '-'
elif abs(t-pi) < 1.e-10: # special case: south pole
t = pi-1.e-10
fmt = '-'
else:
fmt =':'
gratlines.append(self.projplot(phi*0.+t, phi,fmt,
coord=coord,
direct=local,**kwds))
if not equator and pmin <= pi/2. and pi/2 <= pmax:
gratlines.append(self.projplot(phi*0.+pi/2., phi,'-',
coord=coord,
direct=local,**kwds))
for p in phi_list:
if abs(p)<1.e-10: fmt = '-'
else: fmt =':'
gratlines.append(self.projplot(theta, theta*0.+p,fmt,
coord=coord,
direct=local,**kwds))
# Now the borders (only useful for full sky projection)
if hasattr(self,'_do_border') and self._do_border:
theta = np.arange(0,181)*dtor
gratlines.append(self.projplot(theta, theta*0-pi,'-k',
lw=1,direct=True))
gratlines.append(self.projplot(theta, theta*0+0.9999*pi,'-k',
lw=1,direct=True))
phi = np.arange(-180,180)*dtor
gratlines.append(self.projplot(phi*0+1.e-10, phi,'-k',
lw=1,direct=True))
gratlines.append(self.projplot(phi*0+pi-1.e-10, phi,'-k',
lw=1,direct=True))
if hasattr(self,'_graticules'):
self._graticules.append((gratargs,gratkwds,gratlines))
else:
self._graticules = [(gratargs,gratkwds,gratlines)]
return dpar,dmer
def delgraticules(self):
"""Delete all graticules previously created on the Axes.
"""
if hasattr(self,'_graticules'):
for dum1,dum2,g in self._graticules:
for gl in g:
for l in gl:
if l in self.lines:
self.lines.remove(l)
else:
print('line not in lines')
del self._graticules
def _get_interv_graticule(self,pmin,pmax,dpar,mmin,mmax,dmer,verbose=True):
def set_prec(d,n,nn=2):
arcmin=False
if d/n < 1.:
d *= 60
arcmin = True
nn = 1
x = d/n
y = nn*x
ex = np.floor(np.log10(y))
z = np.around(y/10**ex)*10**ex/nn
if arcmin:
z = 1./np.around(60./z)
return z
max_n_par = 18
max_n_mer = 36
n_par = (pmax-pmin)/dpar
n_mer = (mmax-mmin)/dmer
if n_par > max_n_par:
dpar = set_prec((pmax-pmin)/dtor,max_n_par/2)*dtor
if n_mer > max_n_mer:
dmer = set_prec((mmax-mmin)/dtor,max_n_mer/2,nn=1)*dtor
if dmer/dpar < 0.2 or dmer/dpar > 5.:
dmer = dpar = max(dmer,dpar)
vdeg = int(np.floor(np.around(dpar/dtor,10)))
varcmin = (dpar/dtor-vdeg)*60.
if verbose: print("The interval between parallels is {0:d} deg {1:.2f}'.".format(vdeg,varcmin))
vdeg = int(np.floor(np.around(dmer/dtor,10)))
varcmin = (dmer/dtor-vdeg)*60.
if verbose: print("The interval between meridians is {0:d} deg {1:.2f}'.".format(vdeg,varcmin))
return dpar,dmer
class GnomonicAxes(SphericalProjAxes):
"""Define a gnomonic Axes to handle gnomonic projection.
Input:
- rot=, coord= : define rotation and coordinate system. See rotator.
- coordprec= : number of digit after floating point for coordinates display.
- format= : format string for value display.
Other keywords from Axes (see Axes).
"""
def __init__(self,*args,**kwds):
kwds.setdefault('coordprec',3)
super(GnomonicAxes,self).__init__(P.GnomonicProj, *args,**kwds)
self._do_border = False
self._gratdef['local'] = True
self._gratdef['dpar'] = 1.
def projmap(self,map,vec2pix_func,xsize=200,ysize=None,reso=1.5,**kwds):
self.proj.set_proj_plane_info(xsize=xsize,ysize=ysize,reso=reso)
return super(GnomonicAxes,self).projmap(map,vec2pix_func,**kwds)
class HpxGnomonicAxes(GnomonicAxes):
def projmap(self,map,nest=False,**kwds):
nside = pixelfunc.npix2nside(pixelfunc.get_map_size(map))
f = lambda x,y,z: pixelfunc.vec2pix(nside,x,y,z,nest=nest)
xsize = kwds.pop('xsize',200)
ysize = kwds.pop('ysize',None)
reso = kwds.pop('reso',1.5)
return super(HpxGnomonicAxes,self).projmap(map,f,xsize=xsize,
ysize=ysize,reso=reso,**kwds)
class MollweideAxes(SphericalProjAxes):
"""Define a mollweide Axes to handle mollweide projection.
