def test_curvelinear3():
fig = plt.figure(figsize=(5, 5))
fig.clf()
tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) +
mprojections.PolarAxes.PolarTransform())
grid_locator1 = angle_helper.LocatorDMS(15)
tick_formatter1 = angle_helper.FormatterDMS()
grid_locator2 = FixedLocator([2, 4, 6, 8, 10])
grid_helper = GridHelperCurveLinear(tr,
extremes=(0, 360, 10, 3),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = FloatingSubplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax1)
r_scale = 10
tr2 = mtransforms.Affine2D().scale(1, 1 / r_scale) + tr
grid_locator2 = FixedLocator([30, 60, 90])
grid_helper2 = GridHelperCurveLinear(tr2,
extremes=(0, 360,
10 * r_scale, 3 * r_scale),
grid_locator2=grid_locator2)
ax1.axis["right"] = axis = grid_helper2.new_fixed_axis("right", axes=ax1)
ax1.axis["left"].label.set_text("Test 1")
ax1.axis["right"].label.set_text("Test 2")
for an in ["left", "right"]:
ax1.axis[an].set_visible(False)
axis = grid_helper.new_floating_axis(1, 7, axes=ax1,
axis_direction="bottom")
ax1.axis["z"] = axis
axis.toggle(all=True, label=True)
axis.label.set_text("z = ?")
axis.label.set_visible(True)
axis.line.set_color("0.5")
ax2 = ax1.get_aux_axes(tr)
xx, yy = [67, 90, 75, 30], [2, 5, 8, 4]
ax2.scatter(xx, yy)
l, = ax2.plot(xx, yy, "k-")
l.set_clip_path(ax1.patch)
def test_curvelinear4():
# Remove this line when this test image is regenerated.
plt.rcParams['text.kerning_factor'] = 6
fig = plt.figure(figsize=(5, 5))
tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) +
mprojections.PolarAxes.PolarTransform())
grid_locator1 = angle_helper.LocatorDMS(5)
tick_formatter1 = angle_helper.FormatterDMS()
grid_locator2 = FixedLocator([2, 4, 6, 8, 10])
grid_helper = GridHelperCurveLinear(tr,
extremes=(120, 30, 10, 0),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = FloatingSubplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.axis["left"].label.set_text("Test 1")
ax1.axis["right"].label.set_text("Test 2")
for an in ["top"]:
ax1.axis[an].set_visible(False)
axis = grid_helper.new_floating_axis(1,
70,
axes=ax1,
axis_direction="bottom")
ax1.axis["z"] = axis
axis.toggle(all=True, label=True)
axis.label.set_axis_direction("top")
axis.label.set_text("z = ?")
axis.label.set_visible(True)
axis.line.set_color("0.5")
ax2 = ax1.get_aux_axes(tr)
xx, yy = [67, 90, 75, 30], [2, 5, 8, 4]
ax2.scatter(xx, yy)
l, = ax2.plot(xx, yy, "k-")
l.set_clip_path(ax1.patch)
def test_curvelinear3():
fig = plt.figure(figsize=(5, 5))
tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) +
mprojections.PolarAxes.PolarTransform())
grid_locator1 = angle_helper.LocatorDMS(15)
tick_formatter1 = angle_helper.FormatterDMS()
grid_locator2 = FixedLocator([2, 4, 6, 8, 10])
grid_helper = GridHelperCurveLinear(
tr,
extremes=(0, 360, 10, 3),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None,
)
ax1 = FloatingSubplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax1)
r_scale = 10
tr2 = mtransforms.Affine2D().scale(1, 1 / r_scale) + tr
grid_locator2 = FixedLocator([30, 60, 90])
grid_helper2 = GridHelperCurveLinear(tr2,
extremes=(0, 360, 10 * r_scale,
3 * r_scale),
grid_locator2=grid_locator2)
ax1.axis["right"] = axis = grid_helper2.new_fixed_axis("right", axes=ax1)
ax1.axis["left"].label.set_text("Test 1")
ax1.axis["right"].label.set_text("Test 2")
for an in ["left", "right"]:
ax1.axis[an].set_visible(False)
axis = grid_helper.new_floating_axis(1,
7,
axes=ax1,
axis_direction="bottom")
ax1.axis["z"] = axis
axis.toggle(all=True, label=True)
axis.label.set_text("z = ?")
