def __init__(self, background, nside=None, fignum=None,
projection='mollweide', coord_bg='G', coord_plot='C',
partialmap=False, config='footprint.cfg',
map_path=None, download_config=False,
title='Survey Footprints', cbar=None, min=1.0,
max=5000.0, log=True, unit='', **kwds):
self.fig = pl.figure(fignum)
self.coord_plot = coord_plot
self.partialmap = partialmap
self.kwds = kwds
self.cbs = []
if projection == 'mollweide':
self.mapview = H.mollview
self.mapcontour = vf.mollcontour
elif projection == 'cartesian':
self.mapview = H.cartview
self.mapcontour = vf.cartcontour
elif projection == 'orthographic':
self.mapview = H.orthview
self.mapcontour = vf.orthcontour
elif projection == 'gnomonic':
self.mapview = H.gnomview
self.mapcontour = vf.gnomcontour
if map_path is None:
full_path = inspect.getfile(inspect.currentframe())
abs_path = os.path.split(full_path)[0]
map_path = os.path.join(abs_path, 'maps/')
self.config = ConfigHandler(config, map_path, nside=nside,
download_config=download_config)
# Could also just call load_survey which will call get_background
if isinstance(background, str):
bgmap, coord_bg, unit2 = self.config.load_survey(background,
get_unit=True)
background = bgmap[0]
if unit2 is not None:
unit = unit2
if nside is None:
nside = H.npix2nside(len(background))
self.nside = nside
coord = [coord_bg, coord_plot]
cm.Greys.set_under(alpha=0.0)
if log:
min = np.log(min)
max = np.log(max)
unit = r'$\log($' + unit + r'$)$'
background = np.log(background)
if self.partialmap:
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
H.cartview(background, title=title, coord=coord,
fig=self.fig.number, cmap=cm.Greys,
notext=True, flip='astro', min=min, max=max,
sub=sub, margins=margins, **kwds)
self.fig.delaxes(self.fig.axes[-1])
else:
self.mapview(background, title=title,
coord=coord, fig=self.fig.number, cmap=cm.Greys,
min=min, max=max, notext=True,
cbar=True, flip='astro', unit=unit, **kwds)
if not cbar:
self.fig.delaxes(self.fig.axes[-1])
H.graticule(dpar=30.0, dmer=30.0, coord='C', verbose=False)
def __init__(self,
background,
nside=None,
fignum=None,
projection='mollweide',
coord_bg='G',
coord_plot='C',
partialmap=False,
config='footprint.cfg',
map_path=None,
download_config=False,
title='Survey Footprints',
cbar=None,
min=1.0,
max=5000.0,
log=True,
unit='',
**kwds):
self.fig = pl.figure(fignum)
self.coord_plot = coord_plot
self.partialmap = partialmap
self.kwds = kwds
self.cbs = []
if projection == 'mollweide':
self.mapview = H.mollview
self.mapcontour = vf.mollcontour
elif projection == 'cartesian':
self.mapview = H.cartview
self.mapcontour = vf.cartcontour
elif projection == 'orthographic':
self.mapview = H.orthview
self.mapcontour = vf.orthcontour
elif projection == 'gnomonic':
self.mapview = H.gnomview
self.mapcontour = vf.gnomcontour
if map_path is None:
full_path = inspect.getfile(inspect.currentframe())
abs_path = os.path.split(full_path)[0]
map_path = os.path.join(abs_path, 'maps/')
self.config = ConfigHandler(config,
map_path,
nside=nside,
download_config=download_config)
# Could also just call load_survey which will call get_background
if isinstance(background, str):
bgmap, coord_bg, unit2 = self.config.load_survey(background,
get_unit=True)
background = bgmap[0]
if unit2 is not None:
unit = unit2
if nside is None:
nside = H.npix2nside(len(background))
self.nside = nside
coord = [coord_bg, coord_plot]
cm.Greys.set_under(alpha=0.0)
if log:
min = np.log(min)
max = np.log(max)
unit = r'$\log($' + unit + r'$)$'
background = np.log(background)
if self.partialmap:
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
H.cartview(background,
title=title,
coord=coord,
fig=self.fig.number,
cmap=cm.Greys,
notext=True,
flip='astro',
min=min,
max=max,
sub=sub,
margins=margins,
**kwds)
self.fig.delaxes(self.fig.axes[-1])
else:
self.mapview(background,
title=title,
coord=coord,
fig=self.fig.number,
cmap=cm.Greys,
min=min,
max=max,
notext=True,
cbar=True,
flip='astro',
unit=unit,
**kwds)
if not cbar:
self.fig.delaxes(self.fig.axes[-1])
H.graticule(dpar=30.0, dmer=30.0, coord='C', verbose=False)
class SurveyStack(object):
'''The SurveyStack class allows us to overlay survey hitmaps upon
an input background. The hitmaps have their transparency associated with
the number of hits (more hits = less transparent).
