def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1
# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
# Now creates floating axis
# floating axis whose first coordinate (theta) is fixed at 60
ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60)
axis.label.set_text(r"$\theta = 60^{\circ}$")
axis.label.set_visible(True)
# floating axis whose second coordinate (r) is fixed at 6
ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 6)
axis.label.set_text(r"$r = 6$")
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def setup_axes(fig, rect, theta, radius):
""" Sets up the axes in such a way we can plot the polarplot. """
tr = PolarAxes.PolarTransform()
pi = np.pi
angle_ticks = [(-4. / 8. * pi, r"$-\frac{1}{2}\pi$"), (-3. / 8. * pi, r""),
(-2. / 8. * pi, r"$-\frac{1}{4}\pi$"), (-1. / 8. * pi, r""),
(0. / 8. * pi, r"$0$"), (1. / 8. * pi, r""),
(2. / 8. * pi, r"$\frac{1}{4}\pi$"), (3. / 8. * pi, r""),
(4. / 8. * pi, r"$\frac{1}{2}\pi$"),
(6. / 8. * pi, r"$\frac{3}{4}\pi$"),
(8. / 8. * pi, r"$\pi$")]
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
grid_locator2 = MaxNLocator(3)
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(theta[0], theta[1], radius[0], radius[1]),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
# adjust axis
# "bottom", "top", "left", "right"
ax1.axis["left"].set_axis_direction("bottom")
ax1.axis["right"].set_axis_direction("top")
ax1.axis["bottom"].set_visible(False)
ax1.axis["top"].set_axis_direction("bottom")
ax1.axis["top"].toggle(ticklabels=True, label=True)
ax1.axis["top"].major_ticklabels.set_axis_direction("top")
ax1.axis["top"].label.set_axis_direction("top")
# create a parasite axes
aux_ax = ax1.get_aux_axes(tr)
return ax1, aux_ax
def plot_taylor_diag(std, corr, ref_std, normalized=True):
import mpl_toolkits.axisartist.grid_finder as gf
import mpl_toolkits.axisartist.floating_axes as fa
# define polar axes transform
tr = PolarAxes.PolarTransform()
# define correlation labels
rlocs = np.concatenate((np.arange(10) / 10., [0.95, 0.99]))
tlocs = np.arccos(rlocs)
gl1 = gf.FixedLocator(tlocs)
tf1 = gf.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
smin = 0
smax = max(2.0, 1.1 * std.max())
# add the curvilinear grid
fig = plt.figure()
ghelper = fa.GridHelperCurveLinear(tr,
extremes=(0, np.pi / 2, smin, smax),
grid_locator1=gl1,
tick_formatter1=tf1)
ax = fa.FloatingSubplot(fig, 111, grid_helper=ghelper)
fig.add_subplot(ax)
# adjust axes
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["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom")
if normalize:
ax.axis["left"].label.set_text("Normalized standard deviation")
else:
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top")
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False)
ax.grid(True)
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 setup_axes2(fig, rect):
"""
With custom locator and formatter.
Note that the extreme values are swapped.
"""
tr = PolarAxes.PolarTransform()
pi = np.pi
angle_ticks = [(.5 * pi, r"90$\degree$"), (.625 * pi, r"112.5$\degree$"),
(.75 * pi, r"135$\degree$"),
(.81111111 * pi, r"146$\degree$"),
(.84444444 * pi, r"152$\degree$"), (pi, r"180$\degree$")]
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
grid_locator2 = MaxNLocator(2)
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(pi, 0.5 * pi, 6371, 0),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
ax1.axis["bottom"].major_ticklabels.set_axis_direction("top")
ax1.axis["left"].toggle(ticklabels=False)
ax1.axis["left"].toggle(ticks=False)
ax1.axis["top"].toggle(ticks=False)
fig.add_subplot(ax1)
# create a parasite axes whose transData in RA, cz
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.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.
return ax1, aux_ax
def setup_axes3(fig, rect):
#menentukan axis_direction
tr_rotate = Affine2D().translate(-95, 0)
tr_scale = Affine2D().scale(np.pi / 180., 1.)
tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()
grid_locator1 = angle_helper.LocatorHMS(4)
tick_formatter1 = angle_helper.FormatterHMS()
grid_locator2 = MaxNLocator(3)
ra0, ra1 = 8. * 15, 14. * 15
cz0, cz1 = 0, 14000
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(ra0, ra1, cz0, cz1),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None,
)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.axis["left"].set_axis_direction("bottom")
ax1.axis["right"].set_axis_direction("top")
ax1.axis["bottom"].set_visible("False")
ax1.axis["top"].set_axis_direction("bottom")
ax1.axis["top"].toggle(ticklabels=True, label=True)
ax1.axis["top"].major_ticklabels.set_axis_direction("top")
ax1.axis["top"].label.set_axis_direction("top")
ax1.axis["left"].label.set_text(r"cz [km$^{-1}$]")
ax1.axis["top"].label.set_text(r"$\alpha_{1950}$]")
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch
#efek dari patch adalah lebih sederhana dan lebih pada tampilan lainnya
ax1.patch.zorder = 0.9
return ax1, aux_ax
def setup_axes(fig, rect):
"""
polar projection, but in a rectangular box.
"""
# see demo_curvelinear_grid.py for details
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
grid_locator2 = grid_finder.MaxNLocator(5)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1)
ax1 = axisartist.Subplot(fig, rect, grid_helper=grid_helper)
#ax1.axis[:].toggle(all=False)
ax1.axis[:].set_visible(False)
fig.add_subplot(ax1)
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
#ax1.grid(True)
return ax1
def setup_axes2(fig, rect):
"""
With custom locator and formatter.
Note that the extreme values are swapped.
"""
#tr_scale = Affine2D().scale(np.pi/180., 1.)
tr = PolarAxes.PolarTransform()
pi = np.pi
angle_ticks = [(0, r"$0$"), (.25 * pi, r"$\frac{1}{4}\pi$"),
(.5 * pi, r"$\frac{1}{2}\pi$")]
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
grid_locator2 = MaxNLocator(2)
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(.5 * pi, 0, 2, 1),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None,
)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
# create a parasite axes whose transData in RA, cz
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.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.
return ax1, aux_ax
def setup_axes(self, fig, rect, n_markers):
"""
With custom locator and formatter.