axis.label.set_visible(True)
axis.line.set_color("0.5")
ax2 = ax1.get_aux_axes(tr)
xx, yy = [67, 90, 75, 30], [2, 5, 8, 4]
ax2.scatter(xx, yy)
(l, ) = ax2.plot(xx, yy, "k-")
l.set_clip_path(ax1.patch)
def __init__(self, figure):
self.figure = figure
self.the_min = 0
self.the_max = 360
self.r_min = 0
self.r_max = 20
self.the_label = 'The'
self.r_label = 'R'
# ---
tr_scale = Affine2D().scale(np.pi / 180., 1.)
tr = tr_scale + PolarAxes.PolarTransform()
grid_helper = GridHelperCurveLinear(tr)
a = FloatingSubplot(self.figure, 111, grid_helper=grid_helper)
self.figure.add_subplot(a)
# ---
# adjust x axis (theta):
a["bottom"].set_visible(False)
a.axis["top"].set_axis_direction("bottom") # tick direction
a.axis["top"].major_ticklabels.set_axis_direction("top")
a.axis["top"].label.set_axis_direction("top")
# adjust y axis (r):
a.axis["left"].set_axis_direction("bottom") # tick direction
a.axis["right"].set_axis_direction("top") # tick direction
# ---
# create a parasite axes whose transData is theta, r:
self.plt = a.get_aux_axes(tr)
# make aux_ax to have a clip path as in a?:
self.patch = a.patch
# this has a side effect that the patch is drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to prevent this:
a.patch.zorder = -2
self.plt.add_artist(plt.Circle([0, 0]))
def plot_polar_heatmap(data,
name,
interp_factor=5.,
color_limits=False,
hide_colorbar=False,
vmin=None,
vmax=None,
log_scale=True,
dpi=200,
output_dir=None):
"""Plots the polar heatmap describing azimuth and latitude / elevation components.
Plots the polar heatmap where each cell of the heatmap corresponds to
the specific element of the array provided by `gather_polar_errors`
function.
Parameters
----------
data : 2D array
Indicates the array containing the sum of angular errors within the
specified angular ranges. It is usually provided by `gather_polar_errors`
function.
name : str
Indicates the name of the output png file.
interp_factor : float
Indicates the interpolation factor of the heatmap.
color_limits : boolean
Specifies if the determined intensity limits should be returned.
hide_colorbar : boolean
Specifies if the colorbar should be hidden.
vmin : float
Indicates the minimum value of the colorbar.
vmax : float
Indicates the maximum value of the colorbar.
log_scale : float
Specifies if the heatmap sould be in the logarithmic scale.
dpi : integer
Indicates the DPI of the output image.
output_dir : str
Indicates the path to the output folder where the image will be stored.
"""
th0, th1 = 0., 180.
r0, r1 = 0, 90
thlabel, rlabel = 'Azimuth', 'Elevation'
tr_scale = Affine2D().scale(np.pi / 180., 1.)
tr = tr_scale + PolarAxes.PolarTransform()
lat_ticks = [(.0 * 90., '0$^{\circ}$'), (.33 * 90., '30$^{\circ}$'),
(.66 * 90., '60$^{\circ}$'), (1. * 90., '90$^{\circ}$')]
r_grid_locator = FixedLocator([v for v, s in lat_ticks])
r_grid_formatter = DictFormatter(dict(lat_ticks))
angle_ticks = [(0 * 180., '90$^{\circ}$'), (.25 * 180., '45$^{\circ}$'),
(.5 * 180., '0$^{\circ}$'), (.75 * 180., '-45$^{\circ}$'),
(1. * 180., '-90$^{\circ}$')]
theta_grid_locator = FixedLocator([v for v, s in angle_ticks])
theta_tick_formatter = DictFormatter(dict(angle_ticks))
grid_helper = GridHelperCurveLinear(tr,
extremes=(th0, th1, r0, r1),
grid_locator1=theta_grid_locator,
grid_locator2=r_grid_locator,
tick_formatter1=theta_tick_formatter,
tick_formatter2=r_grid_formatter)
fig = plt.figure()
ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax)
ax.set_facecolor('white')
ax.axis["bottom"].set_visible(False)
ax.axis["top"].toggle(ticklabels=True, label=True)
ax.axis["top"].set_axis_direction("bottom")
ax.axis["top"].major_ticklabels.set_axis_direction("top")
ax.axis["top"].label.set_axis_direction("top")
ax.axis["left"].set_axis_direction("bottom")
ax.axis["right"].set_axis_direction("top")
ax.axis["top"].label.set_text(thlabel)
ax.axis["left"].label.set_text(rlabel)
aux_ax = ax.get_aux_axes(tr)
aux_ax.patch = ax.patch
ax.patch.zorder = 0.9
rad = np.linspace(0, 90, data.shape[1])
azm = np.linspace(0, 180, data.shape[0])
f = interpolate.interp2d(rad,
azm,
data,
kind='linear',
bounds_error=True,
fill_value=0)
new_rad = np.linspace(0, 90, 180 * interp_factor)
new_azm = np.linspace(0, 180, 360 * interp_factor)
new_data_angle_dist = f(new_rad, new_azm)
new_r, new_th = np.meshgrid(new_rad, new_azm)
new_data_angle_dist += 1.