'''
def __init__(self, background, nside=None, fignum=None,
projection='mollweide', coord_bg='G', coord_plot='C',
partialmap=False, config='footprint.cfg',
map_path=None, download_config=False,
title='Survey Footprints', cbar=None, min=1.0,
max=5000.0, log=True, unit='', **kwds):
self.fig = pl.figure(fignum)
self.coord_plot = coord_plot
self.partialmap = partialmap
self.kwds = kwds
self.cbs = []
if projection == 'mollweide':
self.mapview = H.mollview
self.mapcontour = vf.mollcontour
elif projection == 'cartesian':
self.mapview = H.cartview
self.mapcontour = vf.cartcontour
elif projection == 'orthographic':
self.mapview = H.orthview
self.mapcontour = vf.orthcontour
elif projection == 'gnomonic':
self.mapview = H.gnomview
self.mapcontour = vf.gnomcontour
if map_path is None:
full_path = inspect.getfile(inspect.currentframe())
abs_path = os.path.split(full_path)[0]
map_path = os.path.join(abs_path, 'maps/')
self.config = ConfigHandler(config, map_path, nside=nside,
download_config=download_config)
# Could also just call load_survey which will call get_background
if isinstance(background, str):
bgmap, coord_bg, unit2 = self.config.load_survey(background,
get_unit=True)
background = bgmap[0]
if unit2 is not None:
unit = unit2
if nside is None:
nside = H.npix2nside(len(background))
self.nside = nside
coord = [coord_bg, coord_plot]
cm.Greys.set_under(alpha=0.0)
if log:
min = np.log(min)
max = np.log(max)
unit = r'$\log($' + unit + r'$)$'
background = np.log(background)
if self.partialmap:
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
H.cartview(background, title=title, coord=coord,
fig=self.fig.number, cmap=cm.Greys,
notext=True, flip='astro', min=min, max=max,
sub=sub, margins=margins, **kwds)
self.fig.delaxes(self.fig.axes[-1])
else:
self.mapview(background, title=title,
coord=coord, fig=self.fig.number, cmap=cm.Greys,
min=min, max=max, notext=True,
cbar=True, flip='astro', unit=unit, **kwds)
if not cbar:
self.fig.delaxes(self.fig.axes[-1])
H.graticule(dpar=30.0, dmer=30.0, coord='C', verbose=False)
def superimpose_hpxmap(self, hpx_map, label, color='red', coord_in='C',
cbar=True):
'''Superimpose a Healpix map on the background map.
Parameters
----------
hpx_map : array-like
The hpx_map to superimpose upon the background.
label : string
The label to put on the colorbar for this footprint.
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
coord_in : 'C', 'G', or 'E'
The coordinate system of the input healpix map.
Notes
-----
The input healpix map will have zeros replaced with NaNs to make
those points completely transparent.
'''
idx_nan = (hpx_map == 0)
hpx_map /= np.max(hpx_map)
hpx_map[idx_nan] = np.NaN
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
if self.partialmap:
# Colorbar is added to this and then deleted to make sure there is
# room at the bottom of the map for the labels. Margins are to make
# sure the title is not partially off the figure for a square map
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
map_tmp = H.cartview(hpx_map, title='',
coord=coord, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', sub=sub,
margins=margins, return_projected_map=True,
**self.kwds)
idx = np.isfinite(map_tmp)
cbar = len(map_tmp[idx]) > 0
else:
self.mapview(hpx_map, title='', coord=coord,
cbar=True, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', **self.kwds)
self.fig.delaxes(self.fig.axes[-1])
if cbar:
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0-0.1, 0.05, 0.05])
self.fig.colorbar(im0, cax=ax_color, orientation='horizontal',
label=label, values=[1, 1])
self.cbs.append(ax_color)
# Read just the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0*ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0-0.1, 0.05, 0.05])
left += 1.0 / ncb
def superimpose_fits(self, fns, label, color='red', maptype='WCS',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image.