Note that the extreme values are swapped.
"""
tr = PolarAxes.PolarTransform()
rs = np.linspace(self.r_inner, self.r_outer, n_markers)
angle_ticks = []
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
# set the number of r points you want on plot
grid_locator2 = FixedLocator(rs)
tick_formatter2 = None
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(0.5 * np.pi, -0.5 * np.pi, self.r_outer, self.r_inner),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=tick_formatter2)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
# create a parasite axes whose transData in RA, cz
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.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.
return ax1, aux_ax
def polarplot(self):
tr = PolarAxes.PolarTransform()
grid_locator1 = FixedLocator([v for v, s in self.angle_ticks])
tick_formatter1 = DictFormatter(dict(self.angle_ticks))
grid_locator2 = FixedLocator([a for a, b in self.radius_ticks])
tick_formatter2 = DictFormatter(dict(self.radius_ticks))
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(self.max_angle, self.min_angle, self.max_rad,
self.min_rad),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=tick_formatter2)
ax = floating_axes.FloatingSubplot(self.fig,
self.rect,
grid_helper=grid_helper)
self.fig.add_subplot(ax)
ax.grid(True, color='b', linewidth=0.2, linestyle='-')
aux_ax = ax.get_aux_axes(tr)
aux_ax.patch = ax.patch
ax.patch.zorder = 0.9
ax.axis['bottom'].set_label(r'Angle ($^{\circ}$)')
ax.axis['bottom'].major_ticklabels.set_rotation(180)
ax.axis['bottom'].label.set_rotation(180)
ax.axis['bottom'].LABELPAD += 30
ax.axis['left'].set_label('Range (m)')
ax.axis['left'].label.set_rotation(0)
ax.axis['left'].LABELPAD += 15
ax.axis['left'].set_visible(True)
return ax, aux_ax
def setup_axes2(fig, rect, tmin, tmax, zmin, zmax):
"""
With custom locator and formatter.
Note that the extreme values are swapped.
"""
tr = PolarAxes.PolarTransform()
pi = np.pi
angle_ticks = [(tmin, '%.2f' % tmin), (0, r'$0$'), (tmax, '%.2f' % tmax)]
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
grid_locator2 = MaxNLocator(4)
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(tmax, tmin, zmax, zmin),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper)
fig.add_subplot(ax1)
# create a parasite axes whose transData in RA, cz
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.patch.zorder = 0.95 # 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.
return ax1, aux_ax
def setup_axes2(fig, rect):
"""
With custom locator and formatter.
Note that the extreme values are swapped.
"""
tr = PolarAxes.PolarTransform()
pi = np.pi
angle_ticks = [(0,'180' ),
(.25*pi,'135' ),
(.5*pi, '90'),
(.75*pi,'45' ),
(pi,'0')]
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
grid_locator2 = MaxNLocator(nbins=6)
grid_helper = floating_axes.GridHelperCurveLinear(
tr, extremes=(pi,0, 6371,3481),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=None)
ax1 = floating_axes.FloatingSubplot(fig, rect,
grid_helper=grid_helper)
fig.add_subplot(ax1)
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.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.
return ax1, aux_ax
def get_slice_axes(fig, num, lat1=-90., lat2=90., shrink=1.0):
tr_rotate = Affine2D().translate(0, 90)
# set up polar axis
tr = PolarAxes.PolarTransform() + tr_rotate
angle_ticks = [(np.radians(i), str(i)) for i in np.arange(80, -81, -10)]
# angle_ticks = [(np.radians(80), r"$80$"),
# (np.radians(40), r"$\frac{3}{4}\pi$"),
# (1.*np.pi, r"$\pi$"),
# (1.25*np.pi, r"$\frac{5}{4}\pi$"),
# (1.5*np.pi, r"$\frac{3}{2}\pi$"),
# (1.75*np.pi, r"$\frac{7}{4}\pi$")]
# set up ticks and spacing around the circle
grid_locator1 = FixedLocator([v for v, s in angle_ticks])
tick_formatter1 = DictFormatter(dict(angle_ticks))
# set up grid spacing along the 'radius'
radius_ticks = [
(1., ''),
# (1.5, '%i' % (MAX_R/2.)),
(R / Rb, '')
]
grid_locator2 = FixedLocator([v for v, s in radius_ticks])
tick_formatter2 = DictFormatter(dict(radius_ticks))
# define angle ticks around the circumference:
# set up axis:
# tr: the polar axis setup
# extremes: theta max, theta min, r max, r min
# the grid for the theta axis
# the grid for the r axis
# the tick formatting for the theta axis
# the tick formatting for the r axis
lat1 = np.radians(lat1)
lat2 = np.radians(lat2)
grid_helper = floating_axes.GridHelperCurveLinear(
tr,
extremes=(-lat1, lat2, R / Rb * shrink, 1 * shrink),
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=tick_formatter2)
ax1 = floating_axes.FloatingSubplot(fig, num, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.axis["left"].set_axis_direction("bottom")
ax1.axis["right"].set_axis_direction("bottom")
ax1.axis["left"].major_ticklabels.set_rotation(90)
#ax1.axis["right"].set_tick_direction('out')
#ax1.axis
# ax1.axis["left"].major_ticks.set_tick_out(True)
# # ax1.axis["bottom"].set_visible(False)
ax1.axis["bottom"].set_axis_direction("right")
ax1.axis["bottom"].major_ticks.set_tick_out(True)
# ax1.axis["bottom"].major_ticklabels.locs_angles_labels = [0, 0, 0, 0,0,0,0]
# print(ax1.axis["bottom"].major_ticklabels._locs_angles_labels)
#ax1.axis["bottom"].major_ticklabels.set_ha('right')
majortick_iter, minortick_iter = ax1.axis[
'bottom']._axis_artist_helper.get_tick_iterators(ax1)
tick_loc_angle, ticklabel_loc_angle_label = ax1.axis[
'bottom']._get_tick_info(majortick_iter)
#ax1.axis["bottom"].major_ticklabels.set_pad(1.0)
aux_ax = ax1.get_aux_axes(tr)
aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax
ax1.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.
return ax1, aux_ax
def setup_axes(self, fig, rect=111):
"""Set up Taylor diagram axes, i.e. single quadrant polar
plot, using mpl_toolkits.axisartist.floating_axes.