if log_scale:
data_mesh = aux_ax.pcolormesh(
new_th,
new_r,
new_data_angle_dist,
cmap='jet',
norm=colors.LogNorm(
vmin=1. if vmin is None else vmin,
vmax=new_data_angle_dist.max() if vmax is None else vmax))
else:
data_mesh = aux_ax.pcolormesh(new_th,
new_r,
new_data_angle_dist,
cmap='jet',
vmin=vmin,
vmax=vmax)
cbar = plt.colorbar(data_mesh,
orientation='vertical',
shrink=.88,
pad=.1,
aspect=15)
cbar.ax.set_ylabel('Absolute error, [deg.]')
if hide_colorbar:
cbar.remove()
ax.grid(False)
plt.show()
if output_dir is not None:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
fig.savefig(os.path.join(output_dir, f'{name}_chart.png'),
transparent=False,
bbox_inches='tight',
pad_inches=0.1,
dpi=dpi)
if color_limits:
return 1., new_data_angle_dist.max()
def fractional_polar_axes(f, thlim=(0, 180), rlim=(0, 1), step=(30, 0.25),
thlabel='theta', rlabel='r', ticklabels=True, rlabels = None, subplot=111):
'''Return polar axes that adhere to desired theta (in deg) and r limits. steps for theta
and r are really just hints for the locators.'''
th0, th1 = thlim # deg
r0, r1 = rlim
thstep, rstep = step
# scale degrees to radians:
tr_scale = Affine2D().scale(np.pi/180., 1.)
pa = PolarAxes
tr = tr_scale + pa.PolarTransform()
theta_grid_locator = angle_helper.LocatorDMS((th1-th0)//thstep)
r_grid_locator = MaxNLocator((r1-r0)//rstep)
theta_tick_formatter = angle_helper.FormatterDMS()
if rlabels:
rlabels = DictFormatter(rlabels)
grid_helper = GridHelperCurveLinear(tr,
extremes=(th0, th1, r0, r1),
grid_locator1=theta_grid_locator,
grid_locator2=r_grid_locator,
tick_formatter1=theta_tick_formatter,
tick_formatter2=rlabels)
a = FloatingSubplot(f, subplot, grid_helper=grid_helper)
f.add_subplot(a)
# adjust x axis (theta):
a.axis["bottom"].set_visible(False)
a.axis["top"].set_axis_direction("bottom") # tick direction
a.axis["top"].toggle(ticklabels=ticklabels, label=bool(thlabel))
a.axis["top"].major_ticklabels.set_axis_direction("top")
a.axis["top"].label.set_axis_direction("top")
a.axis["top"].major_ticklabels.set_pad(10)
# adjust y axis (r):
a.axis["left"].set_axis_direction("bottom") # tick direction
a.axis["right"].set_axis_direction("top") # tick direction
a.axis["left"].toggle(ticklabels=True, label=bool(rlabel))
# add labels:
a.axis["top"].label.set_text(thlabel)
a.axis["left"].label.set_text(rlabel)
# create a parasite axes whose transData is theta, r:
auxa = a.get_aux_axes(tr)
# make aux_ax to have a clip path as in a?:
auxa.patch = a.patch
# this has a side effect that the patch is drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to prevent this:
a.patch.zorder = -2
# add sector lines for both dimensions:
thticks = grid_helper.grid_info['lon_info'][0]
rticks = grid_helper.grid_info['lat_info'][0]
for th in thticks[1:-1]: # all but the first and last
auxa.plot([th, th], [r0, r1], ':', c='grey', zorder=-1, lw=0.5)
for ri, r in enumerate(rticks):
# plot first r line as axes border in solid black only if it isn't at r=0
if ri == 0 and r != 0:
ls, lw, color = 'solid', 1, 'black'