Can be a single fits file or a list of them that will be added
together.
Parameters
----------
fns : list of strings
The filenames for the maps to read in
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
maptype : string
'WCS' or 'HPX' describing the type of map in the FITS file.
coord_in : 'C', G', or 'E'
Coordinate system of the input map
'''
if maptype == 'WCS':
hpx_map = util.read_wcs_maps(fns, 256)
elif maptype == 'HPX':
hpx_map = util.read_hpx_maps(fns)
self.superimpose_hpxmap(hpx_map, label, color=color, coord_in=coord_in)
def superimpose_bound_cen(self, center, size, label,
color='red', coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving input radec boundaries for the map. Boundaries are defined
as the center and "radius" in ra/dec.
Parameters
----------
center : array-like with shape (2,)
The center of the survey (degrees). ra/dec, gall/galb, etc.
size : array-like with shape (2,)
The length of the edge of the rectangle in degrees
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default : 'red'
coord_in : 'C', 'G', or 'E'
The coordinate system of the input parameters. 'C' would mean
input values are in ra,dec. Default : 'C'
'''
hpx_map = util.gen_map_centersize(center, size, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color,
coord_in=coord_in)
def superimpose_bound_circ(self, center, radius, label,
color='red', coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving an input center ra/dec and a radius of a disc.
Parameters
----------
center : array-like with shape (2,)
The center of the survey (degrees). ra/dec, gall/galb, etc.
radius : float
The radius of the disc (degrees)
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color for the survey. Either a string recognized by
matplotlib or a rgb triplet. Default : 'red'
coord_in : 'C', 'E', or 'G'
The coordinate system of the input values. Default : 'C'
'''
hpx_map = util.gen_map_disc(center, radius, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color,
coord_in=coord_in)
def superimpose_bound_vtx(self, vertices, label, color='red',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving the ra/dec corners of the image. The enclosed survey
footprint is generated by calling healpy.query_polygon.
Parameters
----------
vertices : array-like with shape (n,2)
The n corners of the survey footprint in degrees.
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default : 'red'
coord_in : 'C', 'E', or 'G'
The coordinate system of the input vertices. Default : 'C'
'''
hpx_map = util.gen_map_polygon(vertices, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color,
coord_in=coord_in)
def superimpose_survey(self, survey_name, color='red',
label=None, cbar=True):
'''Superimpose a specific survey whose Healpix footprints we have
pregenerated and are listed in the configuration file
Parameters
----------
survey_name : string
Name of survey. Valid values are section names in the
configuration file.
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
label : string
The label for the survey. If none, survey_name is used as
the label.
'''
hpx_maps, coord = self.config.load_survey(survey_name)
hpx_map = combine_maps(hpx_maps)
if label is None:
label = survey_name
self.superimpose_hpxmap(hpx_map, label, color=color,
coord_in=coord, cbar=cbar)
def superimpose_survey_outline(self, survey_name, color='red',
label=None, cbar=True):
'''Superimpose an outline of a survey
Parameters
----------
survey_name : string
The name of the survey in the configuration file
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
label : string, optional
The name to use when labeling this survey on the footprint.
If not input, we will use the survey name.
Notes
-----
This function is for survey footprints that are defined in the
configuration file as opposed loading a healpix map
'''
vtxs, coord = self.config.load_survey_outline(survey_name)
if label is None:
label = survey_name
if isinstance(coord, list):
for vtx1, coord1 in zip(vtxs[:-1], coord[:-1]):
self.superimpose_polygon_outline(vtx1, label, color=color,
coord_in=coord1, cbar=False)
vtxs = vtxs[-1]
coord = coord[-1]
self.superimpose_polygon_outline(vtxs, label, color=color,
coord_in=coord, cbar=cbar)
def superimpose_survey_contour(self, survey_name, color='red',
label=None, frac=0.85, **kwds):
'''Superimpose an outline of a survey.