Wouldn't the ideal be to define its own non-linear
transformation, so that coordinates are directly r=stddev and
theta=correlation? I guess it would allow
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
tr = PolarAxes.PolarTransform()
# Correlation labels
rlocs = NP.concatenate((NP.arange(10)/10.,[0.95,0.99]))
tlocs = NP.arccos(rlocs) # Conversion to polar angles
gl1 = GF.FixedLocator(tlocs) # Positions
tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs))))
ghelper = FA.GridHelperCurveLinear(tr,
extremes=(0,NP.pi/2, # 1st quadrant
0,1.5*self.ref.std()),
grid_locator1=gl1,
tick_formatter1=tf1,
)
ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
fig.add_subplot(ax)
# Adjust axes
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
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["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom") # "X axis"
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top") # "Y axis"
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False) # Useless
# Grid
ax.grid()
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point and stddev contour
print("Reference std:", self.ref.std())
self.ax.plot([0],self.ref.std(),'ko', label='_')
t = NP.linspace(0,NP.pi/2)
r = NP.zeros_like(t) + self.ref.std()
self.ax.plot(t,r,'k--', label='_')
return self.ax
def __init__(
self,
ref_point: float,
fig: Optional[plt.figure] = None,
subplot: Optional[int] = 111,
extend_angle: bool = False,
corr_labels: Optional[np.ndarray] = None,
ref_range: Tuple[float, float] = (0, 10),
ref_label: str = "Reference Point",
angle_label: str = "Correlation",
var_label: str = "Standard Deviation",
) -> None:
self.angle_label = angle_label
self.ref_label = ref_label
self.var_label = var_label
self.extend_angle = extend_angle
# correlation labels
if corr_labels is None:
corr_labels = np.array(
[0, 0.2, 0.4, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 1.0])
# extend
if extend_angle:
# Extend to negative correlations
self.tmax = np.pi
corr_labels = np.concatenate((-corr_labels[:0:-1], corr_labels))
else:
# limit to only positive correlations
self.tmax = np.pi / 2.0
# init figure
if fig is None:
fig = plt.figure(figsize=(8, 8))
# extend or not
self.smin = ref_range[0] * ref_point
self.smax = (ref_range[1] / 100) * ref_point + ref_point
corr_ticks = np.arccos(corr_labels)
gl1 = grid_finder.FixedLocator(corr_ticks)
tf1 = grid_finder.DictFormatter(
dict(zip(corr_ticks, map(str, corr_labels))))
# Grid Helper
ghelper = floating_axes.GridHelperCurveLinear(
aux_trans=PolarAxes.PolarTransform(),
extremes=(0, self.tmax, self.smin, self.smax),
grid_locator1=gl1,
tick_formatter1=tf1,
)
# create graphical axes
ax = floating_axes.FloatingSubplot(fig, subplot, grid_helper=ghelper)
fig.add_subplot(ax)
self.graph_axes = ax
self.polar_axes = ax.get_aux_axes(PolarAxes.PolarTransform())
self.sample_points = []
# Setup Axes
self.reset_axes()
def curvelinear_test3(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1, axis
import numpy as np
from . import angle_helper
from matplotlib.projections import PolarAxes
from matplotlib.transforms import Affine2D
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
# Find a grid values appropriate for the coordinate (degree,
# minute, second).
tick_formatter1 = angle_helper.FormatterDMS()
# And also uses an appropriate formatter. Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
for axis in list(six.itervalues(ax1.axis)):
axis.set_visible(False)
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
0, 130, axes=ax1, axis_direction="left")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
grid_helper = ax1.get_grid_helper()
ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
0, 50, axes=ax1, axis_direction="right")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
1, 10, axes=ax1, axis_direction="bottom")
axis.label.set_text("Test 2")
axis.get_helper()._extremes = 50, 130
axis.major_ticklabels.set_axis_direction("top")
axis.label.set_axis_direction("top")
grid_helper.grid_finder.grid_locator1.den = 5
grid_helper.grid_finder.grid_locator2._nbins = 5
# # A parasite axes with given transform
# ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
# # note that ax2.transData == tr + ax1.transData
# # Anthing you draw in ax2 will match the ticks and grids of ax1.
# ax1.parasites.append(ax2)
# intp = cbook.simple_linear_interpolation
# ax2.plot(intp(np.array([0, 30]), 50),
# intp(np.array([10., 10.]), 50))
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True)
def __init__(self,
refstd,
refcorrcoef=1.0,
fig=None,
rect=111,
label='_',
srange=(0, 1.5)):
"""Set up Taylor diagram axes, i.e. single quadrant polar
plot, using `mpl_toolkits.axisartist.floating_axes`.
Parameters:
* refstd: reference standard deviation to be compared to
* fig: input Figure or None
* rect: subplot definition
* label: reference label
* srange: stddev axis extension, in units of *refstd*
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
self.refstd = refstd # Reference standard deviation
tr = PolarAxes.PolarTransform()
# Correlation labels
rlocs = NP.concatenate((NP.arange(10) / 10., [0.95, 0.99]))
tlocs = NP.arccos(rlocs) # Conversion to polar angles
gl1 = GF.FixedLocator(tlocs) # Positions
tf1 = GF.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
# Standard deviation axis extent (in units of reference stddev)
self.smin = srange[0] * self.refstd
self.smax = srange[1] * self.refstd
ghelper = FA.GridHelperCurveLinear(
tr,
extremes=(
0,
NP.pi / 2, # 1st quadrant
self.smin,
self.smax),
grid_locator1=gl1,
tick_formatter1=tf1)
if fig is None:
fig = PLT.figure()
ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
fig.add_subplot(ax)
# Adjust axes
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
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["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom") # "X axis"
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top") # "Y axis"
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False) # Useless
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point and stddev contour
l, = self.ax.plot(NP.arccos(refcorrcoef),
self.refstd,
'k*',
ls='',
ms=10,
label=label)
t = NP.linspace(0, NP.pi / 2)
r = NP.zeros_like(t) + self.refstd
self.ax.plot(t, r, 'k--', label='_')
# Collect sample points for latter use (e.g. legend)
self.samplePoints = [l]
def create_cg(fig=None,
subplot=111,
rot=-450,
scale=-1,
angular_spacing=10,
radial_spacing=10,
latmin=0,
lon_cycle=360):
""" Helper function to create curvelinear grid
The function makes use of the Matplotlib AXISARTIST namespace
`mpl_toolkits.axisartist \
<https://matplotlib.org/mpl_toolkits/axes_grid/users/axisartist.html>`_.