else:
ls, lw, color = 'dashed', 0.5, 'grey'
# From http://stackoverflow.com/a/19828753/2020363
auxa.add_artist(plt.Circle([0, 0], radius=r, ls=ls, lw=lw, color=color, fill=False,
transform=auxa.transData._b, zorder=-1))
return auxa
def cone(
ra,
z,
scale=0.5,
orientation='horizontal',
raaxis='min',
ralim=None,
zlim=None,
hms=False,
# cosmology=None, lookbtime=False,
plot=None,
fig=None,
subnum=None,
xlabel=r"$\alpha$",
ylabel=r"$\mathsf{redshift}$",
**kwargs):
"""
Make a wedge plot of RA/Dec vs redshift z, where RA/DEC are in degrees
Parameters
----------
Input data
angle : (n, ) array
RA in degrees
redshift : (n, ) array
scale: 0.5
orientation:
'horizontal': increasing z along +ve xaxis
'vertical': increasing z along +ve yaxis
angle in degrees: increasing z along the tilted axis
raxis: 'min' | 'mid' | float
default is 'min'
RA value along which the cone plot is orientated horizontal
or vertical
ralim, zlim: list [ramin, rmax], list [zmin, zmax]
default is taken from the lower/upper bound of the input data
scatter: any kwargs compatible with plt.scatter
hms: show RA labels in units of hours (if True) or degrees (if False)
lookbtime: True/False
plot: None
'scatter' | 'hexbin' etc
fig: supply figure instance
default None
subnum: subplot number e.g. 111, 221 etc
default None
xlabel: r"$\alpha$"
ylabel: r"$\mathsf{redshift}$"
cosmology: dict
Uses cosmolopy package to compute look-back time
default cosmology is {'omega_M_0': 0.3,
'omega_lambda_0': 0.7,
'h': 0.72}
kwargs
------
scatter = {'s': 4, 'marker': ','}
Notes
-----
--Decorations that can be done outside the code:
Draw grids: ax.grid(True, alpha=0.2)
Set title: ax.set_title('Wedge plot')
--Look-back time as twin axis to redshift not yet implemented
--In future plan is to able to put colorbar in the plot too.
Using cmap option.
"""
# ------ Extents of ra and z
if ralim:
ramin, ramax = ralim
else:
ramin, ramax = ra.min(), ra.max()
if zlim:
zmin, zmax = zlim
else:
zmin, zmax = z.min(), z.max()
# ----- Scale and Orientation of the wedge
if orientation == 'horizontal':
dirn = 0.
elif orientation == 'vertical':
dirn = 90.
else:
dirn = orientation
if raaxis == 'min':
raaxis = ramin
elif raaxis == 'mid':
raaxis = 0.5 * (ramin + ramax)
# Tilt of a cone relative to minimum RA
tr_rotate = Affine2D().translate(dirn / scale - raaxis, 0.0)
# Scaling the opening angle
tr_scale = Affine2D().scale(scale * np.pi / 180., 1.0)
tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()
# ---- Grids
if hms is True:
grid_locator1 = angle_helper.LocatorHMS(4.0)
tick_formatter1 = angle_helper.FormatterHMS()
else:
grid_locator1 = angle_helper.LocatorDMS(4.0)
tick_formatter1 = angle_helper.FormatterDMS()
grid_locator2 = MaxNLocator(10)
grid_helper = GridHelperCurveLinear(tr,
extremes=(ramin, ramax, zmin, zmax),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
# Figure properties
if not fig:
fig = plt.figure(figsize=(8, 7))
subnum = 111
ax = FloatingSubplot(fig, subnum, grid_helper=grid_helper)
fig.add_subplot(ax)