Parameters
----------
survey_name : string
The name of the survey in the configuration file
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
label : string, optional
The name to use when labeling this survey on the footprint.
If not input, we will use the survey name.
Notes
-----
This function is for entries that load Healpix maps. We draw
contours instead of plotting an image of the map. For entries that
are not Healpix maps, but define the survey region, try
superimpose_survey_outline(...)
'''
hpx_maps, coord = self.config.load_survey(survey_name)
if label is None:
label = survey_name
for hpx_map in hpx_maps[:-1]:
self.superimpose_hpxmap_contour(hpx_map, label, color=color,
coord_in=coord, cbar=False,
**kwds)
self.superimpose_hpxmap_contour(hpx_maps[-1], label, color=color,
coord_in=coord, cbar=True, frac=frac,
**kwds)
def superimpose_hpxmap_contour(self, hpx_map, label, color='red',
coord_in='C', cbar=True, frac=0.85,
smooth_map=None):
'''Superimpose a contour of an input healpix map.
Parameters
----------
hpx_map : array-like
The input healpix map
label : string
The name to use as a label for the input map
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
coord_in : 'C', 'E', or 'G', optional
The coordinate system of the input map. Default = 'C'.
cbar : boolean, optional
Whether to add a colorbar labeling the input map. Default = true.
frac : float, optional
The contour level will be drawn containing `frac' levels
of observation time.
smooth_map : float
FWHM to smooth the input map (in arcminutes)
'''
idx_nan = (hpx_map == 0)
# Smoothing makes it more likely that contours don't have holes,
# but it takes some time to smooth each map
if smooth_map:
hpx_map = H.smoothing(hpx_map, fwhm=np.radians(smooth_map/60.0),
verbose=False)
hpx_map /= np.max(hpx_map)
hpx_map[idx_nan] = np.NaN
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
level = determine_level(hpx_map, frac)
if self.partialmap:
# Colorbar is added to this and then deleted to make sure there is
# room at the bottom of the map for the labels. Margins are to make
# sure the title is not partially off the figure for a square map
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
map_tmp = H.cartcontour(hpx_map, 5, title='', coord=coord,
fig=self.fig.number, cmap=cm1, notext=True,
flip='astro', latra=self.latra,
lonra=self.lonra, sub=sub, margins=margins,
return_projected_map=True, **self.kwds)
idx = np.isfinite(map_tmp)
if cbar:
cbar = len(map_tmp[idx]) > 0
else:
self.mapcontour(hpx_map, [-0.1, level], title='', coord=coord,
cbar=True, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', **self.kwds)
self.fig.delaxes(self.fig.axes[-1])
if cbar:
# Temporary axis with a Healpix map so I can get the correct color
# for the colorbar
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
hpx_map = np.ones(12*32**2)
self.mapview(hpx_map, title='', coord=coord,
cbar=None, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', **self.kwds)
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0-0.1, 0.05, 0.05])
self.fig.colorbar(im0, cax=ax_color, orientation='horizontal',
label=label, values=[1, 1])
self.cbs.append(ax_color)
self.fig.delaxes(self.fig.axes[-2])
# Readjust the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0*ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0-0.1, 0.05, 0.05])
left += 1.0 / ncb
def superimpose_polygon_outline(self, vertices, label, color='red',
coord_in='C', cbar=True):
'''Superimpose an outline of a survey given input vertices
Parameters
----------
vertices: array-like (nvtxs, 2)
The vertices of the polygon
label : string
The label for the survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
coord_in : 'C', 'E', or 'G'
The coordinate system for the input vertices
cbar : boolean
Whether to add a colorbar corresponding to this polygon or not
'''
lons = vertices[:, 0]
lats = vertices[:, 1]
if np.abs(lons[-1] - 180.0) > 0.01:
lons = np.append(lons, lons[0])
lats = np.append(lats, lats[0])
#Convert coordinate system for the outline to the one used in the
#plot
r = H.rotator.Rotator(coord=[coord_in, self.coord_plot])
r = H.rotator.Rotator(coord=[coord_in, coord_in])
lonsp = []
latsp = []
for lon, lat in zip(lons, lats):
theta = np.radians(90 - lat)
phi = np.radians(lon)
thetap, phip = r(theta, phi)
lonsp.append(np.degrees(phip))
latsp.append(90 - np.degrees(thetap))
lons = lonsp
lats = latsp
nvertices = len(lons)