Here are some limitations to normal Matplotlib Axes. While using the
Matplotlib `AxesGrid Toolkit \
<https://matplotlib.org/mpl_toolkits/axes_grid/index.html>`_
most of the limitations can be overcome.
See `Matplotlib AxesGrid Toolkit User’s Guide \
<https://matplotlib.org/mpl_toolkits/axes_grid/users/index.html>`_.
Parameters
----------
fig : matplotlib Figure object
If given, the PPI/RHI will be plotted into this figure object.
Axes are created as needed. If None a new figure object will
be created or current figure will be used, depending on "subplot".
subplot : :class:`matplotlib:matplotlib.gridspec.GridSpec`, \
matplotlib grid definition
nrows/ncols/plotnumber, see examples section
defaults to '111', only one subplot
rot : float
Rotation of the source data in degrees, defaults to -450 for PPI,
use 0 for RHI
scale : float
Scale of source data, defaults to -1. for PPI, use 1 for RHI
angular_spacing : float
Spacing of the angular grid, defaults to 10.
radial_spacing : float
Spacing of the radial grid, defaults to 10.
latmin : float
Startvalue for radial grid, defaults to 0.
lon_cycle : float
Angular cycle, defaults to 360.
Returns
-------
cgax : matplotlib toolkit axisartist Axes object
curvelinear Axes (r-theta-grid)
caax : matplotlib Axes object (twin to cgax)
Cartesian Axes (x-y-grid) for plotting cartesian data
paax : matplotlib Axes object (parasite to cgax)
The parasite axes object for plotting polar data
"""
# create transformation
# rotate
tr_rotate = Affine2D().translate(rot, 0)
# scale
tr_scale = Affine2D().scale(scale * np.pi / 180, 1)
# polar
tr_polar = PolarAxes.PolarTransform()
tr = tr_rotate + tr_scale + tr_polar
# build up curvelinear grid
extreme_finder = ah.ExtremeFinderCycle(
360,
360,
lon_cycle=lon_cycle,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(latmin, np.inf),
)
# locator and formatter for angular annotation
grid_locator1 = ah.LocatorDMS(lon_cycle // angular_spacing)
tick_formatter1 = ah.FormatterDMS()
# grid_helper for curvelinear grid
grid_helper = GridHelperCurveLinear(
tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
grid_locator2=None,
tick_formatter1=tick_formatter1,
tick_formatter2=None,
)
# try to set nice locations for radial gridlines
grid_locator2 = grid_helper.grid_finder.grid_locator2
grid_locator2._nbins = (radial_spacing * 2 + 1) // np.sqrt(2)
# if there is no figure object given
if fig is None:
# create new figure if there is only one subplot
if subplot == 111:
fig = pl.figure()
# otherwise get current figure or create new figure
else:
fig = pl.gcf()
# generate Axis
cgax = SubplotHost(fig, subplot, grid_helper=grid_helper)
fig.add_axes(cgax)
# get twin axis for cartesian grid
caax = cgax.twin()
# move axis annotation from right to left and top to bottom for
# cartesian axis
caax.toggle_axisline()
# make right and top axis visible and show ticklabels (curvelinear axis)
cgax.axis["top", "right"].set_visible(True)
cgax.axis["top", "right"].major_ticklabels.set_visible(True)
# make ticklabels of left and bottom axis invisible (curvelinear axis)
cgax.axis["left", "bottom"].major_ticklabels.set_visible(False)
# and also set tickmarklength to zero for better presentation
# (curvelinear axis)
cgax.axis["top", "right", "left", "bottom"].major_ticks.set_ticksize(0)
# show theta (angles) on top and right axis
cgax.axis["top"].get_helper().nth_coord_ticks = 0
cgax.axis["right"].get_helper().nth_coord_ticks = 0
# generate and add parasite axes with given transform
paax = ParasiteAxesAuxTrans(cgax, tr, "equal")
# note that paax.transData == tr + cgax.transData
# Anything you draw in paax will match the ticks and grids of cgax.
cgax.parasites.append(paax)
return cgax, caax, paax
def __init__(self, refstd, fig=None, rect=111, label='_'):
"""Set up Taylor diagram axes, i.e. single quadrant polar
plot, using mpl_toolkits.axisartist.floating_axes. refstd is
the reference standard deviation to be compared to.
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
self.refstd = refstd # Reference standard deviation
tr = PolarAxes.PolarTransform()
# Correlation labels
rlocs = NP.concatenate((NP.arange(10) / 10., [0.95, 0.99]))
tlocs = NP.arccos(rlocs) # Conversion to polar angles
gl1 = GF.FixedLocator(tlocs) # Positions
tf1 = GF.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
# Standard deviation axis extent
self.smin = 0
self.smax = 1.4 * self.refstd
ghelper = FA.GridHelperCurveLinear(
tr,
extremes=(
0,
NP.pi / 2, # 1st quadrant
self.smin,
self.smax),
grid_locator1=gl1,
tick_formatter1=tf1,
)
if fig is None:
fig = PLT.figure()
#ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
# for plot number > 9
ax = FA.FloatingSubplot(fig, rect[0], rect[1], rect[2], \
grid_helper=ghelper)
fig.add_subplot(ax)
# Adjust axes
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
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["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom") # "X axis"
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top") # "Y axis"
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False) # Useless
# Contours along standard deviations
ax.grid(False)
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point and stddev contour
print "Reference std:", self.refstd
l, = self.ax.plot([0], self.refstd, 'k*', ls='', ms=10, label=label)
# ms is the marker size
t = NP.linspace(0, NP.pi / 2)
r = NP.zeros_like(t) + self.refstd
self.ax.plot(t, r, 'k--', label='_')
# Collect sample points for latter use (e.g. legend)
self.samplePoints = [l]
def curvelinear_plot(rayon_max=1000):
"""Fonction pour créer un repère cartésien avec en décoration les coordonnées polaire pour mieux repérer les angles
Cette fonction est basée sur un exemple matplotlib : see demo_curvelinear_grid.py for details
Args:
rayon_max (float, optional): Rayon amximal du plot dans lequel afficher le robot. Defaults to 1000.