Input:
- rot=, coord= : define rotation and coordinate system. See rotator.
- coordprec= : number of digit after floating point for coordinates display.
- format= : format string for value display.
Other keywords from Axes (see Axes).
"""
def __init__(self,*args,**kwds):
kwds.setdefault('coordprec',2)
super(MollweideAxes,self).__init__(P.MollweideProj, *args,**kwds)
self.set_xlim(-2.01,2.01)
self.set_ylim(-1.01,1.01)
def projmap(self,map,vec2pix_func,xsize=800,**kwds):
self.proj.set_proj_plane_info(xsize=xsize)
img = super(MollweideAxes,self).projmap(map,vec2pix_func,**kwds)
self.set_xlim(-2.01,2.01)
self.set_ylim(-1.01,1.01)
return img
class HpxMollweideAxes(MollweideAxes):
def projmap(self,map,nest=False,**kwds):
nside = pixelfunc.npix2nside(pixelfunc.get_map_size(map))
f = lambda x,y,z: pixelfunc.vec2pix(nside,x,y,z,nest=nest)
return super(HpxMollweideAxes,self).projmap(map,f,**kwds)
class CartesianAxes(SphericalProjAxes):
"""Define a cylindrical Axes to handle cylindrical projection.
"""
def __init__(self,*args,**kwds):
kwds.setdefault('coordprec',2)
super(CartesianAxes,self).__init__(P.CartesianProj, *args, **kwds)
self._segment_threshold = 180
self._segment_step_rad = 0.1*pi/180
self._do_border = True
def projmap(self,map,vec2pix_func,xsize=800,ysize=None,lonra=None,latra=None,**kwds):
self.proj.set_proj_plane_info(xsize=xsize,ysize=ysize,lonra=lonra,latra=latra)
return super(CartesianAxes,self).projmap(map,vec2pix_func,**kwds)
class HpxCartesianAxes(CartesianAxes):
def projmap(self,map,nest=False,**kwds):
nside = pixelfunc.npix2nside(pixelfunc.get_map_size(map))
f = lambda x,y,z: pixelfunc.vec2pix(nside,x,y,z,nest=nest)
return super(HpxCartesianAxes,self).projmap(map,f,**kwds)
class OrthographicAxes(SphericalProjAxes):
"""Define an orthographic Axes to handle orthographic projection.
Input:
- rot=, coord= : define rotation and coordinate system. See rotator.
- coordprec= : num of digits after floating point for coordinates display.
- format= : format string for value display.
Other keywords from Axes (see Axes).
"""
def __init__(self,*args,**kwds):
kwds.setdefault('coordprec',2)
super(OrthographicAxes,self).__init__(P.OrthographicProj, *args,**kwds)
self._segment_threshold = 0.01
self._do_border = False
def projmap(self,map,vec2pix_func,xsize=800,half_sky=False,**kwds):
self.proj.set_proj_plane_info(xsize=xsize,half_sky=half_sky)
img = super(OrthographicAxes,self).projmap(map,vec2pix_func,**kwds)
if half_sky: ratio = 1.01
else: ratio = 2.01
self.set_xlim(-ratio,ratio)
self.set_ylim(-1.01,1.01)
return img
class HpxOrthographicAxes(OrthographicAxes):
def projmap(self,map,nest=False,**kwds):
nside = pixelfunc.npix2nside(len(map))
f = lambda x,y,z: pixelfunc.vec2pix(nside,x,y,z,nest=nest)
return super(HpxOrthographicAxes,self).projmap(map,f,**kwds)
class AzimuthalAxes(SphericalProjAxes):
"""Define an Azimuthal Axes to handle azimuthal equidistant or
Lambert azimuthal equal-area projections.
Input:
- rot=, coord= : define rotation and coordinate system. See rotator.
- coordprec= : number of digit after floating point for coordinates display.
- format= : format string for value display.
Other keywords from Axes (see Axes).
"""
def __init__(self,*args,**kwds):
kwds.setdefault('coordprec',3)
super(AzimuthalAxes,self).__init__(P.AzimuthalProj, *args,**kwds)
self._do_border = False
def projmap(self,map,vec2pix_func,xsize=200,ysize=None,reso=1.5,lamb=True,half_sky=False,**kwds):
self.proj.set_proj_plane_info(xsize=xsize,ysize=ysize,reso=reso,lamb=lamb,half_sky=half_sky)
return super(AzimuthalAxes,self).projmap(map,vec2pix_func,**kwds)
class HpxAzimuthalAxes(AzimuthalAxes):
def projmap(self,map,nest=False,**kwds):
nside = pixelfunc.npix2nside(pixelfunc.get_map_size(map))
f = lambda x,y,z: pixelfunc.vec2pix(nside,x,y,z,nest=nest)
xsize = kwds.pop('xsize',800)
ysize = kwds.pop('ysize',None)
reso = kwds.pop('reso',1.5)
lamb = kwds.pop('lamb',True)
return super(HpxAzimuthalAxes,self).projmap(map,f,xsize=xsize,
ysize=ysize,reso=reso,lamb=lamb,**kwds)