# Loop over all vertices and generate lines between adjacent vertices
# in list. This is to ensure the lines are drawn.
linelon = np.array([])
linelat = np.array([])
for i in range(nvertices-1):
tmplon = np.linspace(lons[i], lons[i+1], num=1000)
tmplat = np.linspace(lats[i], lats[i+1], num=1000)
linelon = np.append(linelon, tmplon)
linelat = np.append(linelat, tmplat)
H.projplot(linelon, linelat, lonlat=True, markersize=1,
color=color, coord=coord_in)
if cbar:
# Temporary axis with a Healpix map so I can get the correct color
# for the colorbar
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
hpx_map = np.ones(12*32**2)
self.mapview(hpx_map, title='', coord=coord,
cbar=True, fig=self.fig.number, cmap=cm1,
notext=True, flip='astro', **self.kwds)
self.fig.delaxes(self.fig.axes[-1])
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0-0.1, 0.05, 0.05])
self.fig.colorbar(im0, cax=ax_color, orientation='horizontal',
label=label, values=[1, 1])
self.cbs.append(ax_color)
# Delete the temporary map
self.fig.delaxes(self.fig.axes[-2])
# Readjust the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0*ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0-0.1, 0.05, 0.05])
left += 1.0 / ncb
class SurveyStack(object):
'''The SurveyStack class allows us to overlay survey hitmaps upon
an input background. The hitmaps have their transparency associated with
the number of hits (more hits = less transparent).
'''
def __init__(self,
background,
nside=None,
fignum=None,
projection='mollweide',
coord_bg='G',
coord_plot='C',
partialmap=False,
config='footprint.cfg',
map_path=None,
download_config=False,
title='Survey Footprints',
cbar=None,
min=1.0,
max=5000.0,
log=True,
unit='',
**kwds):
self.fig = pl.figure(fignum)
self.coord_plot = coord_plot
self.partialmap = partialmap
self.kwds = kwds
self.cbs = []
if projection == 'mollweide':
self.mapview = H.mollview
self.mapcontour = vf.mollcontour
elif projection == 'cartesian':
self.mapview = H.cartview
self.mapcontour = vf.cartcontour
elif projection == 'orthographic':
self.mapview = H.orthview
self.mapcontour = vf.orthcontour
elif projection == 'gnomonic':
self.mapview = H.gnomview
self.mapcontour = vf.gnomcontour
if map_path is None:
full_path = inspect.getfile(inspect.currentframe())
abs_path = os.path.split(full_path)[0]
map_path = os.path.join(abs_path, 'maps/')
self.config = ConfigHandler(config,
map_path,
nside=nside,
download_config=download_config)
# Could also just call load_survey which will call get_background
if isinstance(background, str):
bgmap, coord_bg, unit2 = self.config.load_survey(background,
get_unit=True)
background = bgmap[0]
if unit2 is not None:
unit = unit2
if nside is None:
nside = H.npix2nside(len(background))
self.nside = nside
coord = [coord_bg, coord_plot]
cm.Greys.set_under(alpha=0.0)
if log:
min = np.log(min)
max = np.log(max)
unit = r'$\log($' + unit + r'$)$'
background = np.log(background)
if self.partialmap:
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
H.cartview(background,
title=title,
coord=coord,
fig=self.fig.number,
cmap=cm.Greys,
notext=True,
flip='astro',
min=min,
max=max,
sub=sub,
margins=margins,
**kwds)
self.fig.delaxes(self.fig.axes[-1])
else:
self.mapview(background,
title=title,
coord=coord,
fig=self.fig.number,
cmap=cm.Greys,
min=min,
max=max,
notext=True,
cbar=True,
flip='astro',
unit=unit,
**kwds)
if not cbar:
self.fig.delaxes(self.fig.axes[-1])
H.graticule(dpar=30.0, dmer=30.0, coord='C', verbose=False)
def superimpose_hpxmap(self,
hpx_map,
label,
color='red',
coord_in='C',
cbar=True):
'''Superimpose a Healpix map on the background map.