Returns:
matplotlib.fig, matplotlib.ax : figure et axe matplotlib dans lequel on fait l'affichage
"""
tr_rotate = Affine2D().translate(90, 0)
tr_scale = Affine2D().scale(np.pi / 180., 1.)
tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(-np.inf, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(8)
# tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(
tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
# tick_formatter1=tick_formatter1
)
fig = plt.figure()
ax1 = fig.add_subplot(axes_class=HostAxes, grid_helper=grid_helper)
# Now creates floating axis
# ax1.scatter(5, 5)
# floating axis whose first coordinate (theta) is fixed at 60
ax1.axis["ax"] = axis = ax1.new_floating_axis(0, 0)
axis.set_axis_direction("top")
axis.major_ticklabels.set_axis_direction("left")
ax1.axis["ax1"] = axis = ax1.new_floating_axis(0, -90)
axis.set_axis_direction("left")
axis.major_ticklabels.set_axis_direction("top")
# axis.label.set_text(r"$\theta = 60^{\circ}$")
# axis.label.set_visible(True)
# floating axis whose second coordinate (r) is fixed at 6
ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 150)
axis.label.set_pad(10)
# axis.label.set_text(r"$r = 1$")
ax1.set_aspect(1.)
ax1.set_xlim(-rayon_max, rayon_max)
ax1.set_ylim(-rayon_max, rayon_max)
ax1.grid(True)
return fig, ax1
def taylor_normalized(scores, colors, angle_lim):
import mpl_toolkits.axisartist.floating_axes as floating_axes
from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist.grid_finder import (FixedLocator,
DictFormatter)
fig = plt.figure()
tr = PolarAxes.PolarTransform()
min_corr = np.round(np.cos(angle_lim), 1)
CCgrid = np.concatenate(
(np.arange(min_corr * 10, 10, 2.0) / 10., [0.9, 0.95, 0.99]))
CCpolar = np.arccos(CCgrid)
gf = FixedLocator(CCpolar)
tf = DictFormatter(dict(zip(CCpolar, map(str, CCgrid))))
STDgrid = np.arange(0, 2.0, .5)
gfs = FixedLocator(STDgrid)
tfs = DictFormatter(dict(zip(STDgrid, map(str, STDgrid))))
ra0, ra1 = 0, angle_lim
cz0, cz1 = 0, 2
grid_helper = floating_axes.GridHelperCurveLinear(tr,
extremes=(ra0, ra1, cz0,
cz1),
grid_locator1=gf,
tick_formatter1=tf,
grid_locator2=gfs,
tick_formatter2=tfs)
ax1 = floating_axes.FloatingSubplot(fig, 111, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.axis["top"].set_axis_direction("bottom")
ax1.axis["top"].toggle(ticklabels=True, label=True)
ax1.axis["top"].major_ticklabels.set_axis_direction("top")
ax1.axis["top"].label.set_axis_direction("top")
ax1.axis["top"].label.set_text("Correlation")
ax1.axis['top'].label.set_size(14)
ax1.axis["left"].set_axis_direction("bottom")
ax1.axis["left"].label.set_text("Normalized Standard Deviation")
ax1.axis['left'].label.set_size(14)
ax1.axis["right"].set_axis_direction("top")
ax1.axis["right"].toggle(ticklabels=True)
ax1.axis["right"].major_ticklabels.set_axis_direction("left")
ax1.axis["bottom"].set_visible(False)
ax1 = ax1.get_aux_axes(tr)
plt.grid(linestyle=':', alpha=0.5)
for i, r in enumerate(scores.iterrows()):
theta = np.arccos(r[1].CORRELATION)
rr = r[1].MSTD / r[1].OSTD
print(rr)
print(theta)
ax1.plot(theta, rr, 'o', label=r[0], color=colors[i])
ax1.plot(0, 1, 'o', label='Obs')
plt.legend(loc='upper right', bbox_to_anchor=[1.3, 1.15])
plt.show()
rs, ts = np.meshgrid(np.linspace(0, 2), np.linspace(0, angle_lim))
rms = np.sqrt(1 + rs**2 - 2 * rs * np.cos(ts))
ax1.contour(ts, rs, rms, 3, colors='0.5')
#contours = ax1.contour(ts, rs, rms,3,colors='0.5')
#plt.clabel(contours, inline=1, fontsize=10)
plt.grid(linestyle=':', alpha=0.5)
for i, r in enumerate(scores.iterrows()):
crmse = np.sqrt(1 + (r[1].MSTD/scores.OSTD[i])**2 \
- 2*(r[1].MSTD/scores.OSTD[i])*r[1].CORRELATION)
print(crmse)
c1 = ax1.contour(ts, rs, rms, [crmse], colors=colors[i])
plt.clabel(c1, inline=1, fontsize=10, fmt='%1.2f')
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 TaylorDiagram(stddev,corrcoef,refstd,fig,colors,normalize=True):
"""Plot a Taylor diagram.