# adjust axis
# Left, right, top represent z, lookbacktime, RA respectively.
# right axes is for look-back time yet to be coded
ax.axis["left"].set_axis_direction("bottom")
ax.axis["right"].set_axis_direction("top")
ax.axis["bottom"].set_visible(False)
ax.axis["top"].set_axis_direction("bottom")
ax.axis["top"].toggle(ticklabels=True, label=True)
ax.axis["top"].major_ticklabels.set_axis_direction("top")
ax.axis["top"].label.set_axis_direction("top")
ax.axis["left"].label.set_text(ylabel)
ax.axis["top"].label.set_text(xlabel)
# create a parasite axes that transData in RA, z
aux = ax.get_aux_axes(tr)
aux.patch = ax.patch # for aux_ax to have a clip path as in ax
ax.patch.zorder = 0.9 # but this has a side effect that the patch is
# drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to
# prevent this.
if plot == 'scatter':
aux.scatter(ra, z, **kwargs)
# plt.tight_layout()
# plt.show()
return ax, aux, fig
def fractional_polar_axes(f, thlim=(0, 180), rlim=(0, 1), step=(30, 0.01),
thlabel=r'$\alpha$', rlabel='z', ticklabels=True):
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist import angle_helper
from mpl_toolkits.axisartist.grid_finder import MaxNLocator
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, FloatingSubplot
from mpl_toolkits.axisartist.grid_finder import (FixedLocator, MaxNLocator,
DictFormatter)
"""Return polar axes that adhere to desired theta (in deg) and r limits. steps for theta
and r are really just hints for the locators. Using negative values for rlim causes
problems for GridHelperCurveLinear for some reason"""
th0, th1 = thlim # deg
r0, r1 = rlim
thstep, rstep = step
# scale degrees to radians:
tr_scale = Affine2D().scale(np.pi/180., 1.)
tr = tr_scale + PolarAxes.PolarTransform()
theta_grid_locator = angle_helper.LocatorDMS((th1-th0) // thstep)
r_grid_locator = MaxNLocator((r1-r0) // rstep)
theta_tick_formatter = angle_helper.FormatterDMS()
theta_tick_formatter = None
theta_ticks = [(0, r"$90^{\circ}$"),
(30, r"$120^{\circ}$"),
(60, r"$150^{\circ}$"),
(90, r"$180^{\circ}$"),
(120, r"$210^{\circ}$"),
(150, r"$270^{\circ}$"),
(180, r"$0^{\circ}$")]
theta_tick_formatter = DictFormatter(dict(theta_ticks))
grid_helper = GridHelperCurveLinear(tr,
extremes=(th0, th1, r0, r1),
grid_locator1=theta_grid_locator,
grid_locator2=r_grid_locator,
tick_formatter1=theta_tick_formatter,
tick_formatter2=None)
a = FloatingSubplot(f, 111, grid_helper=grid_helper)
f.add_subplot(a)
# adjust x axis (theta):
a.axis["bottom"].set_visible(False)
a.axis["top"].set_axis_direction("bottom") # tick direction
a.axis["top"].toggle(ticklabels=ticklabels, label=bool(thlabel))
a.axis["top"].major_ticklabels.set_axis_direction("top")
a.axis["top"].label.set_axis_direction("top")
# adjust y axis (r):
a.axis["left"].set_axis_direction("bottom") # tick direction
a.axis["right"].set_axis_direction("top") # tick direction
a.axis["left"].toggle(ticklabels=ticklabels, label=bool(rlabel))
# add labels:
a.axis["top"].label.set_text(thlabel)
a.axis["left"].label.set_text(rlabel)
# create a parasite axes whose transData is theta, r:
auxa = a.get_aux_axes(tr)
# make aux_ax to have a clip path as in a?:
auxa.patch = a.patch
# this has a side effect that the patch is drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to prevent this:
a.patch.zorder = -2
# add sector lines for both dimensions:
thticks = grid_helper.grid_info['lon_info'][0]
rticks = grid_helper.grid_info['lat_info'][0]
for th in thticks[1:-1]: # all but the first and last
auxa.plot([th, th], [r0, r1], '--', c='grey', zorder=-1)
for ri, r in enumerate(rticks):
# plot first r line as axes border in solid black only if it isn't at r=0
if ri == 0 and r != 0:
ls, lw, color = 'solid', 2, 'black'
else:
ls, lw, color = 'dashed', 1, 'grey'
# From http://stackoverflow.com/a/19828753/2020363
auxa.add_artist(plt.Circle([0, 0], radius=r, ls=ls, lw=lw, color=color, fill=False,
transform=auxa.transData._b, zorder=-1))
return auxa