Parameters
----------
hpx_map : array-like
The hpx_map to superimpose upon the background.
label : string
The label to put on the colorbar for this footprint.
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
coord_in : 'C', 'G', or 'E'
The coordinate system of the input healpix map.
Notes
-----
The input healpix map will have zeros replaced with NaNs to make
those points completely transparent.
'''
idx_nan = (hpx_map == 0)
hpx_map /= np.max(hpx_map)
hpx_map[idx_nan] = np.NaN
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
if self.partialmap:
# Colorbar is added to this and then deleted to make sure there is
# room at the bottom of the map for the labels. Margins are to make
# sure the title is not partially off the figure for a square map
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
map_tmp = H.cartview(hpx_map,
title='',
coord=coord,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
sub=sub,
margins=margins,
return_projected_map=True,
**self.kwds)
idx = np.isfinite(map_tmp)
cbar = len(map_tmp[idx]) > 0
else:
self.mapview(hpx_map,
title='',
coord=coord,
cbar=True,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
**self.kwds)
self.fig.delaxes(self.fig.axes[-1])
if cbar:
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0 - 0.1, 0.05, 0.05])
self.fig.colorbar(im0,
cax=ax_color,
orientation='horizontal',
label=label,
values=[1, 1])
self.cbs.append(ax_color)
# Read just the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0 * ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0 - 0.1, 0.05, 0.05])
left += 1.0 / ncb
def superimpose_fits(self,
fns,
label,
color='red',
maptype='WCS',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image.
Can be a single fits file or a list of them that will be added
together.
Parameters
----------
fns : list of strings
The filenames for the maps to read in
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
maptype : string
'WCS' or 'HPX' describing the type of map in the FITS file.
coord_in : 'C', G', or 'E'
Coordinate system of the input map
'''
if maptype == 'WCS':
hpx_map = util.read_wcs_maps(fns, 256)
elif maptype == 'HPX':
hpx_map = util.read_hpx_maps(fns)
self.superimpose_hpxmap(hpx_map, label, color=color, coord_in=coord_in)
def superimpose_bound_cen(self,
center,
size,
label,
color='red',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving input radec boundaries for the map. Boundaries are defined
as the center and "radius" in ra/dec.
Parameters
----------
center : array-like with shape (2,)
The center of the survey (degrees). ra/dec, gall/galb, etc.
size : array-like with shape (2,)
The length of the edge of the rectangle in degrees
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default : 'red'
coord_in : 'C', 'G', or 'E'
The coordinate system of the input parameters. 'C' would mean
input values are in ra,dec. Default : 'C'
'''
hpx_map = util.gen_map_centersize(center, size, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color, coord_in=coord_in)
def superimpose_bound_circ(self,
center,
radius,
label,
color='red',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving an input center ra/dec and a radius of a disc.
Parameters
----------
center : array-like with shape (2,)
The center of the survey (degrees). ra/dec, gall/galb, etc.
radius : float
The radius of the disc (degrees)
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color for the survey. Either a string recognized by
matplotlib or a rgb triplet. Default : 'red'
coord_in : 'C', 'E', or 'G'
The coordinate system of the input values. Default : 'C'
'''
hpx_map = util.gen_map_disc(center, radius, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color, coord_in=coord_in)
def superimpose_bound_vtx(self,
vertices,
label,
color='red',
coord_in='C'):
'''Superimpose the footprint of a survey on the background image
by giving the ra/dec corners of the image. The enclosed survey
footprint is generated by calling healpy.query_polygon.