This is adapted from the code by Yannick Copin found here:
https://gist.github.com/ycopin/3342888
Parameters
----------
stddev : numpy.ndarray
an array of standard deviations
corrcoeff : numpy.ndarray
an array of correlation coefficients
refstd : float
the reference standard deviation
fig : matplotlib figure
the matplotlib figure
colors : array
an array of colors for each element of the input arrays
normalize : bool, optional
disable to skip normalization of the standard deviation
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
# define transform
tr = PolarAxes.PolarTransform()
# correlation labels
rlocs = np.concatenate((np.arange(10)/10.,[0.95,0.99]))
tlocs = np.arccos(rlocs)
gl1 = GF.FixedLocator(tlocs)
tf1 = GF.DictFormatter(dict(zip(tlocs,map(str,rlocs))))
# standard deviation axis extent
if normalize:
stddev = stddev/refstd
refstd = 1.
smin = 0
smax = max(2.0,1.1*stddev.max())
# add the curvilinear grid
ghelper = FA.GridHelperCurveLinear(tr,
extremes=(0,np.pi/2,smin,smax),
grid_locator1=gl1,
tick_formatter1=tf1)
ax = FA.FloatingSubplot(fig, 111, grid_helper=ghelper)
fig.add_subplot(ax)
# adjust axes
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["top"].label.set_text("Correlation")
ax.axis["left"].set_axis_direction("bottom")
if normalize:
ax.axis["left"].label.set_text("Normalized standard deviation")
else:
ax.axis["left"].label.set_text("Standard deviation")
ax.axis["right"].set_axis_direction("top")
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["bottom"].set_visible(False)
ax.grid(True)
ax = ax.get_aux_axes(tr)
# Plot data
corrcoef = corrcoef.clip(-1,1)
for i in range(len(corrcoef)):
ax.plot(np.arccos(corrcoef[i]),stddev[i],'o',color=colors[i],mew=0,ms=8)
# Add reference point and stddev contour
l, = ax.plot([0],refstd,'k*',ms=12,mew=0)
t = np.linspace(0, np.pi/2)
r = np.zeros_like(t) + refstd
ax.plot(t,r, 'k--')
# centralized rms contours
rs,ts = np.meshgrid(np.linspace(smin,smax),
np.linspace(0,np.pi/2))
rms = np.sqrt(refstd**2 + rs**2 - 2*refstd*rs*np.cos(ts))
contours = ax.contour(ts,rs,rms,5,colors='k',alpha=0.4)
ax.clabel(contours,fmt='%1.1f')
return ax
def plotGsrResidue(theta,
phi,
residue,
optTheta,
optPhi,
mvabTheta=None,
mvabPhi=None,
mvubTheta=None,
mvubPhi=None):
fig = figure()
fig.clf()
# some matplotlib setup stuff which I don't fully understand but it works
tr = Affine2D().scale(pi / 180., 1.) + PolarAxes.PolarTransform()
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
fig.add_subplot(ax1)
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
ax1.axis["right"].get_helper().nth_coord_ticks = 0
ax1.axis["bottom"].get_helper().nth_coord_ticks = 1
# draw the filled contoured map in polar coordinates
ax1.contour(transpose(mat(theta)) * mat(cos(phi * pi / 180)),
transpose(mat(theta)) * mat(sin(phi * pi / 180)),
1 / transpose(reshape(residue, (phi.size, -1))),
100,
lw=0.1)
cc = ax1.contourf(
transpose(mat(theta)) * mat(cos(phi * pi / 180)),
transpose(mat(theta)) * mat(sin(phi * pi / 180)),
1 / transpose(reshape(residue, (phi.size, -1))), 100)
# remove gaps between the contour lines
for c in cc.collections:
c.set_antialiased(False)
# show the MVAB direction
if mvabTheta is not None and mvabPhi is not None:
ax1.plot(mvabTheta * cos(mvabPhi * pi / 180),
mvabTheta * sin(mvabPhi * pi / 180),
'sk',
markersize=8)
# show the MVUB direction
if mvubTheta is not None and mvubPhi is not None:
ax1.plot(mvubTheta * cos(mvubPhi * pi / 180),
mvubTheta * sin(mvubPhi * pi / 180),
'dk',
markersize=8)
# show the optimal direction
ax1.plot(optTheta * cos(optPhi * pi / 180),
optTheta * sin(optPhi * pi / 180),
'.k',
markersize=15)
# aspect and initial axes limits
ax1.set_aspect(1.)
ax1.set_xlim(-90, 90)
ax1.set_ylim(-90, 90)
# add grid
ax1.grid(True)
# add colobar
cb = colorbar(cc, pad=0.07)
cb.locator = MaxNLocator(14)
cb.update_ticks()
cb.set_label(r"$1/\tilde{\mathcal{R}}$")
# save
if toSave:
savefig(resultsDir + '/eps/gsr_ResidualMap.eps', format='eps')
savefig(resultsDir + '/png/gsr_ResidualMap.png', format='png')
def curvelinear_test2(fig, gs=None, xcenter=0.0, ycenter=17.3, xwidth=1.5, ywidth=1.5,
rot_angle=0.0, xlabel=xlabel, ylabel=ylabel, xgrid_density=8, ygrid_density=5):
"""
polar projection, but in a rectangular box.
"""
tr = Affine2D().translate(0,90)
tr += Affine2D().scale(np.pi/180., 1.)
tr += PolarAxes.PolarTransform()
tr += Affine2D().rotate(rot_angle) # This rotates the grid
extreme_finder = angle_helper.ExtremeFinderCycle(10, 60,
lon_cycle = 360,
lat_cycle = None,
lon_minmax = None,
lat_minmax = (-90, np.inf),
)
grid_locator1 = angle_helper.LocatorHMS(xgrid_density) #changes theta gridline count
tick_formatter1 = angle_helper.FormatterHMS()
grid_locator2 = angle_helper.LocatorDMS(ygrid_density) #changes theta gridline count
tick_formatter2 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
grid_locator2=grid_locator2,
tick_formatter1=tick_formatter1,
tick_formatter2=tick_formatter2
)
# ax1 = SubplotHost(fig, rect, grid_helper=grid_helper)
if gs is None:
ax1 = SubplotHost(fig, 111, grid_helper=grid_helper)
else:
ax1 = SubplotHost(fig, gs, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(False)
ax1.axis["top"].major_ticklabels.set_visible(False)
ax1.axis["bottom"].major_ticklabels.set_visible(True) #Turn off?