###################################################################
#
# Table color for mollview, gnomview, and orthview.
# Currently defined for so that the default colormap, found in
# matplotlib.rcParams['image.cmap'], the data is displayed with
# values greater than vmax as the final element of the colormap,
# masked indices gray, and the background set to white.
#
# With matplotlib.rcParams['image.cmap'] assigned to a string
# corresponding to a standard matplotlib colormap, one can call
# hp.mollview(m) and have the map projected in the standard way,
# whereas using just, e.g., hp.mollview(m, cmap='jet') will display
# the data with a non-white background.
#
# One can set the default colormap in the matplotlibrc file, or set
# it in situ:
# >>> matplotlib.rcParam['image.cmap'] = 'coolwarm'
# >>> hp.mollview(m)
# Note that custom colormaps can also be used, but they need to be
# registered ahead fo time, as shown in
# http://matplotlib.org/examples/pylab_examples/custom_cmap.html
def get_color_table(vmin,vmax,val,cmap=None,norm=None):
# Create color table
newcmap = create_colormap(cmap)
if type(norm) is str:
if norm.lower().startswith('log'):
norm = LogNorm2(clip=False)
elif norm.lower().startswith('hist'):
norm = HistEqNorm(clip=False)
else:
norm = None
if norm is None:
norm = LinNorm2(clip=False)
norm.vmin = vmin
norm.vmax = vmax
norm.autoscale_None(val)
return newcmap,norm
def create_colormap(cmap):
if cmap is not None:
return cmap
cmap0 = matplotlib.cm.get_cmap(matplotlib.rcParams['image.cmap'])
if hasattr(cmap0, '_segmentdata'):
newcm = matplotlib.colors.LinearSegmentedColormap('newcm',cmap0._segmentdata,
cmap0.N)
else:
newcm = cmap0
newcm.set_over(newcm(1.0))
newcm.set_under('w')
newcm.set_bad('gray')
return newcm
##################################################################
#
# A Locator that gives the bounds of the interval
#
class BoundaryLocator(matplotlib.ticker.Locator):
def __init__(self,N=2):
if N < 2:
raise ValueError("Number of locs must be greater than 1")
self.Nlocs=N
def __call__(self):
if matplotlib.__version__ < '0.98':
vmin,vmax = self.viewInterval.get_bounds()
else:
vmin, vmax = self.axis.get_view_interval()
locs = vmin + np.arange(self.Nlocs)*(vmax-vmin)/(self.Nlocs-1.)
return locs
def autoscale(self):
self.verify_intervals()
vmin,vmax = self.dataInterval.get_bounds()
if vmax<vmin:
vmin,vmax = vmax,vmin
if vmin==vmax:
vmin -= 1
vmax += 1
return vmin,vmax
##################################################################
#
# A normalization class to get color table equalised by
# the histogram of data
#
class HistEqNorm(matplotlib.colors.Normalize):
def __init__(self, vmin=None, vmax=None, clip=False):
matplotlib.colors.Normalize.__init__(self,vmin,vmax,clip)
self.xval = None
self.yval = None
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if matplotlib.cbook.iterable(value):
vtype = 'array'
val = np.ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = np.ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = float(self.vmin), float(self.vmax)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return 0.0 * val
else:
if clip:
mask = np.ma.getmask(val)
val = np.ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = np.ma.array(np.interp(val, self.xval, self.yval),
mask=np.ma.getmask(val))
result[np.isinf(val.data)] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
if matplotlib.cbook.iterable(value):
vtype='array'
val = np.ma.array(value)
else:
vtype='scalar'
val = np.ma.array([value])
result = np.ma.array(self._lininterp(val, self.yval, self.xval),
mask=np.ma.getmask(val))
result[np.isinf(val.data)] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
def autoscale_None(self, val):
changed = False
if self.vmin is None:
self.vmin = val.min()
changed = True
if self.vmax is None:
self.vmax = val.max()
changed = True
if changed or self.xval is None or self.yval is None:
self._set_xyvals(val)
def autoscale(self, val):
self.vmin = val.min()
self.vmax = val.max()
self._set_xyvals(val)
def _set_xyvals(self,val):
data = np.ma.asarray(val).ravel()
w=np.isinf(data.data)
if data.mask is not np.ma.nomask:
w = w|data.mask
data2 = data.data[~w]
bins = min(data2.size//20, 5000)
if bins < 3: bins=data2.size
try:
# for numpy 1.1, use new bins format (left and right edges)
hist, bins = np.histogram(data2,bins=bins,
range=(self.vmin,self.vmax),
new=True)
except TypeError:
# for numpy <= 1.0 or numpy >= 1.2, no new keyword
hist, bins = np.histogram(data2,bins=bins,
range=(self.vmin,self.vmax))
if bins.size == hist.size+1:
# new bins format, remove last point
bins = bins[:-1]
hist = hist.astype(np.float)/np.float(hist.sum())
self.yval = np.concatenate([[0.], hist.cumsum(), [1.]])