Parameters
----------
vertices : array-like with shape (n,2)
The n corners of the survey footprint in degrees.
label : string
The label to put on the colorbar for this survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default : 'red'
coord_in : 'C', 'E', or 'G'
The coordinate system of the input vertices. Default : 'C'
'''
hpx_map = util.gen_map_polygon(vertices, self.nside)
self.superimpose_hpxmap(hpx_map, label, color=color, coord_in=coord_in)
def superimpose_survey(self,
survey_name,
color='red',
label=None,
cbar=True):
'''Superimpose a specific survey whose Healpix footprints we have
pregenerated and are listed in the configuration file
Parameters
----------
survey_name : string
Name of survey. Valid values are section names in the
configuration file.
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
label : string
The label for the survey. If none, survey_name is used as
the label.
'''
hpx_maps, coord = self.config.load_survey(survey_name)
hpx_map = combine_maps(hpx_maps)
if label is None:
label = survey_name
self.superimpose_hpxmap(hpx_map,
label,
color=color,
coord_in=coord,
cbar=cbar)
def superimpose_survey_outline(self,
survey_name,
color='red',
label=None,
cbar=True):
'''Superimpose an outline of a survey
Parameters
----------
survey_name : string
The name of the survey in the configuration file
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
label : string, optional
The name to use when labeling this survey on the footprint.
If not input, we will use the survey name.
Notes
-----
This function is for survey footprints that are defined in the
configuration file as opposed loading a healpix map
'''
vtxs, coord = self.config.load_survey_outline(survey_name)
if label is None:
label = survey_name
if isinstance(coord, list):
for vtx1, coord1 in zip(vtxs[:-1], coord[:-1]):
self.superimpose_polygon_outline(vtx1,
label,
color=color,
coord_in=coord1,
cbar=False)
vtxs = vtxs[-1]
coord = coord[-1]
self.superimpose_polygon_outline(vtxs,
label,
color=color,
coord_in=coord,
cbar=cbar)
def superimpose_survey_contour(self,
survey_name,
color='red',
label=None,
frac=0.85,
**kwds):
'''Superimpose an outline of a survey.
Parameters
----------
survey_name : string
The name of the survey in the configuration file
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
label : string, optional
The name to use when labeling this survey on the footprint.
If not input, we will use the survey name.
Notes
-----
This function is for entries that load Healpix maps. We draw
contours instead of plotting an image of the map. For entries that
are not Healpix maps, but define the survey region, try
superimpose_survey_outline(...)
'''
hpx_maps, coord = self.config.load_survey(survey_name)
if label is None:
label = survey_name
for hpx_map in hpx_maps[:-1]:
self.superimpose_hpxmap_contour(hpx_map,
label,
color=color,
coord_in=coord,
cbar=False,
**kwds)
self.superimpose_hpxmap_contour(hpx_maps[-1],
label,
color=color,
coord_in=coord,
cbar=True,
frac=frac,
**kwds)
def superimpose_hpxmap_contour(self,
hpx_map,
label,
color='red',
coord_in='C',
cbar=True,
frac=0.85,
smooth_map=None):
'''Superimpose a contour of an input healpix map.
Parameters
----------
hpx_map : array-like
The input healpix map
label : string
The name to use as a label for the input map
color : string or array-like with shape (3,), optional
The color to use when overlaying the survey footprint. Either a
string or rgb triplet. Default = 'red'
coord_in : 'C', 'E', or 'G', optional
The coordinate system of the input map. Default = 'C'.
cbar : boolean, optional
Whether to add a colorbar labeling the input map. Default = true.
frac : float, optional
The contour level will be drawn containing `frac' levels
of observation time.