# let right and bottom axis show ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks=0
ax1.axis["bottom"].get_helper().nth_coord_ticks=0
fig.add_subplot(ax1)
grid_helper = ax1.get_grid_helper()
# These move the grid
ax1.set_xlim(xcenter-xwidth, xcenter+xwidth) # moves the origin left-right in ax1
ax1.set_ylim(ycenter-ywidth, ycenter+ywidth) # moves the origin up-down
if xlabel is not None: ax1.set_xlabel(xlabel)
if ylabel is not None: ax1.set_ylabel(ylabel)
ax1.grid(True, linestyle='-')
return ax1,tr
def __init__(self,
refstd=1,
fig=None,
rect=111,
label='REF',
std_range=(0, 1.65),
std_level=np.arange(0, 1.51, 0.25)):
"""Create base Taylor Diagram.
Parameters
----------
refstd : float, optional
reference standard deviation
fig : matplotlib.figure.Figure, optional
Optional input figure. Default is None
rect : int, optional
Optional subplot definition
label : string, optional
Optional reference label string indentifier
std_range : Tuple, optional
Optional stddev axis extent
std_level : list, optional
Optional list of tick locations for stddev axis
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as fa
import mpl_toolkits.axisartist.grid_finder as gf
# Pull and set optional constructor variables
# Set figure
self.fig = fig
if fig is None:
self.fig = plt.figure(figsize=(8, 8))
# Reference standard deviation
self.refstd = refstd
# Standard deviation axis extent (in units of reference stddev)
self.smin = std_range[0]
self.smax = std_range[1]
# Set polar transform
tr = PolarAxes.PolarTransform()
# Set correlation labels
rlocs = np.concatenate((np.arange(10) / 10., [0.95, 0.99, 1]))
tlocs = np.arccos(rlocs) # Conversion to polar angles
gl1 = gf.FixedLocator(tlocs) # Positions
tf1 = gf.DictFormatter(dict(list(zip(tlocs, list(map(str, rlocs))))))
# Set standard deviation labels
gl2 = gf.FixedLocator(std_level)
# format each label with 2 decimal places
format_string = list(map(lambda x: "{0:0.2f}".format(x), std_level))
index = np.where(std_level == self.refstd)[0][0]
format_string[index] = label
tf2 = gf.DictFormatter(dict(list(zip(std_level, format_string))))
# Use customized GridHelperCurveLinear to define curved axis
ghelper = fa.GridHelperCurveLinear(
tr,
extremes=(
0,
np.pi / 2, # 1st quadrant only
self.smin,
self.smax),
grid_locator1=gl1,
tick_formatter1=tf1,
grid_locator2=gl2,
tick_formatter2=tf2)
# Create graphical axes
ax = fa.FloatingSubplot(self.fig, rect, grid_helper=ghelper)
self.fig.add_subplot(ax)
# Adjust axes for Correlation
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
ax.axis["top"].toggle(ticklabels=True, label=True)
ax.axis["top"].major_ticklabels.set_axis_direction("right")
ax.axis["top"].label.set_axis_direction("top")
ax.axis["top"].label.set_text("Correlation")
# Standard deviation ("X axis")
ax.axis["left"].set_axis_direction("bottom")
# Standard deviation ("Y axis")
ax.axis["right"].set_axis_direction("top")
ax.axis["right"].toggle(ticklabels=True, label=True)
ax.axis["right"].major_ticklabels.set_axis_direction("left")
ax.axis["right"].label.set_text("Standard deviation (Normalized)")
# Set font sizes, tick sizes, and padding
ax.axis['top', 'right'].label.set_fontsize(18)
ax.axis['top', 'right', 'left'].major_ticklabels.set_fontsize(16)
ax.axis['top', 'right', 'left'].major_ticks.set_ticksize(10)
ax.axis['top', 'right', 'left'].major_ticklabels.set_pad(8)
ax.axis['top', 'right'].label.set_pad(6)
# Bottom axis is not needed
ax.axis["bottom"].set_visible(False)
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point stddev contour
t = np.linspace(0, np.pi / 2)
r = np.zeros_like(t) + self.refstd
h, = self.ax.plot(t,
r,
linewidth=1,
linestyle=(0, (9, 5)),
color='black',
zorder=1)
# Set aspect ratio
self.ax.set_aspect('equal')
# Store the reference line
self.referenceLine = h
# Collect sample points for latter use (e.g. legend)
self.modelMarkerSet = []
# Set number for models outside axes
self.modelOutside = -1
def curvelinear_test2(fig):
"""
polar projection, but in a rectangular box.
"""
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
# Find a grid values appropriate for the coordinate (degree,
# minute, second).
tick_formatter1 = angle_helper.FormatterDMS()
# And also uses an appropriate formatter. Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 2, 2, grid_helper=grid_helper)
# make ticklabels of right and top axis visible.
ax1.axis["right"].major_ticklabels.set_visible(True)
ax1.axis["top"].major_ticklabels.set_visible(True)
# let right axis shows ticklabels for 1st coordinate (angle)
ax1.axis["right"].get_helper().nth_coord_ticks = 0
# let bottom axis shows ticklabels for 2nd coordinate (radius)
ax1.axis["bottom"].get_helper().nth_coord_ticks = 1
fig.add_subplot(ax1)
# A parasite axes with given transform
ax2 = ParasiteAxesAuxTrans(ax1, tr, "equal")
# note that ax2.transData == tr + ax1.transData
# Anthing you draw in ax2 will match the ticks and grids of ax1.
ax1.parasites.append(ax2)
intp = cbook.simple_linear_interpolation
ax2.plot(intp(np.array([0, 30]), 50),
intp(np.array([10., 10.]), 50),
linewidth=2.0)
ax1.set_aspect(1.)
ax1.set_xlim(-5, 12)
ax1.set_ylim(-5, 10)
ax1.grid(True, zorder=0)
def __init__(self,
refstd,
fig=None,
rect=111,
label='_',
srange=(0, 1.5),
extend=False):
"""
Set up Taylor diagram axes, i.e. single quadrant polar
plot, using `mpl_toolkits.axisartist.floating_axes`.