self.xval = np.concatenate([[self.vmin], bins + 0.5*(bins[1]-bins[0]),
[self.vmax]])
def _lininterp(self,x,X,Y):
if hasattr(x,'__len__'):
xtype = 'array'
xx=np.asarray(x).astype(np.float)
else:
xtype = 'scalar'
xx=np.asarray([x]).astype(np.float)
idx = X.searchsorted(xx)
yy = xx*0
yy[idx>len(X)-1] = Y[-1] # over
yy[idx<=0] = Y[0] # under
wok = np.where((idx>0) & (idx<len(X))) # the good ones
iok=idx[wok]
yywok = Y[iok-1] + ( (Y[iok]-Y[iok-1])/(X[iok]-X[iok-1])
* (xx[wok]-X[iok-1]) )
w = np.where( ((X[iok]-X[iok-1]) == 0) ) # where are the nan ?
yywok[w] = Y[iok[w]-1] # replace by previous value
wl = np.where(xx[wok] == X[0])
yywok[wl] = Y[0]
wh = np.where(xx[wok] == X[-1])
yywok[wh] = Y[-1]
yy[wok] = yywok
if xtype == 'scalar':
yy = yy[0]
return yy
##################################################################
#
# A normalization class to get logarithmic color table
#
class LogNorm2(matplotlib.colors.Normalize):
"""
Normalize a given value to the 0-1 range on a log scale
"""
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if matplotlib.cbook.iterable(value):
vtype = 'array'
val = np.ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = np.ma.array([value]).astype(np.float)
val = np.ma.masked_where(np.isinf(val.data),val)
self.autoscale_None(val)
vmin, vmax = float(self.vmin), float(self.vmax)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin<=0:
raise ValueError("values must all be positive")
elif vmin==vmax:
return type(value)(0.0 * np.asarray(value))
else:
if clip:
mask = np.ma.getmask(val)
val = np.ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (np.ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
result.data[result.data<0]=0.0
result.data[result.data>1]=1.0
result[np.isinf(val.data)] = -np.inf
if result.mask is not np.ma.nomask:
result.mask[np.isinf(val.data)] = False
if vtype == 'scalar':
result = result[0]
return result
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None or self.vmax is None:
val = np.ma.masked_where(np.isinf(A.data),A)
matplotlib.colors.Normalize.autoscale_None(self,val)
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = float(self.vmin), float(self.vmax)
if matplotlib.cbook.iterable(value):
val = np.ma.asarray(value)
return vmin * np.ma.power((vmax/vmin), val)
else:
return vmin * np.pow((vmax/vmin), value)
##################################################################
#
# A normalization class to get linear color table
#
class LinNorm2(matplotlib.colors.Normalize):
"""
Normalize a given value to the 0-1 range on a lin scale
"""
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if matplotlib.cbook.iterable(value):
vtype = 'array'
val = np.ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = np.ma.array([value]).astype(np.float)
winf = np.isinf(val.data)
val = np.ma.masked_where(winf,val)
self.autoscale_None(val)
vmin, vmax = float(self.vmin), float(self.vmax)
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
return type(value)(0.0 * np.asarray(value))
else:
if clip:
mask = np.ma.getmask(val)
val = np.ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * (1./(vmax-vmin))
result.data[result.data<0]=0.0
result.data[result.data>1]=1.0
result[winf] = -np.inf
if result.mask is not np.ma.nomask:
result.mask[winf] = False
if vtype == 'scalar':
result = result[0]
return result
def autoscale_None(self, A):
' autoscale only None-valued vmin or vmax'
if self.vmin is None or self.vmax is None:
val = np.ma.masked_where(np.isinf(A.data),A)
matplotlib.colors.Normalize.autoscale_None(self,val)
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = float(self.vmin), float(self.vmax)
if matplotlib.cbook.iterable(value):
val = np.ma.asarray(value)
return vmin + (vmax-vmin) * val
else:
return vmin + (vmax-vmin) * value
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