smooth_map : float
FWHM to smooth the input map (in arcminutes)
'''
idx_nan = (hpx_map == 0)
# Smoothing makes it more likely that contours don't have holes,
# but it takes some time to smooth each map
if smooth_map:
hpx_map = H.smoothing(hpx_map,
fwhm=np.radians(smooth_map / 60.0),
verbose=False)
hpx_map /= np.max(hpx_map)
hpx_map[idx_nan] = np.NaN
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
level = determine_level(hpx_map, frac)
if self.partialmap:
# Colorbar is added to this and then deleted to make sure there is
# room at the bottom of the map for the labels. Margins are to make
# sure the title is not partially off the figure for a square map
sub = (1, 1, 1)
margins = (0.01, 0.025, 0.01, 0.03)
map_tmp = H.cartcontour(hpx_map,
5,
title='',
coord=coord,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
latra=self.latra,
lonra=self.lonra,
sub=sub,
margins=margins,
return_projected_map=True,
**self.kwds)
idx = np.isfinite(map_tmp)
if cbar:
cbar = len(map_tmp[idx]) > 0
else:
self.mapcontour(hpx_map, [-0.1, level],
title='',
coord=coord,
cbar=True,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
**self.kwds)
self.fig.delaxes(self.fig.axes[-1])
if cbar:
# Temporary axis with a Healpix map so I can get the correct color
# for the colorbar
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
hpx_map = np.ones(12 * 32**2)
self.mapview(hpx_map,
title='',
coord=coord,
cbar=None,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
**self.kwds)
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0 - 0.1, 0.05, 0.05])
self.fig.colorbar(im0,
cax=ax_color,
orientation='horizontal',
label=label,
values=[1, 1])
self.cbs.append(ax_color)
self.fig.delaxes(self.fig.axes[-2])
# Readjust the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0 * ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0 - 0.1, 0.05, 0.05])
left += 1.0 / ncb
def superimpose_polygon_outline(self,
vertices,
label,
color='red',
coord_in='C',
cbar=True):
'''Superimpose an outline of a survey given input vertices
Parameters
----------
vertices: array-like (nvtxs, 2)
The vertices of the polygon
label : string
The label for the survey
color : string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string or rgb triplet.
coord_in : 'C', 'E', or 'G'
The coordinate system for the input vertices
cbar : boolean
Whether to add a colorbar corresponding to this polygon or not
'''
lons = vertices[:, 0]
lats = vertices[:, 1]
if np.abs(lons[-1] - 180.0) > 0.01:
lons = np.append(lons, lons[0])
lats = np.append(lats, lats[0])
#Convert coordinate system for the outline to the one used in the
#plot
r = H.rotator.Rotator(coord=[coord_in, self.coord_plot])
r = H.rotator.Rotator(coord=[coord_in, coord_in])
lonsp = []
latsp = []
for lon, lat in zip(lons, lats):
theta = np.radians(90 - lat)
phi = np.radians(lon)
thetap, phip = r(theta, phi)
lonsp.append(np.degrees(phip))
latsp.append(90 - np.degrees(thetap))
lons = lonsp
lats = latsp
nvertices = len(lons)
# Loop over all vertices and generate lines between adjacent vertices
# in list. This is to ensure the lines are drawn.
linelon = np.array([])
linelat = np.array([])
for i in range(nvertices - 1):
tmplon = np.linspace(lons[i], lons[i + 1], num=1000)
tmplat = np.linspace(lats[i], lats[i + 1], num=1000)
linelon = np.append(linelon, tmplon)
linelat = np.append(linelat, tmplat)
H.projplot(linelon,
linelat,
lonlat=True,
markersize=1,
color=color,
coord=coord_in)
if cbar:
# Temporary axis with a Healpix map so I can get the correct color
# for the colorbar
cm1 = util.get_color_map(color)
coord = [coord_in, self.coord_plot]
hpx_map = np.ones(12 * 32**2)
self.mapview(hpx_map,
title='',
coord=coord,
cbar=True,
fig=self.fig.number,
cmap=cm1,
notext=True,
flip='astro',
**self.kwds)
self.fig.delaxes(self.fig.axes[-1])
# First add the new colorbar axis to the figure
im0 = self.fig.axes[-1].get_images()[0]
box = self.fig.axes[0].get_position()
ax_color = pl.axes([len(self.cbs), box.y0 - 0.1, 0.05, 0.05])
self.fig.colorbar(im0,
cax=ax_color,
orientation='horizontal',
label=label,
values=[1, 1])
self.cbs.append(ax_color)
# Delete the temporary map
self.fig.delaxes(self.fig.axes[-2])
# Readjust the location of every colorbar
ncb = len(self.cbs)
left = 1.0 / (2.0 * ncb) - 0.025
for ax_tmp in self.cbs:
ax_tmp.set_position([left, box.y0 - 0.1, 0.05, 0.05])
left += 1.0 / ncb