Parameters:
* refstd: reference standard deviation to be compared to
* fig: input Figure or None
* rect: subplot definition
* label: reference label
* srange: stddev axis extension, in units of *refstd*
* extend: extend diagram to negative correlations
"""
from matplotlib.projections import PolarAxes
import mpl_toolkits.axisartist.floating_axes as FA
import mpl_toolkits.axisartist.grid_finder as GF
self.refstd = refstd # Reference standard deviation
tr = PolarAxes.PolarTransform()
# Correlation labels
rlocs = NP.array([0, 0.2, 0.4, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 1])
if extend:
# Diagram extended to negative correlations
self.tmax = NP.pi
rlocs = NP.concatenate((-rlocs[:0:-1], rlocs))
else:
# Diagram limited to positive correlations
self.tmax = NP.pi / 2
tlocs = NP.arccos(rlocs) # Conversion to polar angles
gl1 = GF.FixedLocator(tlocs) # Positions
tf1 = GF.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
# Standard deviation axis extent (in units of reference stddev)
self.smin = srange[0] * self.refstd
self.smax = srange[1] * self.refstd
ghelper = FA.GridHelperCurveLinear(tr,
extremes=(0, self.tmax, self.smin,
self.smax),
grid_locator1=gl1,
tick_formatter1=tf1)
if fig is None:
fig = PLT.figure()
ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
fig.add_subplot(ax)
# Adjust axes
ax.axis["top"].set_axis_direction("bottom") # "Angle axis"
ax.axis["top"].toggle(ticklabels=True, label=True)
ax.axis["top"].major_ticklabels.set_axis_direction("top")
ax.axis["top"].major_ticklabels.set_fontsize(15)
ax.axis["top"].label.set_axis_direction("top")
ax.axis["top"].label.set_text("Standardized gauge density")
ax.axis["top"].label.set_fontsize(15)
ax.axis["left"].set_axis_direction("bottom") # "X axis"
ax.axis["left"].label.set_text("RMSD (mm/h)")
ax.axis["left"].label.set_fontsize(15)
ax.axis["left"].major_ticklabels.set_fontsize(15)
ax.axis["right"].set_axis_direction("top") # "Y-axis"
ax.axis["right"].toggle(ticklabels=True)
ax.axis["right"].major_ticklabels.set_axis_direction(
"bottom" if extend else "left")
ax.axis["right"].major_ticklabels.set_fontsize(15)
ax.axis["right"].label.set_text("RMSD (mm/h)")
ax.axis["right"].label.set_fontsize(15)
if self.smin:
ax.axis["bottom"].toggle(ticklabels=False, label=False)
else:
ax.axis["bottom"].set_visible(False) # Unused
self._ax = ax # Graphical axes
self.ax = ax.get_aux_axes(tr) # Polar coordinates
# Add reference point and stddev contour
l, = self.ax.plot([0], self.refstd, '.', ls='', ms=10, label=label)
t = NP.linspace(0, self.tmax)
r = NP.zeros_like(t) + self.refstd
self.ax.plot(t, r, 'k--', label='_')
# Collect sample points for latter use (e.g. legend)
self.samplePoints = [l]
def curvelinear_test3(fig):
"""
polar projection, but in a rectangular box.
"""
global ax1, axis
import numpy as np
from . import angle_helper
from matplotlib.projections import PolarAxes
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
grid_locator1 = angle_helper.LocatorDMS(15)
# Find a grid values appropriate for the coordinate (degree,
# minute, second).
tick_formatter1 = angle_helper.FormatterDMS()
# And also uses an appropriate formatter. Note that,the
# acceptable Locator and Formatter class is a bit different than
# that of mpl's, and you cannot directly use mpl's Locator and
# Formatter here (but may be possible in the future).
from .grid_finder import FixedLocator
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)
#ax1.axis["top"].set_visible(False)
#ax1.axis["bottom"].major_ticklabels.set_axis_direction("top")
fig.add_subplot(ax1)
#ax1.grid(True)
r_scale = 10.
tr2 = 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)
#grid_helper2 = ax1.get_grid_helper()
ax1.axis["z"] = axis = grid_helper.new_floating_axis(
1, 7, axes=ax1, axis_direction="bottom")
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")
#axis.label.set_visible(True)
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_axis_direction():
fig = plt.figure(figsize=(5, 5))
# PolarAxes.PolarTransform takes radian. However, we want our coordinate
# system in degree
tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
# polar projection, which involves cycle, and also has limits in
# its coordinates, needs a special method to find the extremes
# (min, max of the coordinate within the view).
# 20, 20 : number of sampling points along x, y direction
extreme_finder = angle_helper.ExtremeFinderCycle(
20,
20,
lon_cycle=360,
lat_cycle=None,
lon_minmax=None,
lat_minmax=(0, np.inf),
)
grid_locator1 = angle_helper.LocatorDMS(12)
tick_formatter1 = angle_helper.FormatterDMS()
grid_helper = GridHelperCurveLinear(tr,
extreme_finder=extreme_finder,
grid_locator1=grid_locator1,
tick_formatter1=tick_formatter1)
ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper)
for axis in ax1.axis.values():
axis.set_visible(False)
fig.add_subplot(ax1)
ax1.axis["lat1"] = axis = grid_helper.new_floating_axis(
0, 130, axes=ax1, axis_direction="left")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lat2"] = axis = grid_helper.new_floating_axis(
0, 50, axes=ax1, axis_direction="right")
axis.label.set_text("Test")
axis.label.set_visible(True)
axis.get_helper()._extremes = 0.001, 10
ax1.axis["lon"] = axis = grid_helper.new_floating_axis(
1, 10, axes=ax1, axis_direction="bottom")
axis.label.set_text("Test 2")
axis.get_helper()._extremes = 50, 130
axis.major_ticklabels.set_axis_direction("top")
axis.label.set_axis_direction("top")
grid_helper.grid_finder.grid_locator1.den = 5
grid_helper.grid_finder.grid_locator2._nbins = 5
ax1.set_aspect(1.)
ax1.set_xlim(-8, 8)
ax1.set_ylim(-4, 12)
ax1.grid(True)